Skip to main content
Download PDF

Validity of InterVA model versus physician review of verbal autopsy for tracking tuberculosis-related mortality in Ethiopia

BMC Infectious Diseases volume 22, Article number: 200 (2022) Cite this article

Abstract

Background

In most African countries where a legitimate vital registration system is lacking, physicians often review verbal autopsy (VA) data to determine the cause of death, while there are concerns about the routine practicality, accuracy, and reliability of this procedure. In Ethiopia where the burden of tuberculosis (TB) remains unacceptably high, reliable VA data are needed to guide intervention strategies. This study aimed to validate the InterVA model against the physician VA in tracking TB-related mortality in Ethiopia.

Methods

From a sample of deaths in Addis Ababa, Ethiopia, VAs were conducted on TB-related mortality, physician-certified verbal autopsy (PCVA) through multiple steps to ascertain the causes of death. InterVA model was used to interpret the causes of death. Estimates of TB-related deaths between physician reviews and the InterVA model were compared using Cohen’s Kappa (k), Receiver-operator characteristic (ROC) curve analysis, sensitivity, and specificity to compare agreement between PCVA and InterVA.

Results

A total of 8952 completed PCVA were used. The InterVA model had an optimal likelihood cut-off point sensitivity of 0.64 (95% CI: 59.0–69.0) and specificity of 0.95 (95% CI: 94.9–95.8). The area under the ROC curve was 0.79 (95% CI: 0.78–0.81). The level of agreement between physician reviews and the InterVA model to identifying TB-related mortality was moderate (k = 0.59, 95% CI: 0.57–0.61).

Conclusion

The InterVA model is a viable alternative to physician review for tracking TB-related causes of death in Ethiopia. From a public health perspective, InterVA helps to analyze the underlying causes of TB-related deaths cost-effectively using routine survey data and translate to policies and strategies in resource-constrained countries.

Peer Review reports

Background

Tuberculosis (TB) remains one of the major public health threats worldwide [1]. According to the WHO 2020 annual TB report [2], an estimated 10 million people fell ill with TB in 2019, and close to half a million people developed rifampicin-resistant TB (RR-TB), of which 78% had multidrug-resistant TB (MDR-TB). In Ethiopia, TB is still a major public health concern [3,4,5,6]. The country is among the 30 countries with the highest burden of TB, TB/HIV, and multi-drug resistant TB. Although the TB burden in Ethiopia has steadily declined, the estimated incidence remains high at 151 per 100,000 populations and a death rate of 24 per 100,000 population.

In developing countries and Africa in particular, accurate and reliable data on causes of death is essentially lacking [7,8,9]. Ethiopia is one of those countries with an impaired vital registration system [9,10,11]. Mortality estimates, so far, are derived poorly from demographic and health surveys, surveillance systems, and mathematical models. The Addis Ababa Mortality Surveillance Program (AAMSP) is a unique undertaking, which monitors citywide mortality based on burial surveillance in all cemeteries within the city boundary [12]. The program collects further information on causes of death and other socio-demographic characteristics through Verbal Autopsy (VA) from cases sampled out of burial registries.

The verbal autopsy expert algorithm (InterVA) is a compter-driven model developed and used to interpret VA data into probable causes of death [13]. It calculates the probability of a set of causes of death, given the presence of indicators (circumstances, signs, and symptoms) reported in VA interviews [14]. It is faster and cheaper, although statistical modeling of this sort may not reflect the subjective subtleties of physicians’ review [15], it is advantageous in terms of efficiency, consistency, and standardization. As a result, there is an increasing trend to shift from physician’s based review to InterVA model; however, the reliability of this model across different settings has not been studied well [15].

In most of the African countries, including Ethiopia, where a legitimate vital registration system is lacking, physicians often review VA data to determine the cause of death, while there are concerns on the routine practicality, accuracy, and reliability of this procedure. In Ethiopia where the burden of TB remains unacceptably high, reliable VA data are needed to guide decision-making strategies. The country needs reliable data on the level and causes of TB-related mortality for effective TB program implementation and realization of the End TB strategy by 2035. This study aimed to validate the InterVA model against the physician VA in tracking TB-related mortality in Ethiopia.

