|Year : 2021 | Volume
| Issue : 2 | Page : 45-49
Sociodemographic determinants of tuberculosis burden in India
Manas Pratim Roy
Department of Pediatrics, Safdarjung Hospital, New Delhi, India
|Date of Submission||10-May-2021|
|Date of Acceptance||21-May-2021|
|Date of Web Publication||09-Aug-2021|
Dr. Manas Pratim Roy
Department of Pediatrics, Safdarjung Hospital, New Delhi.
Source of Support: None, Conflict of Interest: None
Introduction: Tuberculosis (TB), the eighth common cause of disability-adjusted life years in India, is a major problem for public health. Social factors such as economic condition and smoking have been implicated among the risk factors for TB. This paper reports the role of different sociodemographic factors in deciding the burden of TB in India. Materials and Methods: In an ecological approach, data from major national surveys were analyzed: National Family Health Survey 4, Global Adult Tobacco Survey 2, and India: Health of the Nation’s States. Spearman correlation coefficient and multivariate linear regression were used for state-wise analysis. Results: North India seems to be the major contributor to the national TB burden. Several factors such as the use of clean fuel, tobacco use, and economic condition were seen to impact TB burden. On multivariate analysis, only clean fuel was found to be significant (r = −0.540, P = 0.002). Conclusion: A multi-pronged approach for appropriate policy decisions for focussing on sociodemographic factors is the need of the hour for reducing the mortality burden due to TB.
Keywords: Economic condition, TB, tobacco
|How to cite this article:|
Roy MP. Sociodemographic determinants of tuberculosis burden in India. Amrita J Med 2021;17:45-9
| Introduction|| |
Tuberculosis (TB), with a 3.2% share of disability-adjusted life years (DALY), is one of the major causes of morbidity in India. Over the years, with a surge in non-communicable diseases, the proportion has reduced, in comparison to the status in 1990. Still, with 2.79 million patients, India contributes a major bulk in the global burden of TB, which stands at a mammoth 10.4 million.
As sustainable development goals aim for ending the epidemic of TB by 2030, the need to understand the distribution of TB assumes more importance. With ongoing TB treatment averting 49 million deaths globally between 2000 and 2015, the strategy for prevention of TB should supplement the success, thereby paving the way for achieving our target. As complications such as drug resistance loom large, the preventive approach gets imperative in our fight against TB.
It is evident from the literature that the mathematical model on social determinants of TB is limited. However, limited studies indicate that economic condition, smoking, alcohol abuse, and presence of co-morbidity such as diabetes may play a vital role for TB.,, Rapid urbanization could also fuel the spread of TB. However, there are not many efforts on record for putting sociodemographic aspects in national perspective and determining their roles in the distribution of TB across India. As the difference between States in terms of the burden of TB is huge, the role of sociodemographic factors in deciding such variation needs to be ascertained. The present article aimed to find the relationship of sociodemographic factors with TB burden in India.
| Materials and Methods|| |
India: Health of the Nation’s States, a report based on the Global Burden of Disease Study 2016, was used as the primary source for state-wise data on the burden of TB and diabetes. Tobacco-related data were taken from the Global Adult Tobacco Survey (GATS) 2: India 2016–17, whereas for use of clean fuel, National Family Health Survey (2015–16) (NFHS 4) was used. Under smokeless tobacco, betel quid with tobacco, khaini, gutka, and paan masala with tobacco; mishri, gul, bajjar and gudakhu, and snuff were considered. Electricity, LPG/natural gas, biogas were considered as a clean fuel., Per capita Net State Domestic Product (NSDP) for 2015–2016 was considered as an indicator of economic condition and expressed in INR (Indian currency). State/union territories (UTs) was the unit for analysis. A total of 30 units (29 states and one UT) were included for the analysis.
