Social risk and its association with tuberculosis mortality in a context of high inequality in South Brazil: A geo-epidemiology analysis

Social determinants of respiratory health from birth: still of concern in the 21st century?

Respiratory symptoms are ubiquitous in children and, even though they may be the harbinger of poor long-term outcomes, are often trivialised. Adverse exposures pre-conception, antenatally and in early childhood have lifetime impacts on respiratory health. For the most part, lung function tracks from the pre-school years at least into late middle age, and airflow obstruction is associated not merely with poor respiratory outcomes but also early all-cause morbidity and mortality. Much would be preventable if social determinants of adverse outcomes were to be addressed. This review presents the perspectives of paediatricians from many different contexts, both high and low income, including Europe, the Americas, Australasia, India, Africa and China. It should be noted that there are islands of poverty within even the highest income settings and, conversely, opulent areas in even the most deprived countries. The heaviest burden of any adverse effects falls on those of the lowest socioeconomic status. Themes include passive exposure to tobacco smoke and indoor and outdoor pollution, across the entire developmental course, and lack of access even to simple affordable medications, let alone the new biologicals. Commonly, disease outcomes are worse in resource-poor areas. Both within and between countries there are avoidable gross disparities in outcomes. Climate change is also bearing down hardest on the poorest children. This review highlights the need for vigorous advocacy for children to improve lifelong health. It also highlights that there are ongoing culturally sensitive interventions to address social determinants of disease which are already benefiting children.

Tuberculosis mortality: quantifying agreement in clinical cause of death assessments

OBJECTIVES

Mortality is a key statistic for public health globally, and mortality reduction is a key target of 'End TB' strategy. However, cause of death in relation to tuberculosis (TB) may be controversial, and we aimed to evaluate classification in Australia.

METHODS

We surveyed Australian clinicians and public health officers, presenting a variety of scenarios. Respondents were asked to classify each scenario with regards to whether TB was considered causative, contributory or not related to death.

RESULTS

Fifty-nine individuals completed the survey. Respondents were experienced TB clinicians and public health officers (median 14 years of TB care experience), with a majority having recently been involved in death certification/classification. In most scenarios, there was substantial variation, particularly where death was related to TB medications, or if an alternative contributing process was recognised, such as cardiovascular complications. Variation in classification was not evidently associated with classification experience.

CONCLUSION

We found significant variation in cause of death classification among experienced TB clinicians and public health officers, using representative TB death scenarios.

IMPLICATIONS FOR PUBLIC HEALTH

Consensus and transparency with regards to classification would assist in more uniform cause of death classification across jurisdictions and allow for better tracking of this critical performance measure.

Investigating the effect of macro-scale estimators on worldwide COVID-19 occurrence and mortality through regression analysis using online country-based data sources

OBJECTIVE

To investigate macro-scale estimators of the variations in COVID-19 cases and deaths among countries.

DESIGN

Epidemiological study.

SETTING

Country-based data from publicly available online databases of international organisations.

PARTICIPANTS

The study involved 170 countries/territories, each of which had complete COVID-19 and tuberculosis data, as well as specific health-related estimators (obesity, hypertension, diabetes and hypercholesterolaemia).

PRIMARY AND SECONDARY OUTCOME MEASURES

The worldwide heterogeneity of the total number of COVID-19 cases and deaths per million on 31 December 2020 was analysed by 17 macro-scale estimators around the health-related, socioeconomic, climatic and political factors. In 139 of 170 nations, the best subsets regression was used to investigate all potential models of COVID-19 variations among countries. A multiple linear regression analysis was conducted to explore the predictive capacity of these variables. The same analysis was applied to the number of deaths per hundred thousand due to tuberculosis, a quite different infectious disease, to validate and control the differences with the proposed models for COVID-19.

RESULTS

In the model for the COVID-19 cases (R²=0.45), obesity (β=0.460), hypertension (β=0.214), sunshine (β=-0.157) and transparency (β=0.147); whereas in the model for COVID-19 deaths (R²=0.41), obesity (β=0.279), hypertension (β=0.285), alcohol consumption (β=0.173) and urbanisation (β=0.204) were significant factors (p<0.05). Unlike COVID-19, the tuberculosis model contained significant indicators like obesity, undernourishment, air pollution, age, schooling, democracy and Gini Inequality Index.

CONCLUSIONS

This study recommends the new predictors explaining the global variability of COVID-19. Thus, it might assist policymakers in developing health policies and social strategies to deal with COVID-19.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT04486508).

