Using community wealth ranking to identify the poor for subsidies: a case study of community-based health insurance in Nouna, Burkina Faso

Health Soc Care Community. 2010 Jul;18(4):363-8. doi: 10.1111/j.1365-2524.2009.00905.x. Epub 2010 Feb 18.


Access to health-care is low in developing countries. Poor people are less likely to seek care than those who are better off. Community-based health insurance (CBI) aims to improve healthcare utilisation by removing financial barriers, unfortunately CBI has been less effective in securing equity than expected. Poor people, who probably require greater protection from catastrophic health expenses, are less likely to enrol in such schemes. Therefore, it is important to implement targeted interventions so that the most in need are not left out. CBI has been offered to a district in Burkina Faso, comprising 7762 households in 41 villages and the district capital of Nouna since 2004. Community wealth ranking (CWR) was used in 2007 to identify the poorest quintile of households who were subsequently offered insurance at half the usual premium rate. The CWR is easy to implement and requires minimal resources such as interviews with local informants. As used in this study, the agreement between the key informants was more (37.5%) in the villages than in Nouna town (27.3%). CBI management unit only received nine complaints from villagers who considered that some households had been wrongly identified. Among the poorest, the annual enrolment increased from 18 households (1.1%) in 2006 to 186 (11.1%) in 2007 after subsidies. CWR is an alternative methodology to identify poor households and was found to be more cost and time efficient compared to other methods. It could be successfully replicated in low-income countries with similar contexts. Moreover, targeted subsidies had a positive impact on enrolment.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Burkina Faso
  • Focus Groups
  • Government Programs / economics
  • Government Programs / statistics & numerical data*
  • Health Status Disparities
  • Humans
  • Income / statistics & numerical data*
  • Insurance, Health / economics
  • Insurance, Health / statistics & numerical data*
  • Poverty / statistics & numerical data*
  • Residence Characteristics* / statistics & numerical data
  • Socioeconomic Factors
  • Urban Population / statistics & numerical data