204 Bulletin of the World Health Organization | March 2006, 84 (3)
Objective Verbal autopsy (VA) — the interviewing of family members or caregivers about the circumstances of a death after the event — is an established tool in areas where routine death registration is non-existent or inadequate. We assessed the performance of a probabilistic model (InterVA) for interpreting community-based VA interviews, in order to investigate patterns of cause-specific mortality in a rural Ethiopian community. We compared results with those obtained after review of the VA by local physicians, with a view to validating the model as a community-based tool. Methods Two-hundred and eighty-nine VA interviews were successfully completed; these included most deaths occurring in a defined community over a 1-year period. The VA interviews were interpreted by physicians and by the model, and cause-specific mortality fractions were derived for the whole community and for particular age groups using both approaches. Findings The results of the two approaches to interpretation correlated well in this example from Ethiopia. Four major cause groups accounted for over 60% of all mortality, and patterns within specific age groups were consistent with expectations for an underdeveloped high-mortality community in sub-Saharan Africa. Conclusion Compared with interpretation by physicians, the InterVA model is much less labour intensive and offers 100% consistency. It is a valuable new tool for characterizing patterns of cause-specific mortality in communities without death registration and for comparing patterns of mortality in different populations.
Keywords Autopsy; Interviews; Questionnaires; Cause of death; Probability; Mortality/trends; Ethiopia (source: MeSH, NLM). Mots clés Autopsie; Entretien; Questionnaires; Cause décès; Probabilité ; Mortalité/orientations; Ethiopie (source: MeSH, INSERM). Palabras clave Autopsia; Entrevistas; Cuestionarios; Causa de muerte; Probabilidad; Mortalidad/tendencias; Etiopía (fuente: DeCS, BIREME).
Bulletin of the World Health Organization 2006;84:204-210.
Voir page 209 le résumé en français. En la página 209 figura un resumen en español.
Introduction Verbal autopsy (VA) — the interviewing of family members or caregivers about the circumstances of a death after the event — is a useful tool in areas where routine death registration is non-existent or inadequate. Processing VA interview material to arrive at a cause of death (COD) has often used physician review, although this demands a considerable amount of (often scarce) physician time, and frequently more than one physician is used to interpret each VA to increase the possibility of obtaining an objective consensus. Other methods of interpreta-- tion of the VA, such as algorithms and neural networks, have been explored
a Department of Community Health, Addis Ababa University, Addis Ababa, Ethiopia. b Epidemiology, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden. c Obstetrics and Gynecology, Department of Clinical Science, Umeå University, Umeå, Sweden. d Epidemiology, Department of Public Health and Clinical Medicine, Umeå University, Umeå 90185, Sweden. Correspondence to this author
(email: peter.byass@epiph.umu.se). Ref. No. 05-028712 (Submitted: 29 November 2005 – Final revised version received: 29 January 2006 – Accepted: 29 January 2006)
Assessing a new approach to verbal autopsy interpretation in a rural Ethiopian community: the InterVA model Mesganaw Fantahun,a,b Edward Fottrell,b Yemane Berhane,a,b Stig Wall,b Ulf Högberg,b,c & Peter Byass d
inconclusively in terms of validity.1 A new probabilistic approach has recently been proposed that has given promising preliminary results.2,3 This approach ap-- plies Bayes’ theorem to VA interpretation in an attempt to overcome concerns associ-- ated with expert assessment while also addressing the limitations of algorithmic approaches. This model has already been evaluated on a preliminary basis in Viet Nam,2 and is now known as InterVA.
