Women, older age groups, and individuals who rated their health as “bad” had much higher rates of health care utilisation (Table 1). Higher levels of health and education were associated to a small extent with greater health care use. Having health insurance (particularly civil servant or private insurance) showed a strong association.

Table 2 shows the variations in service density and service proximity among districts. In terms of service density, the variation of median GP-to-population ratio among districts can be as high as three times, with similar differentials observed in the ratios of nurses and public primary health care centres (PHCs) to district population. The ratios of hospital beds showed even greater inter-district variation, with the variation between districts can be as high as seven times. In terms of service proximity, wide variations in travel times and travel costs were observed, particularly for inpatient care.

Figure 1 depicts the overall geographical patterns of service density among Indonesian districts. Districts with higher population ratios of GPs, PHCs, and nurses were clustered in western Sumatra, eastern Kalimantan, and Papua. In terms of hospital beds, most districts in Java had higher ratios, while substantial numbers of districts in middle Sumatra, Maluku, and Papua had low ratios. The geographical patterns of service proximity among districts are displayed in Figure 2. The average travel times and costs to the nearest primary care facility or hospital were especially long and high in most districts in Sumatra, Kalimantan, and Papua, as well as in remoter districts, such as in Maluku and Nusa Tenggara. Geographical patterns of outpatient and inpatient health care utilisation are depicted in Figure 3. Most districts in Java and Bali had relatively high outpatient and inpatient rates. Most districts in Sumatra, Kalimantan, Maluku, had low rates of outpatient and inpatient care. The detail data of health care utilisation, service density, and service proximity for 497 district are displayed in Table S1 in the Online Supplementary Document.

Figure 1.  Service density by districts.

Figure 2.  Service proximity by districts.

Figure 3.  Healthcare utilisation by districts.

Relationships between supply-side factors and the utilisation of individual outpatient care are analysed in Table 3. In terms of supply density, model 2 shows that a higher ratio of PHCs to district population was associated with greater individual uptake of outpatient care (OR = 1.22, 95% CI = 1.01-1.48). In model 3, we added service proximity variables and found that higher travel costs were associated with lower outpatient uptake (OR = 0.82, 95% CI = 0.70-0.97). The overall extent of geographic inequalities in outpatient care utilisation in Indonesia (model 1) is reflected by the median odds ratios at province level (MOR = 1.21, 95% CrI = 1.09-1.29) and at district level (MOR = 1.65, 95% CrI = 1.59-1.70). Model 2 highlights the contribution of service density to the geographic inequalities, as indicated by declining MOR values at province level (from 1.21, 95% CrI = 1.09-1.29 to 1.17, 95% CrI = 1.07-1.26) and at district level (1.65, 95% CrI = 1.59-1.70 to 1.63, 95% CrI = 1.58-1.69). Service proximity did not contribute to geographic inequalities at province level (MOR unchanged at 1.17 from model 2 to 3) but did contribute slightly at district level (MOR decreasing from 1.63, 95% CrI = 1.58-1.69 to 1.61, 95% CrI, 1.56-1.67).

Focusing on inpatient health care, Table 4 analyses relationships between supply-side factors and the individual uptake of inpatient care, also in relation to the overall geographic inequalities. Model 2 shows that a higher nurse-to-population ratio was associated with higher inpatient care utilisation (OR = 1.30, 95% CI = 1.11-1.52). The addition of service proximity variables (model 3) strengthened the positive association between hospital bed ratio and inpatient care (OR = 1.23, 95% CI = 1.05-1.43), whereas long travel times correlated with lower inpatient care utilisation (OR = 0.72, 95% CI = 0.61-0.86). The overall extent of geographic inequalities in inpatient care utilisation at province and district levels in Indonesia is indicated by the respective MORs of 1.23 (95% CrI = 1.04-1.31) and 1.63 (95% CrI = 1.55-1.69) as displayed in model 1. Contributions by service density (model 2) are reflected in decreasing MOR values at province level (from 1.23, 95% CrI = 1.04-1.31 to 1.19, 95% CrI = 1.05-1.27) and district level (1.63, 95% CrI = 1.55-1.69 to 1.62, 95% CrI = 1.55-1.68). Contributions by service proximity (model 3) are shown by additional decreases at province level (1.19, 95% CrI = 1.05-1.27 to 1.16, 95% CrI = 1.00-1.23) and district level (1.62, 95% CrI = 1.55-1.68 to 1.60, 95% CrI = 1.54-1.66).

