Setting health care capitations through diagnosis-based risk adjustment: a suitable model for the English NHS?

Predicting healthcare expenditure based on Adjusted Morbidity Groups to implement a needs-based capitation financing system

BACKGROUND

Due to population aging, healthcare expenditure is projected to increase substantially in developed countries like Spain. However, prior research indicates that health status, not merely age, is a key driver of healthcare costs. This study analyzed data from over 1.25 million residents of Spain's Murcia region to develop a capitation-based healthcare financing model incorporating health status via Adjusted Morbidity Groups (AMGs). The goal was to simulate an equitable area-based healthcare budget allocation reflecting population needs.

METHODS

Using 2017 data on residents' age, sex, AMG designation, and individual healthcare costs, generalized linear models were built to predict healthcare expenditure based on health status indicators. Multiple link functions and distribution families were tested, with model selection guided by information criteria, residual analysis, and goodness-of-fit statistics. The selected model was used to estimate adjusted populations and simulate capitated budgets for the 9 healthcare districts in Murcia.

RESULTS

The gamma distribution with logarithmic link function provided the best model fit. Comparisons of predicted and actual average costs revealed underfunded and overfunded areas within Murcia. If implemented, the capitation model would decrease funding for most districts (up to 15.5%) while increasing it for two high-need areas, emphasizing allocation based on health status and standardized utilization rather than historical spending alone.

CONCLUSIONS

AMG-based capitated budgeting could improve equity in healthcare financing across regions in Spain. By explicitly incorporating multimorbidity burden into allocation formulas, resources can be reallocated towards areas with poorer overall population health. Further policy analysis and adjustment is needed before full-scale implementation of such need-based global budgets.

Impact of free maternal health care policy on maternal health care utilization and perinatal mortality in Ghana: protocol design for historical cohort study

Background

Ghana introduced what has come to be known as the 'Free’ Maternal Health Care Policy (FMHCP) in 2008 via the free registration of pregnant women to the National Health Insurance Scheme to access healthcare free of charge. The policy targeted every pregnant woman in Ghana with a full benefits package covering comprehensive maternal healthcare.

Purpose

This study seeks to measure the contribution of the FMHCP to maternal healthcare utilization; antenatal care uptake, and facility delivery and determine the utilization impact on stillbirth, perinatal, and neonatal deaths using quasi-experimental methods. The study will also contextualize the findings against funding constraints and operational bottlenecks surrounding the policy operations in the Upper East Region of Ghana.

Methods

This study adopts a mixed-method design to estimate the treatment effect using variables generated from historical data of Ghana and Kenya Demographic and Health Survey data sets of 2008/2014, as treatment and comparison groups respectively. As DHS uses complex design, weighting will be applied to the data sets to cater for clustering and stratification at all stages of the analysis by setting the data in STATA and prefix Stata commands with ‘svy’. Thus, the policy impact will be determined using quasi-experimental designs; propensity score matching, and difference-in-differences methods. Prevalence, mean difference, and test of association between outcome and exposure variables will be achieved using the Rao Scot Chi-square. Confounding variables will be adjusted for using Poisson and multiple logistics regression models. Statistical results will be reported in proportions, regression coefficient, and risk ratios. This study then employs intrinsic-case study technique to explore the current operations of the ‘free’ policy in Ghana, using qualitative methods to obtain primary data from the Upper East Region of Ghana for an in-depth analysis.

Discussion

The study discussions will show the contributions of the ‘free’ policy towards maternal healthcare utilization and its performance towards stillbirth, perinatal and neonatal healthcare outcomes. The discussions will also centre on policy designs and implementation in resource constraints settings showing how SDG3 can be achievement or otherwise. Effectiveness of policy proxy and gains in the context of social health insurance within a broader concept of population health and economic burden will also be conferred.

Protocol approval

This study protocol is registered for implementation by the Ghana Health Service Ethical Review Committee, number: GHS-ERC 002/04/19.