Method

This study used a completed verbal autopsy between 2007 and 2017. The cause of death assigned by physicians and InterVA was compared at the individual level.

Source of data

The source of data was from VA and physician review records of the AAMSP database. The AAMSP used to register all deaths that happen in Addis Ababa on regular basis. The primary source of AAMSP data was death registration followed by verbal autopsy interviews. Information gathered from a standardized interview about the circumstances of death from relatives or friends or close caregivers of the deceased was reviewed by physicians to assign causes of death. The AAMSP was established in 2001 in Addis Ababa, the capital of Ethiopia with the main objective of monitoring cause-specific mortality with a special focus on HIV/AIDS. From a total of 89 cemeteries, the program collected information about decedents' background information and lay the cause of death from family members[13, 16].

Data collection

Burial surveillance

A burial surveillance form has been used by cemetery clerks who trained about death registrations in training workshops. In each of the cemeteries, one or two cemetery-based clerks were assigned to register deaths using the burial surveillance form prepared for this purpose. The variables registered by the cemetery clerks include the name of the deceased, date of burial, age, sex, birth region, marital status, ethnicity, religion, specific address, and lay cause of death. More than 15,000 deaths have been registered annually from all the cemeteries. The data that was collected from all cemeteries of Addis Ababa was entered into computer software to serve as a sampling frame for VA.

Verbal autopsy

A random sample of about ten percent of burial records from all cemeteries except ‘Baytewar’ cemetery was selected for VA interviews. ‘Baytewar’ is an Amharic word used to refer to a stranger or someone who is socially isolated. In Baytewar cemetery, bodies with no close relatives or friends to facilitate a funeral were buried. The Baytewar cemetery alone accommodated around 15% of the total number of burials. Most of these were infant bodies delivered by the obstetric wards of hospitals and remain unidentified cemetery and those with complete addresses were not eligible for VA interviews.

Verbal autopsies were conducted by trained interviewers after 2 to 3 months of the mourning period. The interviewers were trained on how to contact respondents when to interview and complete the questionnaires. Of the total records that were sampled for VA interviews, about 7.6% were not completed with the main reasons either respondents refused the interview or the households were not found.

VA employs a standardized questionnaire to produce information about the causes of death. Through this process, the information on the sign, symptoms, medical history, and circumstances preceding deaths were elicited by interviewing the next kin or caregiver. The cause or sequence of causes that led to death were assigned based on data collected from the questionnaire and any health records and narrative section that has been available. The VA questionnaire was adapted from one used at the INDEPTH Network site [17] and included a set of questions previously used in Ethiopia [18,19,20] and modified on the WHO 2012 VA instrument developed by the WHO, Health Metrics Network (HMN), and the IN-DEPTH-Network [21].

Physician review

Physicians reviewed the completed VA questionnaire to ascertain the causes of deaths which were done in multiple steps. Initially, the cause of death was assigned independently by two physicians after they review the completed VA questionnaire. Then, the two-physicians diagnosis was checked by the surveillance team members. If the two-physician diagnosis (for the assigned cause of death) contradicts, a third physician reviewed the case, and the final assignment was made based on the agreement between any two of the three physicians. However, if the assigned cause of death by the third physician was not in agreement with any of the two-physician diagnoses, the cause for death was assigned on a panel discussion between them. In the situation where the three physicians could not reach an agreement, the cause of death was assigned as undetermined. The physicians were participating to review the process which was second- or third-year internal medicine residents of Addis Ababa University recruited to join the university after serving two or more years as a General Practitioner (GP) in any of the public hospitals. We provided them training and annual refreshments on the standard verbal autopsy method.

InterVA

The physician and the InterVA-3.2 model independently assessed the same basic data from the VA questionnaire. The study tested the concordance of assigning any kind of TB as a cause of death between physician review and the interval model. The model's input data include signs, symptoms, medical history, and situations collected from the VA questionnaires' close-ended questions. Compiling the same VA data into an input file for the InterVA model and processing it into the cause of death data, data adaptations were made to match the model. The model additionally demands a "high" or "low" input to describe the local prevalence of two specific causes, which can vary by order of magnitude between settings. The WHO verbal autopsy tool does not have data on some InterVA markers, thus they were left blank.

