Suspension of face-to-face instruction in schools during the COVID-19 pandemic has led to concerns about consequences for students’ learning. So far, data to study this question have been limited. Here we evaluate the effect of school closures on primary school performance using exceptionally rich data from The Netherlands (n ≈ 350,000). We use the fact that national examinations took place before and after lockdown and compare progress during this period to the same period in the 3 previous years. The Netherlands underwent only a relatively short lockdown (8 wk) and features an equitable system of school funding and the world’s highest rate of broadband access. Still, our results reveal a learning loss of about 3 percentile points or 0.08 standard deviations. The effect is equivalent to one-fifth of a school year, the same period that schools remained closed. Losses are up to 60% larger among students from less-educated homes, confirming worries about the uneven toll of the pandemic on children and families. Investigating mechanisms, we find that most of the effect reflects the cumulative impact of knowledge learned rather than transitory influences on the day of testing. Results remain robust when balancing on the estimated propensity of treatment and using maximum-entropy weights or with fixed-effects specifications that compare students within the same school and family. The findings imply that students made little or no progress while learning from home and suggest losses even larger in countries with weaker infrastructure or longer school closures.

Suspension of face-to-face instruction in schools during the COVID-19 pandemic has led to concerns about consequences for student learning. So far, data to study this question have been limited. Here we evaluate the effect of school closures on primary school performance using exceptionally rich data from the Netherlands (n≈350,000). The Netherlands represents a best-case scenario with a relatively short lockdown (8 weeks) and a high degree of technological preparedness. We use the fact that national exams took place before and after lockdown, and compare progress during this period to the same period in the three previous years using a difference-in-differences design. Our results reveal a learning loss of about 3 percentile points or 0.08 standard deviations. These results remain robust when balancing on the estimated propensity of treatment and using maximum entropy weights, or with fixed-effects specifications that compare students within the same school and family. Losses are up to 55% larger among students from less-educated homes. Investigating mechanisms, we find that most of the effect reflects the cumulative impact of knowledge learned rather than transitory influences on the day of testing. The average learning loss is equivalent to a fifth of a school year, nearly exactly the same period that schools remained closed. These results imply that students made little or no progress whilst learning from home, and suggest much larger losses in countries less prepared for remote learning.

BackgroundHigh blood pressure is a leading risk factor for death and disability in sub-Saharan Africa (SSA). We evaluated the costs and cost-effectiveness of hypertension care provided within the Kwara State Health Insurance (KSHI) program in rural Nigeria.MethodsA Markov model was developed to assess the costs and cost-effectiveness of population-level hypertension screening and subsequent antihypertensive treatment for the population at-risk of cardiovascular disease (CVD) within the KSHI program. The primary outcome was the incremental cost per disability-adjusted life year (DALY) averted in the KSHI scenario compared to no access to hypertension care. We used setting-specific and empirically-collected data to inform the model. We defined two strategies to assess eligibility for antihypertensive treatment based on 1) presence of hypertension grade 1 and 10-year CVD risk of >20%, or grade 2 hypertension irrespective of 10-year CVD risk (hypertension and risk based strategy) and 2) presence of hypertension in combination with a CVD risk of >20% (risk based strategy). We generated 95% confidence intervals around the primary outcome through probabilistic sensitivity analysis. We conducted one-way sensitivity analyses across key model parameters and assessed the sensitivity of our results to the performance of the reference scenario.ResultsScreening and treatment for hypertension was potentially cost-effective but the results were sensitive to changes in underlying assumptions with a wide range of uncertainty. The incremental cost-effectiveness ratio for the first and second strategy respectively ranged from US$ 1,406 to US$ 7,815 and US$ 732 to US$ 2,959 per DALY averted, depending on the assumptions on risk reduction after treatment and compared to no access to antihypertensive treatment.ConclusionsHypertension care within a subsidized private health insurance program may be cost-effective in rural Nigeria and public-private partnerships such as the KSHI program may provide opportunities to finance CVD prevention care in SSA.

