Predicting the distribution of under-five deaths by cause in countries without adequate vital registration systems

Saul S Morris1, Robert E Black2 and Lana Tomaskovic3

1 Department of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
2 Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
3 Rue Ancienne, 1227 Carouge, Geneva, Switzerland.

Correspondence: Saul S Morris, Public Health Nutrition Unit, London School of Hygiene & Tropical Medicine, 49–51 Bedford Square, London WC1B 3DP, UK. E-mail: saul.morris{at}lshtm.ac.uk


    Abstract
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 Abstract
 Materials and Methods
 Results
 Discussion
 References
 
Background The absence of complete vital registration and atypical nature of the locations where epidemiological studies of cause of death in children are conducted make it difficult to know the true distribution of child deaths by cause in developing countries. A credible method is needed for generating valid estimates of this distribution for countries without adequate vital registration systems.

Methods A systematic review was undertaken of all studies published since 1980 reporting under-5 mortality by cause. Causes of death were standardized across studies, and information was collected on the characteristics of each study and its population. A meta-regression model was used to relate these characteristics to the various proportional mortality outcomes, and predict the distribution in national populations of known characteristics. In all, 46 studies met the inclusion criteria.

Results Proportional mortality outcomes were significantly associated with region, mortality level, and exposure to malaria; coverage of measles vaccination, safe delivery care, and safe water; study year, age of children under surveillance, and method used to establish definitive cause of death. In sub-Saharan Africa and in South Asia, the predicted distribution of deaths by cause was: pneumonia (23% and 23%), malaria (24% and <1%), diarrhoea (22% and 23%), ‘neonatal and other’ (29% and 52%), measles (2% and 1%).

Conclusions For countries without adequate vital registration, it is possible to estimate the proportional distribution of child deaths by cause by exploiting systematic associations between this distribution and the characteristics of the populations in which it has been studied, controlling for design features of the studies themselves.


Keywords Cause of death, mortality, preschool child, infant mortality, Sub-Saharan Africa Asia

Accepted 14 May 2003

Setting appropriate public health targets and measuring progress toward their achievement requires information on patterns of disease and factors that increase or decrease risk. Unfortunately, the epidemiological evidence base for the distribution of child mortality by cause is inadequate to support sound public health decision-making in most countries or even at regional and global levels. Both of its main sources of data are flawed. Vital registration data, the first source, are usually collected in countries with under-5 mortality rates that are low by global standards. For example, in the whole of Africa, only the island states of Mauritius and the Seychelles have vital registration systems with coverage of 95% or more.1 South Africa has the most complete system in continental Sub-Saharan Africa. Although an analysis of the quality of this system in the early 1990s found a coverage of only 19% of all births,2 it is said to have improved since that time. India has a sample registration system, the quality of which has also been questioned.3 The second source of relevant data consists of epidemiological studies of cause-specific child mortality in specific populations. These studies are limited in number, and are generally conducted in populations that are either atypically easy to access or have atypically high mortality rates. The resulting data have limited utility for public health planning, even in the countries where the studies are conducted, unless a valid means can be found of extrapolating to other populations with—most likely—different characteristics.

This paper outlines a method for extracting generalizable results on the proportional distribution of under-5 deaths by cause in countries without reliable vital registration systems. This method involves systematic searching for all relevant epidemiological studies, standardizing the way causes of death are categorized across studies, and using regression techniques to relate the proportional distribution of deaths by cause in each study to a limited number of characteristics of the study populations and study designs. Finally, these relationships are exploited to estimate the proportional distribution of deaths by in national populations of known size and characteristics. We present estimates of the proportional distribution of under-5 deaths by cause in two regions that together accounted for 75% of all child deaths in the year 2000: sub-Saharan Africa and South Asia.


    Materials and Methods
 Top
 Abstract
 Materials and Methods
 Results
 Discussion
 References
 
We conducted a systematic search of all studies published since 1980 that report the distribution of child mortality by cause. Both MEDLINE and POPLINE were searched using the terms (infant OR child) AND (death* OR mortality OR fatal outcome) AND (ARI OR ALRI OR pneumonia OR respiratory) AND (diarrhoea OR diarrhoea). No restriction was placed on publication language. The reference sections of these studies were then reviewed to identify additional studies. We also contacted researchers conducting cause-specific mortality reviews for the Child Health Epidemiology Reference Group of the World Health Organization to ensure that any appropriate articles included in these reviews were also included in the current study. Members of the Reference Group, as well as members of the staff of both the London School of Hygiene & Tropical Medicine and the Bloomberg School of Public Health at Johns Hopkins University, were asked to identify any unpublished studies known to them.

The initial pool of studies was then screened to ensure that they met the inclusion criteria for the present study. These were (1) surveillance period no earlier than 1980, (2) mortality surveillance for either an exact multiple of 12 months (prospective or retrospective surveys) or exactly the same age range for all study subjects in the case of birth cohorts, to minimize seasonal effects, (3) use of a standardized verbal autopsy questionnaire with or without supporting clinical records to ascertain cause of death, (4) a unique cause (or fully identified combination of causes) for each death, and (5) no more than one-third of all deaths attributed to ‘undetermined’ causes. Forty-seven studies were identified that met these criteria, 46 of them described in 43 different publications, and one as yet unpublished in any form (4–44; Henry Perry, Hospital Albert Schweitzer, personal communication, 2002).

A total of 231 different ‘causes’ of death were identified in the data set. Causes were grouped into 14 subsets: diarrhoea, pneumonia, malaria, measles, diarrhoea plus pneumonia (combined), diarrhoea plus malaria (combined), pneumonia plus malaria (combined), malnutrition, ‘neonatal causes’, injury, sepsis/ septicaemia/meningitis, other specified, other unspecified, and undetermined. We contacted study investigators and asked them to identify the cause of death when non-specific terms were used in the original publication. Where needed we also asked them to distinguish between measles deaths and deaths from other vaccine-preventable causes.

