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Demands for community services and associated factors among residents in smart communities: a case study of xuzhou city.

1. Introduction

2. literature review and hypotheses, 2.1. different types of community services in smart communities, 2.2. factors influencing residents’ demands for community services in smart communities, 2.3. hypotheses of this study, 3.1. variables and measures.

• A succinct explanation of community services in smart communities and the intention of this survey;
• Respondents’ basic information;
• The measurement of respondents’ demands for community services in smart communities. The question “Are you in need of this type of community service?” was used in the measurement and residents’ responses were measured as a dichotomous variable, with 1 representing a need for this type of community service and 0 otherwise. In light of the literature review, seven categories of community services in smart communities mentioned above were chosen as outcome variables and measured through residents’ responses to the question;
• Factors influencing respondents’ demands for community services in smart communities, including respondents’ sociodemographic characteristics, living characteristics, economic characteristics, and individual attitude characteristics.

3.2. Sampling and Data Collection

3.3. statistical model and analysis, 4.1. descriptive statistics of the respondents, 4.2. residents’ demands for community services in smart communities in xuzhou, 4.3. results of the binary logistic regression test, 4.3.1. assessment of model fit, 4.3.2. validation of predicted probabilities, 4.3.3. explanation of coefficients in the binary logistic regression, 5. discussion, 6. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, abbreviations.

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Chen, J.; Wang, L.; Gu, T.; Wang, C.; Hao, E. Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City. Int. J. Environ. Res. Public Health 2023 , 20 , 3750. https://doi.org/10.3390/ijerph20043750

Chen J, Wang L, Gu T, Wang C, Hao E. Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City. International Journal of Environmental Research and Public Health . 2023; 20(4):3750. https://doi.org/10.3390/ijerph20043750

Chen, Jiongxun, Linxiu Wang, Tiantian Gu, Chenyang Wang, and Enyang Hao. 2023. "Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City" International Journal of Environmental Research and Public Health 20, no. 4: 3750. https://doi.org/10.3390/ijerph20043750

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Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City

Affiliation.

• 1 School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China.
• PMID: 36834442
• PMCID: PMC9964050
• DOI: 10.3390/ijerph20043750

Smart community enables a sustainable and livable community future, in which residents' demands play an important role in its success. Though great efforts have been made to encourage residents' participation in the implementation of smart communities, inefficient service supply still exists. Thus, this study aimed to classify residents' demands for community services in smart communities and to explore relevant influencing factors based on the developed conceptual framework. Data from 221 respondents in Xuzhou city of China were analyzed by using binary logistic regression. The results indicated that more than 70% of respondents had demands for all community services in smart communities. Moreover, the demands were influenced by distinct factors, including sociodemographic characteristics, living characteristics, economic characteristics, and individual attitude characteristics. The types of community services in smart communities are clarified and fresh insights are provided into associated factors related to residents' demands for these services in this study, through which enhanced provision of community services and effective implementation of smart communities can be achieved.

Keywords: binary logistic regression; influencing factors; residents’ demands; smart community.

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The authors declare no conflict of interest.

The conceptual framework for this…

The conceptual framework for this research.

Respondents’ expressed demands for 39…

Respondents’ expressed demands for 39 types of community services in smart communities.

A combination of the ROC…

A combination of the ROC curves for initial demands and prediction performance. Note:…

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Community-Based Supports and Services for Older Adults: A Primer for Clinicians

Eugenia l. siegler.

1 Division of Geriatrics and Palliative Medicine, Weill Cornell Medical College, New York, NY 10065, USA

Sonam D. Lama

Michael g. knight.

2 Department of Medicine, New York Presbyterian Hospital, Weill Cornell Campus, New York, NY 10065, USA

Evelyn Laureano

3 Neighborhood Self Help by Older Persons Project (SHOPP), Bronx, NY 10459, USA

M. Carrington Reid

Although 20% of adults 60 years and older receive community-based supports and services (CBSS), clinicians may have little more than a vague awareness of what is available and which services may benefit their patients. As health care shifts toward more creative and holistic models of care, there are opportunities for CBSS staff and primary care clinicians to collaborate toward the goal of maintaining patients’ health and enabling them to remain safely in the community. This primer reviews the half-century history of these organizations in the United States, describes the most commonly used services, and explains how to access them.

1. Introduction

Community-based supports and services (CBSS) are designed to help community-dwelling older adults remain safely in their homes and delay or prevent institutionalization. CBSS provide (and act as a link to) specific resources for older adults and their caregivers that include wellness programs, nutritional support, educational programs about health and aging, and counseling services for caregivers, as well as general assistance with housing, finances, and home safety. CBSS also provide opportunities for community and civic engagement through various volunteer programs [ 1 ] and can enhance individuals’ skills and attitudes “to live in and gain more control over local aspects of their communities” [ 2 ].

More than 20% of older adults (i.e., those aged 60 and above) currently receive CBSS [ 3 ]. Older adults who use these services need them: over 90% of service users have multiple chronic conditions [ 4 ] and corresponding activity of daily living (ADL) deficits [ 5 ]. With the rapid aging of our population, even as overall health improves the number of older adults who could benefit from CBSS is expected to increase significantly in the coming years [ 6 ].

A recent nationwide survey of community-dwelling older adults found that a substantial majority were very interested in receiving information about CBSS [ 7 ]. However, respondents often did not know the range of services provided or where (or how) to access them. Survey respondents viewed health care providers as one of their major sources for information about CBSS and were less likely to contact community-based agencies directly [ 7 ]. Many older adults and caregivers feel most comfortable discussing health and social issues with their health care provider. As such, health care providers are ideally positioned to educate older patients and their caregivers about CBSS and to refer them for services and supports when appropriate.

