Estimating cause-specific mortality from community- and facility-based data sources in the United Republic of Tanzania: options and implications for mortality burden estimates.

940 Bulletin of the World Health Organization | December 2006, 84 (12) Objective To compare mortality burden estimates based on direct measurement of levels and causes in communities with indirect estimates based on combining health facility cause-specific mortality structures with community measurement of mortality levels. Methods Data from sentinel vital registration (SVR) with verbal autopsy (VA) were used to determine the cause-specific mortality burden at the community level in two areas of the United Republic of Tanzania. Proportional cause-specific mortality structures from health facilities were applied to counts of deaths obtained by SVR to produce modelled estimates. The burden was expressed in years of life lost. Findings A total of 2884 deaths were recorded from health facilities and 2167 recorded from SVR/VAs. In the perinatal and neonatal age group cause-specific mortality rates were dominated by perinatal conditions and stillbirths in both the community and the facility data. The modelled estimates for chronic causes were very similar to those from SVR/VA. Acute febrile illnesses were coded more specifically in the facility data than in the VA. Injuries were more prevalent in the SVR/VA data than in that from the facilities. Conclusion In this setting, improved International classification of diseases and health related problems, tenth revision (ICD-10) coding practices and applying facility-based cause structures to counts of deaths from communities, derived from SVR, appears to produce reasonable estimates of the cause-specific mortality burden in those aged 5 years and older determined directly from VA. For the perinatal and neonatal age group, VA appears to be required. Use of this approach in a nationally representative sample of facilities may produce reliable national estimates of the cause-specific mortality burden for leading causes of death in adults. Bulletin of the World Health Organization 2006;84:940-948. Voir page 946 le résumé en français. En la página 947 figura un resumen en español. Introduction Accurate information on the levels and causes of death should be central to sett ting policy priorities in the health sector. However, few countries in the developt ing world have such basic demographic knowledge about their own populations. Growing interest has recently been shown in ways to assess and improve the quality of sources of routine mortality data,1–3 and to make maximum use of existing nont routine sources for estimating mortality burdens, particularly among children.4t8 Given the lack of reliable vital statist tics, the options for estimating mortality burden are extremely limited in resourcet constrained settings. They include: household surveys for estimates of tot tal child and, occasionally, maternal mortality; • Estimating cause-specific mortality from community- and facility-based data sources in the United Republic of Tanzania: options and implications for mortality burden estimates David R Whiting,a Philip W Setel,b Daniel Chandramohan,c Lara J Wolfson,d Yusuf Hemed,b & Alan D Lopez e .947 ةحفص في ةيبرعلاب صخللما لىع علاطلاا نكيم a MEASURE Evaluation, Carolina Population Center, University of North Carolina at Chapel Hill, United States of America; and Department of Medicine, School of Population and Health Sciences, Newcastle University, England. Correspondence to this author at the latter address (email: david.whiting@ncl.ac.uk). b MEASURE Evaluation, Carolina Population Center, and Department of Epidemiology School of Public Health, University of North Carolina at Chapel Hill, NC, United States of America. c Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, England. d Initiative for Vaccine Research, Vaccines, and Biologicals, World Health Organization, Geneva, Switzerland. e School of Population Health, University of Queensland, Queensland, Australia. Ref No. 05-028910 (Submitted: 9 December 2005 – Final revised version received: 9 May 2006 – Accepted: 11 May 2006) facilitytbased death statistics from routine health management informat tion systems; communitytbased mortality statistics derived from the application of “vert bal autopsy” (VA) in demographic surveillance systems (DSS) and sent tinel/sample vital registration (SVR) systems. Although each of these options has its limitations,3 two of them, routine health service statistics and VAtderived mort tality statistics, allow the attribution of causes of death at all ages; to date, household surveys have not provided this information.6,9 Within this context, we examine two ways of deriving the causetspecific mortality structure of populations in resourcetconstrained settings. The first • • is through the application of “indirect” measures of causetspecific mortality fractions from health facility statistics, to directly measured levels of mortalt ity derived from communitytbased surveillance and reporting. The second is through the direct measurement of both the causes and levels of mortalt ity, with causes of death derived from the application of a welltvalidated VA instrument.10,11 This paper draws upon SVR data from the Tanzanian Ministry of Health,12 and healthtfacilitytbased data collected during a study to validate the VA procedures used in the Tanzanian system.11 We report the findings for three age groups in one urban and one rural setting. Subsequently the term “communityt based” will be used interchangeably with “SVR”. 