The contribution of service density and proximity to geographical inequalities in health care utilisation in Indonesia

A nation-wide multilevel analysis

Bibliographic Data

ID19551024
AuthorsJoko Mulyanto (0000-0002-4653-2470, University of Amsterdam, corresponding author), Anton E Kunst (0000-0002-3313-5273, Amsterdam University Medical Centers), Dionne Kringos (0000-0003-2711-4713, University of Amsterdam), Dionne S Kringos
Year2020
Volume10
Issue2
Pages020428-020428
Publication date2020-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Global Health (JOURNAL)
Journal identifiersISSN: 2047-2978 • E-ISSN: 2047-2986
PublisherInternational Society of Global Health (PUBLISHER • GB)
DOI10.7189/jogh.10.020428
PMID33312501
OpenAlexW3112221603
LanguageEN
Citations received5
References cited26

BACKGROUND: Geographical inequalities in access to health care have only recently become a global health issue. Little evidence is available about their determinants. This study investigates the associations of service density and service proximity with health care utilisation in Indonesia and the parts they may play in geographic inequalities in health care use. METHODS: Using data from a nationally representative survey (N = 649 625), we conducted a cross-sectional study and employed multilevel logistic regression to assess whether supply-side factors relating to service density and service proximity affect the variability of outpatient and inpatient care utilisation across 497 Indonesian districts. We used median odds ratios (MORs) to estimate the extent of geographical inequalities. Changes in the MOR values indicated the role played by the supply-side factors in the inequalities. RESULTS: Wide variations in the density and proximity of health care services were observed between districts. Outpatient care utilisation was associated with travel costs (odds ratio (OR) = 0.82, 95% confidence interval (CI) = 0.70-0.97). Inpatient care utilisation was associated with ratios of hospital beds to district population (OR = 1.23, 95% CI = 1.05-1.43) and with travel times (OR = 0.72 95% CI = 0.61-0.86). All in all, service density and proximity provided little explanation for district-level geographic inequalities in either outpatient (MOR = 1.65, 95% CrI = 1.59-1.70 decreasing to 1.61, 95% CrI = 1.56-1.67) or inpatient care utilisation (MOR = 1.63, 95% CrI = 1.55-1.69 decreasing to 1.60 95% CrI = 1.54-1.66). CONCLUSIONS: Supply-side factors play important roles in individual health care utilisation but do not explain geographical inequalities. Variations in other factors, such as the price and responsiveness of services, may also contribute to the inequalities. Further efforts to address geographical inequalities in health care should go beyond the physical presence of health care infrastructures to target issues such as regional variations in the prices and responsiveness of services

Business · Confidence interval · Economic growth · Economics · Environmental health · Geography · Health care · Inequality · Logistic regression · Odds · Odds ratio · Population · Sociology · Demography · Global Health Workforce Issues · Global Maternal and Child Health · Healthcare Systems and Reforms · Medicine

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