Patterns of Health Care Service Utilization by Individuals with Mental Health Problems: a Predictive Cluster Analysis

A grounded theory exploration of the enablers and barriers of public healthcare access for people with comorbid serious mental and chronic physical illnesses in Jamaica

Chronic physical illnesses (CPI) are highly prevalent among people with serious mental illnesses (PWSMI) yet people in this population experience significant challenges accessing healthcare. This study utilised a constructivist grounded theory approach to collect and analyse data related to the enablers and barriers to public healthcare access for PWSMI & CPI. Data were collected through semi-structured interviews conducted with fifty-seven participants comprising PWSMI &CPI and their caregivers, health policymakers, primary care physicians, psychiatrists, and mental health nurses. Enablers and barriers to healthcare access were represented using a socio-ecological model consisting of five levels: wider society, health system, clinician, family and community, and individual. Jamaica's free public healthcare system was the most pronounced enabler of healthcare access, while poverty, stigma, and discrimination were the most pronounced barriers. Factors such as social support, time, clinician beliefs, attitudes and training, and individual characteristics were identified as consisting of dimensions that were both enablers and barriers to healthcare access. These findings indicated that factors that influenced healthcare access for PWSMI & CPI were aligned with the social determinants of health. Improved healthcare access for PWSMI & CPI necessitates strategies that incorporate a multi-sectoral approach to address social and environmental factors influencing healthcare access across all levels of the socio-ecological model.

Using unsupervised clustering approaches to identify common mental health profiles and associated mental health-care service-use patterns in Ontario, Canada

Mental health is a complex, multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress, and well-being. In this study, we aimed to use unsupervised clustering approaches to identify multidimensional mental health profiles that exist in the population, and their associated service-use patterns. The data source was the 2012 Canadian Community Health Survey-Mental Health, linked to administrative health-care data; all Ontario, Canada, adult respondents were included. We used a partitioning around medoids clustering algorithm with Gower's proximity to identify groups with distinct combinations of mental health indicators and described them according to their sociodemographic and service-use characteristics. We identified 4 groups with distinct mental health profiles, including 1 group that met the clinical threshold for a depressive diagnosis, with the remaining 3 groups expressing differences in positive mental health, life stress, and self-rated mental health. The 4 groups had different age, employment, and income profiles and exhibited differential access to mental health-care services. This study represents the first step in identifying complex profiles of mental health at the population level in Ontario. Further research is required to better understand the potential causes and consequences of belonging to each of the mental health profiles identified. This article is part of a Special Collection on Mental Health.

Healthcare use of young finnish adults with mental disorders - profiles and profile membership determinants

BACKGROUND

Comprehensive, timely, and integrated primary care services have been proposed as a response to the increased demand for mental health and substance use services especially among young people. However, little is known about the care utilization patterns of young people with mental and substance use disorders. Our aim was to characterize profiles of care use in young Finnish adults with mental or substance use disorders, and the potential factors associated with the service use profiles.

METHODS

Primary and specialized care visits of young adults (16-29 years) diagnosed with a psychiatric or a substance use disorder (n = 7714) were retrieved from the national health care register from years 2020 and 2021. K-Means clustering was used to detect different profiles based on the utilization of care services. Multinomial logistic regression was used to analyze the factors associated with different profiles of care use.

RESULTS

Five different profiles were identified: low care use (75%), and use of principally primary health care (11%), student health services (9%), psychiatric services (5%), or substance use services (1%). Female gender was associated with membership in the primary health care focused profiles (OR 2.58 and OR 1.99), and patients in the primary health care and student health services profiles were associated with a better continuity of care (OR 1.04 and OR 1.05). Substance use disorders were associated with psychiatric service use (OR: 2.51) and substance use services (OR: 58.91). Living in smaller municipalities was associated with lower service use when comparing to the largest city.

