MANEJO AMBIENTAL

Methods to assign causes of death (COD) using verbal autopsy data (VA) are categorized in two as; physician-certified verbal autopsy (PCVA) or Computer-coded verbal autopsy (CCVA). PCVA typically involves at least two physicians examining each record of a patient/client, with adjudication done by a consensus review or by a third physician (Fottrell & Byass 2010; Leitao et al. 2013). VA data has most commonly been analysed by medical/professional staff, but this has proven to be expensive, slow and non-reproducible process in many situations, and may provide VA cause of death information that cannot readily be compared between settings (Lozano et al. 2011a). In recent years, there has been interest in using CCVA to improve consistency, inter-observer agreement and comparability and to make the coding of VAs faster and cheaper (Byass et al. 2006; Murray et al. 2007) [Figure 3.5].

Figure 3.5: Analysis of Verbal autopsy data

The development and details of other CCVA models have been described in detail in literature before (Fottrell & Byass 2010).This section will dwell on InterVA which was used to assign cause of death in this study.

The InterVA (Interpreting Verbal Autopsy) model is a probabilistic model that can be used to determine the cause of death for each case by processing successive indicators to generate up to three likely causes of death for each case in a computer. This model was initially used by a global network (most notably by INDEPTH), which conducted longitudinal health and demographic evaluation of populations in low and middle income countries (LMICs). The model was developed using an expert panel and was designed to be generic and not context dependent and to produce relatively broad cause of death categories. InterVA is freely available in the public domain.

A series of InterVA models have been developed over the past decade. The model was first tested on VA data from Vietnam for all deaths including maternal deaths, where over 70% of individual causes of death were identical with those determined by two physicians, (Byass, Huong & Minh 2003). These models progressed to InterVA-3, which has been used in a variety of settings across Asia, Africa, and the Latin America (Bauni et al. 2011b; Byass et al. 2011; Fantahun et al. 2006a; Ferri et al. 2012; Herbst, Mafojane & Newell 2011; Oti & Kyobutungi 2010; Ramroth et al. 2012; Tensou et al. 2010) and an associated model, InterVA-M, which analyses deaths among WRA separately (Bell et al. 2008; Fottrell et al. 2007b). The InterVA- M was not used in this study because it was under review when data were analysed.

The InterVA model generates up to three likely causes of death from 35 broad categories, with probabilities for each cause of death (WHO 2005). Fewer than three causes are displayed if the probability of the second or third cause is less than 50% of the probability of the preceding cause (Fottrell et al. 2010; Ramroth et.al 2012). Thus, the sums of the likelihood do not usually add up to 100. It also considers the levels malaria and HIV ⁄AIDS prevalence in the region. The use of the InterVA model to interpret VA data has the advantage of achieving maximum consistency in interpreting VA data (Byass, Huong & Minh 2003; Fantahun et al. 2006b). It also requires relatively minimal time and labour resources, especially in comparison to the physician review method.

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InterVA-4 was released in 2012 and is aligned to the 2012 WHO VA instrument. The new model has been designed to use the VA input indicators defined in the 2012 WHO VA instrument and to provide causes of death compatible with the International Classification of Diseases version 10 (ICD-10). The new model integrates experience accumulated from previous versions (interVA-3), latest data and research findings, and revisions by an expert panel. In addition, known shortcomings of previous InterVA models have been addressed in the current revision, as well as integrating other work on maternal and perinatal deaths (Byass et al. 2012). Further validation opportunities are still being explored now.InterVA-4 aims to provide a consistent and generally applicable means of interpreting VA data and is applicable in both prospective and retrospective data. The model is intended for use both within already- enumerated populations and as a stand-alone death registration tool, both in research and in civil registration system. The model is designed to interpret VA data for deaths from all causes and all ages in the community (Byass et al. 2012).

In this review we identified seven relevant studies which compared cause of death classification

methods using VA data [Appendix 3]. One study in Pakistan compared hospital assigned

causesof death (HCOD) and PCVA (Midhet 2008). There were 128 deaths and there was a

complete agreement between the two groups of MDs. However, the agreement was weak for all other cause categories including indirect MDs (kappa test P-value of 0.055) (Midhet 2008). Another study in India which used HCOD as the gold standard for validating both PCVA and

the InterVA model on causes of adult deathsreported thatall the methods yielded the same top

five underlying causes of adult deaths. However, the InterVA overestimated tuberculosis as a cause of death compared to the HCOD. On the other hand, PCVA overestimated diabetes. Overall, cause-specific mortality fractions (CSMF) for the five major cause groups by the InterVA, PCVA, and HCOD were 70%, 65%, and 60%, respectively. PCVA versus HCOD yielded a higher kappa value = 0.52, 95% confidence interval [CI]: 0.48, 0.54) than the InterVA versus HCOD kappa value of 0.32 (95% CI: 0.30, 0.38). Overall, agreement across the three methods was 0.41 (95% CI: 0.37, 0.48).

Both the InterVA and PCVA compared well with the HCOD at a population level (Bauni et al. 2011a). A study in Ethiopia showed the physicians’ review and the probabilistic model in

determining causes of death, both approaches yielded very similar findings for the major CSMFs in this community, despite that the fact that the two approaches were applied independently to the same data, and that the model was built without direct reference to the data. In the study, patterns of mortality revealed were consistent with those anticipated for an underdeveloped population in sub-Saharan Africa (Mesganaw et al. 2006)

On the other hand, a systematic review of 19 comparison studies, most of which used hospital- based deaths as the reference standard, reported no single best-performing coding method for VA across various the studies and found little current justification for CCVA to replace PCVA (Leitao et al. 2014), Lozano et.al (2011) who compared interVA to PCVA and Simplified Symptom Pattern (SSP), across all age groups, reported that InterVA performed worse than PCVA and SPP, both on an individual and population level.