Seroprevalence of SARS -CoV-2 (seroprevalence = n/N*100, N = Total participants and n = SARS-COV-2 positive for IgG, Where N = 1884 and n = 485 for the first round and N = 1721 and n = 588 for second round respectively).

Seroprevalence of SARS -CoV-2 (seroprevalence = n/N*100, N = Total participants and n = SARS-COV-2 positive for IgG, Where N = 1884 and n = 485 for the first round and N = 1721 and n = 588 for second round respectively).</p

Multiple logistic regression of SARS-CoV-2 and association with seroincidence, December 2020 to April 2021.

Multiple logistic regression of SARS-CoV-2 and association with seroincidence, December 2020 to April 2021.</p

Supporting information consiting of all supporting tables and figures.

(DOCX)</p

Study district and participants’ socio-demographic characteristics by study rounds, base line (December, 2020) and follow up (April, 2021).

Study district and participants’ socio-demographic characteristics by study rounds, base line (December, 2020) and follow up (April, 2021).</p

Consort diagram representing cohort description for points of inclusion and exclusion (n = number of students at each step).

The mean (±SD) age of the participants at baseline was 15.8 (2.6) years, ranging from 10 to 27 years; 1116 (59.2%) of them were in the age group of 15 to 18 years. Females accounted for 1033 (54.8%) of the participants. The majority of the students, 1740 (92.4%), lived in a family size of three or more per household. Mask use and physical distance practices were low among the study participants. There are no marked differences in background characteristics between the first and second rounds of the students (Table 1).</p

Trend changes in SARS -CoV-2 seroprevalence in hotspot districts, national RT-PCR positivity rate and daily new cases.

Each orange dot represents hotspot districts. Data from the national RT-PCR positivity rate and daily new cases were obtained from the daily COVID-19 report by the Ministry of Health of Ethiopia [3].</p

Seroprevalence of SARS-CoV-2 and percent change in seroprevalence after school reopening in hotspot districts in Oromia, Ethiopia during study period, first round (December 2020) to second round (April, 2021).

Seroprevalence of SARS-CoV-2 and percent change in seroprevalence after school reopening in hotspot districts in Oromia, Ethiopia during study period, first round (December 2020) to second round (April, 2021).</p

Map showing the selected schools, zones, or towns for the seroprevalence of SARS CoV-2 study among students in Oromia, Ethiopia December 2020 to April 2021.

Map showing the selected schools, zones, or towns for the seroprevalence of SARS CoV-2 study among students in Oromia, Ethiopia December 2020 to April 2021.</p

Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: a systematic analysis from the Global Burden of Disease Study 2015

Background The scale-up of tobacco control, especially after the adoption of the Framework Convention for TobaccoControl, is a major public health success story. Nonetheless, smoking remains a leading risk for early death anddisability worldwide, and therefore continues to require sustained political commitment. The Global Burden ofDiseases, Injuries, and Risk Factors Study (GBD) offers a robust platform through which global, regional, andnational progress toward achieving smoking-related targets can be assessed.Methods We synthesised 2818 data sources with spatiotemporal Gaussian process regression and produced estimatesof daily smoking prevalence by sex, age group, and year for 195 countries and territories from 1990 to 2015. We analysed38 risk-outcome pairs to generate estimates of smoking-attributable mortality and disease burden, as measured bydisability-adjusted life-years (DALYs). We then performed a cohort analysis of smoking prevalence by birth-year cohortto better understand temporal age patterns in smoking. We also did a decomposition analysis, in which we parsed outchanges in all-cause smoking-attributable DALYs due to changes in population growth, population ageing, smokingprevalence, and risk-deleted DALY rates. Finally, we explored results by level of development using theSocio-demographic Index (SDI).Findings Worldwide, the age-standardised prevalence of daily smoking was 25·0% (95% uncertainty interval [UI]24·2–25·7) for men and 5·4% (5·1–5·7) for women, representing 28·4% (25·8–31·1) and 34·4% (29·4–38·6)reductions, respectively, since 1990. A greater percentage of countries and territories achieved significant annualisedrates of decline in smoking prevalence from 1990 to 2005 than in between 2005 and 2015; however, only four countrieshad significant annualised increases in smoking prevalence between 2005 and 2015 (Congo [Brazzaville] andAzerbaijan for men and Kuwait and Timor-Leste for women). In 2015, 11·5% of global deaths (6·4 million [95% UI5·7–7·0 million]) were attributable to smoking worldwide, of which 52·2% took place in four countries (China, India,the USA, and Russia). Smoking was ranked among the five leading risk factors by DALYs in 109 countries andterritories in 2015, rising from 88 geographies in 1990. In terms of birth cohorts, male smoking prevalence followedsimilar age patterns across levels of SDI, whereas much more heterogeneity was found in age patterns for femalesmokers by level of development. While smoking prevalence and risk-deleted DALY rates mostly decreased by sex andSDI quintile, population growth, population ageing, or a combination of both, drove rises in overall smokingattributableDALYs in low-SDI to middle-SDI geographies between 2005 and 2015.Interpretation The pace of progress in reducing smoking prevalence has been heterogeneous across geographies,development status, and sex, and as highlighted by more recent trends, maintaining past rates of decline should notbe taken for granted, especially in women and in low-SDI to middle-SDI countries. Beyond the effect of the tobaccoindustry and societal mores, a crucial challenge facing tobacco control initiatives is that demographic forces arepoised to heighten smoking’s global toll, unless progress in preventing initiation and promoting cessation can besubstantially accelerated. Greater success in tobacco control is possible but requires effective, comprehensive, andadequately implemented and enforced policies, which might in turn require global and national levels of politicalcommitment beyond what has been achieved during the past 25 years.</p

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59·4 (IQR 35·4–67·3), ranging from a low of 11·6 (95% uncertainty interval 9·6–14·0) to a high of 84·9 (83·1–86·7). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030. Funding: Bill & Melinda Gates Foundation