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Rulla Alsaedi , Kimberly McKeirnan; Literature Review of Type 2 Diabetes Management and Health Literacy. Diabetes Spectr 1 November 2021; 34 (4): 399–406. https://doi.org/10.2337/ds21-0014

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The purpose of this literature review was to identify educational approaches addressing low health literacy for people with type 2 diabetes. Low health literacy can lead to poor management of diabetes, low engagement with health care providers, increased hospitalization rates, and higher health care costs. These challenges can be even more profound among minority populations and non-English speakers in the United States.

A literature search and standard data extraction were performed using PubMed, Medline, and EMBASE databases. A total of 1,914 articles were identified, of which 1,858 were excluded based on the inclusion criteria, and 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 articles were reviewed in detail.

Patients, including ethnic minorities and non-English speakers, who are engaged in diabetes education and health literacy improvement initiatives and ongoing follow-up showed significant improvement in A1C, medication adherence, medication knowledge, and treatment satisfaction. Clinicians considering implementing new interventions to address diabetes care for patients with low health literacy can use culturally tailored approaches, consider ways to create materials for different learning styles and in different languages, engage community health workers and pharmacists to help with patient education, use patient-centered medication labels, and engage instructors who share cultural and linguistic similarities with patients to provide educational sessions.

This literature review identified a variety of interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy.

Diabetes is the seventh leading cause of death in the United States, and 30.3 million Americans, or 9.4% of the U.S. population, are living with diabetes ( 1 , 2 ). For successful management of a complicated condition such as diabetes, health literacy may play an important role. Low health literacy is a well-documented barrier to diabetes management and can lead to poor management of medical conditions, low engagement with health care providers (HCPs), increased hospitalizations, and, consequently, higher health care costs ( 3 – 5 ).

The Healthy People 2010 report ( 6 ) defined health literacy as the “degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.” Diabetes health literacy also encompasses a wide range of skills, including basic knowledge of the disease state, self-efficacy, glycemic control, and self-care behaviors, which are all important components of diabetes management ( 3 – 5 , 7 ). According to the Institute of Medicine’s Committee on Health Literacy, patients with poor health literacy are twice as likely to have poor glycemic control and were found to be twice as likely to be hospitalized as those with adequate health literacy ( 8 ). Associations between health literacy and health outcomes have been reported in many studies, the first of which was conducted in 1995 in two public hospitals and found that many patients had inadequate health literacy and could not perform the basic reading tasks necessary to understand their treatments and diagnoses ( 9 ).

Evaluation of health literacy is vital to the management and understanding of diabetes. Several tools for assessing health literacy have been evaluated, and the choice of which to use depends on the length of the patient encounter and the desired depth of the assessment. One widely used literacy assessment tool, the Test of Functional Health Literacy in Adults (TOFHLA), consists of 36 comprehension questions and four numeric calculations ( 10 ). Additional tools that assess patients’ reading ability include the Rapid Estimate of Adult Literacy in Medicine (REALM) and the Literacy Assessment for Diabetes. Tests that assess diabetes numeracy skills include the Diabetes Numeracy Test, the Newest Vital Sign (NVS), and the Single-Item Literacy Screener (SILS) ( 11 ).

Rates of both diabetes and low health literacy are higher in populations from low socioeconomic backgrounds ( 5 , 7 , 12 ). People living in disadvantaged communities face many barriers when seeking health care, including inconsistent housing, lack of transportation, financial difficulties, differing cultural beliefs about health care, and mistrust of the medical professions ( 13 , 14 ). People with high rates of medical mistrust tend to be less engaged in their care and to have poor communication with HCPs, which is another factor HCPs need to address when working with their patients with diabetes ( 15 ).

The cost of medical care for people with diabetes was $327 billion in 2017, a 26% increase since 2012 ( 1 , 16 ). Many of these medical expenditures are related to hospitalization and inpatient care, which accounts for 30% of total medical costs for people with diabetes ( 16 ).

People with diabetes also may neglect self-management tasks for various reasons, including low health literacy, lack of diabetes knowledge, and mistrust between patients and HCPs ( 7 , 15 ).

These challenges can be even more pronounced in vulnerable populations because of language barriers and patient-provider mistrust ( 17 – 19 ). Rates of diabetes are higher among racial and ethnic minority groups; 15.1% of American Indians and Alaskan Natives, 12.7% of Non-Hispanic Blacks, 12.1% of Hispanics, and 8% of Asian Americans have diagnosed diabetes, compared with 7.4% of non-Hispanic Whites ( 1 ). Additionally, patient-provider relationship deficits can be attributed to challenges with communication, including HCPs’ lack of attention to speaking slowly and clearly and checking for patients’ understanding when providing education or gathering information from people who speak English as a second language ( 15 ). White et al. ( 15 ) demonstrated that patients with higher provider mistrust felt that their provider’s communication style was less interpersonal and did not feel welcome as part of the decision-making process.

To the authors’ knowledge, there is no current literature review evaluating interventions focused on health literacy and diabetes management. There is a pressing need for such a comprehensive review to provide a framework for future intervention design. The objective of this literature review was to gather and summarize studies of health literacy–based diabetes management interventions and their effects on overall diabetes management. Medication adherence and glycemic control were considered secondary outcomes.

Search Strategy

A literature review was conducted using the PubMed, Medline, and EMBASE databases. Search criteria included articles published between 2015 and 2020 to identify the most recent studies on this topic. The search included the phrases “diabetes” and “health literacy” to specifically focus on health literacy and diabetes management interventions and was limited to original research conducted in humans and published in English within the defined 5-year period. Search results were exported to Microsoft Excel for evaluation.

Study Selection

Initial screening of the articles’ abstracts was conducted using the selection criteria to determine which articles to include or exclude ( Figure 1 ). The initial search results were reviewed for the following inclusion criteria: original research (clinical trials, cohort studies, and cross-sectional studies) conducted in human subjects with type 2 diabetes in the United States, and published in English between 2015 and 2020. Articles were considered to be relevant if diabetes was included as a medical condition in the study and an intervention was made to assess or improve health literacy. Studies involving type 1 diabetes or gestational diabetes and articles that were viewpoints, population surveys, commentaries, case reports, reviews, or reports of interventions conducted outside of the United States were excluded from further review. The criteria requiring articles to be from the past 5 years and from the United States were used because of the unique and quickly evolving nature of the U.S. health care system. Articles published more than 5 years ago or from other health care systems may have contributed information that was not applicable to or no longer relevant for HCPs in the United States. Articles were screened and reviewed independently by both authors. Disagreements were resolved through discussion to create the final list of articles for inclusion.

PRISMA diagram of the article selection process.

Data Extraction

A standard data extraction was performed for each included article to obtain information including author names, year of publication, journal, study design, type of intervention, primary outcome, tools used to assess health literacy or type 2 diabetes knowledge, and effects of intervention on overall diabetes management, glycemic control, and medication adherence.

A total of 1,914 articles were collected from a search of the PubMed, MEDLINE, and EMBASE databases, of which 1,858 were excluded based on the inclusion and exclusion criteria. Of the 56 articles that met criteria for abstract review, 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 studies identified various diabetes management interventions, including diabetes education tools such as electronic medication instructions and text message–based interventions, technology-based education videos, enhanced prescription labels, learner-based education materials, and culturally tailored interventions ( 15 , 20 – 28 ). Figure 1 shows the PRISMA diagram of the article selection process, and Table 1 summarizes the findings of the article reviews ( 15 , 20 – 28 ).

Findings of the Article Reviews (15,20–28)

SAHLSA, Short Assessment of Health Literacy for Spanish Adults.

Medical mistrust and poor communication are challenging variables in diabetes education. White et al. ( 15 ) examined the association between communication quality and medical mistrust in patients with type 2 diabetes. HCPs at five health department clinics received training in effective health communication and use of the PRIDE (Partnership to Improve Diabetes Education) toolkit in both English and Spanish, whereas control sites were only exposed to National Diabetes Education Program materials without training in effective communication. The study evaluated participant communication using several tools, including the Communication Assessment Tool (CAT), Interpersonal Processes of Care (IPC-18), and the Short Test of Functional Health Literacy in Adults (s-TOFHLA). The authors found that higher levels of mistrust were associated with lower CAT and IPC-18 scores.

Patients with type 2 diabetes are also likely to benefit from personalized education delivery tools such as patient-centered labeling (PCL) of prescription drugs, learning style–based education materials, and tailored text messages ( 24 , 25 , 27 ). Wolf et al. ( 27 ) investigated the use of PCL in patients with type 2 diabetes and found that patients with low health literacy who take medication two or more times per day have higher rates of proper medication use when using PCL (85.9 vs. 77.4%, P = 0.03). The objective of the PCL intervention was to make medication instructions and other information on the labels easier to read to improve medication use and adherence rates. The labels incorporated best-practice strategies introduced by the Institute of Medicine for the Universal Medication Schedule. These strategies prioritize medication information, use of larger font sizes, and increased white space. Of note, the benefits of PCL were largely seen with English speakers. Spanish speakers did not have substantial improvement in medication use or adherence, which could be attributed to language barriers ( 27 ).

Nelson et al. ( 25 ) analyzed patients’ engagement with an automated text message approach to supporting diabetes self-care activities in a 12-month randomized controlled trial (RCT) called REACH (Rapid Education/Encouragement and Communications for Health) ( 25 ). Messages were tailored based on patients’ medication adherence, the Information-Motivation-Behavioral Skills model of health behavior change, and self-care behaviors such as diet, exercise, and self-monitoring of blood glucose. Patients in this trial were native English speakers, so further research to evaluate the impact of the text message intervention in patients with limited English language skills is still needed. However, participants in the intervention group reported higher engagement with the text messages over the 12-month period ( 25 ).

Patients who receive educational materials based on their learning style also show significant improvement in their diabetes knowledge and health literacy. Koonce et al. ( 24 ) developed and evaluated educational materials based on patients’ learning style to improve health literacy in both English and Spanish languages. The materials were made available in multiple formats to target four different learning styles, including materials for visual learners, read/write learners, auditory learners, and kinesthetic learners. Spanish-language versions were also available. Researchers were primarily interested in measuring patients’ health literacy and knowledge of diabetes. The intervention group received materials in their preferred learning style and language, whereas the control group received standard of care education materials. The intervention group showed significant improvement in diabetes knowledge and health literacy, as indicated by Diabetes Knowledge Test (DKT) scores. More participants in the intervention group reported looking up information about their condition during week 2 of the intervention and showed an overall improvement in understanding symptoms of nerve damage and types of food used to treat hypoglycemic events. However, the study had limited enrollment of Spanish speakers, making the applicability of the results to Spanish-speaking patients highly variable.

Additionally, findings by Hofer et al. ( 22 ) suggest that patients with high A1C levels may benefit from interventions led by community health workers (CHWs) to bridge gaps in health literacy and equip patients with the tools to make health decisions. In this study, Hispanic and African American patients with low health literacy and diabetes not controlled by oral therapy benefited from education sessions led by CHWs. The CHWs led culturally tailored support groups to compare the effects of educational materials provided in an electronic format (via iDecide) and printed format on medication adherence and self-efficacy. The study found increased adherence with both formats, and women, specifically, had a significant increase in medication adherence and self-efficacy. One of the important aspects of this study was that the CHWs shared cultural and linguistic characteristics with the patients and HCPs, leading to increased trust and satisfaction with the information presented ( 22 ).

Kim et al. ( 23 ) found that Korean-American participants benefited greatly from group education sessions that provided integrated counseling led by a team of nurses and CHW educators. The intervention also had a health literacy component that focused on enhancing skills such as reading food package labels, understanding medical terminology, and accessing health care services. This intervention led to a significant reduction of 1–1.3% in A1C levels in the intervention group. The intervention established the value of collaboration between CHW educators and nurses to improve health information delivery and disease management.

A collaboration between CHW educators and pharmacists was also shown to reinforce diabetes knowledge and improve health literacy. Sharp et al. ( 26 ) conducted a cross-over study in four primary care ambulatory clinics that provided care for low-income patients. The study found that patients with low health literacy had more visits with pharmacists and CHWs than those with high health literacy. The CHWs provided individualized support to reinforce diabetes self-management education and referrals to resources such as food, shelter, and translation services. The translation services in this study were especially important for building trust with non-English speakers and helping patients understand their therapy. Similar to other studies, the CHWs shared cultural and linguistic characteristics with their populations, which helped to overcome communication-related and cultural barriers ( 23 , 26 ).

The use of electronic tools or educational videos yielded inconclusive results with regard to medication adherence. Graumlich et al. ( 20 ) implemented a new medication planning tool called Medtable within an electronic medical record system in several outpatient clinics serving patients with type 2 diabetes. The tool was designed to organize medication review and patient education. Providers can use this tool to search for medication instructions and actionable language that are appropriate for each patient’s health literacy level. The authors found no changes in medication knowledge or adherence, but the intervention group reported higher satisfaction. On the other hand, Yeung et al. ( 28 ) showed that pharmacist-led online education videos accessed using QR codes affixed to the patients’ medication bottles and health literacy flashcards increased patients’ medication adherence in an academic medical hospital.

Goessl et al. ( 21 ) found that patients with low health literacy had significantly higher retention of information when receiving evidence-based diabetes education through a DVD recording than through an in-person group class. This 18-month RCT randomized participants to either the DVD or in-person group education and assessed their information retention through a teach-back strategy. The curriculum consisted of diabetes prevention topics such as physical exercise, food portions, and food choices. Participants in the DVD group had significantly higher retention of information than those in the control (in-person) group. The authors suggested this may have been because participants in the DVD group have multiple opportunities to review the education material.

Management of type 2 diabetes remains a challenge for HCPs and patients, in part because of the challenges discussed in this review, including communication barriers between patients and HCPs and knowledge deficits about medications and disease states ( 29 ). HCPs can have a positive impact on the health outcomes of their patients with diabetes by improving patients’ disease state and medication knowledge.

One of the common themes identified in this literature review was the prevalence of culturally tailored diabetes education interventions. This is an important strategy that could improve diabetes outcomes and provide an alternative approach to diabetes self-management education when working with patients from culturally diverse backgrounds. HCPs might benefit from using culturally tailored educational approaches to improve communication with patients and overcome the medical mistrust many patients feel. Although such mistrust was not directly correlated with diabetes management, it was noted that patients who feel mistrustful tend to have poor communication with HCPs ( 20 ). Additionally, Latino/Hispanic patients who have language barriers tend to have poor glycemic control ( 19 ). Having CHWs work with HCPs might mitigate some patient-provider communication barriers. As noted earlier, CHWs who share cultural and linguistic characteristics with their patient populations have ongoing interactions and more frequent one-on-one encounters ( 12 ).

Medication adherence and glycemic control are important components of diabetes self-management, and we noted that the integration of CHWs into the diabetes health care team and the use of simplified medication label interventions were both successful in improving medication adherence ( 23 , 24 ). The use of culturally tailored education sessions and the integration of pharmacists and CHWs into the management of diabetes appear to be successful in reducing A1C levels ( 12 , 26 ). Electronic education tools and educational videos alone did not have an impact on medication knowledge or information retention in patients with low health literacy, but a combination of education tools and individualized sessions has the potential to improve diabetes medication knowledge and overall self-management ( 20 , 22 , 30 ).

There were several limitations to our literature review. We restricted our search criteria to articles published in English and studies conducted within the United States to ensure that the results would be relevant to U.S. HCPs. However, these limitations may have excluded important work on this topic. Additional research expanding this search beyond the United States and including articles published in other languages may demonstrate different outcomes. Additionally, this literature review did not focus on A1C as the primary outcome, although A1C is an important indicator of diabetes self-management. A1C was chosen as the method of evaluating the impact of health literacy interventions in patients with diabetes, but other considerations such as medication adherence, impact on comorbid conditions, and quality of life are also important factors.

The results of this work show that implementing health literacy interventions to help patients manage type 2 diabetes can have beneficial results. However, such interventions can have significant time and monetary costs. The potential financial and time costs of diabetes education interventions were not evaluated in this review and should be taken into account when designing interventions. The American Diabetes Association estimated the cost of medical care for people with diabetes to be $327 billion in 2017, with the majority of the expenditure related to hospitalizations and nursing home facilities ( 16 ). Another substantial cost of diabetes that can be difficult to measure is treatment for comorbid conditions and complications such as cardiovascular and renal diseases.

Interventions designed to address low health literacy and provide education about type 2 diabetes could be a valuable asset in preventing complications and reducing medical expenditures. Results of this work show that clinicians who are considering implementing new interventions may benefit from the following strategies: using culturally tailored approaches, creating materials for different learning styles and in patients’ languages, engaging CHWs and pharmacists to help with patient education, using PCLs for medications, and engaging education session instructors who share patients’ cultural and linguistic characteristics.

Diabetes self-management is crucial to improving health outcomes and reducing medical costs. This literature review identified interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy. Clinicians seeking to implement diabetes care and education interventions for patients with low health literacy may want to consider drawing on the strategies described in this article. Providing culturally sensitive education that is tailored to patients’ individual learning styles, spoken language, and individual needs can improve patient outcomes and build patients’ trust.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

Both authors conceptualized the literature review, developed the methodology, analyzed the data, and wrote, reviewed, and edited the manuscript. R.A. collected the data. K.M. supervised the review. K.M. is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation

Portions of this research were presented at the Washington State University College of Pharmacy and Pharmaceutical Sciences Honors Research Day in April 2019.

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Diabetes Mellitus Review

• PMID: 27093761

Diabetes mellitus is a group of physiological dysfunctions characterized by hyperglycemia resulting directly from insulin resistance, inadequate insulin secretion, or excessive glucagon secretion. Type 1 diabetes (T1D) is an autoimmune disorder leading to the destruction of pancreatic beta-cells. Type 2 diabetes (T2D), which is much more common, is primarily a problem of progressively impaired glucose regulation due to a combination of dysfunctional pancreatic beta cells and insulin resistance. The purpose of this article is to review the basic science of type 2 diabetes and its complications, and to discuss the most recent treatment guidelines.

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Type 2 diabetes and pre-diabetes mellitus: a systematic review and meta-analysis of prevalence studies in women of childbearing age in the Middle East and North Africa, 2000–2018

• Rami H. Al-Rifai   ORCID: orcid.org/0000-0001-6102-0353 1 ,
• Maria Majeed 1 ,
• Maryam A. Qambar 2 ,
• Ayesha Ibrahim 2 ,
• Khawla M. AlYammahi 2 &
• Faisal Aziz 1  

Systematic Reviews volume  8 , Article number:  268 ( 2019 ) Cite this article

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Investing in women’s health is an inevitable investment in our future. We systematically reviewed the available evidence and summarized the weighted prevalence of type 2 diabetes (T2DM) and pre-diabetes mellitus (pre-DM) in women of childbearing age (15–49 years) in the Middle East and North African (MENA) region.

