METHOD: This 2016 study located every dental practice in Malaysia (private and public) and mapped these practices against population, using Geographic Information Systems (GIS) tools. Population clusters within 5, 10 and 20 km of a dental clinic were identified, and clinic-to-population ratios were ascertained. Population data were obtained from the Population and Housing Census of Malaysia 2010. Population relative wealth was obtained from the 2014 Report on Household Income and Basic Amenities Survey for Malaysia. The physical address for each dental practice in Malaysia was gathered from the Official Portal of Ministry of Health Malaysia. All data for analysis were extracted from the integrated database in Quantum GIS (QGIS) into Microsoft Excel.
RESULT: The population of Malaysia (24.9 million) was distributed across 127 districts, with 119 (94%) having at least one dental clinic. Sixty-four districts had fewer than 10 dental clinics, and 11.3% of Malaysians did not reside in the catchment of 20 km from any dental clinic. The total dental clinic-to-population ratio was 1:9,000: for public dental clinics it was 1:38,000 and for private clinics it was 1:13,000.
CONCLUSION: Dental services were distributed relative to high population density, were unevenly distributed across Malaysia and the majority of people with the highest inaccessibility to a dental service resided in Malaysian Borneo.
METHODS: We selected two medicines on the 2013 Thai national list of essential medicines (NLEM) [letrozole and imatinib] and three unlisted medicines for the same indications [trastuzumab, nilotinib and dasatinib]. We created timelines of access policies and programs for these products based on scientific and grey literature. Using IMS Health sales data, we described the trajectories of sales volumes of the study medicines between January 2001 and December 2012. We compared estimated average numbers of patients treated before and after the implementation of policies and programs for each product.
RESULTS: Different stakeholders implemented multiple interventions to increase access to the study medicines for different patient populations. During 2007-2009, the Thai Government created a special NLEM category with different coverage requirements for payers and issued compulsory licenses; payers negotiated prices with manufacturers and engaged in pooled procurement; pharmaceutical companies expanded patient assistance programs and lowered prices in different ways. Compared to before the interventions, estimated numbers of patients treated with each medicine increased significantly afterwards: for letrozole from 645 (95% CI 366-923) to 3683 (95% CI 2,748-4,618); for imatinib from 103 (95% CI 72-174) to 350 (95% CI 307-398); and for trastuzumab from 68 (95% CI 45-118) to 412 (95% CI 344-563).
CONCLUSIONS: Government, payers, and manufacturers implemented multi-pronged approaches to facilitate access to targeted cancer therapies for the Thai population, which differed by medicine. Routine monitoring is needed to assess clinical and economic impacts of these strategies in the health system.