A STATA do file used to validate the model in 2003 was revised and applied to produce parameters essential for the model. Kappa statistics, ROC curve, sensitivity, and specificity were applied to compare agreement between PCVA and InterVA. Causes of death are assigned to a predefined matrix of evaluated probabilities of occurrence.

Interpretation of the InterVA model

The model relates a range of input indicators, like sex, age, physical signs and symptoms, medical record, and therefore the circumstances of death to likely CODs using Bayesian probabilities [22]. The model leads to up to 3 likely causes per case when possible; each related to a quantified likelihood. To assign an estimate of the certainty for that patient, the model gives the common likelihood for a maximum of three CODs [23]. during this study, a high prevalence of Malaria and HIV/AIDS were used as basic epidemiological parameters for the model as their prevalence varies from place to put. Data were entered case-by-case into Microsoft visual FoxPro window of the InterVA version 3.2 to assign the possible COD responsible for the death of every individual.

Distribution of N subjects by physician review and InterVA model category

The formula we used to determine the distribution of subjects by physician review and InterVA model category was:

$${\text{K}} = \frac{{{\text{P}}_{{\text{o}}} - {\text{P}}_{{\text{e}}} }}{{1 - {\text{P}}_{{\text{e}}} }} = \frac{{1 - 1 - {\text{P}}_{{\text{o}}} }}{{1 - {\text{P}}_{{\text{e}}} }}$$

where Po = the relative observed agreement among raters.

Pe = the hypothetical probability of chance agreement

K = Kappa statis

  

Physician review

  

Category 1 (Yes)

Category 2 (No)

Total

 

InterVA Model

Category 1 (Yes)

a

B

a + b

P1. = (a + b)/N

 

Category 2 (No)

c

D

c + d

P2. = (c + d)/N

 

Total

a + c

b + d

N

 
  

P 0.1 = (a + c)/N

P 0.2 = (b + d)/N

  
$$Po=\frac{a+d}{N}$$

where a = the total number of instances that both raters said were correct. The raters are in agreement.

b = the total number of instances that rater 2 said was incorrect, but rater 1 said were correct. This is also disagreement.

c = the total number of instances that rater 1 said was incorrect, but rater 2 said were correct. This is also disagreement.

d = the total number of instances that both raters said was incorrect. Raters are in agreement.

In Cohen’s kappa, the chance agreement is defined as the sum of the products of marginal distributions, i.e.

$$Pe\left( k \right)\, = \,P.{1}*P{1}.\, + \,P.{2}*P{2}{\text{.}}$$

Receiver operator characteristics (ROC) curve

For both PCVA and InterVA, the area under the receiver operator characteristics (ROC) curve was used to assess overall diagnostic performance (properly diagnosing all diseases). The area under the curve (AUC) of a procedure should be near one for it to be highly sensitive and specific. The approach is more accurate if the curve closely aligns the left-hand border and the top border of the ROC space. If the area under the ROC curve was more than 0.75, we evaluated our methods to be appropriate.

Validity measures: sensitivity and specificity

Sensitivity and specificity with their 95% confidence intercal (CI) were compared for PCVA and InterVA model. The formula for the calculation were defined as:

$${\text{Sensitivity}} = \frac{{{\text{TP}}}}{{({\text{TP}} + {\text{FN}})}}$$
$${\text{Specificity}} = \frac{{{\text{TN}}}}{{({\text{FP}} + {\text{TN}})}}$$

Where: TP = true positive; FP = false positive; TN = true negative; FN = false negative.

Inclusion and exclusion criteria

The inclusion criteria were all records where causes of death were assigned and those with all the information like identification of decedents, sex, age, house address, and date of birth.

The exclusion criteria were records with missing in address, name, inconsistency in the address including place of burial. Causes of death assigned by the InterVA model as “Indeterminate” were also excluded from the analysis.

Data management and analysis

Data analysis was conducted using STATA 14 software. Individual decedents had a unique ID and all datasets were merged using this ID before analysis. Two separate variables (TB_inerva and TB_pr) were generated for comparison purposes and documenting the trend of TB-related death. Coding and recoding, labeling, and analysis were done.

