Microsimulation Models on Mental Health: A Critical Review of the Literature.

OBJECTIVES: To retrieve and synthesize the literature on existing mental health-specific microsimulation models or generic microsimulation models used to examine mental health, and to critically appraise them. METHODS: All studies on microsimulation and mental health published in English in MEDLINE, Embase, PsycINFO, and EconLit between January 1, 2010, and September 30, 2022, were considered. Snowballing, Google searches, and searches on specific journal websites were also undertaken. Data extraction was done on all studies retrieved and the reporting quality of each model was assessed using the Quality Assessment Reporting for Microsimulation Models checklist, a checklist developed by the research team. A narrative synthesis approach was used to synthesize the evidence. RESULTS: Among 227 potential hits, 19 studies were found to be relevant. Some studies covered existing economic-demographic models, which included a component on mental health and were used to answer mental-health-related research questions. Other studies were focused solely on mental health and included models that were developed to examine the impact of specific policies or interventions on specific mental disorders or both. Most models examined were of medium quality. The main limitations included the use of model inputs based on self-reported and/or cross-sectional data, small and/or nonrepresentative samples and simplifying assumptions, and lack of model validation. CONCLUSIONS: This review found few high-quality microsimulation models on mental health. Microsimulation models developed specifically to examine mental health are important to guide healthcare delivery and service planning. Future research should focus on developing high-quality mental health-specific microsimulation models with wide applicability and multiple functionalities.

Multimorbidity clusters among people with serious mental illness: a representative primary and secondary data linkage cohort study.

BACKGROUND: People with serious mental illness (SMI) experience higher mortality partially attributable to higher long-term condition (LTC) prevalence. However, little is known about multiple LTCs (MLTCs) clustering in this population. METHODS: People from South London with SMI and two or more existing LTCs aged 18+ at diagnosis were included using linked primary and mental healthcare records, 2012-2020. Latent class analysis (LCA) determined MLTC classes and multinominal logistic regression examined associations between demographic/clinical characteristics and latent class membership. RESULTS: The sample included 1924 patients (mean (s.d.) age 48.2 (17.3) years). Five latent classes were identified: 'substance related' (24.9%), 'atopic' (24.2%), 'pure affective' (30.4%), 'cardiovascular' (14.1%), and 'complex multimorbidity' (6.4%). Patients had on average 7-9 LTCs in each cluster. Males were at increased odds of MLTCs in all four clusters, compared to the 'pure affective'. Compared to the largest cluster ('pure affective'), the 'substance related' and the 'atopic' clusters were younger [odds ratios (OR) per year increase 0.99 (95% CI 0.98-1.00) and 0.96 (0.95-0.97) respectively], and the 'cardiovascular' and 'complex multimorbidity' clusters were older (ORs 1.09 (1.07-1.10) and 1.16 (1.14-1.18) respectively). The 'substance related' cluster was more likely to be White, the 'cardiovascular' cluster more likely to be Black (compared to White; OR 1.75, 95% CI 1.10-2.79), and both more likely to have schizophrenia, compared to other clusters. CONCLUSION: The current study identified five latent class MLTC clusters among patients with SMI. An integrated care model for treating MLTCs in this population is recommended to improve multimorbidity care.

Impact of prevention in primary care on costs in primary and secondary care for people with serious mental illness.

A largely unexplored part of the financial incentive for physicians to participate in preventive care is the degree to which they are the residual claimant from any resulting cost savings. We examine the impact of two preventive activities for people with serious mental illness (care plans and annual reviews of physical health) by English primary care practices on costs in these practices and in secondary care. Using panel two-part models to analyze patient-level data linked across primary and secondary care, we find that these preventive activities in the previous year are associated with cost reductions in the current quarter both in primary and secondary care. We estimate that there are large beneficial externalities for which the primary care physician is not the residual claimant: the cost savings in secondary care are 4.7 times larger than the cost savings in primary care. These activities are incentivized in the English National Health Service but the total financial incentives for primary care physicians to participate were considerably smaller than the total cost savings produced. This suggests that changes to the design of incentives to increase the marginal reward for conducting these preventive activities among patients with serious mental illness could have further increased welfare.

