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Kernel density estimation as a technique for assessing availability of health services in Nicaragua

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Abstract

Typically, accessibility ratios have been calculated through a simple mathematical division of the number of people in an area by the number of facilities (or staff) in that area. This approach does not take into account the service area of the facility or its proximity to population centers, and is often performed using aggregate numbers for an administrative region. This paper describes an approach to calculating accessibility ratios such as population to facility ratios or population to staff ratios using Kernel density estimation (KDE) within a geographic information system. KDE disperses discrete phenomena across continuous space and is unrestrained by administrative boundaries. Therefore it provides a better representation of the spread of people and services across the landscape. Two types of accessibility ratios are calculated on a national level for Nicaragua: population-per-facility and population-per-staff; the merits of using KDE over traditional approaches are discussed.

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Notes

  1. In 2001, the largest provider of health services in Nicaragua was the public sector. The private sector was composed of few facilities mainly concentrated in Managua, the capital city, and the two main largest cities, Granada and León.

  2. This criticism has been the basis for the application of gravity models to measure access to health services where distance is inversely related to the influence of services on population groups (Guptil 1975; Carrothers 1956; Gesler et al. 1986).

  3. The variance of the normal distribution is determined when the kernel size is chosen.

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Acknowledgments

We thank Margel Beteta, Carlos Rojas and Luis Blandón for their assistance on the use of the Nicaragua census population data. This study was conducted with funds provided by MEASURE Evaluation which is a cooperative agreement (GPO-A-00-03-00003-00) between the U.S. Agency for International Development (USAID) and the Carolina Population Center, University of North Carolina at Chapel Hill. The opinions expressed are those of the authors and do not necessarily reflect the views of USAID or the United States government.

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  1. Carolina Population Center, University of North Carolina at Chapel Hill, 123 W, Franklin St, Chapel Hill, NC, 27516, USA

    John Spencer

  2. Department of Maternal and Child Health and Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA

    Gustavo Angeles

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  1. John Spencer
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  2. Gustavo Angeles
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Correspondence to John Spencer.

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Spencer, J., Angeles, G. Kernel density estimation as a technique for assessing availability of health services in Nicaragua. Health Serv Outcomes Res Method 7, 145–157 (2007). https://doi.org/10.1007/s10742-007-0022-7

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  • DOI: https://doi.org/10.1007/s10742-007-0022-7

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