Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases

Investigating socioeconomic disparities in prescribing new diabetes medications in individuals with type 2 diabetes and very high cardiovascular risk in the Netherlands

AIMS

This study aims to analyze prescription patterns of new diabetes medication and assess socioeconomic disparities in their initiation among individuals with T2DM with very high cardiovascular risk.

METHODS

Individuals diagnosed with T2DM and very high cardiovascular risk were identified (N = 10,768) based on general practitioner's electronic health record data. SGLT-2is and GLP-1RAs prescription patterns were examined. Furthermore, the association between SES and the prescription of SGLT-2is and GLP-1RAs in 2022 was investigated.

RESULTS

Despite the increase in prescription rates of SGLT-2is and GLP-1RAs between 2019 and 2022, approximately 85 % and 93 % of eligible individuals did not receive SGLT-2is and GLP-1RAs in 2022, respectively. We found a positive association between SGLT-2is prescription and SES in only the 4th quintile compared to 1st quintile (referent) in the fully adjusted model (OR 1.29 95 % CI:1.08-1.54).

CONCLUSIONS

The prescription rates among eligible individuals highlight significant room for improvement in aligning prescribing practices with guidelines. We found no profound socioeconomic gradient in initiation of SGLT-2is and GLP-1RAs. The latter may be due to guidelines' clear indication of the eligible population and GP education. Future development and potential disparities in initiation and maintenance should be monitored to ensure equitable prescribing.

Systematic population-wide ecological analysis of regional variability in disease prevalence

The prevalence of diseases often varies substantially from region to region. Besides basic demographic properties, the factors that drive the variability of each prevalence are to a large extent unknown. Here we show how regional prevalence variations in 115 different diseases relate to demographic, socio-economic, environmental factors and migratory background, as well as access to different types of health services such as primary, specialized and hospital healthcare. We have collected regional data for these risk factors at different levels of resolution; from large regions of care (Versorgungsregion) down to a 250 by 250 m square grid. Using multivariate regression analysis, we quantify the explanatory power of each independent variable in relation to the regional variation of the disease prevalence. We find that for certain diseases, such as acute heart conditions, diseases of the inner ear, mental and behavioral disorders due to substance abuse, up to 80% of the variance can be explained with these risk factors. For other diagnostic blocks, such as blood related diseases, injuries and poisoning however, the explanatory power is close to zero. We find that the time needed to travel from the inhabited center to the relevant hospital ward often contributes significantly to the disease risk, in particular for diabetes mellitus. Our results show that variations in disease burden across different regions can for many diseases be related to variations in demographic and socio-economic factors. Furthermore, our results highlight the relative importance of access to health care facilities in the treatment of chronic diseases like diabetes.

Examining annual transitions in healthcare spending among U.S. medicare beneficiaries using multistate Markov models: Analysis of medicare current beneficiary survey data, 2003-2019

Many studies have examined factors associated with individuals of high or low healthcare spending in a given year. However, few have studied how healthcare spending changes over multiple years and which factors are associated with the changes. In this study, we examined the dynamic patterns of healthcare spending over a three-year period, among a nationally representative cohort of Medicare beneficiaries in the U.S. and identified factors associated with these patterns. We extracted data for 30,729 participants from the national Medicare Current Beneficiary Survey (MCBS), for the period 2003-2019. Using multistate Markov (MSM) models, we estimated the probabilities of year-to-year transitions in healthcare spending categorized as three states (low (L), medium (M) and high (H)), or to the terminal state, death. The participants, 13,554 (44.1%), 13,715 (44.6%) and 3,460 (11.3%) were in the low, medium and high spending states at baseline, respectively. The majority of participants remained in the same spending category from one year to the next (L-to-L: 76.8%; M-to-M: 71.7%; H-to-H: 56.6 %). Transitions from the low to high spending state were significantly associated with older age (75-84, ≥85 years), residing in a long-term care facility, greater assistance with activities of daily living, enrollment in fee-for-service Medicare, not receiving a flu shot, and presence of specific medical conditions, including cancer, dementia, and heart disease. Using data from a large population-based longitudinal survey, we have demonstrated that MSM modelling is a flexible framework and useful tool for examining changes in healthcare spending over time.

