This appendix describes the sampling and survey design of the longitudinal study on which the papers in this collection are based. Special attention is paid to the design of the treatment and comparison groups, tracking of households, and the integrated nature of the quantitative and qualitative phases in the 2006-2007 re-survey.Bangladesh, long-term impact, sampling, survey design,

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As COVID-19 vaccines are becoming available, governments will need to assess the number and location of the most vulnerable people within their populations. However, problematically, tracking data for most low-and middle-income countries are only available at the national level. To support the COVID-19 relief effort, the Gender, Climate Change, and Nutrition Integration Initiative (GCAN) was commissioned to develop a subnational dataset of key COVID-19 risk indicators and potential risk hotspots.Based on patient data compiled and analyzed worldwide, the science community's consensus is that key COVID-19 risk factors include age, sex, and obesity. Being old, male, and obese increases both vulnerability to infection and the likelihood of negative outcomes. Based on each indicator's COVID-19 death hazard ratio, a composite index for the second-level subnational administrative units was constructed using exploratory factor analysis (a statistical technique that reduces the number of variables). The results of the subnational risk index (map a) and the risk indicators (maps b, c, and d) are presented visually below, resulting in hotspots (the redder colors) and cold spots (the bluer colors).The age-related risk is highest in the Far-Western Region (Mahakali and Seti Zones), while the sex-related risk (i.e., more male) is highest in the East Region (Mechi and Sagarmatha Zones). The obesity-related risk is highest in the West Region (Bagmati and Dhaualagiri Zones). Overall, the highest risk is estimated in the West Region, followed by East and Central Regions.

As COVID-19 vaccines are becoming available, governments will need to assess the number and location of the most vulnerable people within their populations. However, problematically, tracking data for most low-and middle-income countries are only available at the national level. To support the COVID-19 relief effort, the Gender, Climate Change, and Nutrition Integration Initiative (GCAN) was commissioned to develop a subnational dataset of key COVID-19 risk indicators and potential risk hotspots.Based on patient data compiled and analyzed worldwide, the science community's consensus is that key COVID-19 risk factors include age, sex, and obesity. Being old, male, and obese increases both vulnerability to infection and the likelihood of negative outcomes. Based on each indicator's COVID-19 death hazard ratio, a composite index for the second-level subnational administrative units was constructed using exploratory factor analysis (a statistical technique that reduces the number of variables). The results of the subnational risk index (map a) and the risk indicators (maps b, c, and d) are presented visually below, resulting in hotspots (the redder colors) and cold spots (the bluer colors).The age-related risk is high across the Eastern and Central Regions. The sex-related risk (i.e., more male) is highest in the Central Region. The obesity-related risk is high in the Central and Western Regions. Overall, the risk index pattern follows the age-related risk, which is highest in the Eastern and Central Regions.