Testing survey-based methods for rapid monitoring of child mortality, with implications for summary birth history data

Brady, Eoghan; Hill, Kenneth

doi:10.1371/journal.pone.0176366

Cited by 5 publications

(5 citation statements)

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“…The use of SBH data is appealing, but in any one country the appropriateness of inclusion must be carefully determined as there are many sources of potential bias. 35,36 Existing methods for subnational estimation of child mortality in LMIC fall into two main categories: discrete space and continuous space models. They differ in whether the spatial structure of the data is modeled on the administrative area scale (eg, counties, districts) or on the continuous scale using the GPS locations of sampled clusters.…”

Section: Discussionmentioning

confidence: 99%

“…A number of different methods have been described to analyze summary birth history data. 18,[34][35][36][37][38] We describe and use the Brass method as it is the most commonly used method to analyze SBH data, 39,40 and has the most straightforward implementation. None of the other methods have an available implementation in the R programming environment.…”

Section: Brass Methodsmentioning

confidence: 99%

“…Thus, the probability of death before a child's fifth birthday (60th month of life), is denoted 60 q 0 . Following previous work, 5,6 we do not have a distinct discrete hazard for each of the 60 months, but rather we assume that the hazard is constant within each of six age-bands indexed by a = 1, 2, … , 6, and corresponding to months in the intervals [0, 1), [1,12), [12,24), [24,36), [36,48), [48,60). This allows the model the flexibility to be able to make estimates for other important demographic measures such as the neonatal mortality rate, 1 q 0 , and the infant mortality rate, 11 q 1 .…”

Section: Review Of Smoothed Direct Estimationmentioning

confidence: 99%

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Space‐time modeling of child mortality at the Admin‐2 level in a low and middle income countries context

Godwin

Wakefield

2021

Statistics in Medicine

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The Sustainable Development Goals call for a total reduction of preventable child mortality before 2030. Further, the goals state the desirability to have subnational mortality estimates. Estimates at this level are required for health interventions at the subnational level. In a low and middle income countries context, the data on mortality typically consist of household surveys, which are carried out with a stratified, cluster design, and census microsamples. Most household surveys collect full birth history (FBH) data on birth and death dates of a mother's children, but censuses collect summary birth history (SBH) data which consist only of the number of children born and the number that died. In previous work, direct (survey-weighted) estimates with associated variances were derived from FBH data and smoothed in space and time. Unfortunately, the FBH data from household surveys are usually not sufficiently abundant to obtain yearly estimates at the Admin-2 level (at which interventions are often made). In this paper we describe four extensions to previous work: (i) combining SBH data with FBH data, (ii) modeling on a yearly scale, to combine data on a yearly scale with data at coarser time scales, (iii) adjusting direct estimates in Admin-2 areas where we do not observe any deaths due to small sample sizes, (iv) acknowledge differences in data sources by modeling potential bias arising from the various data sources. The methods are illustrated using household survey and census data from Kenya and Malawi, to produce mortality estimates from 1980 to the time of the most recent survey, and predictions to 2020.

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Section: Discussionmentioning

confidence: 99%

Section: Brass Methodsmentioning

confidence: 99%

Section: Review Of Smoothed Direct Estimationmentioning

confidence: 99%

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Space‐time modeling of child mortality at the Admin‐2 level in a low and middle income countries context

Godwin

Wakefield

2021

Statistics in Medicine

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“…The FBH data are then used to impute the dates of births and deaths of SBH women's children. This approach gave disappointing results when validating mortality estimates obtained from this method and comparing to estimates computed from a later FBH survey (Hill et al ., 2015; Brady and Hill, 2017). The authors attribute this to incompatibility of the SBH and FBH data, stemming from data quality issues with SBH data.…”

