RésuméMother to child transmission of HIV (MTCT)

BackgroundDuring the Millennium Development Goal (MDG) era (1990-2015) the government in Mainland Tanzania and partners launched numerous initiatives to advance child survival including the comprehensive One Plan for Maternal Newborn and Child Health in 2008-2015 and a “sharpened” One Plan strategy in early 2014. Moving into the Sustainable Development Goal era, the government needs to learn from successes and challenges of striving towards MDG 4.MethodsWe expand previous work by presenting data for the full MDG period and sub-national results. We used data from six nationally-representative household surveys conducted between 1999 and 2015 to examine trends in coverage of 22 lifesaving maternal, newborn, child health and nutrition (MNCH&N) interventions, nutritional status (stunting; wasting) and breastfeeding practice across Mainland Tanzania and sub-nationally in seven standardized geographic zones. We used the Lives Saved Tool (LiST) to model the relative contribution of included interventions which saved under 5 lives during the period from 2000-2015 compared to 1999 on a national level and within the seven zones.FindingsChild survival and nutritional status improved across Mainland Tanzania and in each of the seven zones across the 15-year period. MNCH&N intervention coverage varied widely and across zones with several key interventions declining across Mainland Tanzania or in specific geographical zones during all or part the period. According to our national LiST model, scale-up of 22 MNCH&N interventions – together with improvements in breastfeeding practice, stunting and wasting – saved 838 460 child lives nationally between 2000 and 2015.ConclusionsMainland Tanzania has made significant progress in child survival and nutritional outcomes but progress cannot be completely explained by changes in intervention coverage alone. Further examination of the implementation and contextual factors shaping these trends is important to accelerate progress in the SDG era.

The ideal approach for calculating effective coverage of health services using ecological linking requires accounting for variability in facility readiness to provide health services and patient volume by incorporating adjustments for facility type into estimates of facility readiness and weighting facility readiness estimates by service-specific caseload. The aim of this study is to compare the ideal caseload-weighted facility readiness approach to two alternative approaches 1) facility-weighted readiness and 2) observation-weighted readiness to assess the suitability of each as a proxy for caseload-weighted facility readiness. We utilized the 2014-2015 Tanzania Service Provision Assessment along with routine health information system data to calculate facility readiness estimates using the three approaches. We then conducted equivalence testing, using the caseload-weighted estimates as the ideal approach and comparing with the facility-weighted estimates and observation-weighted estimates to test for equivalence. Comparing the facility-weighted readiness estimates to the caseload-weighted readiness estimates, we found 58% of estimates met the requirements for equivalence. In addition, the facility-weighted readiness estimates consistently underestimated, by a small percentage, facility readiness as compared to the caseload-weighted readiness estimates. Comparing the observation-weighted readiness estimates to the caseload-weighted readiness estimates, we found 64% of estimates met the requirements for equivalence. We found that, in this setting, both facility-weighted readiness and observation-weighted readiness may be reasonable proxies for caseload-weighted readiness. However, in a setting with more variability in facility readiness or larger differences in facility readiness between low caseload and high caseload facilities, the observation-weighted approach would be a better option than the facility-weighted approach. While the methods compared showed equivalence, our results suggest that selecting the best method for weighting readiness estimates will require assessing data availability alongside knowledge of the country context.

The ideal approach for calculating effective coverage of health services using ecological linking requires accounting for variability in facility readiness to provide health services and patient volume by incorporating adjustments for facility type into estimates of facility readiness and weighting facility readiness estimates by service-specific caseload. The aim of this study is to compare the ideal caseload-weighted facility readiness approach to two alternative approaches: (1) facility-weighted readiness and (2) observation-weighted readiness to assess the suitability of each as a proxy for caseload-weighted facility readiness. We utilised the 2014–2015 Tanzania Service Provision Assessment along with routine health information system data to calculate facility readiness estimates using the three approaches. We then conducted equivalence testing, using the caseload-weighted estimates as the ideal approach and comparing with the facility-weighted estimates and observation-weighted estimates to test for equivalence. Comparing the facility-weighted readiness estimates to the caseload-weighted readiness estimates, we found that 58% of the estimates met the requirements for equivalence. In addition, the facility-weighted readiness estimates consistently underestimated, by a small percentage, facility readiness as compared to the caseload-weighted readiness estimates. Comparing the observation-weighted readiness estimates to the caseload-weighted readiness estimates, we found that 64% of the estimates met the requirements for equivalence. We found that, in this setting, both facility-weighted readiness and observation-weighted readiness may be reasonable proxies for caseload-weighted readiness. However, in a setting with more variability in facility readiness or larger differences in facility readiness between low caseload and high caseload facilities, the observation-weighted approach would be a better option than the facility-weighted approach. While the methods compared showed equivalence, our results suggest that selecting the best method for weighting readiness estimates will require assessing data availability alongside knowledge of the country context.