Data on deaths in the community have been collected at the Butajira Rural Health Program (BRHP) Demographic Surveillance Site (DSS) since 1987.4 The routine death registration form records perceived causes of death, but this is of limited use. A better understanding of
cause-specific mortality in such com-- munities is a prerequisite for reducing unacceptably high rates of mortality. Reliable VA methods can be used to assess trends in cause-specific mortality, mortality differentials between popula-- tion groups and the effects of interven-- tions. For example, a study of childhood mortality in a Senegalese DSS provided useful information on the mortality structure responsible for a major peak in mortality.5 Efficient and valid tools for the assessment of cause-specific mortality are also necessary for monitoring prog-- ress towards achieving the Millennium Development Goals.6
The aim of this study was to assess the performance of the InterVA model
Arabic
205Bulletin of the World Health Organization | March 2006, 84 (3)
Special Theme – Estimating Mortality Mesganaw Fantahun et al. The InterVA model: verbal autopsy interpretation in rural Ethiopia
on a series of community-based VA in-- terviews from rural Ethiopia, comparing cause-specific mortality as determined by the model and by local physicians, and identifying areas for further develop-- ment of the model.
Methods The BRHP was established in 1987 by selecting a sample of ten communities (with probabilities proportional to size) in the Butajira District, some 130 km south-west of Addis Ababa, Ethiopia. Butajira is set in the Ethiopian Rift Valley, with altitudes ranging from 1750 m to 3400 m above sea level, and local communities depending largely on sub-- sistence agriculture and some cash-crop cultivation.7 Although predominantly rural, the district includes the market town of Butajira, which accounts for about 10% of the total population, as reflected in the sample.
At the time of this study, which was conducted between August 2003 and July 2004, the sample constituted about 15% of the population of the district, and comprised about 45 000 people. In this prospective study, about 350 deaths were expected, 325 were ac-- tually recorded and VA interviews were successfully conducted for 89% of these (289 cases).
The verbal autopsy interview and questionnaire The VA questionnaire used was prepared by WHO and INDEPTH (International Network of field sites with continuous Demographic Evaluation of Populations and Their Health in developing coun-- tries)8,9 and was adapted to the local situation. The questionnaire included open narrative and closed questions. The narrative was used to record free explanations of the circumstances of death while the closed questions dealt with specific symptoms and conditions. Five interviewers administered the ques-- tionnaire to those who had witnessed the deaths and/or took care of the deceased. The interviewers had all completed secondary (high school) education and had previous experience in community- based data collection. Training of the interviewers emphasized issues such as preferred respondents, period of inter-- views, approaching grieving respondents and compiling narrative material (en-- suring that duration, frequency, sever-- ity and sequence of the symptoms were mentioned). Pretesting resulted in small
modifications to facilitate understand-- ing by the study population. Each inter-- viewer was assigned two communities on the basis of his/her previous place of work and experience. Details of deaths were notified by DSS staff, who also regu-- larly reviewed death registration forms and BRHP registers to identify deaths for which VA interviews could be carried out. A supervisor coordinated their activi-- ties, oversaw the data collection process, checked questionnaires for completeness and consistency, and conducted random quality checks by re-interviewing about 5% of the respondents.
Data were collected between 45 and 60 days after death in most cases. Forty-five days is considered to be the usual period of mourning in the study area. Two months was chosen as an upper guideline to minimize recall bias concerning details of symptoms and circumstances of death. In about 15% of the cases, data collection took place between 4 and 5 months after death, for operational reasons.
Two physicians reviewed each VA form independently to assign one or more causes of death, and subsequently met to reach consensus for cases where there were differences of opinion. If no physician consensus could be reached after discussion, the COD was regarded as indeterminate. The physicians were trained in procedures to assign causes of death and given details of the study area, population and the surveillance system. They were briefed on the com-- mon local terms used to express signs, symptoms, causes and conditions of death. They were also provided with a list of ICD-9 (International classification of diseases, ninth revision, http://www.cdc. gov/nchs/icd9.htm) causes of death and required as much information as possible to identify main and underlying causes of death. The physicians were not spe-- cifically briefed about the probabilistic model, in order to preserve their inde-- pendence. The causes of death assigned by the physicians were coded and entered into a database.
Data analysis The probabilistic model, as previously described,3 requires the extraction of a defined set of indicators (signs, symp-- toms, history, circumstances) as the input to the model, and which can be derived from both the open narrative and closed questions in the VA interview. Run-- ning the model on these indicators
then generates a database with up to three likely causes of death for each case, together with respective likelihoods. Table 1 shows an example of how likely causes of death for an individual case are determined stepwise by the model as successive indicators are processed. In this case, the final causes of death were human immunodeficiency virus/acquired immune deficiency syndrome (HIV/ AIDS) with pulmonary tuberculosis.