Development of a Supported Self-management Intervention for People With Severe Mental Illness and Type 2 Diabetes: Theory and Evidence-Based Co-design Approach.

BACKGROUND: Type 2 diabetes is 2 to 3 times more common among people with severe mental illness (SMI). Self-management is crucial, with additional challenges faced by people with SMI. Therefore, it is essential that any diabetes self-management program for people with SMI addresses the unique needs of people living with both conditions and the inequalities they experience within health care services. OBJECTIVE: We combined theory, empirical evidence, and co-design approaches to develop a type 2 diabetes self-management intervention for people with SMI. METHODS: The development process encompassed 4 steps: step 1 involved prioritizing the mechanisms of action (MoAs) and behavior change techniques (BCTs) for the intervention. Using findings from primary qualitative research and systematic reviews, we selected candidate MoAs to target in the intervention and candidate BCTs to use. Expert stakeholders then ranked these MoAs and BCTs using a 2-phase survey. The average scores were used to generate a prioritized list of MoAs and BCTs. During step 2, we presented the survey results to an expert consensus workshop to seek expert agreement with the definitive list of MoAs and BCTs for the intervention and identify potential modes of delivery. Step 3 involved the development of trigger films using the evidence from steps 1 and 2. We used animations to present the experiences of people with SMI managing diabetes. These films were used in step 4, where we used a stakeholder co-design approach. This involved a series of structured workshops, where the co-design activities were informed by theory and evidence. RESULTS: Upon the completion of the 4-step process, we developed the DIAMONDS (diabetes and mental illness, improving outcomes and self-management) intervention. It is a tailored self-management intervention based on the synthesis of the outputs from the co-design process. The intervention incorporates a digital app, a paper-based workbook, and one-to-one coaching designed to meet the needs of people with SMI and coexisting type 2 diabetes. CONCLUSIONS: The intervention development work was underpinned by the MoA theoretical framework and incorporated systematic reviews, primary qualitative research, expert stakeholder surveys, and evidence generated during co-design workshops. The intervention will now be tested for feasibility before undergoing a definitive evaluation in a pragmatic randomized controlled trial.

Association of environmental and socioeconomic indicators with serious mental illness diagnoses identified from general practitioner practice data in England: A spatial Bayesian modelling study.

