Dataset Information

Linking household surveys and health facility assessments to estimate intervention coverage for the Lives Saved Tool (LiST).

ABSTRACT: Calls have been made for improved measurement of coverage for maternal, newborn and child health interventions. Recently, methods linking household and health facility surveys have been used to improve estimation of intervention coverage. However, linking methods rely the availability of household and health facility surveys which are temporally matched. Because nationally representative health facility assessments are not yet routinely conducted in many low and middle income countries, estimates of intervention coverage based on linking methods can be produced for only a subset of countries. Estimates of intervention coverage are a critical input for modelling the health impact of intervention scale-up in the Lives Saved Tool (LiST). The purpose of this study was to develop a data-driven approach to estimate coverage for a subset of antenatal care interventions modeled in LiST.Using a five-step process, estimates of population level coverage for syphilis detection and treatment, case management of diabetes, malaria infection, hypertensive disorders, and pre-eclampsia, were computed by linking household and health facility surveys. Based on data characterizing antenatal care and estimates of coverage derived from the linking approach, predictive models for intervention coverage were developed. Updated estimates of coverage based on the predictive models were compared, first with current default proxies, then with estimates based on the linking approach. Model fit and accuracy were assessed using three measures: the coefficient of determination, Pearson's correlation coefficient, and the root mean square error (RMSE).The ability to predict intervention coverage was fairly accurate across all interventions considered. Predictive models accounted for 20-63% of the variance in intervention coverages, and correlation coefficients ranged from 0.5 to 0.83. The predictive model used to estimate coverage of management of pre-eclampsia performed relatively better (RMSE = 0.11) than the model estimating coverage of diabetes case management (RMSE = 0.19).The new approach to estimate coverage represents an improvement over current default proxies in LiST. As the availability of reliable coverage data improves, impact estimates generated by LiST will improve. This study underscores the need for continued efforts to improve coverage measurement, while bringing to the fore the importance of health facility assessments as complementary data sources.

SUBMITTER: Kanyangarara M

PROVIDER: S-EPMC5688485 | biostudies-other | 2017 Nov

REPOSITORIES: biostudies-other

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Linking household surveys and health facility assessments to estimate intervention coverage for the Lives Saved Tool (LiST).

Kanyangarara Mufaro M Chou Victoria B VB

BMC public health 20171107 Suppl 4

<h4>Background</h4>Calls have been made for improved measurement of coverage for maternal, newborn and child health interventions. Recently, methods linking household and health facility surveys have been used to improve estimation of intervention coverage. However, linking methods rely the availability of household and health facility surveys which are temporally matched. Because nationally representative health facility assessments are not yet routinely conducted in many low and middle income ...[more]

PMID: 29143639

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Project description:BackgroundLinking facility and household surveys through geographic methods is a popular technique to draw conclusions about the relationship between health services and population health outcomes at local levels. These methods are useful tools for measuring effective coverage and tracking progress towards Universal Health Coverage, but are understudied. This paper compares the appropriateness of several geospatial methods used for linking individuals (within displaced survey cluster locations) to their source of family planning (at undisplaced health facilities) at a national level.MethodsIn Malawi, geographic methods linked a population health survey, rural clusters from the Woman's Questionnaire of the 2015 Malawi Demographic and Health Survey (MDHS 2015), to Malawi's national health facility census to understand the service environment where women receive family planning services. Individuals from MDHS 2015 clusters were linked to health facilities through four geographic methods: (i) closest facility, (ii) buffer (5 km), (iii) administrative boundary, and (iv) a newly described theoretical catchment area method. Results were compared across metrics to assess the number of unlinked clusters (data lost), the number of linkages per cluster (precision of linkage), and the number of women linked to their last source of modern contraceptive (appropriateness of linkage).ResultsThe closest facility and administrative boundary methods linked every cluster to at least one facility, while the 5-km buffer method left 288 clusters (35.3%) unlinked. The theoretical catchment area method linked all but one cluster to at least one facility (99.9% linked). Closest facility, 5-km buffer, administrative boundary, and catchment methods linked clusters to 1.0, 1.4, 21.1, and 3.3 facilities on average, respectively. Overall, the closest facility, 5-km buffer, administrative boundary, and catchment methods appropriately linked 64.8%, 51.9%, 97.5%, and 88.9% of women to their last source of modern contraceptive, respectively.ConclusionsOf the methods studied, the theoretical catchment area linking method loses a marginal amount of population data, links clusters to a relatively low number of facilities, and maintains a high level of appropriate linkages. This linking method is demonstrated at scale and can be used to link individuals to qualities of their service environments and better understand the pathways through which interventions impact health.

