Nonlinear sufficient dimension reduction for distribution-on-distribution regression

We introduce a new approach to nonlinear sufficient dimension reduction in cases where both the predictor and the response are distributional data, modeled as members of a metric space. Our key step is to build universal kernels (cc-universal) on the metric spaces, which results in reproducing kernel Hilbert spaces for the predictor and response that are rich enough to characterize the conditional independence that determines sufficient dimension reduction. For univariate distributions, we construct the universal kernel using the Wasserstein distance, while for multivariate distributions, we resort to the sliced Wasserstein distance. The sliced Wasserstein distance ensures that the metric space possesses similar topological properties to the Wasserstein space, while also offering significant computation benefits. Numerical results based on synthetic data show that our method outperforms possible competing methods. The method is also applied to several data sets, including fertility and mortality data and Calgary temperature data.

Inference under superspreading: Determinants of SARS-CoV-2 transmission in Germany

Superspreading, under-reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid-19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age-specific changes in transmission, adjusting for weather, reported prevalence, and testing and tracing. Several factors were associated with a reduction in transmission: public awareness rising, information on local prevalence, testing and tracing, high temperature, stay-at-home orders, and restaurant closures. However, substantial uncertainty remains for other interventions including school closures and mandatory face coverings. The challenge of disentangling the effects of different determinants is discussed and examined through a simulation study. On a broader perspective, the study illustrates the potential of surveillance data with demographic information and date of symptom onset to improve inference in the presence of under-reporting and reporting delay.

Penalized weighted smoothed quantile regression for high-dimensional longitudinal data

Quantile regression, known as a robust alternative to linear regression, has been widely used in statistical modeling and inference. In this paper, we propose a penalized weighted convolution-type smoothed method for variable selection and robust parameter estimation of the quantile regression with high dimensional longitudinal data. The proposed method utilizes a twice-differentiable and smoothed loss function instead of the check function in quantile regression without penalty, and can select the important covariates consistently using the efficient gradient-based iterative algorithms when the dimension of covariates is larger than the sample size. Moreover, the proposed method can circumvent the influence of outliers in the response variable and/or the covariates. To incorporate the correlation within each subject and enhance the accuracy of the parameter estimation, a two-step weighted estimation method is also established. Furthermore, we prove the oracle properties of the proposed method under some regularity conditions. Finally, the performance of the proposed method is demonstrated by simulation studies and two real examples.

Associations of humor, morale, and unit cohesion on posttraumatic stress disorder symptoms

Military personnel experience many stressors during deployments that can lead to symptoms of posttraumatic stress disorder (PTSD). However, not all military personnel who are exposed to deployment stressors develop PTSD symptoms. Recent research has explored factors that contribute to military personnel resilience, a multifaceted and multidetermined construct, as a means to mitigate and prevent PTSD symptoms. Much of this research has focused on the effects of individual-level factors (e.g., use of coping strategies like humor, the morale of individual unit members), with some research focusing on unit-level factors (e.g., the cohesiveness of a unit). However, there is little research exploring how these factors relate to each other in mitigating or reducing PTSD symptoms. In this study, we examined the association between deployment stressors, perceived unit cohesion, morale, humor, and PTSD symptoms in a sample of 20,901 active-duty military personnel using structural equation modeling. Results indicated that perceived unit cohesion, humor, and morale were positively associated with each other and negatively associated with PTSD symptoms over and above the effect of deployment stressors. These findings highlight the influence of resilience factors on PTSD symptoms beyond their substantial overlap and have implications for future research as well as the potential development of interventions for military personnel.

The impact of health on labour market outcomes: A rapid systematic review

The relationship between an individual's health and their labour market outcomes has long been a subject of health economics research. This review aims to provide an up-to-date, global review of the substantive findings in the existing literature. We pay particular attention to causal effects, acknowledging the methodological complexities that have long challenged the research and emphasizing the importance of overcoming them to present robust, policy-relevant evidence. The recent literature shows a notable advancement in addressing these methodological issues compared to previous work. The evidence reviewed suggests that individuals with better health overwhelmingly exhibit higher earnings and often enhanced labour supply. These findings extend beyond geographical boundaries, as evidence from diverse regions underscores the global significance of this association. The review covers evidence from a wide range of health indicators and conditions - including e.g. self-reported health, chronic diseases, disability, nutritional health, infections, mental health, addictions and others. Within and across the different health domains, the health-related factors exert varying degrees of influence on labour market outcomes, highlighting the multifaceted nature of the health-labour relationship and its potentially profound implications for individuals, communities, and economies.

Regional variations in vaccination against COVID-19 in Germany

Vaccination willingness against COVID-19 is generally perceived as low. Moreover, there is large heterogeneity across and within countries. As a whole, Germany has average vaccination rates compared to other industrialized countries. However, vaccination rates in the 16 different German federal states differ by more than 20 percentage points. We describe variation in vaccination rates on the level of the 400 German counties using data on all vaccinations carried out until December 2022. Around 52-72% of that variation can be explained by regional differences in demographic characteristics, housing, education and political party preferences. We find indications that the remaining part may be due to differences in soft factors such as risk aversion, trust in the German government, trust in science, and beliefs in conspiracy theories regarding the origins of the Corona virus. We conclude that improving the trust in science and the fight against conspiracy theories may possibly be effective tools to improve vaccination rates and effectively fight pandemics.