Changes in German Mental Health Care by Implementing a Global Treatment Budget-A Mixed-Method Process Evaluation Study

BACKGROUND

Internationally, there is a broad spectrum of outreach and integrative care models, whereas in Germany acute psychiatric treatment is still mostly provided in inpatient settings. To overcome this, a new legal framework (§64b Social Code V) has been introduced, promoting "Flexible and Integrative Treatment" Models (FIT64b), based on a "Global Treatment Budget" (GTB) financing approach. 23 hospitals have implemented the framework according to local needs and concepts. Prior research has already identified specific components of FIT64b. Based on this, our paper aims to examine the implementation process and underpinning change mechanisms of GTB-based FIT64b models from a staff, service user and caregiver perspective.

METHOD

31 focus groups and 15 semi-structured interviews were conducted with hospital staff (n = 138), service users (n = 63), and caregivers (n = 35) in 10 psychiatric hospitals implementing FIT64b. Using qualitative analysis, we identified 5 core themes describing the implementation process, which were theoretically modeled into a logical diagram. The core mechanisms of change were thus identified across themes. Additional structural and semi-quantitative performance data was collected from all study departments.

RESULTS

The qualitative analysis showed that the shift from a daily- and performance-based payment to a lump-sum GTB and the shift of resources from in- to outpatient settings were of crucial importance for the process of change. Saved budget shares could be reinvested to integrate in-, out-, and day-patient units and to set up outreach home care. Clinicians reported feeling relieved by the increase of treatment options. They also emphasized a stronger relationship with and a better understanding of service users and a simplification of bureaucracy. Finally, service users and caregivers experienced higher need-adaptedness of treatment, a feeling of deeper understanding and safety, and the possibility to maintain everyday life during treatment. Finally, two FIT64b implementation prototypes were classified according to the semi-quantitative performance data.

CONCLUSION

Based on the results, we developed 3 core mechanisms of change of FIT64b models: (1) Need-adaptedness and flexibility; (2) Continuity of care; (3) Maintaining everyday life. Our findings outline and emphasize the potential a GTB approach may have for improving psychiatric hospital services.

The National Health Service (NHS) in 'crisis': the role played by a shift from horizontal to vertical principles of equity

Explanations of the state of 'crisis' in the English National Health Service (NHS) generally focus on the overall level of health care funding rather than the way in which funding is distributed. Describing systematic patterns in the way different areas are experiencing crisis, this paper suggests that NHS organisations in older, rural and particularly coastal areas are more likely to be 'failing' and that this is due to the historic underfunding of such areas. This partly reflects methodological and technical shortcomings in NHS resource allocation formulae. It is also the outcome of a philosophical shift from horizontal (equal access for equal needs) to vertical (unequal access to equalise health outcomes) principles of equity. Insofar as health inequalities are determined by factors well beyond health care, we argue that this is an ineffective approach to addressing health inequalities. Moreover, it sacrifices equity in access to health care by failing to adequately fund the health care needs of older populations. The prioritisation of vertical over horizontal equity also conflicts with public perspectives on the NHS. Against this background, we ask whether the time has come to reassert the moral and philosophical case for the principle of equal access for equal health care need.

Key aspects related to implementation of risk stratification in health care systems-the ASSEHS study

BACKGROUND

The lack of proven efficacy of new healthcare interventions represents a problem for health systems globally. It is partly related to suboptimal implementation processes, leading to poor adoption of new interventions. Activation of Stratification Strategies and Results of the interventions on frail patients of Healthcare Services (ASSEHS) EU project (N° 2013 12 04) aims to study current existing health Risk Stratification (RS) strategies and tools on frail elderly patients. This paper aims at identifying variables that make the implementation of population RS tools feasible in different healthcare services.

METHODS

Two different methods have been used to identify the key elements in stratification implementation; i) a Scoping Review, in order to search and gather scientific evidence and ii) Semi-structured interviews with six key experts that had been actively involved in the design and/or implementation of RS strategies. It aims to focus the implementation construct on real-life contextual understandings, multi-level perspectives, and cultural influences.

