a Alfred Weber-Institute, University of Heidelberg, Germany.
b Centre de Recherche en Santé de Nouna (CRSN), Burkina Faso.
c Department of Tropical Hygiene and Public Health, Heidelberg, Germany.
d Centre for Economic Policy Research (CEPR), London, UK.
Christoph M Schmidt, Alfred Weber-Institute, Grabengasse 14, 69117 Heidelberg, Germany. E-mail: Schmidt{at}Uni-Hd.De
Abstract
Background An effective health policy necessitates a reliable characterization of the burden of disease (BOD) by cause. The Global Burden of Disease Study (GBDS) aims to deliver this information. For sub-Saharan Africa (SSA) in particular, the GBDS relies on extrapolations and expert guesses. Its results lack validation by locally measured epidemiological data.
Methods This study presents locally measured BOD data for a health district in Burkina Faso and compares them to the results of the GBDS for SSA. As BOD indicator, standard years of life lost (age-weighted YLL, discounted with a discount rate of 3%) are used as proposed by the GBDS. To investigate the influence of different age and time preference weights on our results, the BOD pattern is again estimated using, first, YLL with no discounting and no age-weighting, and, second, mortality figures.
Results Our data exhibit the same qualitative BOD pattern as the GBDS results regarding age and gender. We estimated that 53.9% of the BOD is carried by men, whereas the GBDS reported this share to be 53.2%. The ranking of diseases by BOD share, though, differs substantially. Malaria, diarrhoeal diseases and lower respiratory infections occupy the first three ranks in our study and in the GBDS, only differing in their respective order. Protein-energy malnutrition, bacterial meningitis and intestinal nematode infections occupy ranks 5, 6 and 7 in Nouna but ranks 15, 27 and 38 in the GBDS. The results are not sensitive to the different age and time preference weights used. Specifically, the choice of parameters matters less than the choice of indicator.
Conclusions Local health policy should rather be based on local BOD measurement instead of relying on extrapolations that might not represent the true BOD structure by cause.
KEY MESSAGES
Keywords Burden of disease, years of life lost, verbal autopsy, sub-Saharan Africa
Accepted 12 October 2000
An effective health policy necessitates a reliable characterization of the burden of disease (BOD) and its distribution by cause. The Global Burden of Disease Study1 (GBDS) is a major step towards the development of such a rational information-based health policy. It provides a comprehensive assessment of epidemiological conditions and the disease burden for all regions of the world in an attempt at facilitating priority setting in health policy and research, and the development of cost-effective health interventions.
A fundamental problem for less developed regions, in particular those of sub-Saharan Africa (SSA), is the dearth of epidemiological and demographic data.2 In the absence of routine vital event registration in most of this region, the GBDS extrapolated the epidemiological data that was available (mainly from a vital registration system in South Africa), using cause-of-death models and expert judgements.3 These results await validation by thorough analyses of mortality and morbidity in SSA.
In this paper we present the results of a study measuring the BOD in a health district in rural Burkina Faso. In a study population of 31 000 people under demographic surveillance, deaths are recorded via a vital events registration system, and causes of death are assigned through verbal autopsy (VA). Our principal objective is the analysis of locally measured BOD by cause of death, age, and gender in direct comparison with the GBDS results. A special emphasis is laid on the ranking of diseases by disease burden. We ask whether local health policy needs to be based on local BOD measurement. Furthermore, providing estimates based on two different indicators (YLL and deaths) and on two alternative YLL specifications concerning age-weighting and discounting, we investigate the robustness of our conclusions.
Study Population and Methods
Burkina Faso had an estimated population of approximately 10.7 millions in 1998.4 This small West African state is divided into 11 administrative health regions, which comprise 53 health districts overall, each covering a population of 200 000300 000 individuals. At least one health care facility in each district is a hospital with surgery capacities.5 The districts themselves are again sub-divided in smaller areas of responsibility which are organized around either a hospital or a so-called CSPS (Centre de Santé et de Promotion Sociale), the basic health care facility in the Burkinian health system.