We comprehensively searched six electronic databases to retrieve published literature and prevalence studies on T2DM and pre-DM in women of childbearing age in the MENA. Retrieved citations were screened and data were extracted by at least two independent reviewers. Weighted T2DM and pre-DM prevalence was estimated using the random-effects model.

Of the 10,010 screened citations, 48 research reports were eligible. Respectively, 46 and 24 research reports on T2DM and pre-DM prevalence estimates, from 14 and 10 countries, were included. Overall, the weighted T2DM and pre-DM prevalence in 14 and 10 MENA countries, respectively, were 7.5% (95% confidence interval [CI], 6.1–9.0) and 7.6% (95% CI, 5.2–10.4). In women sampled from general populations, T2DM prevalence ranged from 0.0 to 35.2% (pooled, 7.7%; 95% CI, 6.1–9.4%) and pre-DM prevalence ranged from 0.0 to 40.0% (pooled, 7.9%; 95% CI, 5.3–11.0%). T2DM was more common in the Fertile Crescent countries (10.7%, 95% CI, 5.2–17.7%), followed by the Arab Peninsula countries (7.6%, 95% CI, 5.9–9.5%) and North African countries and Iran (6.5%, 95% CI, 4.3–9.1%). Pre-DM prevalence was highest in the Fertile Crescent countries (22.7%, 95% CI, 14.2–32.4%), followed by the Arab Peninsula countries (8.6%, 95% CI, 5.5–12.1%) and North Africa and Iran (3.3%, 95% CI, 1.0–6.7%).

Conclusions

T2DM and pre-DM are common in women of childbearing age in MENA countries. The high DM burden in this vital population group could lead to adverse pregnancy outcomes and acceleration of the intergenerational risk of DM. Our review presented data and highlighted gaps in the evidence of the DM burden in women of childbearing age, to inform policy-makers and researchers.

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The global burden of type 2 diabetes mellitus (T2DM) is rapidly increasing, affecting individuals of all ages. The global T2DM prevalence nearly doubled in the adult population over the past decade from 4.7% in 1980 to 8.5% in 2014 [ 1 ]. The global burden of T2DM in people 20–79 years is further projected to increase to 629 million in 2045 compared to 425 million in 2017 [ 1 ]. Low- and middle-income countries will be the most affected with the rise in the burden of T2DM. For the period between 2017 and 2045, the projected increase in the prevalence of T2DM in the Middle East and North Africa (MENA) region is 110% compared to 16% in Europe, 35% in North Africa and the Caribbean, and 62% in South and Central America [ 1 ]. Pre-diabetes (pre-DM) or intermediate hyperglycaemia is defined as blood glucose levels above the normal range, but lower than DM thresholds [ 1 ]. The burden of pre-DM is increasing worldwide. By 2045, the number of people aged between 20 and 79 years old with pre-DM is projected to increase to 587 million (8.3% of the adult population) compared to 352.1 million people worldwide in 2017 (i.e., 7.3% of the adult population of adults aged 20 to 79 years) [ 1 ]. About three quarters (72.3%) of people with pre-DM live in low- and middle-income countries [ 1 ].

Pre-DM or T2DM are associated with various unfavorable health outcomes. People with pre-DM are at high risk of developing T2DM [ 1 ]. Annually, it is estimated that 5–10% of people with pre-DM will develop T2DM [ 2 , 3 ]. Pre-DM and T2DM are also associated with early onset of nephropathy and chronic kidney disease [ 4 , 5 , 6 , 7 ], diabetic retinopathy [ 6 , 8 , 9 ], and increased risk of macrovascular disease [ 10 , 11 ]. T2DM is also reported to increase the risk of developing active [ 12 ] and latent tuberculosis [ 13 ]. The rising levels of different modifiable key risk factors, mainly body overweight and obesity, driven by key changes in lifestyle, are the attributes behind the continued burgeoning epidemics of pre-DM and T2DM [ 14 , 15 , 16 ]. Women of childbearing age (15–49 years) [ 17 ] are also affected by the global rise in pre-DM and T2DM epidemics. Rising blood glucose levels in women of childbearing age has pre-gestational, gestational, and postpartum consequences, including increased intergenerational risk of DM [ 18 ].

The total population in 20 countries (Algeria, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Malta, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Syria, Tunisia, the United Arab Emirates, and Yemen) in the Middle East and North Africa region comprises almost 6.7% (~ 421 million people) of the world’s population, with about 200 million females as of July 1, 2015 [ 19 ]. In adults ≥ 18 years, T2DM prevalence rose sharply by 2.3 times in each of the Eastern Mediterranean regions and the African region, between 1980 and 2014 [ 20 ]. This sharp increase in these two regions is higher than that reported in the region of the Americas (1.7 times), the European region (1.4 times), and the Western Pacific Region (1.9 times) [ 20 ].

Key pre-DM and T2DM risk factors, body overweight and obesity, are highly prevalent in people in the MENA countries. In 2013, the age-standardized prevalence of overweight and obesity among women ≥ 20 years was 65.5% (obese 33.9%) [ 21 ]. The high burden of overweight and obesity in several MENA countries attributed to the interrelated economic, dietary, lifestyle behavioral factors. The nutrition transitions and changes in the food consumption habits were supported by the witnessed economic development in most of the MENA countries. For instance, in the past five decades, the economic development in the Arab Gulf countries linked to the discovery of oil and gas reserves led to changes in eating habits towards the consumption of foods rich in fat and calories as well as increasing behavioral habits towards a sedentary lifestyle [ 22 , 23 ]. This is particularly true with the significant shift from the consumption of traditional low-fat food to fat-rich foods, as well as with a major change from an agricultural lifestyle to an urbanized lifestyle that is often accompanied by decreased levels of physical activity. The urbanized lifestyle increases exposure to fast foods through the high penetration of fast food restaurants serving fat-rich foods, the reliance on automobiles for transport, and the increasing penetration of cell phones, all of which facilitate low levels of physical activity. Globally, physical inactivity is estimated to cause around 27% of diabetes cases [ 24 ]. In eight Arab countries, based on national samples, low levels of physical activity in adults ranged from 32.1% of the population in Egypt in 2011–2012 to as high as 67% of the population in Saudi Arabia in 2005 [ 25 ]. Furthermore, fruit and vegetable consumption is inversely associated with weight gain [ 26 ]. Studies indicated a low intake of fruit and vegetables in some of the MENA countries [ 27 , 28 ]. The growing burden of the possible risk factors of body overweight and obesity in women may further affect and exacerbate the burden of DM and its associated complications in the MENA countries.

To develop effective prevention and control interventions, there is a need for understanding the actual burden of pre-DM and T2DM epidemics in vital population groups, such as women of childbearing age (15–49 years), in the MENA region. Thus, individual studies need to be compiled and summarized. According to our previously published protocol (with a slight deviation) [ 29 ], here, we present the results of the systematically reviewed published quantitative literature (systematic review “1”), to assess the burden (prevalence) of T2DM and pre-DM in women of childbearing age in the MENA region, from 2000 to 2018.

Investing in women’s health paves the way for healthier families and stronger economies. Societies that prioritize women’s health are likely to have better population health overall and to remain more productive for generations to come [ 30 ]. Against this background, our review was aimed at characterizing the epidemiology of T2DM and pre-DM in population groups of women of childbearing age in the MENA through (1) systematically reviewing and synthesizing all available published records of T2DM and pre-DM and (2) estimating the mean T2DM and pre-DM prevalence at national, sub-regional, and regional levels, from January 2000 to July 2018. The findings of the review fill an evidence gap to inform policy-makers on the epidemiologic burden of T2DM and pre-DM in women of childbearing age.

Following our published protocol [ 29 ] that is registered with the International Prospective Registry of Systematic Reviews (PROSPERO registration number “CRD42017069231” dated 12/06/2017), we reported here systematic review “1”. This review adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2009 guidelines [ 31 , 32 , 33 ]. The PRISMA checklist is provided in the Additional file  1 .

Data source and search strategy

To identify eligible studies on T2DM and pre-DM prevalence measures in MENA countries, we implemented a comprehensive computerized search of six electronic databases (MEDLINE, EMBASE, Web of Science, SCOPUS, Cochrane library, and Academic Search Complete) from January 1, 2000, to July 12, 2018, using variant Medical Subject Headings (MeSH) and free-text (Text) terms. The detailed search strategy is presented in an additional box file (see Additional file  2 ). We also hand-searched the reference lists of eligible studies for further studies that might have been missed.

We defined the participants, exposure, comparator, outcome(s), and type of study “PECO(T)”. The PECO(T) statement provides the framework for the identification and selection of studies for inclusion [ 34 ]. As we were looking for prevalence studies, we only considered participants and the outcomes.

Inclusion and exclusion criteria

Participants : Women of childbearing age were defined according to the World Health Organization (WHO) as women aged between 15 and 49 years (thereafter, women of childbearing age) [ 35 ]. Pregnant women were also considered in this review as long as they were tested for T2DM and/or pre-DM according to what was reported in the individual studies.

Outcomes : T2DM and pre-DM. The included studies should have reported quantitative or calculable pre-DM or T2DM prevalence estimate(s) in women of childbearing age regardless of the sample size, pregnancy status, or pre-DM/T2DM ascertainment methodology, in any of the 20 MENA region countries [ 36 ]. We excluded studies of self-reported pre-DM/T2DM not supported with either anti-DM medications or a documented diagnosis. We also excluded studies on metabolic syndrome as long as there was no clear information on the proportion of women of childbearing age with pre-DM or T2DM. Studies were also excluded if they pooled women of childbearing age with pre-DM/T2DM with other non-communicable diseases in the same category, or together with males, or for each gender separately but without age stratification. We excluded studies with incalculable pre-DM/T2DM prevalence after attempting to contact the authors at least twice with no response.

Types of studies : We included observational studies if they were cross-sectional, comparative cross-sectional, case-control (not comparing T2DM/pre-DM vs. no T2DM/pre-DM), or cohort study designs. We excluded observational studies of other study designs.

Detailed eligibility criteria are available in the published protocol [ 29 ]. The PRISMA flow chart for the selection of studies is shown in Fig.  1 .

PRISMA flow chart

Identifying eligible studies

Titles and abstracts of the remaining citations were screened independently by four reviewers (AI, KA, MM, and MQ) for any potential study on pre-DM/T2DM in childbearing age women. Full-texts of the identified potentially eligible studies were thoroughly screened and independently assessed by the four reviewers. The qualities of the extracted studies were independently assessed by two other reviewers (RHA and FA). Discrepancies in data extraction were discussed and resolved.

Data extraction

Data from fully eligible studies were extracted into a pre-defined data extraction excel file using a pre-defined list of variables [ 29 ]. Our outcome of interest was the national/regional weighted pooled prevalence of T2DM and pre-DM in women of childbearing age in the MENA. We extracted the following data on the baseline characteristics of the eligible research reports (author names, year of publication, country, city, and study setting), study methodology (design, time period, sampling strategy, and T2DM/pre-DM ascertainment methodology), and study population (age, pregnancy status, co-morbidity, and number of women with the outcomes of interest).

In research reports which provided stratified T2DM/pre-DM prevalence estimates, the prevalence of the total sample was replaced with the stratified estimates keeping the rule of having at least 10 tested subjects per strata, otherwise we extracted information on the whole tested sample. We followed a pre-defined sequential order when extracting stratified prevalence estimates. Outcome measures stratified according to body mass index (BMI) were prioritized, followed by age and year. This prioritization scheme was used to identify the strata with more information on the tested women. When the strata were not prioritized, the overall outcome prevalence measured was extracted. For a research report that stratified the prevalence of the outcome of interest at these different levels (i.e., age and BMI), one stratum per research report was considered and included to avoid double counting. If the outcome measure was ascertained by more than one ascertainment guideline, we extracted relevant information based on the most sensitive and reliable ascertainment assay (i.e., prioritizing fasting blood glucose “FBG” over self-reported DM status), or the most recent and updated criteria (i.e., prioritizing WHO 2006 over WHO 1999 criteria).

We generated a funnel plot to explore the small-study effect on the pooled prevalence estimates. The funnel plot was created by plotting each prevalence measure against its standard error. The asymmetry of the funnel plot was tested using the Egger’s test [ 37 ] (see Additional files  3 and 4 ).

Quality appraisal and risk of bias

We assessed the methodological quality and risk of bias (ROB) of the studies on T2DM or pre-DM prevalence measures using six-quality items adapted from the National Heart, Lung, and Blood Institute (NIH) tool [ 38 ]. Of the 14 items proposed for observational studies on the NIH tool, eight items were not used as they are relevant only for cohort studies assessing the relationship between an exposure and an outcome [ 38 ]. We also assessed the robustness of the implemented sampling methodology and the ascertainment methodology of the measured outcome(s) using three additional quality criteria (sampling methodology, ascertainment methodology, and precision of the estimate). Studies were considered as having “high” precision if at least 100 women tested for T2DM/pre-DM; a reasonable precision, given a pooled prevalence of 7.2% for T2DM or 7.6% for pre-DM estimated in this study, was obtained. We computed the overall proportion of research reports with potentially low risk of bias across each of the nine quality criteria. We also computed the proportion (out of nine) of quality items with potentially a low risk of bias for each of the included research reports.

Quantitative synthesis: meta-analysis

Meta-analyses of the extracted data to estimate the weighted pooled prevalence of T2DM and pre-DM and the corresponding 95% confidence interval (CI) were executed. The variances of prevalence measures were stabilized by the Freeman-Tukey double arcsine transformation method [ 39 , 40 ]. The estimated pooled prevalence measures were weighted using the inverse variance method [ 40 ], and an overall pooled prevalence estimate was generated using a Dersimonian–Laird random-effects model [ 41 ]. Heterogeneity measures were also calculated using the Cochran’s Q statistic and the inconsistency index; I –squared ( I 2 ) [ 42 ]. In addition to the pooled estimates, the prevalence measures were summarized using ranges and medians. The prediction interval, which estimated the 95% interval in which the true effect size in a new prevalence study will lie, was also reported [ 42 , 43 ].

Country-level pooled estimates were generated according to the population group of tested women (general population, pregnant, non-pregnant with history of gestational DM (GDM), and patients with co-morbidity), and the overall country-level pooled prevalence, regardless of the tested population and study period. To assess if the prevalence of T2DM and pre-DM is changing over time, we stratified studies into two time periods: 2000–2009 and 2010–2018. In order not to miss any important data when estimating country-level, sub-regional, and regional prevalence, the period for studies that overlapped these two periods was defined as “overlapping”. In studies with an unclear data collection period, we used the median (~ 2 years) that was obtained from subtracting the year of publication from the year of data collection to estimate the year of data collection in those studies. The “patients with co-morbidity” included women of childbearing age with organ transplant, kidney dialysis, cancer, HIV, chronic obstructive pulmonary disease, polycystic ovarian syndrome (PCOS), or schizophrenia. Categorization of the study period was arbitrary with an aim to estimate the change in T2DM and pre-DM at the country-level and overall, over time.

We also estimated the weighted pooled prevalence, regardless of country, according to the tested women’s population group, study period, T2DM/pre-DM ascertainment guidelines (WHO guidelines, American DM Association (ADA) guidelines, International DM Association (IDF) guidelines, or medical records/anti-DM medications/self-reported), and sample size (< 100 or ≥ 100). The overall weighted pooled prevalence of T2DM and pre-DM regardless of the country, tested population, study period, ascertainment guidelines, and sample size was also generated. Providing pooled estimates regardless of the ascertainment guidelines was justified by the fact that the subject women were defined and treated as T2DM or pre-DM patients following each specific ascertainment guidelines.

To provide prevalence estimates at a more sub-regional level, countries in the MENA region were re-grouped into three sub-regions, namely, “Arab Peninsula, Fertile crescent, and North Africa and Iran.” The pooled prevalence in these three sub-regions was estimated according to the tested population group, study period, ascertainment guidelines, and sample size, as well as overall for each sub-region.

We also estimated the weighted pooled prevalence of T2DM and pre-DM according to age group. We categorized women of childbearing age into three age groups (15–29 years, 30–49 years) and not specified/overlapping. The “not specified/overlapping” category covers women who did fell in the other two age groups. For example, women with an age range of 25–34 years or 18–40 years. The age group weighted pooled prevalence produced regardless of the country, sub-region, and tested population as well as study period.

All meta-analyses were performed using the metaprop package [ 33 ] in Stata/SE v15 [ 44 ].

Sources of heterogeneity: meta-regression

Random-effects univariate and multivariable meta-regression models were implemented to identify sources of between-study heterogeneity and to quantify their contribution to variability in the T2DM and pre-DM prevalence. In univariate meta-regression models, analysis was performed by country, tested population, study period, ascertainment guidelines, and sample size. All variables with a p  < 0.1, in the univariate models, were included in the multivariable model. In the final multivariable model, a p value ≤ 0.05 was considered statistically significant, contributing to heterogeneity in prevalence estimates.

All meta-regression analyses were performed using the metareg package in Stata/SE v15 [ 44 ].

Search and eligible research reports

Of the 12,825 citations retrieved from the six databases, 48 research reports were found eligible (Fig. 1 ); 46 reported T2DM prevalence [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 ] while 24 reported pre-DM prevalence [ 48 , 49 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 60 , 62 , 63 , 66 , 67 , 70 , 73 , 75 , 81 , 85 , 88 , 89 , 90 ].

Scope of reviewed T2DM reports

The 46 research reports on T2DM prevalence yielded 102 T2DM prevalence studies. The 46 reports were from 14 countries (Algeria, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, the United Arab Emirates [UAE], and Yemen); ranging by year between 2000 in Saudi Arabia [ 79 ] and 2018 in UAE [ 81 ]. Sixteen (34.9%) research reports were reported in Saudi Arabia [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ], followed by 19.6% in the UAE [ 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ], and 15.2% in Iran [ 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. Over one third (37.3%) of the yielded 102 T2DM prevalence studies were in Saudi Arabia. Of the 102 T2DM prevalence studies, 79.4% were in women sampled from general populations and 11.8% in pregnant women. Over two thirds (69.6%) of the T2DM prevalence studies were in or before 2009 and 82.4% tested ≥ 100 women (Table  1 ).