Impact of socio-economic factors on Tuberculosis treatment outcomes in north-eastern Uganda: a mixed methods study

BACKGROUND

Tuberculosis (TB) is a major public health problem and at 48%, Karamoja in North-Eastern Uganda has the lowest treatment success rate nationally. Addressing the social determinants of TB is crucial to ending TB. This study sought to understand the extent and ways in which socio-economic factors affect TB treatment outcomes in Karamoja.

METHODS

We conducted a convergent parallel mixed methods study in 10 TB Diagnostic and Treatment Units. The study enrolled former TB patients diagnosed with drug-susceptible TB between April 2018 and March 2019. Unit TB and laboratory registers were reviewed to identify pre-treatment losses to follow-up. Four focus group discussions with former TB patients and 18 key informant interviews with healthcare workers were conducted. Principle component analysis was used to generate wealth quintiles that were compared to treatment outcomes using the proportion test. The association between sociodemographic characteristics and TB treatment outcomes was evaluated using the chi-square test and multiple logistic regression.

RESULTS

A total of 313 participants were randomly selected from 1184 former TB patients recorded in the unit TB registers. Of these, 264 were contacted in the community and consented to join the study: 57% were male and 156 (59.1%) participants had unsuccessful treatment outcomes. The wealthiest quintile had a 58% reduction in the risk of having an unsuccessful treatment outcome (adj OR = 0.42, 95% CI 0.18-0.99, p = 0.047). People who were employed in the informal sector (adj OR = 4.71, 95% CI 1.18-18.89, p = 0.029) and children under the age of 15 years who were not in school or employed (adj OR = 2.71, 95% CI 1.11-6.62, p = 0.029) had significantly higher odds of unsuccessful treatment outcome. Analysis of the pre-treatment loss to follow-up showed that 17.2% of patients with pulmonary bacteriologically confirmed TB did not initiate treatment with a higher proportion among females (21.7%) than males (13.5%). Inadequate food, belonging to migratory communities, stigma, lack of social protection, drug stock-outs and transport challenges affected TB treatment outcomes.

CONCLUSIONS

This study confirmed that low socio-economic status is associated with poor TB treatment outcomes emphasizing the need for multi- and cross-sectoral approaches and socio-economic enablers to optimise TB care.

Spatial analysis of tuberculosis cure in primary care in Rio de Janeiro, Brazil

Background

Tuberculosis (TB) presents a high burden of disease and is considered a global emergency by the World Health Organization (WHO), as the leading cause of death from infectious disease in adults. TB incidence is related directly to access to health services and socioeconomic determinants and inequality. Providing primary care settings can lead to improved access, shorter waiting times for patients, and enhanced TB case detection. The article aims to identify the spatial and temporal risk areas for TB and the relationship between TB cure and primary healthcare coverage from 2012 to 2014 in Rio de Janeiro, Brazil.

Methods

A cross-sectional study was conducted in Rio de Janeiro, Brazil. All cases of TB reported to the Information System on Diseases of Notification (SINAN) from 2012 to 2014 were included. Socioeconomic variables from the 2010 Brazilian national census were also added. Socioeconomic variables were selected from multivariate analysis using principal factors analysis. Spatial association was verified with generalized additive model (GAM). It was possible to identify areas at higher risk of failure to cure TB.

Results

TB rates showed strong positive spatial autocorrelation. TB cure rate varied according to schooling (individuals with complete secondary schooling had higher cure rates than illiterate individuals; OR 1.72, 95% CI 1.30–2.29), alcohol consumption (OR 0.47, 95% CI 0.35–0.64), contact investigation (OR 2.00, 95% CI 1.56–2.57), positive HIV serology (OR 0.31, 95% CI 0.23–0.42), and census tracts with higher elderly rates (OR 9.39, 95% CI 1.03–85.26). Individuals who had been covered by primary healthcare (PHC) for 35 to 41 months had 1.64 higher odds of cure, compared to those with no PHC coverage (95% CI 1.07–2.51).

Conclusion

A comprehensive risk map was developed, allowing public health interventions. Spatial analysis allowed identifying areas with lower odds of TB cure in the city of Rio de Janeiro. TB cure was associated statistically with time of coverage by primary healthcare. TB cure rate also varied according to sociodemographic factors like schooling, alcohol abuse, and population density.

This methodology can be generalized to other areas and/or other public health problems.

Highlights

We studied standardized municipal TB cure rates in an area of social inequality in Brazil.

TB rates showed strong positive spatial autocorrelation.

Higher rates were associated with population density and socioeconomic conditions. Illiterate individuals were less likely to achieve TB cure.

TB cure was less likely in individuals with HIV and alcohol abuse.

TB cure was greater in areas with high primary healthcare coverage.