An analysis of health system resources in relation to pandemic response capacity in the Greater Mekong Subregion

Abstract

Background: There is increasing perception that countries cannot work in isolation to militate against the threat of pandemic influenza. In the Greater Mekong Subregion (GMS) of Asia, high socio-economic diversity and fertile conditions for the emergence and spread of infectious diseases underscore the importance of transnational cooperation. Investigation of healthcare resource distribution and inequalities can help determine the need for, and inform decisions regarding, resource sharing and mobilisation.

Methods: We collected data on healthcare resources deemed important for responding to pandemic influenza through surveys of hospitals and district health offices across four countries of the GMS (Cambodia, Lao PDR, Thailand, Vietnam). Focusing on four key resource types (oseltamivir, hospital beds, ventilators, and health workers), we mapped and analysed resource distributions at province level to identify relative shortages, mismatches, and clustering of resources. We analysed inequalities in resource distribution using the Gini coefficient and Theil index.

Results: Three quarters of the Cambodian population and two thirds of the Laotian population live in relatively underserved provinces (those with resource densities in the lowest quintile across the region) in relation to health workers, ventilators, and hospital beds. More than a quarter of the Thai population is relatively underserved for health workers and oseltamivir. Approximately one fifth of the Vietnamese population is underserved for beds and ventilators. All Cambodian provinces are underserved for at least one resource. In Lao PDR, 11 percent of the population is underserved by all four resource items. Of the four resources, ventilators and oseltamivir were most unequally distributed. Cambodia generally showed higher levels of inequalities in resource distribution compared to other countries. Decomposition of the Theil index suggests that inequalities result principally from differences within, rather than between, countries.

Conclusions: There is considerable heterogeneity in healthcare resource distribution within and across countries of the GMS. Most inequalities result from within countries. Given the inequalities, mismatches, and clustering of resources observed here, resource sharing and mobilization in a pandemic scenario could be crucial for more effective and equitable use of the resources that are available in the GMS.

Figure 1

Availability (per population) of healthcare resources for responding to pandemic influenza across provinces in four countries of the Greater Mekong Subregion. Boxes represent the median and interquartile range, and whiskers represent the most extreme values within 1.5 times of the interquartile range. cm = Cambodia; la = Lao PDR; th = Thailand; vn = vietnam; HRH = human resources for health (medical doctors and nurses).

Figure 2

Geographic distribution of healthcare resources (adjusted for population size) for responding to pandemic influenza across provinces in four countries of the Greater Mekong Subregion. Relatively underserved and over-supplied provinces are operationally defined as those in the lowest and highest quintiles, respectively.

Figure 3

Spatial clusters and outliers of healthcare resources across provinces in the Greater Mekong Subregion. “High-high” and “low-low” clusters define a group of adjacent provinces with similarly high and low resource densities, respectively. A“high-low” cluster defines a province with relatively high resource density that is adjacent to provinces with relatively low resource densities, while a “low-high” cluster defines the converse pattern.

Figure 4

Spatial clusters and outliers of healthcare resources across provinces within each country in the Greater Mekong Subregion. In this analysis, resource densities are only compared among provinces within the same country (unlike in Figure  3). “High-high” and “low-low” clusters define a group of adjacent provinces with similarly high and low resource densities, respectively. A“high-low” cluster defines a province with relatively high resource density that is adjacent to provinces with relatively low resource densities, while a “low-high” cluster defines the converse pattern.