Comparing estimates of child mortality reduction modelled in LiST with pregnancy history survey data for a community-based NGO project in Mozambique
© Ricca et al; licensee BioMed Central Ltd. 2011
Published: 13 April 2011
There is a growing body of evidence that integrated packages of community-based interventions, a form of programming often implemented by NGOs, can have substantial child mortality impact. More countries may be able to meet Millennium Development Goal (MDG) 4 targets by leveraging such programming. Analysis of the mortality effect of this type of programming is hampered by the cost and complexity of direct mortality measurement. The Lives Saved Tool (LiST) produces an estimate of mortality reduction by modelling the mortality effect of changes in population coverage of individual child health interventions. However, few studies to date have compared the LiST estimates of mortality reduction with those produced by direct measurement.
Using results of a recent review of evidence for community-based child health programming, a search was conducted for NGO child health projects implementing community-based interventions that had independently verified child mortality reduction estimates, as well as population coverage data for modelling in LiST. One child survival project fit inclusion criteria. Subsequent searches of the USAID Development Experience Clearinghouse and Child Survival Grants databases and interviews of staff from NGOs identified no additional projects. Eight coverage indicators, covering all the project’s technical interventions were modelled in LiST, along with indicator values for most other non-project interventions in LiST, mainly from DHS data from 1997 and 2003.
The project studied was implemented by World Relief from 1999 to 2003 in Gaza Province, Mozambique. An independent evaluation collecting pregnancy history data estimated that under-five mortality declined 37% and infant mortality 48%. Using project-collected coverage data, LiST produced estimates of 39% and 34% decline, respectively.
LiST gives reasonably accurate estimates of infant and child mortality decline in an area where a package of community-based interventions was implemented. This and other validation exercises support use of LiST as an aid for program planning to tailor packages of community-based interventions to the epidemiological context and for project evaluation. Such targeted planning and assessments will be useful to accelerate progress in reaching MDG4 targets.
Although there are encouraging trends in some key countries, meeting Millennium Development Goal (MDG) 4 for reduction of child mortality will be challenging, given current trends. Community-based intervention packages are not commonly implemented at large scale, although recent evidence demonstrates that they are effective for neonatal and child mortality reduction at moderate scale in various resource-constrained settings. [2, 3] This has prompted calls for greater emphasis on community-level delivery, especially preventive interventions and integrated strategies. [2, 4, 5]
Analysis of effectiveness of this type of programming is hampered by its cost and complexity. It is difficult to estimate the mortality impact of packages of interventions in realistic field settings, as well as effectiveness of component interventions within packages.  Projects implementing interventions under these conditions usually lack the resources necessary to carry out mortality impact evaluations. The Lives Saved Tool (LiST) produces mortality reduction estimates by modelling the mortality effect of increases in population coverage for key child health interventions. LiST calculates this by combining coverage change data with data on effectiveness of each intervention against common serious child illnesses, and country-specific cause of death profiles. This is explained in detail elsewhere.  By producing intuitive and equivalent outputs from otherwise disparate data, such as the percentage reduction in mortality rates and number of deaths averted, LiST facilitates comparisons that are otherwise difficult to make. Population based surveys in which mortality is directly measured are costly, difficult, and time-consuming, and LiST modelling could be an attractive alternative to estimate mortality reduction.
In order to validate LiST-produced estimates of child mortality reduction in community-based NGO programming, a search was done of such projects with complete coverage data for their child health interventions and independent child mortality reduction estimates. One met criteria for inclusion.
Search for community-based NGO projects
Key characteristics, strategies, interventions, and results of World Relief Mozambique Vurhonga II project (explained in detail in Edward, et. al.)