Since the objective of this study was to determine the cause-specific mortal-- ity fraction (CSMF) for each important COD in the community, individual cases that were assigned more than one cause (by physicians or by the model) were taken to represent two or three fractional causes. For the physicians, for cases with two causes of death each was assigned a weight of 0.5, while for the model the respective likelihoods were used for ap-- portionment where there were two or three causes. Thus the two approaches processed the same basic data from the VA questionnaire independently, result-- ing in individually assigned causes of death by both methods, which were then aggregated to respective CSMFs at the community level.
Ethical approval for the study was obtained from the Faculty of Medicine, Addis Ababa University and the Ethiopian National Ethics Committee.
Results A total of 289 VA interviews were suc-- cessfully completed. Infants (aged less than 1 year) accounted for 48 cases (16.6%), children aged 1–14 years for 101 cases (34.9%) and adults 140 cases (48.4%). Of the deaths in childhood, 87% occurred before the fifth birthday. Deaths in rural areas amounted to 235 cases (81.3%). For the 289 VA inter-- views, the physicians reached consensus without further discussion in 201 cases (69.3%). In four cases, no consensus was reached after discussion, resulting in an indeterminate outcome. In 57 cases (19.7%), two causes of death were as-- signed. In the model’s output, 84 cases (29.1%) were assigned three causes and 73 (25.3%) had two causes. Examination of the range of causes assigned by the physicians and by the model, respectively, revealed some important differences in terminology. The physicians differenti-- ated between “pneumonia” and “sepsis”, while the model used a single category
206 Bulletin of the World Health Organization | March 2006, 84 (3)
Special Theme – Estimating Mortality The InterVA model: verbal autopsy interpretation in rural Ethiopia Mesganaw Fantahun et al.
Table 1. Example of verbal autopsy interpretation showing selected causes of death from the InterVA model
The example shows the case of an adult male aged 15–49 years, whose final illness lasted more than 3 weeks, was accompanied by oral candidiasis, coughing up blood and coughing for more than 3 weeks, with the conclusion of death related to human immunodeficiency virus/ acquired immune deficiency syndrome (HIV/AIDS) (67% likelihood) and pulmonary tuberculosis (33% likelihood) (shown in bold type).
Indicator Probability of selected causes of death
Acute HIV/AIDS- Acute Ma- Malig- Maternal Suicide Pulmonary Diar- Pneu- cardiac related infection laria nancy causes tuberculosis rhoea monia
Unconditional probability 0.005 0.050 0.005 0.050 0.020 0.002 0.002 0.050 0.050 0.050 1st: Male aged 15–49 years 0.003 0.332 0.003 0.033 0.070 0 0.013 0.332 0.003 0.033 2nd: Final illness lasted more 0.000 0.403 0.000 0.004 0.081 0 0.000 0.403 0.001 0.004 than 3 weeks 3rd: Oral candidiasis 0.000 0.971 0.000 0.000 0.004 0 0.000 0.019 0.000 0.000 4th: Coughing up blood 0.000 0.828 0.000 0.000 0.003 0 0.000 0.166 0.000 0.000 5th: Cough for more than 0.000 0.665 0.000 0.000 0.001 0 0.000 0.332 0.000 0.000 3 weeks
for these conditions. The physicians also tended to use more etiological descrip-- tions in some cases, for example “shigel-- losis” in preference to “bloody diarrhoea”. Fig. 1 represents a comparison of prin-- cipal CSMFs. The physicians’ stated causes are listed in the left-hand column and the model’s descriptors are listed in the right-hand column. The two central columns represent a rationalized version of the physicians’ and model’s CSMFs respectively, for the sake of easier com-- parison across common categories.