There is little information in the literature about health care providers’ knowledge of and referral patterns to agencies providing CBSS. One Canadian study published almost 25 years ago found that physicians lacked basic information about these services; almost half (47%) acknowledged that lack of information contributed to their failure to refer patients for CBSS [ 8 ]. Although we did not identify any recent studies on this topic, we suspect that most health care providers still lack basic knowledge about the types of services provided by these agencies, which types of patients are eligible to receive them, and how to refer older patients (and/or caregivers) for services when appropriate.

This paper seeks to address this gap by (1) briefly describing the history of and funding sources for agencies providing CBSS; (2) defining the specific types of CBSS available and describing several types of agencies that provide them; (3) defining who is eligible to receive these services; and finally (4) providing practical tips about how to access CBSS. For the purposes of this paper, we define an agency providing CBSS as one that delivers services (e.g., home delivered meals) or programs (e.g., chronic disease management classes at senior centers) in a community-based setting. We exclude certified home health services (such as visiting nurse or home physical therapy) and state Medicaid waiver programs to focus the discussion on individual organizations less familiar to clinicians.

2. A Brief History of Agencies Providing Community-Based Supports and Services and Their Funding Sources

Although clinicians ultimately must look locally to find out what their patients need and which CBSS are available to help address that need, the origins of nationally supported CBSS begin in Washington. Locally run agencies providing CBSS owe their growth to a federal infrastructure that has enabled and supported them through funding for administration, services, and demonstration projects.

Federal funds were first allocated for social service programs targeting older adults in 1952 [ 9 ]. More than a decade later, the passage of the Older Americans Act (OAA) in 1965—the same year Medicare and Medicaid were established—created the formal framework for large-scale federal support of agencies providing CBSS. The OAA established the Administration on Aging (AoA) and mandated creation of state Agencies on Aging to promote delivery of social services to older Americans [ 9 ].

Currently, the AoA is one of the units of the Administration for Community Living in the Department of Health and Human Services. The AoA is divided into five offices: (1) Supportive and Caregiver Services; (2) Nutritional and Health Promotion Programs; (3) Elder Rights; (4) American Indian, Alaskan Native and Native Hawaiian Programs; and (5) Long-Term Care Ombudsman Programs [ 10 ]. The national aging services network through which the AoA promotes home and community-based services consists of 56 state (and territorial) units on aging, 629 area agencies on aging (AAA), 256 Native American and Native Hawaiian organizations, together with the tens of thousands of direct service providers and volunteers [ 3 ].

The OAA funds services under several different titles. Title III, which accounts for nearly three-quarters of the AoA’s budget (1.2 billion dollars in 2012) [ 11 ], funds the State Units on Aging and AAAs. The target population consists of individuals aged 60 and over. Although there is no means testing, 30% of those receiving Title III services in fiscal year 2010 had an income below the federal poverty line [ 11 ]. People who participate in OAA programs also often need and receive non-Title III services, as well. For example 29% of those who receive home delivered meals also receive Medicaid; 22% of those who receive homemaker services also receive energy assistance [ 12 ].

Federal dollars only partially fund AAAs and the organizations they support, and a community organization’s funding is often quite precarious. Only 57% of senior centers received OAA funding in fiscal year 2012 [ 3 ]. New York City’s AAA, the NYC Department for the Aging, is the largest in the country with a proposed budget of over $262,000,000 for 2014–2015. Approximately 29% of this amount is expected to come from federal funds, with the remaining amount coming from state (14%) and municipal (57%) sources [ 13 ]. Thus, while the OAA has established an infrastructure to oversee, plan, and fund CBSS for older Americans, its budget is a fraction of what is needed to pay for all of the services provided by these agencies. The survival of the CBSS network depends on a combination of national, state, and local government support along with private contributions, business support, and other philanthropy.

3. Examples of Services and Supports Provided by Community-Based Organizations

Community-based organizations provide a broad range of programs for older adults and caregivers. Most health care providers are familiar with nutrition, homemaker, and transportation services as well as senior centers but many other services are available, including legal assistance and case management services for clients and counseling and respite services for caregivers. Table 1 lists the primary CBSS available for use by older adults and their caregivers; although broad in scope, the amount of services any one individual receives is often quite limited and not a substitute for formal or family caregiving.

Specific community-based supports and services.

From Administration on Aging: FY 2012 Report to Congress [ 3 ]. Each column is listed in descending order based on units of service.

The following section summarizes information about four commonly used community-based agencies that provide these services.

3.1. Nutrition Service Programs

Although often colloquially thought of as “Meals on Wheels,” subsidized nutrition encompasses a far broader range of services. Elder nutrition services constitute the largest OAA program [ 14 ]; total federal expenditures for the three main programs (Congregate Nutrition Services, Home Delivered Nutrition Service, and Nutrition Services Incentive Program) totaled $816,289,000 in fiscal year 2012. Despite this significant federal outlay, Title III support provides only 35% of funding for these meals; the rest of the funding comes from state and local government and philanthropic and private sources [ 3 ].

These Title III programs were designed not just to relieve “food insecurity” but also to promote socialization and physical health and well-being [ 14 ]. Socialization occurs in the setting of congregate meals that are served in the community through senior centers, day health programs, and other venues.

Nutritional service programs are directed toward those with significant impairments. A 2009 survey of recipients determined that 41% of those receiving congregate meals and 63% of those receiving home delivered meals had 6 or more chronic conditions. In this same study, 9% of those receiving congregate meals and 31% of those receiving home delivered meals reported at least 3 ADL limitations [ 4 ]. The effectiveness of nutrition support programs has been studied; data suggest that home delivered meals can reduce nursing home admissions [ 15 , 16 ].