Community-based maternal child health project covering all 48 villages of Chokwe District (excluding Chokwe town), Gaza Province, Mozambique
• Funding from October 1999 – September 2003
• After initial planning and baseline studies, project implementation began March 2000
• Population surveys for coverage of key maternal child health services and behaviors in October 1999 (baseline) and July 2003 (endline)
• Additional evaluation studies conducted in May 2004: Retrospective complete pregnancy history survey, mortality results analyzed from March 1998 to February 2004, and reported in six separate 12 month periods
Main Project Strategies
• Health related behavior change of mothers of children under five through 173 Care Groups (mothers’ groups with 10-15 volunteers each) trained in monthly supervisory visits, whose members performed monthly visits to 8-10 households in immediate vicinity
• Train health workers and religious leaders in health counseling techniques and content
• Outreach and community-facility links through training of socorristas (community outreach workers) in health posts and formation of village health committees
• Strengthen first level of facility-based health care through establishment of health posts in villages that lacked them and health worker training in IMCI
• Train traditional birth attendants and build small delivery rooms with cement floors in several villages for use by project-trained TBAs
• Nutrition promotion and community-based nutritional rehabilitation
• Promotion of improved care seeking for sick children
• AIDS prevention messaging
• Latrine construction
• TBAs: clean deliveries and essential obstetric and neonatal care (clean cord care, drying and wrapping newborn, skin-to-skin contact, immediate breastfeeding)
• Community case management of diarrhea and pneumonia
• Care of children with diarrhea: promotion of ORT and nutritional support
Selected Key Results/Outputs
• Monthly home visits by Care Group (mothers’ group) members, with 100% coverage of households with children under five throughout project period
• Village health committee coverage increased from 0 to 95%
• Outreach workers (socorristas) increased in number from 3 to 32
• Increase in access to trained providers of care for sick children from 65% to 99%
• Health providers trained in IMCI increased from 0% to 100% in project area
Main health activities of other organizations in Gaza District during project period
• Oxfam assisted in distribution of ITNs to all women of fertile age and children under 5.
• NGO assistance to MOH – train socorristas in community-based child health activities.
• National vaccination campaigns, polio eradication campaigns x 2
Coverage data used for modeling in LiST
World Relief Mozambique Vurhonga II project coverage data and mapping to LiST indicators for modelling
Project coverage indicator
2000, % (95% CI)
2003, % (95% CI)
Children with diarrhea treated with ORT
Children with diarrhea treated with ORT or Recommended Home Fluids
53 (44 – 62)
94 (90 – 98)
Households with latrine
Improved excreta disposal (latrine/toilet)
28 (10 – 46)
75 (70 – 80)
Households with children that own an insecticide treated net
Households with children that own an insecticide treated net
80 (65 – 85)
Children with fever treated at health facility within 24 hours
Children with fever treated with antimalarial
38 (25 – 51)
95 (86 – 100)
Children with fast or difficult breathing treated at health facility within 24 hours
Antibiotics for pneumonia
26 (14 – 38)
60 (35 – 85)
Delivery of last child by trained birth attendant (trained in clean delivery, immediate breastfeeding, thermal care)
Clean home delivery (LiST also includes delivery of immediate breastfeeding and thermal care in this indicator)
65 (60 – 70)
87 (83 – 91)
Children fully immunized according to national EPI scheme
74 (66 – 82)
89 (84 – 94)
Mothers reporting increased food intake in last pregnancy
Coverage of this indicator was entered in LiST as complementary feeding since the project’s nutrition education interventions included nutrition in pregnancy and complementary feeding
44 (38 – 50)
82 (78 – 86)
Coverage data was collected at baseline and endline using a small-sample survey instrument known as the Knowledge, Practices, and Coverage (KPC) survey, based on DHS questions. Households were selected according to a standard cluster sampling methodology, with 30 independent clusters and 10 households in each cluster. Cluster selection was based on village level population data, with probability of selection proportional to population size. The method used is explained in detail elsewhere  The project target geographic area remained invariant from baseline to endline and comprised the entire district of Chokwe except Chokwe town – 48 villages with an estimated population of 119,467 at baseline). The KPC survey collects data on multiple indicators important to the project, and the sample is designed to detect statistically significant baseline/endline differences of at least ± 16% (alpha = 0.05, beta = 0.20) if no sub-sampling is done and an indicator starts at a baseline of 50%. Ninety five percent confidence intervals for project data used for LiST modelling are shown in Table 2. KPC surveys were carried out in October 1999 and July 2003. Project activities started in March 2000, so this is taken as the baseline year for LiST modelling.