There was a general similarity be-- tween the two approaches in terms of cause-specific mortality, but with some important differences. For all deaths in this community, four major groups of causes accounted for more than 60% of all mortality, as determined both by the physicians and by the model. These were pneumonia/sepsis, pulmonary tuberculosis, malaria, and diarrhoeal disease/malnutrition (Fig. 1). Among infants in the first year of life, prematu-- rity/low birth weight, perinatal causes and pneumonia/sepsis accounted for four out of five deaths, as assessed both by the physicians’ review and the model. For children, pneumonia/sepsis, diar-- rhoeal disease/malnutrition and malaria were the major causes of death. Even among adults, infectious causes (pneu-- monia/sepsis, tuberculosis, malaria and diarrhoeal disease) accounted for more than 50% of deaths according to both methods.
There were also good rank correla-- tions between causes of death assigned by the physicians and by the model, both for the whole community (Fig. 1) and for specific age groups, particularly when including the “others” category.
This category included different diseases assigned with a low frequency (e.g. other infectious diseases, other respiratory problems, kidney or urinary problems, malignancies, other digestive problems) and a group of cases for which the COD was indeterminate (four cases by physi-- cians’ diagnosis and five by the probabi-- listic method).
Some important differences in-- cluded the lower frequency with which meningitis and malaria were assigned as a COD by the model than by the physi-- cians. Perinatal problems appeared to be responsible for a higher proportion of deaths among infants according to the physicians. On the other hand, HIV/ AIDS was less frequently diagnosed by the physicians than by the model.
Fig. 2 shows principal CSMFs ac-- cording to the model for the urban area (Butajira town) and the surrounding ru-- ral areas. In the town, the model detected higher rates of HIV/AIDS, tuberculosis and cardiovascular mortality than in the rural area, all of which are credible findings.
Discussion Comparison of the physicians’ review and the probabilistic model in deter-- mining causes of death showed that both approaches yielded very similar findings for the major CSMFs in this community, despite that the fact that the two approaches were applied inde-- pendently to the same data, and that the model was built without direct refer-- ence to the data for Butajira. Patterns of mortality revealed were consistent with those anticipated for an underdeveloped population in sub-Saharan Africa. This
approach would be equally applicable to cause-specific mortality rates, given appropriate denominator data. However, there are some differences in interpreta-- tion that deserve further attention. There is a need for intelligent interpretation of categorizations that may be largely equivalent without being exactly the same, for example, the preference of the physicians for “shigellosis”, imputing a likely specific etiology to reports of bloody diarrhoea, but in the absence of any microbiological data.
Diagnosis of the disease group com-- prising malnutrition, kwashiorkor, ma-- rasmus and diarrhoea was an important and more complex issue for these data. This version of the InterVA model was not designed to differentiate between kwashiorkor and marasmus, and pre-- sented a single possible category of “malnutrition”. However, most people dying as a result of malnutrition would also have a strong history of diarrhoea reported in the VA interview; ma-- rasmics would be reported as losing weight, and people with kwashiorkor may be reported as having weight loss or no weight loss, oedema, and skin and hair changes. This whole complex area is something that needs further attention in the probabilistic model, although the results for the composite group of malnutrition, marasmus, kwashiorkor and diarrhoea obtained by the physicians and by the model were still comparable.
Meningitis in young infants may not be accompanied by the traditional signs of the disease (neck stiffness, fever, severe headache and other meningeal signs), or the signs may not be noted by
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the caregiver, which makes it difficult to diagnose the disease using specific indi-- cators. In principle, these signs might be expected in older children, but the physicians (rightly or wrongly) perhaps used more general symptoms of acute infection to lead towards a diagnosis of meningitis.
Malaria in the Ethiopian highlands follows a somewhat atypical unstable hy-- poendemic pattern,10 which may not be optimally reflected in what is intended to be a generally applicable model. This kind of endemicity tends to result in more deaths than might otherwise be expected in relatively immune-naive adults, when compared with mortality attributable to the more common child-- hood malaria seen in areas of higher en-- demicity. Choosing between the model’s options of “low” and “high” malaria prevalence did not substantially narrow the gap between physicians’ diagnosis and the probabilistic method, although at the “high” setting the model per-- formed better.
