Evaluation of spatial accessibility to primary healthcare using GIS

Abstract

Primary health care is considered to be one of the most important aspects of the health care system in any country, which directly helps in improving the health of the population. Potential spatial accessibility is a very important component of the primary health care system. One technique for studying spatial accessibility is by computing a gravity-based measure within a geographic information system (GIS) framework. In this study, straight-line distances between the associated population clusters and the health facilities and the provider-to-population ratio were used to compute the spatial accessibility of the population clusters for the whole country. Bhutan has been chosen as the case study area because it is quite easy to acquire and process data for the whole country due to its small size and population. The spatial accessibility measure of the 203 sub-districts shows noticeable disparities in health care accessibility in this country with about only 19 sub-districts achieving good health accessibility ranking. This study also examines a number of different health accessibility policy scenarios which can assist in identifying the most effective health policy from amongst many probable planning scenarios. Such a health accessibility measuring system can be incorporated into an existing spatial health system in developing countries to facilitate the proper planning and equitable distribution of health resources.

Spatio-Temporal Analysis of Spatial Accessibility to Primary Health Care in Bhutan

“…The NN method was originally developed to accommodate large variation in distances between some population clusters from their nearest health facilities while evaluating spatial accessibility of the entire nation [11]. For Bhutan, no particular distance cut-off value can be used since it has been observed from the health centres and population clusters network data that some of the population clusters (located in Lunana sub-district of Gas district) are located 53 kilometres away from their nearest health facilities.…”

“…The NN method delineates catchment area by identifying a finite number of nearest service centres for each population cluster instead of identifying variable number of service centres falling within a finite-size buffer ring around each cluster. All the FCA-based accessibility studies so far, save for Jamtsho and Corner [11], have used the BR method of delineating population catchment areas. Wang has noted that the choice of distance weighting functions and delineation of population catchment areas can only be resolved by conducting actual surveys on health care utilization behaviour [12].…”

Spatial Patterns Associating Low Birth Weight with Environmental and Behavioral Factors

“…Descriptive analysis of characteristics of mother with sample matching age, residence, and parity was found with sum of samples 75 LBW and 75 control were adequate TBA care total 32 cases (82.05%) and 7 controls (17.95%), inadequate ANC 33 cases (71.74%) and 13 controls (28.26%), drinking popa with total cases 16 47 (62.67%), and >500 were 3 (4%). The variable mercury exposure was at level 0.0144/0.0172 about 24 (16%), and at level > 0.0425 were 4 (2.67%).…”

Modelo não linear de localização de instalações de serviços de saúde com indicador de acessibilidade incorporado

“…O indicador de acessibilidade proposto é classificado como gravitacional 5,6,10,13,14,15 , já que incorpora a separação espacial entre os pares origem e destino e uma medida de atratividade. Nesse tipo de indicador, quanto maior a distância (ou tempo de viagem), menor a acessibilidade; quanto maior a atratividade, maior a acessibilidade.…”