Regional differences in healthcare costs further explained: The contribution of health, lifestyle, loneliness and mastery

Healthcare costs in the Netherlands are rising and vary considerably among regions. Explaining regional differences in healthcare costs can help policymakers in targeting appropriate interventions in order to restrain costs. Factors usually taken into account when analyzing regional differences in healthcare costs are demographic structure and socioeconomic status (SES). However, health, lifestyle, loneliness and mastery have also been linked to healthcare costs. Therefore, this study analyzes the contribution of health, lifestyle factors (BMI, alcohol consumption, smoking and physical activity), loneliness, and mastery to regional differences in healthcare costs. Analyses are performed in a linked dataset (n = 334,721) from the Dutch Public Health Services, Statistics Netherlands, the National Institute for Public Health and the Environment (year 2016), and the healthcare claims database Vektis (year 2017) with Poisson and zero-inflated binomial regressions. Regional differences in general practitioner consult costs remain significant even after taking into account health, lifestyle, loneliness, and mastery. Regional differences in costs for mental, pharmaceutical, and specialized care are less pronounced and can be explained to a large extent. For total healthcare costs, regional differences are mostly explained through the factors included in this study. Hence, addressing lifestyle factors, loneliness and mastery can help policymakers in restraining healthcare costs. In this study, the region of Zuid-Limburg represents the reference region. Use compare regions for health and healthcare costs (Regiovergelijker gezondheid en zorgkosten) in order to select all other Dutch regions as reference region.

Supplementary Information

The online version of this article (10.1007/s12508-022-00369-4) contains supplementary material, which is available to authorized users.

Regional variation in long-term care spending in Japan

Background

Health inequalities are widening in Japan, and thus, it is important to understand whether (and to what extent) there is a regional variation in long-term care (LTC) spending across municipalities. This study assesses regional variation in LTC spending and identifies the drivers of such variation.

Methods

We conducted a cross-sectional study using publicly available municipality-level data across Japan in 2019, in which the unit of analysis was municipality. The outcome of interest was per-capita LTC spending, which was estimated by dividing total LTC spending in a municipality by the number of older adults (people aged ≥ 65). To further identify drivers of regional variation in LTC spending, we conducted linear regression of per-capita spending against a series of demand, supply, and structural factors. Shapley decomposition approach was used to highlight the contribution of each independent variable to the goodness of fit of the regression model.

Results

In Fiscal 2019, per-capita LTC spending varied from 133.1 to 549.9 thousand yen (max/min ratio 4.1) across the 1460 municipalities analyzed, showing considerable regional variation. The included covariates explained 84.0% of the total variance in LTC spending, and demand-determined variance was remarkably high, which contributed more than 85.7% of the overall R². Specifically, the highest contributing factor was the proportion of severe care-need level and care level certification rate.

Conclusions

Our results demonstrate that, even after adjusting for different municipalities’ age and sex distribution, there is a large variation in LTC spending. Furthermore, our findings highlight that, to reduce the spending gap between municipalities, the issues underlying large variations in LTC spending across municipalities must be identified and addressed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14194-6.

Geographic and temporal variation in the treatment and outcomes of atrial fibrillation: a population-based analysis of national quality indicators

BACKGROUND

Assessment of potential geographic variation in quality indicators of atrial fibrillation care may identify opportunities for improvement in the quality of atrial fibrillation care. The objective of this study was to assess for potential geographic variation in the quality of atrial fibrillation care in Alberta, Canada.

METHODS

In a population-based cohort of adults (age ≥ 18 yr) with incident nonvalvular atrial fibrillation (NVAF) diagnosed between Apr. 1, 2008, and Mar. 31, 2016, in Alberta, we investigated the variation in national quality indicators of atrial fibrillation care developed by the Canadian Cardiovascular Society. Specifically, we assessed the geographic and temporal variation in the proportion of patients with initiation of oral anticoagulant therapy, persistence with therapy, ischemic stroke and major bleeding outcomes 1 year after atrial fibrillation diagnosis using linked administrative data sets. We defined stroke risk using the CHADS₂ score. We assessed geographic variation using small-area variation statistics and geospatial data analysis.

RESULTS

Of the 64 093 patients in the study cohort (35 019 men [54.6%] and 29 074 women [45.4%] with a mean age of 69 [standard deviation 15.9] yr), 36 199 were at high risk for stroke and 14 411 were at moderate risk. Within 1 year of NVAF diagnosis, 20 180 patients (55.7%) in the high-risk group and 6448 patients (44.7%) in the moderate-risk group were prescribed anticoagulation. A total of 2187 patients (3.4%) had an ischemic stroke, and 2996 patients (4.7%) experienced a major bleed. There was substantial regional variation observed in initiation of oral anticoagulant therapy but not in the proportion of patients with ischemic stroke or major bleeding. Among the 64 Health Status Areas in Alberta, therapy initiation rates ranged from 22.6% to 71.2% among patients at high stroke risk and from 22.7% to 55.8% among those at moderate stroke risk, with clustering of lower therapy initiation rates in rural northern regions.