Section: Introductionmentioning

confidence: 99%

Child mortality estimation incorporating summary birth history data

Wilson

Wakefield

2020

Biometrics

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The United Nations' Sustainable Development Goal 3.2 aims to reduce under‐five child mortality to 25 deaths per 1000 live births by 2030. Child mortality tends to be concentrated in developing regions where information needed to assess achievement of this goal often comes from surveys and censuses. In both, women are asked about their birth histories, but with varying degrees of detail. Full birth history (FBH) data contain the reported dates of births and deaths of every surveyed mother's children. In contrast, summary birth history (SBH) data contain only the total number of children born and total number of children who died for each mother. Specialized methods are needed to accommodate this type of data into analyses of child mortality trends. We develop a data augmentation scheme within a Bayesian framework where for SBH data, birth and death dates are introduced as auxiliary variables. Since we specify a full probability model for the data, many of the well‐known biases that exist in this data can be accommodated, along with space‐time smoothing on the underlying mortality rates. We illustrate our approach in a simulation, showing robustness to model misspecification and that uncertainty is reduced when incorporating SBH data over simply analyzing all available FBH data. We also apply our approach to data from the Central region of Malawi and compare with the well‐known Brass method.

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“…Other methods such as cohort change and birth history imputation have been proposed [ 10 , 16 ], but in general, the development of new methods for indirect estimation of age-specific mortality has been understudied. Furthermore, none of the major existing methods have explored the use of predictive covariates measured at the individual mother or child level.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a new method for indirect estimation of neonatal, infant, and child mortality trends using summary birth histories

et al. 2018

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BackgroundThe addition of neonatal (NN) mortality targets in the Sustainable Development Goals highlights the increased need for age-specific quantification of mortality trends, detail that is not provided by summary birth histories (SBHs). Several methods exist to indirectly estimate trends in under-5 mortality from SBHs; however, efforts to monitor mortality trends in important age groups such as the first month and first year of life have yet to utilize the vast amount of SBH data available from household surveys and censuses.Methods and findingsWe analyzed 243 Demographic and Health Surveys (DHS) from 76 countries, which collected both complete and SBHs from 8.5 million children from 2.3 million mothers to develop a new empirically based method to indirectly estimate time trends in age-specific mortality. We used complete birth history (CBH) data to train a discrete hazards generalized additive model in order to predict individual hazard functions for children based on individual-, mother-, and country-year-level covariates. Individual-level predictions were aggregated over time by assigning probability weights to potential birth years from mothers from SBH data. Age-specific estimates were evaluated in three ways: using cross-validation, using an external database of an additional 243 non-DHS census and survey data sources, and comparing overall under-5 mortality to existing indirect methods.Our model was able to closely approximate trends in age-specific child mortality. Depending on age, the model was able to explain between 80% and 95% of the variation in the validation data. Bias was close to zero in every age, with median relative errors spanning from 0.96 to 1.09. For trends in all under-5s, performance was comparable to the methods used for the Global Burden of Disease (GBD) study and significantly better than the standard indirect (Brass) method, especially in the 5 years preceding a survey. For the 15 years preceding surveys, the new method and GBD methods could explain more than 95% of the variation in the validation data for under-5s, whereas the standard indirect variants tested could only explain up to 88%. External validation using census and survey data found close agreement with concurrent direct estimates of mortality in the NN and infant age groups. As a predictive method based on empirical data, one limitation is that potential issues in these training data could be reflected in the resulting application of the method out of sample.ConclusionsThis new method for estimating child mortality produces results that are comparable to current best methods for indirect estimation of under-5 mortality while additionally producing age-specific estimates. Use of such methods allows researchers to utilize a massive amount of SBH data for estimation of trends in NN and infant mortality. Systematic application of these methods could further improve the evidence base for monitoring of trends and inequalities in age-specific child mortality.

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Testing survey-based methods for rapid monitoring of child mortality, with implications for summary birth history data

Cited by 5 publications

References 8 publications

Space‐time modeling of child mortality at the Admin‐2 level in a low and middle income countries context

Space‐time modeling of child mortality at the Admin‐2 level in a low and middle income countries context

Child mortality estimation incorporating summary birth history data

Development and validation of a new method for indirect estimation of neonatal, infant, and child mortality trends using summary birth histories

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