Background: Computer-coded verbal autopsy (CCVA) methods to assign causes of death (CODs) for medically unattended deaths have been proposed as an alternative to physician-certified verbal autopsy (PCVA). We conducted a systematic review of 19 published comparison studies (from 684 evaluated), most of which used hospital-based deaths as the reference standard. We assessed the performance of PCVA and five CCVA methods: Random Forest, Tariff, InterVA, King-Lu, and Simplified Symptom Pattern. Methods: The reviewed studies assessed methods’ performance through various metrics: sensitivity, specificity, and chance-corrected concordance for coding individual deaths, and cause-specific mortality fraction (CSMF) error and CSMF accuracy at the population level. These results were summarized into means, medians, and ranges. Results: The 19 studies ranged from 200 to 50,000 deaths per study (total over 116,000 deaths). Sensitivity of PCVA versus hospital-assigned COD varied widely by cause, but showed consistently high specificity. PCVA and CCVA methods had an overall chance-corrected concordance of about 50% or lower, across all ages and CODs. At the population level, the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs. Random Forest had the best CSMF accuracy performance, followed closely by PCVA and the other CCVA methods, but with lower values for InterVA-3. Conclusions: There is no single best-performing coding method for verbal autopsies across various studies and metrics. There is little current justification for CCVA to replace PCVA, particularly as physician diagnosis remains the worldwide standard for clinical diagnosis on live patients. Further assessments and large accessible datasets on which to train and test combinations of methods are required, particularly for rural deaths without medical attention.

Background: Verbal autopsy (VA) has been proposed to determine the cause of death (COD) distributions in settings where most deaths occur without medical attention or certification. We develop performance criteria for VA-based COD systems and apply these to the Registrar General of India’s ongoing, nationally-representative Indian Million Death Study (MDS). Methods: Performance criteria include a low ill-defined proportion of deaths before old age; reproducibility, including consistency of COD distributions with independent resampling; differences in COD distribution of hospital, home, urban or rural deaths; age-, sex- and time-specific plausibility of specific diseases; stability and repeatability of dual physician coding; and the ability of the mortality classification system to capture a wide range of conditions. Results: The introduction of the MDS in India reduced the proportion of ill-defined deaths before age 70 years from 13% to 4%. The cause-specific mortality fractions (CSMFs) at ages 5 to 69 years for independently resampled deaths and the MDS were very similar across 19 disease categories. By contrast, CSMFs at these ages differed between hospital and home deaths and between urban and rural deaths. Thus, reliance mostly on urban or hospital data can distort national estimates of CODs. Age-, sex- and time-specific patterns for various diseases were plausible. Initial physician agreement on COD occurred about two-thirds of the time. The MDS COD classification system was able to capture more eligible records than alternative classification systems. By these metrics, the Indian MDS performs well for deaths prior to age 70 years. The key implication for low- and middle-income countries where medical certification of death remains uncommon is to implement COD surveys that randomly sample all deaths, use simple but high-quality field work with built-in resampling, and use electronic rather than paper systems to expedite field work and coding. Conclusions: Simple criteria can evaluate the performance of VA-based COD systems. Despite the misclassification of VA, the MDS demonstrates that national surveys of CODs using VA are an order of magnitude better than the limited COD data previously available.

Background: Physician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (CODs) in countries where medical certification of death is uncommon. Computer-coded verbal autopsy (CCVA) methods have been proposed as a faster and cheaper alternative to PCVA, though they have not been widely compared to PCVA or to each other. Methods: We compared the performance of open-source random forest, open-source tariff method, InterVA-4, and the King-Lu method to PCVA on five datasets comprising over 24,000 verbal autopsies from low- and middle-income countries. Metrics to assess performance were positive predictive value and partial chance-corrected concordance at the individual level, and cause-specific mortality fraction accuracy and cause-specific mortality fraction error at the population level. Results: The positive predictive value for the most probable COD predicted by the four CCVA methods averaged about 43% to 44% across the datasets. The average positive predictive value improved for the top three most probable CODs, with greater improvements for open-source random forest (69%) and open-source tariff method (68%) than for InterVA-4 (62%). The average partial chance-corrected concordance for the most probable COD predicted by the open-source random forest, open-source tariff method and InterVA-4 were 41%, 40% and 41%, respectively, with better results for the top three most probable CODs. Performance generally improved with larger datasets. At the population level, the King-Lu method had the highest average cause-specific mortality fraction accuracy across all five datasets (91%), followed by InterVA-4 (72% across three datasets), open-source random forest (71%) and open-source tariff method (54%). Conclusions: On an individual level, no single method was able to replicate the physician assignment of COD more than about half the time. At the population level, the King-Lu method was the best method to estimate cause-specific mortality fractions, though it does not assign individual CODs. Future testing should focus on combining different computer-coded verbal autopsy tools, paired with PCVA strengths. This includes using open-source tools applied to larger and varied datasets (especially those including a random sample of deaths drawn from the population), so as to establish the performance for age- and sex-specific CODs.

