Tuberculosis mortality and the male survival deficit in rural South Africa:An observational community cohort study

BACKGROUND: Women live on average five years longer than men, and the sex difference in longevity is typically lower in populations with high mortality. South Africa-a high mortality population with a large sex disparity-is an exception, but the causes of death that contribute to this difference are not well understood. METHODS: Using data from a demographic surveillance system in rural KwaZulu-Natal (2000-2014), we estimate differences between male and female adult life expectancy by HIV status. The contribution of causes of death to these life expectancy differences are computed with demographic decomposition techniques. Cause of death information comes from verbal autopsy interviews that are interpreted with the InSilicoVA tool. RESULTS: Adult women lived an average of 10.4 years (95% confidence Interval 9.0-11.6) longer than men. Sex differences in adult life expectancy were even larger when disaggregated by HIV status: 13.1 (95% confidence interval 10.7-15.3) and 11.2 (95% confidence interval 7.5-14.8) years among known HIV negatives and positives, respectively. Elevated male mortality from pulmonary tuberculosis (TB) and external injuries were responsible for 43% and 31% of the sex difference in life expectancy among the HIV negative population, and 81% and 16% of the difference among people living with HIV. CONCLUSIONS: The sex differences in adult life expectancy in rural KwaZulu-Natal are exceptionally large, atypical for an African population, and largely driven by high male mortality from pulmonary TB and injuries. This is the case for both HIV positive and HIV negative men and women, signalling a need to improve the engagement of men with health services, irrespective of their HIV status

Automated versus physician assignment of cause of death for verbal autopsies: randomized trial of 9374 deaths in 117 villages in India

Abstract Background Verbal autopsies with physician assignment of cause of death (COD) are commonly used in settings where medical certification of deaths is uncommon. It remains unanswered if automated algorithms can replace physician assignment. Methods We randomized verbal autopsy interviews for deaths in 117 villages in rural India to either physician or automated COD assignment. Twenty-four trained lay (non-medical) surveyors applied the allocated method using a laptop-based electronic system. Two of 25 physicians were allocated randomly to independently code the deaths in the physician assignment arm. Six algorithms (Naïve Bayes Classifier (NBC), King-Lu, InSilicoVA, InSilicoVA-NT, InterVA-4, and SmartVA) coded each death in the automated arm. The primary outcome was concordance with the COD distribution in the standard physician-assigned arm. Four thousand six hundred fifty-one (4651) deaths were allocated to physician (standard), and 4723 to automated arms. Results The two arms were nearly identical in demographics and key symptom patterns. The average concordances of automated algorithms with the standard were 62%, 56%, and 59% for adult, child, and neonatal deaths, respectively. Automated algorithms showed inconsistent results, even for causes that are relatively easy to identify such as road traffic injuries. Automated algorithms underestimated the number of cancer and suicide deaths in adults and overestimated other injuries in adults and children. Across all ages, average weighted concordance with the standard was 62% (range 79–45%) with the best to worst ranking automated algorithms being InterVA-4, InSilicoVA-NT, InSilicoVA, SmartVA, NBC, and King-Lu. Individual-level sensitivity for causes of adult deaths in the automated arm was low between the algorithms but high between two independent physicians in the physician arm. Conclusions While desirable, automated algorithms require further development and rigorous evaluation. Lay reporting of deaths paired with physician COD assignment of verbal autopsies, despite some limitations, remains a practicable method to document the patterns of mortality reliably for unattended deaths. Trial registration ClinicalTrials.gov , NCT02810366. Submitted on 11 April 2016

Summary statistics by gender and HIV status, 2010–2014.

<p>Summary statistics by gender and HIV status, 2010–2014.</p

Trends of the female advantage in adult life expectancy (overall and by HIV status), 2000–2014.

<p>Notes: ¹ The difference is the average number of extra years that adult women are expected to live compared to adult men in uMkhanyakude. ² The confidence interval for the HIV positive population in 2010 has been truncated and should extend from -21.7 to 19.0.</p

Contributions of causes of death to the total sex difference in adult life expectancy, by HIV status (2010–2014).

<p>Contributions of causes of death to the total sex difference in adult life expectancy, by HIV status (2010–2014).</p

Adult life expectancy by sex and WHO region, 2013.

<p>Source: WHO Global Health Observatory Data, <a href="http://www.who.int/gho/en/" target="_blank">http://www.who.int/gho/en/</a>.</p