Burden of disease among the world's poorest billion people: An expert-informed secondary analysis of Global Burden of Disease estimates

Abstract

Background: The health of populations living in extreme poverty has been a long-standing focus of global development efforts, and continues to be a priority during the Sustainable Development Goal era. However, there has not been a systematic attempt to quantify the magnitude and causes of the burden in this specific population for almost two decades. We estimated disease rates by cause for the world's poorest billion and compared these rates to those in high-income populations.

Methods: We defined the population in extreme poverty using a multidimensional poverty index. We used national-level disease burden estimates from the 2017 Global Burden of Disease Study and adjusted these to account for within-country variation in rates. To adjust for within-country variation, we looked to the relationship between rates of extreme poverty and disease rates across countries. In our main modeling approach, we used these relationships when there was consistency with expert opinion from a survey we conducted of disease experts regarding the associations between household poverty and the incidence and fatality of conditions. Otherwise, no within-country variation was assumed. We compared results across multiple approaches for estimating the burden in the poorest billion, including aggregating national-level burden from the countries with the highest poverty rates. We examined the composition of the estimated disease burden among the poorest billion and made comparisons with estimates for high-income countries.

Results: The composition of disease burden among the poorest billion, as measured by disability-adjusted life years (DALYs), was 65% communicable, maternal, neonatal, and nutritional (CMNN) diseases, 29% non-communicable diseases (NCDs), and 6% injuries. Age-standardized DALY rates from NCDs were 44% higher in the poorest billion (23,583 DALYs per 100,000) compared to high-income regions (16,344 DALYs per 100,000). Age-standardized DALY rates were 2,147% higher for CMNN conditions (32,334 DALYs per 100,000) and 86% higher for injuries (4,182 DALYs per 100,000) in the poorest billion, compared to high-income regions.

Conclusion: The disease burden among the poorest people globally compared to that in high income countries is highly influenced by demographics as well as large disparities in burden from many conditions. The comparisons show that the largest disparities remain in communicable, maternal, neonatal, and nutritional diseases, though NCDs and injuries are an important part of the "unfinished agenda" of poor health among those living in extreme poverty.

Fig 1. Percent of population in each age group, for the poorest billion versus high-income regions.

High-income regions defined as Western Europe, high-income North America, high-income Asia-Pacific, Australia, and New Zealand from the GBD 2017. Percentages shown within each group: percentages in the poorest billion total to 100% and in high-income regions total to 100%.

Fig 2

Proportion of DALYs by broad cause groups, crude (a) and age-standardized (b); Rates of DALYs by broad cause groups, crude (c) and age-standardized (d), for the poorest billion and World Bank income groups. Age-standardized results were standardized using the GBD 2017 population standard [28].

Fig 3

Crude DALY rate by condition in the poorest billion (a) and in high-income regions (b). High-income regions defined as Western Europe, high-income North America, high-income Asia-Pacific, Australia, and New Zealand from the GBD 2017.

Fig 4

YLL and YLD (DALY) rates by age in the poorest billion (a) and high-income regions (b). Approaches described in detail in Methods. Results for other approaches in S1 Appendix (pp 38–44). High-income regions defined as Western Europe, high-income North America, high-income Asia-Pacific, Australia, and New Zealand from the GBD 2017.

Fig 5

Age-specific YLLs and YLDs as a proportion of all-age DALYS in the poorest billion (a) high-income regions (b). Approaches described in detail in Methods. Results for other approaches in S1 Appendix (pp 45–51). High-income regions defined as Western Europe, high-income North America, high-income Asia-Pacific, Australia, and New Zealand from the GBD 2017.

Fig 6

Crude DALY rate in the poorest billion and difference between crude DALY rate in poorest billion and high-income regions (a) and difference between age-standardized DALY rate in poorest billion and high-income regions (b). Approaches described in detail in Methods, results for other approaches and more focused graphs on NCDs and injuries in S1 Appendix (pp 69–78). Results for other approaches in S1 Appendix (pp 52–59). High-income regions defined as Western Europe, high-income North America, high-income Asia-Pacific, Australia, and New Zealand from the GBD 2017. Age-standardized results were standardized using the GBD 2017 population standard [28].

Fig 7

Crude DALY rate in the poorest billion and ratio of crude DALY rate in poorest billion and high-income regions (a) and ratio of age-standardized DALY rate in poorest billion and high-income regions (b). Approaches described in detail in Methods. High-income regions defined as Western Europe, high-income North America, high-income Asia-Pacific, Australia, and New Zealand from the GBD 2017. There are infinite ratios (zero DALYs in high income regions) for the following causes- African trypanosomiasis, Guinea worm, Onchocerciasis, Schistosomiasis, Vitamin A deficiency, Yellow fever). Ratios on y-axis expressed on a logarithmic scale. Age-standardized results were standardized using the GBD 2017 population standard [28].