BackgroundAttaining the perfect balance of health care resources is probably impracticable; however, it is possible to achieve improvements in the distribution of these resources. In terms of the distribution of health resources, equal access to these resources would make health services available to all people. The aim of this study was to compare the distributions of health care resources in urban, suburban, and rural areas of Mongolia.MethodsWe compared urban and rural areas using the Mann–Whitney U test and further investigated the distribution equality of physicians, nurses, and hospital beds throughout Mongolia using the Gini coefficient—a common measure of distribution derived from the Lorenz curve. Two indicators were calculated: the distribution per 10 000 population and the distribution per 1000 km2 area.ResultsUrban and rural areas were significantly different only in the distribution of physicians per population. However, in terms of the distribution per area, there were statistical differences in physicians, nurses, and hospital beds. We also found that distributions per population unit were equal, with Gini coefficients for physicians, nurses, and hospital beds of 0.18, 0.07, and 0.06, respectively. Distributions per area unit were highly unequal, with Gini coefficients for physicians, nurses, and hospital beds of 0.74, 0.67, and 0.69, respectively.ConclusionsAlthough the distributions of health care resources per population were adequate for the population size, a striking difference was found in terms of the distributions per geographical area. Because of the nomadic lifestyle of rural and remote populations in Mongolia, geographical imbalances need to be taken into consideration when formulating policy, rather than simply increasing the number of health care resources.

Indonesia has been decentralized since 2001, and we evaluated the distribution trends of physicians, puskesmas (community health centers), hospitals, and hospital beds in 34 provinces in Indonesia for 2000 to 2014. Inequality index of Gini showed improvement of the distribution of physicians and decreased from 0.38 to 0.29. The indices in distributions of hospitals and hospital beds also decreased from 0.26 to 0.17 and from 0.25 to 0.18, respectively. However, the index in the distribution of puskesmas increased from 0.19 to 0.28. We also investigated the legislative transitions of the laws concerning health resources and found the strong affects of compulsory work laws for physicians and the increment of health budget. In the decentralization era, the local governments have some political autonomy for the development of health resources; however, the national government should monitor the nationwide distribution of health resources and advice necessary recommendations to the local governments.

Background: Life expectancy acts as a population measure of the performance of healthcare systems. Regional disparities on life expectancy in Indonesia has been persisted and become a public health policy challenge. A systematic clustering of provinces can be a valuable alternative for organizing cooperation that aimed to increase life expectancy and reduce disparities. This study aimed to identify determinants of life expectancy and designate clusters of Indonesian provinces with similar characteristics. This approach can be useful in generating alternative cooperation strategies to improve life expectancy. Methods: We carefully selected variables that have been shown to impact life expectancy and gathered 2015 data from Indonesia's Ministry of Health. All 34 Indonesian provinces were included as analysis units. We performed structural equation modeling (SEM) to select domains that needed to work on from theoretical models. Based on SEM results, we performed cluster analysis to arrange cooperation groups. Results: Life expectancy showed correlations with mean years of schooling, expenditure per capita, health workforce, healthcare facilities, and environment. Expenditure per capita also was the strongest of all constructs. Based on SEM results, we performed cluster analysis to arrange cooperation groups of total 34 provinces and generated five clusters of provinces. Conclusions: Enhancing the economy is the most effective approach for improving life expectancy and other constructs. These clusters can build cooperation that is new, within, and across clusters. These results may be useful in formulating cooperation strategies aimed at increasing life expectancy.