Background: COVID-19 poses one of the most profound public health crises for a hundred years. As of mid-May 2020, across the world, almost 300,000 deaths and over 4 million confirmed cases were registered. Reaching over 30,000 deaths by early May, the UK had the highest number of recorded deaths in Europe, second in the world only to the USA. Hospitalization and death from COVID-19 have been linked to demographic and socioeconomic variation. Since this varies strongly by location, there is an urgent need to analyse the mismatch between health care demand and supply at the local level. As lockdown measures ease, reinfection may vary by area, necessitating a real-time tool for local and regional authorities to anticipate demand. Methods: Combining census estimates and hospital capacity data from ONS and NHS at the Administrative Region, Ceremonial County (CC), Clinical Commissioning Group (CCG) and Lower Layer Super Output Area (LSOA) level from England and Wales, we calculate the number of individuals at risk of COVID-19 hospitalization. Combining multiple sources, we produce geospatial risk maps on an online dashboard that dynamically illustrate how the precrisis health system capacity matches local variations in hospitalization risk related to age, social deprivation, population density and ethnicity, also adjusting for the overall infection rate and hospital capacity. Results: By providing fine-grained estimates of expected hospitalization, we identify areas that face higher disproportionate health care burdens due to COVID-19, with respect to pre-crisis levels of hospital bed capacity. Including additional risks beyond age-composition of the area such as social deprivation, race/ethnic composition and population density offers a further nuanced identification of areas with disproportionate health care demands. Conclusions: Areas face disproportionate risks for COVID-19 hospitalization pressures due to their socioeconomic differences and the demographic composition of their populations. Our flexible online dashboard allows policymakers and health officials to monitor and evaluate potential health care demand at a granular level as the infection rate and hospital capacity changes throughout the course of this pandemic. This agile knowledge is invaluable to tackle the enormous logistical challenges to re-allocate resources and target susceptible areas for aggressive testing and tracing to mitigate transmission.

COVID-19 poses one of the most profound public health crises for a hundred years. As of late March 2020, over 25,000 deaths and above a half million confirmed cases were registered across more than 175 countries or regions. The virus will infect a sizeable proportion of the worlds population, leading to unprecedented pressures on national health care systems. Although national estimates of hospital bed capacity are available, these obscure important differences at local and regional levels. COVID-19 appears especially dangerous for the oldest age groups and those with serious comorbidities. It is crucial to understand how health system capacity matches local variations in population structure. Using England and Wales, we illustrate how the interaction of local demography, a high burden of COVID-19 hospitalization at older ages, and regional variation in hospital resources may culminate in "hospital deserts" with too few resources to cope with a wave of critical cases. We demonstrate how local capacity could rapidly become overwhelmed. By providing fine-grained local estimates of expected hospitalization, we identify areas that have higher risks of health care burden. An online companion to this paper can be used by policymakers to identify and monitor high-risk areas, and predict the expected healthcare demand in real-time as the actual epidemic spreads. This agile knowledge will be invaluable to tackle the enormous logistical challenge COVID-19 will pose to health care systems.

In times of crisis, real-time data mapping population displacements are invaluable for targeted humanitarian response. The Russian invasion of Ukraine on February 24, 2022 forcibly displaced millions of people from their homes including nearly 6m refugees flowing across the Nowcasting population displacement in Ukraine using social media border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged near real-time social media marketing data to estimate subnational population sizes every day disaggregated by age and sex. Our metric of internal displacement estimated that 5.3m people had been internally displaced away from their baseline administrative region by March 14. Results revealed four distinct displacement patterns: large scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics. While this innovative approach provided one of the only quantitative estimates of internal displacement in virtual real-time, we conclude by acknowledging risks and challenges for the future.

In times of volatility and crisis, it is essential to have real-time data mapping population movements to facilitate a rapid and effective humanitarian response. Considerable attention has been placed on the 5.8 million Ukrainian refugees crossing the border as of early May 2022, but information is scarce to quantify and locate over 38 million people who remain in the country, many of whom have been displaced from their homes or remain vulnerable to conflict. This report and its supplementary data leverage near real-time digital trace data from social media users, along with demographic and geo-spatial methods to produce daily estimates of current population sizes and changes sub-nationally disaggregated by age and sex. Using our estimation methods, we quantify large reductions in populations from conflict areas (e.g. Kyiv city), particularly women and children, and population increases in western Ukraine (e.g. Lviv Oblast). Examining additional Oblasts (administrative regions) such as Cherkasy, we show evolving and mixed demographic changes as populations transit through different areas. Mapping population dynamics through time, we illustrate the net changes in population sizes in Oblasts from the start of the war until present, providing a daily metric of total negative population drops. As of May 06, this metric suggested that 6,529,949 people were displaced away from their baseline Oblast. While this data and approach is innovative and is one of the few estimates available to quantify and map internal displacement in virtual real-time during a crisis, we conclude by acknowledging deficiencies and future extensions.

Prediction is an underused tool in the social sciences, often for the wrong reasons. Many social scientists confuse prediction with unnecessarily complicated methods or with narrowly predicting the future. This is unfortunate. When we view prediction as the simple process of evaluating a model’s ability to approximate an outcome of interest, it becomes a more generally applicable and disarmingly simple technique. For all its simplicity, the value of prediction should not be underestimated. Prediction can address enduring sources of criticism plaguing the social sciences, like a lack of assessing a model’s ability to reflect the real world, or the use of overly simplistic models to capture social life. The author illustrates these benefits with empirical examples that merely skim the surface of the many and varied ways in which prediction can be applied, staking the claim that prediction is a truly illustrious “free lunch” that can greatly benefit social scientists in their empirical work.