941Bulletin of the World Health Organization | December 2006, 84 (12) Research Cause-specific mortality in the United Republic of Tanzania David R Whiting et al. The modelled structures were calculated by applying the proportion of deaths due to each cause in each age group from the facilitytbased data to the number of deaths in the same age groups in the communitytbased data and then summing the YLLs by cause and age group: Where: r = region; a = age group; x = cause; n = deaths counted by SVR; prax = proportion of deaths due to cause “x” derived from medical record ret view in region r, age group a; YLL = years of life lost; PYLLsP = potential years of life lost of resident population derived from SVR (life expectancy values were those used by Murray & Lopez 23 — 80.0 years for males and 82.5 years for females). Nontparametric bootstrap 95% confit dence intervals were calculated.24 Three criteria for “good perfort mance” of VA were established:11 SensitivityVA >0.50; SpecificityVA >(1–CSMF) (causetspet cific mortality fractions); Relative difference between CSMFVA and CSMFMR <0.20 (where “MR” = cause according to reference medical recordtbased death certificate). Causes meeting these criteria are indit cated in the analysis. Ethical approval Ethical approval for this secondary analysis study was obtained from the University of North Carolina at Chapel Hill Institutional Review Board. Cleart ance for the verbal autopsy validation study was obtained from the Harvard University Human Subjects Protection Committee, the University of Newcastle upon Tyne Ethics Committee and the Tanzanian National Institute for Medical Research. The study was also implementt ed as part of the aims and objectives of the Tanzanian Ministry of Health under its Adult Morbidity and Mortality Project (AMMP). • • • The purpose of this paper is to consider the options for providing act curate and readily obtainable estimates of mortality burden for use in policy planning, monitoring and evaluation. As such, we do not claim to validate either method, but to look at whether and how a retanalysis of facilitytbased records might produce estimates of mortality burden that differ substantially from those derived from direct community measurement through SVR, a technique which preliminary investigations have established as having a reasonable level of costteffectiveness.13 Data and methods The communitytbased mortality data were obtained from one urban (Dar es Salaam) and one rural (Hai, Kilimanjaro region) site during 2002. This entailed the regular enumeration of resident populations, vital events and migrations in defined geographical locations, as well as a continuous, active mortality surveilt lance system in which VA procedures were applied to all incident deaths.14t16 For VAs threetline death certificates were completed, and cause of death was coded as a single underlying cause using a 39titem tabulation list (available on request) based on the International classification of diseases and health rel lated problems, tenth revision (ICDt10).17 Each VA was coded independently by two physicians and discrepancies were resolved by taking the majority verdict after independent coding by a third physician; if all three disagreed the cause was recorded as “undetermined”. First and second coders agreed on all three lines of the VA death certificate 42.5% of the time for communicable, maternal, perinatal and nutritional conditions and for noncommunicable conditions. For injuries, agreement was 60%. Details of the field methods have been previously documented.14,16,18 Community surveilt lance included all deaths that occurred in residents of households in the study areas regardless of place of death — home, health facility or elsewhere (e.g. on roads as a result of traffic fatalities) — VA was the source of the cause of death detert mination in all cases. The cause structure of mortality in health facilities in Dar es Salaam was obt tained from seven health facilities (three facilities adjacent to the SVR area) and for Kilimanjaro from six facilities (four situated within the SVR area).18 All deaths from 2001 to 2003 were included, except for those among individuals resident outside a specified geographical radius around the facility, and cases in which surviving family members det clined to participate in the study. Physicians trained in ICDt10 coding rules reviewed all medical records.17,19,20 Two physicians prepared independent death certificates that included undert lying, immediate and associated causes of death; any disagreements were then resolved by consensus. The death certifit cates were coded according to ICDt10, and underlying causes were validated using TRANSAX software.21 Each death was assigned to one of the groups in the mortality tabulation list. It should be noted