RESULTS

A Feasibility Framework has been drawn. Two dimensions impact the feasibility of RS: (i) Planning, deployment and change management and (ii) Care intervention. The former comprises communication, training and mutual learning, multidisciplinarity of the team, clinicians' engagement, operational plan and ICT display and functionalities. The latter includes case finding and selection of the target population, pathway definition and quality improvement process.

CONCLUSIONS

The Feasibility Framework provides a list of key elements that should be considered for an effective implementation of population risk stratification interventions. It helps to identify, plan and consider relevant elements to ensure a proper RS implementation.

Predicting patient 'cost blooms' in Denmark: a longitudinal population-based study

OBJECTIVES

To compare the ability of standard versus enhanced models to predict future high-cost patients, especially those who move from a lower to the upper decile of per capita healthcare expenditures within 1 year-that is, 'cost bloomers'.

DESIGN

We developed alternative models to predict being in the upper decile of healthcare expenditures in year 2 of a sample, based on data from year 1. Our 6 alternative models ranged from a standard cost-prediction model with 4 variables (ie, traditional model features), to our largest enhanced model with 1053 non-traditional model features. To quantify any increases in predictive power that enhanced models achieved over standard tools, we compared the prospective predictive performance of each model.

PARTICIPANTS AND SETTING

We used the population of Western Denmark between 2004 and 2011 (2 146 801 individuals) to predict future high-cost patients and characterise high-cost patient subgroups. Using the most recent 2-year period (2010-2011) for model evaluation, our whole-population model used a cohort of 1 557 950 individuals with a full year of active residency in year 1 (2010). Our cost-bloom model excluded the 155 795 individuals who were already high cost at the population level in year 1, resulting in 1 402 155 individuals for prediction of cost bloomers in year 2 (2011).

PRIMARY OUTCOME MEASURES

Using unseen data from a future year, we evaluated each model's prospective predictive performance by calculating the ratio of predicted high-cost patient expenditures to the actual high-cost patient expenditures in Year 2-that is, cost capture.

RESULTS

Our best enhanced model achieved a 21% and 30% improvement in cost capture over a standard diagnosis-based model for predicting population-level high-cost patients and cost bloomers, respectively.

CONCLUSIONS

In combination with modern statistical learning methods for analysing large data sets, models enhanced with a large and diverse set of features led to better performance-especially for predicting future cost bloomers.

Equity of utilisation of cardiovascular care and mental health services in England: a cohort-based cross-sectional study using small-area estimation

Background

A strong policy emphasis on the need to reduce both health inequalities and unmet need in deprived areas has resulted in the substantial redistribution of English NHS funding towards deprived areas. This raises the question of whether or not socioeconomically disadvantaged people continue to be disadvantaged in their access to and utilisation of health care.

Objectives

To generate estimates of the prevalence of cardiovascular disease (CVD) and common mental health disorders (CMHDs) at a variety of scales, and to make these available for public use via Public Health England (PHE). To compare these estimates with utilisation of NHS services in England to establish whether inequalities of use relative to need at various stages on the health-care pathway are associated with particular sociodemographic or other factors.

Design

Cross-sectional analysis of practice-, primary care trust- and Clinical Commissioning Group-level variations in diagnosis, prescribing and specialist management of CVD and CMHDs relative to the estimated prevalence of those conditions (calculated using small-area estimation).