Project description:BackgroundDiarrhea is a leading cause of morbidity and mortality among children under five years of age. The Lives Saved Tool (LiST) is a model used to calculate deaths averted or lives saved by past interventions and for the purposes of program planning when costly and time consuming impact studies are not possible.DiscussionLiST models the relationship between coverage of interventions and outputs, such as stunting, diarrhea incidence and diarrhea mortality. Each intervention directly prevents a proportion of diarrhea deaths such that the effect size of the intervention is multiplied by coverage to calculate lives saved. That is, the maximum effect size could be achieved at 100% coverage, but at 50% coverage only 50% of possible deaths are prevented. Diarrhea mortality is one of the most complex causes of death to be modeled. The complexity is driven by the combination of direct prevention and treatment interventions as well as interventions that operate indirectly via the reduction in risk factors, such as stunting and wasting. Published evidence is used to quantify the effect sizes for each direct and indirect relationship. Several studies have compared measured changes in mortality to LiST estimates of mortality change looking at different sets of interventions in different countries. While comparison work has generally found good agreement between the LiST estimates and measured mortality reduction, where data availability is weak, the model is less likely to produce accurate results. LiST can be used as a component of program evaluation, but should be coupled with more complete information on inputs, processes and outputs, not just outcomes and impact.SummaryLiST is an effective tool for modeling diarrhea mortality and can be a useful alternative to large and expensive mortality impact studies. Predicting the impact of interventions or comparing the impact of more than one intervention without having to wait for the results of large and expensive mortality studies is critical to keep programs focused and results oriented for continued reductions in diarrhea and all-cause mortality among children under five years of age.

Project description:Achieving the Sustainable Development Goals will require careful allocation of resources in order to achieve the highest impact. The Lives Saved Tool (LiST) has been used widely to calculate the impact of maternal, neonatal and child health (MNCH) interventions for program planning and multi-country estimation in several Lancet Series commissions. As use of the LiST model increases, many have expressed a desire to cost interventions within the model, in order to support budgeting and prioritization of interventions by countries. A limited LiST costing module was introduced several years ago, but with gaps in cost types. Updates to inputs have now been added to make the module fully functional for a range of uses.This paper builds on previous work that developed an initial version of the LiST costing module to provide costs for MNCH interventions using an ingredients-based costing approach. Here, we update in 2016 the previous econometric estimates from 2013 with newly-available data and also include above-facility level costs such as program management. The updated econometric estimates inform percentages of intervention-level costs for some direct costs and indirect costs. These estimates add to existing values for direct cost requirements for items such as drugs and supplies and required provider time which were already available in LiST Costing.Results generated by the LiST costing module include costs for each intervention, as well as disaggregated costs by intervention including drug and supply costs, labor costs, other recurrent costs, capital costs, and above-service delivery costs. These results can be combined with mortality estimates to support prioritization of interventions by countries.The LiST costing module provides an option for countries to identify resource requirements for scaling up a maternal, neonatal, and child health program, and to examine the financial impact of different resource allocation strategies. It can be a useful tool for countries as they seek to identify the best investments for scarce resources. The purpose of the LiST model is to provide a tool to make resource allocation decisions in a strategic planning process through prioritizing interventions based on resulting impact on maternal and child mortality and morbidity.