Pooled T2DM prevalence

In the 14 countries, the weighted T2DM prevalence in women of childbearing age estimated at 7.5% (95% CI, 6.1–9.0%, I 2 , 98.2%) (Table  2 , Fig.  2 ). The weighted T2DM prevalence was not significantly different ( p  = 0.4) in studies reported between 2000 and 2009 (7.9%, 95% CI, 6.2–9.7%, I 2 , 97.9%) and studies reported between 2010 and 2018 (5.8%, 95% CI, 3.4–8.7%, I 2 , 95.4%) (Table 2 ). The weighted T2DM prevalence was higher in women with an age range of 15–19 years (10.9%, 95% CI, 8.8–13.3%, I 2 , 97.9%) than women with an age range of 30–49 years (2.5%, 95% CI, 1.8–3.2%, I 2 , 83.6%) (see Additional file  5 ).

Forest plot of the meta-analyses for the 14 MENA countries’ studies on T2DM

Pooled findings of 102 T2DM prevalence estimates reported in 14 countries in the MENA region. The individual 102 estimates and their 95% confidence interval (CI) omitted to fit the plot. The diamond is centered on the summary effect estimate, and the width indicates the corresponding 95% CI. UAE, United Arab Emirates; T2DM, type 2 diabetes mellitus; MENA, Middle East and Northern Africa

The highest two weighted T2DM estimates were observed in infertile women of childbearing age in Egypt (28.2%, 95% CI, 17.4–40.3%) and in non-pregnant women with a history of GDM in Iran (24.7%, 95% CI, 18.5–31.5%) (Table 2 ). In general populations, the weighted T2DM prevalence ranged between 1.3% (95% CI, 0.0–4.7%) in 2001–2002 in Morocco [ 60 ] and 16.4% (95% CI, 6.5–29.8%, I 2 , 96.5%) in Iraq in 2007 [ 55 ] and in 2011–2012 [ 54 ]. In Saudi Arabia, in women of childbearing age sampled from general populations, the pooled T2DM prevalence estimated at 8.0% (95% CI, 5.3–11.3%, I 2 , 96.5%) (Table 1 ). In Saudi Arabia, the weighted T2DM prevalence in women of childbearing age, regardless of source of population and timeline, estimated at 7.2% (95% CI, 4.6–10.2%, I 2 , 98.6%) (Table 2 ). In Oman, the weighted T2DM prevalence in women of childbearing age sampled from general populations estimated at 8.0% (95% CI, 2.9–15.4%, I 2 , 95.9%) in 2000. In Qatar, the weighted T2DM was prevalence in women of childbearing age sampled from general populations 10.7% (95% CI, 2.2–24.4%, I 2 , 93.7%) between 2007 and 2008. In the UAE, in women of childbearing age sampled from general populations, the pooled T2DM prevalence estimated at 8.0% (95% CI, 4.8–11.9%, I 2 , 98.9%) that declined from 9.4% (95% CI, 5.6–14.1%, I 2 , 95.1%) between 2000 and 2009 to 6.0% (95% CI, 3.3–6.5%, I 2 , 90.5%) between 2010 and 2018 (Table 2 ).

Sub-regional pooled T2DM prevalence

The pooled T2DM prevalence measures estimated at 6.5% (95% CI, 4.3–9.1%, I 2 , 96.0%) in North African countries including Iran, 10.7% (95% CI 5.2–17.7%, I 2 , 90.7%) in the Fertile Crescent countries, and 7.6% (95% CI, 5.9–9.5%, I 2 , 98.5%) in the Arabian Peninsula countries (see Additional file  6 ).

Additional file  7 shows figures presenting the sub-regional-weighted prevalence of T2DM (Fig. 1 ) in women of childbearing age from 2000 to 2009 and from 2010 to 2018. Additional file  8 shows figures presenting timeline view of the weighted prevalence of T2DM (Fig. 1 ) by publication year.

Meta-bias in T2DM prevalence

The asymmetry in the funnel plot examining the small-study effects on the pooled T2DM prevalence among women of childbearing age indicates evidence for the presence of a small-study effect (Egger’s test p  < 0.0001). The funnel plot is presented in an additional figure file (see Additional file  3 ).

Predictors of heterogeneity in T2DM prevalence

In the univariate meta-regression models, all variables except study period, T2DM ascertainment criteria, and sample size were associated with T2DM prevalence at p value < 0.1. In the adjusted meta-regression model, none of the included variables was significantly associated with T2DM prevalence at p value < 0.05. In two studies in infertile women of childbearing age in Egypt, the T2DM prevalence was higher (adjusted odds ratio (aOR), 5.26, 95% CI, 0.87–32.1) compared to women of childbearing age in Saudi Arabia. Overall, compared to women of childbearing age sampled from general populations, T2DM prevalence in non-pregnant women of childbearing age with a history of GDM was 234% higher (aOR, 3.34%, 95% CI, 0.90–12.41) (see Additional file  9 ).

Scope of reviewed pre-DM reports

The 24 research reports on pre-DM prevalence yielded 52 pre-DM prevalence studies and were from 10 countries (Iran, Iraq, Jordan, Kuwait, Morocco, Oman, Qatar, Saudi Arabia, UAE, and Yemen); ranging by year between 2002 in Oman [ 62 ] and 2018 in Saudi Arabia [ 81 ]. Thirteen (25.0%), 11 (21.2%), and 11 (21.2%) of the pre-DM prevalence studies were from Iran, Saudi Arabia, and UAE, respectively. Approximately 87.0% of the pre-DM prevalence studies tested women of childbearing age sampled from general populations. The pre-DM prevalence estimates ranged from 0.0% in various age groups in multiple countries [ 51 , 60 , 70 ] to 40.0% in Iraq in women aged 20–39 years, recruited from the general population [ 55 ] (Table 1 ).

Pooled pre-DM prevalence

In the 10 countries, the weighted pre-DM prevalence in women of childbearing age was estimated at 7.6% (95% CI, 5.2–10.4%, I 2 , 99.0%) (Table  3 , Fig.  3 ). The weighted pre-DM prevalence in studies reported between 2000 and 2009 (4.8%, 95% CI 4.0–7.8%, I 2 , 97.1%) was significantly lower ( p  < 0.001) than the weighted prevalence estimated in studies reported between 2010 and 2018 (9.3%, 95%, 4.7–15.2%, I 2 , 93.9%) (Table 3 ). Weighted pre-DM prevalence was 1.70 times higher in women with an age range of 15–19 years (9.0%, 95% CI, 4.9–14.1%, I 2 , 99.2%) than women with an age range of 30–49 years (5.3%, 95% CI, 1.8–10.3%, I 2 , 99.0%) (see Additional file 5 ).

Forest plot of the meta-analyses for the 10 MENA countries’ studies on pre-DM pooled findings of 52 pre-DM prevalence estimates reported in 10 countries in the MENA region. The individual 52 estimates and their 95% confidence interval (CI) omitted to fit the plot. The diamond is centered on the summary effect estimate, and the width indicates the corresponding 95% CI. UAE, United Arab Emirates; pre-DM, pre-diabetes mellitus; MENA, Middle East and Northern Africa

In general populations, the highest three weighted pre-DM prevalence estimates were observed in women of childbearing age in Iraq (25.5%, 95% CI, 15.4–37.1%, I 2 , 92.2%), followed by UAE (15.5%, 95% CI, 10.5–21.2%, I 2 , 99.0%), and Kuwait (13.8%, 95% CI, 7.7–21.4%, I 2 , 96.8%) (Table 3 ). In 13 studies in Iran (7 from the general population), the prevalence of pre-DM ranged from 0.0 to 21.4% with an overall weighted prevalence of 3.8% (95% CI, 1.2–7.6%, I 2 , 98.3%). The 11 pre-DM studies in Saudi Arabia were in women of childbearing age sampled from the general population, with an overall weighted pre-DM prevalence of 6.6% (95% CI, 3.7–10.3%, I 2 , 93.5%) (2000–2009: 9.4% vs. 2010–2018: 4.4%). Regardless of the tested population in UAE, the weighted pre-DM prevalence was 6.6% (95% CI, 5.1–8.3%, I 2 , 65.6%) in studies reported between 2000 and 2009, and 12.0% (95% CI, 8.9–15.5%) in studies reported between 2010 and 2018 with an overall pre-DM prevalence of 14.4% (95% CI, 9.5–20.0%, I 2 , 99.1%) (Table 3 ).

Sub-regional pooled pre-DM prevalence

The pooled pre-DM prevalence estimated at 3.3% (95% CI, 1.0–6.7%, I 2 , 98.1%) in North African countries including Iran, 22.7% (95% CI, 14.2–32.4%, I 2 , 90.0%) in the Fertile crescent countries, and 8.6% (95% CI, 5.5–12.1%, I 2 , 99.1%) in the Arabian Peninsula countries (see Additional files  10 ). Additional file 7 shows figures presenting the sub-regional weighted prevalence of pre-DM (Fig. 2 ) in women of childbearing age from 2000 to 2009 and from 2010 to 2018. Additional file 8 shows figures presenting timeline view of the weighted prevalence of pre-DM (Fig. 2 ) by publication year.

Meta-bias in pre-DM prevalence measures

The asymmetry in the funnel plot examining the small-study effects on the pooled pre-DM prevalence among women of childbearing age indicates evidence for the presence of a small-study effect (Egger’s test p  < 0.0001). The funnel plot is presented in an additional figure file (Additional file  4 ).

Predictors of heterogeneity in pre-DM prevalence

Country, study period, and pre-DM ascertainment criteria were associated with a difference in the pre-DM prevalence in the univariate meta-regression models at p value < 0.1. In the univariate meta-regression models, pre-DM prevalence in women of childbearing age in Iraq was 424% higher compared to such women in Saudi Arabia (OR, 5.24, 95% CI, 1.45–18.94%). This significant association turned insignificant in the multivariable model (aOR, 2.20, 95% CI, 0.52–10.82%). In the multivariable model, compared to Saudi Arabia, pre-DM prevalence in women of childbearing age was 70% lower in Iran (aOR, 0.30, 95% CI, 0.11–0.79%) and 88% lower in Morocco (aOR, 0.12, 95% CI, 0.01–0.91%) (see Additional file  11 ).

Quality assessment of the T2DM/pre-DM research reports

Findings of our summarized and research report-specific quality assessments for relevant DM prevalence studies can be found in Additional file  12 . Briefly, all the 48 research reports clearly stated their research questions or objectives, clearly specified and defined their study populations, and selected or recruited the study subjects from the same or similar populations. There was a clear gap in the reporting or justifying of the sample size calculation in 79.2% of the research reports. The majority (87.5%) of the research reports tested ≥ 100 women of childbearing age, and they were classified as having high precision.

Overall, the 48 research reports were of reasonable quality with potentially low ROB in an average of 7.2 items (range, 6–9). Four (8.3%) of the 48 reports had potentially low ROB in all the measured nine quality items [ 66 , 82 , 83 , 86 ] (see Additional file  12 ).

We provided, to our knowledge, the first regional study that comprehensively reviewed and estimated the regional, sub-regional, and country-level burden of T2DM and pre-DM in various populations of women of childbearing age in the MENA. Based on the available data from 14 and 10 studies in MENA countries, the present findings document the comparable burden of T2DM (7.5%, 95% CI 6.9–9.0%) and pre-DM (7.6%, 95% CI 5.2–10.4%) in women of childbearing age. The estimated prevalence of T2DM and pre-DM in 14 countries in the MENA is similar to the estimated worldwide crude diabetes prevalence of 8.2% (95% credible interval (CI) 6.6–9.9%) in adult women in 2014 (age-standardized 7.9%, 95% CI 6.4–9.7%) [ 91 ]. The T2DM and pre-DM prevalence in women of childbearing age varied across the three sub-regions in the MENA, by population group, time period, DM ascertainment criteria, and sample size. The obvious common prevalence of T2DM and pre-DM in women of childbearing age in the MENA countries reflects the highest prevalence of adult diabetes estimated for the MENA [ 91 ]. In this region, the crude diabetes prevalence in adult women increased from 5.0% in 1980 to 9.0% in 2014 [ 91 ]. This increase in diabetes prevalence among adult populations in the MENA over time is higher than many other regions including Europe and Central and West Africa [ 91 ]. The highest national adult diabetes prevalence estimates documented in the MENA is 5–10 times greater than the lowest national prevalence estimates documented in Western European countries [ 91 ].

T2DM is a significant public health problem in both developed and developing countries that can lead to various health complications including increased overall risk of dying prematurely [ 20 ]. The common burden of T2DM and pre-DM in women of childbearing age, which is reflected in the high burden of adult diabetes in this region [ 91 ], might be mainly driven by the sociodemographic changes in this region. In recent decades, there was an increase in median age, sedentary lifestyle, and physical inactivity in the MENA [ 92 ]. These lifestyle changes are linked to an increase in the burden of body overweight and obesity that are shared predisposing factors for pre-DM and T2DM [ 20 ]. At the population level, physical inactivity was very common in many MENA countries (Saudi Arabia 67.6% in 2005; Kuwait 62.6% in 2014; Qatar 45.9% in 2012; Egypt 32.1% in 2011–2012; Iraq 47.0% in 2015) [ 25 ]. The burden of body overweight and obesity is higher in many low-income and middle-income countries in the MENA than in Europe and Asia Pacific countries [ 93 ]. Obesity in women in several Middle Eastern countries was 40–50% [ 93 ]. The age-standardized prevalence of obesity was 32.0% in Egypt, 35.5% in Jordan, 30.4% in Iraq, 32.5% in Libya, and 35.4% in Saudi Arabia [ 94 ]. In Tunisia, 43.7% and 24.1% of 35–70-year-old females in urban and rural areas, respectively, were obese [ 95 ]. In 2016, in almost all of the countries in MENA, the mean BMI for people aged ≥ 18 years was ≥ 25.0 [ 96 ].

To curb the burden of DM and its associated complications in women of childbearing age in the MENA countries, our results suggest three main implications for care. First, based on the estimated 5–10% progression rate from pre-DM to T2DM [ 3 , 10 ], out of the 47,958 tested women of childbearing age for pre-DM (Table 3 ), we estimate that 2398 to 4796 women are expected to progress to T2DM. This risk of progression to T2DM could be reduced through lifestyle and drug-based interventions as it was reported elsewhere [ 97 , 98 , 99 ]. In England, 55–80% of participants with hyperglycemia at baseline had normal glycaemia at 10 year follow-up [ 3 ]. The high burden of DM along with pre-DM in women of childbearing age could accelerate maternal complications including GDM leading to increased intergenerational risk of DM. Programs to halt the growing epidemic of DM among different population groups could start by addressing the key risk factors including sedentary lifestyle and increased body weight. Addressing this problem would require social and public policies and efforts to reduce the national and regional burden of increased body weight and obesity through enhancing healthy eating behaviors and physical activity. Second, there is a critical need for strengthened surveillance systems that match the scale and nature of the DM epidemic in women of childbearing age in the MENA. Enhancing early detection and management of high-risk individuals requires accessible and affordable health care systems, outreach campaigns to raise public awareness, and social and medical support to induce and maintain a healthy lifestyle. Adult people at increased risk of T2DM and pre-DM can be predicted based on good screening tools from the Centers for Disease Control and Prevention (CDC) [ 100 ] and the American Diabetes Association (T2DM Risk Test) [ 101 ]. Early screening and detection will require government-funded prevention programs. Third, controlling the burden of T2DM and pre-DM in MENA countries requires strong and successful partnerships between public health and clinical departments. Physicians have a fundamental role in the care of individual patients to screen, diagnose, and treat both pre-DM and T2DM in clinical settings. In addition, physicians have a fundamental role in working to raise awareness and participating in developing prevention programs and engaging communities. Concerted efforts and partnership between physicians, health departments, and community agencies are needed to strengthen health care services, encouraging and facilitating early screening and detection, and promoting healthy diets and physical activity.

Providing summary estimates and up-to-date mapping gaps-in-evidence of T2DM and pre-DM prevalence in women of childbearing age in different MENA countries provides the opportunities for future public health interventions and research to better characterize the T2DM and pre-DM epidemiology nationally and regionally. Nevertheless, present review findings suggest that the DM burden in women of childbearing age in MENA countries is capturing only the tip of the iceberg. Identifying gaps-in-evidence through systematically reviewing and summarizing the literature has public health research implications. Our review shows that in many countries, the estimation of the burden of T2DM or pre-DM in women of childbearing age in general populations occurred more than a decade ago (Table 1 ). Additionally, the review shows that there was no data on the burden of T2DM and pre-DM in women of childbearing age in several countries in the MENA region. This lack of evidence on a key public heath outcome requires a strongly resourced research capacity and research funding schemes. There is evidence that federally funded research can impact important health issues that affect a large segment of the population [ 102 ].

This robust approach to the literature search and review as well as in retrieving and extracting relevant data from the published literature allowed us to provide summary estimates on the burden of T2DM and pre-DM in women of childbearing age from the 14 and 10 countries in the MENA, respectively. Once the diagnosis was established, regardless of the ascertainment criteria, patients were treated as having diabetes or pre-diabetes. Thus, generating pooled estimates, regardless of the DM ascertainment criteria, stratified according to various population groups, provided more insights into the actual burden of T2DM and pre-DM in various populations of women of childbearing age. The meta-regression analysis identified sources of variations in T2DM and pre-DM prevalence and sources of between-study heterogeneity in prevalence estimates. (Additional files 9 and 11 show these in more detail). The country-stratified and population-stratified T2DM and pre-DM prevalence reports revealed gaps in evidence that can help strengthen research and DM control programs in the most affected countries and populations. The use of probability sampling was very common in the studies included, which may provide broader insights on the representation of our findings to the general or specific group of women of childbearing age at the national, but not at the regional, level.

Limitations

There are important but unavoidable limitations when interpreting the results of our review. Despite the estimated DM prevalence, the actual DM burden could have been underestimated, at country, sub-regional, or regional level, due to several reasons. The inaccessibility of data on pre-DM or T2DM in women of childbearing age from several countries in the MENA may not necessarily mean an actual lack of data. To meet the aim of our review of estimating the burden of pre-DM and T2DM in women of childbearing age, in several published studies reviewed, women of childbearing age were found to have been combined with those of other age groups or with men. The presented overall pooled estimates, regardless of the tested population group, should not be interpreted as the total burden of the outcome at the population level. Utilizing data on T2DM and pre-DM from only 14 and 10 countries may limit the findings from being generalizable to the entire MENA region. Although we followed a thorough and well-defined search strategy, there is a potential of publication bias as shown in funnel plots (Additional files 3 and 4 ). The estimated T2DM and pre-DM prevalence suggest that only the tip of the iceberg was captured. The presented estimates may not be representative of the true prevalence for each population. This underestimation may be particularly true in low-resource settings where necessary resources and capacity in investigating pre-DM at the community level are lacking. The wide array of blood glucose cut-off points and criteria used for T2DM and pre-DM ascertainment also suggests that overestimation and underestimation bias cannot be excluded. Unless estimated from individual population-based studies only, the presented weighted pooled estimates at the country, sub-regional, or regional level should not be interpreted as the burden of the measured outcomes at the population level. Also, the presented pooled estimates according to the two time periods, from 2000 to 2009 and from 2010 to 2018, should not be interpreted as an over-time change in the burden of the measured outcomes. While our meta-analyses revealed substantial heterogeneity across studies, the meta-regression analyses identified the potential sources of between-study heterogeneity within the framework of the present study and the level of detail that can be used in describing these sources (Tables  1 and 2 ). Thus, much of the variability in T2DM and pre-DM prevalence across studies might remain unexplained.

