Urban‐rural and sex differentials in tuberculosis mortality in Bangladesh: results from a population‐based survey

Abstract

Objective To assess tuberculosis mortality in Bangladesh through a population‐based survey using a Verbal Autopsy tool. Methods Nationwide mortality survey employing the WHO‐recommended Verbal Autopsy (VA) tool, and using InsilicoVA, a data‐driven method, to assign the cause of death. Using a three‐stage cluster sampling method, 3997 VA interviews were conducted in both urban and rural areas of Bangladesh. Cause‐specific mortality fractions (CSMF) were estimated using Bayesian probabilistic models. Results 6.8% of total deaths in the population were due to TB [95% CI: (5.1, 8.9)], comprising 12.0% [95% CI: (11.1, 12.8)] and 6.42% [95% CI: (5.4, 7.3)] of total male and female deaths, respectively. This proportion was highest among adults age 15–49 years [12.2%, 95% CI: (9.4, 14.6)]. The urban population is more likely to die from TB, and urban males have highest CSMF [13.6%, 95% CI: (9.1, 16.9)]. Conclusion Our survey results show that TB is the fifth major cause of death in the general population and that sex and place of residence (urban/rural) have a significant effect on TB mortality in Bangladesh. The underlying causes of higher rates of TB‐related deaths in urban areas and particularly among urban males, who have better knowledge and higher enrollment in the DOTS Program, need to be explored.

Availability and price changes of potential medicines and equipment for the prevention and treatment of COVID-19 among pharmacy and drug stores in Bangladesh; findings and implications

“…Encouragingly for patients, prices of essential antibiotics in private pharmacies in Bangladesh, including model stores, were not much higher than international prices in a recent study by , with only limited price increases between 2003 and 2019 44 . The increase in cases with dengue 21 , and the continued challenges with tuberculosis in Bangladesh, with patients typically seeking help from non-qualified practitioners before seeking help from qualified professionals, also needs addressing [45][46][47][48] . Encouragingly, whilst there are still cases of malaria in Bangladesh, co-ordinated activities have reduced these by more than 50% in 2016 versus 2010 49 .…”

Spatio-temporal variation in tuberculosis incidence and risk factors for the disease in a region of unbalanced socio-economic development

“…This finding was likely because both health and economic conditions are good in the cities in the study region, with few low-income groups. In contrast, in the study conducted in Bangladesh, regional differences in TB death rates were associated with a high prevalence of the disease in urban areas [ 29 ]. These urban areas had a high population density, with one-third of the residents living in slums, a scenario that is favourable for TB transmission [ 29 ].…”

“…Our findings support those of other studies, which reported different urban and rural epidemiological characteristics of elderly TB patients, however, there were inconsistencies between these studies in terms of the TB risk level. For example, in a study carried out in Bangladesh, the authors reported higher TB-related mortality rates in urban than rural regions [ 29 ]. Furthermore, results from Tanzania revealed significant differences in the epidemiological characteristics of TB in urban versus rural areas, with recurrent TB in rural but not urban regions [ 30 ].…”

Verbal autopsy models in determining causes of death

“…InterVA assumes that the symptoms are conditionally independent given a cause, an assumption that is likely reasonable for many symptoms but discards valuable information in particular cases that could be used to improve the overall performance of the model. (ii) InSilicoVA: is a probabilistic statistical tool that uses a hierarchical Bayesian framework to estimate the CoD for individuals and the population distribution [21,30]. InSilicoVA can be deployed where no access to the gold standard, and no labelled outcomes.…”

“…However, the model requires a training dataset from a prior validation study [25]. Bayes rule PHRMC [8,18,21,26] HDSS [30] China and Tanzania [21] Philippines, Mexico, India [8,30], China [26] Hospital & Community Statistical Features [21,30] Hospital Statistical Narratives Features [8] ~9.4% (single cause) ~46.0% (multiple cause) (Adult) [26] 91% [21] 70% [8] ~18 (single cause) ~68% (multiple cause) (Adult) [26] 64% [30] Better [21] 68% [8] Less [26] Random Forest [8,21] Random Forest PHRMC China, Tanzania [21] Philippines, Mexico, Tanzania, India [8] Hospital & Community Statistical Features [21] Hospital Statistical & Narratives Features [8] 41% [21] 45% [8] 71% [21] 77% [8] Better [21] 68% [8] Symptom Pattern (SP) [27] Bayes 45.8% adults [26], 51.5% for children [26], and 32.5% for neonates [26] 74% [8] 0.710 for adults [26], 0.739 for children [26], and 0.751 for neonates [26] 68% [8] Less [26] SCI, CCC, CCVA, and PCVA.…”