CONCLUSIONS

Young adults diagnosed with a psychiatric or a substance use disorder had remarkably different and heterogeneous care patterns. Most of the participants had low care utilization, indicating potential gaps in service use and care needs. Measures should be taken to ensure equal access to and availability of mental health services. The profiles that utilized the most services highlights the importance of integrated services and patient-oriented improvement of treatment.

Machine learning identifies a COVID-19-specific phenotype in university students using a mental health app

Background

Advances in smartphone technology have allowed people to access mental healthcare via digital apps from wherever and whenever they choose. University students experience a high burden of mental health concerns. Although these apps improve mental health symptoms, user engagement has remained low. Studies have shown that users can be subgrouped based on unique characteristics that just-in-time adaptive interventions (JITAIs) can use to improve engagement. To date, however, no studies have examined the effect of the COVID-19 pandemic on these subgroups.

Objective

Here, we sought to examine user subgroup characteristics across three COVID-19-specific timepoints: during lockdown, immediately following lockdown, and three months after lockdown ended.

Methods

To do this, we used a two-step machine learning approach combining unsupervised and supervised machine learning.

Results

We demonstrate that there are three unique subgroups of university students who access mental health apps. Two of these, with either higher or lower mental well-being, were defined by characteristics that were stable across COVID-19 timepoints. The third, situational well-being, had characteristics that were timepoint-dependent, suggesting that they are highly influenced by traumatic stressors and stressful situations. This subgroup also showed feelings and behaviours consistent with burnout.

Conclusions

Overall, our findings clearly suggest that user subgroups are unique: they have different characteristics and therefore likely have different mental healthcare goals. Our findings also highlight the importance of including questions and additional interventions targeting traumatic stress(ors), reason(s) for use, and burnout in JITAI-style mental health apps to improve engagement.

Associated factors of nurse-sensitive patient outcomes: A multicentred cross-sectional study in psychiatric inpatient hospitals

WHAT IS ALREADY KNOWN?: The nurse-patient relationship in mental health care is an important focus of mental health nursing theories and research. There is limited evidence about which factors influence nurse-sensitive patient outcomes of the nurse-patient relationship. This hinders the development, planning, delivering, and quality assurance of the nurse-patient relationship in nursing practice and nursing education. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: To our best knowledge, this is the first study to examine associations between nurse-sensitive patient outcomes of the nurse-patient relationship and a range of patient characteristics and relationship-contextual factors. In this study, we found that gender, age, hospital characteristics, nurse availability when needed, nurse contact, and nurse stimulation were associated with the scores on the nurse-sensitive patient outcome scale. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Having insight into the factors associated with nurse-sensitive patient outcomes of the nurse-patient relationship can help nurses, nursing students, nursing management and also patients to enhance the nurse-patient relationship, trying to influence outcomes of nursing care. ABSTRACT: Introduction The lack of evidence on patient characteristics and relational-contextual factors influencing nurse-sensitive patient outcomes of a nurse-patient relationship is a possible threat to the quality and education of the nurse-patient relationship. Aim To measure nurse-sensitive patient outcomes of the nurse-patient relationship and to explore the associations between nurse-sensitive patient outcomes and a range of patient characteristics and relational-contextual factors. Method In a multicenter cross-sectional study, 340 inpatients from 30 units in five psychiatric hospitals completed the Mental Health Nurse-Sensitive Patient Outcome Scale. Descriptive, univariate and Linear Mixed Model analyses were conducted. Results Overall, patient-reported outcomes were moderate to good. Female participants, nurse availability when needed, more nurse contact and nurse stimulation were associated with higher outcomes. Age differences were observed for some of the outcomes. Outcomes also varied across hospitals but were not related to the number of times patients were hospitalized or to their current length of stay in the hospital. Discussion The results may help nurses to become more sensitive and responsive to factors associated with nurse-sensitive patient outcomes of the nurse-patient relationship. Implications The nurse-sensitive results can support nurses in designing future nurse-patient relationships.