Most studies identified only a single cause for each death. Deaths attributed to combined causes such as diarrhoea plus pneumonia were therefore re-distributed between their single-cause components. For example, if in study j there were d1 deaths attributable to diarrhoea, d2 attributed to pneumonia, and d1.2 attributed to the combined effect of the two illnesses, then the d1.2 combined-cause deaths were redistributed to diarrhoea and pneumonia in the ratio d1:d2.

Deaths attributed to malnutrition were re-allocated among all other categories of infectious illness based on the relative importance of the single-cause deaths in the same studies. Labelling a small proportion of deaths as ‘due to malnutrition’ would underestimate the true contribution of malnutrition to child mortality in poor communities. Underweight is a risk factor for deaths from all infectious causes, with attributable fractions of 52% for pneumonia, 61% for diarrhoea, 57% for malaria, and 45% for measles.45

The nature of the available data thwarted our intention to examine the proportion of deaths occurring in the neonatal period. Ten of the 47 studies did not report neonatal deaths separately, choosing instead to include them in a residual category. Even studies that did separate neonatal deaths used one of two irreconcilable approaches to categorize them: either age at death or—more commonly—disease category (tetanus, congenital malformation, etc.). We therefore combined deaths from ‘neonatal causes’ with the remaining deaths from injury, sepsis/septicaemia/meningitis, other specified causes, and other unspecified causes into a single residual category, which we call ‘neonatal and other causes’.

We collected information on the characteristics of each study and its population, including: study location (rural/urban, UN region, longitude, latitude, and altitude); range of years encompassed by the mortality surveillance; age of children under surveillance; overall level of mortality encountered; access to safe drinking water (using a standardized definition); vaccination coverage rates; percentage of births attended by qualified personnel; population at risk of falciparum malaria; adult female literacy rate; and the anthropometric status of children in the study population. All relevant information in the publications themselves was abstracted onto a standard record form. Partially completed forms were then sent to investigators with a request that they provide missing information. For data not provided by investigators, we identified other studies conducted in the same or similar sites within a few years of the index study. In the absence of more specific information, estimates were taken from national or sub-national surveys or population censuses conducted within a few years of the index study. Estimates of the proportion of the population at risk of falciparum malaria were taken from Mara LITe, version 3.0.0 (Mapping Malaria in Africa, Durban, South Africa) for African populations, and were supplied by the Centers for Disease Control Division of Parasitic Diseases (Rick Steketee, personal communication, 2002) for all other populations.

Estimates of the level of all-cause mortality were transformed to a single metric (the risk of a child dying before reaching age 5, 5q0), because they were variously reported as rates, risks, or ratios in the publications and often for non-standard age groups. We generated this standard measure by (1) choosing an appropriate model life table, usually from the series developed by the INDEPTH demographic surveillance sites,46 (2) using the method of Brass and Blacker47 to ‘fill out’ the model life table to every month of age between birth and 10 years, (3) varying the level of mortality in the model life table on the logit scale48 until the measure of mortality reported in the publication was replicated in the model, and (4) reading off the implied level of 5q0. Two studies did not report any information that would permit the estimation of 5q0. For one we developed an estimate based on sub-national data available from a survey conducted at approximately the same time; the other study was dropped from the analysis.

We used a regression framework to relate the distribution of mortality from different causes to the characteristics of the study designs and study populations. One approach would have been to use the proportional mortality, P, due to cause k (k = diarrhoea, pneumonia, malaria, measles, other, undetermined) as the dependent variable in six separately estimated equations. However, this strategy ignores the fact that for any given study, the six proportions must sum to one. The correct regression model therefore involves just five equations plus a constraint forcing the proportions to sum to one.

Salomon & Murray49 considered this problem in the context of overall mortality from communicable and non-communicable causes and injury. Although they adapted their model from the work of Katz and King,50 the original mathematical development is attributed to Aitchison.51 In brief, five dependent variables are developed by dividing the proportion of deaths in each study due to cause k by the proportion due to a selected ‘base’ cause K, and taking the natural logarithm of this value. The vector of dependent variables is then assumed to be multivariate Normal, and a linear function of the explanatory variables in the model. The model can be written as


where alpha, beta, and gamma are regression coefficients, and epsilon is a random error term specific to the study and the particular cause k. The resulting set of five equations could be solved by Ordinary Least Squares regression. However, to account for the correlated nature of the five equations, we estimated the entire set simultaneously using Seemingly Unrelated Regression.52

The compositional model outlined above is invariant to the choice of ‘base’ cause, K, holding constant all other variables in the model. We experimented first with taking diarrhoea as our base cause, and then pneumonia, because both causes of death were present in each of the 46 studies under analysis. Presumably because many variables are associated equally with mortality from diarrhoea and mortality from other causes, it was more difficult to identify significant associations with the k-cause:diarrhoea ratios than with the k-cause:pneumonia ratios. The latter specification is therefore presented in this paper.

We experimented with various approaches to weighting the different studies in the regression analysis, based on adaptations of the methods described by Thompson & Sharp.53 ‘Optimal’ weights were almost identical for the studies with the smallest and largest sample sizes, and the results presented in this paper are therefore unweighted. To develop the final model, we first used Ordinary Least Squares regression to explore bivariate associations one cause (or rather, ratio of causes) at a time. At an early stage we discarded child anthropometric status, urban/ rural location, and adult female literacy as potential explanatory variables because they were not associated with any of the dependent variables (ratios) after adjusting for 5q0. For the remaining variables, possible non-linearities were explored ratio by ratio and explanatory variable by explanatory variable using fractional polynomial models,54 a highly flexible method of curve fitting. We examined statistical best-fit models visually to see whether they could have been influenced by outliers, and made minor adjustments in a few cases. All associations significant at the P < 0.2 level were considered for entry in multivariate models. We first selected a parsimonious set of explanatory variables for each dependent variable using forward stepwise regression, and then re-ran the same models using Seemingly Unrelated Regression. Only variables significant at the P < 0.1 level were retained in the final model. This relatively generous cut-off was selected because of the small sample size and associated limited power of the analysis. For each model we report the per cent of variance explained by the predictors. We denote this ‘R2’, with the single quotes cautioning that R-squared is no more than descriptive when generalized least squares estimators are used, as in this case.55 Because of missing values for one or more covariates, 38 studies were included in the final model.

