Revealing the Burden of maternal mortality: a probabilistic model for determining pregnancy-related causes of death from verbal autopsies

Immpact, University of Aberdeen, Aberdeen, Scotland, UK.
Population Health Metrics (Impact Factor: 2.11). 02/2007; 5(1):1. DOI: 10.1186/1478-7954-5-1
Source: PubMed


Substantial reductions in maternal mortality are called for in Millennium Development Goal 5 (MDG-5), thus assuming that maternal mortality is measurable. A key difficulty is attributing causes of death for the many women who die unaided in developing countries. Verbal autopsy (VA) can elicit circumstances of death, but data need to be interpreted reliably and consistently to serve as global indicators. Recent developments in probabilistic modelling of VA interpretation are adapted and assessed here for the specific circumstances of pregnancy-related death.
A preliminary version of the InterVA-M probabilistic VA interpretation model was developed and refined with adult female VA data from several sources, and then assessed against 258 additional VA interviews from Burkina Faso. Likely causes of death produced by the model were compared with causes previously determined by local physicians. Distinction was made between free-text and closed-question data in the VA interviews, to assess the added value of free-text material on the model's output.
Following rationalisation between the model and physician interpretations, cause-specific mortality fractions were broadly similar. Case-by-case agreement between the model and any of the reviewing physicians reached approximately 60%, rising to approximately 80% when cases with a discrepancy were reviewed by an additional physician. Cardiovascular disease and malaria showed the largest differences between the methods, and the attribution of infections related to pregnancy also varied. The model estimated 30% of deaths to be pregnancy-related, of which half were due to direct causes. Data derived from free-text made no appreciable difference.
InterVA-M represents a potentially valuable new tool for measuring maternal mortality in an efficient, consistent and standardised way. Further development, refinement and validation are planned. It could become a routine tool in research and service settings where levels and changes in pregnancy-related deaths need to be measured, for example in assessing progress towards MDG-5.

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Available from: Ann Fitzmaurice, Oct 10, 2015
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    • "On top of this, the performance related to VA questionnaires and physicians in detecting exact causes of death for a given case could be mentioned as a limitation. It is also important to note that interpreting the VAs and assigning causes of death by physicians have been questioned for its reliability and repeatability [34]. "
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    ABSTRACT: Methods. Under Kilite-Awlealo Health and Demographic Surveillance System, we investigated mortality rates and causes of death in a cohort of female population from 1st of January 2010 to 31st of December 2012. At the baseline, 33,688 females were involved for the prospective follow-up study. Households under the study were updated every six months by fulltime surveillance data collectors to identify vital events, including deaths. Verbal Autopsy (VA) data were collected by separate trained data collectors for all identified deaths in the surveillance site. Trained physicians assigned underlining causes of death using the 10th edition of International Classification of Diseases (ICD). We assessed overall, age- and cause-specific mortality rates per 1000 person-years. Causes of death among all deceased females and by age groups were ranked based on cause specific mortality rates. Analysis was performed using Stata Version 11.1.
    BMC Research Notes 09/2014; 7(1):629. DOI:10.1186/1756-0500-7-629
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    • "Completion of steps 1-3 above allows the application of Bayes’ theorem whereby the probability of severe acute maternal morbidity in general, and of each specific maternal morbidity cause category in Table 1, can be determined given the presence of specific self-reported signs, symptoms or indicators: in mathematical terms P(C|I). Associated with each indicator (I) and each morbidity (C) is the probability of occurrence among all pregnant or recently delivered women approximated a priori in step 2 using a semi-qualitative scale [12]. The a priori estimate of the baseline probability of any woman reporting an indicator (P(I)) can reflect the sensitivity, specificity and reliability of women’s self-reports of specific symptoms. "
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    ABSTRACT: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India. Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified. The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.
    Emerging Themes in Epidemiology 03/2014; 11(1):3. DOI:10.1186/1742-7622-11-3 · 2.59 Impact Factor
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    • "Various studies have been conducted to compare the performance of the InterVA model as a physician alternative method to interpret VA data [12,20,24,25]. However, the results still show some discrepancies in comparison to the physician review. "
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    ABSTRACT: In the absence of routine death registration, the InterVA model is a new methodology being used as a physician alternative method to interpret verbal autopsy (VA) data in resource-poor settings. However, various studies indicate that there are significant discrepancies between the two approaches in assigning causes of deaths. This study evaluated the role of recall period and characteristics that were specific to the deceased and the respondent in affecting the level of agreement between the approaches. A population-based cross-sectional study was conducted from March to April, 2012. All adults aged >=14 years and died between 01 January, 2010, and 15 February, 2012, were included in the study. Data were collected by using a pre-tested and modified WHO designed verbal autopsy questionnaire. The verbal autopsy interviews were reviewed by the InterVA-4 model and the physicians. Cohen's kappa statistic with 95% CI was applied to compare the strength of the agreement between the model and the physician review. A total of 408 VA interviews were successfully completed and reviewed by the InterVA model and the physicians. Both approaches showed an overall agreement in 294 (72.1%) of the cases [kappa = 0.48, 95% CI: 0.42 - 0.60]. The level of agreement between the approaches was low [kappa <=0.40] when the deceased was female, 50 and above years old, single, illiterate, rural dweller, belonged to a family of 1--4 people living together, and died at home. This was also true when the recall period was <=1 year, and the respondent was a relative other than parent/marital partner, lived with the deceased, and had medical information. This study identified important variables affecting the strength of agreement between the InterVA-4 model and the physician in assigning causes of death. The results are believed to significantly contribute to the process of identifying the actual underlying causes of deaths in the population, and may thus serve to promote informed health policy decisions in resource-poor settings.
    Archives of Public Health 11/2013; 71(1):28. DOI:10.1186/2049-3258-71-28
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