BACKGROUND: The evidence is sparse regarding the associations between serious mental illnesses (SMIs) prevalence and environmental factors in adulthood as well as the geographic distribution and variability of these associations. In this study, we evaluated the association between availability and proximity of green and blue space with SMI prevalence in England as a whole and in its major conurbations (Greater London, Birmingham, Liverpool and Manchester, Leeds, and Newcastle). METHODS AND FINDINGS: We carried out a retrospective analysis of routinely collected adult population (≥18 years) data at General Practitioner Practice (GPP) level. We used data from the Quality and Outcomes Framework (QOF) on the prevalence of a diagnosis of SMI (schizophrenia, bipolar affective disorder and other psychoses, and other patients on lithium therapy) at the level of GPP over the financial year April 2014 to March 2018. The number of GPPs included ranged between 7,492 (April 2017 to March 2018) to 7,997 (April 2014 to March 2015) and the number of patients ranged from 56,413,719 (April 2014 to March 2015) to 58,270,354 (April 2017 to March 2018). Data at GPP level were converted to the geographic hierarchy unit Lower Layer Super Output Area (LSOA) level for analysis. LSOAs are a geographic unit for reporting small area statistics and have an average population of around 1,500 people. We employed a Bayesian spatial regression model to explore the association of SMI prevalence in England and its major conurbations (greater London, Birmingham, Liverpool and Manchester, Leeds, and Newcastle) with environmental characteristics (green and blue space, flood risk areas, and air and noise pollution) and socioeconomic characteristics (age, ethnicity, and index of multiple deprivation (IMD)). We incorporated spatial random effects in our modelling to account for variation at multiple scales. Across England, the environmental characteristics associated with higher SMI prevalence at LSOA level were distance to public green space with a lake (prevalence ratio [95% credible interval]): 1.002 [1.001 to 1.003]), annual mean concentration of PM2.5 (1.014 [1.01 to 1.019]), and closeness to roads with noise levels above 75 dB (0.993 [0.992 to 0.995]). Higher SMI prevalence was also associated with a higher percentage of people above 24 years old (1.002 [1.002 to 1.003]), a higher percentage of ethnic minorities (1.002 [1.001 to 1.002]), and more deprived areas. Mean SMI prevalence at LSOA level in major conurbations mirrored the national associations with a few exceptions. In Birmingham, higher average SMI prevalence at LSOA level was positively associated with proximity to an urban green space with a lake (0.992 [0.99 to 0.998]). In Liverpool and Manchester, lower SMI prevalence was positively associated with road traffic noise ≥75 dB (1.012 [1.003 to 1.022]). In Birmingham, Liverpool, and Manchester, there was a positive association of SMI prevalence with distance to flood zone 3 (land within flood zone 3 has ≥1% chance of flooding annually from rivers or ≥0.5% chance of flooding annually from the sea, when flood defences are ignored): Birmingham: 1.012 [1.000 to 1.023]; Liverpool and Manchester: 1.016 [1.006 to 1.026]. In contrast, in Leeds, there was a negative association between SMI prevalence and distance to flood zone 3 (0.959 [0.944 to 0.975]). A limitation of this study was because we used a cross-sectional approach, we are unable to make causal inferences about our findings or investigate the temporal relationship between outcome and risk factors. Another limitation was that individuals who are exclusively treated under specialist mental health care and not seen in primary care at all were not included in this analysis. CONCLUSIONS: Our study provides further evidence on the significance of socioeconomic associations in patterns of SMI but emphasises the additional importance of considering environmental characteristics alongside socioeconomic variables in understanding these patterns. In this study, we did not observe a significant association between green space and SMI prevalence, but we did identify an apparent association between green spaces with a lake and SMI prevalence. Deprivation, higher concentrations of air pollution, and higher proportion of ethnic minorities were associated with higher SMI prevalence, supporting a social-ecological approach to public health prevention. It also provides evidence of the significance of spatial analysis in revealing the importance of place and context in influencing area-based patterns of SMI.

Healthcare resource use and costs for people with type 2 diabetes mellitus with and without severe mental illness in England: longitudinal matched-cohort study using the Clinical Practice Research Datalink.

BACKGROUND: Approximately 60 000 people in England have coexisting type 2 diabetes mellitus (T2DM) and severe mental illness (SMI). They are more likely to have poorer health outcomes and require more complex care pathways compared with those with T2DM alone. Despite increasing prevalence, little is known about the healthcare resource use and costs for people with both conditions. AIMS: To assess the impact of SMI on healthcare resource use and service costs for adults with T2DM, and explore the predictors of healthcare costs and lifetime costs for people with both conditions. METHOD: This was a matched-cohort study using data from the Clinical Practice Research Datalink linked to Hospital Episode Statistics for 1620 people with comorbid SMI and T2DM and 4763 people with T2DM alone. Generalised linear models and the Bang and Tsiatis method were used to explore cost predictors and mean lifetime costs respectively. RESULTS: There were higher average annual costs for people with T2DM and SMI (£1930 higher) than people with T2DM alone, driven primarily by mental health and non-mental health-related hospital admissions. Key predictors of higher total costs were older age, comorbid hypertension, use of antidepressants, use of first-generation antipsychotics, and increased duration of living with both conditions. Expected lifetime costs were approximately £35 000 per person with both SMI and T2DM. Extrapolating nationally, this would generate total annual costs to the National Health Service of around £250 m per year. CONCLUSIONS: Our estimates of resource use and costs for people with both T2DM and SMI will aid policymakers and commissioners in service planning and resource allocation.

