Malaysia has a unique public-private mix health care delivery system, where public clinics were principally managed by the Ministry of Health (MOH) whereas private clinic largely funded through out-of-pocket payment or health insurance, registered under MOH. Although public clinic is the main primary care service provider for both urban and rural population catering for about 60% of total outpatient attendance as compared to private [46, 63], public primary care services mainly focuses on serving rural population and poorer population. On top of static or landed clinics, there are also mobile services which attend to scheduled sites to serve population at targeted remote areas . For private clinics, they were mainly located in urban areas (Fig. 1b), similar as previously reported [12, 13].
The rationale of calculating the spatial accessibility score to primary care services separately based on public-private sector is to provide insight on how much the private sector and public sector contribute to the health care delivery system in our local context respectively. Public clinics should be accessible to all rural population knowing of its affordable price, but may not for private clinics due financial barrier. Total accessibility score to primary care services accounting both public and private clinics reflects the ideal accessibility level of the rural population when the financial barrier is lifted. In fact, the state authorities had introduced a health care subsidization scheme called “Peduli Sihat” to alleviate health care cost targeting the poorer population to utilize private clinic . Assuming that no financial barrier among the rural population through benefiting from the subsidiary scheme, the total accessibility score to primary care services was approximately doubled (Fig. 2c) as compared to accessibility score that based on public clinics only (Fig. 2a). Another viable reason of understanding the accessibility score to public or private clinics separately is because of higher tendency of those rural population and poorer population to utilize or favor public clinics [50, 63], despite of having choice to opt for private clinics. Our study also found that rural population of Selangor actually had good spatial accessibility to private clinics and most districts have equal or higher Aspri score compared to the Aspub score. Nonetheless, this study found that total E2SFCA (Astot) score was contributed 59% by the private sector (Aspri score), this indicate that health policy related to public-private collaborative such as subsidization for poorer population to utilize private clinic is fairly important for improving access to primary care services.
Spatial pattern of the E2SFCA scores exhibited in Fig. 3 showed that discrepancies of access existed throughout rural areas of Selangor where higher score tends to appears near to any urban area. It can be observed that the E2SFCA scores, were higher near or surrounding the urban area in each district especially surrounding the large urban agglomeration at the center of the state. This explains why districts that had closer proximity to the large urban center of the state such as GBK and KLG tend to have higher E2SFCA scores, regardless of Aspub, Aspri or Astot score. These findings exhibit similar to commonly reported, where higher access primarily occurs near or surrounding the large urban areas [14–16, 66]. In our study, possible reasons were because the private clinics heavily concentrated in the large urban core at the center of the state, alongside with the presence of extensive road network, because the public clinics appears to had more disperse distribution across both urban and rural areas throughout the state (Fig. 1b) and the Gini value also proved that the public clinics were more equally distributed compared to the private clinics.
Meanwhile, areas with lower E2SFCA scores were more prominent at the northern part of Selangor and along the coastal regions (Fig. 3). Descriptive statistics (Fig. 2) also revealed that three districts at the northern part of Selangor state such as SBK, KUS and HUS have the lowest E2SFCA scores, regardless of Aspub, Aspri or Astot score. Based on Household Income and Expenditure Survey (HIES) 2016, those three districts also amongst the lowest median household income , which could possibly link the association between lower accessibility and low socio-economic status of the population that were commonly reported [24, 29, 40, 68].
Although spatial pattern of the E2SFCA scores can be observed, hotspot analysis provides better indication by distinguishing and pin-pointing specific areas that were statistically significant of high and low E2SFCA scores that clustered together. Hot spot analysis (Fig. 4) confirms that the cold spots mostly located further from the urban center region of the state, particularly on the coastal area of SBK and KUS, middle HUS, as well as at the southern coastal areas of Selangor. Low E2SFCA scores along the coastal areas (except at the center region) probably due to the imbalance between high population density and number of clinics at those areas. Furthermore, the road network also not as extensive as at road near the urban center region (which can be observed in Fig. 1b). Though, the association between population density and road network could be confirmed using appropriate statistical analysis however that is not the scope of this paper. Although hot spot areas mainly concentrated surrounding the large urban center of the state, there were some hot spot areas located up north of the HUS district, which is due to the existence of public mobile services there. This finding could suggest that mobile services were relatively effective in alleviating access to primary care at the targeted area.