INTERPRETATION

The rate of initiation of oral anticoagulant therapy among adults with incident atrial fibrillation was less than 60% in patients in whom oral anticoagulant therapy would be considered guideline-appropriate care. The large geographic variation in oral anticoagulant prescribing warrants additional study into patient, provider and health care system factors that contribute to variation and drive disparities in high-quality, equitable atrial fibrillation care.

Assessment of variation in long-term outcomes of integrated care initiatives in Dutch health care

Introduction

The care for many patients with diabetes mellitus type 2 in the Netherlands, is contracted by a local care group. The healthcare providers, who collectively shape a care group, provide protocolled diabetes care. Differences exist between care groups in terms of their organizational and financial arrangements. These differences may result in variation in outcomes. The aim of this study is to assess whether variation in healthcare costs, diabetes complications and related hospital admissions on the level of care groups exist.

Methods

A quantitative cohort study was conducted. Patients who used diabetes medication (more than 180 days of defined daily doses per year) for the first time between the years 2014 and 2019 were included. Data were extracted from health insurance claims between 2014 and 2019. Generalized linear mixed models were used to analyse patient variation in healthcare costs (two and six years follow-up), diabetes-related complications and hospital admission days. Intraclass correlation coefficients were calculated to estimate the amount of variation that was attributable to the care groups.

Results

A large variation in outcome variables was observed between patients and a small variation between care groups. The intraclass correlation coefficient for long-term costs was 0.4%; for short-term costs between 0.1% and 0.3%; for complications 1% and for hospital days 4%.

Discussion

A large variation between patients with diabetes mellitus type 2 exists in terms of their healthcare costs and complications. In our study, care groups accounted minimally for this variation. A generalized linear mixed model in combination with year cohorts is a tool to study variations in the long-term outcomes of integrated care initiatives.

A Framework for Understanding How Variation in Health Care Service Delivery Affects Work Disability Management

Introduction Differences in disability duration after work injury have been observed across jurisdictions, regions and urban and rural settings. A key aspect of effective disability management is the access and utilization of appropriate and high quality health care. This paper presents a framework for analyzing and thus understanding how health service spending and utilization vary across and within work disability management schemes and affect work disability management. Methods Our framework was developed through a literature review and policy analysis. Existing frameworks describing geographic variation in general health care systems identified factors believed to drive that variation. A review of policy and practice documents from Canada's no-fault cause-based work disability management system identified factors relevant to work disability systems. Results We expand on previous frameworks by taking a systems approach that centers on factors relevant to the work disability management system. We further highlight predisposing, enabling, workplace environment and need-based factors that could lead to variation in health care spending and utilization across and within jurisdictions. These factors are described as shaping the interactions between workers, health care providers, employers and work disability management system actors, and influencing work disability management health and employment outcomes. Conclusion Our systems-focused approach offers a guide for researchers and policymakers to analyze how various factors may influence spending and utilization across regions and to identify areas for improvement in health care delivery within work disability management systems. Next steps include testing the framework in an analysis looking at geographic variation in spending and utilization across and within Canadian work disability management systems.

The Gini coefficient as a useful measure of malaria inequality among populations

Background

Understanding inequality in infectious disease burden requires clear and unbiased indicators. The Gini coefficient, conventionally used as a macroeconomic descriptor of inequality, is potentially useful to quantify epidemiological heterogeneity. With a potential range from 0 (all populations equal) to 1 (populations having maximal differences), this coefficient is used here to show the extent and persistence of inequality of malaria infection burden at a wide variety of population levels.

Methods

First, the Gini coefficient was applied to quantify variation among World Health Organization world regions for malaria and other major global health problems. Malaria heterogeneity was then measured among countries within the geographical sub-region where burden is greatest, among the major administrative divisions in several of these countries, and among selected local communities. Data were analysed from previous research studies, national surveys, and global reports, and Gini coefficients were calculated together with confidence intervals using bootstrap resampling methods.

Results

Malaria showed a very high level of inequality among the world regions (Gini coefficient, G = 0.77, 95% CI 0.66–0.81), more extreme than for any of the other major global health problems compared at this level. Within the most highly endemic geographical sub-region, there was substantial inequality in estimated malaria incidence among countries of West Africa, which did not decrease between 2010 (G = 0.28, 95% CI 0.19–0.36) and 2018 (G = 0.31, 0.22–0.39). There was a high level of sub-national variation in prevalence among states within Nigeria (G = 0.30, 95% CI 0.26–0.35), contrasting with more moderate variation within Ghana (G = 0.18, 95% CI 0.12–0.25) and Sierra Leone (G = 0.17, 95% CI 0.12–0.22). There was also significant inequality in prevalence among local village communities, generally more marked during dry seasons when there was lower mean prevalence. The Gini coefficient correlated strongly with the standard coefficient of variation, which has no finite range.

