The impact of social protection and poverty elimination on global tuberculosis incidence: a statistical modelling analysis of Sustainable Development Goal 1
The reduced framework linked SDG 1 subtargets directly to tuberculosis incidence, validated by correlational analyses and an assessment for mediation. Ending extreme poverty and expanding social protection coverage each resulted in a reduction in the global incidence of tuberculosis by 2035. Both direct effects together also resulted in a reduction in incidence, suggesting social protection expansion is key.
The scale of the reduction in tuberculosis incidence projected is particularly important in view of a 2017 study14x14Abajobir, AA, Abate, KH, Abbafati, C et al. Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016. Lancet. 2017;
Summary | Full Text | Full Text PDF | PubMed | Scopus (1) | Google ScholarSee all References suggesting that fewer than 5% of countries will reach the goal for tuberculosis elimination outlined in SDG 3. The 11% annual proportional decrease in tuberculosis projected by this model is commensurate with the scale of the proportional annual decrease that the End TB Strategy suggests would be necessary to reach the 2035 targets and is likely to have an effect on transmission dynamics.4x4Uplekar, M, Weil, D, Lönnroth, K et al. WHO’s new End TB Strategy. Lancet. 2015; 385: 1799–1801
Summary | Full Text | Full Text PDF | PubMed | Scopus (190) | Google ScholarSee all References Regardless of whether the SDG 1, SDG 3, or End TB targets are reached completely, our study shows that eliminating extreme poverty and especially increasing social protection coverage are key to reducing tuberculosis incidence.
A major strength of this study is the data-driven approach to generating and analysing the modelling framework. Validation of the modelling framework with an analytical approach means we are measuring the association of interest—namely, SDG 1 and tuberculosis incidence—while still accounting for major risk factor pathways. Our approach provides a reasonable balance between complexity and clarity. The statistical model extends beyond point associations and provides projections of future tuberculosis burden that are key for policy discussions. However, we avoid the escalating assumptions and uncertainties of using a mechanistic transmission model to capture these associations.
This study has some data-centric limitations. Not all countries yet collect routine prevalence estimates for social protection coverage or for extreme poverty. However, a robust protocol for the handling of missing data was implemented, and missing data on key exposure variables were not associated with tuberculosis risk factors or WHO region. Improved data availability and quality would have allowed for more refinements to the overall statistical model. Particularly, the production of regional estimates would have been preferable to the global model in view of the different effects of different types of social protection in different regions. The annual proportional decrease in tuberculosis incidence is probably lower than our estimate in high-income regions and higher than our estimate in regions with a greater proportion of low-income and middle-income countries, although in this global model, all regions are given equal weight.
Because the burden of tuberculosis and of poverty is concentrated in low-income and middle-income countries, the proportional annual decrease provided is probably a conservative estimate for the reduction in incidence expected were SDG 1 to be globally attained. With improved data quality, we could also assess whether the relation between poverty, social protection, and tuberculosis is non-linear in some regions, which could result in a more precise estimate than obtained by assuming linearity. Studies using similar analytical projection methodologies for tuberculosis have used regional models and non-linear associations to inform global estimates.15x15Odone, A, Houben, RMGJ, White, RG, and Lönnroth, K. The effect of diabetes and undernutrition trends on reaching 2035 global tuberculosis targets. Lancet Diabetes Endocrinol. 2014;
Summary | Full Text | Full Text PDF | PubMed | Scopus (40) | Google ScholarSee all References,16x16Dye, C, Trunz, BB, Lönnroth, K, Roglic, G, and Williams, BG. Nutrition, diabetes and tuberculosis in the epidemiological transition. PLoS One. 2011; 6: e21161
Crossref | PubMed | Scopus (50) | Google ScholarSee all References However, in this analysis, regional models resulted in wide confidence intervals for β due to sparsity of data. Although we acknowledge that, in a global model, countries will have heterogeneous profiles for risk factors, this heterogeneity must be considered in a trade off with statistical power. Sufficient statistical power to make robust conclusions is unlikely to be attained in subgroup analyses, particularly in view of the missing data in this study; this is evident in the sensitivity analysis done on complete cases.
The estimated annual proportional decrease in tuberculosis incidence projected assumes that the association between the SDG 1 subtargets remains constant as progress is made towards eliminating poverty. Because tuberculosis remains concentrated in the most vulnerable populations, elimination of the last 1% of extreme poverty might have a larger effect on incidence than elimination of the first 1%,17x17Ravallion, M. How long will it take to lift one billion people out of poverty?. World Bank Res Obs. 2013;
Crossref | Scopus (11) | Google ScholarSee all References and thus the value for β might increase over time, which is not captured by this model. This would imply the projections are conservative. However, the elimination of the last pockets of extreme poverty is likely to be more challenging than elimination at baseline, which is captured by the exponential decay component of the statistical model; the absolute decrease slows as progress is made towards reducing poverty. As such, the projections might overestimate the amount of poverty elimination (or social protection) that is feasible within the given timeframe. Both the changing value of β and the changing difficulty of eliminating poverty over time are likely to be heterogeneous between nations and regions, and it is not possible to tell from the collected data whether taken together these factors bias the global estimate upwards or downwards.
The sensitivity analysis investigating non-linearities in β suggest that the assumption of a constant β is not unreasonable. Although other shapes might be possible, little evidence is available to guide either selection of an appropriate non-linear term to replace our analysis or to estimate appropriate standard errors for the CrIs. Although we recognise that β can change over time, in practice incorporation of this change into the model while maintaining the present balance between complexity and interpretability is impossible. Similar concerns arise for changes in the scaling of poverty reduction or social protection expansion; what a reasonable value is for non-linearity with respect to Δx is unclear, as is how to propagate the uncertainty involved with a variable value of Δx.