Table 5 and Table 6 further analyse the associations between supply-side factors and individual health care utilisation, applying stratification by type of district (cities or regencies with high or low population densities). For outpatient care, higher service density generally tended to correspond to higher service use in regency districts. In all types of districts, service proximity tended to associate with higher service use. For inpatient care, hospital bed ratio was associated with higher service uptake regardless of district type, and service proximity tended to correspond with higher service use in all types of districts.

Our study focused on associations between supply-side factors, chosen to reflect district-level service density and proximity, and the individual utilisation of outpatient and inpatient health care in Indonesia. We assessed whether those factors help to explain geographical inequalities in health care use. We found large district-level variations across Indonesia, both in service density and proximity and in the individual use of health care. For outpatient care, however, none of our service density variables showed associations with utilisation rates. Higher rates of inpatient care utilisation were seen in districts with higher ratios of hospital beds to the population. In relation to service proximity, higher travel costs were associated with a lower uptake of outpatient care, and longer travel times were associated with lower uptake of inpatient care. Although supply-side factors in terms of service density and proximity thus showed some associations with individual health care use, those factors provided little overall explanation for the observed district-level geographical inequalities in health care utilisation.

Using data from a nationally representative survey with a large sample size, and combining individual and district-level data, we were able to provide a unique, detailed description of the distributions of supply-side factors and the rates of health care utilisation corresponding to them. Another innovative feature of our study is the use of median odds ratios (MORs) to estimate the magnitude of geographical inequalities in health care utilisation in Indonesia. Multilevel analysis is a standard practice in addressing issues of hierarchical data structure, but MORs have rarely been included in such analyses to enable more precise quantifications of geographic inequalities in health and health care, particularly in low- and middle-income countries (LMICs). The use of MORs allows us, moreover, to assess factors that contribute to geographic inequalities, further clarifying the nature of such inequalities.

We should consider several limitations to our study. First, we used only self-assessed health (SAH) data to adjust health care utilisation to health care need, without inclusion of other possible health conditions. Our data set lacked data on the actual health status of individuals based on objective measurement. Although our data set contained data on self-reported health conditions (diseases), such data are known to lack validity and reliability for use in inequality estimations, especially in LMICs. Second, the use of population ratios of GPs and public primary health care centres (PHCs) as a proxy for service density may not fully capture the overall district-level spectrum of primary health care provision. In Indonesia, GPs are legally permitted and commonly found to have multiple practice sites [12]. We also have not included private clinics in our analysis, due to data unavailability. With the rapid expansion of private primary care providers, their share in outpatient care services has grown [20]. Third, data on travel time and travel to indicate the service proximity were collected based on respondents’ response. Although the responses have been validated by interviewers who are familiar with the local situation, we cannot fully exclude the possibility of recall bias and response inaccuracies.

Large variations in service density among districts were found in Indonesia, even within provinces. In Indonesia, the availability of health care providers within a particular district depends on many conditions, such as local socioeconomic development and local health policy. There are vast differences in regional economic development in Indonesia which lead to wide inter-district variations in terms of living standards, education levels, and physical infrastructure [21]. Districts in more favourable socioeconomic situations provide more incentives for the growth of private health care provision, as in private physician practices and private hospitals.

In public health care provision, most PHCs and public hospitals are owned by district governments. Inadequate management by district health offices after decentralisation has affected the development of public provision [22]. Moreover, in terms of health sector development, district governments do not have to be accountable to the national health ministry and to provincial governments, even when failing to meet national standards [23]. A wide array of Indonesian government district-level decentralisation measures since 2001 may have contributed to that situation. Decentralisation has frequently been discussed as a cause of health care-related geographic inequalities both in LMICs and in higher-income countries (HICs) [24].

Surprisingly, the district-level GP and PHC ratios did not associate with individual outpatient care utilisation after adjustment with service proximity variables. Although distributions of GPs and PHCs were unequal among districts, the majority of districts in Indonesia had GP and PHC ratios above the national average. It is therefore likely that the density of supply of outpatient care was adequate in most districts. A study in Switzerland also found that the association between the number of primary care providers and outpatient uptake is not linear, which to an extent diminishes such an association [25].

For inpatient care, higher service density in terms of hospital bed ratios was associated with higher rates of inpatient care. The expansion of the Indonesian government’s health insurance programme has improved people’s access to health care and increased the demand for health care services, including inpatient care [26]. Districts with higher hospital bed ratios may better meet increased demand, resulting in higher utilisation [20]. A systematic review of studies conducted in the eastern Mediterranean countries also found that when the supply of hospital beds is adequate but not excessive, inpatient care use was increased to an optimum level and hospitals were able to perform efficiently [27].