Identifying user profiles of healthcare, social and employment services in a working-age population: A cluster analysis with linked individual-level register data from Finland

A thorough understanding of the use of services in the population is important in order to comprehend the varying service needs of different groups. This explorative study aimed to find distinct user profiles in a working-age population based on individuals' annual use of healthcare, social and employment services and to explore socio-demographic and morbidity-related predictors of the user groups. Administrative register data on the use of various services and individual-level covariates from year 2018 were linked for all residents aged 18-64 of the municipality of Oulu, Finland (N = 119,740). K-means cluster analysis was used to group the study subjects into clusters, based on their frequency of using 22 distinct healthcare, social and employment services during 2018. Multinomial logistic regression models were utilized to assess the associations of cluster assignment with socio-demographic and health-related covariates (sex, age, marital status, education, occupational class, income, days in employment, chronic disease and receipt of different social benefits). Five distinct clusters were identified in terms of service use, labelled low to moderate users of healthcare (82.0%), regular employment services users with moderate use of healthcare (9.6%), supported employment services users with moderate use of healthcare with an emphasis on preventive care (2.9%), frequent users of healthcare, social and employment services (2.9%), and rehabilitation, disability services and specialized healthcare users (2.6%). Each cluster not only showed different patterns of service use but were also differently associated with demographic, socio-economic and morbidity-related covariates, creating distinct service user types. Knowledge on the different user profiles and their determinants may help predict future need and use of services in a population, plan timely, coordinated and integrated services, and design early interventions and prevention measures. This is important in order to save costs and improve the effectiveness of services for groups with different care needs.

Classifying children with ASD by service utilization and treatment type: A cluster analysis of a nationally representative United States survey

BACKGROUND

If there are patterns of the distribution of services and treatments across the population of people with ASD, these patterns should be based along clinical characteristics or other service needs and not sociodemographic characteristics unrelated to evidence-based care. We examined how individuals in a broad, nationally representative sample "grouped together" based on service utilization and services needed but not covered by insurance. By understanding various treatment patterns, clinicians, researchers, policymakers, and self-advocates and their families can better advocate for high-quality, evidence-based services to be provided equitably.

METHODS

Using the 2011 Survey of Pathways to Diagnosis and Services, a cluster analysis was performed to explore patterns in this population based on medication use, private services use, school-based service use, and services not covered by insurance. Differences in clusters were then explored through multinomial logistic regression.

RESULTS

Six clusters emerged, showing differences in the level of service/medication usage and insurance coverage. Differences across clusters were associated with the level of functional limitation and age at ASD diagnosis. Disparities by insurance type, functional limitation, and age at diagnosis exist among patterns of ASD service provision.

CONCLUSIONS

Our analysis showed that intervention for children with ASD can be across several scales - high and low users of services (both private and school-based), high and low users of medications, and high and low levels of reported non-covered services. The differences were clustered in multiple ways. Further research should incorporate longitudinal and nationally representative data to explore these relationships further.

EISI: Extended inter-spike interval for mental health patients clustering based on mental health services and medications utilisation

Mental health is vital in all human life stages, and managing mental healthcare service resources is crucial for providers. This paper presents a new method, called Extended Inter-Spike Interval (EISI), on identifying the patients with a similar utilisation of mental health services and medications. The EISI measures the distance between the utilisation patterns of the patients. Then, the pairwise distances are given to a developed split-and-merge Partitioning Around Medoids (PAM) clustering algorithm to identify the patients with similar utilisation patterns. To evaluate the proposed method, we use two years (2013-2014) of the 10% publicly available sample of the Australian Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) administrative data. Results show that mental health patients can be grouped into ten clusters with distinct and interpretable utilisations patterns. The largest cluster comprises individuals who only visit general practitioners and take psycholeptics medications for a short time. The smallest group contains occasional visits with general practitioners and regularly utilises psycholeptics and psychoanaleptics medications over long periods. The proposed method provides insights on whom to target and how to structure services for different groups of individuals with mental health conditions.