Despite these potential limitations, our study provided a characterization of the scale of T2DM and pre-DM among women of childbearing age in several MENA countries based on the best available evidence. Data presented in this review can be used to (a) understand the burden of T2DM and pre-DM among a vital population group and to identify at high-risk populations within this specific population group; (b) guide the planning, implementation, and evaluation of programs to prevent and control DM; (c) implement immediate public health actions to prioritize the allocation of public health resources; and (d) formulate research hypotheses and provide a basis for epidemiologic studies. Future research opportunities should prioritize large country-level and multicenter comparable studies, to determine the prevalence of T2DM and pre-DM in various population groups of women of childbearing age. A definitive characterization of the burden of DM in women of childbearing age at the regional and sub-regional level would require comparable and empirical studies using standardized methodology and comparable DM ascertainment assays.

In conclusion, women of childbearing age in the MENA region bear an appreciable burden of T2DM and pre-DM. The estimated burden of T2DM and pre-DM was higher in the Arabian Peninsula and Fertile Crescent countries compared to the rest of the MENA countries identified with prevalence estimates in this review. Although both T2DM (7.5%) and pre-DM (7.6%) had similar overall estimated prevalence, there is need for a more focused attention on early detection and control by public health authorities to avoid DM-associated pre-gestational, gestational, and post-gestational complications. Country-level early DM detection and control programs should consider the key risk factors of DM, mainly the growing burden of body overweight and obesity. Furthermore, facilitating high-quality research and surveillance programs in countries with limited data on DM prevalence and reporting of DM prevalence estimates in women of childbearing age warrant focus.

Availability of data and materials

The datasets used and/or analyzed during the current study and its supplementary information files are available from the corresponding author on reasonable request.

Abbreviations

American DM association

Adjusted odds ratio

Confidence interval

Diabetes mellitus

Gestational diabetes mellitus

International Diabetes Mellitus Association

Middle East and North Africa

Medical Subject Headings

National Heart, Lung, and Blood Institute

Participants, exposure, comparator, and outcome

• Pre-diabetes mellitus

Preferred Reporting Items for Systematic Review and Meta-Analysis

Risk of bias

• Type 2 diabetes

United Arab Emirates

World Health Organization

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Acknowledgments

Authors are grateful to the Institute of Public Health, College of Medicine and Health Sciences at the United Arab Emirates University for the infrastructure provided.

This systematic review was funded by the Summer Undergraduate Research Experience (SURE) PLUS-Grant of the United Arab Emirates University, 2017 (Research grant: 31M348). The funder had no role in the study design, collection, analysis, or interpretation of the data, nor in writing and the decision to submit this article for publication.

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RHA conceptualized and designed the study. AI, MM, MQ, KA, and FA assessed the eligibility of the retrieved citations in the titles/abstracts and full-text screening phases. RHA, MM, and FA critically assessed the eligible studies and extracted data. RHA analyzed and interpreted the data. RHA drafted the manuscript. All authors critically reviewed the manuscript. RHA read and approved the final manuscript. All authors read and approved the final manuscript.

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Supplementary information

Additional file 1..

PRISMA checklist.

Additional file 2.

Search strategies for the six databases, from January 1, 2000 to July 12, 2018.

Additional file 3

Funnel plots examining small-study effects on the pooled T2DM prevalence among women of childbearing age. Egger’s test p <0.0001.

Additional file 4

Funnel plots examining small-study effects on the pooled pre-DM prevalence among women of childbearing age. Egger’s test p <0.0001.

Additional file 5.

Weighted prevalence of T2DM and pre-DM in childbearing age women in MENA countries according to age group.

Additional file 6.

Sub-regional weighted prevalence of T2DM in women of childbearing age according to the tested population, data collection period, T2DM ascertainment, sample size, and overall, in 14 MENA countries.

Additional file 7.

Sub-regional weighted prevalence of T2DM (Figure 1 ) and pre-DM (Figure 2 ) in women of childbearing age from 2000 to 2009 and from 2010 to 2018. Square represents the estimated prevalence and lines around the square represent the upper and lower limit of the 95% confidence interval of the prevalence.

Additional file 8.

Timeline view of the weighted prevalence of T2DM (Figure 1 ) and pre-DM (Figure 2 ) in women of childbearing age, by publication year.

Additional file 9.

Univariate and multivariable meta-regression analyses to identify sources of heterogeneity in studies reporting on T2DM prevalence in women of childbearing age by the different measured characteristics.

Additional file 10.

Sub-regional weighted prevalence of pre-DM in childbearing age women according to the tested population, data collection period, Pre-DM ascertainment, sample size, and overall, in the four sub regions of the 10 MENA countries.

Additional file 11.

Univariate and multivariable meta-regression analyses to identify sources of heterogeneity in studies reporting on pre-DM prevalence in women of childbearing age by the different measured characteristics.

Additional file 12.

Quality assessment of the 48 research reports included in the analysis.

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Al-Rifai, R.H., Majeed, M., Qambar, M. et al. Type 2 diabetes and pre-diabetes mellitus: a systematic review and meta-analysis of prevalence studies in women of childbearing age in the Middle East and North Africa, 2000–2018. Syst Rev 8 , 268 (2019). https://doi.org/10.1186/s13643-019-1187-1

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Introduction, case 1: patient a, case 2: patient b, conflict of interest statement, ‘stumped’ by stump appendicitis—a case report and literature review.

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Chien Lin Soh, Shraddha Shetty, Sala Abdalla, Fiammetta Soggiu, ‘Stumped’ by stump appendicitis—a case report and literature review, Journal of Surgical Case Reports , Volume 2024, Issue 9, September 2024, rjae573, https://doi.org/10.1093/jscr/rjae573

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Stump appendicitis, a rare postoperative complication of appendicectomy, is inflammation of the remnant appendix tissue due to incomplete removal of the appendix at the index operation. Due to a past surgical history of appendicectomy, there is often a diagnostic delay. This delay can result in increased morbidity and mortality for patients. This series seeks to describe two cases encountered in a London district general hospital to elucidate the diagnostic, management, and operative challenges of stump appendicitis. Our case series demonstrates the importance of recognition of stump appendicitis as a differential for patients presenting with abdominal pain and previous appendicectomy. Active exclusion of this differential diagnosis in a patient with previous appendicectomy who presents with right iliac fossa pain is vital. Early identification and treatment can prevent morbidity in the patient population. We highlight that complete operative documentation and access to medical records are useful for this diagnosis.

Acute appendicitis remains one of the most common causes of attendance to the emergency department that culminates in urgent surgery [ 1 , 2 ]. Stump appendicitis, a rare postoperative complication of appendicectomy, is inflammation of the remnant appendix tissue due to incomplete removal of the appendix at the index operation. The incidence is described in the literature as ranging from 0.002 to 0.15%, though it is estimated to be higher than previously reported [ 3 ]. Several factors are hypothesized to predispose to stump appendicitis such as the length of stump left in the index operation, difficult dissection or the presence of a faecolith [ 4 ].

Due to a past surgical history of appendicectomy, there is often a diagnostic delay. The clinical findings of stump appendicitis are similar to those of acute appendicitis - abdominal pain in the right lower quadrant, anorexia, and vomiting. This condition is often diagnosed with radiological imaging such as computed tomography (CT) scanning or ultrasonography [ 5 , 6 ]. This presents a diagnostic challenge since stump appendicitis may not be recognized early, leading to delays in treatment and potential morbidity [ 7 , 8 ].

This case series seeks to describe two cases encountered in a London district general hospital to elucidate the diagnostic, management, and operative challenges of stump appendicitis. It builds upon existing literature and highlights the importance of awareness of such condition. We highlight that complete operative documentation and access to medical records are useful for this diagnosis.

Patient A, a 41-year-old male, presented to the emergency department with a history of generalized abdominal pain over 2 days with no inciting event. It was established from the history that the pain was acute in onset, sharp, and constant, with radiation to the right side of the back. The pain was exacerbated by movement and relieved by rest. He felt feverish with rigors and was constipated for 5 days with a complete loss of appetite. He did not experience any nausea, vomiting, or urinary symptoms. He had a past medical history of type 2 diabetes and hypercholesterolemia, and a history of appendicectomy 2 years prior to this presentation. Examination revealed a soft yet tender right upper quadrant with no peritonism. The patient was hemodynamically stable but had a low-grade fever of 37.6 °C. Urine dipstick was only positive for glucose. He had a normal white cell count however a C-reactive protein (CRP) of 37 (normal range < 2 g/dL). The initial chest X-ray was unremarkable with no free air under the diaphragm.

The working diagnosis was biliary colic and the patient was discharged with antibiotics after obtaining some pain control, with a planned review in the ambulatory care unit in 2 days. On review 2 days later, the patient’s symptoms had worsened and on examination there was now tenderness in the right flank and right renal angle, therefore the patient underwent a computerized tomography of the kidney, ureter and bladder (CT KUB) which revealed a thickened distal ileum, caecum and ileocecal junction, consistent with acute inflammation in the right iliac fossa.

Having had a previous appendicectomy with for perforated acute appendicitis with a postoperative collection, the differential diagnoses raised on CT KUB were either a stump appendicitis or a terminal ileitis. The patient was commenced on IV co-amoxiclav and formal contrast enhanced computer tomography of the chest, abdomen, and pelvis (CT CAP) was organized, revealing a residual long appendiceal stump infection associated with a small localized collection and caecal thickening.

After informed consent, the patient underwent a diagnostic laparoscopy with the intraoperative finding of multiple dense adhesions with the anterior abdominal wall that precluded safe approach to the right iliac fossa laparoscopically. After conversion to lower midline laparotomy and adhesiolysis, the inflamed stump was identified and the appendicectomy was completed with ligation of the appendiceal base at the convergence of the taenia coli. The postoperative course was uneventful, and the patient was discharged on postoperative Day 4.

The final histopathological examination revealed appendicitis, with an 80 mm length and 10 mm diameter with an attached mesoappendix. Microscopic analysis revealed mucosal ulceration and dense transmural neutrophilic inflammation and luminal fibrous obliteration.

Review of previous notes revealed that the patient had a laparoscopic appendicectomy 2 years prior from a CT-confirmed acute retrocaecal appendicitis with localized perforation at the tip ( Fig. 1 ). Intraoperatively, the dissection proved difficult due to the presence on multiple inflammatory adhesions and the retrocaecal position of the appendix, however a retrograde appendicectomy was completed laparoscopically. The postoperative period was complicated by a right iliac fossa abscess that was successfully treated with IV antibiotics and CT-guided drainage. The index histology revealed multiple pieces of appendiceal tissue aggregating to 60 mm × 40 mm × 20 mm, with acute inflammation and necrosis.

Patient A. Appendiceal stump of Patient A identified within the red circle on CT scan before the second operation.

Patient B, an 18-year-old male, presented to the emergency department with sudden onset cramping generalized abdominal pain radiating to the right iliac fossa and testicle. This pain was notably described by the patient as similar to ‘appendicitis pain’ he experienced a few months ago, which culminated in an emergency appendicectomy 2 months prior to this presentation. Examination revealed guarding and tenderness in the right iliac fossa. McBurney’s sign was positive. There was no peritonism. Testicular examination was normal with no tenderness or swelling, and no clinical concern for torsion. He had a low-grade fever but was hemodynamically stable. Urine dipstick revealed blood in the urine. Blood tests revealed neutrophilia of 13.1 and CRP of 5.9 .

With a differential diagnosis of stump appendicitis versus nephrolithiasis, a CT KUB was done which revealed no significant findings in the appendix or kidneys—the differential was revised to mesenteric adenitis or inflammatory bowel disease. The patient was counseled to be booked for an outpatient colonoscopy. However, in view of ongoing symptoms, serial examinations and investigations revealed a rising white cell count and CRP to 264. The patient was started on intravenous (IV) antibiotics. A formal CT CAP demonstrated mural thickening and enhancement of the caecal pole and fat stranding ( Fig. 2 ). With a diagnosis of stump appendicitis, IV antibiotics were escalated to metronidazole and piperacillin-tazobactam.

Patient B. Appendiceal stump of Patient B identified within the red circle on CT scan before the second operation.

The patient was counseled and consented for a diagnostic laparoscopy and completion appendicectomy. The caecum was mobilized from adhesions to the lateral abdominal wall and the convergence of taenia coli was followed to the appendicular stump. The operation revealed an acutely inflamed long retrocaecal appendiceal stump of ~3 cm length, with pus. Loose suture material was found in the vicinity of the stump. The remainder of the caecum and terminal ileum appeared normal. The patient had an uncomplicated recovery and was discharged 3 days later. Histological examination showed an appendix stump that was 3 cm long with chronic inflammation. There was no obvious malignant activity or periappendicitis.

Patient B had an uncomplicated laparoscopic appendicectomy 2 months prior in a different hospital. He presented to the hospital with fever, right iliac fossa pain and positive Rovsing’s sign. His blood tests revealed a raised white blood cell count of 14.7 and a neutrophilia of 11.65. The measured CRP was 5. The CT report described acute uncomplicated appendicitis with an associated large faecolith at the appendix base. He was treated with IV antibiotics and a same day laparoscopic appendicectomy, which was described as uncomplicated. The histology report revealed acute suppurative appendicitis with no parasites. The appendix measured 48 × 15 × 8 mm with an attached mesoappendix measuring 25 × 15 mm.

This case series seeks to describe the rare but challenging diagnosis of stump appendicitis in patient who present to the emergency department with right-sided abdominal pain on a background of previous appendicectomy.

This case series aims to highlight that appropriate clinical examination, urgent radiological imaging, and prompt surgical intervention are vital for good clinical care. However, there remains a diagnostic dilemma with no consensus on the ideal investigations or surgical approach nor on the risk factors on developing this delayed complication through national or international guidelines. Our case series aims to increase awareness of the condition with the hope of reducing diagnostic delay and encouraging timely intervention.

There have been previous case reports and literature reviews written about the subject of stump appendicitis [ 4 , 5 , 9–15 ]. The incidence of stump appendicitis is thought to be higher than previously documented [ 4 ]. Stump appendicitis may lead to the same complications of acute appendicitis including perforation, peritonitis, and septic shock, with significant risks of poor outcomes if the diagnosis is overlooked or delayed.

History taking in all patients revealed generalized abdominal pain that may or may not have radiated to the right iliac fossa—these signs unfortunately are nonspecific. All of our patients presented with neutrophilia and a rise in the CRP. With advancements in radiological imaging and the changing landscape of clinical practice, more patients with undifferentiated abdominal pain are referred for early imaging. Due to the diagnostic uncertainty and the concomitant rise in inflammatory markers, all patients in this case series had a CT scan for confirmation, however, imaging modalities such as abdominal ultrasound or MRI can also be utilized. CT scan with contrast was successful for identifying the inflamed appendiceal stump, however the findings may mimic those of acute appendicitis such as thickening of caecal wall, fat stranding, or localized fluid collection [ 16 ] ( Table 1 ).

Case summary describing patient demographics, primary diagnosis, complications, and time to surgery and discharge.

Management of stump appendicitis can be complicated by the prior surgery. Patient A had to be converted to open surgery due to intra-operative challenges such as significant adhesions and difficulty identifying the base of the appendix. However, Patient B had laparoscopic surgery. Both patients had initial laparoscopic appendectomies that may have been complicated, however, were successful through consistent identification of the stump and judicious dissection of adhesions. The operating surgeons were different for each operation within this case series which may have had an impact on surgical technique during the operation.

Risk factors for developing stump appendicitis include technical aspects of the index appendicectomy such as the lack of correct identification of the base of the appendix or its retrocaecal position that leads to a more difficult exposure. The presence of peritonitis, perforation, and adhesions can also increase the chances of not identifying the base. This is a critical step of laparoscopic appendicectomy, and Subramanian and Liang discuss a ‘critical view’ much like that of laparoscopic cholecystectomy that can prevent conversion to open surgery. The identification of the appendix, taenia caecum, and the terminal ileum is required to confirm position of the stump [ 13 ]. Another risk factors could be the length of stump left in the first surgery. Burbano et al. describe a stump as long as 7 cm being left behind [ 4 ]. In Patient A, the length of stump remaining was 8 cm, which is significant. General recommendation is that the appendix should be resected completely, with a stump <3 mm in length. Primary laparoscopic approach was initially thought to be a contributing factor due to lack of tactile response, however this school of thought remains controversial and not supported by the literature [ 17 ].

Stump appendicitis is usually treated with a surgical intervention. While there is evidence that non complicated acute appendicitis may be treated conservatively with antibiotics, no such strong evidence is available for stump appendicitis. A literature review from 2011 showed that out of 40 cases of stump appendicitis, all were operated on, and only 33% were managed laparoscopically [ 18 ]. In patients where operative management may not be the appropriate option, conservative management with IV antibiotics as described by Paudyal et al. has shown clinical effectiveness [ 19 ].

Time between primary surgery and re-do appendicectomy in our study ranged from 2 months to 2 years—this has been described in the literature as ranging from 4 days to 50 years [ 7 , 17 , 18 ].

Another challenge we identified in our case series was the lack of comprehensive original operative notes when the index surgery is performed at a different hospital. Clear intra-operative documentation and discharge documentation is essential to this diagnosis. There was difficulty in obtaining documentation regarding the first operation for Patient B due to different hospitals for each operation. Therefore, judicious communication between surgeons and hospitals is required to support the diagnosis and decision-making regarding surgery. This was a limiting factor in our ability to ascertain the exact surgical techniques and findings of the index operations—highlighting the need for further research into operative techniques to prevent stump appendicitis.

In conclusion, our case series demonstrates the importance of recognition of stump appendicitis as a differential for patients presenting with abdominal pain and previous appendicectomy. Active exclusion of this differential diagnosis in a patient with previous appendicectomy who presents with right iliac fossa pain is vital. Early identification and treatment can prevent morbidity in the patient population. Surgeons must take note of the importance of complete operative documentation, particularly the difficulties encountered in the previous surgery as these can provide valuable clues to the cause for the current presentation.

None declared.

Patient consent was obtained for publication of the case report and associated radiology images.