that the causes of death for the facilitytbased data used in this paper were not taken directly from hospital records or routine facility statistics, but were derived from a rigort ous independent review of the records. Routine health service statistics in the United Republic of Tanzania only dist aggregate age according to two groups (younger than five years and older than five years), are not coded according to ICDt10, and are not disaggregated by sex. Furthermore, previous assessment has shown that important causes of death including human immunodeficiency vit rus/acquired human immunodeficiency syndrome (HIV/AIDS) and tuberculot sis are either grossly undercounted or absent.22 Years of life lost (YLLs) due to premature mortality were calculated in 5tyear intervals using the Coale and Demeny West Model Level 26 life table23 and summed (for ages 5 years and above). Denominators were generated from the SVR data as follows: perinatal and neonatal period: total number of live births recorded by the sentinel system for each area; age 29 days to 5 years: potential YLLs of children younger than 5 years mit nus onettwelfth of those aged 1 year; age 5 years and above: potential YLLs of resident population (aged 5 years and older calculated on current age in 5tyear intervals) in the surveillance areas. Mortality structures and rates derived from the communitytbased data were calculated as follows: • • • 942 Bulletin of the World Health Organization | December 2006, 84 (12) Research Cause-specific mortality in the United Republic of Tanzania David R Whiting et al. Results Samples Two thousand eight hundred and eightyt four deaths were recorded from health facilities in Kilimanjaro and Dar es Salaam combined, from 2001 to 2003. During 2002, the SVR system recorded 2167 deaths in the two areas. In Dar es Salaam, data on almost three times more facility deaths than SVR deaths were available for the study, whereas in Kilimanjaro records of 1.7 times more deaths were available from SVR than from the facilities. The age distributions of deaths in the community and facilit ties were significantly different in Dar es Salaam, where the community had a far greater proportion of deaths occurring at age 5 years and older. Many more of the facility deaths were among children, especially in the perinatal and neonatal age group. The age patterns of mortality in Kilimanjaro were similar in the facility and the community. In Dar es Salaam, 98.5% of facility deaths were reported from three large hospitals (n = 2004). Six per cent of these deaths were of residents of the surveillance area. In Kilimanjaro there were six participating facilities, of which four accounted for 93% (n = 791) of deaths; 59.5% of these deaths were of residents of the surveillance area. Perinatal and neonatal mortality The comparison between causetspecific mortality fractions (CSMFs) derived from the community and modelled from the facilites for all perinatal and neonatal deaths and causes is shown in Table 1, and the comparisons of mortality rates for the leading causes of perinatal and neonatal death are shown in Fig. 1. In Dar es Salaam, the communitytbased cause structure comprised 11 causes. The healthtfacility cause structure comt prised four causes. No deaths due to malaria in this age group appear to have been recorded in the facility data from the urban setting. Causetspecific mortalt ity rates based on direct measurement in the community in Dar es Salaam were dominated by perinatal conditions and stillbirth followed by: prematurity and low birth weight; malaria; and pneut monia. The facilitytbased mortality rates were also dominated by perinatal causes and stillbirth, followed by prematurity and low birth weight. There were no sigt nificant differences between the facilityt derived and the communitytbased rates for the four causes occurring in the facility data. In Kilimanjaro, 16 cause groups were assigned in the communitytbased mortality cause structure (n = 192), comt pared to five causes for the facilitytbased Table 1. Number and proportion of deaths, by cause, in children younger than 1 month Dar es Salaam Kilimanjaro Community Modelled Community Modelled Cause n % n % n % n % Abdominal pain 1 0.5 All other external causes; falls; accidental drowning and submersion; exposure to smoke, fire and flames 2 2.9 Anaemias 1 1.4 1 0.5 Congenital malformations of the central nervous system 1 1.5 2 1.1 3 2.5 Human immunodeficiency virus 1 1.4 5 2.7 Hypertensive diseases 1 1.4 Intestinal infectious diseases 6 3.2 Malaria 5 7.1 6 3.2 Malnutrition 2 1.1 Other causes 2 2.9 3 1.6 Perinatal conditionsa 25 35.7 26 37.7 48 25.4 75 63.6 Pneumoniaa 2 2.9 18 9.5 5 4.2 Prematurity and low birth weight 9 12.9 2 2.9 25 13.2 9 7.6 Remaining noncommunicable diseases 1 0.5 Remaining respiratory diseases (infections) 1 0.5 Stillbirthsa 21 30.0 40 58.0 55 29.1 26 22.0 Transport accidents 1 0.5 Unspecified acute febrile illness and other infectious causes 1 1.4 14 7.4 a Sensitivity >0.50, and specificity >(1–CSMFVA). mortality structure (n = 91). Significant causes in the communitytbased mortalt ity structure that were absent from the facilitytbased data included: unspecified acute febrile