The statistical model estimates use social insurance as the main measure of social protection, which tends to be more associated with higher-income countries relative to social assistance and labour protection measures (appendixappendix). However, we found an association between social insurance and reduced tuberculosis incidence even after accounting for gross domestic product and the Gini coefficient, which suggests that social insurance is not simply acting as a proxy measure for being in a high-income country or for having low-income inequality (appendixappendix). Social insurance might also be the only SDG 1 measure correlated with tuberculosis incidence because social insurance systems (eg, welfare and unemployment) have wide-ranging effects on society compared with social assistance schemes, which might be more directly targeted at groups that probably, but might not necessarily, overlap with patients with tuberculosis (eg, conditional cash transfers, disability grants, and child benefits). Social insurance is also likely to act more tangibly and more rapidly on tuberculosis trends than labour market reforms or social assistance programmes by enhancing access to tuberculosis care (eg, through health insurance) and by mitigating the effect of tuberculosis-related catastrophic costs and, more broadly, the financial consequences of tuberculosis (ie, unemployment programmes). Although an aggregate measure of social protection prevalence would be a useful metric for SDG 1.3, it is not provided by the SDG databank.
There are some limitations to the use of social insurance as a proxy measure of social protection coverage. We consider prevalence of social insurance to be a measure of a wider construct that includes the structural and social factors required to implement a social insurance system, which might bias our estimate higher. This is probably why social protection was found to be more efficacious than extreme poverty elimination alone; promotion of social insurance systems is likely to entail efforts to eradicate poverty. For a country to expand social insurance, its citizens need to be able to contribute in some way to a system of wealth redistribution, which in turn requires a degree of household financial stability that is unlikely to occur without the elimination of other factors associated with tuberculosis incidence, such as malnutrition, poor housing, and catastrophic costs. In this way, the floor of poverty is first raised by implementation of policy towards expanding social insurance, which might include forms of social protection that are comparatively easier to implement, such as social assistance and labour market interventions. As such, we might conceive of social insurance as a measure that indicates concomitant progress on the other SDG 1 targets.
Evidence is coalescing around a positive effect of social protection in tuberculosis control.5x5Reeves, A, Basu, S, McKee, M, Stuckler, D, Sandgren, A, and Semenza, J. Social protection and tuberculosis control in 21 European countries, 1995–2012: a cross-national statistical modelling analysis. Lancet Infect Dis. 2014;
Summary | Full Text | Full Text PDF | PubMed | Scopus (19) | Google ScholarSee all References,6x6Siroka, A, Ponce, NA, and Lönnroth, K. Association between spending on social protection and tuberculosis burden: a global analysis. Lancet Infect Dis. 2016; 16: 473–479
Summary | Full Text | Full Text PDF | PubMed | Scopus (22) | Google ScholarSee all References,18x18Boccia, D, Hargreaves, J, Lönnroth, K et al. Cash transfer and microfinance interventions for tuberculosis control: review of the impact evidence and policy implications. Int J Tuberc Lung Dis. 2011; 15: S37–S49
Crossref | PubMed | Scopus (44) | Google ScholarSee all References,19x19Nery, JS, Rodrigues, LC, Rasella, D et al. Effect of Brazil’s conditional cash transfer programme on tuberculosis incidence. Int J Tuberc Lung Dis. 2017; 21: 790–796
Crossref | PubMed | Scopus (4) | Google ScholarSee all References This is the first study to directly link policies that form part of the End TB Strategy to SDG 1 or to provide projections of potential future tuberculosis incidence reduction from SDG achievement. Through this alignment there are high level opportunities to enable common progress towards both sets of targets. In view of the strong potential of social protection in tuberculosis, even compared with extreme poverty elimination, further policy assessment and consideration should be given to the role that various social protection programmes might have in tuberculosis control efforts, including the added value of social protection to mitigate catastrophic costs.
In November, 2017, Russia hosted an unprecedented WHO Global Interministerial Conference on ending tuberculosis to stimulate action and commitments. The resulting Moscow resolution aimed for the development of a framework working towards improved integration between tuberculosis care and social protection services. This framework entails the development of cross-sectoral delivery models able to rapidly detect eligible patients on the basis of clinical and vulnerability criteria, rather than just income; identify the most suitable social protection scheme on the basis of the patient’s profile; and facilitate access and enrolment by supporting the patients administratively and legally.
Because national social protection programmes are often under the jurisdiction of governmental development and welfare agencies or non-governmental organisations, the aforementioned cross-sectoral approaches are urgently needed if we are to capitalise on the opportunities to address the distal and proximal risk factors for tuberculosis. Political commitment is one of the key tenets of the End TB Strategy, and responsibility for work towards a sustainable solution to ending the tuberculosis epidemic rests in part with stakeholders outside the biomedical community capable of modifying its upstream drivers.
The scale of reduction in tuberculosis incidence seen by the attainment of SDG 1 would have a substantial effect on the progress towards tuberculosis elimination, accelerating the global downwards trend in tuberculosis incidence. Irrespective of whether the SDG 1, SDG 3, or End TB targets are reached completely, our research suggests that eliminating extreme poverty and especially increasing social protection coverage are key to reducing tuberculosis incidence. Cross-sectoral approaches that promote poverty reduction and social protection expansion will be crucial to the achievement of SDG 1 and provide the reduction in tuberculosis incidence that SDG 3 and the End TB Strategy are aiming to achieve. The approach of this study should be used to assess how other SDGs might affect the reduction of tuberculosis incidence to inform future advocacy and policy efforts.