Estimates of TB-related deaths between physician reviews and the InterVA model were compared using Cohen’s Kappa (k) and ROC curve analysis. A Kappa value of < 0 indicates no agreement and 0–0.20 as slight, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1 as almost perfect agreement.

In this paper, sensitivity refers to the ability of the InterVA model to correctly identify TB-related deaths as assigned by the physician method; whereas specificity refers to the proportion of Death from other causes that are correctly identified as non TB. These two measures were closely related to type I and type II errors. Both sensitivity and specificity were calculated.

Ethical considerations

The study has been reviewed and approved by the Ethiopian National Research Ethics Review Committee of the Ethiopian Ministry of Science and Technology, and the Institutional Review Board of the College of Health Sciences, Addis Ababa University. Informed consent was obtained from caregivers or another eligible adult in the family during the VA interview. Access to the AAMSP data was obtained from the Addis Ababa University Mortality surveillance team that manages the project. The participants remained anonymous and were not identified in the research report or in other means used to disseminate findings.

Results

Background characteristics

A total of 8952 VA was completed with physician diagnosis in the period September 2007 to 2017. Of these, 4618 (51.6%) were male and 4086 (45.6%) were from the age group 65 years and above. Regarding educational status, 3836 (42.8%) had completed elementary and/or secondary school, while 3721 (41.6%) were illiterates. About 3936 (44%) and 2471 (27.6%) of decedents were married and widowed, respectively (Table 1).

Table 1 Background characteristics of decedents 2007–2017 Addis Ababa, Ethiopia
Full size table

TB-related cause of death

From the total 8952 PCVA conducted, 972 (10.9%) were assigned as TB-related deaths by physicians. Of these, 313 VA were excluded from the InterVA analysis due to important missing variables. Of the 8,639 VAs able to be analyzed using InterVA, 975 (11.3%) were assigned as TB-related deaths (Table 2).

Table 2 Comparison of all causes of death by Physician and InterVA model
Full size table

Of those classified by physicians as TB-deaths, 533 (54.8%) were female, and from the InterVA model, 520 (53.3%) were female. The proportion of male TB-related death by Inter VA model was comparable with physician review, 65 (47.1%) and 66 (48.5%), for the period 2009. In the same period, the proportion of female TB-related death by InterVA and physician review were 73 (52.9%) and 70 (51.5%), respectively. However, male and female TB-related death varies from year to year for both InterVA model and physician review (Fig. 1).

Fig. 1
figure 1

Proportion of TB-related deaths based on gender in physician review and InterVA model in the years 2007 to 2017

Full size image

Agreement in diagnosis between physician and InterVA Model

According to the analysis, the results of the level of agreement was 92.9%, with a kappa value of 0.59 (95% CI: 0.57–0.61), indicating that there is a moderate agreement (Table 3).

Table 3 Distribution of N subjects by physician review and InterVA model category
Full size table

The observed agreement was defined as:

$$Po=\frac{622+7314}{8639}=0.918$$
$${\text{Pe}}\left( {\text{k}} \right) = \left( {0.{112}*0.{112}} \right) + \left( {0.{887}*0.{887}} \right)$$
$$Pe\left( k \right) = 0.{799}$$
$$\mathrm{k}=\frac{0.918-0.799}{(1-0.799)}= 0.59$$

From this analysis, the level of agreement in the Kappa value was 0.59, which indicates that there was a moderate agreement.

ROC analysis

Figure 2 presented the ROC analysis in a one-to-one square. The area under the curve captures the relationship between the sensitivity and specificity of the Inter VA method and is indicative of how the method performed. The curve follows the left-hand border and then the top border of the ROC space indicating an acceptable level of accuracy.

Fig. 2
figure 2

Receiver operating characteristic curve of sensitivity and specificity

Full size image

In this study, the ROC curve indicated that the InterVA model predicted the cause of TB-related death with an area under curve or probability of 0.79 when compared with physician review which indicates the good diagnostic performance of the method. The sensitivity and specificity of the InterVA model were 0.64 and 0.95, respectively.