illness and other infectious causes; HIV; intestinal infectious dist eases; and malaria. Significantly higher rates of stillbirth were noted in the Kilimanjaro communitytbased data than in the data derived from health facilities. This was the only cause for which statistit cally significant differences in mortality rates were found. Age 29 days to 5 years CSMFs for children aged 29 days to 5 years are shown in Table 2 and mortalt ity rates are shown in Fig. 2. For Dar es Salaam, community surveillance prot duced a cause structure based on fewer than 100 deaths attributed to 10 causes, led by “unspecified acute febrile illness” and malaria. These causes accounted for 25% and 37% of deaths, respectively. The facilitytderived cause structure (n = 449) identified nine causes of death, with malaria, pneumonia, intestinal infectious diseases and HIV predominating. There were significant differences between communitytbased and facilitytbased mortality rates for unspecified acute febrile illness and pneumonia. 943Bulletin of the World Health Organization | December 2006, 84 (12) Research Cause-specific mortality in the United Republic of Tanzania David R Whiting et al. In Kilimanjaro, 15 causes comt prised the communitytderived mortality structure (n = 88) whereas there were 14 different causes in the facilitytbased cause structure (n = 91). Deaths due to conditions including malnutrition and injuries were considerably more comt mon in the community data, whereas more mortality from malaria and pneut monia was recorded in the facilityt derived data. Nevertheless, with the exception of malnutrition, mortality rate estimates from the two sources did not differ significantly for any of the leadt ing causes. Age 5 years and above Table 3 and Fig. 3 show findings for the population aged 5 years and older. The communitytlevel mortality structure in Dar es Salaam included 24 different causes (n = 554); the facility data (n = 1090) included 20 causes. HIV/AIDS was by far the most prominent cause of death in both the community and facilitytbased data. In the community data, HIV/AIDS was followed by “unt specified acute febrile illness and other infectious causes”, noncommunicable diseases (NCDs), malaria and tubercut losis. In the health facilities, the leading causes after HIV/AIDS were malaria, tuberculosis, NCDs and pneumonia. Unspecified acute febrile illness and malaria were the most discrepant bet tween the community and facilitytbased datasets. In Dar es Salaam, this discrept ancy was also seen in the mortality rate estimates. Facility and community data estimated virtually identical proportions and rates of mortality due to HIV/AIDS, NCDs and neoplasms. Death rates from malaria, tuberculosis and pneumonia were higher in the facilitytbased data than in the communitytbased data. In Kilimanjaro, the community mortality structure (n = 1169) consisted of 33 causes, compared to 25 causes in the facilitytbased data (n = 668). In the communitytbased VA mortality structure, HIV/AIDS was most prevat lent, followed by NCDs; unspecified acute febrile illness and other infectious Table 2. Number and proportion of deaths, by cause, in children aged from 29 days to 5 years Dar es Salaam Kilimanjaro Community Modelled Community Modelled Cause n % n % n % n % All other external causesa 4 4.3 5 5.7 1 0.9 Anaemias 7 7.5 1 1.1 Congenital malformations of the central nervous system 1 1.1 5 5.7 4 3.5 Disorders of the kidney 1 1.1 Human immunodeficiency virus 8 8.5 10 10.6 16 18.2 21 18.1 Hypertensive diseases 1 0.9 Intestinal infectious diseases 4 4.3 11 11.7 9 10.2 13 11.2 Malariab 27 28.7 37 39.4 8 9.1 19 16.4 Malnutrition 2 2.1 6 6.8 1 0.9 Meningitis 1 1.1 1 1.1 6 5.2 Other causes 1 1.1 1 1.1 2 2.3 4 3.5 Perinatal conditions 2 2.3 1 0.9 Pneumoniaa 8 8.5 28 29.8 14 15.9 30 25.9 Prematurity and low birth weight 1 1.1 Remaining noncommunicable diseases 2 2.3 8 6.9 Remaining respiratory diseases (infections) 1 1.1 Tuberculosis 2 2.1 1 0.9 Unspecified acute febrile illness and other infectious causes 33 35.1 2 2.1 15 17.1 6 5.2 a Sensitivity >0.50, and specificity >(1–CSMFVA). b Sensitivity >0.50, specificity >(1–CSMFVA), and (CSMFVA – CSMFMR) <0.20. Fig. 1. Comparison of reference and modelled mortality rates in a perinatal and neonatal group 05-028910 - Fig.1 Dar es Salaam community Years of Life Lost (1000s per 1000 live births) 0.0 Perinatal conditions Dar es Salaam modelled Kilimanjaro community Kilimanjaro modelled 0.2 0.4 0.6 0.8 1.0 Stillbirths Prematurity and low birth weight Pneumonia Other causes Unspecified acute febrile illness and other infectious conditions Malaria Congenital malformations of the central nervous system 944 Bulletin of the World Health Organization | December 2006, 84 (12) Research Cause-specific mortality in the United Republic of Tanzania David R Whiting et al. causes; cancers; and pneumonia. In the facilitytbased data HIV/AIDS was most prevalent followed by NCDs, malaria, tuberculosis, neoplasms, hypertensive diseases, all other external causes, pneut monia, other causes, and diabetes melt litus. There was no significant difference in the rates for chronic diseases, whereas the rates for infectious diseases such as malaria and