3.2. Senior Centers

The first senior center (William Hodson) opened in 1943 in New York City. Over the past 70 years, senior centers have proliferated nationwide (totaling 10,000 in FY 2012 [ 3 ]) and now serve as “community focal points” and gateways to health, educational, social, and recreational services for as many as 1 million older adults every day [ 17 ].

Although there is a general sense that senior centers improve physical and mental health, this is not well investigated. Many studies examining the effectiveness of senior centers have been cross-sectional or had methodological weaknesses [ 18 ]; a few controlled trials have examined specific interventions (e.g., exercise, education) delivered at senior centers and suggested improved outcomes [ 18 , 19 ].

Over the past decade participation in senior centers has declined, especially for the younger, healthier segments of the older population [ 19 ], giving rise to a movement to create more flexible and responsive models that will attract a broader range of individuals and be able to meet a diversity of needs. Pardasani and Tompson [ 20 ] have investigated and classified innovative models into six types, reflecting foci on greater age diversity, health promotion, and intellectual stimulation:

• community centers for all ages,
• wellness centers for active adults over 50,
• lifelong learning/arts centers for adults over 50,
• continuum of care/transitions for older people to age in place,
• entrepreneurial centers focusing in employment and productivity,
• café programs for adults 50 years and over that mix age groups and provide a community space for meals, education, and entertainment.

Although locating a convenient senior center is an important first step, it is important to determine if choices are available, and if so, which senior center could most closely serve a patient’s needs.

3.3. Adult Day Services Centers

Adult day services (ADS) centers provide coordinated services in a community setting. There are three types: social, medical/health, and specialized (e.g., providing programs for demented individuals) [ 21 ]. ADS use is growing. As of 2010, there were 4,601 ADS centers in the USA, 98% of which were open Monday–Friday. Two-thirds of participants attend at least 3 days/week [ 22 ]. Three-fourths of these programs offer medication management for mental health disorders [ 23 ].

As with other CBSS, it is difficult to measure effectiveness in the absence of randomized controlled trials. Some studies have failed to demonstrate clearly positive outcomes [ 24 , 25 ], but others suggest that these programs may enhance quality of life and reduce stress [ 26 , 27 ].

3.4. Naturally Occurring Retirement Communities: An Example of a Creative Solution to a Demographic Challenge

Formally known as Naturally Occurring Retirement Community-Supportive Service Program (NORC-SSP), this model of care is geographically based rather than service based. The first NORC was created in 1986 at a housing development (Penn South Houses) in New York City to support a group of the elderly who had aged in place but required a support system to enable them to continue to live independently in the community [ 28 ]. The development partnered with a local social service agency (United Jewish Appeal Federation of New York) to establish the services necessary to convert what was an apartment complex into housing that could meet the needs of those in declining health. NORCs are public-private partnerships and receive support both from local agencies and the federal government, via Title IV of the OAA [ 29 ].

There are approximately 100 NORCs, half in New York and the rest scattered throughout the USA [ 30 ]. NORCs are formal organizations, with paid staff and volunteers who provide services including socialization, care coordination, and transportation, in addition to expedited referrals to other community services such as home health, nutrition, or legal services [ 29 ]. A newer alternative, known as Villages, is membership-driven and privately funded, originating most often in areas of greater wealth [ 30 ]. Another option is independent senior housing programs that employ service coordinators who link residents to CBSS. Coordinators’ positions can be funded locally or federally [ 31 ].

4. Locating and Determining Eligibility for Services and Supports

Navigating CBSS can be challenging. Eligibility for CBSS benefits depends upon a host of factors that are individual and agency/service-related. OAA requires only that clients be 60 and over; locally funded programs may require means testing. A brochure “You Gave, Now Save” published by the National Council on Aging lists the basic services and general information about eligibility and access: http// www.ncoa.org/assets/files/pdf/center-for-benefits/You-Gave-Now-Save-Guide-to-Benefits.pdf . Online, the Benefits Checkup ( https://www.benefitscheckup.org/ ) helps a patient or caregiver determine eligibility for services and benefits by entering personal information about needs, assets, and expenditures.

The services themselves can be located via local or national sites. Nationally, the AoA sponsors the Eldercare Locator, which is accessible via the web or phone. States or regions may have their own government information phone number or website offering assistance. Information about how to access CBSS is shown below.

Accessing Community-Based Services

• by zip code,
• by service,
• toll-free number: 1-800-677-1116.
• Family caregiver alliance navigator: https://caregiver.org/family-care-navigator .
• National Association of Area Agencies on Aging: http://n4a.membershipsoftware.org/content.asp?contentid=146 ,
• Administration on Aging’s AAA finder: http://www.aoa.gov/AoA_programs/OAA/How_To_Find/Agencies/find_agencies.aspx ,
• search at the state level: office or department of aging (which will usually list the AAAs by county),
• search by county (e.g., “Area Agency on Aging, Cayuga County, New York”).
• search globally (rather than just “meals on wheels”) for home delivered or congregate meals or nutrition assistance,
• counseling and other forms of nutrition support may be available,
• these will usually be called senior center but they may be listed as a subcategory under community resources or congregate meals,
• example 3: adult day services centers: http://nadsa.org/consumers/choosing-a-center/ ,
• example 4: naturally occurring retirement communities: https://www.norcs.org/ .

A newer model of agency, already available in 52 states/territories (as of 2013, 70% of the population was covered), is the Aging and Disability Resource Center [ 32 ]. These centers are programs jointly managed by the Administration for Community Living and Centers for Medicare and Medicaid Services (and in some cases, the Department of Veterans Affairs) to expedite and simplify access to long-term care services and hence the motto: “No wrong door.” The programs offer assistance with care transitions in order to help individuals avoid long-term institutionalization; they also help people access benefits such as Medicaid [ 33 ].