How Do Health Systems Address Patient Flow When Services Are Misaligned With Population Needs? A Qualitative Study

BACKGROUND

Patient flow through health services is increasingly recognized as a system issue, yet the flow literature has focused overwhelmingly on localized interventions, with limited examination of system-level causes or remedies. Research suggests that intractable flow problems may reflect a basic misalignment between service offerings and population needs, requiring fundamental system redesign. However, little is known about health systems' approaches to population-capacity misalignment, and guidance for system redesign remains underdeveloped.

METHODS

This qualitative study, part of a broader investigation of patient flow in urban Western Canada, explored health-system strategies to address or prevent population-capacity misalignment. We conducted in-depth interviews with a purposive sample of managers in 10 jurisdictions across 4 provinces (N = 300), spanning all healthcare sectors and levels of management. We used the constant comparative method to develop an understanding of relevant strategies and derive principles for system design.

RESULTS

All regions showed evidence of pervasive population-capacity misalignment. The most superficial level of response - mutual accommodation (case-by-case problem solving) - was most prevalent; capacity (re)allocation occurred less frequently; population redefinition most rarely. Participants' insights yielded a general principle: Define populations on the basis of clusters of co-occurring need. However, defining such clusters demands a difficult balance between narrowness/rigidity and breadth/flexibility. Deeper analysis suggested a further principle: Populations that can be divided into homogeneous subgroups experiencing similar needs (eg, surgical patients) are best served by narrow/ rigid models; heterogeneous populations featuring diverse constellations of need (eg, frail older adults) require broad/ flexible models.

CONCLUSION

To remedy population-capacity misalignment, health system planners should determine whether clusters of population need are separable vs. fused, select an appropriate service model for each population, allocate sufficient capacity, and only then promote mutual accommodation to address exceptions. Overreliance on case-by-case solutions to systemic problems ensures the persistence of population-capacity misalignment.

Anxiety/Depression Predominance in Liaison-Psychiatry Users of a South-East Mexico Tertiary Hospital

Patients at tertiary hospitals may find themselves in need of mental health support due to the distress associated with the illness that may or not lead to a psychiatric condition. Here is an overview of the clinical cases treated by the liaison psychiatry service of a public tertiary hospital from Southeast Mexico during its first years of operation (2008–2018), with the purpose of gathering information about the status and needs of this population. A sample of 304 clinical records of patients treated for the first time by the psychiatry service was reviewed, and the distribution by demographic characteristics, diagnosis of mental illness and medical area of reference was analyzed. Anxiety and depression symptoms were the most frequent. Most patients were women, lived in Merida and returned after the first appointment. The neurology service referred most patients, yet most attended directly. General tertiary hospitals should prioritize integrating ad hoc mental and physical health care. Adult women with a profile of anxiety and/or depression would be the first target group. Some areas of opportunity for further research and improvement of mental health services are: preventive services for anxiety and depression, follow-up of patients, attention to relatives of patients at intensive care units, implementation of telehealth alternatives, training on mental health screening and inter- and intra-institutional collaboration.

Predictors of mental health service use among individuals with high psychological distress and mental disorders

This study identified predictors of mental health service use over 12 months among 746 individuals with mental disorders and high psychological distress from a catchment area in southwest Montreal, Quebec (Canada). Data collected in 2011 and 2014 were analyzed using Andersen's Behavioral Model of Health Services Use. A hierarchical logistic regression identified predictors of mental health service use. In all, 29% of participants reported using mental health services in the previous 12 months. Three key enabling variables predicted mental health service use: having a family doctor, previous experience with mental health services, and employment. Self-perception of mental health, stressful events, and unmet needs marginally (Needs factors: non-clinical variables) were also associated with the outcome variable. Mental health service utilization depends primarily on organization of the health system, and patient perceptions of its condition (non-clinical needs). Mental health policy should focus on increasing the availability of services and professionals, especially family doctors. Other measures for encouraging service use and overall population wellbeing include raising public awareness around the signs and symptoms of mental illness as a way to promote more rapid response to patient needs, and protecting workplace mental health by reducing stress and stigma toward individuals affected by mental distress.