Finally, the regression coefficients from the final model were used to generate predictions of the distributions of under-5 deaths by cause for the year 2000 for countries in sub-Saharan Africa and South Asia (Afghanistan, Bangladesh, Bhutan, Nepal, Pakistan, and Sri Lanka). The predictions were based on the characteristics and populations of these countries in the year 2000, with the prediction variable ‘surveillance year’ also set to 2000. Each state in India was treated as a separate country because of their size and the relative ease of obtaining necessary data. Because the model-development data set only included populations with under-5 mortality rates of >=26 per 1000, no predictions were made for countries/states in these regions with lower mortality rates (Seychelles and Mauritius in Sub-Saharan Africa, and Sri Lanka and Kerala state in South Asia). Population data and mortality rates were provided by WHO/EIP (Colin Mathers, personal communication, 2002) with one exception. For the Indian states, population data were taken from the 2001 census and mortality rates were taken from the 1998–1999 National Family Health Survey, which is representative at the state level. Coverage data for measles-containing vaccine were official WHO/UNICEF/World Bank estimates,56 except for the Indian states, for which values were taken from the 1998–1999 survey. Estimates of covariate values were taken from the UNICEF statistics website (www.childinfo.org) and for India, from the survey. The regional estimates presented here do not include countries or Indian states with incomplete data for the predictor variables. Countries excluded on this basis are the Comoros, Congo, Djibouti, Gabon, and Liberia in sub-Saharan Africa, and Afghanistan, Tripura, and six small Indian union territories in South Asia. It should be noted that since the estimation model implied that different methods of determining the definitive cause of death resulted in different proportional distributions, it was necessary to choose a uniform method for the predictions; we specified this to be the use of a standardized diagnostic algorithm, a method which minimizes the proportion of deaths attributed to ‘unknown’ causes.

It was not possible to validate the model externally using vital registration data because no country in sub-Saharan Africa or South Asia with an under-5 mortality rate of >=26 per 1000 has complete or near-complete vital registration. We therefore opted to validate the model internally using jackknife estimation.57 This involves re-estimating the model m times (where m is the total number of studies contributing to the regression model), each time omitting one study. On each run, the estimated regression coefficients are used to predict the proportional mortality outcomes in the omitted study. It was thus possible to determine the difference between the actual and predicted proportional mortality outcomes for each study in the regression sample. The mean difference between predicted and actual values can be interpreted as a measure of bias, and the standard deviation of the differences can be interpreted as the standard error of an out-of-sample prediction based on all the data. Assuming these standard errors are distributed Normally and independently for all countries in the same region, we then estimated 95% CI for the regional aggregates (proportional mortality by cause for the whole of sub-Saharan Africa or the whole of South Asia) using Monte Carlo-type simulations. This was done by adding a random disturbance (of mean zero and standard deviation equal to the estimated prediction standard error) to the predictions for each country and then aggregating to the regional level; the whole procedure was repeated 1000 times, and 95% CI for each proportion were estimated by determining the 2.5th and 97.5th centiles of the relevant distributions.

All analyses were undertaken using Stata 7.0 (Stata Corporation, College Station, TX).


    Results
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 Abstract
 Materials and Methods
 Results
 Discussion
 References
 
Among the 46 studies for which 5q0 could be estimated, 19 (41%) were conducted in South Asia, with 8 studies each from Bangladesh and India. A further 18/46 studies (39%) were conducted in sub-Saharan Africa, with 12 countries represented. No studies were identified from Central America, Oceania, or Central or Western Asia, and the three studies from Northern Africa were all from a single country (Egypt). Table 1Go shows, for each of the 46 studies, the proportional distribution of mortality by cause after recoding and re-allocation of deaths attributed to malnutrition and/or combined causes.


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Table 1 Data used in the meta-regression analysis
 
Table 2Go presents selected characteristics of the study populations. The studies have been divided into five equal-sized groups, based on the under-5 mortality risk in each population. The African studies were disproportionately concentrated in the highest mortality quintiles, while the studies from South Asia were concentrated in the middle three quintiles. All of the studies from the Western hemisphere were in the lowest mortality quintile. The studies were distributed around an average mid-study surveillance year of 1990, with low-mortality studies more recent than high mortality studies, reflecting the secular downward trend in child mortality. Low mortality levels were associated with higher proportions of deaths due to ‘neonatal and other’ causes, and lower proportions due to measles and—especially—malaria.


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Table 2 Characteristics of the 46 study populations
 
Thirty-nine studies included study children in mortality surveillance from birth, while eight initiated surveillance at older ages, up to age one year. Six studies were true cohort studies, while 30 identified a relevant population cross-sectionally and then followed them through time. Eleven were cross-sectional surveys with recall of deaths over a specified period of time. Fourteen studies required multiple experts to reach consensus on the cause of death, while nine required only a majority decision. Four further studies used a standardized diagnostic algorithm and three a computerized algorithm to assign cause of death, while five studies relied on a single expert without a standardized algorithm. Twelve studies did not specify the method they used to assign cause of death.