The Nouna health district, which is identical to the province of Kossi, covers 16 CSPS, one district hospital and a population of roughly 230 000 inhabitants.6 In this district a demographic surveillance system (DSS) has been implemented, surveying the population of four CSPS with a study population of 31 280 inhabitants (mid-year population 1998). Periodically updated censuses (the first census was performed in 1992, a first control census in 1994, and a second control census in 1998) are supplemented by a vital events registration system, recording approximately every 3 months births, deaths and migrations. For each recorded death, the cause of death is determined through VA.79 Age was assessed through identifying the date of birth. This was done either based on birth certificates (only in a relatively small number of cases), or using a local events calendar which incorporates seasonal landmarks, feasts, political events, and village events (e.g. initiation rites, death of a village headman, famines, etc.).10
Some 416 weeks after a death is recorded, a structured questionnaire is administered to the best informed relative(s) of the deceased by lay people having a minimum education of 10 years of schooling. The review of the questionnaires is performed independently by two physicians. Three causes of death can be assigned to each case. An underlying cause has to be assigned, which is used as the cause of death in our study, since our results are aimed at informing health policy. In the case of malnutrition, for example, a child might die from a supervening acute disease, but to be successful, health policy must aim to improve nutrition instead of promoting intervention against the supervening disease. Additionally, one associated cause of death and one immediate cause of death can be assigned. If the two physicians do not agree on the underlying cause of death, a third physician is consulted as a referee. If his determination agrees with one of the initial diagnoses, the case is coded accordingly. If not, the case is coded as undetermined.
Of the 464 deaths analysed, 10 deaths (2.2%) were not classified according to the International Classification of Diseases, 10th Revision (ICD-10), and for 76 deaths (16.4%) a cause of death could not be ascertained. Instead of distributing the undetermined cases proportionately across the disease categories as was done in the GBDS, we left them in a separate residual category. A proportional redistribution of these cases would only overstate the precision of our estimates.
Numerous conceptually different measures have been proposed for measuring the BOD, for instance mortality rates, different forms of years of life lost11,12 (YLL), and disability-adjusted life years (DALY),13 with the latter comprising YLL as one of their major elements. We concentrate on standard YLL according to the methodology proposed in the GBDS. On the one hand, we want to ensure comparability, and on the other hand, deaths and remaining life expectancy can be measured relatively reliably by cause and typically contribute most to the overall BOD in SSA. The GBDS, for example, attributes 77% of overall BOD in SSA to YLL, whereas only 23% are attributed to years lived with disability, the morbidity measure of the GBDS.
For our analysis individual YLL were calculated for all recorded 464 deaths in the Nouna health district over a period of 17 months (November 1997 to March 1999), and cross-classified by age, gender, and cause of death. The measure is not standardized according to population size or age,14,15 since our major objective is the characterization of the local burden of disease in Burkina Faso, not a cross-country comparison of standardized figures.
For each individual i, years of life lost due to premature death are calculated as
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where a is the age of the individual, L(a) is the remaining life expectancy at age a, r is the discount rate, and k and ß are the parameters of the age-weighting function. In particular, k = 1 implies full age-weighting, and k = 0 no age-weighting. The YLL(0.03,1) are the benchmark mortality measure in the GBDS.
To test the possibility that the deviations of our results from the GBDS can be explained through mere sample variation, we applied a 2 goodness-of-fit test. The distribution of the null hypothesis is the multinomial distribution of deaths over distinct disease categories of the GBDS. The test statistic is
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with Ej = n*pj, where pj is the expected probability of disease category j as published by the GBDS and n is the sample size. Oj is the number of deaths of category j that occur in the sample. The test is based on the distribution of deaths over distinct cause-of-death categories instead of YLL, since a test statistic computed with YLL would not be 2-distributed. The test is asymptotically valid if Ei
5
i. Categories with an expected occurrence of deaths of less than 5 were grouped together to fulfil this prerequisite.
Results
Appendix Table 1 documents YLL by cause of death, sex, and age group, using a format identical to the tables presented in the GBDS. The classification system is the one used in the GBDS, which can basically be translated into the ICD-10 system. Overall mortality is divided into three broad disease categories I (communicable, maternal, perinatal, and nutritional conditions), II (non-communicable) and III (injuries). Their respective BOD shares are 90.0% for Nouna as compared to 76.7% for the GBDS for group I, 4.2% for group II compared to 12.9% and finally 5.8% and 10.5% for group III, respectively. (In this calculation we excluded YLL caused by war from the GBDS figures to ensure comparability in a case where the deviation can obviously and easily be reduced.)