Discussion

This study compared the cause of TB-related death agreement between the InterVA model and physician review methods. The study showed that level of agreement between physician review and the InterVA model was 92.9% with the kappa value of 0.59, which indicated a moderate agreement. The finding was similar to a previous study in the country [24], Kappa 0.58% (95% CI: 0.50–0.65), but higher when compared with another study[25], kappa = 0.50, (95% CI: 0.4–0.6). This indicated the potential of the IntraVA model to be used to establish TB-related death data.

When it comes to assigning cause-specific mortalities using VA data at the population level, the probabilistic InterVA model produced substantially similar results as the physicians in this study, which was in line with previous studies [14, 26]. The InterVA model's cause-specific mortality data was consistent with existing knowledge about the burden of diseases in the sub-Saharan Africa context [27, 28], suggesting that the model performed well in generating cause-specific mortality data from VA.

The study finding showed that the overall cause of TB-related death both by physicians and the model were low when compared with previous studies from Ethiopia which has given a kappa value of 31%, with TB-related death to be 36.0% and 23.0% by the InterVA model and the physicians respectively [24]. This variation might be due to the study population and time of the study.

The issue of how to obtain a true gold standard in VA validation research arises repeatedly. CODs based on hospital diagnoses have been considered the gold standard in many studies [29, 30]. Hospital diagnoses, on the other hand, have limitations as a gold standard since the composition and distribution of hospital CODs may not be indicative of community mortality. Furthermore, in resource-constrained healthcare settings, when hospital diagnoses are available, they are of poor quality and are limited by insufficient clinical data and record keeping [31,32,33]. Moreover, hospital users and residential users may have varied abilities to notice, recall, and report indicators of sickness. Physician review was used as a reference standard in this study to investigate InterVA. For this study's population, physician review was the only option for COD assessment. This option, however, has drawbacks. Physicians' experiences, perceptions, and interpretations of local epidemiology might contribute to variations in COD data, making it difficult to make valid temporal and regional comparisons. Furthermore, they frequently make decisions based on open history and may not account for all indicators consistently. Our report showed that the use of the physician review has helped to find the most relevant facts relating to the cause of death by tuberculosis, but the choice had limitations. The physicians have considered the detailed information by going through the questionnaire, using their clinical skills and experiences in determining the cause of death. They might however be influenced by their own biases.

Deaths during TB treatment signify gaps in the accurate implementation of TB programs [34, 35] and this has been higher in countries like Ethiopia where the healthcare system is hindered by infrastructure and human resource constraints [36,37,38,39]. The current study sheds light on the role and feasibility of using the InterVA model alternative to physician review of verbal autopsy that competes for the scarce health human resources.

The limitation of this study was that it used secondary data for the primary analysis which is because the questionnaire is developed for all causes of death not specifically for TB-related death identification. Another drawback of using physician review as a gold standard is that physicians may misinterpret some of the VA data, leading to a potentially incorrect cause of death result. The experience, observation, and interpretation of the physicians might also influence interpretations and reach a biased decision of cause of death. Otherwise, the study was conducted carefully and the data management and analysis were conducted in line with the standard and appropriate procedures and statistics methods.

Conclusion

The InterVA model is a viable alternative to physician review for tracking TB-related causes of death in Ethiopia. From a public health perspective, InterVA helps to analyze the underlying causes of TB-related deaths cost-effectively using routine survey data and translate to policies and strategies in resource-constrained countries.

Availability of data and materials

The dataset supporting the conclusions of this article is included in the article.

Abbreviations

VA:

Verbal autopsy

TB:

Tuberculosis

PCVA:

Physician-certified verbal autopsy

k:

Cohen’s Kappa

MDR-TB:

Multidrug-resistant TB

RR-TB:

Rifampicin-resistant TB

AAMSP:

Ababa Mortality Surveillance Program

GP:

General practitioner

PR:

Physicians review

WHO:

World Health Organization

ROC:

Receiver operator characteristics

InterVA:

Verbal Autopsy Expert Algorithm

References

  1. El Bcheraoui C, Mimche H, Miangotar Y, Krish VS, Ziegeweid F, Krohn KJ. Burden of disease in francophone Africa, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Glob Health. 2020;8(3):e341–51.