Table 3Go shows the results of the meta-regression analysis, based on 38 studies with no missing covariate information. The regression coefficients were difficult to interpret because the dependent variables were logarithms of the ratios of two proportions, and the model includes several complex non-linearities. The Table therefore presents ‘interpretations of effects’ rather than the coefficients themselves. A table of coefficients and their standard errors is available from the corresponding author.


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Table 3 Results of the simultaneously estimated regression models relating proportional mortality outcomes to characteristics of the study designs and study populations
 
The regression results show that exposure to endemic, African-pattern falciparum malaria was associated with malaria deaths, and that measles vaccination was protective against measles deaths. Other effects include a greater proportion of deaths from ‘neonatal and other’ causes (relative to pneumonia deaths) at the lowest levels of overall under-5 mortality, a lower proportion of deaths due to pneumonia (relative to malaria deaths) associated with better coverage of safe delivery care, and a secular decline in the ratio of measles deaths to pneumonia deaths. The model results also show that design features of mortality studies can affect the observed proportions of deaths due to different causes: the way that the definitive cause of death was determined was associated both with the proportion of deaths declared underdetermined and with the balance between diarrhoea and ‘neonatal and other’ cause deaths relative to pneumonia deaths. Excluding the youngest infants from mortality surveillance was also found to depress artificially the proportion of deaths attributable to pneumonia (relative to diarrhoea deaths). Whilst virtually all the between-study variability in the ratio of malaria deaths to pneumonia deaths (‘R2’ = 0.90) could be explained as a function of the explanatory variables considered, as could most of the between-study variability in the ratio of deaths from ‘other causes’ to pneumonia deaths (‘R2 = 0.59), the model explained less than half the variability in the other ratios.

The proportional distribution of deaths by cause was estimated for 41 countries in sub-Saharan Africa, accounting for 99% of all child deaths in the region in the year 2000. The proportions were converted to numbers of deaths by cause by multiplying them by the estimated total number of under-5 deaths in each country in 2000. The resulting proportional distribution of deaths by cause for the whole region is shown in Figure 1Go. The four major causes of death—‘neonatal and other’ causes, pneumonia, malaria, and diarrhoea—were each found to be responsible for between 20% and 30% of all child deaths.



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Figure 1 Distribution of under-5 deaths by cause, 2000. AIDS deaths are not accounted for, due to lack of studies in the affected areas

 
The proportional distribution of deaths by cause was also estimated for South Asia, for areas accounting for 93% of child deaths in the region in 2000. The predicted distribution of deaths by cause (Figure 1Go) differed from that of sub-Saharan Africa, with over 50% of child deaths being due to ‘neonatal and other’ causes. Equal proportions of deaths were attributed to diarrhoea and to pneumonia. Few measles deaths were predicted except in several Indian states with low vaccination coverage, and virtually no children were predicted to have died of malaria in this region.

Table 4Go shows the results of the internal validation. This analysis indicates that for the group of 38 studies with no missing covariates, the model successfully reproduced the observed proportional distribution of deaths by cause. Bias exceeded two percentage points only for deaths from undetermined and from ‘neonatal and other’ causes. In relative terms, however, measles deaths and deaths from undetermined causes were underestimated by approximately 30%. The substantial unexplained heterogeneity of the studies was reflected in large prediction standard errors, reaching 12 percentage points for diarrhoea, malaria, and ‘neonatal and other’ causes. If prediction errors of the same magnitude were associated with our national (/state) predictions, and assuming that these errors were independent across countries and across causes of death, then the 95% CI for the regional distributions would be: sub-Saharan Africa, diarrhoea 15.5–28.2%, pneumonia 16.1–24.0%, malaria 17.0–37.0%, measles 1.6–12.8%, ‘neonatal and other’ causes 20.1–33.1%, and undetermined 0.4–6.0%; South Asia, diarrhoea 16.9–32.8%, pneumonia 17.8–26.4%, malaria 0.1–0.8%, measles 1.0–11.4%, ‘neonatal and other’ causes 40.6–57.3%, and undetermined 1.0–4.7%.


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Table 4 Results of the internal validation using jackknife estimation. Based on the 38 studies with no missing covariates
 

    Discussion
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 Abstract
 Materials and Methods
 Results
 Discussion
 References
 
This analysis unifies and summarizes best available data about the distribution of under-5 deaths by cause in countries with mortality rates of >=26 per 1000. Based on population data58 and reported mortality rates,59 we estimate that these countries accounted for 86% of all births in the world in 2000, and 98% of all under-5 deaths. Virtually none of these countries, with the possible exceptions of Mexico and Suriname, have vital registration systems complete enough to support valid estimates of the distribution of causes of death among children under 5. Current estimates of the distribution of child deaths by cause must therefore draw on epidemiological studies using verbal autopsy methods, sometimes supplemented by clinical records. This study is the first to propose a systematic method for exploiting the information from these studies that does not assume that the locations where they were conducted are representative of entire countries or even supra-national regions.

Several issues should be kept in mind when interpreting these results. First, studies of childhood deaths in developing countries have shown that causes of death established using verbal autopsy methods are not always consistent with diagnoses based on more complete clinical data.60–62 In this study, however, we are interested only in the proportion of deaths attributable to each cause, and ‘...the fact that there is misclassification, in and of itself, does not necessarily imply that the resulting verbal autopsy estimate of the cause-specific mortality fraction will be inaccurate’ if there are equal numbers of false positives and false negatives.63 Whether there are counterbalancing numbers of false positives and false negatives is likely to vary by cause of death, location, and type of data collection instrument. We did not adjust our estimates for possible misclassification because we did not know the technical properties of the instruments used, and adjusting for misclassification error based on sensitivities and specificities derived from a validation study population with a cause of death distribution different from that of the general population can lead to spurious results.64