Conclusions

The Gini coefficient is a useful descriptor of epidemiological inequality at all population levels, with confidence intervals and interpretable bounds. Wider use of the coefficient would give broader understanding of malaria heterogeneity revealed by multiple types of studies, surveys and reports, providing more accessible insight from available data.

What explains the regional variation in the use of general practitioners in Australia?

BACKGROUND

Regional variation in the use of health care services is widespread. Identifying and understanding the sources of variation and how much variation is unexplained can inform policy interventions to improve the efficiency and equity of health care delivery.

METHODS

We examined the regional variation in the use of general practitioners (GPs) using data from the Social Health Atlas of Australia by Statistical Local Area (SLAs). 756 SLAs were included in the analysis. The outcome variable of GP visits per capita by SLAs was regressed on a series of demand-side factors measuring population health status and demographic characteristics and supply-side factors measuring access to physicians. Each group of variables was entered into the model sequentially to assess their explanatory share on regional differences in GP usage.

RESULTS

Both demand-side and supply-side factors were found to influence the frequency of GP visits. Specifically, areas in urban regions, areas with a higher percentage of the population who are obese, who have profound or severe disability, and who hold concession cards, and areas with a smaller percentage of the population who reported difficulty in accessing services have higher GP usage. The availability of more GPs led to higher use of GP services while the supply of more specialists reduced use. 30.56% of the variation was explained by medical need. Together, both need-related and supply-side variables accounted for 32.24% of the regional differences as measured by the standard deviation of adjusted GP-consultation rate.

CONCLUSIONS

There was substantial variation in GP use across Australian regions with only a small proportion of them being explained by population health needs, indicating a high level of unexplained clinical variation. Supply factors did not add a lot to the explanatory power. There was a lot of variation that was not attributable to the factors we could observe. This could be due to more subtle aspects of population need or preferences and therefore warranted. However, it could be due to practice patterns or other aspects of supply and be unexplained. Future work should try to explain the remaining unexplained variation.

Regional variation in healthcare spending and mortality among senior high-cost healthcare users in Ontario, Canada: a retrospective matched cohort study

Background

Senior high cost health care users (HCU) are a priority for many governments. Little research has addressed regional variation of HCU incidence and outcomes, especially among incident HCU. This study describes the regional variation in healthcare costs and mortality across Ontario’s health planning districts [Local Health Integration Networks (LHIN)] among senior incident HCU and non-HCU and explores the relationship between healthcare spending and mortality.

Methods

We conducted a retrospective population-based matched cohort study of incident senior HCU defined as Ontarians aged ≥66 years in the top 5% most costly healthcare users in fiscal year (FY) 2013. We matched HCU to non-HCU (1:3) based on age, sex and LHIN. Primary outcomes were LHIN-based variation in costs (total and 12 cost components) and mortality during FY2013 as measured by variance estimates derived from multi-level models. Outcomes were risk-adjusted for age, sex, ADGs, and low-income status. In a cost-mortality analysis by LHIN, risk-adjusted random effects for total costs and mortality were graphically presented together in a cost-mortality plane to identify low and high performers.

Results

We studied 175,847 incident HCU and 527,541 matched non-HCU. On average, 94 out of 1000 seniors per LHIN were HCU (CV = 4.6%). The mean total costs for HCU in FY2013 were 12 times higher that of non-HCU ($29,779 vs. $2472 respectively), whereas all-cause mortality was 13.6 times greater (103.9 vs. 7.5 per 1000 seniors).

Regional variation in costs and mortality was lower in senior HCU compared with non-HCU. We identified greater variability in accessing the healthcare system, but, once the patient entered the system, variation in costs was low. The traditional drivers of costs and mortality that we adjusted for played little role in driving the observed variation in HCUs’ outcomes. We identified LHINs that had high mortality rates despite elevated healthcare expenditures and those that achieved lower mortality at lower costs. Some LHINs achieved low mortality at excessively high costs.

Conclusions

Risk-adjusted allocation of healthcare resources to seniors in Ontario is overall similar across health districts, more so for HCU than non-HCU. Identified important variation in the cost-mortality relationship across LHINs needs to be further explored.

Electronic supplementary material

The online version of this article (10.1186/s12877-018-0952-7) contains supplementary material, which is available to authorized users.