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, 4951 Bedford Square, London WC1B 3DP, UK. E-mail: saul.morris{at}lshtm.ac.uk
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Abstract |
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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.
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 withmost likelydifferent 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.
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Materials and Methods |
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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 (444; 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 ormore commonlydisease 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 19981999 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 19981999 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).
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Results |
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Table 3 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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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 1. The four major causes of deathneonatal and other causes, pneumonia, malaria, and diarrhoeawere each found to be responsible for between 20% and 30% of all child deaths.
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Table 4 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 15.528.2%, pneumonia 16.124.0%, malaria 17.037.0%, measles 1.612.8%, neonatal and other causes 20.133.1%, and undetermined 0.46.0%; South Asia, diarrhoea 16.932.8%, pneumonia 17.826.4%, malaria 0.10.8%, measles 1.011.4%, neonatal and other causes 40.657.3%, and undetermined 1.04.7%.
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Discussion |
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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.6062 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 causesuch as the prevalence of undernutrition or maternal literacyturned 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 studies6,910,14,17,40,44 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 20002001, 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, 0.05.1%). 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.7576 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
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Acknowledgments |
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References |
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