Geographical inequalities in healthcare utilisation and the contribution of compositional factors: A multilevel analysis of 497 districts in Indonesia

Mulyanto, Joko; Kunst, Anton E.; Kringos, Dionne

doi:10.1016/j.healthplace.2019.102236

Cited by 31 publications

(33 citation statements)

References 34 publications

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“…Studies in other low and middle income countries report similar findings. 26–29 Our study is also in line with earlier work in Indonesia, showing that JKN claims per capita for non-communicable diseases are consistently higher in Jakarta province (the national capital) than in largely rural NTT, 30 31 a difference attributed to differences in supply. 30 …”

Section: Discussionsupporting

confidence: 90%

“…[26][27][28][29] Our study is also in line with earlier work in Indonesia, showing that JKN claims per capita for noncommunicable diseases are consistently higher in Jakarta province (the national capital) than in largely rural NTT, 30 31 a difference attributed to differences in supply. 30 Table 5 Use of health services and out-of-pocket (OOP) spending-regression model including district health needs index and access to services, personal insurance status and household wealth…”

Section: Discussionmentioning

confidence: 99%

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Is Indonesia achieving universal health coverage? Secondary analysis of national data on insurance coverage, health spending and service availability

et al. 2021

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ObjectivesTo analyse the relationship between health need, insurance coverage, health service availability, service use, insurance claims and out-of-pocket spending on health across Indonesia.DesignSecondary analysis of nationally representative quantitative data. We merged four national data sets: the National Socioeconomic Survey 2018, National Census of Villages 2018, Population Health Development Index 2018 and National Insurance Records to end 2017. Descriptive analysis and linear regression were performed.SettingIndonesia has one of the world’s largest single-payer national health insurance schemes. Data are individual and district level; all are representative for each of the country’s 514 districts.ParticipantsAnonymised secondary data from 1 131 825 individual records in the National Socioeconomic Survey and 83 931 village records in the village census. Aggregate data for 220 million insured citizens.Primary outcome measuresHealth service use and out-of-pocket payments, by health need, insurance status and service availability. Secondary outcome: insurance claims.ResultsSelf-reported national health insurance registration (60.6%) is about 10% lower compared with the insurer’s report (71.1%). Insurance coverage is highest in poorer areas, where service provision, and thus service use and health spending, are lowest. Inpatient use is higher among the insured than the uninsured (OR 2.35, 95% CI 2.27 to 2.42), controlling for health need and access), and poorer patients are most likely to report free inpatient care (53% in wealth quintile 1 vs 41% in Q5). Insured patients spend US$ 3.14 more on hospitalisation than the uninsured (95% CI 1.98 to 4.31), but the difference disappears when controlled for wealth. Lack of services is a major constraint on service use, insurance claims and out-of-pocket spending.ConclusionsThe Indonesian public insurance system protects many inpatients, especially the poorest, from excessive spending. However, others, especially in Eastern Indonesia cannot benefit because few services are available. To achieve health equity, the Indonesian government needs to address supply side constraints and reduce structural underfunding.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Is Indonesia achieving universal health coverage? Secondary analysis of national data on insurance coverage, health spending and service availability

et al. 2021

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“…Previous studies support the finding that children living in rural areas have a higher risk of stunting than their peers in urban areas [see, for instance, 15,23,28]. According to Mahendradata et al [51] and Mulyanto, Kurst and Kringos [52], both the demand and the supply of health care vary between urban and rural areas. People living in urban areas have more access to health care and other related infrastructure, such as roads that reduce the travel time to health care facilities.…”

Section: Plos Onementioning

confidence: 79%

Beyond personal factors: Multilevel determinants of childhood stunting in Indonesia

et al. 2021

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Background Stunting is still a major public health problem in low- and middle-income countries, including Indonesia. Previous studies have reported the complexities associated with understanding the determinants of stunting. This study aimed to examine the household-, subdistrict- and province-level determinants of stunting in Indonesia using a multilevel hierarchical mixed effects model. Methods We analyzed data for 8045 children taken from the 2007 and 2014 waves of the Indonesian Family and Life Surveys (IFLS). We included individual-, family-/household- and community-level variables in the analyses. A multilevel mixed effects model was employed to take into account the hierarchical structure of the data. Moreover, the model captured the effect of unobserved household-, subdistrict- and province-level characteristics on the probability of children being stunted. Results Our findings showed that the odds of childhood stunting vary significantly not only by individual child- and household-level characteristics but also by province- and subdistrict-level characteristics. Among the child-level covariates included in our model, dietary habits, neonatal weight, a history of infection, and sex significantly affected the risk of stunting. Household wealth status and parental education are significant household-level covariates associated with a higher risk of stunting. Finally, the risk of stunting is higher for children living in communities without access to water, sanitation and hygiene. Conclusions Stunting is associated with not only child-level characteristics but also family- and community-level characteristics. Hence, interventions to reduce stunting should also take into account family and community characteristics to achieve effective outcomes.

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“…These problems are particularly acute in rural settings where the CHCs have even fewer healthcare workers (Strasser et al, 2016). A recent study (Mulyanto et al, 2019a) even suggests that local governments should subsidize travel costs for poor people living in rural regencies due to the extreme low rates of healthcare utilization found in those regencies.…”

Section: Discussionmentioning

confidence: 99%

Spatial and socioeconomic inequalities in the availability of community health centres in the Jakarta region, Indonesia

Winata

McLafferty

2021

Geospat Health

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In the late 1960s, Indonesia established community health centres (CHCs) throughout the country to provide basic healthcare services for the poor. However, CHC expenditures and investments vary widely at the sub-provincial level, among administrative areas known as cities and regencies, raising concern that facilities and services do not correspond to population needs. This study aimed to examine spatial and socioeconomic inequalities in the availability of CHCs in the Jakarta region. We used spatial and statistical analysis methods at the village level to investigate these inequalities based on CHC data from the Ministry of Health and socioeconomic data from Indonesia Statistics. Results show that CHCs and the healthcare workers within them are unevenly distributed. In areas with high need, the availability of CHCs and healthcare workers were found to be low. There is a mismatch in healthcare services and delivery for low-income, unemployed populations at the village level that needs to be addressed. The findings discussed in this paper suggest that Jakarta Department of Health should coordinate with local public health districts to determine locations for new CHCs and assign healthcare workers to each CHC based on need as this would improve access to essential health services for the low-income population.

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Geographical inequalities in healthcare utilisation and the contribution of compositional factors: A multilevel analysis of 497 districts in Indonesia

Cited by 31 publications

References 34 publications

Is Indonesia achieving universal health coverage? Secondary analysis of national data on insurance coverage, health spending and service availability

Is Indonesia achieving universal health coverage? Secondary analysis of national data on insurance coverage, health spending and service availability

Beyond personal factors: Multilevel determinants of childhood stunting in Indonesia

Spatial and socioeconomic inequalities in the availability of community health centres in the Jakarta region, Indonesia

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