tuberculosis were higher in the facilitytbased estimates and the rate of unspecified acute febrile illness and other infectious causes was higher in the communitytbased estimates. Discussion Limitations and implications for mortality burden measurement We have presented mortality burdens calculated by: direct estimation from an SVR system with VA, and indirect estimates obtained by applying the cause structure in adjacent or nearby health facilities to sentinel counts of deaths. It is notable that, in general, many more causes were coded in the comt munity than in the facilitytbased data. There are several possible explanations for this. For example, for causes occurt ring infrequently in the facility data, the modelling process may have rounded them to zero. Identifying the precise reasons for this finding, however, would require further investigation. Some implications from this comt parison of data sources for calculation of mortality burden should be noted. For newborns and children, stillbirths are often excluded from demographic statistics, yet they account for a signifit cant proportion of the mortality burden in the perinatal and neonatal age group. In Dar es Salaam the stillbirth rate was substantially higher in the modelled results than in the community, whereas in the rural area the opposite was true. This could be due to the not uncomt mon practice of recording early neonatal deaths as stillbirths in health facilities.25 For example, a review of the VA data showed that according to parents and caregivers, 10% of “stillborn” children (as determined from the medical records) breathed after birth, and half of these cried before dying. The community estimates of the burden due to prematurity and low birth weight are significantly higher than the facilitytbased estimates. There are various possible reasons for this. For instance, premature birth may occur when there is insufficient time to access health services and low birth weight might be more common in the poor who are known to access health services less often.12 Alternatively, those who do reach health services may be less likely to die. Among deaths of those aged 5 years and older, community and facilitytderived estimates were remarkably similar for most chronic conditions including HIV/AIDS, NCDs and neoplasms. These conditions accounted for a substantial proportion of the total mortality burden and are causes for which the VA tool performed well.11 The facilitytbased model estimates for most chronic conditions therefore appear to accurately represent the community burden in these two areas. Estimates for specific acute febrile illnesses, such as malaria and pneumonia obtained from the facilitytbased model were higher than the community estit mates. However, the mortality rates from unspecified acute febrile illness and other infectious conditions were higher in the community estimates. In these situat tions, medical records are more likely to contain information such as blood slide results, whereas VA data are likely to have far less specific information. This could well have inclined coders to select “unspecified acute febrile illness” over “malaria” or “pneumonia” when coding VA. For acute infectious conditions, therefore, the modelled estimates appear to be more specific than the community estimates. Although onetthird of “mat laria” deaths among children occurred in those aged less than 6 months (who might have been protected by maternal immunity), the proportion of deaths due to malaria did not change when these children were excluded. Finally, there were few injuries in children younger than 5 years overall, and only one from the health facility data. Combining all injuries and act cidents in those aged 5 years and above, the proportion of deaths is higher (significantly in Dar es Salaam) in the communitytbased figures than in the modelled estimates. This may be due to injuries that prove fatal before a health facility can be reached; alternatively if health care is effective, those who do obt tain treatment may be less likely to die. Conclusions Study limitations There are several factors that may affect the conclusions drawn from the data. First, the distribution of deaths was skewed towards a few leading causes in each age group; sample sizes for less comt mon causes were small and confidence intervals for estimates of these causes were consequently large. Second, it is possible that the geographical criterion used to select deaths for inclusion in the validation was not sufficient to ensure that those included from outside the SVR areas had similar characteristics to those that were included. Analysis of the facility cause structure excluding those who were resident in the surveilt lance area, however, did not result in a substantially different cause structure in the health facility. We believe that those Fig. 2. Comparison of reference and modelled mortality rates in children aged 29 days to 5 years 05-028910 - Fig.2 Dar es Salaam community Years of Life Lost (YLLs) (per 1000 potential YLLs) 0 Malaria Dar es Salaam modelled Kilimanjaro community Kilimanjaro modelled 1 2 3 4 6 Pneumonia Unspecified acute febrile illness and other infectious conditions