Analyzing regional variation in health care utilization using (rich) household microdata

Ziebarth, Nicolas R.

doi:10.1016/j.healthpol.2013.04.015

Cited by 33 publications

(20 citation statements)

References 34 publications

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“…The most striking difference between the genders is that females have higher outpatient spending outside of hospitals (41 per cent versus 33 per cent), whereas males have higher hospital care spending (24 per cent versus 19 per cent). This mirrors a finding by Eibich and Ziebarth () at the state level for Germany. They find that whereas outpatient care utilisation is lower in East Germany than in West Germany, inpatient care utilisation is actually higher.…”

Section: Medical Spending In the Cross‐sectionsupporting

confidence: 89%

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Skewed, Persistent and High before Death: Medical Spending in Germany

2016

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We use claims panel data from a big German private health insurer to provide detailed individual‐level evidence on medical spending between 2005 and 2011. This includes evidence on the distribution of medical spending, the dependence of medical spending on age and other demographic characteristics, its persistence, and how medical spending evolves in the years before death. Our main findings are that health care spending more than doubles between ages 50 and 80 and that spending is very concentrated: the top 10 per cent of all spenders are responsible for 53 per cent of all medical spending in a given year. There is a 50 per cent probability that individual expenditures lie in the same quintile of the distribution after five years, for both very‐high‐ and very‐low‐cost individuals. Medical spending in the year of death is six times higher for the deceased than for everybody else in that year and accounts for 7.9 per cent of lifetime spending. Females use more office‐based care and have higher spending at younger ages, whereas males have higher spending at older ages, particularly for hospital care, and die younger. The presentation of these empirical facts is framed by an institutional discussion of the German health care system, a comparison between publicly‐ and privately‐insured individuals, and a discussion of medical spending trends in aggregate‐level data.

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Section: Medical Spending In the Cross‐sectionsupporting

confidence: 89%

“…16 See, for example, Hullegie and Klein (2010). 17 Eibich, Schmitz and Ziebarth, 2012. based on pre-existing conditions, previous health care utilisation, age, gender and body mass index (BMI). 18 Private insurers are legally required to build up reserves for rising expenditures with increased age.…”

Section: Premiumsmentioning

confidence: 99%

Skewed, Persistent and High before Death: Medical Spending in Germany

2016

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“…Recently, other studies used the German setting to analyse regional differences in health care, too. These are Eibich & Ziebarth () and Sundmacher & Busse () on variations in health, Augurzky et al (), Kopetsch & Schmitz () and Eibich & Ziebarth () on utilization of health services. All these papers find significant regional variations which cannot be fully explained.…”

Section: Introductionmentioning

confidence: 99%

Determinants of Regional Variation in Health Expenditures in Germany

Göpffarth

Kopetsch²,

Schmitz

2015

Health Economics

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Health care expenditure in Germany shows clear regional differences. Such geographic variations are often seen as an indicator for inefficiency. With its homogeneous health care system, low co-payments and uniform prices, Germany is a particularly suited example to analyse regional variations. We use data for the year 2011 on expenditure, utilization of health services and state of health in Germany's statutory health insurance system. This data, which originate from a variety of administrative sources and cover about 90% of the population, are enriched with a wealth of socio-economic variables, data on pollutants, prices and individual preferences. State of health and demography explains 55% of the differences as measured by the standard deviation while all control variables account for a total of 72% of the differences at county level. With other measures of variation, we can account for an even greater proportion. A higher proportion of variation than usually supposed can thus be explained. Whilst this study cannot quantify inefficiencies, our results contradict the thesis that regional variations reflect inefficiency. Copyright © 2015 John Wiley & Sons, Ltd.

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“…The NHIS asked every respondent annually, "Did you visit the emergency room (ER) in the past 12 months"; and "Did you stay in the hospital overnight in the past 12 months." These measures of healthcare utilization were often used in the literature (Eibich & Ziebarth, 2013).…”

Section: Measurementsmentioning

confidence: 99%

Disabilities among veterans and their utilization of health care

Lewis

2015

Health Psychology and Behavioral Medicine

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Objectives: This study introduces a conceptual model to investigate factors that influence healthcare utilization. The model included three components: Need for Care, Facilitators and Barriers of Access, and Health Care Utilization. Methods: A nationally representative sample of 13,244 male veterans aged 18 and older was extracted from the 2011 National Health Interview Survey. The sample included 2890 veterans with disabilities. Descriptive analyses and multiple logistic regression analyses were performed, along with significance tests for differences in distributions. Results: Need for services was operationalized by perceived health and number of types of disabilities. Both these variables, predicted healthcare utilization, making visits to the emergency room and staying in the hospital overnight. Facilitators and barriers were operationalized as political structures, societal structure, and demographic variables partially contributed to the model. The political variables, which wars the veterans fought, predicted utilization, but the societal variable, poverty, did not. There were no differences between African-Americans and Caucasians in utilization of health care. Differences did emerge based on region of domicile, marital status, and age. Conclusion: The conceptual model introduced in this paper does not support the notion that healthcare utilization is predicted by poverty. It may be that governmental insurance provides a safety net that insures access to acute medical care. Utilization also appears to be predicted by access to care, from regional resources and social support. Future research should tease apart reasons why regional differences emerge and inform policy-makers who appropriate funds for establishment of healthcare centers.

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Analyzing regional variation in health care utilization using (rich) household microdata

Cited by 33 publications

References 34 publications

Skewed, Persistent and High before Death: Medical Spending in Germany

Skewed, Persistent and High before Death: Medical Spending in Germany

Determinants of Regional Variation in Health Expenditures in Germany

Disabilities among veterans and their utilization of health care

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