Sources of regional variation in healthcare utilization in Germany

Salm, Martin; Wübker, Ansgar

doi:10.1016/j.jhealeco.2019.102271

Cited by 24 publications

(39 citation statements)

References 19 publications

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“…The remaining 72.6% correspond to the share of regional variation attributed to the demand side. This demand share is higher than the 50% share attributable to patients for healthcare utilization of Medicare beneficiaries in the United States (Finkelstein et al, ), but it is lower than the 80–90% share attributable to patients for outpatient care utilization in Germany (Salm & Wübker, ). The relative importance of demand‐ and supply‐side factors as sources of regional variation differs little across different categories of care.…”

Section: Resultsmentioning

confidence: 97%

“…These measures include, among others, issuing detailed treatment guidelines for supply‐sensitive procedures and releasing information depicting existing variations in healthcare expenditures across regions (Wammes, Atsma, de Mann, Groenewound, & Westert, ). However, such policies may be failing to target some of the main causes of regional variation in healthcare expenditures, as recent studies show that demand‐side factors are an important source of regional variation for Medicare patients in the United States (Finkelstein, Gentzkow, & Williams, ) and for outpatient care in Germany (Salm & Wübker, ).…”

Section: Introductionmentioning

confidence: 99%

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Causes of regional variation in Dutch healthcare expenditures: Evidence from movers

et al. 2019

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Summary We assess the relative importance of demand and supply factors as determinants of regional variation in healthcare expenditures in the Netherlands. Our empirical approach follows individuals who migrate between regions. We use individual data on annual healthcare expenditures for the entire Dutch population between the years 2006 and 2013. Regional variation in healthcare expenditures is mostly driven by demand factors, with an estimated share of around 70%. The relative importance of different causes varies with the groups of regions being compared.

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Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Causes of regional variation in Dutch healthcare expenditures: Evidence from movers

et al. 2019

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“…In general, hospitals are not allowed to provide ambulatory care. There are around 130,000 physicians in the ambulatory care sector that are mostly self-employed and work in individual or small group practices (Salm and Wübker 2016).…”

Section: Institutional Settings 21 the German Insurance Systemmentioning

confidence: 99%

“…The RLV defines the maximum quantity of services that a physician can charge without any discount. Services performed beyond the budget limits are reimbursed at a much lower rate (Salm and Wübker 2016). On average, PHI holders have a higher income and thus face higher opportunity costs of waiting times (Johannesson et al 1998).…”

Section: Institutional Settings 21 the German Insurance Systemmentioning

confidence: 99%

Waiting Times for Outpatient Treatment in Germany: New Experimental Evidence from Primary Data

Heinrich

Wübker

Wuckel

2018

Jahrbücher Für Nationalökonomie Und Statistik

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Long waiting times are a common feature and a major concern in many public health care systems. They are often characterized as inefficient because they are a burden to patients without generating any gains for providers. There is an ongoing debate in Germany regarding the preferential treatment given to private health insurance (PHI) holders while statutory health insurance (SHI) holders face continuously increasing waiting times. In order to tackle this problem in the outpatient sector, Germany initiated a reform in 2015 which was aimed at providing SHI holders with appointments within an acceptable time frame. We exploit longitudinal experimental data to examine waiting times for six elective outpatient treatments in Germany for PHI and SHI holders before and after the reform. We find a considerable difference in waiting times favoring private patients. For SHI holders, waiting times remained stable over time (27.5 days in 2014, 30.7 days in 2016, Δ 3.2 days, p-value=0.889) while PHI holders experienced a significant improvement (13.5 days in 2014; 7.8 days in 2016; Δ 5.7 days, p-value=0.002). The results indicate that even after the reform there is still an unequal access to elective outpatient treatment depending on the patient’s insurance status.

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“…Moreover, the spatial variability of the health technology used and the organizational characteristics of institutions contribute considerably. [29]. Study of geographical resource allocation also has long-term history [30,31].…”

Section: Introductionmentioning

confidence: 99%

Self-Declared Roma Ethnicity and Health Insurance Expenditures: A Nationwide Cross-Sectional Investigation at the General Medical Practice Level in Hungary

Kasabji

Alrajo

Vincze

et al. 2020

IJERPH

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The inevitable rising costs of health care and the accompanying risk of increasing inequalities raise concerns. In order to make tailored policies and interventions that can reduce this risk, it is necessary to investigate whether vulnerable groups (such as Roma, the largest ethnic minority in Europe) are being left out of access to medical advances. Objectives: The study aimed to describe the association between general medical practice (GMP) level of average per capita expenditure of the National Health Insurance Fund (NHIF), and the proportion of Roma people receiving GMP in Hungary, controlled for other socioeconomic and structural factors. Methods: A cross-sectional study that included all GMPs providing care for adults in Hungary (N = 4818) was conducted for the period 2012–2016. GMP specific data on health expenditures and structural indicators (GMP list size, providing care for adults only or children also, type and geographical location of settlement, age of GP, vacancy) for secondary analysis were obtained from the NHIF. Data for the socioeconomic variables were from the last census. Age and sex standardized specific socioeconomic status indicators (standardized relative education, srEDU; standardized relative employment, srEMP; relative housing density, rHD; relative Roma proportion based on self-reported data, rRP) and average per capita health expenditure (standardized relative health expenditure, srEXP) were computed. Multivariate linear regression model was applied to evaluate the relationship of socioeconomic and structural indicators with srEXP. Results: The srEDU had significant positive (b = 0.199, 95% CI: 0.128; 0.271) and the srEMP had significant negative (b = −0.282, 95% CI: −0.359; −0.204) effect on srEXP. GP age > 65 (b = −0.026, 95% CI: −0.036; −0.016), list size <800 (b = −0.043, 95% CI: −0.066; −0.020) and 800–1200 (b = −0.018, 95% CI: −0.031; −0.004]), had significant negative association with srEXP, and GMP providing adults only (b = 0.016, 95% CI: 0.001;0.032) had a positive effect. There was also significant expenditure variability across counties. However, rRP proved not to be a significant influencing factor (b = 0.002, 95% CI: −0.001; 0.005). Conclusion: As was expected, lower education, employment, and small practice size were associated with lower NHIF expenditures in Hungary, while the share of self-reported Roma did not significantly affect health expenditures according to our GMP level study. These findings do not suggest the necessity for Roma specific indicators elaborating health policy to control for the risk of widening inequalities imposed by rising health expenses.

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Sources of regional variation in healthcare utilization in Germany

Cited by 24 publications

References 19 publications

Causes of regional variation in Dutch healthcare expenditures: Evidence from movers

Causes of regional variation in Dutch healthcare expenditures: Evidence from movers

Waiting Times for Outpatient Treatment in Germany: New Experimental Evidence from Primary Data

Self-Declared Roma Ethnicity and Health Insurance Expenditures: A Nationwide Cross-Sectional Investigation at the General Medical Practice Level in Hungary

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