5. Counseling Patients and Caregivers Who Would Benefit from Community-Based Supports and Services

CBSS are underutilized by older adults and caregivers for several reasons, including a lack of awareness, reluctance, unavailability, and unaffordability [ 34 ]. Clinicians can address the first two of these barriers directly; social work services are occasionally necessary to help patients gain access to services that may substitute for those that are not local or require payment.

Even when services and programs are available, older patients and caregivers sometimes refuse them. They may lack experience in accessing services or have difficulty accepting that they need them [ 35 ]. They may resist congregating with “old people” or feel that services are not sensitive to their ethnic group. They may resent subjecting themselves to unnecessary requirements or loss of control; they may feel judged or may feel services are not specific to their needs [ 36 ]. It may be useful to anticipate these attitudinal barriers and provide evidence for the usefulness of local programs.

Assessing a patient’s faith community may also help the clinician when thinking about options for community support. Religious institutions are commonly a well-trusted component of affiliated seniors’ lives, especially in ethnic minorities where a level of mistrust of medical institutions can influence their receptiveness to medical senior/social services. Older adults are more likely to be affiliated with religious congregations and attend services (67–69% above age 65), with an even greater percentage participating in ethnic minority communities [ 37 ]. Many of these congregations have some sort of senior outreach, ranging from home visitations to more formalized programs.

Finally, visiting CBSS programs and meeting the staff can be invaluable for the clinician to provide personal experience and anecdote to go along with the generic advice. Local programs generally welcome the opportunity to have clinicians come in to do presentations on specific topics related to health; both the clinician and the CBSS can establish a mutually beneficial collaborative relationship.

6. Conclusion

Clinicians should develop familiarity with CBSS and the agencies that provide them. Knowledge of and coordination with CBSS are essential if clinicians are to create more flexible and responsive models of care (e.g., medical homes) for their older patients [ 38 , 39 ]. Services and supports provided by these agencies can be a critical link in helping older adults remain in the community.

Acknowledgments

The paper was supported by a grant from the John A. Hartford Foundation and an Edward R. Roybal Center for Translational Research on Aging Award (P30AG22845).

Conflict of Interests

Dr. M. Carrington Reid has been a consultant for Endo Pharmaceuticals. Dr. Eugenia L. Siegler receives royalties from Springer Publishing Company. There is not any other potential conflict of interests reported by the authors.

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• Published: 04 September 2024

Predicting anxiety treatment outcome in community mental health services using linked health administrative data

• Kevin E. K. Chai 1 ,
• Kyran Graham-Schmidt 2 ,
• Crystal M. Y. Lee 1 ,
• Daniel Rock 3 , 4 , 5 ,
• Mathew Coleman 6 ,
• Kim S. Betts 1 ,
• Suzanne Robinson 1 , 7 &
• Peter M. McEvoy 1 , 8  

Scientific Reports volume  14 , Article number:  20559 ( 2024 ) Cite this article

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• Health care

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• Psychiatric disorders

Anxiety disorders is ranked as the most common class of mental illness disorders globally, affecting hundreds of millions of people and significantly impacting daily life. Developing reliable predictive models for anxiety treatment outcomes holds immense potential to help guide the development of personalised care, optimise resource allocation and improve patient outcomes. This research investigates whether community mental health treatment for anxiety disorder is associated with reliable changes in Kessler psychological distress scale (K10) scores and whether pre-treatment K10 scores and past health service interactions can accurately predict reliable change (improvement). The K10 assessment was administered to 46,938 public patients in a community setting within the Western Australia dataset in 2005–2022; of whom 3794 in 4067 episodes of care were reassessed at least twice for anxiety disorders, obsessive–compulsive disorder, or reaction to severe stress and adjustment disorders (ICD-10 codes F40–F43). Reliable change on the K10 was calculated and used with the post-treatment score as the outcome variables. Machine learning models were developed using features from a large health service administrative linked dataset that includes the pre-treatment K10 assessment as well as community mental health episodes of care, emergency department presentations, and inpatient admissions for prediction. The classification model achieved an area under the receiver operating characteristic curve of 0.76 as well as an F1 score, precision and recall of 0.69, and the regression model achieved an R 2 of 0.37 with mean absolute error of 5.58 on the test dataset. While the prediction models achieved moderate performance, they also underscore the necessity for regular patient monitoring and the collection of more clinically relevant and contextual patient data to further improve prediction of treatment outcomes.

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Introduction.

Anxiety disorders are the most common class of mental illness in Australia, affecting 3.4 million adults aged 16 years and older or 17.2% of the population in 2020–2022 1 . Similarly in the United States, anxiety disorders are also the most common estimated to affect 30.6% of the population aged 18 years and older in 2020–2022 2 . These disorders are characterized by excessive worry, fear, and nervousness that can interfere with daily life. There are several different types of anxiety disorders, including generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobias. Historically, obsessive compulsive disorder and fear and stressor-related disorders (e.g., posttraumatic stress disorder) were considered anxiety disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV, American Psychiatric Association, APA, 1994) although more recent nosologies consider them separate but related classes of disorders (DSM-5, APA, 2013). Within the International Classification of Diseases (ICD version 10, 2019; ICD version 11, 2023), these disorders are three categories within the mental, behavioural or neurodevelopmental disorders.

Primary care is the main source of treatment for anxiety disorders and, where required, providers more commonly refer patients to private specialist services than to public services 3 . Nonetheless, community mental health services remain important for patients who cannot afford or access private providers 4 . Public services refer to government funded and operated specialised mental health care provided by community and hospital based ambulatory care services, such as outpatient and day clinics 5 and offer a variety of ongoing treatment options including psychotherapy, medication, and support groups. A continuing challenge for clinicians and services in all settings is to predict how well an individual will respond to treatment. There are many factors that can influence outcomes, such as the severity of the disorder, the patient's readiness for change, the quality of the treatment they receive, and external factors that reflect the overall complexity of human lives (e.g., relationship breakdown, financial hardship, workplace redundancy, bereavement) 5 , 6 , 7 .