KPC surveys cover mothers/caretakers of children 0-23 months of age. The surveys were carried out by the project staff themselves. In order to minimize possible bias, interviewers were not assigned clusters in which they themselves were working in their day-to-day project activities. The data was checked for consistency by an independent team at ICF Macro before being entered in a publically available database (http://www.mchipngo.net).
LiST indicators not in project data – estimated values and data sources
DHS, national data, 4 or more ANC visits
Folic acid supplementation or fortification
LiST calculates based on 4 or more ANC visits
Case management during pregnancy
LiST calculates as subcomponent of ANC
Syphilis detection and treatment
LiST calculates as subcomponent of ANC
Intermittent preventive treatment for malaria
No data available. Set at 0%
Tetanus toxoid vaccination x 2, last pregnancy
DHS, Gaza Province
Facility based birth / Skilled Birth Attendance
Estimated from project data - residual percentage of women not delivering with trained TBAs at endline
Essential Newborn Care
LiST calculates these coverage data as proportion of facility-based birth coverage
Basic Emergency Obstetric and Newborn Care
LiST calculates as a proportion of facility-based birth coverage
Comprehensive Obstetric and Newborn Care
LiST calculcates as a proportion of facility-based birth coverage
Antibiotics for preterm premature rupture of membranes
LiST calculates from facility-based coverage data
Newborn Resuscitation - Facility/Home
LiST calculates from facility and clean home delivery coverage
DHS, national data
Vitamin A Supplementation
DHS 2003, Gaza Province data; 1997 data from reference 
DHS, Gaza Province data
DHS, Gaza Province data
DHS, Gaza Province data
Case management of severe neonatal infection
LiST calculates from DHS facility-based birth coverage data
Use of water connection within 30 minutes of home
Pregnancy history survey (Edward, et. al.). Baseline value for 1999.
Antibiotics for dysentery
No data available. Set at 20% for both 1997 and 2003.
Vitamin A for measles treatment
No data available. Set at 90% for both 1997 and 2003.
LiST is a cohort model of child survival from 0-59 months of age. Its structure and assumption are described in detail elsewhere.[7, 10] LiST provides estimates of the cause-specific child mortality impact of over 40 interventions with strong evidence of effect on child survival. The user must supply the values of changes in coverage for these interventions. LiST has country-specific baseline under-five and infant mortality rates and cause of death profiles needed for its calculations. These parameters can be manipulated by the user if desired. The Child Health Epidemiology Reference Group (CHERG) meets periodically to weigh published evidence, determine which interventions to include in the model and what effect sizes to assign them. The under-five mortality modeling is contained within the Spectrum platform which models demographic trends, given assumptions about population growth rates and prevalence of use of family planning methods.  Version 4.2 of the LiST tool was used for modeling and was downloaded from the Johns Hopkins Institute for International Programs web site.  The under-five and infant mortality rates used were those specific to the project area at baseline, as measured in the pregnancy history and described in detail elsewhere.  National cause of death profiles, population structure, and fertility data were used.
All available coverage data both from the World Relief Mozambique project and other sources were examined to determine which coverage indicators matched those in LiST. The authors discussed the indicator definitions and corresponding coverage data that best fit the interventions in LiST. Eight project indicators (Table 2) were mapped to LiST interventions. The fit between project indicators and LiST was exact for seven of the indicators. For one LiST indicator (complementary feeding) the project had no direct data, but had intervened for a package of behavior change practices that included both maternal nutritional practices during pregnancy and child feeding practices. The project had data on the coverage for increased food intake during last pregnancy, and this was used as a proxy for child complementary feeding practices. Of the other 21 indicators in LiST for interventions being implemented in Mozambique at the time, information was available from other sources for eight; LiST estimates the value of nine others from available data (e.g. LiST estimates coverage for syphilis screening from ANC coverage). Non-project data used for LiST modelling is summarized in Table 3. In summary, data was available for all but four of the indicators in LiST for interventions being implemented in Mozambique in the relevant time period.
Sensitivity analyses were run for the LiST estimates, by varying all the parameters used in the model: Coverage data was varied within the limits of the 95% confidence intervals. The values assigned for intervention effectiveness, baseline mortality figures, and cause of death profiles were varied by ± 10% as well.