Although discrepancies of access existed across rural of Selangor, but the disparities may tolerable and the distribution of the E2SFCA scores were reasonably equal with exception of the Aspri score. Amongst the districts, HUS appear to had the least degree of equality in term of all three E2SFCA scores, this could be the geographic distribution of the population settlements and clinics was spatially heterogenous (Fig. 1), causing the large variation in the E2SFCA scores. On the other hand, KUS and SBK were the better-off in term of equality. Though, having equal distribution of the E2SFCA scores alone may not suffice to indicate how good the accessibility in that area. For example, KUS and SBK had good Gini coefficient values but they were both at the lower end in term of E2SFCA scores. Rural areas in northern part of Selangor had bigger primary care accessibility issues as compared to other districts, due to relatively low accessibility scores (KUS and SBK), and even worst for HUS because it also had a low degree of equality as well. Nonetheless, access to public (Aspub score) appear to had higher degree of equality in the northern coastal part, particularly KUS and SBK, except for HUS which was the worst. Meanwhile, private had higher degree of equality near the urban center (GBK and HUL) region of the Selangor state.
During the E2SFCA scores calculation, all rural EBs in Selangor can reach public clinic within catchment size of 30 minutes, meaning that none of the EB had zero Aspub score. Meanwhile, most EBs can reach any private clinic within the catchment size and only small number of EBs have zero Aspri score (results not shown). This indicates that the use of 30 minutes catchment size is suffice and applicable for rural Selangor setting, although 30 minutes catchment size was initially recommended for urban areas . But if a larger scale study to be conducted (macro or national level), using multiple catchment sizes will be crucial due to different threshold distances amongst the population. For example, urban population should have smaller catchment size due to services and population are densely located. But for rural population, they are have to travel further as the health services are more dispersed, hence requires larger catchment size to avoid zero E2SFCA scores . As for our national context, it is possible to use the National Health and Morbidity Survey data to gauge optimal catchment size for each state or region for the variable catchment size approach, based on reported mean distance to seek primary care for each states or region. Similar approach in determining specific catchment sizes for specific region or states could be applied for other country given the required variables were available in their national household survey or census data.
Considering the dual public and private providers, this paper depicts a comprehensive situation of the spatial accessibility to primary care of our local context specifically in rural Selangor state which has not has been conducted before. In order to produce accurate results, this study used smallest area aggregation data (i.e., EB) to produce homogenous areal unit and also includes the data on population and health facilities from adjacent states to eliminates edge effect . The conceptualization of the spatial accessibility in this study accounts for health need (HN) of the population and availability of each clinic. Higher HN population implies that higher utilization of primary care services among them, causing reduced access due to higher competition and congestion. This is also consistent with the National Health Morbidity Survey findings where vulnerable populations (i.e., toddler, elderly and women aged 15–45) had higher number of annual outpatient visit [50, 63, 69]. Khakh et al., (2019) in his study suggested that service hours and days of clinics’ working can be considered to further improve the measure of access , in which this study had incorporated the clinics’ availability factor in the E2SFCA calculation. Higher clinic availability means that the population had more opportunity window to obtain health care services, given that the clinics had longer operating hours. Future study could be conducted to explore how the accessibility differs between office hours (day), and after office hours (night), as temporal differences of accessibility could exists depending on the clinic or facility schedule .
In the future, data such as actual number of doctors and clinic’s operating hours can be incorporated into the E2SFCA calculation to further improve precision of the accessibility scores estimation as per Malaysia’s context. This study also only use fixed catchment size of 30 minutes, regardless of type of clinic although bigger clinic should theoretically have bigger catchment size or service coverage [54, 72]. Therefore the use of multiple catchment sizes depending on type of clinic or different types of rural areas also could further improve the precision of the E2SFCA measure, especially for a national-level or a larger scale study [32, 73]. This study also demonstrates the practicability of data integration, benefiting from multiple databases that routinely collected by the government agencies. Continuous monitoring on the performance of the primary health care accessibility could be conducted feasibly.