Additional file 1 of Secular trends in incidence and mortality of cervical cancer in India and its states, 1990-2019: data from the Global Burden of Disease 2019 Study

Additional file 1: Supplementary Table 1. Percentage changes in cervical cancer incidence among women of all ages in India and its states over the period 1990 to 2019. Supplementary Table 2. Percentage changes in cervical cancer mortality among women of all ages in India and its states over the period 1990 to 2019. Supplementary Figure 1. Trends in age standardized cervical cancer Incidence rate using joinpoint regression analysis across states of India. Supplementary Figure 2. Trends in age standardized mortality rate of cervical cancer using joinpoint regression analysis across states of Indi

Data_Sheet_1_Long-term trends of HIV/AIDS incidence in India: an application of joinpoint and age–period–cohort analyses: a gendered perspective.docx

BackgroundMonitoring the transmission patterns of human immunodeficiency virus (HIV) in a population is fundamental for identifying the key population and designing prevention interventions. In the present study, we aimed to estimate the gender disparities in HIV incidence and the age, period, and cohort effects on the incidence of HIV in India for identifying the predictors that might have led to changes in the last three decades.Data and methodsThis study utilizes data from the Global Burden of Disease Study for the period 1990–2019. The joinpoint regression analysis was employed to identify the magnitude of the changes in age-standardized incidence rates (ASIRs) of HIV. The average annual percentage changes in the incidence were computed, and the age–period–cohort analysis was performed.ResultsA decreasing trend in the overall estimates of age-standardized HIV incidence rates were observed in the period 1990–2019. The joinpoint regression analysis showed that the age-standardized incidence significantly declined from its peak in 1997 to 2019 (38.0 and 27.6 among males and females per 100,000 in 1997 to 5.4 and 4.6, respectively, in 2019). The APC was estimated to be 2.12 among males and 1.24 among females for the period 1990–2019. In recent years, although the gender gap in HIV incidence has reduced, females were observed to bear a proportionately higher burden of HIV incidence. Age effect showed a decline in HIV incidence by 91.1 and 70.1% among males and females aged between 15–19 years and 75–79 years. During the entire period from 1990–1994 to 2015–2019, the RR of HIV incidence decreased by 36.2 and 33.7% among males and females, respectively.ConclusionIndia is experiencing a decline in new HIV infections in recent years. However, the decline is steeper for males than for females. Findings highlight the necessity of providing older women and young women at risk with effective HIV prevention. This study emphasizes the need for large-scale HIV primary prevention efforts for teenage girls and young women.</p

Table_1_Long-term trends of HIV/AIDS incidence in India: an application of joinpoint and age–period–cohort analyses: a gendered perspective.docx

BackgroundMonitoring the transmission patterns of human immunodeficiency virus (HIV) in a population is fundamental for identifying the key population and designing prevention interventions. In the present study, we aimed to estimate the gender disparities in HIV incidence and the age, period, and cohort effects on the incidence of HIV in India for identifying the predictors that might have led to changes in the last three decades.Data and methodsThis study utilizes data from the Global Burden of Disease Study for the period 1990–2019. The joinpoint regression analysis was employed to identify the magnitude of the changes in age-standardized incidence rates (ASIRs) of HIV. The average annual percentage changes in the incidence were computed, and the age–period–cohort analysis was performed.ResultsA decreasing trend in the overall estimates of age-standardized HIV incidence rates were observed in the period 1990–2019. The joinpoint regression analysis showed that the age-standardized incidence significantly declined from its peak in 1997 to 2019 (38.0 and 27.6 among males and females per 100,000 in 1997 to 5.4 and 4.6, respectively, in 2019). The APC was estimated to be 2.12 among males and 1.24 among females for the period 1990–2019. In recent years, although the gender gap in HIV incidence has reduced, females were observed to bear a proportionately higher burden of HIV incidence. Age effect showed a decline in HIV incidence by 91.1 and 70.1% among males and females aged between 15–19 years and 75–79 years. During the entire period from 1990–1994 to 2015–2019, the RR of HIV incidence decreased by 36.2 and 33.7% among males and females, respectively.ConclusionIndia is experiencing a decline in new HIV infections in recent years. However, the decline is steeper for males than for females. Findings highlight the necessity of providing older women and young women at risk with effective HIV prevention. This study emphasizes the need for large-scale HIV primary prevention efforts for teenage girls and young women.</p