Pregnancy history survey
Infant and child mortality were estimated for the period from two years before the start of project activities through one year after it ended (1998-2004), with calculations for these parameters at one year intervals. The information for these calculations was derived from a complete pregnancy history survey of 998 women in the project area, performed in May 2004. The questionnaire used was adapted from the birth history in the DHS 2003 women’s questionnaire and was implemented by a group of surveyors that included personnel from the National Institute of Statistics who had implemented the 2003 DHS, as described in detail in Edward, et.al.  The pregnancy history covered all pregnancies, but the period analyzed and reported was the period from March 1998 to February 2004. The baseline period used to match LiST estimates was March 2000 – February 2001 and the endline period used was March 2003-Feb. 2004.
An unpublished Fortran program written by one of the authors of Edward, et. al. takes as input the time period (beginning and end dates in months) and age group (minimum and maximum) for mortality estimation and calculates m(x) for this age group in the time period. Using the formula of Chiang  and the calculation of mean time lived in the interval for those dying in the interval, 1q0 and 5q0 were calculated and the data plotted in a lexis diagram. Standard errors were calculated assuming a Poisson distribution.
Under-five and infant mortality, comparison of measured and LiST modelled changes
Baseline measured value (95% CI)
Endline measured value (95% CI)
Measured mortality reduction (%)
Endline modeled value
180 (130 – 230)
114 (75 – 153)
102 (64 – 141)
53 (25 – 81)
Accuracy and completeness of coverage data
We used survey data generated as part of standard program monitoring and evaluation activities to model mortality impacts using LiST. Although the available data was not collected as part of a research project, the coverage data input into LiST was of sufficient quality to generate relatively accurate estimates within the limits of the tool. A standard survey instrument was used; data was collected by professional project staff; the potential for bias reduced by avoiding having interviewers collect information from villages where they worked; supervisory spot checks were performed for reliability of information; and data was reviewed for quality by technical support staff from ICF Macro on entry into the online child survival project database.
Although project interventions targeted children 0-59 month olds, which is the cohort modeled in LiST and whose mortality was measured directly in the pregnancy history, the coverage data used for LiST was collected for children 0-23 months old. The inaccuracy caused by this is likely to be small for the following reasons: (1) Even though the KPC measures are collected for children 0-23 months of age, in fact the project implemented interventions for the entire 0-59 month cohort of children, so we expect that the coverage for 0-59 month olds to be substantially the same. (2) we expect that 79% of deaths in children 0-59 months occurred in 0 to 23 month olds, so coverage of 0-23 month olds is the most critical. This calculation was done using the Model Life Tables for Developing Countries published by the United Nations  for an area with the project’s baseline U5MR and IMR. (3) LiST assigns the same effect size to relevant age groups 0-23 months and 24-59 months for all modelled project interventions. As part of the sensitivity analysis presented in Table 5, the effect was examined of halving the coverage change among 24-59 month olds compared to that measured in the KPC for 0-23 month olds. This dropped the estimate of U5MR reduction by 4.8%.
Sensitivity analysis results for LiST modelling
Change in parameter modeled in LiST
Change in estimated decline in U5MR
Change in estimated decline in IMR
ITN coverage change raised 10%
Increase < 0.1%
Increase < 0.1%
ITN coverage change lowered 10%
Decrease < 0.1%
Decrease < 0.1%
ITN effect size raised 10%
Increase < 0.1%
Increase < 0.1%
ITN effect size lowered 10%
Baseline U5MR raised 10%
Baseline U5MR lowered 10%
Proportion of diarrhea deaths raised 10%
Proportion of diarrhea deaths lowered 10%
All age-specific coverage changes among 24-59 month olds reduced to half that measured in the KPC surveys for 0-23 month olds
Accuracy of modelled mortality estimates
The accuracy of the U5MR reduction estimate (39% LiST; 37% Pregnancy History) was better than the IMR reduction estimate (34% LiST; 48% Pregnancy History). Both were within the 95% CI of the parameter, but LiST’s underestimation of the reduction in IMR may be caused by the fact that the only nutritional intervention with probable infant mortality impact that could be modelled in LiST was complementary feeding.