Approval from the Ethical Committee was not sought as it was a secondary analysis. The correlation was assessed between the burden of TB and other independent variables. For finding out predictors for TB mortality, bivariate and multivariate regressions were used. Statistical Package for the Social Sciences (version 19.0; SPSS Inc., Chicago, IL, USA) was used. P-value less than 0.05 was considered significant.
| Results|| |
States vary widely in terms of DALY contributed by TB, with Uttar Pradesh recording seven times than that of Kerala. Most of the contribution to the TB burden was from the northern half of the country, with Assam, Rajasthan, and Odisha playing significant roles. Notably, all these states recorded low economic conditions [Figure 1].
Moderately negative relation was noted between the use of clean fuel and TB burden (r = – 0.540, P = 0.002). A similar association was seen with economic conditions (r = –0.461, P = 0.010). A moderately positive relation was there with the use of tobacco (r = 0.332, P = 0.074). Similarly, smokeless tobacco was having a positive association with TB burden (r = 0.398, P = 0.029). Second-hand smoke (SHS) did not seem to contribute much in TB morbidity. The relation between tobacco use and TB burden is depicted in [Figure 2].
When the top 5 states were compared with the bottom 5, it was seen that the use of smokeless tobacco was a common factor for DALY attributed to TB [Figure 3].
|Figure 3: Prevalence of smokeless tobacco use and TB burden in some states|
Click here to view
The bivariate analysis [Table 1] found the use of clean fuel, economic condition, and smokeless tobacco having a significant relationship with TB burden. On stepwise multivariate linear regression, only clean fuel stood significant.
| Discussion|| |
State-wise variation was notable. While Uttar Pradesh recorded seven times higher TB burden than Kerala, Rajasthan, Odisha, and Madhya Pradesh are also contributing much to the DALY. The data sets allow comparing the states in different parameters. In fact, because of multifactorial causation, the disease burden of individual states may not be explained in a similar way. For example, a high smoking rate may have contributed to Meghalaya for a high burden. For Uttar Pradesh, it might be per capita income. Complex interactions of several factors, thus, decide the extent of TB burden in the community.
Tobacco seems to be an important factor in determining the TB burden. Comparatively stronger relation was noted between smokeless tobacco (SLT) and TB. A previous study from India indicates more frequency of SLT among TB patients. Another study from Malaysia found a similar trend, attributed to a common belief that SLT is a harmless product. Cigarettes and Other Tobacco Products Act (COTPA) has been implemented since 2003 in the country. The integration between Revised National Tuberculosis Control Programme (RNTCP) and COTPA would increase the effectiveness of both interventions. Under the National Tobacco Control Programme (NTCP), Tobacco Cessation Centre (TCC) is being set up at the district level. In the long run, this strategy is expected to pay rich dividends. For strengthening integration, DOTS providers may routinely advise tobacco users on cessation. It has been noted that tobacco does not get sufficient importance in policies tailored for managing TB, resulting in limited or no role of health workers addressing tobacco issues while addressing TB. In a review of south-east Asia, it was found that no country except India has formal coordination between these two programs. An avenue encouraging the merger of tobacco control and TB program would do wonder and reach a wider population.
Passive smoking was found to be associated with TB., Similar to smoking, SHS also exposes individuals to the risk of developing TB, just because the chemicals are the same. The intensity might be different from active smoking but at the population level, the same air is being polluted from smoking and being inhaled by everyone. One study found a dose–response relation in which a high number of cigarettes consumed in the household increases the risk of TB. Another research from South Africa suggested an association between a positive tuberculin test and the presence of a smoker in the household. A meta-analysis suggests an increase in the risk of acquiring TB infection and progression to TB, following exposure to passive smoking. Be it at home or work, we need to protect the vulnerable non-smokers from SHS. Mass education in this regard is pivotal.