Living with diabetes alongside a severe mental illness: A qualitative exploration with people with severe mental illness, family members and healthcare staff.

AIMS: Diabetes is two to three times more prevalent in people with severe mental illness, yet little is known about the challenges of managing both conditions from the perspectives of people living with the co-morbidity, their family members or healthcare staff. Our aim was to understand these challenges and to explore the circumstances that influence access to and receipt of diabetes care for people with severe mental illness. METHODS: Framework analysis of qualitative semi-structured interviews with people with severe mental illness and diabetes, family members, and staff from UK primary care, mental health and diabetes services, selected using a maximum variation sampling strategy between April and December 2018. RESULTS: In all, 39 adults with severe mental illness and diabetes (3 with type 1 diabetes and 36 with type 2 diabetes), nine family members and 30 healthcare staff participated. Five themes were identified: (a) Severe mental illness governs everyday life including diabetes management; (b) mood influences capacity and motivation for diabetes self-management; (c) cumulative burden of managing multiple physical conditions; (d) interacting conditions and overlapping symptoms and (e) support for everyday challenges. People living with the co-morbidity and their family members emphasised the importance of receiving support for the everyday challenges that impact diabetes management, and identified barriers to accessing this from healthcare providers. CONCLUSIONS: More intensive support for diabetes management is needed when people's severe mental illness (including symptoms of depression) or physical health deteriorates. Interventions that help people, including healthcare staff, distinguish between symptoms of diabetes and severe mental illness are also needed.

Investigating the relationship between social care supply and healthcare utilization by older people in England.

Since 2010, adult social care spending in England has fallen significantly in real terms whilst demand has risen. Reductions in social care supply may also have impacted demand for NHS services, particularly for those whose care is provided at the interface of the health and care systems. We analyzed a panel dataset of 150 local authorities (councils) to test potential impacts on hospital utilization by people aged 65 and over: emergency admission rates for falls and hip fractures ("front-door" measures); and extended stays of 7 days or longer; and 21 days or longer ("back-door" measures). Changes in social care supply were assessed in two ways: gross current expenditure (per capita 65 and over) adjusted by local labor costs and social care workforce (per capita 18 and over). We ran negative binomial models, controlling for deprivation, ethnicity, age, unpaid care, council class, and year effects. To account for potential endogeneity, we ran instrumental variable regressions and dynamic panel models. Sensitivity analysis explored potential effects of funding for integrated care (the Better Care Fund). There was no consistent evidence that councils with higher per capita spend or higher social care staffing rates had lower hospital admission rates or shorter hospital stays.

Examining equity in the utilisation of psychiatric inpatient care among patients with severe mental illness (SMI) in Ontario, Canada.

