Title |
Using a community-based definition of poverty for targeting poor households for premium subsidies in the context of a community health insurance in Burkina Faso
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Published in |
BMC Public Health, February 2015
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DOI | 10.1186/s12889-014-1335-4 |
Pubmed ID | |
Authors |
Germain Savadogo, Aurelia Souarès, Ali Sié, Divya Parmar, Gilles Bibeau, Rainer Sauerborn |
Abstract |
One of the biggest challenges in subsidizing premiums of poor households for community health insurance is the identification and selection of these households. Generally, poverty assessments in developing countries are based on monetary terms. The household is regarded as poor if its income or consumption is lower than a predefined poverty cut-off. These measures fail to recognize the multi-dimensional character of poverty, ignoring community members' perception and understanding of poverty, leaving them voiceless and powerless in the identification process. Realizing this, the steering committee of Nouna's health insurance devised a method to involve community members to better define 'perceived' poverty, using this as a key element for the poor selection. The community-identified poor were then used to effectively target premium subsidies for the insurance scheme. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Nigeria | 1 | 8% |
New Zealand | 1 | 8% |
United States | 1 | 8% |
Canada | 1 | 8% |
Senegal | 1 | 8% |
United Kingdom | 1 | 8% |
Unknown | 7 | 54% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 11 | 85% |
Scientists | 2 | 15% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 1 | <1% |
Unknown | 133 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 26 | 19% |
Student > Ph. D. Student | 18 | 13% |
Researcher | 12 | 9% |
Student > Doctoral Student | 9 | 7% |
Student > Bachelor | 9 | 7% |
Other | 24 | 18% |
Unknown | 36 | 27% |
Readers by discipline | Count | As % |
---|---|---|
Social Sciences | 28 | 21% |
Economics, Econometrics and Finance | 15 | 11% |
Medicine and Dentistry | 15 | 11% |
Nursing and Health Professions | 9 | 7% |
Business, Management and Accounting | 8 | 6% |
Other | 17 | 13% |
Unknown | 42 | 31% |