Smoking is another recognized factor. It has been estimated that more than one in every six TB cases is due to smoking, particularly in countries with a high TB burden. The present study, however, fails to find any such relationship. Not considering the frequency and duration of smoking may be responsible for such a result. Smoking, being a self-reported event, depends on the perceived stigma of the individuals. Therefore, under-reporting could be another potential cause of not finding any association. Earlier, Kapoor et al. could not find any relation between TB and smoking. The present analysis could not find a relationship with alcohol consumption also. It may be noted that the risk of TB was found insignificant for males, when daily ethanol intake is less than 38 g. An analysis focussing on individual patient history with quantity of daily alcohol intake is more likely to fetch the exact nature of the relationship.
The relationship between lower socio-economic status and TB was seen earlier in developing countries., Associated factors such as migration, overcrowding, poor ventilation, and malnutrition play considerable roles in precipitating higher risk for developing TB. There might be catalytic action by lack of knowledge and restricted access to health care. The vicious cycle of poverty and illness makes economically challenged people more likely to get TB and less compliant to its treatment. They are more likely to experience unsuccessful treatment outcomes. To address the issue, there is a need for multi-sectoral coordination. We should collaborate and extend our reach beyond the health sector to ensure a continuous reduction in poverty. Under the RNTCP, there is provision for direct bank transfer of Rs. 500 per month to each TB patient for nutritional support. Preferential targeting of the poor can benefit TB control.
Indoor air pollution may render individuals susceptible to TB. About 17% of the TB cases are thought to be the result of such pollution. Analysis of NFHS data also indicates a higher TB prevalence in households using biomass fuel. Lack of separate cooking areas may also impact the occurrence. Women, because of their involvement in cooking, are the common victims of TB. Pradhan Mantri Ujjwala Yojana, introduced in 2016, is a step to ensure clean fuel even in the poorest households. With its reach in more than 80 million families, it is expected to be a game-changer in the future. Similar interventions on smoking and indoor air pollution could accelerate TB decline. Prospective study may be planned to explore this association further.
Among different factors, diabetes may increase the chance of acquiring TB three times. The convergence of epidemics of TB and diabetes in low- and middle-income countries remains a major challenge to address in the twenty-first century. With national programs adopting the strategy for checking every diabetes patient for TB, positive results may indicate potential treatment failure in the future. The present study, however, could not find a positive relationship between these two. Lack of individual level analysis may be one reason for that. In India, a coordination mechanism exists for diabetes and TB, in which a patient diagnosed with one is screened for the other. It helps in early diagnosis, making intervention possible at the pre-diabetic stage and leading to the prevention of co-morbidity. There are studies suggesting that diabetes might not be a risk factor for TB, particularly when most of the TB patients are in the younger age group.
It is understood that interventions by RNTCP like active case finding and contact tracing may be able to alter relations between variables and TB burden. There is also a need for raising levels of awareness and reduction of stigma in the community. A better understanding is required to design community intervention for mitigating TB morbidity. To end TB by 2025, integration between different stakeholders in public and private platforms is required for the successful implementation of the program.
The present paper is an effort to bring all states to a comparable platform and analyze the distribution of TB burden, in terms of some sociodemographic perspectives. Apart from tobacco and indoor air pollution, the study took economic status, alcohol abuse, and the presence of diabetes into account. The setbacks of ecological studies are there, as determining temporal relationship is beyond the scope of this paper. Lack of individual patient-centric approach is an area where future studies could improvize. Some individuals may have been exposed to both SHS and biomass fuel. In some cases, the presence of a well-ventilated kitchen might have negated the side effect of indoor pollution. Still, utilizing standard data sets for a better understanding of the differences in the TB burden was the need of the hour, particularly if we set our target for elimination. Discussing a topic of global importance and putting the focus on sociodemographic issues make this paper unique. We need to have a clear conceptual framework for delineating the role of different sociodemographic factors in deciding the burden of TB and to determine further course of action for bringing in mechanisms for balanced dynamics. It is expected to open avenues for further epidemiological studies on TB with a focus on sociodemographic factors.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3]