BACKGROUND: Severe mental illness (SMI) comprises a range of chronic and disabling conditions, such as schizophrenia, bipolar disorder and other psychoses. Despite affecting a small percentage of the population, these disorders are associated with poor outcomes, further compounded by disparities in access, utilisation, and quality of care. Previous research indicates there is pro-poor inequality in the utilisation of SMI-related psychiatric inpatient care in England (in other words, individuals in more deprived areas have higher utilisation of inpatient care than those in less deprived areas). Our objective was to determine whether there is pro-poor inequality in SMI-related psychiatric admissions in Ontario, and understand whether these inequalities have changed over time. METHODS: We selected all adult psychiatric admissions from April 2006 to March 2011. We identified changes in socio-economic equity over time across deprivation groups and geographic units by modeling, through ordinary least squares, annual need-expected standardised utilisation as a function of material deprivation and other relevant variables. We also tested for changes in socio-economic equity of utilisation over years, where the number of SMI-related psychiatric admissions for each geographic unit was modeled using a negative binomial model. RESULTS: We found pro-poor inequality in SMI-related psychiatric admissions in Ontario. For every one unit increase in deprivation, psychiatric admissions increased by about 8.1%. Pro-poor inequality was particularly present in very urban areas, where many patients with SMI reside, and very rural areas, where access to care is problematic. Our main findings did not change with our sensitivity analyses. Furthermore, this inequality did not change over time. CONCLUSIONS: Individuals with SMI living in more deprived areas of Ontario had higher psychiatric admissions than those living in less deprived areas. Moreover, our findings suggest this inequality has remained unchanged over time. Despite the debate around whether to make more or less use of inpatient versus other care, policy makers should seek to address suboptimal supply of primary, community or social care for SMI patients. This may potentially be achieved through the elimination of barriers to access psychiatrist care and the implementation of universal coverage of psychotherapy.

Factors Associated with Patient Satisfaction of Community Mental Health Services: A Multilevel Approach.

Community care is increasingly the mainstay of mental healthcare provision in many countries and patient satisfaction is an important barometer of quality of patient care. This paper explores the key factors associated with patient satisfaction with community mental health services in England and then compares providers' performance on patient satisfaction. Our analysis is based on patient-level responses from the community mental health survey, which is run annually by the Care Quality Commission (CQC) for the years 2010 to 2013. We perform a repeated cross-section analysis, identifying factors associated with patient satisfaction via a multi-level ordered probit model, including both patient- and provider-level variables. We identify hospital-specific effects via empirical Bayes estimation. Our analysis identifies a number of novel results. First, patient characteristics such as older age, being employed, and being able to work, are associated with higher satisfaction, while being female is associated with lower satisfaction. Service contact length, time since last visit, condition severity and admission to a mental health institution, are all associated with lower satisfaction. Second, treatment type affects satisfaction, with patients receiving talking therapies or being prescribed medications being more satisfied. Third, care continuity and involvement, as proxied by having a care plan, is associated with higher satisfaction. Fourth, seeing a health professional closer to the community improves satisfaction, with patients seeing a community-psychiatric nurse, a social worker or a mental-health support worker being more satisfied. Finally, our study identifies the need for service integration, with patients experiencing financial, accommodation, or physical health needs being less satisfied. At a provider level, we find a negative association between the percentage of occupied beds and satisfaction. We further identify significant provider-specific effects after accounting for observable differences in patient and provider characteristics which suggests significant differences in provider quality of care.

Prospective payment systems and discretionary coding-Evidence from English mental health providers.

Reimbursement of English mental health hospitals is moving away from block contracts and towards activity and outcome-based payments. Under the new model, patients are categorised into 20 groups with similar levels of need, called clusters, to which prices may be assigned prospectively. Clinicians, who make clustering decisions, have substantial discretion and can, in principle, directly influence the level of reimbursement the hospital receives. This may create incentives for upcoding. Clinicians are supported in their allocation decision by a clinical clustering algorithm, the Mental Health Clustering Tool, which provides an external reference against which clustering behaviour can be benchmarked. The aims of this study are to investigate the degree of mismatch between predicted and actual clustering and to test whether there are systematic differences amongst providers in their clustering behaviour. We use administrative data for all mental health patients in England who were clustered for the first time during the financial year 2014/15 and estimate multinomial multilevel models of over, under, or matching clustering. Results suggest that hospitals vary systematically in their probability of mismatch but this variation is not consistently associated with observed hospital characteristics.

Measuring the Activity of Mental Health Services in England: Variation in Categorising Activity for Payment Purposes.

In the context of international interest in reforming mental health payment systems, national policy in England has sought to move towards an episodic funding approach. Patients are categorised into care clusters, and providers will be paid for episodes of care for patients within each cluster. For the payment system to work, clusters need to be appropriately homogenous in terms of financial resource use. We examine variation in costs and activity within clusters and across health care providers. We find that the large variation between providers with respect to costs within clusters mean that a cluster-based episodic payment system would have substantially different financial impacts across providers.