Results

The utilisation of CVD care appears more equitable than the utilisation of care for CMHDs. In contrast to the reviewed literature, we found little evidence of underutilisation of services by older populations. Indeed, younger populations appear to be less likely to access care for some CVD conditions. Nor did deprivation emerge as a consistent predictor of lower use relative to need for either CVD or CMHDs. Ethnicity is a consistent predictor of variations in use relative to need. Rates of primary management are lower than expected in areas with higher percentages of black populations for diabetes, stroke and CMHDs. Areas with higher Asian populations have higher-than-expected rates of diabetes presentation and prescribing and lower-than-expected rates of secondary care for diabetes. For both sets of conditions, there are pronounced geographical variations in use relative to need. For instance, the North East has relatively high levels of use of cardiac care services and rural (shire) areas have low levels of use relative to need. For CMHDs, there appears to be a pronounced ‘London effect’, with the number of people registered by general practitioners as having depression, or being prescribed antidepressants, being much lower in London than expected. A total of 24 CVD and 41 CMHD prevalence estimates have been provided to PHE and will be publicly available at a range of scales, from lower- and middle-layer super output areas through to Clinical Commissioning Groups and local authorities.

Conclusions

We found little evidence of socioeconomic inequality in use for CVD and CMHDs relative to underlying need, which suggests that the strong targeting of NHS resources to deprived areas may well have addressed longstanding concerns about unmet need. However, ethnicity has emerged as a significant predictor of inequality, and there are large and unexplained geographical variations in use relative to need for both conditions which undermine the principle of equal access to health care for equal needs. The persistence of ethnic variations and the role of systematic factors (such as rurality) in shaping patterns of utilisation deserve further investigation, as does the fact that the models were far better at explaining variation in use of CVD than mental health services.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

Spending by primary care practices - does it show what we expect?

BACKGROUND

Over recent years, a number of policies and financial incentives in primary care have been proposed to tackle issues such as deprivation and health outcomes. This article investigates the association between healthcare spending, deprivation and outcomes. It argues that individual practice data are analysed before blanket application and acceptance that one size fits all in a local area.

METHODS

Financial data were analysed alongside key outcome data, including quality and outcomes framework (QOF) indicators for a large urban primary care trust (PCT) in the UK. The PCT had a large population and number of practices, including single-handed practices and an average list size in excess of 5000. The PCT will remain anonymous.

RESULTS

There was no relationship between primary care investment and the practices' deprivation score. There was a strong statistically significant negative correlation between QOF payments and deprivation, (correlation = -0.46, p < 0.001). There were only weak links between primary care investment and health outcomes. There was no relationship between high emergency spending and health outcome.

CONCLUSIONS

The data presented suggest that one size does not necessarily fit all - in terms of providing the appropriate incentives in primary care, nor do national incentives and policies always have the desired effect.

Predictive risk modelling in the Spanish population: a cross-sectional study

BACKGROUND

An increase in chronic conditions is currently the greatest threat to human health and to the sustainability of health systems. Risk adjustment systems may enable population stratification programmes to be developed and become instrumental in implementing new models of care.The objectives of this study are to evaluate the capability of ACG-PM, DCG-HCC and CRG-based models to predict healthcare costs and identify patients that will be high consumers and to analyse changes to predictive capacity when socio-economic variables are added.

METHODS

This cross-sectional study used data of all Basque Country citizens over 14 years of age (n = 1,964,337) collected in a period of 2 years. Data from the first 12 months (age, sex, area deprivation index, diagnoses, procedures, prescriptions and previous cost) were used to construct the explanatory variables. The ability of models to predict healthcare costs in the following 12 months was assessed using the coefficient of determination and to identify the patients with highest costs by means of receiver operating characteristic (ROC) curve analysis.

RESULTS

The coefficients of determination ranged from 0.18 to 0.21 for diagnosis-based models, 0.17-0.18 for prescription-based and 0.21-0.24 for the combination of both. The observed area under the ROC curve was 0.78-0.86 (identifying patients with a cost higher than P-95) and 0.83-0.90 (P-99). The values of the DCG-HCC models are slightly higher and those of the CRG models are lower, although prescription information could not be used in the latter. On adding previous cost data, differences between the three systems decrease appreciably. Inclusion of the deprivation index led to only marginal improvements in explanatory power.

CONCLUSION

The case-mix systems developed in the USA can be useful in a publicly financed healthcare system with universal coverage to identify people at risk of high health resource consumption and whose situation is potentially preventable through proactive interventions.