Designing a model to minimize inequities in hemodialysis facilities distribution
AbstractPortugal has an uneven, city-centered bias in the distribution of hemodialysis centers found to contribute to health care inequities. A model has been developed with the aim of minimizing access inequity through the identification of the best possible localization of new hemodialysis facilities. The model was designed under the assumption that individuals from different geographic areas, ceteris paribus, present the same likelihood of requiring hemodialysis in the future. Distances to reach the closest hemodialysis facility were calculated for every municipality lacking one. Regions were scored by aggregating weights of the “individual burden”, defined as the burden for an individual living in a region lacking a hemodialysis center to reach one as often as needed, and the “population burden”, defined as the burden for the total population living in such a region. The model revealed that the average travelling distance for inhabitants in municipalities without a hemodialysis center is 32 km and that 145,551 inhabitants (1.5%) live more than 60 min away from a hemodialysis center, while 1,393,770 (13.8%) live 30-60 min away. Multivariate analysis showed that the current localization of hemodialysis facilities is associated with major urban areas. The model developed recommends 12 locations for establishing hemodialysis centers that would result in drastically reduced travel for 34 other municipalities, leaving only six (34,800 people) with over 60 min of travel. The application of this model should facilitate the planning of future hemodialysis services as it takes into consideration the potential impact of travel time for individuals in need of dialysis, as well as the logistic arrangements required to transport all patients with end-stage renal disease. The model is applicable in any country and health care planners can opt to weigh these two elements differently in the model according to their priorities.
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Copyright (c) 2011 Teresa M. Salgado, Rebekah Moles, Shalom I. Benrimoj, Fernando Fernandez-Llimos
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