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Figure 1 displays the distribution of the results across age groups. The two bottom bars contrast the standard YLL(0.03,1) for Nouna with the GBDS results. Young age groups (up to 14 years of age) account for the overwhelming majority of estimated YLL both for Nouna and in the GDBS, however, their relative fraction is somewhat smaller in our Nouna study. The most striking difference occurs for small children (04 years), with the GBDS attributing almost 7 percentage points more to this age group (60.6% as compared to 53.7%). The two upper bars of Figure 1
are discussed below.
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An apparent feature of the data is that for the GBDS the BOD is much more evenly distributed across the diseases, whereas for Nouna more than half of the BOD measured in YLL was caused by the three major causes of death. Beyond rank three, substantial differences emerge. Three of the ten leading causes of YLL in the GBDS are not among the major ten causes of YLL in Nouna (intentional injuries, tuberculosis and malignant neoplasms), and the same holds for three of the ten leading causes of DALY (tuberculosis, neuro-psychiatric conditions, and maternal conditions). Protein-energy malnutrition occupies rank 5 in Nouna but only rank 15 in the GBDS, intestinal nematode infections are at rank 7 in Nouna but only at rank 38 in the GBDS. Meningitis is at rank 6 in Nouna in contrast to rank 27 for the GBDS.
A 2 goodness-of-fit test explores whether the differences in ranking between Nouna and the GBDS can be explained through mere sample variation, thus indicating whether, if we observed another sample, we would probably get the same ranking. The computed test statistic is
2 = 391, while the critical value is
2 (k=18, 0.995) = 37.2. The null hypothesis is rejected. There clearly seems to be a statistically significant different cause-of-death pattern in the health district of Nouna. To generate a hypothetical situation most favourable for the GBDS, in a sensitivity analysis we redistributed the residual cases according to the GBDS results and performed the appropriate
2 test once more. Our results are retained.
To analyse the sensitivity of our results to the particular choice of parameters and health-status indicators, we also calculated two alternative measures of the BOD: YLL(0,0) and the number of deaths. (Additional tables presenting the BOD in the same format as Appendix Table 1 but for deaths and YLL(0,0) can be downloaded at www.hyg.uni-heidelberg.de/ sfb544.) In principle, one could imagine that the parameter and indicator choice matters a lot, especially with respect to age, since the difference between YLL(0.03,1), YLL(0,0) and deaths is substantial across most of the age range (Figure 2
).
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With respect to gender, no substantial differences can be observed. While YLL(0.03,1) attribute 53.9% of the BOD to men, the corresponding figures are 53.5% for YLL(0,0) and 54.7% for deaths, respectively. This result confirms intuition, since it would be surprising if the particular choice of gender-insensitive indicators made a large difference.
Similar observations hold for the distribution across the three ICD-10 disease categories. Group I comprises 90.0% of the BOD measured using YLL(0.03,1), 90.8% using YLL(0,0), and 87.8% using deaths. For group II the shares are 4.2%, 3.9% and 6.6%, respectively, and for group III 5.8%, 5.3% and 5.6%. While YLL(0.03,1) and YLL(0,0) do not display perceptible differences, when using deaths as the indicator a relatively larger share of the BOD is attributed to non-communicable diseases. This is a reasonable result, since these diseases tend to be an important cause of death for older people.
Table 2 reports how the ranking of the leading causes of the BOD varies with the chosen health-status measure. There is almost no difference in ranking by cause of death whether or not age and time weighting is implemented. Only rank 7 and 8 change places, the rest retain their positions. Not only does the ranking stay almost the same, but also the shares of the total BOD differ only slightly. For rank 1, there is a difference of 1.1%, for rank 2 the difference is 3.0% and for rank 3 it is 2.3%. The highest proportional difference is for HIV (17.5%), but given its small share in total BOD (1.5% and 1.3%, respectively) even the largest relative difference among the ten major causes of death does not seem to be remarkable.
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Discussion
We have to acknowledge that with our approach we cannot really validate the GBDS study, since so far we cannot give explicit figures on the validity of our implemented VA system itself. However, the results we obtain raise serious doubt that for the health district we are looking at, the GBDS would be the right information base for a local health policy. Moreover, it is plausible to argue that this could very likely be true also for other regions of SSA.