HIV/AIDS was more frequently identified by the model than by the physicians’ review. In children, the cases assigned by the model as HIV/AIDS were identified by the physicians’ review mainly as kwashiorkor, while in adults they were generally diagnosed as tuber-- culosis, or tuberculosis and diarrhoea. International estimates 11 and studies in poor rural African settings 12 have reported that malnutrition is the major underlying COD in children. Thus it seems that the model may have overes-- timated the incidence of HIV/AIDS in children, while the physicians’ review may have underestimated it. This may also reflect some reluctance among health professionals to reach a diagnosis of HIV/AIDS on the basis of circum-- stantial evidence.
Among infant deaths there appeared to be a difference in the diagnosis of perinatal mortality made by the two approaches. Further inspection of the data revealed that the model reached a diagnosis of pneumonia in about five
cases that were labelled as perinatal deaths by the physicians’ review. The physicians’ decisions may have been reached by directly applying the defini-- tion of the perinatal period (the first 7 days of life) as the causal basis for these early deaths. The model’s age grouping, does not define such a narrow interval at the beginning of life. However, in six cases for which the model considered prematurity/low birth weight to be the second likely COD, the physicians restricted themselves to perinatal causes. If prematurity/low birth weight is con-- sidered to be a perinatal problem, in ac-- cordance with the ICD-9 classification, the gap between the two approaches reduces. Thus the apparent differences in diagnoses for the very young may largely arise as a result of differences in definition. In addition, the differences in CSMFs may be attributable to the relatively small number (48) of deaths in this age group.
Another area for consideration was the model”s indicator “rash”, which,
pulmonary TB
malaria
pneumonia/sepsis
diarrhoea
bloody diarrhoea cardiovascular
perinatal causes prematurity
HIV/AIDS related
accidents
liver disease
others
Model
pulmonary TB
malaria
bloody diarrhoea
circulatory
perinatal causes prematurity
HIV/AIDSa related
others
Common categories model
pulmonary TB
pneumonia/sepsis
bloody diarrhoea
diarrhoea
malaria
circulatory
perinatal causes
prematurity
Fig. 1. Cause-specific mortality fractions for 289 deaths in a rural Ethiopian community, derived from verbal autopsies interpreted by physicians and by the InterVA model. The two central columns permit comparison across common categories
WHO 06.23
100
aHIV/AIDS = human immunodeficiency virus/acquired immune deficiency syndrome.
Physicians
80
%
60
40
20
0
others
bTB = tuberculosis.
liver disease meningitis prematurity
perinatal causes
cardiovascular
shigellosis marasmus
kwashiorkor
diarrhoea
malaria
pulmonary TBb
sepsis
pneumonia
others
liver disease meningitis
malnutrition
Common categories physicians
accidents
liver disease
diarrhoea
pneumonia/sepsis
cerebrovascular
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when present, tended to lead to “measles” as a COD. This led to five conclusions of “measles” from the model, compared with 0.5 from the physicians. However, it seemed from the VA material that measles rash was well recognized by respondents as a separate entity, justify-- ing its inclusion as a separate indicator in the next revision of the model. The model also incorporated a possibility of “sickle-cell disease”, not normally found in Ethiopia, and attributed one case erroneously to this cause, which should be generalized to include other haemoglobinopathies.
Both approaches painted a pic-- ture of a high-mortality community in which most deaths were caused by major infectious diseases. While this might be expected in rural Ethiopia, it nevertheless highlights the importance of targeting many preventable infections with improved health-care measures if this burden of communicable disease is to be reduced.13
Mortality patterns in the modest but rapidly developing market town of Butajira show some trends towards higher proportions of deaths related to HIV/AIDS and noncommunicable diseases than in its rural surroundings, as well as a higher CSMF for tuberculosis, possibly associated with more crowded living conditions. These observations may have important implications for future cause-specific trends in mortality in rural communities, as behavioural and lifestyle changes trickle out from urban areas. Age-specific differences in CSMFs followed largely predictable patterns, with infections dominating all age groups.