Second, the coefficients in our model may be biased. There may be characteristics of the study populations or study designs not able to be included in our model that cause uncontrolled confounding. In this respect, the model can only be an improvement on previous work limited to bivariate associations between single-cause mortality and supra-national region65 or total mortality level.66 The fact that many of the explanatory variables may have substantial misclassification is of greater concern, especially those based on sub-national estimates from surveys conducted in years other than those of the index study. Misclassification of explanatory variables in complex multivariate regression models can lead to bias of an unpredictable magnitude or direction.67 Our work benefits, however, from the particular care that we took to minimize misclassification in one important predictor variable in our model: the overall level of under-5 mortality in the different study settings. Our internal validation using jackknife estimation suggests that the model is successful in reproducing the observed proportional distribution of mortality by cause in the intensive mortality studies we reviewed. External validation using vital registration data is unfortunately not possible due to the absence of countries in sub-Saharan Africa or South Asia with an under-5 mortality level of >=26 per 1000 and a complete or near-complete system of vital registration.

Relatively low proportions of the between-study variance in the outcome measures could be explained in our model (except for the malaria:pneumonia ratio, which was well modelled). Some of the variables that we expected to explain well the different proportional distributions by cause—such as the prevalence of undernutrition or maternal literacy—turned out to be associated with most of the major causes in similar ways, or to lose statistical significance after adjustment for the overall under-5 mortality rate. Our findings strongly suggest either that epidemiological patterns in cause of death are much less regular than policy makers would hope, or that the details of mortality study implementation have a major impact on the final results. We are more certain of the latter: the studies reviewed for this analysis used many different categorizations of cause of death and methods for assigning them, and the methods for assigning them were significantly associated with proportional distributions by cause. We urge investigators to use standardized data collection methods such as that developed by WHO/Johns Hopkins/London School of Hygiene & Tropical Medicine.68 It is unfortunately the case that until more and better data become available, our partial understanding of the determinants of local variation in the proportional distribution of deaths by cause will limit our ability to make accurate predictions for any given country. However, if the predictions errors are independent across countries in the same region, then regional aggregate statistics will be less severely affected.

Attributing each death to a single cause oversimplifies a reality in which many children die following multiple illnesses (either consecutively or concurrently). Some studies6,9–10,14,17,40,44 recorded deaths as due to combinations of single causes (pneumonia and diarrhoea, for example), but most did not. More must be learnt about co-morbid events if we are to use epidemiological profiles to make inferences about the impact of public health interventions. In some cases, there may have been a systematic tendency to undercount one illness in the presence of another. For example, pneumonia may have been underreported when accompanied by malaria, a tendency likely to have been accentuated by the diagnostic overlap between these two diseases.69

The cause of death prediction model presented here was constructed using existing data sets, designed for other objectives. Only one of the studies included in our analysis30 recorded deaths from human immunodeficiency virus (HIV)/AIDS, and our predictions therefore ignore AIDS deaths. However, current estimates suggest that even in sub-Saharan Africa, HIV/AIDS causes more than 10% of all child deaths only in 13 severely affected countries.70 In the future, more data are needed on the distribution of child deaths by cause in areas with high HIV prevalence, especially among children and women of childbearing age. Similarly, a lack of standardization forced us to combine deaths in the neonatal period with all deaths coded as ‘other’. We are therefore unable to predict neonatal deaths despite their assumed importance, particularly in South Asia. An important contribution of this analysis is its clear demonstration of the importance of establishing and supporting the use of standards for assigning and classifying the causes of under-5 deaths.

Despite these caveats, the present study provides robust data on the proportional distribution of under-5 deaths by cause in sub-Saharan Africa and South Asia. We do not present estimates for other parts of the world because most of the data on which our model is based are from these two regions. We find that malaria, pneumonia, and diarrhoea are still the major killers in sub-Saharan Africa, and that the latter two causes together also account for nearly one-half of all child deaths in South Asia. ‘Neonatal and other causes’ account for over one-half of child deaths in South Asia, but considerably less in sub-Saharan Africa. The plausibility of this finding is confirmed by the fact that four recent Demographic and Health Surveys (DHS) conducted in South Asia (Bangladesh 1996/97, Bangladesh 1999/2000, India 1998/99, and Nepall 2001) all found that deaths in the neonatal period accounted for over 40% of all under-5 deaths, whereas all DHS surveys conducted in sub-Saharan Africa between 1996 and 2001, with the sole exception of Mauritania 2000–2001, found that deaths in the neonatal period accounted for less than 35% of all child deaths (and often less than one quarter).71 Measles is responsible for relatively few deaths in both regions, a finding that reflects the fact that in the 47 studies we reviewed, the median proportion of deaths due to measles was just 2.2% (interquartile range, 0.0–5.1%). The number of measles deaths predicted by our model is not, however, in agreement with previous work by Stein et al.72 and Miller.73

It is not the purpose of this paper to compare the resulting predictions on proportionate child mortality by cause; however, it is notable that in the mid-1980s, pneumonia and diarrhoea were considered to be the most important causes of child death in both sub-Saharan Africa and South Asia.74 Relatively few under-5 deaths in sub-Saharan Africa were attributed to malaria. This picture has now changed, and malaria is thought to be of similar magnitude to diarrhoea and pneumonia as a cause of child deaths in sub-Saharan Africa.75–76 The number and proportion of deaths due to diarrhoea and pneumonia have decreased, and the number of deaths due to malaria has remained roughly stable. Comparisons with other estimation methods are difficult because the details of previously used methods are not published. Ongoing work of WHO and other groups will utilize various models, including that described here, to refine regional mortality estimates. Meanwhile, interventions to control deaths due to pneumonia, diarrhoea, malaria, and neonatal causes will be essential if under-five mortality is to be reduced.