The Burden of Dementia due to Down Syndrome, Parkinson's Disease, Stroke, and Traumatic Brain Injury: A Systematic Analysis for the Global Burden of Disease Study 2019

Background: In light of the increasing trend in the global number of individuals affected by dementia and the lack of any available disease-modifying therapies, it is necessary to fully understand and quantify the global burden of dementia. This work aimed to estimate the proportion of dementia due to Down syndrome, Parkinson’s disease, clinical stroke, and traumatic brain injury (TBI), globally and by world region, in order to better understand the contribution of clinical diseases to dementia prevalence. Methods: Through literature review, we obtained data on the relative risk of dementia with each condition and estimated relative risks by age using a Bayesian meta-regression tool. We then calculated population attributable fractions (PAFs), or the proportion of dementia attributable to each condition, using the estimates of relative risk and prevalence estimates for each condition from the Global Burden of Disease Study 2019. Finally, we multiplied these estimates by dementia prevalence to calculate the number of dementia cases attributable to each condition. Findings: For each clinical condition, the relative risk of dementia decreased with age. Relative risks were highest for Down syndrome, followed by Parkinson’s disease, stroke, and TBI. However, due to the high prevalence of stroke, the PAF for dementia due to stroke was highest. Together, Down syndrome, Parkinson’s disease, stroke, and TBI explained 10.0% (95% UI: 6.0–16.5) of the global prevalence of dementia. Interpretation: Ten percent of dementia prevalence globally could be explained by Down syndrome, Parkinson’s disease, stroke, and TBI. The quantification of the proportion of dementia attributable to these 4 conditions constitutes a small contribution to our overall understanding of what causes dementia. However, epidemiological research into modifiable risk factors as well as basic science research focused on elucidating intervention approaches to prevent or delay the neuropathological changes that commonly characterize dementia will be critically important in future efforts to prevent and treat disease

Mapping child growth failure across low- and middle-income countries

Childhood malnutrition is associated with high morbidity and mortality globally1. Undernourished children are more likely to experience cognitive, physical, and metabolic developmental impairments that can lead to later cardiovascular disease, reduced intellectual ability and school attainment, and reduced economic productivity in adulthood2. Child growth failure (CGF), expressed as stunting, wasting, and underweight in children under five years of age (0–59 months), is a specific subset of undernutrition characterized by insufficient height or weight against age-specific growth reference standards3–5. The prevalence of stunting, wasting, or underweight in children under five is the proportion of children with a height-for-age, weight-for-height, or weight-for-age z-score, respectively, that is more than two standard deviations below the World Health Organization’s median growth reference standards for a healthy population6. Subnational estimates of CGF report substantial heterogeneity within countries, but are available primarily at the first administrative level (for example, states or provinces)7; the uneven geographical distribution of CGF has motivated further calls for assessments that can match the local scale of many public health programmes8. Building from our previous work mapping CGF in Africa9, here we provide the first, to our knowledge, mapped high-spatial-resolution estimates of CGF indicators from 2000 to 2017 across 105 low- and middle-income countries (LMICs), where 99% of affected children live1, aggregated to policy-relevant first and second (for example, districts or counties) administrative-level units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the ambitious World Health Organization Global Nutrition Targets to reduce stunting by 40% and wasting to less than 5% by 2025. Large disparities in prevalence and progress exist across and within countries; our maps identify high-prevalence areas even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where the highest-need populations reside, these geospatial estimates can support policy-makers in planning interventions that are adapted locally and in efficiently directing resources towards reducing CGF and its health implications

Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning

Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0.71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50.2% exceed this threshold for suitability in at least one 5×5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify

Mapping disparities in education across low- and middle-income countries

Educational attainment is an important social determinant of maternal, newborn, and child health1–3. As a tool for promoting gender equity, it has gained increasing traction in popular media, international aid strategies, and global agenda-setting4–6. The global health agenda is increasingly focused on evidence of precision public health, which illustrates the subnational distribution of disease and illness7,8; however, an agenda focused on future equity must integrate comparable evidence on the distribution of social determinants of health9–11. Here we expand on the available precision SDG evidence by estimating the subnational distribution of educational attainment, including the proportions of individuals who have completed key levels of schooling, across all low- and middle-income countries from 2000 to 2017. Previous analyses have focused on geographical disparities in average attainment across Africa or for specific countries, but—to our knowledge—no analysis has examined the subnational proportions of individuals who completed specific levels of education across all low- and middle-income countries12–14. By geolocating subnational data for more than 184 million person-years across 528 data sources, we precisely identify inequalities across geography as well as within populations

Additional file 3 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 3: Supplemental figures.Figure S1. Prevalence of male circumcision. Figure S2. Prevalence of signs and symptoms of sexually transmitted infections. Figure S3. Prevalence of marriage or living as married. Figure S4. Prevalence of partner living elsewhere among females. Figure S5. Prevalence of condom use during most recent sexual encounter. Figure S6. Prevalence of sexual activity among young females. Figure S7. Prevalence of multiple partners among males in the past year. Figure S8. Prevalence of multiple partners among females in the past year. Figure S9. HIV prevalence predictions from the boosted regression tree model. Figure S10. HIV prevalence predictions from the generalized additive model. Figure S11. HIV prevalence predictions from the lasso regression model. Figure S12. Modeling regions. Figure S13. Age- and sex-specific vs. adult prevalence modeling. Figure S14. Data sensitivity. Figure S15. Model specification validation. Figure S16. Modeled and re-aggregated adult prevalence comparison. Figure S17. HIV prevalence raking factors for males. Figure S18. HIV prevalence raking factors for females. Figure S19. Age-specific HIV prevalence in males, 2000. Figure S20. Age-specific HIV prevalence in females, 2000. Figure S21. Age-specific HIV prevalence in males, 2005. Figure S22. Age-specific HIV prevalence in females, 2005. Figure S23. Age-specific HIV prevalence in males, 2010. Figure S24. Age-specific HIV prevalence in females, 2010. Figure S25. Age-specific HIV prevalence in males, 2018. Figure S26. Age-specific HIV prevalence in females, 2018. Figure S27. Age-specific uncertainty interval range estimates in males, 2000. Figure S28. Age-specific uncertainty interval range estimates in females, 2000. Figure S29. Age-specific uncertainty interval range estimates in males, 2005. Figure S30. Age-specific uncertainty interval range estimates in females, 2005. Figure S31. Age-specific uncertainty interval range estimates in males, 2010. Figure S32. Age-specific uncertainty interval range estimates in females, 2010. Figure S33. Age-specific uncertainty interval range estimates in males, 2018. Figure S34. Age-specific uncertainty interval range estimates in females, 2018. Figure S35. Change in HIV prevalence in males, 2000-2005. Figure S36. Change in HIV prevalence in females, 2000-2005. Figure S37. Change in HIV prevalence in males, 2005-2010. Figure S38. Change in HIV prevalence in females, 2005-2010. Figure S39. Change in HIV prevalence in males, 2010-2018. Figure S40. Change in HIV prevalence in females, 2010-2018. Figure S41. Space mesh for geostatistical models

Additional file 4 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 4: Supplemental results.1. README. 2. Prevalence range across districts. 3. Prevalence range between sexes. 4. Prevalence range between ages. 5. Age-specific district ranges

Additional file 1 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 1: Supplemental information.1. Compliance with the Guidlines for Accurate and Transparent Health Estimates Reporting (GATHER). 2. HIV data sources and data processing. 3. Covariate and auxiliary data. 4. Statistical model. 5. References