As validity is a major concern in any VA application,14–16 possible ways of assessing both approaches in relation to a gold standard were considered. Validation using hospital COD as a gold standard was not possible owing to the extreme rarity of hospital deaths in this setting; most of the deaths occurred in the home without any professional
contact. Thus, reluctantly, we had to conclude that any formal validation was impossible. However the good concor-- dance in CSMFs between the two meth-- ods and the reasonableness of findings compared with other local studies 13,17–19 suggest that validity for both methods is good. In addition, the clear differences between the results produced here for Ethiopia and those previously produced using data from Viet Nam3 demonstrate the model’s capacity to reflect mortality patterns in very different communities, as do the evident urban–rural differences shown in Fig. 2.
Philosophies underlying the applica-- tion of VA in assessing community-based mortality have not always been clear. The term “autopsy” implies some equiva-- lence with a pathologist’s postmortem, in which the major objective is an unambiguous conclusion for a specific individual. However, in terms of the epidemiological and public health im-- plications of cause-specific mortality, the
Fig. 2. Cause-specific mortality fractions for 54 deaths in Butajira town, Ethiopia, and 235 deaths in surrounding villages, derived from verbal autopsies interpreted by the InterVA model
WHO 06.24
100
Rural
80
%
60
40
20
0
others 12.5
Urban
liver disease 4.1
HIV/AIDSa related 3.7
perinatal/premature 6.5
cardiovascular 5.2
accidents 3.8
malaria 9.0
diarrhoea 13.9
pulmonary TBb 12.4
pneumonia/sepsis 28.9
others 11.2
liver disease 5.3
HIV/AIDS related 10.8
perinatal/premature 6.2
cardiovascular 9.2
accidents 4.2
malaria 8.5
diarrhoea 11.1
pulmonary TB 17.5
pneumonia/sepsis 22.6
aHIV/AIDS = human immunodeficiency virus/acquired immune deficiency syndrome. bTB = tuberculosis.
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Résumé
Evaluation d’une nouvelle manière d’interpréter les autopsies verbales dans une communauté rurale en Ethiopie : le modèle InterVA Objectif L’autopsie verbale (AV) - une méthode qui consiste à interroger après coup les membres d’une famille ou les aidants au sujet des circonstances d’un décès - est couramment utilisée lorsque l’enregistrement systématique des décès est inexistant ou laisse à désirer. Nous avons évalué l’efficacité d’un modèle probabiliste (InterVA) d’interprétation des autopsies verbales communautaires afin d’étudier les caractéristiques de la mortalité par cause dans une communauté éthiopienne. Nous avons comparé nos résultats à ceux qui avaient été obtenus par des médecins locaux après examen des AV, le but étant de valider le modèle en tant qu’outil d’enquête dans une communauté. Méthodes Deux cents quatre-vingt neuf autopsies verbales ont été effectuées avec succès ; elles portaient sur la plupart des décès survenus dans une certaine communauté au cours d’une année. Les interrogatoires d’autopsie verbale ont été interprétés d’une part par des médecins et d’autre part en utilisant le modèle et nous en avons déduit les ratios de mortalité par cause pour l’ensemble de
la communauté et pour les différentes classes d’âge en utilisant les deux manières. Résultats Les résultats des deux modes d’interprétation sont en bonne corrélation pour cette communauté éthiopienne. Plus de 60 % des décès ont été attribués à quatre causes principales et les caractéristiques de cette mortalité dans certains groupes d’âge déterminés correspondaient à ce que l’on peut attendre pour une communauté sous-développée à forte mortalité de l’Afrique subsaharienne. Conclusion Comparativement à l’interprétation par un médecin, l’interprétation au moyen du modèle InterVA prend beaucoup moins de temps et l’homogénéité des résultats est de 100 %. Ce modèle est un outil précieux pour déterminer les caractéristiques de la mortalité par cause dans les communautés où les décès ne sont pas enregistrés et pour comparer ces caractéristiques d’une population à l’autre.