    Article  Google Scholar 

  2. Global tuberculosis report 2020. https://www.who.int/publications-detail-redirect/9789240013131. Accessed 5 Dec 2021.

  3. Mohammed H, Oljira L, Roba KT, Ngadaya E, Ajeme T, Haile T. Burden of tuberculosis and challenges related to screening and diagnosis in Ethiopia. J Clin Tuberc Other Mycobact Dis. 2020;19: 100158.

    Article  Google Scholar 

  4. Manyazewal T, Woldeamanuel Y, Holland DP, Fekadu A, Blumberg HM, Marconi VC. Electronic pillbox-enabled self-administered therapy versus standard directly observed therapy for tuberculosis medication adherence and treatment outcomes in Ethiopia (SELFTB): protocol for a multicenter randomized controlled trial. Trials. 2020;21(1):383.

    Article  Google Scholar 

  5. Mohammed H, Oljira L, Roba KT, Yimer G, Fekadu A, Manyazewal T. Containment of COVID-19 in Ethiopia and implications for tuberculosis care and research. Infect Dis Poverty. 2020;9(1):131.

    Article  Google Scholar 

  6. Manyazewal T, Woldeamanuel Y, Blumberg HM, Fekadu A, Marconi VC. The fight to end tuberculosis must not be forgotten in the COVID-19 outbreak. Nat Med. 2020;26(6):811–2.

    Article  CAS  Google Scholar 

  7. Uneke CJ, Uro-Chukwu HC, Chukwu OE. Validation of verbal autopsy methods for assessment of child mortality in sub-Saharan Africa and the policy implication: a rapid review. Pan Afr Med J. 2019;33:318.

    Article  Google Scholar 

  8. Karat AS, Maraba N, Tlali M, Charalambous S, Chihota VN, Churchyard GJ. Performance of verbal autopsy methods in estimating HIV-associated mortality among adults in South Africa. BMJ Glob Health. 2018;3(4): e000833.

    Article  Google Scholar 

  9. Gebremedhin S. Development of a new model for estimating maternal mortality ratio at national and sub-national levels and its application for describing sub-national variations of maternal death in Ethiopia. PLoS ONE. 2018;13(8): e0201990.

    Article  Google Scholar 

  10. Silva R, Amouzou A, Munos M, Marsh A, Hazel E, Victora C, et al. RMM Working Group. Can community health workers report accurately on births and deaths? Results of field assessments in Ethiopia, Malawi and Mali. PLoS One. 2016;11(1):e0144662.

  11. Weldearegawi B, Spigt M, Berhane Y, Dinant G. Mortality level and predictors in a rural Ethiopian population: community based longitudinal study. PLoS ONE. 2014;9(3): e93099.

    Article  Google Scholar 

  12. Araya T, Tensou B, Davey G, Berhane Y. Burial surveillance detected significant reduction in HIV–related deaths in Addis Ababa, Ethiopia. Trop Med Int Health. 2011;16(12):1483–9.

    Article  Google Scholar 

  13. Araya T, Reniers G, Schaap A, Kebede D, Kumie A, Nagelkerke N, et al. Lay diagnosis of causes of death for monitoring AIDS mortality in Addis Ababa, Ethiopia. Trop Med Int Health. 2004;9(1):178–86.

    Article  Google Scholar 

  14. Oti SO, Kyobutungi C. Verbal autopsy interpretation: a comparative analysis of the InterVA model versus physician review in determining causes of death in the Nairobi DSS. Popul Health Metr. 2010;8:21.

    Article  Google Scholar 

  15. Fottrell E, Byass P. Verbal autopsy: methods in transition. Epidemiol Rev. 2010;32:38–55.

    Article  Google Scholar 

  16. Misganaw A, Mariam DH, Araya T, Aneneh A. Validity of verbal autopsy method to determine causes of death among adults in the urban setting of Ethiopia. BMC Med Res Methodol. 2012;12:130.

    Article  Google Scholar 

  17. INDEPTH Network. Accra, Ghana. http://www.indepth-network.org/resources/tools.

  18. Weldearegawi B, Ashebir Y, Gebeye E, Gebregziabiher T, Yohannes M, Mussa S, et al. Emerging chronic non-communicable diseases in rural communities of Northern Ethiopia: evidence using population-based verbal autopsy method in Kilite Awlaelo surveillance site. Health Policy Plan. 2013;28(8):891–8.