Project description:Evidence has been accumulating that community health workers (CHWs) providing evidence-based interventions as part of community-based primary health care (CBPHC) can lead to reductions in maternal, neonatal and child mortality. However, investments to strengthen and scale-up CHW programs still remain modest.We used the Lives Saved Tool (LiST) to estimate the number of maternal, neonatal and child deaths and stillbirths that could be prevented if 73 countries effectively scaled up the population coverage of 30 evidence-based interventions that CHWs can deliver in these high-burden countries. We set population coverage targets at 50%, 70%, and 90% and summed the country-level results by region and by all 73 high-burden countries combined. We also estimated which specific interventions would save the most lives.LiST estimates that a total of 3.0 (sensitivity bounds 1.8-4.0), 4.9 (3.1-6.3) and 6.9 (3.7-8.7) million deaths would be prevented between 2016 and 2020 if CBPHC is gradually scaled up during this period and if coverage of key interventions reaches 50%, 70%, and 90% respectively. There would be 14%, 23%, and 32% fewer deaths in the final year compared to a scenario assuming no intervention coverage scale up. The Africa Region would receive the most benefit by far: 58% of the lives saved at 90% coverage would be in this region. The interventions contributing the greatest impact are nutritional interventions during pregnancy, treatment of malaria with artemisinin compounds, oral rehydration solution for childhood diarrhea, hand washing with soap, and oral antibiotics for pneumonia.Scaling up CHW programming to increase population-level coverage of life-saving interventions represents a very promising strategy to achieve universal health coverage and end preventable maternal and child deaths by 2030. Numerous practical challenges must be overcome, but there is no better alternative at present. Expanding the coverage of key interventions for maternal nutrition and treatment of childhood illnesses, in particular, may produce the greatest gains. Recognizing the millions of lives of mothers and their young offspring that could be achieved by expanding coverage of evidence-based interventions provided by CHWs and strengthening the CBPHC systems that support them underscores the pressing need for commitment from governments and donors over the next 15 years to prioritize funding, so that robust CHW platforms can be refined, strengthened, and expanded.

Project description:BackgroundCurrently many measures of intervention coverage obtained from household surveys do not measure actual health intervention/service delivery, resulting in a need for linking reports of care-seeking with assessments of the service environment in order to improve measurements. This systematic review aims to identify evidence of different methods used to link household surveys and service provision assessments, with a focus on reproductive, maternal, newborn and child health care, in low- and middle-income countries.MethodsUsing pre-defined search terms, articles published in peer-reviewed journals and the grey literature after 1990 were identified, their reference lists scanned and linking methods synthesized.FindingsA total of 59 articles and conference presentations were carefully reviewed and categorized into two groups based on the linking method used: 1) indirect/ecological linking that included studies in which health care-seeking behavior was linked to all or the nearest facilities or providers of certain types within a geographical area, and 2) direct linking/exact matching where individuals were linked with the exact provider or facility where they sought care. The former approach was employed in 51 of 59 included studies, and was particularly common among studies that were based on independent sources of household and facility data that were nationally representative. Only eight of the 59 reviewed studies employed direct linking methods, which were typically done at the sub-national level (eg, district level) and often in rural areas, where the number of providers was more limited compared to urban areas.ConclusionsDifferent linking methods have been reported in the literature, each category has its own set of advantages and limitations, in terms of both methodology and practicality for scale-up. Future studies that link household and provider/facility data should also take into account factors such as sources of data, the timing of surveys, the temporality of data points, the type of services and interventions, and the scale of the study in order to produce valid and reliable results.

Dataset Information

Linking household surveys and health facility assessments to estimate intervention coverage for the Lives Saved Tool (LiST).

Publications

Linking household surveys and health facility assessments to estimate intervention coverage for the Lives Saved Tool (LiST).

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