Extreme heat and heat waves have been established as disasters which can lead to a great loss of life. Several studies over the years, both within and outside of India, have shown how extreme heat events lead to an overall increase in mortality. However, the impact of extreme heat, similar to other disasters, depends upon the vulnerability of the population. This study aims to assess the extreme heat vulnerability of the population of four cities with different characteristics across India. This cross-sectional study included 500 households from each city across the urban localities (both slum and non-slum) of Ongole in Andhra Pradesh, Karimnagar in Telangana, Kolkata in West Bengal and Angul in Odisha. Twenty-one indicators were used to construct a household vulnerability index to understand the vulnerability of the cities. The results have shown that the majority of the households fell under moderate to high vulnerability level across all the cities. Angul and Kolkata were found to be more highly vulnerable as compared to Ongole and Karimnagar. Further analysis also revealed that household vulnerability is more significantly related to adaptive capacity than sensitivity and exposure. Heat Vulnerability Index can help in identifying the vulnerable population and scaling up adaptive practices.

Food adequacy and dietary quality in the lactation period are fundamental for maternal and child health. Lactating mothers are vulnerable to malnutrition because of increased physiological demand, monotonous diet, lactogenesis process, and increased nutrient requirements. The micronutrient adequacy especially among women is not ensured in Indian diet. The dual course of gender bias and poverty, along with lack of knowledge about diet quality are significant impediments in maintaining minimum dietary diversity among Indian women. The study aimed to assess the prevalence of minimum dietary diversity and associated factors among lactating women. A community-based cross-sectional study was conducted among 1236 lactating women through a multistage sampling procedure in Haryana state, India. Data were collected in Computer-assisted personal interviewing (CAPI) using a pretested structured interview schedule. Minimum Dietary Diversity for Women by Food and Agriculture Organization (FAO) was used to calculate the minimum dietary diversity. The mean dietary diversity score among lactating women from the ten food groups was 6.35 ± 2.57 and the prevalence of minimum dietary diversity was 77.1%. The complete model revealed that both individual and household factors can explain the variation in dietary diversity intake. Furthermore, the result of model 2 explained that women aged 31 to 35 years (AOR 5.92,95% (1.87–18.77), graduation and above qualified women (AOR 1.98, 95% (0.96–4.09) and lactating women with high knowledge on nutrition (AOR 2.00, 95% (1.34–4.57) were the significant factors promoting minimum dietary diversity. Three-fourths of the lactating women reached adequate minimum dietary diversity. Younger age, low educational level, and poor nutritional knowledge were significant constraints to achieving minimum dietary diversity. Further improvement in the minimum dietary diversity among lactating women is very much required. It is also advised that exiting platforms dispersing awareness on nutrition should be supported and strengthened.

Objective: This study aims to estimate the prevalence of human papillomavirus (HPV) vaccine usage and determine the factors for awareness about HPV vaccine among women in reproductive age group. Methods: This is a cross-sectional survey under a cervical cancer prevention study. The sample size was 1020 women, aged 15-49 years [550 in Delhi and 470 in Rohtak]. Bivariate analysis and Fisher exact test along with binary logistic regression analysis were used to determine the factors for awareness. Result: About 18.0 % [Delhi: 24.2 % and Rohtak: 10.9 %] of the respondents had heard about the vaccine against cervical cancer. The women aged more than 30 years [AOR: 1.35; CI: 0.94, 1.94] were more likely to be aware of cervical cancer vaccine as compare to women of 30 years and less. However, the women from Rohtak [AOR: 0.90; CI: 0.48, 1.66] were less likely to be aware of vaccine against cervical cancer in reference to women aged 30 years and more [AOR: 1.61; CI: 1.01, 2.56] from Delhi. About 0.6 % [Delhi: 1.1 % and Rohtak: 0.0 %] of the respondents had received HPV vaccine. Conclusion: Women tend to have limited knowledge about cervical cancer vaccine and immunisation practices. The women’s demographic makeup varied significantly between the two sites, i.e , Rohtak and Delhi, which had an impact on how well they understood and utilised the cervical cancer vaccination. It is worth mentioning that none of the women from Rohtak had received the immunisation. The awareness of the cervical cancer vaccine among women from the Rohtak was lower than the Delhi women.