    Article  Google Scholar 

  19. Tadesse S, Tadesse T. Evaluating the performance of interpreting Verbal Autopsy 3.2 model for establishing pulmonary tuberculosis as a cause of death in Ethiopia: a population-based cross-sectional study. BMC Public Health. 2012;12:1039.

    Article  Google Scholar 

  20. Lulu K, Berhane Y. The use of simplified verbal autopsy in identifying causes of adult death in a predominantly rural population in Ethiopia. BMC Public Health. 2005;5:58.

    Article  Google Scholar 

  21. Byass P, Chandramohan D, Clark SJ, D’Ambruoso L, Fottrell E, Graham WJ, et al. Strengthening standardised interpretation of verbal autopsy data: the new InterVA-4 tool. Glob Health Action. 2012;5:1–8.

    Article  Google Scholar 

  22. Byass P, Fottrell E, Huong DL, Berhane Y, Corrah T, Kahn K, et al. Refining a probabilistic model for interpreting verbal autopsy data. Scand J Public Health. 2006;34(1):26–31.

    Article  Google Scholar 

  23. Inter VA 3.2 model. http://www.interva.net. Accessed 2012 Feb 12.

  24. Tensou B, Araya T, Take DS, Byass P, Berhane Y, Kebebew T. Evaluating the InterVA model for determining AIDS mortality from verbal autopsies in the adult population of Addis Ababa. Trop Med Int Health. 2010;15(5):547–53.

    PubMed  PubMed Central  Google Scholar 

  25. Tadesse S. Validating the InterVA model to estimate the burden of mortality from verbal autopsy data: a population-based cross-sectional study. PLoS ONE. 2013;8(9): e73463.

    Article  CAS  Google Scholar 

  26. Fantahun M, Fottrell E, Berhane Y, Wall S, Högberg U, Byass P. Assessing a new approach to verbal autopsy interpretation in a rural Ethiopian community: the InterVA model. Bull World Health Organ. 2006;84(3):204–10.

    Article  Google Scholar 

  27. Jamison DT, Feachem RG, Makgoba MW, Bos ER, Baingana FK, Hofman KJ, et al. Disease and Mortality in Sub-Saharan Africa. 2nd ed. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2006.

  28. de-Graft Aikins A, Unwin N, Agyemang C, Allotey P, Campbell C, Arhinful D. Tackling Africa’s chronic disease burden: from the local to the global. Global Health. 2010;6:5.

    Article  Google Scholar 

  29. Setel PW, Macfarlane SB, Szreter S, Mikkelsen L, Jha P, Stout S, et al. Monitoring of vital events. A scandal of invisibility: making everyone count by counting everyone. Lancet. 2007;370(9598):1569–77.

    Article  Google Scholar 

  30. Bauni E, Ndila C, Mochamah G, Nyutu G, Matata L, Ondieki C, et al. Validating physician-certified verbal autopsy and probabilistic modeling (InterVA) approaches to verbal autopsy interpretation using hospital causes of adult deaths. Popul Health Metr. 2011;9:49.

    Article  Google Scholar 

  31. Mohammed H, Oljira L, Roba KT, Ngadaya E, Tesfaye D, Manyazewal T, et al. Impact of early chest radiography on delay in pulmonary tuberculosis case notification in Ethiopia. Int J Mycobacteriol. 2021;10(4):364–72.

    Article  Google Scholar 

  32. Mohammed H, Oljira L, Teji Roba K, Ngadaya E, Mehari R, Manyazewal T, et al. Who to involve and where to start integrating tuberculosis screening into routine healthcare services: positive cough of any duration as the first step for screening tuberculosis in Ethiopia. Risk Manag Healthc Policy. 2021;14:4749–56.

    Article  Google Scholar 

  33. Said B, Charlie L, Getachew E, Said B, Wanjiru CL, Abebe M, Manyazewal T. Molecular bacterial load assay versus culture for monitoring treatment response in adults with tuberculosis. SAGE Open Med. 2021;9:20503121211033470.