emphasis is not so much on the individu-- al’s COD but on eliciting the causes that have a major impact on communities as a whole. The approach taken here reflects this whole-community emphasis, by allowing attributions of cause to be ap-- portioned for individuals with multiple likely causes. While the physicians had the advantage of being able to consider detailed, nuanced information by going through the questionnaire and using their clinical skills and experiences in determining cause(s) of death, they may have also been influenced by their own biases, particularly for less obvious causes of death for which decisions had to be made between equally likely diagnoses. This might have contributed to some of the discordance observed between the two approaches. It seems fair to conclude that the InterVA model, subject to some limitations identified above, represents a valuable new tool in the quest to characterize patterns of cause-specific mortality in communities without death registration. While its use may be more limited for identifying COD at the individual level and its performance in terms of determining CSMFs may not be obviously better than that of the local
physicians in this setting, it offers two major advantages as community-level tool for identifying COD patterns. It is much less labour-intensive, since at an estimate of 5 minutes per physician per VA interpretation, even a relatively small study such as this demands more than one week of physician time purely for VA interpretation. At the same time, pre-- cisely the same model can be applied to VA material from a range of settings, or over extended periods of time, without introducing any interobserver variation in interpretation. These two advantages outweigh the possible losses associated with subtle interpretation carried out by physicians, at least in a public health context. Possibilities for future compari-- sons of patterns of mortality at different surveillance sites, for example, among various INDEPTH members, are thus created, paving the way for building a more reliable global overview of patterns of mortality.
Current developments This exercise in Butajira provided some important indicators for modifications to be made at a detailed level, which would be needed in the next version of
the model, and which have now been implemented. This revised version of the InterVA model is now available for general release (www.interva.net). The development of more specialized versions of the model (e.g. specifically for maternal deaths) is in progress. A version of the model that can be used on a handheld computer (PDA) will also be available for download from this website; this system could be used dur-- ing the VA interview and thus eliminate the need to transfer the VA data into the model after the interview. We hope that making these resources generally available will stimulate progress towards consistent comparisons of patterns of mortality. O
Acknowledgements We acknowledge Dr Derege Ketema and Dr Amha Fantaye, who assigned causes of death.
Funding: This study received financial support from the Swedish Agency for Research Co-operation with Developing Countries (SAREC).
Competing interests: none declared.
Resumen
Evaluación de un nuevo sistema de interpretación (modelo InterVA) de las autopsias verbales en una comunidad de la Etiopía rural Objetivo La autopsia verbal (AV) -entrevista a miembros de la familia o a cuidadores acerca de las circunstancias de una defunción después del evento- es un instrumento de uso arraigado en las zonas con sistemas inadecuados o inexistentes de registro
sistemático de las defunciones. Evaluamos los resultados de un modelo probabilístico (InterVA) de interpretación de las entrevistas de AV comunitarias, a fin de investigar las pautas de mortalidad por causas específicas en una comunidad etíope rural, y comparamos
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esos resultados con los obtenidos por médicos locales a los que se dio a examinar las AV, con miras a validar el modelo como instrumento comunitario. Métodos Se completaron con éxito doscientas ochenta y nueve entrevistas de AV, que incluían la mayor parte de las defunciones ocurridas en una determinada comunidad durante un periodo de un año. Las entrevistas de AV fueron interpretadas por médicos y mediante el modelo, y se procedió a calcular las fracciones de mortalidad por causas específicas para el conjunto de la comunidad y para grupos de edad particulares utilizando los dos sistemas. Resultados Se observó una buena correlación entre los resultados de los dos sistemas de interpretación en este ejemplo de Etiopía.
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Cuatro grandes grupos de causas explicaban más del 60% de toda la mortalidad, y los perfiles internos observados en grupos de edad específicos fueron compatibles con lo que podía esperarse para una comunidad subdesarrollada de alta mortalidad del África subsahariana. Conclusión En comparación con la interpretación realizada por médicos, el modelo InterVA requiere mucho menos trabajo y tiene una coherencia del 100%. Es un nuevo y valioso instrumento para caracterizar la distribución de la mortalidad por causas específicas en comunidades sin registros de defunción y para comparar las pautas de mortalidad de distintas poblaciones.
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