Being able to accurately predict patient outcomes would be beneficial 7 , 8 , 9 , 10 . First, it would allow clinicians to tailor treatment plans to the individual needs of each patient, for example, by targeting known risk factors for disengagement or poor clinical outcomes. This could improve patient outcomes and reduce the need for patients to try multiple standardised treatments before finding one that works. Second, it would allow clinical planners in mental health services to allocate resources more effectively. For example, services could focus on providing more intensive treatment to patients who are at high risk of deterioration. Third, it could help identify patients who are unlikely to respond to treatment and may need additional support.

Promising methods for predicting patient outcomes for anxiety disorders and other mental illnesses include clinical prediction tools, patient-reported outcome measures, and machine learning 9 , 10 , 11 . These methods are commonly based on predictors such as patient demographics, clinical symptoms, treatment history, from different modes of data such as electronic health records, biometrics, and radiology and machine learning techniques such as logistic regression, random forests, support vector machines, gradient boosting and neural networks on datasets comprising of 4184 undergraduate students 9 and 1249 participants from a mental healthcare provider 11 .

Research on the prediction of treatment outcomes in mental health show that it is difficult, either because treatment outcomes genuinely do not vary based on individual differences or due to a range of methodological limitations, such as investigations of variables based on convenience rather than strong theory; the lack of consideration of the complex interplay between relational and content components of psychotherapy; low statistical power due to studies being designed to evaluate main effects of treatments rather than moderators of symptom change; overly homogenous samples due to exclusion criteria in randomised trials; over-reliance on significance testing without due consideration to effect sizes; failure to probe interactions to understand patterns of effects; and neglecting non-linear relationships within the context of complex relationships for humans in the real world 8 , 12 .

The alternative of relying on clinician intuition is also fraught. The biases clinicians bring to predicting psychotherapy outcomes have been long known 13 , 14 , 15 . Researchers have recently suggested that machine learning approaches that use large databases, theory-informed parameters and include complex relationships with multiple predictors of responder status, could address many of these issues 8 , 16 , 17 . Models that explain patterns in historical data and predict future outcomes, would hold promise for informing and improving the quality of care for people with anxiety disorders.

The aims of this study were to (a) investigate associations between demographic, treatment, and clinical variables and changes in psychological distress while patients were engaged with community mental health services and (b) develop machine learning models to predict reliable change in Kessler (K10) psychological distress scores using a patient’s pre-treatment (K10) scores within a community mental health setting and their past health service interactions for anxiety disorders. No previous research has used a large sample of demographic, clinical, and treatment service data administratively collected within community mental health services over a 17-year period to predict changes in psychological distress using machine learning models.

Study population

This study was approved by the Department of Health Western Australia Human Research Ethics Committee (approval number: RGS0000004782) and the Curtin University Human Research Ethics Committee (approval number: HRE2022-0001) with a waiver of informed consent obtained from the Department of Health Western Australia Human Research Ethics Committee. All methods in this study were performed in accordance with the relevant guidelines and regulations.

The study cohort was collated from a linked mental health dataset provided by the Department of Health Western Australia which is described elsewhere 18 . The linked dataset is comprised of records related to mental health assessments, community mental health service usage, emergency department presentations and inpatient admissions from 2005–2022.

For this study, we restricted the dataset to records from community mental health services where an anxiety disorder (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD): F40–F43) 19 was recorded at any time in the episode of care and to episodes of care with at least two assessments (pre and post treatment ≥ 2 weeks and ≤ 4 months apart) for determining the outcome of the treatment. Based on community mental health dataset collection rules, assessments are not to be reported for brief community interventions (< 2 weeks) and that assessments should be completed at least every three months (we adjusted to 4 months to allow delays and scheduling issues). Data is included from eligible patient episodes of care, with the first pre/post assessment used for each individual episode. Allowing multiple care episodes per patient better represents real-world conditions, providing a more accurate evaluation of the predictive model’s performance on each patient encounter. We conducted a sensitivity analysis comparing the use of single and multiple episodes of care in Supplementary Discussion 1 . ICD-10 was used as 99% of records in the community mental health data collection period within the study population used this classification.

The dataset preparation steps for defining the study population (Table 1 ) and the number of records from each anxiety disorder ICD-10 code (Table 2 ) are presented below.

Primary outcome measure

The K10 assessment is a self-reported measure of anxiety and depression symptoms characteristic of the broad construct of psychological distress 20 . It comprises of 10 questions about emotional states assessed on a five-level response scale (1 = none of the time, 2 = a little of the time, 3 = some of the time, 4 = most of the time, 5 = all of the time). The responses from the 10 questions can be summed to a total ranging from 10 to 50, where lower scores represent lower levels of distress. The K10 has high internal reliability (Cronbach's alpha = 0.93) 21 , distinguishes people with and without anxiety disorders 22 , and has been shown to be highly sensitive to change during psychotherapy 23 . We calculated Cronbach’s alpha for each ICD-10 code in our dataset using the Pingouin Python statistical package 24 .

Data analysis plan

Treatment outcome.