Investigating the impact of primary care payments on underdiagnosis in dementia: A difference-in-differences analysis.

OBJECTIVE: In England, two primary care incentive schemes were introduced to increase dementia diagnosis rates to two-thirds of expected levels. This study assesses the effectiveness of these schemes. METHODS: We used a difference-in-differences framework to analyse the individual and collective impacts of the incentive schemes: (1) Directed Enhanced Service 18 (DES18: facilitating timely diagnosis of and support for dementia) and (2) the Dementia Identification Scheme (DIS). The dataset included 7529 English general practices, of which 7142 were active throughout the 10-year study period (April 2006 to March 2016). We controlled for a range of factors, including a contemporaneous hospital incentive scheme for dementia. Our dependent variable was the percentage of expected cases that was recorded on practice dementia registers (the "rate"). RESULTS: From March 2013 to March 2016, the mean rate rose from 51.8% to 68.6%. Both DES18 and DIS had positive and significant effects. In practices participating in the DES18 scheme, the rate increased by 1.44 percentage points more than the rate for non-participants; DIS had a larger effect, with an increase of 3.59 percentage points. These combined effects increased dementia registers nationally by an estimated 40 767 individuals. Had all practices fully participated in both schemes, the corresponding number would have been 48 685. CONCLUSION: The primary care incentive schemes appear to have been effective in closing the gap between recorded and expected prevalence of dementia, but the hospital scheme had no additional discernible effect. This study contributes additional evidence that financial incentives can motivate improved performance in primary care.

The impact of waiting time on patient outcomes: Evidence from early intervention in psychosis services in England.

Recently, new emphasis was put on reducing waiting times in mental health services as there is an ongoing concern that longer waiting time for treatment leads to poorer health outcomes. However, little is known about delays within the mental health service system and its impact on patients. We explore the impact of waiting times on patient outcomes in the context of early intervention in psychosis (EIP) services in England from April 2012 to March 2015. We use the Mental Health Services Data Set and the routine outcome measure the Health of the Nation Outcome Scale. In a generalised linear regression model, we control for baseline outcomes, previous service use, and treatment intensity to account for possible endogeneity in waiting time. We find that longer waiting time is significantly associated with a deterioration in patient outcomes 12 months after acceptance for treatment for patients that are still in EIP care. Effects are strongest for waiting times longer than 3 months, and effect sizes are small to moderate. Patients with shorter treatment periods are not affected. The results suggest that policies should aim to reduce excessively long waits in order to improve outcomes for patients waiting for treatment for psychosis.

Costs and Performance of English Mental Health Providers.