KEY MESSAGES

  • Small-scale studies of child mortality, with cause of death ascertained by post-mortem interview with the child’s carers, provide a rich source of data on causes of under-5 death in countries without adequate vital registration systems.
  • Systematic associations can be detected between the proportional distribution of deaths by cause and a number of characteristics of study populations and designs.
  • The studies suggest that pneumonia, malaria, diarrhoea, and ‘neonatal plus other’ causes are all major causes of death in sub-Saharan Africa, while diarrhoea, pneumonia, and particularly ‘neonatal plus other’ causes are important in South Asia.
  • The considerable residual heterogeneity observed in the proportional distributions of under-5 deaths by cause indicates the need for more and better standardized studies of under-5 mortality in poor countries.

 


    Acknowledgments
 
The authors would like to thank the following investigators, who kindly supplemented the published data on their studies with additional information: Dr K Anand, All India Institute of Medical Sciences; Dr S Arifeen, International Centre for Diarrhoea Disease Research, Bangladesh; Dr S Awasthi, King George’s Medical College, Lucknow; Dr A Menezes, Federal University of Pelotas; Dr U D’Alessandro, Prince Leopold Institute of Tropical Medicine; Dr J Schellenberg, London School of Hygiene & Tropical Medicine; Dr C Delacollette, World Health Organization; Dr J-P Chippaux, Institut de Recherche pour le Développement; Dr V Fauveau, United Nations Population Fund; Dr S Hirve, King Edward Memorial Hospital, Pune; Dr W Huang, Guiyang Medical College; Dr K Kahn, University of Witwatersrand; Dr F Jalil, Lahore; Dr J Katz, Johns Hopkins University; Dr H Perry, Hospital Albert Schweitzer. No specific funding was received by any author or institution for this work. However, the work was conducted under the auspices of the Child Health Epidemiology Reference Group, which operates with the financial support of the Bill and Melinda Gates Foundation and is coordinated by the Department of Child and Adolescent Health and Development of the World Health Organization. Dr J Bryce convened this group and identified the need for the current analysis to be undertaken, providing useful feedback at many points. We are also grateful to Dr B Zaba, London School of Hygiene & Tropical Medicine, for assistance with demographic modelling, and to Dr C Boschi-Pinto for useful comments on earlier drafts of this manuscript. The views represented in this article are those of the individual authors and do not necessarily represent the views of their institutions.


    References
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 Abstract
 Materials and Methods
 Results
 Discussion
 References
 
1 Lopez AD, Ahmad OB, Guillot M et al. Life Tables for 191 Countries for 2000: Data, Methods, Results. Geneva: World Health Organization, 2001. (GPE Discussion Paper No. 40).

2 Nannan N, Bradshaw D, Mazur R et al. What is the infant mortality rate in South Africa? The need for improved data. S Afr Med J 1998; 88:1583–87.[ISI][Medline]

3 Mahapatra P, Chalapatri Rao PV. Cause of death reporting systems in India: a performance analysis. Natl Med J India 2001;14:129–31.[ISI][Medline]

4 Afari EA, Nkrumah FK, Nakana T et al. Impact of primary health care on childhood and mortality in rural Ghana: the Gomoa experience. Cent Afr J Med 1995;41:148–53.[Medline]

5 Anand K, Kant S, Kumar G et al. Development is not essential to reduce infant mortality rate in India: experience from the Ballabgarh project. J Epidemiol Community Heath 2000;54:247–53.[Abstract/Free Full Text]

6 Arifeen S, Black RE, Antelmann G et al. Exclusive breastfeeding reduces acute respiratory infection and diarrhea deaths among infants in Dhaka slums. Pediatrics 2001;108:E67.[Medline]

7 Awasthi S, Pande VK, Glick H. Under fives mortality in the urban slums of Lucknow. Indian J Pediatr 1996;63:363–68.[Medline]

8 Bang AT, Bang RA, Tale O et al. Reduction in pneumonia mortality and total childhood mortality by means of community-based intervention trial in Gadchiroli, India. Lancet 1990;336:201–06.[ISI][Medline]

9 Baqui AH, Black RE, Arifeen SE et al. Causes of childhood deaths in Bangladesh: results of a nationwide verbal autopsy study. Bull World Health Organ 1998;76:161–71.[ISI][Medline]

10 Baqui AH, Sabir AA, Begum N et al. Causes of childhood deaths in Bangladesh: an update. Acta Paediatr 2001;90:682–90.[ISI][Medline]

11 Barreto ICHC, Pontes LK, Corrêa L. Vigilância de óbitos infantis em sistemas locais de saúde: avaliação da autópsia verbal e das informaçóes de agentes de saúde (Portuguese). Rev Panam Salud Publica 2000;7:303–12.[Medline]

12 Barros FC, Victora CG, Vaughan JP et al. Infant mortality in southern Brazil: a population based study of causes of death. Arch Dis Child 1987;65:487–90.

13 Victora CG, Barros FC, Huttly SRA et al. Early childhood mortality in a Brazilian cohort: the roles of birthweight and socioeconomic status. Int J Epidemiol 1992;21:911–15.[Abstract]

14 Becker S, Waheeb Y, El-Deeb B et al. Estimating the completeness of under-5 death registration in Egypt. Demography 1996;33:329–39.[ISI][Medline]

15 Bhatia S. Patterns and causes of neonatal and postneonatal mortality in rural Bangladesh. Stud Fam Plann 1989;20:136–46.[ISI][Medline]

16 Binka FN, Kubaje A, Adjuik M et al. Impact of permethrin impregnated bednets on child mortality in Kassena-Nankana district, Ghana: a randomized controlled trial. Trop Med Int Health 1996;1:147–54.[ISI][Medline]

17 D’Alessandro U, Olaleye BO, McGuire W et al. Mortality and morbidity from malaria in Gambian children after introduction of an impregnated bednet programme. Lancet 1995;345:479–83.[CrossRef][ISI][Medline]