In our analysis we operate with a limited sample sizewe are distributing 464 deaths over more than 40 disease categories. This necessarily restricts our ability to accurately estimate the prevalence or incidence of single diseases, even though our number of reported cases is relatively high compared to other mortality studies in SSA.8 For example, there are no reported deaths from tuberculosis in women below age 70. Findings like this are most probably merely a result of chance. For this reason, we will not discuss the detailed results (Appendix Table 1). Yet our data enable us to discuss the aggregate findings on the distribution of BOD by age and sex and to present a ranking of the most prevalent causes of death.
With respect to gender and age the Nouna results confirm more or less the general BOD structure reported by the GBDS for SSA as a whole, even if the GBDS seems to overstate the fraction of infant and child mortality compared to the Nouna results. The ranking of diseases by the share of disease burden, however, displays considerable differences. Basically, ranking, and thereby priority setting in health policy, depends on three things: the choice of indicator, the values incorporated in the indicator (age and time preferences) and the epidemiological data base that is used. Our results demonstrate a significant difference in ranking between the GBDS and Nouna, whereas the ranking by cause for Nouna shows little variation if a different indicator or different age and time weights are used.
Three competing explanations might be offered for the divergence between the Nouna and the GBDS data. First, instead of being an ideal weighted average of local BOD estimates over all regions of SSA, the GBDS results are an extrapolation of mortality data from a few parts of Africa, using cause-of-death models and a variety of expert judgements. Thus, while being a convincing pragmatic approach in the absence of local data, the GBDS might misrepresent the BOD structure of SSA as a whole. Only further local BOD analysis for other SSA regions would be able to validate the GBDS results as reliable mean estimations for SSA.
Second, measurement errors might have biased our estimates. For example, the large BOD shares attributed to the major diseases suggest that the medical doctors who reviewed the VA questionnaires might have tended to cluster deaths in the major categories they experienced in their daily work. While it seems unlikely that this problem could fully account for the observed differences to the GBDS, further validation studies of the VA method are warranted. In the literature the potential and limitations of VA methods are examined critically.1619 On balance, it is argued that the VA method is the best option in a situation where the majority of deaths occur without recourse to modern health care facilities.
Another measurement error problem could be the overrepresentation of the months November through March, since we used the whole available sample size of 17 months. To check whether this would affect our conclusions, we recalculated our results on a 12-month-basis (January to December 1998). The conclusions remain unchanged.
The third explanation for the divergence between the GBDS and our results could be that rural Burkina Faso might be very different from other parts of SSA. There is evidence that it is poorer and less developed than the average SSA country,4 implying a relatively young population, fewer medical facilities, low vaccination coverage, a lower quality of housing, water supply and storage facilities, and generally a low level of hygiene. Not only will this have consequences for the high BOD share of disease category I (Nouna seems to lag behind in the epidemiological transition), but also for the high occurrence of protein-energy malnutrition and intestinal nematode infections.
Furthermore, Burkina Faso lies inside the meningitis belt, which is of course not the case for SSA as a whole, and it is a region of high malaria transmission. Malaria is probably endemic in most regions of SSA, but its endemicity varies widely. Chandramohan,16 for example, reports the BOD shares of meningitis and malaria to be 11.4% and 8.9% in a region in Tanzania, 15.7% and 2.0% in Ethiopia, and 4.3% and 14.2% in Ghana, respectively.
Thus, even if the GBDS provided an accurate portrait of SSA as a whole, SSA would be quite heterogeneous in terms of BOD. Burkinian deviations from this typical BOD structure could not be an isolated phenomenon, but would have to be outweighed by countervailing deviations in other SSA regions. Overall, these arguments clearly underscore the need for local BOD measurement and priority setting. The available expert estimates from the GBDS are apparently not sufficient to provide a characterization of the BOD in SSA which is detailed and accurate enough to provide a basis for local health policy.
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This research was supported by the Deutsche Forschungsgemeinschaft under the research grant Sonderforschungsbereich 544, Control of Tropical Infectious Diseases. We thank all the staff of the CRSN, the Ministry of Health of Burkina Faso, and the households surveyed for their valuable help and co-operation. We are grateful to Sarosh Kuruvilla, Frederick Mugisha and two anonymous referees for valuable comments and suggestions.
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