    Article  Google Scholar 

  34. Naik PR, Moonan PK, Nirgude AS, Shewade HD, Satyanarayana S, Raghuveer P, et al. Use of verbal autopsy to determine underlying cause of death during treatment of multidrug-resistant tuberculosis, India. Emerg Infect Dis. 2018;24(3):478–84.

    Article  Google Scholar 

  35. Charlie L, Saidi B, Getachew E, Wanjiru CL, Abebe M, Tesfahunei HA, et al. Programmatic challenges in managing multidrug-resistant tuberculosis in Malawi. Int J Mycobacteriol. 2021;10(3):255–9.

    CAS  PubMed  Google Scholar 

  36. Mussie KM, Gradmann C, Yimer SA, Manyazewal T. Pragmatic management of drug-resistant tuberculosis: a qualitative analysis of human resource constraints in a resource-limited country context-Ethiopia. Int J Public Health. 2021;66: 633917.

    Article  Google Scholar 

  37. Temesgen E, Belete Y, Haile K, Ali S. Prevalence of active tuberculosis and associated factors among people with chronic psychotic disorders at St. Amanuel Mental Specialized Hospital and Gergesenon Mental Rehabilitation center, Addis Ababa, Ethiopia. BMC Infect Dis. 2021;21(1):1100.

    Article  Google Scholar 

  38. Manyazewal T, Woldeamanuel Y, Blumberg HM, Fekadu A, Marconi VC. The potential use of digital health technologies in the African context: a systematic review of evidence from Ethiopia. NPJ Digit Med. 2021;4(1):125.

    Article  Google Scholar 

  39. Mussie KM, Yimer SA, Manyazewal T, Gradmann C. Exploring local realities: perceptions and experiences of healthcare workers on the management and control of drug-resistant tuberculosis in Addis Ababa, Ethiopia. PLoS ONE. 2019;14(11): e0224277.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors are thankful to the Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), Addis Ababa University, for the successful coordination of the work. The authors are grateful for their permission to use the needed data for the Addis Ababa Mortality surveillance program. We are grateful to Professor Peter Byass, who trained the application of InterVA to the Addis Ababa Mortality surveillance research team.

Funding

This work was supported in part by the European and Developing Countries Clinical Trials Partnership (EDCTP2) program supported by the European Union under grant number CSA2016S-1608. TM was supported by the Fogarty International Center and National Institute of Allergy and Infectious Diseases of the U.S. National Institutes of Health under Award Number D43TW009127.

Author information

Authors and Affiliations

  1. Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, P.O. Box 9086, Addis Ababa, Ethiopia

    Haileleuel Bisrat, Tsegahun Manyazewal, Hussen Mohammed, Bilal Shikur & Getnet Yimer

  2. Department of Public Health, College of Medicine and Health Science, Dire Dawa University, Dire Dawa, Ethiopia

    Hussen Mohammed

  3. Department of Public Health, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia

    Bilal Shikur & Getnet Yimer

Authors
  1. Haileleuel Bisrat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tsegahun Manyazewal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hussen Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bilal Shikur
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Getnet Yimer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HB conceived the idea, analyzed the data, and write the first draft; TM, HM, GY, and BS revised the draft; and all authors approved the final version for publication. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Haileleuel Bisrat.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Institutional Review Board (IRB) of the College of Health Sciences, Addis Ababa University (Ref. No. AAUMF 03-008) and The Federal Democratic Republic of Ethiopia Ministry of Science and Technology (Ref. No. 310/616/06). Letters of permission were obtained from respective facilities. After enough time is given by responding to any question raised by study participants, interviews and procedures were performed. All procedures were performed as per Helsinki Declaration and national ethical standard. Informed consent for participation was obtained from caregivers or another eligible adult in the family. It was performed in a private space. The English language consent or parental consent and/or assent form was translated to Amharic back-translated to English for validation.

Consent for publication

Not applicable.

Competing of interests

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bisrat, H., Manyazewal, T., Mohammed, H. et al. Validity of InterVA model versus physician review of verbal autopsy for tracking tuberculosis-related mortality in Ethiopia. BMC Infect Dis 22, 200 (2022). https://doi.org/10.1186/s12879-022-07193-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12879-022-07193-w

Keywords

BMC Infectious Diseases

ISSN: 1471-2334

Contact us