The treatment outcome and its effectiveness were determined by subtracting the post-treatment score from the pre-treatment score. Given that changes in scores reflect true change plus measurement error, Jacobson and Traux proposed the Reliable Change Index (RCI) to evaluate the effectiveness of therapies and interventions based on pre/post treatment scores 25 . The RCI estimates the magnitude of change in a measure’s observed score required before assuming that true change has occurred (i.e., not attributable to measurement error). The RCI is calculated by dividing the difference between the two scores by the standard error of the difference. RCI values ≥ 1.96 represent reliable improvement, RCI values ≤ 1.96 represent reliable deterioration and RCI values between − 1.96 and 1.96 represent no reliable change. The K10 was used as both a continuous outcome variable (post-treatment score) and to classify individuals with respect to whether they reliably improved, deteriorated, or remained unchanged between pre-treatment and post-treatment. The calculation of the RCI and subsequent analysis were conducted using Python 3.9.

The dataset of the study population was prepared with the prediction model features restricted to data from the K10 pre-assessment and previous community mental health episodes of care, in addition to emergency department and inpatient mental health service events (Fig.  1 ).

The data sources and features that are available for the prediction model at pre-assessment are depicted to the left of the dashed line. The first pre/post assessment is used for each episode of care and patients may have multiple eligible episodes of care in the dataset. ED emergency department.

The features extracted and created from these data sources are presented in Table 3 with definitions provided in Supplementary Table 1 . The dataset is split into a 70%/30% training and test set using fivefold random subsampling stratified cross validation in machine learning experiments.

Classification and regression models are used to predict the reliable change category (deterioration/no reliable change vs. reliable improvement) and post treatment score as a continuous variable, respectively. Models were trained using the Python scikit-learn library 26 . Training (70%) and testing (30%) datasets were created using a stratified fivefold repeated random sub-sampling cross-validation method.

Model selection

PyCaret 27 , an automated machine learning (AutoML) software library, was used to initially experiment with several machine learning algorithms by splitting only the training dataset into 70/30% using fivefold random sampling cross validation. These initial results will be used to select the most suitable classification and regression methods for subsequent experiments.

Model evaluation

The classification models are evaluated using the Receiver Operating Characteristic (ROC) Area Under the Curve (AUC), precision, recall, F1 score (harmonic mean of precision and recall) and a confusion matrix to identify how often a model gets predictions right (true positives/negatives) and wrong (false positives/negatives) for each reliable change category. An AUC of 1 is considered to have perfect predictive power while an AUC 0.5 suggests no predictive power beyond random chance 28 . The regression models are evaluated using predicted R squared (R 2 ) and the mean absolute error 29 . The predicted post-treatment scores from the regression model were also used to classify episodes of care into the reliable change categories for evaluation.

Feature importance and selection

Shapley Additive Explanations (SHAP) is a game theory inspired technique commonly used to explain the importance and contribution of features in prediction modelling 30 , 31 . It is a model agnostic approach applied to both classification and regression models in our experiments using the SHAP Python library 31 . Furthermore, a greedy forward feature selection method 32 was applied, which involved sequentially adding the feature that provides the largest contribution to the model until a pre-defined stopping criterion was met. The stopping criteria used in experiments for classification were F1 improvement > 0.01 and mean absolute error (MAE) improvement < 0.001 for regression.

The distribution of score changes between pre/post-treatment is shown in Fig.  2 . 2882 (71%) episodes of care showed a reduction in K10 score after treatment, 872 (21%) exhibited an increase in K10 after treatment and 313 (8%) remained unchanged.

The difference (score change) between pre/post treatment Kessler psychological distress scale (K10) total scores.

The RCI method was applied on the dataset, where K10 reliability coefficients (Cronbach's alphas) of 0.92–0.94 were calculated for each of the ICD codes. The pattern of reliable change for F43 (Reaction to severe stress, and adjustment disorders) is illustrated in Fig.  3 . These boundaries vary for other ICD codes (F40, F41, F42) as the reliable change index was calculated and applied separately for each diagnosis (Supplementary Fig.  1 ).

Pre/post treatment scores for F43: Reaction to severe stress, and adjustment disorders. The dashed green lines represent the boundaries of the reliable change index, with the area to the left representing reliable deterioration and the area to the right representing reliable improvement. The area between the green lines represents no reliable change.

Descriptive statistics for the dataset are reported in Table 4 . Altogether, 4067 episodes of care were available for analysis that comprised predominately of females (67%) and a mean (SD) age of 40.2 (17.9) years. The deteriorated reliable change category had low representation (212 records or 5%) and was merged with the no reliable change category (total of 2446 records or 60%) for machine learning experiments.

The machine learning results are presented in two sections (a) classification for predicting the reliable change category and (b) regression for predicting post-assessment scores.

Classification

PyCaret (AutoML) was used to initially experiment with several classification models on the training dataset using cross-validation as presented in Table 5 . Gradient boosting achieved the highest AUC (0.72) and F1 score (0.57). All the models outperform the baseline classifier (AUC = 0.5) that predicts all records as the majority class (deteriorated/no reliable change). Based on these results, gradient boosting was selected for subsequent experiments.

The gradient boosting model was run on both the train and test datasets achieving an average F1 score of 0.66 (0.66–0.69) over fivefold cross validation, with the best model achieving an AUC of 0.77 and F1 of 0.69 (Table 6 ).

The confusion matrix and ROC of the best model is presented in Fig.  4 . The confusion matrix highlighted that the model performed better in classifying episodes of care with deterioration/no reliable change (551 out of 734 (75%) correctly classified) than those that demonstrated reliable improvement (306 out of 487 (63%) correct).

( A ) Classification confusion matrix shows how often the model correctly predicted each class (true positives/negatives) and how often it made mistakes (false positives/negatives). ( B ) The receiver operating characteristic curve on the test dataset shows the sensitivity and specificity at different thresholds for prediction.

The top 20 features based on the SHAP values and feature selection results are shown in Supplementary Table 2 and Supplementary Fig.  2 . The top 2 features from both methods were the pre-assessment score and the collection stage (review). Only using the pre-assessment score achieved a 0.62 F1 score with the admission collection stage increasing the prediction performance to 0.66 and years since the previous emergency contact to 0.69. The additional 4 selected features only improve the model performance to 0.70 (+ 0.1 F1 score).

