Limits...
Verbal autopsy interpretation: a comparative analysis of the InterVA model versus physician review in determining causes of death in the Nairobi DSS.

Oti SO, Kyobutungi C - Popul Health Metr (2010)

Bottom Line: Physician review (PR) is the most common method of interpreting VA, but this method is a time- and resource-intensive process and is liable to produce inconsistent results.The InterVA model showed promising results as a community-level tool for generating cause of death data from VAs.We recommend further refinement to the model, its adaptation to suit local contexts, and its continued validation with more extensive data from different settings.

View Article: PubMed Central - HTML - PubMed

Affiliation: African Population and Health Research Center, P,O, Box 10787 GPO-00100, Nairobi, Kenya. soti@aphrc.org.

ABSTRACT

Background: Developing countries generally lack complete vital registration systems that can produce cause of death information for health planning in their populations. As an alternative, verbal autopsy (VA) - the process of interviewing family members or caregivers on the circumstances leading to death - is often used by Demographic Surveillance Systems to generate cause of death data. Physician review (PR) is the most common method of interpreting VA, but this method is a time- and resource-intensive process and is liable to produce inconsistent results. The aim of this paper is to explore how a computer-based probabilistic model, InterVA, performs in comparison with PR in interpreting VA data in the Nairobi Urban Health and Demographic Surveillance System (NUHDSS).

Methods: Between August 2002 and December 2008, a total of 1,823 VA interviews were reviewed by physicians in the NUHDSS. Data on these interviews were entered into the InterVA model for interpretation. Cause-specific mortality fractions were then derived from the cause of death data generated by the physicians and by the model. We then estimated the level of agreement between both methods using Kappa statistics.

Results: The level of agreement between individual causes of death assigned by both methods was only 35% (kappa = 0.27, 95% CI: 0.25 - 0.30). However, the patterns of mortality as determined by both methods showed a high burden of infectious diseases, including HIV/AIDS, tuberculosis, and pneumonia, in the study population. These mortality patterns are consistent with existing knowledge on the burden of disease in underdeveloped communities in Africa.

Conclusions: The InterVA model showed promising results as a community-level tool for generating cause of death data from VAs. We recommend further refinement to the model, its adaptation to suit local contexts, and its continued validation with more extensive data from different settings.

No MeSH data available.


Related in: MedlinePlus

Cause-specific mortality fractions for 572 deaths of children aged less than 5 years in the NUHDSS from 2002-2008, derived from verbal autopsies interpreted by physicians and by the InterVA model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2902422&req=5

Figure 2: Cause-specific mortality fractions for 572 deaths of children aged less than 5 years in the NUHDSS from 2002-2008, derived from verbal autopsies interpreted by physicians and by the InterVA model.

Mentions: For children aged less than 5 years, there were 572 deaths assigned causes by both physicians and the model. Figure 2 shows the CSMF as assigned by physicians and the model. Of all deaths in this age group, the majority (382 cases or 67%) was due to infectious diseases as assigned by physicians. An even greater number of cases (446 or 78%) were attributed to infectious diseases by the model. A notable difference between the two methods is the frequencies with which deaths in this age group were attributed to HIV/AIDS. The InterVA model attributed 110 out of 572 (19.2%) deaths to HIV/AIDS, whereas the physicians only attributed 10 (1.7%) deaths to HIV/AIDS. On the other hand, physicians attributed 105 (18.4%) deaths to diarrheal diseases, while the model attributed only 48 (8.4%) deaths. Also, the model assigned twice as many deaths to pulmonary tuberculosis as the physicians. Other infectious diseases such as pneumonia, malaria, and measles only showed slight differences in proportions assigned by the two methods. Injuries and noncommunicable diseases accounted for less than 8% of deaths as assigned by both methods.


Verbal autopsy interpretation: a comparative analysis of the InterVA model versus physician review in determining causes of death in the Nairobi DSS.

Oti SO, Kyobutungi C - Popul Health Metr (2010)

Cause-specific mortality fractions for 572 deaths of children aged less than 5 years in the NUHDSS from 2002-2008, derived from verbal autopsies interpreted by physicians and by the InterVA model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902422&req=5

Figure 2: Cause-specific mortality fractions for 572 deaths of children aged less than 5 years in the NUHDSS from 2002-2008, derived from verbal autopsies interpreted by physicians and by the InterVA model.
Mentions: For children aged less than 5 years, there were 572 deaths assigned causes by both physicians and the model. Figure 2 shows the CSMF as assigned by physicians and the model. Of all deaths in this age group, the majority (382 cases or 67%) was due to infectious diseases as assigned by physicians. An even greater number of cases (446 or 78%) were attributed to infectious diseases by the model. A notable difference between the two methods is the frequencies with which deaths in this age group were attributed to HIV/AIDS. The InterVA model attributed 110 out of 572 (19.2%) deaths to HIV/AIDS, whereas the physicians only attributed 10 (1.7%) deaths to HIV/AIDS. On the other hand, physicians attributed 105 (18.4%) deaths to diarrheal diseases, while the model attributed only 48 (8.4%) deaths. Also, the model assigned twice as many deaths to pulmonary tuberculosis as the physicians. Other infectious diseases such as pneumonia, malaria, and measles only showed slight differences in proportions assigned by the two methods. Injuries and noncommunicable diseases accounted for less than 8% of deaths as assigned by both methods.

Bottom Line: Physician review (PR) is the most common method of interpreting VA, but this method is a time- and resource-intensive process and is liable to produce inconsistent results.The InterVA model showed promising results as a community-level tool for generating cause of death data from VAs.We recommend further refinement to the model, its adaptation to suit local contexts, and its continued validation with more extensive data from different settings.

View Article: PubMed Central - HTML - PubMed

Affiliation: African Population and Health Research Center, P,O, Box 10787 GPO-00100, Nairobi, Kenya. soti@aphrc.org.

ABSTRACT

Background: Developing countries generally lack complete vital registration systems that can produce cause of death information for health planning in their populations. As an alternative, verbal autopsy (VA) - the process of interviewing family members or caregivers on the circumstances leading to death - is often used by Demographic Surveillance Systems to generate cause of death data. Physician review (PR) is the most common method of interpreting VA, but this method is a time- and resource-intensive process and is liable to produce inconsistent results. The aim of this paper is to explore how a computer-based probabilistic model, InterVA, performs in comparison with PR in interpreting VA data in the Nairobi Urban Health and Demographic Surveillance System (NUHDSS).

Methods: Between August 2002 and December 2008, a total of 1,823 VA interviews were reviewed by physicians in the NUHDSS. Data on these interviews were entered into the InterVA model for interpretation. Cause-specific mortality fractions were then derived from the cause of death data generated by the physicians and by the model. We then estimated the level of agreement between both methods using Kappa statistics.

Results: The level of agreement between individual causes of death assigned by both methods was only 35% (kappa = 0.27, 95% CI: 0.25 - 0.30). However, the patterns of mortality as determined by both methods showed a high burden of infectious diseases, including HIV/AIDS, tuberculosis, and pneumonia, in the study population. These mortality patterns are consistent with existing knowledge on the burden of disease in underdeveloped communities in Africa.

Conclusions: The InterVA model showed promising results as a community-level tool for generating cause of death data from VAs. We recommend further refinement to the model, its adaptation to suit local contexts, and its continued validation with more extensive data from different settings.

No MeSH data available.


Related in: MedlinePlus