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• appendectomy
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• differential diagnosis
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• Published: 13 September 2024

Can social media encourage diabetes self-screenings? A randomized controlled trial with Indonesian Facebook users

• Manuela Fritz 1 , 2 ,
• Michael Grimm 1 , 3 , 4 ,
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• Elad Yom-Tov   ORCID: orcid.org/0000-0002-2380-4584 6 &
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Nudging individuals without obvious symptoms of non-communicable diseases (NCDs) to undergo a health screening remains a challenge, especially in middle-income countries, where NCD awareness is low but the incidence is high. We assess whether an awareness campaign implemented on Facebook can encourage individuals in Indonesia to undergo an online diabetes self-screening. We use Facebook’s advertisement function to randomly distribute graphical ads related to the risk and consequences of diabetes. Depending on their risk score, participants receive a recommendation to undergo a professional screening. We were able to reach almost 300,000 individuals in only three weeks. More than 1400 individuals completed the screening, inducing costs of about US $ 0.75 per person. The two ads labeled “diabetes consequences” and “shock” outperform all other ads. A follow-up survey shows that many high-risk respondents have scheduled a professional screening. A cost-effectiveness analysis suggests that our campaign can diagnose an additional person with diabetes for about US $ 9.

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Introduction.

Non-communicable diseases (NCDs), such as cardiovascular diseases, diabetes, and cancer, have overtaken infectious diseases as the leading cause of death worldwide 1 . Screening for metabolic NCD risk factors, such as high blood sugar and blood pressure, provides an effective tool to prevent more severe long-term health consequences. Also, behavioral risk factors, such as smoking, drinking, unhealthy diets, and a lack of physical activity, can be addressed once an individual is aware of its personal risk. Yet, nudging individuals to undergo such a screening in case of no apparent symptoms remains a challenge. This holds especially true in low- and middle-income countries (LMICs), where health literacy and the awareness of and screening for NCDs remain limited 2 , 3 , 4 . At the same time, NCDs are increasing at an unprecedented rate in many LMICs, requiring innovative solutions to increase NCD screening 5 , 6 , 7 .

To increase NCD awareness and screening in LMICs, the World Health Organization (WHO) promotes mass media awareness campaigns as a cost-effective instrument 8 , 9 . Yet, their focus is largely on traditional media such as TV, radio and print, whereas public health campaigns via social media advertising remain unmentioned. Social media public health campaigns and health advertisements have been shown to be promising to address a variety of health aspects and health behaviors. For example, social media public health campaigns have been used to address vaccination rates 10 , 11 , 12 , 13 , 14 , Covid-19 infections 15 , drinking during pregnancy 16 , smoking cessation 17 , sexual behaviors 18 , food choices and physical activity 19 , 20 .

Our study adds to this literature, but goes beyond these studies in multiple aspects. First, the major share of these campaigns is implemented and evaluated in high-income countries and addresses health topics of which the general public is broadly aware off. The question of whether such social media health campaigns work similarly well in LMIC contexts, especially if they address a disease for which there is little knowledge and awareness 4 , 21 , 22 , remains unanswered and we address this research gap. Thereby, we also directly speak to the literature that evaluates which other means and nudges (e.g., messages through community leaders or reminders) are effective in LMICs in encouraging better health-related outcomes and behavior 23 , 24 .

Second, most campaigns are limited to the pure provision of information and do not observe and engage viewers in concrete measurable actions other than those happening online (e.g., clicks or likes). Instead, users in our campaign were redirected to our campaign website, on which they could engage in an actual screening activity. Moreover, through a follow-up survey with part of the participants, we elicited behavior that happened (offline) after the campaign exposure. Notable exceptions to mention here are a study on Covid-19 infections 15 , which also expands the research design to offline measurements of user mobility and actual infection rates, and a study on HPV vaccination 14 which measures actual vaccination rates.

Lastly, only a limited number of studies address the aspect of cost-effectiveness, despite the major advantage of online campaigns being cheap in comparison to other mass media campaigns. More specifically, while some studies evaluate the cost per person reached or the cost per person recruited with such campaigns 25 , 26 , they do not go as far as evaluating the cost per actual diagnosed case or prevented case. Hence, we conduct a cost-effectiveness analysis of our campaign to provide insights about the cost-saving potential of social media public health campaigns (beyond the cost per person reached), which is especially relevant in contexts of limited public health budgets as it is the case in Indonesia and in many other LMICs 27 .

We design, implement, and evaluate a diabetes health campaign and assess whether health advertisements (“ads”) distributed via Facebook can serve as a promising instrument to foster the individual decision to undergo a diabetes risk screening in Indonesia. Indonesia is a relevant setting for our campaign since diabetes is currently the third leading cause of death 28 . Moreover, the country ranks fifth in the list of absolute numbers of diabetes cases and third among the countries with the highest number of undiagnosed cases worldwide. In 2021, more than 19 million individuals were estimated to be living with the disease in Indonesia, with more than 70% of the cases remaining undiagnosed 29 . At the same time, the usage rate of Facebook is high, which lends itself as a perfect showcase to study whether social media campaigns are suitable to encourage people to engage in preventive health behavior such as diabetes screening. Given this setting, our results are relevant for many other middle-income countries with similar high rates of diabetes and large numbers of Facebook users, such as other countries in Southeast Asia, as well as for example India, Brazil, Mexico or Pakistan.

Facebook is becoming an increasingly relevant tool for scientific research, especially in terms of implementing randomized controlled trials (RCTs) with a large outreach 12 , 13 , 15 , 30 . Given the platform’s possibilities to specify concrete population targeting criteria and using Facebook’s A/B split test function, it allows us to target our campaign to Facebook users in the cities with the highest diabetes rates in Indonesia (Jakarta and Yogyakarta), and to provide causal evidence on the effectiveness of different ad designs. Specifically, we use an RCT on Facebook and distribute ads that differ in their framing, i.e., in their message and graphical design, but equally invite viewers to visit our campaign website and to complete a diabetes self-screening. We are especially interested in whether loss-framed, i.e., shocking, messages work better than more neutral ads. Theoretical work by Rothman et al. 31 , 32 suggests that loss-framed or shocking messages should be more effective in inducing health behaviors that might be perceived as risky (i.e., have an uncertain outcome), such as disease detection activities. Following this argument, we hypothesize that a diabetes awareness campaign that encourages diabetes screening might be most effective if a shocking or loss-framed perspective is taken and investigate this proposition experimentally. Thereby, we also add to the empirical literature that explores what kind of information, framings or pictorial content drive health-related decisions 33 , 34 , 35 , 36 , 37 , 38 , 39 , in particular health screening activities 40 , 41 , 42 . Specifically, we provide evidence about which ads can effectively nudge individuals to learn about their risk of having or developing diabetes in a country where general disease awareness is low.

We then assess whether the most persuasive ad is good enough to design a cost-effective awareness campaign. Hence, in this second part of our analysis, we are interested in whether a campaign based on the cost and effectiveness parameters of the best-performing ad can be considered a cost-effective public health intervention. To this end, we follow-up with a subset of participants that completed the self-screening and investigate their compliance rate with the recommendation to schedule an appointment for a professional screening if they were found to be at high risk.

Campaign outreach and engagement

From March 15 until April 5, 2022, we ran a diabetes health campaign entitled “Ada Gula, Ada Diabetes”. The title is related to the traditional Indonesian saying “Ada gula, ada semut”, which literally means “When there is sugar, there must be ants”. Figuratively, the saying means that for every action there is an equal and opposite reaction. Our adapted campaign name hence figuratively interprets diabetes as the reaction to too much sugar – also in relation to the fact that diabetes is known as “Sakit Gula” (“sugar disease” or “sugar sickness”) in Indonesia. We ran the campaign in Jakarta and Yogyakarta and used five different ads, two of which took on a loss-framed and rather disquieting perspective, with the remaining three referring to the family, religion, and the local diabetes prevalence rate (see Methods for a detailed description of the campaign and ads). After clicking on one of the ads, users were re-directed to our campaign website, where they were offered the opportunity to complete a diabetes risk screening questionnaire similar to the diabetes risk test of the American Diabetes Association and the diabetes FINDRISC (Finnish Diabetes Risk Score) screening test but adapted to the Indonesian population (see Methods section for details and Supplementary Tables 1 and 2 for the complete questionnaire). Based on the individual answers, a risk score between 0 and 16 points was calculated and participants received an assessment of their personal risk. Additionally, the assessment contained recommendations on how to keep the risk low, how the diabetes risk can be reduced and to visit a health center or a physician if the risk score was too high. Six weeks after the end of the campaign we sent a follow-up survey to (voluntarily left) e-mail addresses to elicit information about actual compliance with the recommendations received.

Table 1 presents the Facebook engagement statistics of our campaign by age, gender, and location (statistics by ad are presented in Supplementary Table 3 ). These descriptive statistics show that our Facebook campaign can be deemed effective in distributing diabetes-related ads and reaching the general public: Within only three weeks, we reached in total 286,776 individuals with our campaign, generated 758,977 impressions (distinct views of the ads) and 5274 link clicks. This amounts to a click rate of 1.84% (relative to the number of reached individuals), which is higher than the rates achieved in studies with a similar setup, for example in Tjaden et al. 12 (1.7%), Choi et al. 43 (1.4%) or Orazi 30 (0.2%). Overall, we spent approximately US $ 1060 and the campaign resulted in 2052 started and 1469 completed screening questionnaires, implying a conversion-to-reach rate of 0.51% (1469/286,776) and a conversion-to-click rate of 27.85% (1469/5274). Moreover, this relates to a cost of around US $ 0.75 per person conducting such a self-screening. The age and gender patterns reflect the Indonesian Facebook user rates, with slightly more men than women using the platform and the elderly having the lowest user rates 44 , 45 .

Due to changes in Apple’s data policy, Facebook is unable to track users who opted out of tracking under iOS 14 or users who prohibit tracking in any other form and therefore relies on statistical modeling to estimate the total number of conversions 46 . Moreover, Facebook is unable to differentiate by age or gender once the users leave the platform and thus only provides aggregated data on conversions. Hence, for the results in terms of conversions (Column (5)), we rely on the more accurate data that was collected directly on our campaign website from which we could extract – without any loss or modeling – the absolute number of completed (and started) screening questionnaires by age, gender, and location.

Screening participation

Once redirected to our campaign website, participants could fill out the screening questionnaire. We used Facebook’s dynamic URL parameters 47 to generate ad-specific referrer links containing information about the ad id, ad name, and ad placement. These URL parameters could then be read out whenever an individual started to fill out the screening questionnaire. For those individuals using an Apple device who opted out of tracking, the ad-specific URL parameters within the referrer link would not be displayed. However, given that a vast majority of smartphone users in Indonesia rely on an Android system, only 26 (out of 1469) completed screening questionnaires could not be linked to the ad from which users were redirected to our campaign website.

Respondents had the possibility to complete the screening questionnaire multiple times on our website, either for themselves or for other relatives and friends. This was to allow for possible spillover effects, for example, if a user, after completion of the screening questionnaire, re-did the screening for another person. This, however, also implies that the same person could fill out the screening questionnaire multiple times with different information, for example, to check for related changes in the obtained diabetes risk score. The individual link id together with the IP address and browser information, however, allowed us to identify repeated survey questionnaires that were completed from the same device. We therefore construct a data sample in which we drop the observations stemming from repeated questionnaires, i.e., for each link id × IP address combination we keep only the first completed observation in our sample. We use this first observation based on the assumption that a person filling out the questionnaire multiple times would do so first for him- or herself and only afterward for another person. Similarly, we assume that if it was filled out multiple times simply out of curiosity, the respondent would enter the true data the first time and hypothetical data only afterward. This procedure led to a reduction from 1533 completed questionnaires (with duplicates) to an individual sample containing the 1469 completed screening questionnaires presented in the summary statistics.

Table 2 presents the summary statistics of the completed screening questionnaires for the main sample. Summary statistics, including the information for all started questionnaires and for the sample of completed questionnaires including any duplicates are presented in Supplementary Tables 4 and 5 .

The greatest proportion of users completing the risk screening questionnaire on our campaign website were in the 45–54 age group, the average BMI was about 26 and the users had on average a high diabetes risk with a risk score of 6.4. Sixty-one percent of them were found to be at high risk of diabetes, indicating that we were indeed able to reach out to persons who could benefit from such a self-screening. Men and women are almost equally represented. Half of the respondents report ever having been told that they have high blood sugar levels and one-third have ever been diagnosed with high blood pressure levels. In terms of smoking, 34% of participants report being ever-smokers, (i.e., either currently smoking or smoking previously but have now stopped). This average smoking rate, however, obscures a strong gender heterogeneity, with 8% of women and 57% of men in our sample being ever-smokers; a trend that is also well in line with the tobacco consumption pattern in Indonesia observed in the Indonesian Basic Health Research (RISKESDAS 48 , with 3.2% female and 65% male ever-smokers, respectively, for the total Indonesian population above the age of 10). Sixty percent of the respondents report doing at least 30 minutes of physical activity per day, while only 45% report consuming fruit or vegetables on a daily basis. Thirty percent of the respondents report consuming sugary beverages every day.

The summary statistics of the started screening questionnaires (Supplementary Table 4 ) reveal that a large share of survey starters dropped out after the first question (9%) and another large share before the question about participants’ weight and height (10%). Overall, 75% of started screening questionnaires were completed. Of all completers, 205 (14%) left their e-mail address to be contacted for further study activities. We sent a follow-up survey to this sub-sample six weeks after the end of the campaign. The full workflow and the number of observations at each step are presented in Fig. 1 .

The number 1533 in parentheses at Step 4 refers to the number of completed questionnaires when duplicated questionnaires are also counted.

Results from the follow-up survey

Of the 205 participants who left their e-mail addresses and agreed to be re-contacted for further research activities, 53 participated in the follow-up survey. The primary aim of the follow-up survey was to elicit whether individuals with a high risk of diabetes complied with the recommendation they received to schedule an appointment in a primary healthcare facility or with their physician to undergo a blood test for diabetes. Also, if they reported not planning to schedule an appointment, we were interested in the reasons. Of the 53 individuals participating in this survey, 32 (60%) had received a high-risk score in the screening, 15 (28)% a medium-risk score, and 6 (11%) a low-risk score. Obviously, we must assume that the group of respondents is not necessarily representative of the overall sample of 1469 individuals that participated in the screening, as survey participation was voluntary. However, when comparing their observable characteristics with those of the overall sample we did not find any statistically significant differences in their characteristics, as displayed in Supplementary Table 6 . The power of these tests is of course limited, given the small sample size, but even the absolute size of the differences is in most cases surprisingly small. Moreover, we cannot detect any selection in terms of the ad the individual was exposed to (Supplementary Table 7 ), i.e., we do not find any significant effects of the different ads or the final risk score on the probability of participating in the follow-up survey.

We asked those individuals who either were at high risk according to their screening results or who mentioned remembering that they had a high risk about their plans for a professional appointment (n = 35). Of those individuals, 12 (34%) reported that they had already been aware that they had diabetes and hence no further professional test was needed, 13 (37%) reported that they did not plan to schedule a professional appointment, and 10 (28%) reported that they had already scheduled an appointment after participating in our screening or that they intended to do so in the next month (Supplementary Fig. 1 ). Hence, almost one-third of those deemed to be at high risk, corresponding to 43% of those who were unaware of their disease status, seem to comply with the recommendation to undergo a professional blood test for diabetes. If we extrapolate this share to the full sample, it amounts to 250 complying individuals at high risk. These numbers suggest that the campaign not only attracted individuals who were already aware that they had diabetes but that it also reached a substantial share of individuals at high risk of diabetes who were not aware of their status.

To account for a potential desirability bias in our survey, i.e., individuals simply reporting complying with the received recommendation because they expected this to be the socially desirable answer, we randomized two different framings of the same question. One highlighted the importance of scheduling a professional appointment given the possible severe health consequences of diabetes, the other implied that the time that had passed since the screening was probably too short to already have scheduled a meeting (the exact framings are shown in Supplementary Material 3 ). Whereas the first framing should increase the psychological cost of admitting to not having made an appointment, the second framing makes it psychologically rather easy to admit to not having made an appointment. If both framings lead to a comparable share of respondents who report having made an appointment, we can interpret this as evidence that a desirability bias is not at work. Indeed, we do not find any significant differences in the response pattern to the questions, which increases our trust in the reported answers (Supplementary Table 8 ).

Individuals reporting not intending to schedule an appointment for a professional blood test were further asked for the main reasons keeping them from doing so (Supplementary Fig. 2 ). More than half of the respondents answered being afraid of the possible costs of such a test. Given the small sample size for this question, the results have to be interpreted carefully. Yet, since preventive health care visits, including tests for chronic diseases, are free of charge for those covered by the JKN national health insurance scheme (which around 80% in our sample are), a potentially promising strategy to increase screening rates could be to distribute detailed information about the services covered in the scheme.

Ad performance

Next to the assessment of the outreach and engagement with our campaign, we were interested in which ad design and framing would be most effective in creating clicks and conversions (completed screening questionnaires). In particular, we were interested in whether the two loss-framed ads would outperform the more neutrally framed ads (see the Methods section for the different designs). To assess ad performance, we estimate the following logistic regression models:

where λ is the logistic function, \(\mathop{\sum }\nolimits_{j = 1}^{4}A{d}_{i}^{j}\) is a set of four dummy variables that are equal to one whenever person i saw ad j (the ad “family” serves as the reference group), Z i is a vector of control variables (age, gender, region), and u i ( e i ) is the error term. Note that the coefficients β j and δ j can be interpreted as causal effects since the ads were randomly assigned to Facebook users. Additionally, we investigate the effects separately by gender, since previous empirical evidence suggests that the effects of framing and information differ significantly for men and women 49 , 50 , 51 , 52 .

Figures 2 and 3 together with Supplementary Tables 9 and 10 in Supplementary Material 4 show the results for link clicks and conversions for the total sample and separately for men and women. Figures 2 and 3 show the relative increases in comparison to the “family” ad, which implies a reference click-to-reach-ratio of 1.7% and a reference conversion-to-reach-ratio of 0.4%. Supplementary Tables 9 and 10 display the regression coefficients and marginal effects (with and without controls and by gender) from the logit model, together with the p -values of pairwise Wald tests for the different coefficients.

Figure 2 shows the effectiveness of the different ads in terms of link clicks for a the full sample and b by gender. The effects are presented as relative effect to the “family ad'', which serves as a reference category. Black whiskers present the 95% confidence intervals.

Figure 3 shows the effectiveness of the different ads in terms of conversions for a the full sample and b by gender. The effects are presented as relative effects to the “family ad'', which serves as reference category. Black whiskers present the 95% confidence intervals.