BACKGROUND: Despite limited resources in mental health care, there is little research exploring variations in cost performance across mental health care providers. In England, a prospective payment system for mental health care based on patient needs has been introduced with the potential to incentivise providers to control costs. The units of payment under the new system are 21 care clusters. Patients are allocated to a cluster by clinicians, and each cluster has a maximum review period. AIMS OF THE STUDY: The aim of this research is to explain variations in cluster costs between mental health providers using observable patient demographic, need, social and treatment variables. We also investigate if provider-level variables explain differences in costs. The residual variation in cluster costs is compared across providers to provide insights into which providers may gain or lose under the new financial regime. METHODS: The main data source is the Mental Health Minimum Data Set (MHMDS) for England for the years 2011/12 and 2012/13. Our unit of observation is the period of time spent in a care cluster and costs associated with the cluster review period are calculated from NHS Reference Cost data. Costs are modelled using multi-level log-linear and generalised linear models. The residual variation in costs at the provider level is quantified using Empirical Bayes estimates and comparative standard errors used to rank and compare providers. RESULTS: There are wide variations in costs across providers. We find that variables associated with higher costs include older age, black ethnicity, admission under the Mental Health Act, and higher need as reflected in the care clusters. Provider type, size, occupancy and the proportion of formal admissions at the provider-level are also found to be significantly associated with costs. After controlling for patient- and provider-level variables, significant residual variation in costs remains at the provider level. DISCUSSION AND LIMITATIONS: The results suggest that some providers may have to increase efficiency in order to remain financially viable if providers are paid national fixed prices (tariffs) under the new payment system. Although the classification system for payment is not based on diagnosis, a limitation of the study is the inability to explore the effect of diagnosis due to poor coding in the MHMDS. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: We find that some mental health care providers in England are associated with higher costs of provision after controlling for characteristics of service users and providers. These higher costs may be associated with higher quality care or with inefficient provision of care. IMPLICATIONS FOR HEALTH POLICIES: The introduction of a national tariff is likely to provide a strong incentive to reduce costs. Policies may need to consider safe-guarding local health economies if some providers make substantial losses under the new payment regime. IMPLICATIONS FOR FURTHER RESEARCH: Future research should consider the relationship between costs and quality to ascertain whether reducing costs may potentially negatively impact patient outcomes.

Variations in Performance of Mental Health Providers in the English NHS: An Analysis of the Relationship Between Readmission Rates and Length-of-Stay.

Length-of-stay (LOS) for inpatient mental health care is a major driver of variation in resource use internationally. We explore determinants of LOS in England, focusing on the impact of emergency readmission rates which can serve as a measure of the quality of care. Data for 2009/2010 and 2010/2011 are analysed using hierarchical and non-hierarchical models. Unexplained residual variation among providers is quantified using Empirical Bayes techniques. Diagnostic, treatment and patient-level demographic variables are key drivers of LOS. Higher emergency readmission rates are associated with shorter LOS. Ranking providers by residual variation reveals significant differences, suggesting some providers can improve performance.

Determinants of hospital length of stay for people with serious mental illness in England and implications for payment systems: a regression analysis.

BACKGROUND: Serious mental illness (SMI), which encompasses a set of chronic conditions such as schizophrenia, bipolar disorder and other psychoses, accounts for 3.4 m (7 %) total bed days in the English NHS. The introduction of prospective payment to reimburse hospitals makes an understanding of the key drivers of length of stay (LOS) imperative. Existing evidence, based on mainly small scale and cross-sectional studies, is mixed. Our study is the first to use large-scale national routine data to track English hospitals' LOS for patients with a main diagnosis of SMI over time to examine the patient and local area factors influencing LOS and quantify the provider level effects to draw out the implications for payment systems. METHODS: We analysed variation in LOS for all SMI admissions to English hospitals from 2006 to 2010 using Hospital Episodes Statistics (HES). We considered patients with a LOS of up to 180 days and estimated Poisson regression models with hospital fixed effects, separately for admissions with one of three main diagnoses: schizophrenia; psychotic and schizoaffective disorder; and bipolar affective disorder. We analysed the independent contribution of potential determinants of LOS including clinical and socioeconomic characteristics of the patient, access to and quality of primary care, and local area characteristics. We examined the degree of unexplained variation in provider LOS. RESULTS: Most risk factors did not have a differential effect on LOS for different diagnostic sub-groups, however we did find some heterogeneity in the effects. Shorter LOS in the pooled model was associated with co-morbid substance or alcohol misuse (4 days), and personality disorder (8 days). Longer LOS was associated with older age (up to 19 days), black ethnicity (4 days), and formal detention (16 days). Gender was not a significant predictor. Patients who self-discharged had shorter LOS (20 days). No association was found between higher primary care quality and LOS. We found large differences between providers in unexplained variation in LOS. CONCLUSIONS: By identifying key determinants of LOS our results contribute to a better understanding of the implications of case-mix to ensure prospective payment systems reflect accurately the resource use within sub-groups of patients with SMI.