18 De Francisco A, Hall AJ, Armstrong-Schellenberg JRM et al. The pattern of infant and childhood mortality in Upper River Division, The Gambia. Ann Trop Paediatr 1993;13:345–52.[ISI][Medline]

19 Delacollette C, van der Stuyft P, Molima K et al. Etude de la mortalité globale et de la mortalité liée au paludisme dans le Kivu montagneux, Zaïre (French). Rev Epidemiol Sante Publique 1989;37:161–66.[ISI][Medline]

20 Delacollette C, Barutwanayo M. Mortalité et morbidité aux jeunes âges dans une région à paludisme hyperendémique stable, commune de Nyanza-Lac, Imbo Sud, Burundi (French). Bull Soc Pathol Exot 1993;86:373–79.[ISI][Medline]

21 Delaunay V, Etard JF, Preziosi MP et al. Decline of infant and child mortality rates in rural Senegal over a 37-year period (1963–1999). Int J Epidemiol 2001;30:1286–93.[Abstract/Free Full Text]

22 Ekanem EE, Asindi AA, Okoi OU. Community-based surveillance of paediatric deaths in Cross River State, Nigeria. Trop Geog Med 1994;46:305–08.[ISI][Medline]

23 Fauveau V, Koenig MA, Wojtyniak B. Excess female deaths among rural Bangladeshi children: an examination of cause-specific mortality and morbidity. Int J Epidemiol 1991;20:729–35.[Abstract]

24 Ghana VAST Study Team. Vitamin A supplementation in northern Ghana: effects on clinic attendances, hospital admissions, and child mortality. Lancet 1993;342:7–12.[CrossRef][ISI][Medline]

25 Greenwood BM, Greenwood AM, Bradley AK et al. Deaths in infancy and early childhood in a well-vaccinated, rural, West African population. Ann Trop Paediatr 1987;7:91–99.[ISI][Medline]

26 Hirve S, Ganatra B. Prospective cohort study on the survival experience of under five children in rural Western India. Indian Pediatr 1997;34:995–1001.[Medline]

27 Huang W, Yu H, Wang F et al. Infant mortality among various nationalities in the middle part of Guizhou, China. Soc Sci Med 1997;45:1031–40.[CrossRef][ISI][Medline]

28 Ibrahim MM, Omar HM, Persson, LÅ et al. Child mortality in a collapsing African society. Bull World Health Organ 1996;74: 547–52.[ISI][Medline]

29 Jaffar S, Leach A, Greenwood AM et al. Changes in the pattern of infant and childhood mortality in Upper River Division, The Gambia, from 1989 to 1993. Trop Med Int Health 1997;2:28–37.[CrossRef][ISI][Medline]

30 Kahn K, Tollman SM, Garenne M et al. Who dies from what? Determining cause of death in South Africa’s rural North-East. Trop Med Int Health 1999;4:433–41.[CrossRef][ISI][Medline]

31 Khalique N, Sinha SN, Yunus M et al. Early childhood mortality—a rural study. J Roy Soc Health 1993;113:247–49.[ISI]

32 Khan AJ, Khan JA, Akbar M et al. Acute respiratory infections in children: a case management intervention in Abbottabad District, Pakistan. Bull World Health Organ 1990;68:577–85.[ISI][Medline]

33 Khan SR, Jalil F, Zaman S et al. Early child health in Lahore, Pakistan: X. Mortality. Acta Paediatr 1993;82(Suppl.390):109–17.[ISI]

34 Menezes AMB, Victora CG, Barros FC et al. Mortalidade infantile em duas coortes de base populacional no Sul do Brasil: tendências e diferencias (Portuguese). Cad Saude Publica 1996;12(Suppl.1): 79–86.[Medline]

35 Mølbak K, Aaby P, Ingholt L et al. Persistent and acute diarrhoea as the leading causes of child mortality in urban Guinea Bissau. Trans Roy Soc Trop Med Hyg 1992;86:216–20.[ISI][Medline]

36 Mostafa G, Shaikh MAK, van Ginneken JK et al. Demographic Surveillance System—Matlab. Vol. 28. Registration of Demographic Events—1996. Dhaka, Bangladesh: International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), 1998. (Scientific Report No. 82.)

37 Mtango FDE, Neuvians D, Broome CV et al. Risk factors for deaths in children under 5 years old in Bagamoyo District, Tanzania. Trop Med Parasitol 1992;43:229–33.[ISI][Medline]

38 Nelson CM, Sutanto A, Gessner BD et al. Age- and cause-specific childhood mortality in Lombok, Indonesia, as a factor for determining the appropriateness of introducing Haemophilus influenziae Type b and pneumococcal vaccines. J Health Popul Nutr 2000;18:131–38.[ISI][Medline]

39 Rahmathullah L, Underwood BA, Thulasiraj RD et al. Reduced mortality among children in southern India receiving a small weekly dose of vitamin A. N Engl J Med 1990;323:929–35.[Abstract]

40 Scumacher R, Swedberg E, Diallo MO. Mortality Study in Guinea: Investigating the Causes of Death for Children Under 5. Arlington, VA: BASICS II, 2002.

41 Shamebo D, Muhe L, Sandström A et al. The Butajira Rural Health Project in Ethiopia; mortality pattern of the under fives. J Trop Pediatr 1991;37:254–61.[ISI][Medline]

42 West KP Jr, Pokhrel RP, Katz J et al. Efficacy of vitamin A in reducing preschool child mortality in Nepal. Lancet 1991;338:67–71.[CrossRef][ISI][Medline]

43 WHO/CHD Immunisation-Linked Vitamin A Supplementation Study Group. Randomised trial to assess benefits and safety of vitamin A supplementation linked to immunisation in early infancy. Lancet 1998;352:1257–63.[CrossRef][ISI][Medline]

44 Yassin KM. Indices and sociodemographic determinants of childhood mortality in rural Upper Egypt. Soc Sci Med 2000;51:185–97.[CrossRef][ISI][Medline]

45 Fishman S, Caulfield LE, de Onis M et al. Underweight status. In: Ezzati M, Lopez AD, Rodgers A, et al. (eds). Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Risk Factors. Geneva: World Health Organization (in press).