Graph (a) for the full sample in Fig. 2 shows that we can establish a clear hierarchy in terms of ad effectiveness for generating link clicks, with the two loss-framed ads clearly outperforming the ads “family” and “geography”. Only the effect of the “religion” ad is not statistically different from that of the “shock” ad. The performance of the “consequences” ad is somewhat larger than that of the “shock” ad, yet this difference is only significant at the 10% level (see also Supplementary Table 9 ).

In terms of the effect size, a user seeing one of the two loss-framed ads “shock” or “consequences” was 15% and 23%, respectively, more likely to click on the ad compared to someone who saw the least performing “family” ad. In absolute terms, this implies an increase to a click-to-reach-ratio of 1.9% and 2.1%. Those seeing the “shock” or “consequences” ads were also 3% and 11% more likely to click on the ads in comparison to the “religion” ad, though the differential effect between the “shock” and “religion” ads is not statistically significant. The magnitudes of these effects are comparable to those found in a study with a similar set-up, also based on Facebook’s A/B split function: Tjaden et al. 12 test several ads to increase Covid-19 vaccination rates in Germany and vary the pictured messenger (doctor, governmental representative, religious leader). They report an increase between 20% and 40% in clicks of the best-performing versus other ads.

Differentiating the ads’ effectiveness by gender (Graph (b)), however, shows that the effectiveness of the “consequences” and “shock” ads in terms of link clicks seems to be driven by women, whereas men reacted to all ads in a rather similar manner. In fact, while the effect is still the largest for the two loss-framed ads in qualitative terms, we cannot reject the hypothesis of equal performance of all five ads for the male audience.

Turning to conversions, Fig. 3 , Graph (a) shows a slightly different picture. While the “consequences” ad is again the best-performing ad in generating conversions (significantly different from all but the “shock” ad), the performance of the “religion” ad, which was the one that came closest to the performance of the loss-framed ads in terms of creating link clicks, is no longer significantly different from the least performing “family” ad. This might be a sign that the “religion” ad did not sufficiently relate to the topic of diabetes and viewers of the ad did not proceed to the screening once they realized that the website did not contain religious content.

In contrast, the “geography” ad is significantly more effective than the “family” and “religion” ads and equally effective as the “shock” ad in generating finalized risk screening tests. Differentiating by gender (Graph (b)) reveals, however, that the effectiveness of the “geography” ad is again solely due to female users. For men, responsiveness to the “consequences” ad was greatest and the ad performed significantly better compared to all other ads with the exception of the “shock” ad ( p -value 0.188).

The effect magnitudes are somewhat larger than those for link clicks when comparing the best-performing ad against the others: an individual exposed to the consequences ad was 57%, 48%, 19% and 10% more likely to complete the self-screening than someone seeing the family, religion, geography or shocking ad, respectively.

Women were also more likely overall (+25%) to complete a screening questionnaire conditional on seeing any of the ads compared to their male counterparts. Yet, given that the number of women seeing an ad on Facebook was lower in absolute terms (since there are generally fewer female Facebook users than male users in Indonesia 45 ), the sample of completed questionnaires is balanced in the gender distribution. Although the oldest age group (65+) was more likely to click on the ads than users below the age of 45, they are about equally likely to complete the questionnaire as the youngest age group, which is driven by a higher attrition rate in the oldest age group. Specifically, when we regress the probability of attrition on participants’ characteristics (conditional on having started the screening questionnaire), we find that elderly respondents above the age of 65 were 34 percentage points more likely to drop out in the course of the questionnaire compared to the youngest age groups. This effect is larger for older men, though not statistically different from the effect for older women (results shown in Supplementary Table 11 ).

Overall, we can confirm the hypothesis that an ad with a loss-framed perspective, i.e., highlighting the adverse health consequences of diabetes, performs significantly better than ads referring to the family, religion, or local prevalence rates. Only the second loss-framed and “shocking” ad comes close to the performance of the “consequences” ad in our health awareness campaign. Hence, an online diabetes awareness campaign focusing on the health consequences of diabetes can be an effective tool to induce diabetes self-screenings. When we assess whether the diabetes risk level of the screening completers differs in relation to the ad they saw, we find that those who saw one of the loss-framed ads had a risk score that was on average higher by 0.28 ( p -value 0.039) than the score of those who saw one of the other three ads. This supports the hypothesis by Rothman et al. 31 , 32 by showing that those who do indeed have a higher diabetes risk, and might also perceive it as such, were more responsive to the loss-framing ads than someone with a lower risk.

Our campaign also shows that the content and framing of the ads is particularly important when targeting women. Women reacted more differentially to the different ads, whereas men responded to the ads more equally, especially for the outcome of link clicks. Yet, also for men, the “consequences” ad performed significantly better than the “family”, “geography” and “religion” ads for the conversion outcome, indicating that the loss-perspective was successful in engaging men in the actual self-screening activity.

While such gender-heterogeneous responses are in line with previous research highlighting the moderating effect of gender in loss- versus gain-framing experiments e.g., 49 , 50 , 51 , 52 , we must refrain from a more extensive analysis of the drivers of this effect, simply due to data limitations. We did not collect any information on underlying characteristics that could explain such differential behavior. Yet, the literature suggests that gender-differences in risk perceptions 49 , avoidance orientation 50 or trust 41 can shape these gender-specific responses. Also, we did not explicitly test loss- versus gain-framing but rather loss-focused versus differently focused ads, which limits the comparability of our results with more precise gain- versus loss-framed campaigns. Nevertheless, our results provide important insights into the question of what type of ads can effectively be used to enhance preventive health behavior and how responsiveness differs between men and women.

Comparison of the sample and benchmark populations

A valid concern that might arise at this point is that we were only able to reach out to a particular population group with our Facebook campaign. While the distribution of the ads was random conditional on being in the pre-specified target group, the actual selection into completing the screening questionnaire is endogenous, and hence the results concerning the effectiveness of our campaign might not to be generalized to other population groups. To investigate the importance of such selection effects, we compare our sample of participants who completed the screening questionnaire with the universe of people who met our eligibility criteria in Jakarta and Yogyakarta. This comparison is presented in detail in Supplementary Material 5 and Supplementary Table 12 . It suggests that the sample generated by our experiment is slightly skewed toward the 45-55 age group and to those who seem to be significantly more at risk of having or developing diabetes compared to the total population above the age of 35 in Jakarta and Yogyakarta. We interpret this self-selection as an indication that our campaign was very effective in reaching out to people at high risk who could potentially benefit from such online screening. Since we also showed above in the results of the follow-up survey that only one-third of the individuals who were found to have a high risk and that self-selected into the follow-up survey had already been aware that they have diabetes, we deem this as evidence that our campaign was indeed able to reach out to a large number of individuals who were unaware of their high risk and that our campaign was able to effectively engage them in the diabetes self-screening.

Cost-effectiveness

Having identified that ads focusing on the detrimental health consequences of diabetes can be a particularly well-suited approach to encourage diabetes risk screening among those with a comparably high diabetes risk, we are now interested in the cost-effectiveness of such an online campaign. We analyze the cost-effectiveness of our Facebook health campaign under the assumption that it would be scaled-up to a one-year health campaign across the whole island of Java. This implies a target population of about 25 million Facebook users above the age of 35. We perform a simple cost-effectiveness calculation based on the cost and effectiveness parameters derived from our study and enrich them with a repeated decision-tree model. The final cost parameter of interest is the cost per newly diagnosed person.

The assumptions, results, and sensitivity analysis of the cost-effectiveness analysis are presented in Supplementary Material 6 (Supplementary Tables 13 and 14 and Supplementary Fig. 3 ). We show that the hypothetical up-scaling of the campaign to the whole of Java over the period of one year could lead to about 1.7 million users participating in the online screening, of whom about 250,000 would continue with the professional follow-up screening, and finally to the diagnosis of almost 170,000 previously undetected diabetes cases. This corresponds to an increase from 25% to 29% of diagnosed cases relative to all cases, i.e., an increase of 16%. While the share might still seem small, the absolute number is large, especially in light of the low cost and low effort needed to implement an online health campaign. This low cost is further confirmed when we look at the total cost of the proposed intervention (including the professional follow-up screening), which is slightly higher than US $ 1.5 million. Dividing the total cost by the 170,000 newly diagnosed cases, the cost of detecting one more previously undiagnosed person amounts to approximately US $ 9 (with a lower bound of US $ 5.20 in a best-case scenario and an upper bound of US $ 37 in a worst-case scenario).

Contrasting these amounts to the cost of long-term diabetes care in Indonesia suggests a large cost-saving potential. Hidayat et al. 53 estimate the direct medical costs for a patient in the Indonesian healthcare system with severe diabetes health consequences at US $ 930 per person per year, whereas a patient without severe diabetes consequences costs the healthcare system only US $ 420. Under the premise that early diagnosis reduces the probability of severe diabetic health consequences, an online diabetes health campaign offers the possibility of reducing healthcare expenditures in the long term. Further, the cost per detected case is lower in comparison to other screening strategies, for example, screening with a similar diabetes risk questionnaire during annual health check-ups in Thailand (~US $ 30 per detected case, counting only direct medical cost) 54 .

NCDs are the leading cause of death worldwide. In LMICs, the health and economic burden due to NCDs is rising rapidly and innovative solutions to increase screening activities and encourage healthy lifestyles could counteract this problem. Public health campaigns can help to increase awareness of NCDs and encourage populations at risk to change unhealthy lifestyles, inform them about important preventive health measures such as screening, ensure adequate treatment in the event of a positive diagnosis, and thereby reduce health care costs and productivity losses in the long run.

We show that using social media platforms, such as Facebook, for such health campaigns sets out new opportunities to increase awareness and screening for diabetes in LMICs. Such campaigns can generate high exposure and engagement rates at very low cost. With our campaign, we were able to reach out to almost 300,000 individuals in only three weeks and with a budget of less than US $ 1100. More than 1400 individuals completed the offered online diabetes risk screening on our campaign website, implying a cost of less than US $ 0.75 per person screened in that way. We also relied on insights from psychology and assessed whether such a campaign should rely on ads with a focus on a loss-framed or shocking perspective to effectively induce preventive health screenings. Our randomized experiment shows that this is indeed a promising approach and that ads focusing on the adverse health consequences of diabetes are most effective in nudging viewers to click on the ads and to carry out a diabetes self-screening. In particular, we find that an ad highlighting the risk of losing eyesight or developing heart- and kidney diseases as a consequence of diabetes outperformed all other ads in the number of link clicks and completed screening questionnaires. Only the second loss-framed ad, which focused on the fact that diabetes can result in death, came near the performance of the “consequences” ad. Yet, this framing effect was more pronounced for the female sample in our study. Men responded more equally also to other ads. These gender differences should be considered by policymakers aiming to design an effective public health campaign.

We also find that such a campaign is especially well-suited for reaching out to the population in the 45–55 age range. This is an encouraging finding, given that the risk of diabetes increases after the age of 45 and a diagnosis of elevated blood sugar at this age offers the opportunity for early treatment to prevent further adverse health consequences.

However, while we can establish that loss-framed or more shocking ads are more effective in terms of creating link clicks and completed self-screenings, it is beyond the scope of our study to assess whether such negatively framed ads could have longer-term negative consequences. A potential adverse effect could for example arise if individuals exposed to the loss-framed ads would engage in information avoidance. In the context of our study, we can show that those individuals being exposed to the loss-framed ads were more likely to participate in the self-screening and equally likely to participate in the follow-up survey, indicating that they did not engage in information avoidance in the short term. Yet, we cannot rule out that long-term health behavior after having received a high-risk result in the self-screening could be adversely affected by the prospect of negative health consequences. Moreover, while shocking contents work well in social media networks to go viral, such content could also induce anxiety or trigger mental health consequences. A recent study in the context of Covid-19 55 , for example, shows that loss-framed ads increased anxiety levels. Together with the fact that a diabetes diagnosis can lead to diabetes distress 56 and affected individuals are at increased risk for mental health disorders 57 , our results call for further research in terms of longer-term consequences of using loss-framed ads in public health campaigns, especially when implemented at scale.

A remaining limitation of our study is that our measure of compliance with the received recommendation to visit a physician or the report of an existing diagnosis is self-reported. Even though we control for social desirability bias, we are limited in our ability to measure whether individuals claiming to have scheduled an appointment indeed follow through with the professional screening, or whether an individual indeed was already diagnosed with diabetes before. This leaves ample room for future studies in which actual compliance rates are being measured. This could be done, for example, by cooperating directly with local health centers that verify whether a person was referred via an online campaign (e.g., via a referral voucher). Moreover, to confirm the self-reported diabetes diagnoses, it would be interesting to set up a study aiming to verify existing diagnoses through medical records. Yet, privacy concerns and data protection rules pose a substantial hurdle for such a study design.

While we run our campaign in Indonesia, many other middle-income countries are equally experiencing a rapidly increasing diabetes burden and have high social media usage rates. This suggests that the insights from our campaign and study should not only be transferable to other countries in Southeast Asia but also to countries such as India, Brazil, Mexico, and Pakistan.

Overall, our study suggests that a health awareness campaign implemented on the social media platform Facebook is a useful tool to increase awareness of and (self-)screenings for diabetes, and loss-framed ads work particularly well. Policymakers in Indonesia and comparable countries should consider using such social media health campaigns as an innovative tool to address the increasing diabetes burden.

Campaign and ad design

From March 15 until April 5, 2022, we ran a diabetes health campaign on Facebook, targeting Indonesian Facebook users in Jakarta and Yogyakarta – the two cities with the highest diabetes rates in Indonesia 48 . In Indonesia’s urban areas, which also have higher diabetes prevalence rates than rural areas, internet penetration rates and usage of social media platforms are high. As of January 2022, the internet penetration rate in Indonesia stood at 74%, with 94% of all users accessing the internet via smartphones. Around 190 million Indonesians are active social media users, of which 130-135 million are active Facebook users, according to the audience size to be reached with Facebook’s advertising tool 58 , 59 .

We implemented the campaign via Facebook’s advertisement function which permits the distribution of self-designed ads to Facebook users while using specific demographic and geographic targeting criteria. This advertisement tool was originally developed for businesses to boost their customer base and increase sales, but it is also increasingly used by scientific researchers to recruit survey participants 43 , 60 , 61 , 62 , 63 . While using the tool for the recruitment of survey participants is indisputably practical, it also offers an even more sophisticated and scientifically valuable function that allows researchers to implement randomized controlled trials. Facebook’s A/B split test allows for a random distribution of two or more ads to evenly split and statistically comparable audiences to test which ad performs best in terms of a pre-specified campaign target 64 . The ads can thus differ in their design or placement, depending on which variable is being tested. This A/B test design also ensures that the same budget is allocated to each ad and hence avoids Facebook’s algorithm determining the budget allocation, something which could generate unbalanced Facebook user exposure rates across ads.

We designed five different ads, two of which took on a loss-framed and rather disquieting perspective, with the remaining three referring to the family, religion, and the local diabetes prevalence rate. The two loss-framed ads were entitled “diabetes consequences” and “shock”. The non-loss-framed ads were entitled “family”, “religion” and “geography”. These non-loss-framed ads were inspired by different strands of the literature that link religion and health 65 , family and health 66 , and information about local health conditions and health behavior 67 . While this design does not allow us to infer the effects of loss- versus gain-framing (since we do not include a specifically gain-framed ad), it allows us to compare the effect of loss-framed ads with ads that rely on different psychological channels that have been shown to affect health-related behavior. The ads and their displayed message are described in more detail below and presented in Fig. 4 .

Consequences: The consequences ad contained a statement about the possible health consequences of diabetes, including blindness, kidney- and heart diseases. The graphic showed a wooden mannequin on which the body parts that can be affected by diabetes were marked with a black cross.

Shock: The shocking ad pictured a man in front of a coffin and contained the message that diabetes can have deadly consequences.

Family: The family ad pictured three generations of an Indonesian family and contained the message that every family can be affected by diabetes.

Geography: One geography ad was designed for each of the two regions in our study (Jakarta and Yogyakarta). The graphics showed a landmark of each of the two cities (the National Monument in Jakarta and the Yogyakarta Monument in Yogyakarta, respectively) covered in sweets. The message referred to the local prevalence rate of diabetes in each of the regions.

Religion: The religion ad presented an Indonesian woman in hijab cooking and contained a statement from the Quran that conveyed the message that one should not live a potentially self-harming life.

a Diabetes consequences – Diabetes can cause blindness, heart diseases, and kidney failure. Learn about your diabetes risk now! b Shock – Diabetes can have deadly consequences. Diabetes can be prevented and controlled. Learn about your diabetes risk now! c Family – Diabetes can affect every family. Diabetes can be prevented and controlled. Learn about your diabetes risk now! d Geography (Jakarta) – Jakarta is the city with the highest diabetes prevalence rate in Indonesia. Learn about your diabetes risk now! ( e ) Geography (Yogyakarta) – Yogyakarta is one of the cities with the highest diabetes prevalence rates. Learn about your diabetes risk now! f Religion – “and do not throw [yourselves] with your [own] hands into destruction” (Q.S. Al-Baqarah, 2:195). Diabetes can be prevented and controlled. Learn about your diabetes risk now!

In addition to the messages outlined above, each ad carried the statement “Learn about your diabetes risk now” (“Pelajari tentang risiko diabetes Anda sekarang”) to encourage the ad viewers to click on the ad and visit the campaign website on which they could conduct the risk screening test. Technically, we ran two different campaigns, one for each of the targeted regions, and then pooled the data for the analysis. Each ad received an equal budget of US $ 5 per day, summing to a total daily budget of US $ 50 for both cities. In terms of the target population, we restricted the audience demographically to Facebook users above the age of 35 and geographically to users living in either Jakarta or Yogyakarta.

The campaign objective was chosen to optimize “conversions”, with conversion programmed to be equal to completion of the screening questionnaire. Setting “conversions” as the campaign objective (instead of the other two possibilities “awareness” or “consideration”) allowed us to focus on possible screening questionnaire completers who would thus gain from the campaign, while simultaneously preventing showing the ads to seemingly uninterested users. This conversion objective required the generation of a so-called Facebook Pixel code, which had to be embedded in the code of the website to which the ad viewers were redirected. Facebook could then use this Pixel to track user actions taking place on our website and optimize accordingly. This implies that after a learning phase, Facebook’s algorithm aimed to show the ads to individuals more likely to click on the ads and to complete the screening questionnaire, based on the characteristics of earlier completers. The success of the algorithm is confirmed by the positive trend in the number of daily clicks and conversions over time as presented in Fig. 5 . After a first peak in link clicks, most likely driven by immediate reactions from viewers always responding to such ads, the learning phase sets in and translates into a positive trend in clicks and conversions. While this internal algorithm exaggerates a selection bias per ad if the conversion objective is used in regular campaigns 68 , the use of the A/B split test ensured that, conditional on being in the target audience, the ad version the user saw was random. This randomization procedure allowed us to compare the different ads based on their effectiveness in generating clicks and conversions, i.e., completed screening questionnaires.

