BACKGROUND: In most low-income countries, several child-survival interventions are being implemented. We assessed how these interventions are clustered at the level of the individual child. METHODS: We analysed data from Bangladesh, Benin, Brazil, Cambodia, Eritrea, Haiti, Malawi, Nepal, and Nicaragua. A co-coverage score was obtained by adding the number of interventions received by each child (including BCG, diphtheria-pertussis-tetanus, and measles vaccines), tetanus toxoid for the mother, vitamin A supplementation, antenatal care, skilled delivery, and safe water. Socioeconomic status was assessed through principal components analysis of household assets, and concentration indices were calculated. FINDINGS: The percentage of children who did not receive a single intervention ranged from 0.3% (14/5495) in Nicaragua to 18.8% (1154/6144) in Cambodia. The proportions receiving all available interventions varied from 0.8% (48/6144) in Cambodia to 13.3% (733/5495) in Nicaragua. There were substantial inequities within all countries. In the poorest wealth quintile, 31% of Cambodian children received no interventions and 17% only one intervention; in Haiti, these figures were 15% and 17%, respectively. Inequities were inversely related to coverage levels. Countries with higher coverage rates tended to show bottom inequity patterns, with the poorest lagging behind all other groups, whereas low-coverage countries showed top inequities with the rich substantially above the rest. INTERPRETATION: The inequitable clustering of interventions at the level of the child raises the possibility that the introduction of new technologies might primarily benefit children who are already covered by existing interventions. Packaging several interventions through a single delivery strategy, while making economic sense, could contribute to increased inequities unless population coverage is very high. Co-coverage analyses of child-health surveys provide a way to assess these issues.
BACKGROUND: In most low-income countries, several child-survival interventions are being implemented. We assessed how these interventions are clustered at the level of the individual child. METHODS: We analysed data from Bangladesh, Benin, Brazil, Cambodia, Eritrea, Haiti, Malawi, Nepal, and Nicaragua. A co-coverage score was obtained by adding the number of interventions received by each child (including BCG, diphtheria-pertussis-tetanus, and measles vaccines), tetanus toxoid for the mother, vitamin A supplementation, antenatal care, skilled delivery, and safe water. Socioeconomic status was assessed through principal components analysis of household assets, and concentration indices were calculated. FINDINGS: The percentage of children who did not receive a single intervention ranged from 0.3% (14/5495) in Nicaragua to 18.8% (1154/6144) in Cambodia. The proportions receiving all available interventions varied from 0.8% (48/6144) in Cambodia to 13.3% (733/5495) in Nicaragua. There were substantial inequities within all countries. In the poorest wealth quintile, 31% of Cambodian children received no interventions and 17% only one intervention; in Haiti, these figures were 15% and 17%, respectively. Inequities were inversely related to coverage levels. Countries with higher coverage rates tended to show bottom inequity patterns, with the poorest lagging behind all other groups, whereas low-coverage countries showed top inequities with the rich substantially above the rest. INTERPRETATION: The inequitable clustering of interventions at the level of the child raises the possibility that the introduction of new technologies might primarily benefit children who are already covered by existing interventions. Packaging several interventions through a single delivery strategy, while making economic sense, could contribute to increased inequities unless population coverage is very high. Co-coverage analyses of child-health surveys provide a way to assess these issues.
Authors: Ahmad Reza Hosseinpoor; Cesar G Victora; Nicole Bergen; Aluisio J D Barros; Ties Boerma Journal: Bull World Health Organ Date: 2011-10-04 Impact factor: 9.408
Authors: Cesar Gomes Victora; Gary Joseph; Inacio C M Silva; Fatima S Maia; J Patrick Vaughan; Fernando C Barros; Aluisio J D Barros Journal: Am J Public Health Date: 2018-02-22 Impact factor: 9.308
Authors: Lars T Fadnes; Ingunn M S Engebretsen; Henry Wamani; Nulu B Semiyaga; Thorkild Tylleskär; James K Tumwine Journal: BMC Public Health Date: 2009-05-07 Impact factor: 3.295