Spatial and non-spatial determinants of successful tuberculosis treatment outcomes: An implication of Geographical Information Systems in health policy-making in a developing country

J Epidemiol Glob Health. 2015 Sep;5(3):221-30. doi: 10.1016/j.jegh.2014.11.001. Epub 2015 Jan 12.

Abstract

This retrospective study aimed to address whether or to what extent spatial and non-spatial factors with a focus on a healthcare delivery system would influence successful tuberculosis (TB) treatment outcomes in Urmia, Iran. In this cross-sectional study, data of 452 new TB cases were extracted from Urmia TB Management Center during a 5-year period. Using the Geographical Information System (GIS), health centers and study subjects' locations were geocoded on digital maps. To identify the statistically significant geographical clusters, Average Nearest Neighbor (ANN) index was used. Logistic regression analysis was employed to determine the association of spatial and non-spatial variables on the occurrence of adverse treatment outcomes. The spatial clusters of TB cases were concentrated in older, impoverished and outskirts areas. Although there was a tendency toward higher odds of adverse treatment outcomes among urban TB cases, this finding after adjusting for distance from a given TB healthcare center did not reach statistically significant. This article highlights effects of spatial and non-spatial determinants on the TB adverse treatment outcomes, particularly in what way the policies of healthcare services are made. Accordingly, non-spatial determinants in terms of low socio-economic factors need more attention by public health policy makers, and then more focus should be placed on the health delivery system, in particular men's health.

Keywords: Adverse treatment outcome; Geographical Information System; Health policy-making; Tuberculosis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Cross-Sectional Studies
  • Developing Countries
  • Female
  • Geographic Information Systems*
  • Health Policy*
  • Humans
  • Male
  • Middle Aged
  • Remission Induction
  • Retrospective Studies
  • Spatial Analysis
  • Treatment Outcome
  • Tuberculosis / drug therapy*
  • Young Adult