Figure 5 displays the time trend in a daily link clicks and b daily conversions (i.e., completed screening questionnaires). Vertical gray dashed lines indicate Sundays.

Campaign website

After clicking on one of the ads in Facebook, individuals were redirected to the landing page of the campaign website. Before being able to browse further on the website, the participants were informed about our privacy policy and that data generated on the website were used for an academic study. For both, they had to indicate their informed consent. Individuals were then offered the opportunity to complete a diabetes risk screening questionnaire on this website similar to the diabetes risk test of the American Diabetes Association and the diabetes FINDRISC (Finnish Diabetes Risk Score) screening test. The questionnaire version we used is an adapted and translated version specifically for the Indonesian population. The original FINDRISC questionnaire was developed to identify individuals at risk of diabetes using a Finnish population sample 69 . Since then, the questionnaire has been evaluated and validated many times and has been adjusted to different populations and country samples 70 , 71 , 72 , 73 . The original diabetes risk test of the American Diabetes Association dates back to 1993 and has likewise been adapted multiple times 74 . The version we used is based on the diabetes risk test of the American Diabetes Association 75 , 76 , the ModAsian FINDRISC for Asia, the FINDRISC Bahasa Indonesia 77 , and the Malay version of the American Diabetes Association diabetes risk test 74 . It consisted of eleven questions which could be answered in approximately 90 seconds. Based on the individual answers, a risk score between 0 and 16 points was calculated and participants received an assessment of their personal risk rated as low risk (0-3 points), medium risk (4-5 points), or high risk (6 or more points). Additionally, the assessment contained recommendations on how to keep the risk low, how the diabetes risk can be reduced and to visit a health center or a physician if the risk score was too high.

The website also included a page with factual information on diabetes in Indonesia, including the distribution of prevalence rates across the country, behavioral risk factors, as well as information about how diabetes can be diagnosed and how it can be treated. Furthermore, we provided detailed information about the institutions involved in the research activities, the aim of the campaign, and the notification that the campaign was purely educational and could not replace a professional health visit or screening. We also asked participants to leave their e-mail addresses so that they could get follow-up information and continue to be involved in the study.

Follow-up survey

Six weeks after the end of the campaign we sent a follow-up survey to all these addresses to elicit information about actual compliance with the recommendations received. Since providing the e-mail address was voluntary and hence the sub-sample of respondents was subject to a potential self-selection bias, we provide a description of the sample that completed this follow-up survey and contrast it with the profile of the entire sample (see Results section). In this follow-up survey, we asked the respondents about their plans to comply with the received recommendations. Specifically, we asked whether they plan to schedule a professional medical screening (or have already done so), if yes, when and where they planned to go and if no, what their reasons were for not doing so. We also asked several questions about diabetes risk factors, symptoms, and health consequences, whether the respondent had health insurance, whether this was the first time they had conducted a diabetes risk test, whether they had already been diagnosed with diabetes, and whether they were currently on medication.

IRB approval and RCT registration

This study received ethical approval from the University of Passau Research Ethics Committee (15.03.2022, IRB Approval Number I-07.5090/2022). Informed consent was obtained from all participants who browsed our website. Informed consent for the experiment on Facebook is covered by Facebook’s data use policy. Identifiable images relating to persons in our ads are no patients and no written consent was required since ads were designed by ourselves with pictures taken from openly accessible stocks with license-free images. The study was pre-registered at the AEA RCT Registry (0008781, https://doi.org/10.1257/rct.8781 ). In the final manuscript/study, we deviated in some features from our initial analysis plan, partly for technical reasons, and marginally adjusted our hypotheses after the pilot study. These changes are explained in detail in an appendix to our pre-analysis plan (downloadable under the same registration number). The study was conducted without any support from or connection to Facebook (Meta group) and Facebook had no access to the responses that were generated on our website or during the follow-up survey.

Data availability

All data underlying this study are available from the authors upon request.

Code availability

All codes underlying this study are available from the authors upon request.

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Acknowledgements

We thank Ferdyani Yulia Atikaputri, Ayu Paramudita and Mardha Tilla Septiani for their research assistance. We also thank Gerard van den Berg, Annegret Kuhn, Robert Lensink and participants at seminars at the University of Groningen and the University of Passau as well as at the NCDE 2023 in Gothenburg, GDEC 2023 in Dresden and the Web Conference 2023 in Austin, Texas for very useful comments and suggestions. Part of this work was done while IW was at the Qatar Computing Research Institute, HBKU, Doha, Qatar, EYT was at Microsoft Research, Herzliya, Israel and at the Technion Israel Institute of Technology, Faculty of Industrial Engineering and Management, Haifa, Israel, and MF was at the University of Groningen, Department for Economics, Econometrics and Finance, Groningen, The Netherlands. We acknowledge financial support by the Open Access Publication Fund of the University Library Passau.

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Fritz, M., Grimm, M., Weber, I. et al. Can social media encourage diabetes self-screenings? A randomized controlled trial with Indonesian Facebook users. npj Digit. Med. 7 , 245 (2024). https://doi.org/10.1038/s41746-024-01246-x

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Gestational Diabetes Mellitus—Recent Literature Review

Robert modzelewski.

1 Endocrinology, Diabetology and Internal Medicine Clinic, Department of Internal Medicine, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland

Magdalena Maria Stefanowicz-Rutkowska

2 Department of Endocrinology, Diabetes and Isotope Therapy, Wroclaw Medical University, 50-367 Wroclaw, Poland

Wojciech Matuszewski

Elżbieta maria bandurska-stankiewicz.

Gestational diabetes mellitus (GDM), which is defined as a state of hyperglycemia that is first recognized during pregnancy, is currently the most common medical complication in pregnancy. GDM affects approximately 15% of pregnancies worldwide, accounting for approximately 18 million births annually. Mothers with GDM are at risk of developing gestational hypertension, pre-eclampsia and termination of pregnancy via Caesarean section. In addition, GDM increases the risk of complications, including cardiovascular disease, obesity and impaired carbohydrate metabolism, leading to the development of type 2 diabetes (T2DM) in both the mother and infant. The increase in the incidence of GDM also leads to a significant economic burden and deserves greater attention and awareness. A deeper understanding of the risk factors and pathogenesis becomes a necessity, with particular emphasis on the influence of SARS-CoV-2 and diagnostics, as well as an effective treatment, which may reduce perinatal and metabolic complications. The primary treatments for GDM are diet and increased exercise. Insulin, glibenclamide and metformin can be used to intensify the treatment. This paper provides an overview of the latest reports on the epidemiology, pathogenesis, diagnosis and treatment of GDM based on the literature.

1. Introduction

Gestational diabetes mellitus (GDM) is a state of hyperglycemia (fasting plasma glucose ≥ 5.1 mmol/L, 1 h ≥ 10 mmol/L, 2 h ≥ 8.5 mmol/L during a 75 g oral glucose tolerance test according to IADPSG/WHO criteria) that is first diagnosed during pregnancy [ 1 ]. GDM is one of the most common medical complications of pregnancy, and its inadequate treatment can lead to serious adverse health effects for the mother and child [ 1 , 2 ]. According to the latest estimates of the International Diabetes Federation (IDF), GDM affects approximately 14.0% (95% confidence interval: 13.97–14.04%) of pregnancies worldwide, representing approximately 20 million births annually [ 3 ]. Mothers with GDM are at risk of developing gestational hypertension, pre-eclampsia and termination of pregnancy via Caesarean section [ 4 ]. In addition, GDM increases the risk of complications, including cardiovascular disease, obesity, and impaired carbohydrate metabolism, leading to the development of type 2 diabetes (T2DM) in both mother and infant [ 5 , 6 , 7 ]. The increase in the incidence of GDM also leads to a significant economic burden and deserves greater attention and awareness [ 8 ].

Despite numerous studies, the pathogenesis of GDM remains unclear, and the results obtained so far indicate a complex mechanism of interaction of many genetic, metabolic and environmental factors [ 9 ]. The basic methods of treating GDM include an appropriate diet and increased physical activity, and when these are inadequate, pharmacotherapy, usually insulin therapy, is used. In developing countries, such as Brazil, oral hypoglycemic agents are also used, mainly metformin and glibenclamide (glyburide) [ 10 ]. The prevention and appropriate treatment of GDM are needed to reduce the morbidity, complications and economic effects of GDM that affect society, households and individuals. Though it is well established that the diagnosis of even mild GDM and treatment with lifestyle recommendations and insulin improves pregnancy outcomes, it is controversial as to which type and regimen of insulin are optimal, and whether oral agents can be used safely and effectively to control glucose levels.

2. Aim of the Study

A review of current literature reports on epidemiology, pathogenesis, diagnosis and treatment of GDM.

3. Material and Methods

The study presents an analysis of data that are currently available in the literature that concern the epidemiology, pathogenesis, diagnosis and treatment of GDM. The study was based on reviews, original articles and meta-analyses published in English in the last 10 years.

A literature search was conducted from 1 January 2021 to 31 March 2022 using Web of Science, PubMed, EMBASE, Cochrane, Open Grey and Grey Literature Report. MeSH terms, including “gestational diabetes”, “pregnancy induced diabetes”, “hyperglycemia”, “glucose intolerance”, “insulin resistance”, ”prevalence”, “incidence”, “GDM treatment” and “behavioral treatment”, were used alone or in combination.

4. Results and Discussion

4.1. epidemiology.

The growing problem of overweight and obesity around the world significantly contributes to the steady increase in the incidence of diabetes, including GDM in the population of women of reproductive age [ 11 ]. According to the 2019 report by the International Diabetes Federation (IDF), more than approximately 20.4 million women (14.0% of pregnancies) presented with disorders of carbohydrate metabolism, of which approximately 80% was GDM, i.e., about one in six births was affected by gestational diabetes [ 3 ]. Table 1 presents the analysis of the geographical distribution of GDM [ 3 , 12 ].

The geographical distribution of GDM [ 3 , 12 ].

4.2. GDM Risk Factors

The incidence of hyperglycemia in pregnancy increases with age. According to Mosses et al., GDM was diagnosed in 6.7% of pregnancies in general, but in 8.5% of women over 30 years of age [ 13 ]. Lao et al. showed the highest risk of developing GDM at the ages of 35–39 compared with younger pregnant women (OR 95% CI: 10.85 (7.72–15.25) vs. 2.59 (1.84–3.67)) [ 14 ]. These observations were confirmed by IDF data showing the highest percentage of pregnancies with GDM reaching 37% at the ages of 45–49, which was also conditioned by a lower number of pregnancies with an accompanying general higher percentage of diabetes in this population [ 3 ]. The delivery of a macrosomic child is another important factor that may increase the risk of both GDM and DM2 by up to 20% [ 15 ]. Even after taking into account the age of the woman, pluriparity remains in a linear relationship to the incidence of GDM [ 16 ]. GDM in a previous pregnancy increases the risk of reoccurrence by more than six times [ 17 ]. In women with a BMI of at least 30 kg/m 2 , the GDM frequency is 12.3%, and in women with first-line relatives that have a history of GDM, it is 11.6%. The combination of these two factors increases the risk of GDM up to 61% of cases [ 4 , 18 , 19 ]. More than twice the percentage of pregnancies with GDM was observed in women that were previously treated for polycystic ovary syndrome (PCOS) [ 20 ]. Recent studies indicated that the prevalence of GDM is related to the season and that GDM prevalence increases during the summer compared with winter [ 21 , 22 , 23 ]. Moreover, a 50% increase in the incidence of GDM in pregnancies resulting from in vitro fertilization was described [ 24 ].

4.3. Diagnosing GDM

The decades-long polemic about the diagnosis of GDM has covered two issues: whether to include all pregnant women or only those with risk factors, and whether to use one- or two-stage diagnostic procedures. A GDM diagnosis is only possible if a previous diagnosis of diabetes (i.e., type 1 or type 2 diabetes) had been excluded early in the pregnancy. Screening of only risk groups may result in GDM not being diagnosed in as many as 35–47% of pregnant women, which is certain to affect obstetric results [ 25 ]. The results of the Hyperglycemia Adverse Pregnancy Outcome (HAPO) study of 23,316 women gave a clear outcome that elevated glycemia (but below the threshold for overt diabetes mellitus) showed a linear relationship with the occurrence of maternal and neonatal complications expressed as large for gestational age (LGA) endpoints, the frequency of Caesarean sections, neonatal hypoglycemia and the concentration of the umbilical C-peptide [ 26 ]. The current criteria for the diagnosis of GDM introduced by The International Association of Diabetes and Pregnancy Study Groups (IADPSG), which were based on the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) results, found a threefold increase in GDM diagnoses, which suggests an earlier underestimation. The HAPO group sought to identify new screening values that would better identify pregnancies at risk for perinatal complications. The HAPO study demonstrated a positive linear relationship between screening glucose values and adverse perinatal outcomes. Moreover, the study authors found that perinatal risks began to increase in women with glucose values that were previously considered “normal” [ 27 , 28 ]. Therefore, nowadays, the basis of GDM diagnostics is the administration of 75 g of glucose between 24 and 28 weeks of pregnancy in all pregnant women without previously diagnosed diabetes. The treatment of even mild forms of glucose intolerance in GDM offers an added benefit, as demonstrated by the Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) and Maternal-Fetal Medicine Units Network (MFMU). It was shown that the frequency of obstetric complications is reduced depending on hyperglycemia and pregnancy weight gain. In the ACHOIS study, the composite endpoint (neonatal death, perinatal injury, hyperbilirubinemia, neonatal hypoglycemia and hyperinsulinemia) was significantly reduced with antihyperglycemic intervention, and there was also a lower weight gain (by 1.7 kg on average) and a lower incidence of LGA. In the MFMU study, no changes were noted in the composite endpoint, but the incidence of LGA and shoulder dystocia decreased significantly [ 2 , 29 , 30 ]. The results of these studies showed that most scientific societies implement the recommendations of the IADPSG from 2010 and WHO from 2013 into their daily practice. The introduction of the IADPSG criteria for the screening of GDM increased the prevalence by threefold, albeit with no substantial improvements in GDM-related events for women without risk factors except for reduced risks for LGA, neonatal hypoglycemia and preterm birth [ 31 ]. This led to further research on a group of patients with GDM. In a large randomized trial (among 23,792 pregnant women), Hillier et al. showed that one-step screening, as compared with two-step screening, doubled the incidence of the diagnosis of GDM, but did not affect the risks of LGA, adverse perinatal outcomes, primary Caesarean section, or gestational hypertension or pre-eclampsia [ 32 ]. The GEMS Trial assessed two diagnostic thresholds for GDM—namely, the currently used, higher diagnostic criteria and the IADPSG, lower diagnostic criteria—for their effects on fetal growth, perinatal morbidity, maternal physical and psychological morbidity, and health service utilization. The recently published results of the GEMS Trial showed that lower glycemic criteria (fasting plasma glucose level of at least 92 mg/dL, a 1 h level of at least 180 mg/dL or a 2 h level of at least 153 mg/dL) for the diagnosis of GDM did not result in a lower risk of a large-for-gestational-age infant than the use of higher glycemic criteria (fasting plasma glucose level of at least 99 mg/dL or a 2 h level of at least 162 mg/dL) [ 33 ]. This latest study is another important point in the discussion of the best diagnosis method for GDM. Table 2 presents the criteria for the diagnosis of GDM according to different scientific societies.

The criteria for the diagnosis of GDM according to different scientific societies.

Notes: ADA—American Diabetes Association, ACOG—American College of Obstetricians and Gynecologists, DCCPG—Diabetes Canada Clinical Practice Guidelines, DIPSI—Diabetes in Pregnancy Society Group India, EASD—European Association for the Study of Diabetes, FIGO—International Federation of Gynecology and Obstetrics, ADIPS—Australasian Diabetes in Pregnancy Society, WHO—World Health Organization, IADPSG—International Association of the Diabetes and Pregnancy Study Groups, NICE—National Institute for Health and Care Excellence. 1 There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on a 1 h post-load value. 2 Refers to the whole blood glucose level. 3 Recommends either the IADPSG one-step or two-step approach; initial screening by measuring plasma or serum glucose concentration 1 h after a 50 g oral glucose load (GCT). Those exceeding the cut-off perform either a 100 g OGTT or 75 g OGTT, requiring two or more venous plasma concentrations to be met or exceed the threshold. 4 Listed in the preferred approach, the alternate approach is the IADPSG, which uses a non-fasting 75 g OGTT. 5 Uses a non-fasting 75 g OGTT.

Many potential markers of GDM occurrence are being described more and more frequently. The greatest hopes are connected with afamine, adiponectin and 1,5-anhydroglucitol [ 34 , 35 ]. Due to the fact that in many countries, prenatal care is provided by gynecologists who can consult other specialists, it seems important to develop predictive models that allow for the identification of women at the highest risk for gestational diabetes in early pregnancy. The Benhalim-2 2020 model, which takes into account interview and biochemical data (propensity score model: history of GDM, FPG, height, triglycerides, age, ethnic origin, first trimester weight, family history of diabetes, HbA1c), showed the highest sensitivity [ 36 ].

4.4. Pathogenesis of Carbohydrate Metabolism Disorders in Pregnancy

Several factors may be responsible for the occurrence of GDM, the most important of which are insulin resistance and beta cell dysfunction, as well as genetic, environmental and dietary factors.