AutoML was applied to experiment with several regression models on the training dataset using cross-validation as presented in Table 7 . Gradient boosting achieved the top performance with a 0.33 R 2 and 5.82 MAE. All models, except for decision tree, outperformed the baseline regressor that predicts the mean post-treatment score for all records. The gradient boosting model was selected for subsequent experiments.

The gradient boosting model achieved an average MAE of 5.73 (5.58–5.83) over fivefold cross validation with the best model achieving an R 2 of 0.39, 0.37 and MAE values of 5.65, 5.58 on the train and test dataset, respectively (Table 8 ).

The top 20 features based on the absolute SHAP values and feature selection results are shown in Supplementary Table 3 and Supplementary Fig.  3 . Feature selection identified the pre-assessment score and the collection stage (admission) as the top features achieving a 5.75 and 5.59 MAE. The other 5 selected features only reduced the MAE to 5.52 (− 0.07).

Regression applied classification

The regression model predicted the post-assessment score and was used to classify episodes of care into reliable change. The regression applied classification results (Table 9 ) showed a decline when compared to the classification model with an F1 score of 0.69 vs. 0.67 on the test set. The AUC cannot be computed for comparison as the regression model does not generate classification probabilities.

The confusion matrix of the regression applied classification is shown in Fig.  5 . These results when compared to the classification model showed that the regression model performed poorer in predicting improved reliable change (306 vs. 304), and deterioration/no reliable change (551 vs. 533).

Regression applied classification confusion matrix shows how often the model correctly predicted each class (true positives/negatives) and how often it made mistakes (false positives/negatives).

This study aimed to investigate whether community mental health treatment is related to improvements in psychological distress and develop machine learning models for predicting reliable change and post-treatment scores in anxiety disorder treatments. The discussion will now assess whether the results and findings adequately achieved these aims.

Prediction performance

The classification model achieved an AUC of 0.76 on the test dataset of 1193 patients and an AUC between 0.75 and 0.90 indicates a moderate score in psychology and human behavioural research 33 , 34 . Our results are similar to a study that achieved an AUC of 0.73 on a test dataset of 1255 undergraduate students 9 and outperformed another study that achieved an AUC of 0.60 on 279 patients in their test dataset 11 . The regression model achieved a R 2 on the test dataset and a R 2 between 0.3 and 0.5 is generally considered a weak effect 35 but can be considered as moderate in the context of human behavioural and psychology research 36 . Furthermore, A MAE of 5.58 for the regression model could be interpreted as a relatively large error for downstream tasks such as using the predicted post-treatment scores to classify reliable change. The classification and regression applied classification model achieved similar performance and both outperformed the baseline models. The moderate performance indicates that the models could be further improved with more data and/or better discriminating features. However, there is likely to be an upper limit on prediction performance given the inherent complexity of human lives in predicting the outcome of patient treatments (i.e. Bayes error) 29 .

Classification and regression

The classification model generated probabilities for each class, which helped identify appropriate classification thresholds using the ROC and AUC evaluation metrics. However, a strength of the regression model is that it predicted the post-treatment score, which allowed for the use of classification systems such as reliable change and could potentially be used for other metrics of recovery. Furthermore, the SHAP values of the regression model were easier to interpret as a higher SHAP value indicated a higher predicted post-assessment score (poorer outcomes) compared to classification where a higher SHAP value represents as a lower post-assessment score (improved reliable change). For example, a high pre-assessment score (poor outcome) for classification resulted in the model predicting towards reliable improvement, possibly due to higher pre-assessment scores having more potential to change by post-assessment (i.e. lower scores experiencing a floor effect). However, for regression, a high pre-assessment score (poor outcome) would predict towards high post-assessment scores (poor outcomes).

Model features

The SHAP analysis and feature selection experiments showed that the pre-assessment score was the most important feature, with the assessment collection stage (admission, review) improving prediction with the remaining features providing only a minor contribution to the overall performance. However, a strength of having fewer contributing features is that the model is simpler to implement and translate into clinical software. These top features were, however, not particularly helpful for future treatment-matching, although the challenge of discovering robust predictors of mental health treatment outcomes is well known 8 , 12 . A shift from capturing predominantly health service activity data to capturing more clinically relevant data (e.g., therapeutic process, treatments delivered) along with contextual factors (i.e., non-therapy factors such as life stressors), and implementing more regular patient outcome monitoring 37 to more readily identify when a clinical intervention is not working and could be adapted or stopped, may be required to improve prediction. A cardiologist would not contemplate diagnosing and evaluating interventions for heart disease from single datapoints three months apart, and yet mental health services are expected to do so.