The results of a sensitivity analysis of the LiST model are shown in Table 5. LiST estimates of mortality reduction are calculated based on several inputs: The baseline mortality rate, cause of death profile, change in coverage for each of the interventions in the model, and their effect sizes. Table 5 shows the effect on LiST’s estimates of the reductions in U5MR and IMR caused by changing one of the most critical examples of each of these parameters by 10%. The manipulations of the diarrhea and malaria parameters are shown in the table, as the project had large coverage changes for highly effective interventions for these causes of death. The modelled changes in mortality are more sensitive to changes in parameters that affect the calculation of the overall baseline mortality than they are to changes in the estimation of coverage or intervention effectiveness. This is not surprising, as the value of the baseline mortality affects the calculations for all interventions in the model.
One of the potential strengths of LiST is its ability to simplify analysis of situations in which multiple interventions are implemented simultaneously. Yet to date there have been few published reports on the accuracy of LiST estimates for mortality reductions in areas where packages of community-based child health interventions are being implemented. The LiST validation with data from the evaluation of Accelerated Child Survival and Development programs is similar to this one  and to some extent the national level exercises with DHS data. The current analysis shows that even in the context of relatively complex community-based NGO programming with interventions designed to affect less proximate determinants of child health like level of community organization and women’s empowerment, LiST accurately estimated mortality changes.
Limitations of validation analysis
Although coverage data for project interventions was fairly complete and the time periods coincided well with the mortality estimates calculated from the pregnancy history, the main limitations of the current exercise are (1) that the data was not available from the project for 21 relevant LiST indicators, and had to be estimated mainly from consecutive DHS surveys and (2) the 95% confidence intervals are quite wide for the mortality estimates derived from the pregnancy history.
There are cautions that must be kept in mind when using LiST. The accuracy of its estimates is dependent on having accurate information on the causes of death in the program area. National cause of death profiles are now available through CHERG, but there may be important variation from one region of a country to another. The outputs from LiST must also be interpreted in light of complementary considerations. For example, when used in planning LiST could mistakenly give the impression that mature interventions like vaccination that already have achieved high levels of coverage are not important, as simply maintaining high coverage yields no additional lives saved. LiST also does not take account of the mode of delivery and the fact that delivery of some interventions like antenatal care or vaccination establishes a platform that can serve for adding other interventions, like ITN or vitamin A distribution. Even with an awareness of these limitations and caveats, LiST can be a valuable aid in prioritizing choices for deployment of scarce resources.
Although only a single project was identified for study, it is typical of integrated, community-based NGO programming and implemented under realistic field conditions in a resource-constrained setting typical of conditions of other community-based NGO programming and the type of settings in which greater progress needs to be made to reach MDG4 targets.
A validation exercise has confirmed that in a relatively routine field setting of an NGO child survival project implementing a package of community-based interventions in Mozambique, the Lives Saved Tool (LiST) provides a reasonably accurate estimate of under-five and infant mortality reduction when compared to independent directly measured mortality estimates. These are the kinds of routine programming conditions that LiST attempts to simulate with its modeling. These findings support the use of LiST as a practical tool for estimating the mortality effect of NGO community-based child health programs that is less costly than direct mortality measurement. These findings also support the use of LiST as a planning tool for choosing among child survival interventions in an attempt to maximize mortality impact in pursuit of MDG4.
Role of the funding source
Several authors (JR, DP, LR) have been associated with USAID’s Child Survival and Health Grants Program during manuscript preparation. Several others (MM, PE) were associated with World Relief, whose Vurhonga II project was funded by USAID through this mechanism. The study sponsors had no role in the study, data collection, or analysis. The corresponding author had final decision-making authority over interpretation of the results and the decision to submit this paper.
The authors would like to thank the staff of Food for the Hungry, Foundation of Compassionate American Samaritans, and World Relief for devoting their time to fulfil the requests of the investigators for the necessary data: The authors would also like to thank Ingrid Friberg for assisting them in their use of LiST; Stan Becker for supplying additional information about the pregnancy history analysis previously performed; and Karen Fogg and Claire Boswell for conducting the interviews of NGO staff.
This article has been published as part of BMC Public Health Volume 11 Supplement 3, 2011: Technical inputs, enhancements and applications of the Lives Saved Tool (LiST). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2458/11?issue=S3.
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