HIV/AIDS Intestinal infectious diseases Remainder of noncommunicable diseases Malnutrition Anaemias 5 945Bulletin of the World Health Organization | December 2006, 84 (12) Research Cause-specific mortality in the United Republic of Tanzania David R Whiting et al. included in the validation study were similar to those from the surveillance area and were unlikely to have experit enced significantly different mortality patterns or risks. Considerations for application to other countries “Routine” sources of data, such as vital registration and facilitytbased statistics are unreliable for measuring mortality levels and rates in most lowertincome countries, and in some situations (such as areas experiencing protracted conflict) even facilitytbased data are absent or extremely patchy. Even in areas where coverage is thought to be fairly good, such as Kenya, the usefulness of routine administrative sources for estimation of demographic characteristics may be very limited.2 In a setting where no facilitytbased statistics exist, or where facilitytbased monitoring has collapsed, it is unlikely that sufficient data sources would exist to permit any reliable estimates of mortalt ity levels or cause structures for national populations. In such circumstances it is plausible that social and political inset curity, or severely constrained resources, would make a sustained investment in any longtterm mortality monitoring syst tem a dubious undertaking. However, most countries do have some form of routine health informat tion system. With improved ICD coding practices, the comparison undertaken here shows that applying facilitytbased cause structures to counts of deaths from communities derived from sentinel or SVR in the United Republic of Tanzania may produce reasonable estimates of the directly measured causetspecific mortalt ity (as determined by VA) burden in those aged 5 years and older for leading Table 3. Number and proportion of deaths, by cause, in subjects aged 5 years and older Dar es Salaam Kilimanjaro Community Modelled Community Modelled Cause n % n % n % n % Abdominal pain 7 1.3 2 0.4 19 1.6 Accidental poisoning by and exposure to noxious substances 11 1.0 All other external causesa 11 2.0 4 0.7 24 2.1 30 2.9 Anaemias 2 0.4 3 0.3 1 0.1 Assault b 4 0.7 10 0.9 1 0.1 Chronic obstructive pulmonary diseases 1 0.2 3 0.6 18 1.6 16 1.6 Cirrhosis of the liver 8 0.7 7 0.7 Congenital malformations of the central nervous system 2 0.2 5 0.5 Diabetes mellitus 9 1.6 7 1.3 23 2.0 37 3.6 Disorders of the kidney 8 1.5 5 0.9 23 2.0 17 1.7 Human immunodeficiency virusa 204 37.0 200 36.8 275 23.8 214 20.9 Hypertensive diseases 13 2.4 12 2.2 29 2.5 78 7.6 Intentional self-harmb 17 1.5 4 0.4 Intestinal infectious diseasesa 13 2.4 10 1.8 20 1.7 15 1.5 Malariaa 40 7.3 124 22.8 21 1.8 84 8.2 Malignant neoplasm of breast b 3 0.5 1 0.2 6 0.5 Malignant neoplasm of liver and intrahepatic bile ducts 8 0.7 15 1.5 Malignant neoplasm of trachea, bronchus and lung 1 0.2 1 0.1 Malnutrition 2 0.2 Meningitis 2 0.4 3 0.3 Other causes 40 7.3 19 3.5 155 13.4 26 2.5 Pneumonia 7 1.3 27 5.0 57 4.9 38 3.7 Prematurity and low birth weight 2 0.2 Remaining disorders of the liver 1 0.2 1 0.1 9 0.9 Remaining noncommunicable diseasesb 43 7.8 52 9.6 185 16.0 218 21.3 Remaining neoplasmsa 12 2.2 6 1.1 83 7.2 115 11.2 Remaining respiratory diseases (infections) 1 0.2 13 1.1 Tetanusb 1 0.2 3 0.3 5 0.5 Transport accidents 18 3.3 2 0.4 14 1.2 4 0.4 Tuberculosisa 29 5.3 54 9.9 24 2.1 62 6.1 Unspecified acute febrile illness and other infectious causes 76 13.8 1 0.2 97 8.4 15 1.5 Viral hepatitis 1 0.1 In women of child-bearing age (15–49 years) Maternal (except pregnancy with abortive outcome)a 8 4.6 10 5.9 1 0.4 6 3.3 Pregnancy with abortive outcome 1 0.6 1 0.4 a Sensitivity >0.50, and specificity >(1–CSMFVA). b Sensitivity >0.50, specificity >(1–CSMFVA), and (CSMFVA – CSMFMR) <0.20. 946 Bulletin of the World Health Organization | December 2006, 84 (12) Research Cause-specific mortality in the United Republic of Tanzania David R Whiting et al. causes of death. It would, nevertheless, be necessary to run an active system of SVR with VA in parallel to any facilityt based system for a fixed period of time to provide a basis for any later comparison. For the perinatal and neonatal age group, VA appears to be required. VAtderived mortality burden estimates are assumed to be preferable because they provide direct, if crude, measurements of the community mortality experience. To estimate the burden of disease in Africa, the Global Burden of Disease study 23 included extrapolations from a relatively small number of DSS sites, including those included in this study. This paper suggests that use of health facility data, combined with community levels of mortality might have resulted in similar estimates of the burden for a number of important causes, but only if the following four conditions had been met: the similarities in cause structure shown here also apply to hospitals and their neighbouring communities in other parts of Africa; a reliable alternative source of accut rate mortality levels was available; death certificates had been completed and coded using correct ICDt10 prot cedures; and