4.4.1. Insulin Resistance

In the pathogenesis of GDM, as in type 2 diabetes, a key role is played by insulin resistance and decreased insulin secretion relative to the patient’s needs. We observe GDM in both obese and lean women [ 37 ]. Insulin resistance induced by pregnancy overlaps with the pre-pregnancy insulin resistance that is already present in obese women, while in lean women, an impaired first phase of insulin secretion is also dominant [ 38 ]. Insulin resistance in pregnancy is predisposed by the diabetogenic effect of placental hormones (human placental lactogen (hPL), human placental growth hormone (hPGH), growth hormone (GH), adrenocorticotropic hormone (ACTH), prolactine (PRL), estrogens and gestagens), increased secretion of pro-inflammatory cytokines (tumor necrosis factor alpha (TNF-α), IL-6, resistin and C-reactive protein (CRP)), adiponectin deficiency, hyperleptinemia and central leptin resistance, impaired glucose transport in skeletal muscles, impaired insulin receptor signaling, and decreased expression and abnormal translocation of GLUT-4 to the cell membrane of adipocytes [ 39 , 40 , 41 ]. An increased secretion of insulin-antagonistic hormones (placental hormones, cortisol) during pregnancy results in an increased insulin resistance, which, at the end of the third trimester, reaches a value similar to full-blown type 2 diabetes [ 9 , 42 ]. Subclinical inflammation in pregnant women as a result of the synthesis of pro-inflammatory cytokines in the placenta and adipose tissue also leads to insulin resistance [ 43 , 44 ]. So far, the effects on the development of insulin resistance due to TNF-α, IL-6 and C-reactive protein have been best studied. Kirwan et al. stated that an increase in insulin resistance, which is characteristic of pregnancy, most strongly correlates with the increase in TNF-α concentration, considering that TNF-α as a marker of insulin resistance during pregnancy [ 45 ]. Furthermore, hyperleptinemia in the first weeks of pregnancy is a predictor of the development of gestational diabetes. According to Qui, the determination of the leptin concentration ≥ 31.0 ng/mL in the 13th week of pregnancy causes a 4.7-fold increase in the risk of GDM compared with the risk at the level of leptinemia of ≤14.3 ng/mL. For every 10 ng/mL increase in leptin concentration, the risk of GDM increases by 21% [ 46 ]. At the same time, GDM is characterized by elevated concentrations of leptin, which leads to hyperleptinemia [ 47 ]. However, pre-pregnancy BMI is a stronger predictor of leptinemia than GDM perse [ 48 ]. In women with gestational diabetes, the concentration of adiponectin is lower than in pregnant women without disturbances of carbohydrate metabolism, regardless of their pre-pregnancy BMI [ 49 ]. It was shown that a low adiponectin concentration in the first and second trimesters of pregnancy is a predictor of diabetes development in pregnancy [ 50 ]. In the Barbour study, a 1.5–2-fold increase in the level of the p85α PI-3-kinase regulatory subunit was found in both the muscle and adipose tissue of obese pregnant and pregnant GDM women compared to obese non-pregnant women. In women with GDM, a 62% increase in the phosphorylation activity of IRS-1 serine residues was found in striated muscle cells compared with the control group of pregnant women without GDM, which points to insulin resistance post-receptor mechanisms [ 43 ].

4.4.2. β-Cell Dysfunction

The analysis of insulin secretion disorders in GDM gives inconclusive results. The mechanisms of β-cell hypertrophy and proliferation, resulting in a 300% increase in insulin secretion in the first two trimesters of physiological pregnancy, is insufficient to explain GDM [ 9 , 39 ]. In the pathogenesis of GDM, we also observed the influence of autoimmune and genetic factors, such as the presence of anti-insulin and/or anti-insulin antibodies, which are at risk of developing DM1 and latent autoimmune diabetes in adults (LADA) [ 51 ]. In cross-sectional studies, the prevalence of mutations in the gene variants GCK, HNF1A, HNF4A, HNF1B and INS in maturity-onset diabetes of the young (MODY) was 0–5% [ 52 ]. Great hopes in the search for the genetic causes of GDM are associated with research on the single nucleotide polymorphism (SNP) related to the cyclin-dependent kinase 5 (CDK5) regulatory subunit associated protein1-like1 gene (CKDAL1). Their presence is associated with an impaired first phase of insulin secretion in DM2 and GDM and leads to a decrease in the mass of beta cells and impairment of their function, leading to GDM [ 53 , 54 ].

4.4.3. Other Factors

A study conducted in Spain showed that carriers of the gene rs7903146 T-allele who followed the Mediterranean diet in early pregnancy had a lower risk of developing GDM [ 55 ]. A growing body of research provides evidence of the importance of DNA methylation in the regulation of gene expression associated with metabolic disturbances in pregnant women and in the metabolic programming of the fetus in the setting of GDM-induced hyperglycemia [ 56 , 57 , 58 ]. In subcutaneous and visceral adipose tissue samples, the insulin receptor mRNA/protein expressions were significantly reduced in women with GDM ( p < 0.05) [ 56 ]. Mothers with GDM displayed a significantly increased global placental DNA methylation (3.22 ± 0.63 vs. 3.00 ± 0.46% (±SD), p = 0.013) [ 57 ]. Additional light was shed on the pathogenesis of GDM by studies on disorders of the placental proteome, where the placental proteome was altered in pregnant women affected by GDM with large-for-gestational-age (LGA), with at least 37 proteins being differentially expressed to a higher degree ( p < 0.05) as compared with those with GDM but without LGA [ 59 ]. In addition, Khosrowbeygi et al. showed that women with GDM had higher values of TNF-α (225.08 ± 27.35 vs. 115.68 ± 12.64 pg/mL, p < 0.001) and lower values of adiponectin (4.50 ± 0.38 vs. 6.37 ± 0.59 µg/mL, p = 0.003) and the adiponectin/TNF-α ratio (4.31 ± 0.05 vs. 4.80 ± 0.07, p < 0.001) than normal pregnant women. The ratio of adiponectin/TNF-α, which decrease significantly in GDM compared with normal pregnancy, might be an informative biomarker for the assessment of pregnant women at high risk of insulin resistance and dyslipidemia and for the diagnosis and therapeutic monitoring aims regarding GDM [ 60 ].

4.5. COVID-19 Pandemic and GDM

The second severe acute respiratory distress syndrome (SEA) coronavirus (SARS-CoV-2) causes an acute respiratory disease called coronavirus disease 2019 (COVID-19). There are limited data on the impact of SARS-CoV-2 infection on the onset and course of GDM. A living systematic review and meta-analysis of 435 studies reported the incidence of COVID-19 in pregnant women of approximately 10% (7–14%) [ 61 ]. The COVID-19 pandemic has caused organizational difficulties related to the correct diagnosis of GDM. In Anglo-Saxon countries, in order to minimize the risk of infection with SARS-CoV-2, replacement of the three-point OGTT was proposed and the assessment of fasting blood glucose and Hba1c were introduced. Postpartum screening postponement and the use of telemedicine were also offered [ 62 ]. However, simplifying the diagnosis of GDM in order to avoid the risk of COVID-19 infection was unfortunately associated with the risk of not diagnosing GDM by as much as 20–30%, which may affect obstetric outcomes [ 63 , 64 , 65 ]. This was confirmed by another study that showed that in the “COVID era”, diagnostics toward GDM cannot be abandoned and the procedures for its detection cannot be simplified [ 66 ]. The COVID-19 pandemic increased the incidence of GDM in 2020 compared with 2019 (13.5% vs. 9%, p = 0.01), especially in women in the first trimester of pregnancy. Experiencing lockdown during the first trimester of gestation increased the risk of GDM in these women by a factor of 2.29 ( p = 0.002) compared with women whose pregnancies occurred before and after lockdown [ 67 ]. This is undoubtedly influenced by the sedentary lifestyle of women during the pandemic and reduced physical activity, most often caused by the fear of leaving their homes due to COVID-19 [ 68 ]. The “lockdown effect” caused a marked deterioration in glycemic control, an increase in the percentage of HBA1c, and weight/BMI gain in patients with DM2 and GDM [ 69 , 70 ].

4.6. Treatment of Gestational Diabetes

Regarding women with GDM, due to the lack of randomized clinical trials, it is extremely difficult to propose an unambiguous and uniform model of management in order to achieve obstetric results similar to the population of healthy women. The treatment of GDM is based on consensus and expert opinion. Analyses of Cochrane Database Reviews showed the lack of unambiguous data on the correlation between the intensity of glycemic control and obstetric outcomes [ 71 ]. Based on a meta-analysis from 2014–2019, Mitanchez et al. indicated that the greatest impact on reducing the number of obstetric complications is achieved by combining dietary treatment with exercise [ 72 ].

4.6.1. Nutritional Treatment

Nutritional recommendations help women to achieve normoglycemia, optimal weight gain and proper development of the fetus, and the introduction of a pharmacological treatment does not release the mother from the obligation to follow the diet [ 73 ]. In GDM, it is necessary to develop an individual nutritional plan based on glycemic self-control, optimal weight gain based on pre-pregnancy BMI, and a calculation of energy requirements and macronutrient proportions, as well as taking into account the mother’s nutritional preferences, together with work, rest and exercise [ 73 ]. Chao et al. indicated better results when using individualized recommendations for a specific woman with GDM in contrast to general recommendations [ 74 ]. It is recommended to eat three main meals and 2–3 snacks a day, often with a snack around 9:30 pm to protect against nocturnal hypoglycemia and morning ketosis [ 6 ]. In a prospective observational study using the 24 h online diet and glycemic tool (“Myfood24 GDM”), better glycemic control was demonstrated with more frequent meals [ 75 ]. In women with GDM, carbohydrates are the most important macronutrient, and their high consumption can cause hyperglycemia. However, glucose is the main energy substrate of the placenta and fetus, and thus, is necessary for their proper growth and metabolism [ 76 ]. According to the ATA, the content of carbohydrates in the diet should constitute 40–50% of the energy requirement, not less than 180 g/day, and consist mainly of starchy foods with a low glycemic index (GI) [ 6 , 73 ]. The recommended dietary fiber intake is 25–28 g per day, which means a portion of about 600 g of fruit and vegetables per day with a minimum of 300 g of vegetables, whole grain bread, pasta and rice [ 73 , 77 , 78 ]. Protein should constitute about 30% of the caloric value, that is about 1.3 g/kg of b.w./d, with the minimum recommended daily intake of 71 g of protein [ 73 ]. Increased intakes of plant protein, lean meat and fish, and reduced intakes of red and processed meats are beneficial in the treatment of GDM and may improve insulin sensitivity [ 79 , 80 ]. A diet with a high fat content is contraindicated (20–30% of the caloric value is recommended, including < 10% saturated fat), as it leads to placental dysfunction and infant obesity, increased inflammation and oxidative stress, and impaired maternal muscle glucose uptake [ 80 , 81 , 82 ]. The consumption of saturated fat should be limited in favor of the consumption of the polyunsaturated fatty acids (PUFA) n-3 (linolenic acid) and n-6 (linoleic acid), which are the most important fatty acids for fetal growth and development. A total intake of n-3 in the amount of 2.7 g/day is considered safe during pregnancy [ 77 ], while additional fish oil supplementation gives inconclusive results [ 83 ]. The recommended weight gain in pregnancy amounts to on average 8–12 kg, depending on the initial body weight ( Table 3 ) [ 78 ].

Weight gain in relation to baseline body weight (BMI).

A weight gain of over 18 kg is associated with a twice higher risk of macrosomia [ 84 , 85 ]. Many studies show an increase in the need for vitamins and minerals in pregnancy, mainly folic acid, vitamin D and iron. All pregnant women are recommended to supplement daily with 400 µg of folic acid and 5.0 µg of vitamin D; additionally, depending on the dietary intake, 500–900 mg of calcium and 27–40 mg of iron are recommended [ 77 ]. The influence of gut microbiota on the development of GDM is interesting [ 86 ]. So far, it was shown that in women in the third trimester of pregnancy, GDM was associated with altered intestinal microflora [ 87 ]. However, in the conducted studies on the beneficial effects of probiotics in the prevention or treatment of GDM, the results are still inconclusive [ 88 , 89 , 90 , 91 ].

The main quality-oriented recommendations include the need to limit or eliminate processed products with a high content of salt, sugar and fats; avoiding unpasteurized milk, raw meat, alcohol and caffeine; and ensuring proper hydration of at least 2 L of water per day. In addition, the effect of the Dietary Approach to Stop Hypertension (DASH) diet on glycemic control was confirmed, and Sarathi et al. indicated that eating a high-protein diet based on soy products reduces insulin requirements in GDM patients [ 92 , 93 ]. Myoinositol (vitamin B8) supplementation or a diet rich in the MYO-INS isomer may improve glycemic control in GDM [ 94 , 95 ].

4.6.2. Exercise in GDM

In women with GDM, the quantitative and qualitative recommendations for exercise are ambiguous in terms of improving glycemic control [ 96 ]. Obstetric indications and contraindications should be followed. If there are no contraindications, the available observational studies indicate the safety of physical activity during pregnancy [ 97 ]. Activities that can be safely started and continued are walking, cycling, swimming, selected pilates and low-intensity fitness exercises. It is safe to continue with (but not initiate) the following after consulting with one’s obstetrician: yoga, running, tennis, badminton and strength exercises. Pregnant people should avoid contact sports, horse riding, surfing, skiing and diving. The analysis of Aune et al. showed a reduction in the risk of GDM by 38% (RR 0.62, 95% CI 0.41–0.94) in physically active women [ 98 ]. An intervention study in overweight patients by Nasiri-Amiri et al. showed a 24% reduction in the risk of GDM in women exercising no more than three times a week [ 99 ]. In women with normal body weight, increased physical activity, according to an analysis by Ming et al., resulted in a lower weight gain in pregnancy without affecting the child’s weight or the frequency of Caesarean sections and a 42% reduction in the risk of GDM (RR 0.58, 95% CI 0.37−0.90, p = 0.01) [ 100 ]. A meta-analysis by Harrison et al. of eight randomized trials showed a significant reduction in fasting and postprandial glucose levels in women with 20–30 min of activity 3–4 times a week [ 101 ].

4.6.3. Pharmacological Treatment

Patients who cannot achieve glycemic targets with a properly balanced diet and elimination of dietary errors should be treated pharmacologically [ 29 ]. Most studies indicate insulin therapy as the safest form of treatment, and OAD (orally administrated drugs) treatment should be introduced only in the case of the patient’s lack of consent to insulin therapy or its unavailability [ 102 ]. Insulin therapy is carried out in the model of functional intensive insulin therapy (FIIT) with the use of subcutaneous injections. The safety of human insulin use in pregnancy was demonstrated [ 103 ]. The safety of the use of aspart and detemir analogs was confirmed in randomized trials [ 104 , 105 , 106 ] and the safety of lispro and glargine analogs was shown in observational studies [ 107 ]; none of the studies showed the passage of insulin analogs across the placenta [ 108 , 109 ]. Currently, metformin and glibenclamide are used as oral medications. Metformin and glibenclamide (glyburide) cross the placenta but are unlikely to be teratogenic [ 110 , 111 ]. The metformin in gestational (MiG) diabetes trial was a landmark study; it was one of the largest randomized controlled trials, in which 751 women with GDM prospectively assessed a composite of neonatal complications as the primary outcome and secondary outcome of neonatal anthropometry at birth. It was concluded that metformin alone, or with supplemental insulin, was not associated with increased perinatal complications. This trial was the basis of many subsequent studies to assess the safety and efficacy of metformin use in GDM [ 112 ]. Some studies showed that the use of metformin during pregnancy is associated with higher body weight, more visceral and subcutaneous tissue, and higher blood glucose levels when the offspring is 9 years old [ 113 ]. The use of glibenclamide, despite its high effectiveness, may result in a higher percentage of intrauterine deaths and neonatal complications, such as hypoglycemia, macrosomia and FGR (fetal growth restriction) [ 114 ]. Although there is an increasing amount of evidence that supports the use of glyburide or metformin for GDM, the American Diabetes Association (ADA) and American College of Obstetricians and Gynecologists (ACOG) still recommend insulin as the primary medical treatment if the glycaemic treatment goals are not achieved with lifestyle intervention due to the lack of evidence regarding the long-term safety of the alternatives [ 115 ]. Sodium-glucose cotransporter-2 (SGLT2) inhibitors block the transporter located in the proximal tubule of kidneys that promotes renal tubular reabsorption of glucose, which causes a decrease in blood glucose levels due to an increase in renal glucose excretion. Among women with diabetes, UTI during pregnancy can be associated with pyelonephritis and sepsis and potential long-term effects on the neonate [ 116 ]. There were some adverse events noted in animal reproductive studies, including adverse effects on renal development when SGLT2 inhibitors were used in the second and third trimesters, although there are no human data available. The use of SGLT2 inhibitors during pregnancy is not recommended [ 110 ]. Recently, some studies reported the use of GLP-1 agents in GDM. GLP-1 agents, including dipeptidyl peptidase-4 (DPP-4) inhibitor and glucagon-like peptide-1 receptor agonist (GLP-1 Ra), enhance insulin secretion in pancreatic b-cell and showed many benefits in treating diabetes mellitus type 2 but are not a common choice for GDM [ 117 , 118 ]. In a systematic review that included 516 patients and investigated the use of GLP-1 agents in GDM (at different time points, including the second trimester of pregnancy and after delivery), Chen et al. showed that the use of GLP-1 agents to normalize blood glucose and can improve insulin resistance, as well as reduce the rate of developing postpartum diabetes compared with a placebo. This systematic review suggested that a dipeptidyl peptidase-4 inhibitor and glucagon-like peptide-1 receptor agonist may be beneficial to GDM patients but need rigorously designed clinical trials to demonstrate this. In particular, whether it can be used during pregnancy to improve pregnancy outcomes or better used to prevent developing diabetes after delivery should be investigated [ 119 ]. The data of a randomized controlled trial, namely, The Treatment of Booking Gestational Diabetes Mellitus (TOBOGM), compared pregnancy outcomes among women with booking GDM receiving immediate or deferred treatment can provide new insights into the diagnosis and treatment of GDM [ 120 ].

5. Conclusions

GDM is one of the most common complications of pregnancy and confers lifelong risks to both women and their children. Observational data demonstrated a linear association between maternal glycemic parameters and risks for adverse pregnancy and offspring outcomes. SARS-CoV-2 infection will undoubtedly affect the risk of GDM. Many doubts regarding the diagnostic criteria and treatment of GDM are still under discussion. Treatment with insulin is effective, but costs and patient experiences limit its use in clinical practice. The use of metformin as a first-line agent for GDM remains controversial due to its transplacental passage and limited long-term follow-up data. Further clinical trials are necessary to use other oral hypoglycemic agents to treat GDM. It is very important for patients with GDM to receive behavioral therapy and to closely cooperate with the doctor. Future work in the field should include studies of both clinical and implementation outcomes, examining strategies to improve the quality of care delivered to women with GDM. The screening and treatment for GDM early in pregnancy are very controversial due to the lack of data from large randomized controlled trials. There is an urgent need for well-designed research that can inform decisions on the best practice regarding gestational diabetes mellitus screening and diagnosis.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, R.M., M.M.S.-R. and E.M.B.-S.; methodology, R.M. and W.M.; formal analysis, E.M.B.-S.; investigation, R.M. and M.M.S.-R.; resources, R.M., M.M.S.-R. and W.M.; data curation, E.M.B.-S.; writing—original draft preparation, R.M. and M.M.S.-R.; writing—review and editing, E.M.B.-S.; visualization, R.M. and M.M.S.-R.; supervision, E.M.B.-S.; project administration, R.M. and W.M. All authors have read and agreed to the published version of the manuscript.

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Conflicts of interest.

The authors declare no conflict of interest.

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