In the context of Indonesia, travel time was not associated with outpatient care use, while higher travel costs were associated with a lower use of outpatient care. Most districts had relatively short and similar travel times (less than 25 minutes) to the nearest primary care facilities, which can be considered acceptable for most residents. Given this situation, travel costs could become the main consideration for people to visit an outpatient care provider, and it may be the remaining individual-level financial barrier especially to those with insurance. Our finding is consistent with a systematic review using data from HICs that, in areas with comparable distances to outpatient facilities, travel costs are the main consideration when choosing between different types of health care in outpatient settings [28].

Contrary to outpatient care, higher travel times were associated with lower utilisation of inpatient care, while travel costs showed no association. People’s willingness to use health care will diminish when travel time to facilities becomes very long [28]. A study in England and France has shown that people of low SES with serious health conditions are less willing to make lengthy journeys to health care facilities than those with high SES [29]. In addition to the actual travel expenses, longer travel times may also entail higher opportunity costs, such as lost income, which may financially impact mostly people with a low SES. In addition, because people with serious health conditions are likely to travel with carers, carers’ opportunity costs count as well. A study in Ireland showed that these type of costs have been identified as a major issue but are generally unrecognised by the general public and policymakers [30].

Despite relatively strong association with individual health care use, service density and proximity provided little explanation for district-level geographic inequalities in health care utilisation in Indonesia. This finding implies that such geographic disparities may be more strongly attributable to other district-specific variations related to supply- or demand-side factors – such as variations in price services and service responsiveness. These two factors will be discussed in the next two paragraphs.

The price of services may substantially influence geographic inequalities in health care use in Indonesia because approximately 60% of the Indonesian population was still uninsured in 2013, and thus often, dependent on out-of-pocket payment for health care [12]. For this segment of the population, the price of services is likely a main determinant of health care use. Moreover, the prices of private health services are not government-regulated, but follow market mechanisms [31], which may lead to wide price variations among regions. In addition to that, the prices of health care services provided by district-owned facilities are determined by district governments – a further likely cause of wide inter-district variations. For instance, some districts do not apply user charges for uninsured people obtaining health care in PHCs, and other districts apply relatively affordable charges, but many districts impose rather high user charges, seeing them as a source of local government revenue [12]. A report from the World Bank showed that Thailand faced similar issues, as the local government autonomy to determine prices within health care may have led to geographical inequality in the use of health care [32].

Variations in service responsiveness between districts may also contribute to the geographical inequalities in health care use. While the physical presence of health care facilities is a basic requirement for providing services to the population, additional factors influence whether their services respond to the needs of the population. Such factors include the number and qualification levels of health personnel and the availability of supporting equipment. The numbers of staff members in public facilities who have specific qualifications, such as physicians, specialists, nurses, and midwives, vary widely between Indonesian districts, and this is likely to affect both the types, volumes, and quality of services delivered to the population [33]. Necessary medical equipment and essential medicines are also unequally distributed amongst public facilities, due to differences in local government investment and procurement procedures [23]. Studies in LMICs and HICs showed that disparities in health care resources among local governments likely affected their capability to deliver responsive health care to the local population which may lead to geographical inequalities in health care utilisation [34-36].

District variation in service responsiveness may result from, amongst other factors, variation in financial incentive systems for health personnel who work at district-owned facilities. District-owned facilities such as PHCs and public hospitals are the main health care providers in most Indonesian districts. District government has full authority to manage the financial systems of such institutions, including the financial incentives system for employees like GPs and specialists. Variations in such systems between districts have been reported [13]. A systematic review of studies from LMICs and HICs has documented that financial incentives were a major determinant of the behaviour and performance of health personnel [37].

This study has demonstrated wide inter-district variation in supply-side factors in Indonesian health care. Supply-side factors in terms of service density and service proximity were associated with resident’s use of some type of health care. However, service density and proximity offered little explanation for the geographical inequalities in health care utilisation in Indonesia. This implies that other factors, such as service prices and service responsiveness also contribute to these inequalities. Physical development of health care infrastructure, aimed at expanding the capacity of services and bringing them closer to the residents, may be the initial step towards improving access and reducing geographical disparities. However, to adequately address the geographic inequalities in health care use, additional efforts are needed, for example by targeting regional variations in the price of services and in service responsiveness.