the mortality burden in the DSS sites they had used was similar to the burt den in other African countries. Countries that lack complete vital regist tration might apply a phased approach to obtain estimates of the commut nity mortality burden in the most costt effective manner. Initially, VAs could be conducted as part of a sentinel or SVR system or in conjunction with a large household survey or national census so that comparisons, similar to those presented here, could be made of the mortality burdens estimated directly and those derived from the facilityt based model. Comparisons stratified by socioeconomic status measured at • • • • the household level would also be desirt able. In addition, “catchment areas” of populations and communities actually using nearby facilities would have to be well understood so that characteristics of denominator populations could be accurately worked out. If it can be established that the estimates for any or all age groups are congruent, the VA component may no longer be required. Use of this approach in a nationally representative sample of facilities could thereby be demont strated to produce national estimates of the causetspecific mortality burden for leading causes of death; these will have been demonstrated to be consistent with VAtderived estimates — the only other systematic source of such data likely to be practical and available. O Acknowledgements The authors wish to acknowledge the Adult Morbidity and Mortality Project, Phase2 (AMMP) and the people resident in the AMMP surveillance sites in Dar Fig. 3. Comparison of reference and modelled mortality rates in population aged 5 years and older 05-028910 - Fig.3 Dar es Salaam community Years of Life Lost (YLLs) (per 1000 potential YLLs) 0.0 HIV Dar es Salaam modelled Kilimanjaro community Kilimanjaro modelled 0.5 1.0 1.5 2.0 3.02.5 Malaria Remainder of noncommunicable diseases Tuberculosis Unspecified acute febrile illness and other infectious conditions Other causes Remainder of neoplasms Pneumonia es Salaam and Hai. The views expressed are not necessarily those of the NIH, NIA or USAID. Funding: Funding for the preparation of this article was provided through MEASURE Evaluation, Phase 2, a United States Agency for International Development (USAID) Cooperative Agreement (# GPOtAt00t03t00003t00) implemented by the Carolina Population Center, University of North Carolina at Chapel Hill. Research funding and pubt lication support was also provided by grant P10462t109/9903GLOBt2, The Global Burden of Disease 2000 in Aging Populations, US National Institutes of Health (NIH), National Institute on Ageing (NIA). AMMP was a project of the Tanzanian Ministry of Health funded by the UK Department for International Development and implemented in partt nership with the University of Newcastle upon Tyne. Competing interests: none declared. Résumé Estimation de la mortalité par cause à partir des données de la communauté et des établissements de santé en République-Unie de Tanzanie : options et incidences sur les estimations du poids de la mortalité Objectif Comparer les estimations du poids de la mortalité obtenues en mesurant directement les niveaux et les causes dans les communautés aux estimations indirectes obtenues en associant la structure de mortalité par cause dans les établissements de santé à la mesure des niveaux de mortalité dans la communauté. Méthodes On a utilisé les données des registres d’état civil sentinelles avec autopsie verbale pour déterminer le poids de la mortalité par cause au niveau communautaire dans deux régions de la République-Unie de Tanzanie. La structure proportionnelle de la mortalité par cause obtenue dans les établissements de santé a été appliquée aux chiffres des décès obtenus par les registres sentinelles afin d’obtenir des estimations modélisées. Le poids a été exprimé en années de vie perdues. Résultats Au total, les établissements de santé ont enregistré 947Bulletin of the World Health Organization | December 2006, 84 (12) Research Cause-specific mortality in the United Republic of Tanzania David R Whiting et al. Resumen Estimación de la mortalidad por causas específicas a partir de fuentes de datos de la comunidad y de los establecimientos sanitarios en la República Unida de Tanzanía: opciones y repercusiones en las estimaciones de la carga de mortalidad Objetivo Comparar las estimaciones de la carga de mortalidad basadas en mediciones de su magnitud y sus causas realizadas directamente en la comunidad con las estimaciones indirectas basadas en la combinación de la estructura de la mortalidad por causas específicas registrada en los establecimientos sanitarios y la magnitud de la mortalidad medida en la comunidad. Métodos Se utilizaron datos del sistema de registro civil centinela (RCC) con autopsia verbal (AV) para determinar la carga de mortalidad por causas específicas a nivel comunitario en dos zonas de la República Unida de Tanzanía. A las cifras de muertes obtenidas en los RCC se les aplicaron las estructuras proporcionales de mortalidad por causas específicas registradas en los establecimientos sanitarios, con el fin de obtener estimaciones derivadas de modelos. La carga se expresó en años de