Clinically relevant data

While the study dataset can be seen as a strength (i.e. linked population dataset collected over a 17-year period for training and evaluating prediction models) it is still limited and can be further enhanced. The collection of administrative patient data is often driven by compliance and reporting requirements rather than a clear understanding of its clinical utility. This can lead to the accumulation of vast amounts of data that are difficult to analyse and interpret, providing limited insights into patient care and outcomes. Moreover, the focus on compliance can divert resources away from efforts to collect and curate data that is directly relevant to clinical decision-making while burdening clinicians with onerous data entry administrative tasks. For instance, measures of key individual differences theorised to play a critical role in the aetiology and maintenance of anxiety disorders, such as anxiety sensitivity 38 , intolerance of uncertainty 39 , and experiential avoidance 40 , may help with case formulation, treatment planning, and outcome monitoring. The degree to which interventions successfully modify these factors would be expected to determine downstream impacts on symptom change across the anxiety disorders. Patients’ satisfaction and engagement with the service (e.g., attendance frequency and duration), relational factors between the clinician and patient (e.g., working alliance 41 ), and social determinants (e.g., interpersonal supports and stressors, financial stressors, adverse childhood experiences 42 , 43 ) may also help focus clinicians’ and consumers’ attention on factors likely to have the largest impact on mental health and wellbeing and thereby improve outcomes and their prediction. Outcomes beyond symptom change that capture broader intervention impacts (e.g., quality of life), or monitoring progress on idiographic presenting problems (those specific and of highest priority to the individual), may be particularly valued by consumers 44 , although there is evidence that improvements in quality of life are largely mediated by symptom change 45 . Routine monitoring of known predictors of mental health and wellbeing would facilitate outcome evaluation and benchmarking, whereby novel interventions and service models can be compared over time to previous benchmarks. Without these data, services have no way of knowing if outcomes are worsening, maintaining, or improving over time, which would help with treatment planning. There is evidence that regular and routine outcome monitoring (e.g., session-by-session) that is used collaboratively by consumers and clinicians can improve outcomes, decrease negative outcomes for consumers at risk of not benefiting from treatment, and increase cost-effectiveness of interventions 46 . Future research incorporating and documenting these measures and processes would likely produce more robust and informative predictive models.

The inability of the prediction models to produce higher or more robust performance might suggest that the health administrative data being collected and made available for research lacks clinical relevance, which makes its collection and use difficult to justify. The resources invested in collecting and storing this data could be better utilised towards initiatives that directly improve patient care. Moreover, relying on data that fails to provide meaningful insights could lead to misguided policy decisions and interventions that may not produce the desired outcomes. Administrative data collected solely for service utilisation and planning metrics are insufficient for evaluating quality of care, identifying impacts of service innovations, and ensuring consumer outcomes improve over time. If the priority is maximising patient recovery, then infrastructure (e.g., digital platforms) and measures that routinely, regularly and effectively capture consumer-driven priorities are required to ensure interventions are on track for positive outcomes, or, if not, can be, collaboratively and rapidly responded to by the consumer and healthcare worker to process back on track.

Clinical assessment

A limitation of the model and experiments are features provided by clinicians in their assessments of the patients such as unstructured clinical notes. While these features could aid in prediction, it is noteworthy to highlight that it is also difficult for clinicians to predict, based only from the initial pre-assessment, whether a patient will drop out, be treatment resistant or improve. If this cannot be predicted accurately and reliably by clinical experts 13 , 14 , 15 , then it may be no different when developing and using predictive models. Future research including a combination of clinician, consumer, and administrative data may improve predictive models.

Predicting patient outcomes in mental health is a complex and difficult task but is essential for improving the quality of care for people with anxiety disorders. Research on the prediction of patient outcomes is ongoing and the preliminary findings to date are promising. This study developed classification and regression models that showed moderate prediction performance with features that would be relatively easy to collect and implement in health services organisations and clinics on a linked health administrative dataset collected over a 17-year period. Future research using regular patient outcome monitoring, clinical assessment, consumer and administrative data, may yield more accurate and reliable models for predicting patient outcomes. This will have a significant impact on the lives of people with anxiety disorders and will inform healthcare policy planning.

Data availability

The data that support the findings of this study are available from Government of Western Australia Department of Health ( https://www.datalinkage-wa.org.au/ ) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. The corresponding author can provide clarification of the dataset used for the study but for access to the data, contact the Western Australia Department of Health at [email protected].

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Acknowledgements

This work was supported by the Digital Health Cooperative Research Centre (DHCRC) [DHCRC-0076]. DHCRC is funded under the Australian Commonwealth’s Cooperative Research Centres (CRC) Program. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation for the manuscript. The authors wish to thank Justin Manuel from Western Australia Country Health Service for his ongoing contribution to the overall project and to the staff from the Department of Health WA’s Data Linkage Services and the Hospital Morbidity Data Collection, Emergency Department Data Collection, and Mental Health Data Collection.

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Contributions

K.E.K.C analysed the data, conducted the experiments and drafted the manuscript. K.E.K.C, K.G.S C.M.Y.L, P.M.M., D.R, M.C conceived the design and P.M.M, K.G.S, D.R, M.C provided clinical advice for the project. K.S.B, P.M, D.R, S.R secured funding for the project. All authors contributed to the critical revision of the manuscript and approved the final version of the article to be published.

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Correspondence to Kevin E. K. Chai .

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Chai, K.E.K., Graham-Schmidt, K., Lee, C.M.Y. et al. Predicting anxiety treatment outcome in community mental health services using linked health administrative data. Sci Rep 14 , 20559 (2024). https://doi.org/10.1038/s41598-024-71557-2

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DOI : https://doi.org/10.1038/s41598-024-71557-2

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Integrating Community Services and Research: A Livelihood Needs Assessment at the Countryside of the Philippines

• R. C. Garcia
• Published 2017
• Sociology, Environmental Science
• Journal of Education and Practice

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STATE AND PROSPECTS OF DEVELOPMENT OF ACTIVE TOURISM AMONG YOUTH OF TOMSK REGION

The fact that Tomsk is a student city, with a large number of young active people in it, can be assumed the basic factor in the development of sports and recreational tourism in the Tomsk region. This contingent is the best for the formation of the concept of development of sport routes. But with a rather substantial potential for development, currently active forms of tourism are not popular in the Tomsk region, including among young people. There are several obstacles to positive changes. The first is the lack of awareness of potential tourists on existing routes and active leisure activities. Sports tourism is mostly promoted by small, non-governmental organizations and clubs affected by the lack of qualified instructors, effective advertising and promotion of tourism as it is in educational institutions, the lack of image of the Tomsk region as a favorable tourist region.

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