46 INDEPTH Network. Population and Health in Developing Countries. Vol. 1: Population, Health and Survival at INDEPTH Sites. Ottawa, Canada: International Development Research Centre, 2002.

47 Brass W, Blacker J. The Estimation of Infant Mortality from Proportions Dying Among Recent Births. Centre for Population Studies Research Paper 99–1. London: London School of Hygiene & Tropical Medicine, 1999.

48 David PH, Bisharat L, Hill AG. Measuring Childhood Mortality: A Guide for Simple Surveys. Amman, Jordan: UNICEF Regional Office for the Middle East and North Africa, 1990.

49 Salomon JA, Murray CJL. Compositional Models for Mortality by Age, Sex and Cause. Geneva: World Health Organization, 2001. (GPE Discussion Paper Series: No. 11.)

50 Katz J, King G. A statistical model for multiparty electoral data. Am Pol Sci Rev 1999;93:15–32.[ISI]

51 Aitcheson J. The Statistical Analysis of Compositional Data. New York: John Wiley & Sons, 1986.

52 Zellner A. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. JASA 1962;57:348–68.

53 Thompson SG, Sharp SJ. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med 1999;18:2693–708.[CrossRef][ISI][Medline]

54 Royston P, Altman DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling (with discussion). Appl Stats 1994;43:429–67.

55 StataCorp. Stata Reference Manual Release 7. Vol. 4, Su–Z. College Station, Texas: Stata Corporation, 2001, pp. 8–14.

56 WHO/UNICEF/The World Bank. State of the World’s Vaccines and Immunization. Geneva, World Health Organization, 2002.

57 Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Boca Raton: Chapman & Hall/CRC, 1993, pp. 141–51.

58 United Nations Population Division. World Population Prospects: The 2000 Revision. New York: United Nations Population Division, 2001.

59 UNICEF statistics, child mortality. Available at: http://www.childinfo.org/cmr/revis/db2.thm. Accessed 3 May 2003.

60 Kalter HD, Gray RH, Black RE et al. Validation of post-mortem interviews to ascertain selected causes of death in children. Int J Epidemiol 1990;19:380–86.[Abstract]

61 Snow RW, Armstrong JR, Forster D et al. Childhood deaths in Africa: uses and limitations of verbal autopsies. Lancet 1992;340:351–55.[CrossRef][ISI][Medline]

62 Mobley CC, Boerma JT, Titus S et al. Validation study of a verbal autopsy method for causes of childhood mortality in Namibia. J Trop Pediatr 1996;42:365–69.[Abstract]

63 Anker M. The effect of misclassification error on reported cause-specific mortality fractions from verbal autopsy. Int J Epidemiol 1997;26:1090–96.[Abstract]

64 Chandramohan D, Setel P, Quigley M. Effect of misclassification of causes of death in verbal autopsy: can it be adjusted? Int J Epidemiol 2001;30:509–14.[Abstract/Free Full Text]

65 Bern C, Martines J, de Zoysa I et al. The magnitude of the global problem of diarrhoeal disease: a ten-year update. Bull World Health Organ 1992;70:705–14.[ISI][Medline]

66 Williams BG, Gouws E, Boschi-Pinto C et al. Estimates of world-wide distribution of child deaths from acute respiratory infections. Lancet Inf Dis 2002;2:25–32.[CrossRef][ISI][Medline]

67 Levi M. Errors in the variables bias in the presence of correctly measured variables. Econometrica 1973;41:985–86.[ISI]

68 Anker M, Black RE, Coldham C et al. A Standard Verbal Autopsy Method for Investigating Causes of Death in Infants and Children. Geneva: World Health Organization, 1999. (WHO/CDS/CSR/ISR/99.4.)

69 World Health Organization. The Overlap in the Clinical Presentation and Treatment of Malaria and Pneumonia in Children: Report of a Meeting. Geneva: World Health Organization, 1992 (WHO/ARI/92.23 and WHO/MAL/92.1065).

70 Walker N, Schwartländer B, Bryce J. Meeting international goals in child survival and HIV/AIDS. Lancet 2002;360:284–89.[CrossRef][ISI][Medline]

71 Measure DHS+. Demographic and health surveys, publications & press. Available at: http://www.measuredhs.com/pubs/. Accessed 3 May 2003.

72 Stein CE, Birmingham M, Kurian M, Duclos P, Strebel P. The global burden of measles in the year 2000—a model that uses country-specific indicators. J Infect Dis 2003;187(Suppl.1):S8–S14.[CrossRef][ISI][Medline]

73 Miller MA. Introducing a novel model to estimate national and global measles disease burden. Int J Infect Dis 2000;4:14–20.[Medline]

74 Lopez AD. Causes of death in industrial and developing countries: estimates for 1985–1990. In: Jamison DT, Mosley WH, Measham AR, Bobadilla JL (eds). Disease Control Priorities in Developing Countries. New York: Oxford University Press, 1993, pp. 35–50.

75 Snow RW, Craig M, Deichmann U, March K. Estimating mortality, morbidity and disability due to malaria among Africa’s non-pregnant population. Bull World Health Organ 1999;77:624–40.[ISI][Medline]

76 Mathers CD, Stein C, Fat DM et al. Global burden of disease 2000. Version 2 methods and results. WHO GBD documentation (http:// www.who.int/evidence, accessed 14 March 2003).