vida perdidos. Resultados Se registraron 2884 muertes en los establecimientos sanitarios y 2167 en los RCC/AV. En el grupo de edad perinatal y neonatal, las tasas de mortalidad por causas específicas mostraron un predominio de las afecciones perinatales y las muertes intrauterinas, tanto en los datos obtenidos en la comunidad como en los obtenidos en los establecimientos sanitarios. Las estimaciones de las causas crónicas derivadas de modelos fueron muy similares a las obtenidas en los RCC/AV. Las enfermedades febriles agudas se codificaron de forma más específica en los datos de los establecimientos sanitarios que en la AV. Los traumatismos fueron más prevalentes en los datos de los RCC/AV que en los de los establecimientos sanitarios. Conclusiones En este entorno, la mejora de las prácticas de codificación de la 10ª revisión de la Clasificación Internacional de enfermedades y problemas relacionados con la salud (CIE 10) y la aplicación de las estructuras etiológicas obtenidas en los establecimientos sanitarios a las cifras de muertes registradas en la comunidad, derivadas de los RCC, parecen proporcionar estimaciones razonables de la carga de mortalidad por causas específicas en mayores de 5 años determinada directamente a partir de las AV. En los grupos de edad perinatal y neonatal parecen ser necesarias AV. El empleo de este método en una muestra de establecimientos representativa del país puede proporcionar estimaciones nacionales fiables de la carga de mortalidad por las principales causas específicas de muerte de los adultos. 2884 décès et les registres sentinelles/autopsies verbales 2167. Dans la classe d’âge des nouveau-nés et très jeunes nourrissons, les taux de mortalité par cause sont dominés par les affections périnatales et la mortinatalité aussi bien pour les données communautaires que les données des établissements. Les estimations modélisées relatives aux causes chroniques sont très semblables à celles des registres sentinelles/autopsies verbales. Les maladies fébriles aiguës sont codées de manière plus spécifique dans les données des établissements que dans les autopsies verbales. Les traumatismes sont plus répandus dans les données provenant des registres sentinelles/autopsies verbales que dans celles provenant des établissements. Conclusion Dans cette situation, les meilleures pratiques de codage de la Classification internationale des Maladies et des Problèmes de Santé connexes, Dixième Révision (CIM-10) et l’application de la structure par causes de la mortalité des établissements aux chiffres des décès relevés dans les communautés à partir des registres sentinelles semblent donner des estimations raisonnables du poids de la mortalité par cause chez les sujets de cinq ans et plus, déterminée directement à partir de l’autopsie verbale. Pour la classe d’âge des nouveau-nés et très jeunes nourrissons, il semble nécessaire d’utiliser les autopsies verbales. L’utilisation de cette approche dans un échantillon d’établissements représentatif au plan national peut fournir des estimations nationales fiables du poids de la mortalité par cause pour les principales causes de décès chez l’adulte. صخلم :ةدحتلما اينازـنت ةيروهمج في قفارلما لىعو عمتجلما لىع زكترـت تايطعم لىع دماتعلااب ينعم ببس نع ةمجانلا تايفولا تلادعم ريدقت تايفولا ءبعل تاريدقتلا لىع تايرثأتلاو تارايتخلاا تايوتسملل شرابم سايق لىع ةزكترلما تايفولا ءبع تاريدقت ةنراقم :فدهلا ةصاخلا تان ِّوكلما قفارت لىع ةزكترم تاريدقت عم تاعمتجلما في بابسلألو سايق عم ةيحصلا قفارلما في ينعم ببس نع ةمجانلا تايفولا تلادعبم .تايفولا تايوتسم )ةيتايحلا( ةيندلما تلاجسلا اهحيتـت يتلا تايطعلما انمدختسا :ةقيرطلا ةمجانلا تايفولا ءبع لىع فرعتلل ةيظفللا ةيحيشرتلا ةفصلا عم ةرفاخلا .ةدحتلما اينازـنت ةيروهمج في ْنينَتقطنم في عمتجلما ىوتسم في َّنينعم ببس نع ةيندلما تلاجسلا نم تدمتسا يتلا ةيبسنلا تلادعلما تان ِّوكم تقبط دقو ءبعلا نع ِّبُع دقو .جذمانلا نم ةدمتسم تاريدقت جاتنلإ ةرفاخلا )ةيتايحلا( .ةايحلا نم ةعئاضلا تاونسلاب تلاجسلا في ةافو 2167و ةيحصلا قفارلما في ةافو 2884 تل ِّجُس :تادوجولما تايفو تداس دقو .ةيظفللا ةيحيشرتلا ةفصلاو ةرفاخلا )ةيتايحلا( ةيندلما تايفولا تلادعم لىع محرلا لخاد ةنجلأا تومو ةدلاولاب ةطيحلما ةترفلا ،نادلولاو ةدلاولا ةترفب ةطيحلما ةيرمعلا ةعومجلما في َّنينعم ببس نع ةمجانلا .ةيحصلا قفارلما نمو عمتجلما نم لك نم ةدمتسلما تايطعلما في كلذو لىإ ةهباشم ةنمزلما بابسلأل جذمانلا نم ةدمتسلما تاريدقتلا تناك دقو ةفصلا نمو ةرفاخلا )ةيتايحلا( تلاجسلا نم ةدمتسلما تاريدقتلل ديعب ٍدح ثركأ لكشب ةداحلا ةيو َّنمُحـلا ضارملأا زيمرـت درو دقو .ةيظفللا ةيحيشرتلا ةيحيشرتلا ةفصلا في هنم ةيحصلا قفارلما نم ةدمتسلما تايطعلما في ةيعون تلاجسلا نم ةدمتسلما تايطعلما في ًاراشتنا ثركأ تايذلأا تناك دقو ،ةيظفللا تايطعلما في هنم ةيظفللا ةيحيشرتلا ةفصلا نمو ةرفاخلا )ةيتايحلا( ةيندلما .ةيحصلا قفارلما نم ةدمتسلما فينصتلل ةن َّنسَحُمـلا ةشراعلا ةعجارلما في زيمرـتلا تاسرمام نأ ودبي :جاتنتسلاا بابسلأل تان ِّوكلما قيبطتو ،اهب ةقلعتلما ةيحصلا تلاكشمللو ضارملأل ليودلا ةقتشلماو تاعمتجلما نم ةدمتسلما تايفولا دادعت دنع قفارلما لىع ةزكترلما ءبعل ةلوقعم تاريدقت جتـنيس ،ةرفاخلا )ةيتايحلا( ةيندلما تلاجسلا نم 948 Bulletin of the World Health Organization | December 2006, 84 (12) Research Cause-specific mortality in the United Republic of Tanzania David R Whiting et al. 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