Assessing the uptake of incentivised physical health checks for people with serious mental illness: a cohort study in primary care.

BACKGROUND: People with serious mental illness are more likely to experience physical illnesses. The onset of many of these illnesses can be prevented if detected early. Physical health screening for people with serious mental illness is incentivised in primary care in England through the Quality and Outcomes Framework (QOF). GPs are paid to conduct annual physical health checks on patients with serious mental illness, including checks of body mass index (BMI), cholesterol, and alcohol consumption. AIM: To assess the impact of removing and reintroducing QOF financial incentives on uptake of three physical health checks (BMI, cholesterol, and alcohol consumption) for patients with serious mental illness. DESIGN AND SETTING: Cohort study using UK primary care data from the Clinical Practice Research Datalink between April 2011 and March 2020. METHOD: A difference-in-difference analysis was employed to compare differences in the uptake of physical health checks before and after the intervention, accounting for relevant observed and unobserved confounders. RESULTS: An immediate change was found in uptake after physical health checks were removed from, and after they were added back to, the QOF list. For BMI, cholesterol, and alcohol checks, the overall impact of removal was a reduction in uptake of 14.3, 6.8, and 11.9 percentage points, respectively. The reintroduction of BMI screening in the QOF increased the uptake by 10.2 percentage points. CONCLUSION: This analysis supports the hypothesis that QOF incentives lead to better uptake of physical health checks.

Measuring the overall performance of mental healthcare providers.

To date there have been no attempts to construct composite measures of healthcare provider performance which reflect preferences for health and non-health benefits, as well as costs. Health and non-health benefits matter to patients, healthcare providers and the general public. We develop a novel provider performance measurement framework that combines health gain, non-health benefit, and cost and illustrate it with an application to 54 English mental health providers. We apply estimates from a discrete choice experiment eliciting the UK general population's valuation of non-health benefits relative to health gains, to administrative and patient survey data for years 2013-2015 to calculate equivalent health benefit (eHB) for providers. We measure costs as forgone health and quantify the relative performance of providers in terms of equivalent net health benefit (eNHB): the value of the health and non-health benefits minus the forgone benefit equivalent of cost. We compare rankings of providers by eHB, eNHB, and by the rankings produced by the hospital sector regulator. We find that taking account of the non-health benefits in the eNHB measure makes a substantial difference to the evaluation of provider performance. Our study demonstrates that the provider performance evaluation space can be extended beyond measures of health gain and cost, and that this matters for comparison of providers.

The Role of Mental Health on Workplace Productivity: A Critical Review of the Literature.

BACKGROUND: Mental health disorders in the workplace have increasingly been recognised as a problem in most countries given their high economic burden. However, few reviews have examined the relationship between mental health and worker productivity. OBJECTIVE: To review the relationship between mental health and lost productivity and undertake a critical review of the published literature. METHODS: A critical review was undertaken to identify relevant studies published in MEDLINE and EconLit from 1 January 2008 to 31 May 2020, and to examine the type of data and methods employed, study findings and limitations, and existing gaps in the literature. Studies were critically appraised, namely whether they recognised and/or addressed endogeneity and unobserved heterogeneity, and a narrative synthesis of the existing evidence was undertaken. RESULTS: Thirty-eight (38) relevant studies were found. There was clear evidence that poor mental health (mostly measured as depression and/or anxiety) was associated with lost productivity (i.e., absenteeism and presenteeism). However, only the most common mental disorders were typically examined. Studies employed questionnaires/surveys and administrative data and regression analysis. Few studies used longitudinal data, controlled for unobserved heterogeneity or addressed endogeneity; therefore, few studies were considered high quality. CONCLUSION: Despite consistent findings, more high-quality, longitudinal and causal inference studies are needed to provide clear policy recommendations. Moreover, future research should seek to understand how working conditions and work arrangements as well as workplace policies impact presenteeism.