Long-term exposure to ambient ozone and adult-onset asthma: A prospective cohort study

BACKGROUND

The association between long-term exposure to ozone (O3) and adult-onset asthma (AOA) remains inconclusive, and analysis of causality is lacking.

OBJECTIVES

To examine the causal association between long-term O3 exposure and AOA.

METHODS

A prospective cohort study of 362,098 participants was conducted using the UK Biobank study. Incident cases of AOA were identified using health administrative data of the National Health Services. O3 exposure at participants' residential addresses was estimated by a spatio-temporal model. Instrumental variable (IV) modelling was used to analyze the causal association between O3 exposure and AOA, by incorporating wind speed and planetary boundary layer height as IVs into time-dependent Cox model. Negative control outcome (accidental injury) was also used to additionally evaluate unmeasured confounding.

RESULTS

During a mean follow-up of 11.38 years, a total of 10,973 incident AOA cases were identified. A U-shaped concentration-response relationship was observed between O3 exposure and AOA in the traditional Cox models with HR of 0.917 (95% CI: 0.889, 0.946) for O3 at low levels (<38.17 ppb), and 1.198 (95% CI: 1.162, 1.236) for O3 at high levels (≥38.17 ppb). However, in the IV analysis we only found a statistically significant association between high-level O3 exposure and AOA risk, but not for low-level O3 exposure. No significant associations between O3 exposure and accidental injury were observed.

CONCLUSION

Our findings suggest a potential causal relationship between long-term exposure to high-level ambient O3 and increased risks of AOA.

Adolescents' height and cognitive ability in China

Cognitive ability, as an early human capital, has always been an important research object in modern education and labor economics. Despite growing awareness of the importance of height in individual growth and development, there are few empirical studies on height and cognitive ability. Using the data from the China Education Panel Survey, this paper examined the impact of height on the cognitive ability of adolescents and explored the reasons behind the Chinese pursuit of height growth and the potential impact mechanism. In this paper, comprehensive analysis ability was taken as the representative of cognitive ability. The empirical results showed that height was positively correlated with cognitive ability. From the perspective of the influence mechanism, the hypothesis that height reflected self-esteem, health, non-cognitive ability, and other influences on cognitive ability was excluded. To correct the errors that endogenous problems may cause, we used the PSM method and "age at first menstruation " and "age at first wet dream" as instrumental variables to correct them. The results showed that height still affected cognitive ability, with taller people having higher cognitive ability.

Effect of face-covering use on adherence to other COVID-19 protective behaviours: A systematic review

During the COVID-19 pandemic, concerns were raised that face covering use may elicit risk compensation; a false sense of security resulting in reduced adherence to other protective behaviours such as physical distancing. This systematic review aimed to investigate the effect of face covering use on adherence to other COVID-19 related protective behaviours. Medline, Embase, PsychInfo, EmCare, medRxiv preprints, Research Square and WHO COVID-19 Research Database were searched for all primary research studies published from 1st January 2020 to 17th May 2022 that investigated the effect of face covering use on adherence to other protective behaviours in public settings during the COVID-19 pandemic. Papers were selected and screened in accordance with the PRISMA framework. Backwards and forwards citation searches of included papers were also conducted on 16th September 2022, with eligible papers published between 1st January 2020 and that date being included. A quality appraisal including risk of bias was assessed using the Academy of Nutrition and Dietetics' Quality Criteria Checklist. This review is registered on PROSPERO, number CRD42022331961. 47 papers were included, with quality ranging from low to high. These papers investigated the effects of face covering use and face covering policies on adherence to six categories of behaviour: physical distancing; mobility; face-touching; hand hygiene; close contacts; and generalised protective behaviour. Results reveal no consistent evidence for or against risk compensation, with findings varying according to behaviour and across study types, and therefore confident conclusions cannot be made. Any policy decisions related to face coverings must consider the inconsistencies and caveats in this evidence base.

Impact of health investment on household income distribution: insights from China's longitudinal survey data

This paper investigates the impact of health investment on household income distribution, drawing from data spanning over 10 years from the China Nutrition and Health Survey. The study aims to contribute to the literature by examining the nuanced pathways through which health investment influences income distribution. Utilizing a rich dataset, rigorous empirical methods including quantile regression and cross-sectional data modeling are employed to explore the relationship between health investment and income distribution. The analysis reveals a robust positive association between health investment and both absolute and relative income levels across various demographic and occupational groups. Additionally, the study elucidates the pathways through which health investment influences income, including its effects on illness duration, employment opportunities, effective working time, and educational attainment. The findings demonstrate the dynamic nature of the relationship, indicating that as income levels rise, the impact of health investment on income becomes more pronounced. Moreover, the analysis highlights the role of health investment in facilitating upward income mobility, particularly for low-income households. Overall, these findings provide valuable insights for policymakers, suggesting that strategic health investment initiatives can contribute to achieving more equitable income distribution.

Artificial Neural Network Prediction of COVID-19 Daily Infection Count

This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output set of our training process. To achieve this, we implement a regularized backcasting technique that utilize death counts and the infection fatality ratio (IFR), as the death statistics and serological surveys (providing the IFR) as more reliable COVID-19 data sources. Addressing the impact of factors such as age distribution, vaccination, and emerging variants on the IFR time series is a pivotal aspect of our analysis. We expect our study to enhance our understanding of the genuine implications of the COVID-19 pandemic, subsequently benefiting mitigation strategies.

Modeling Long-Term Budgetary Impacts of Prevention: An Overview of Meta-analyses of Relationships Between Key Health Outcomes Across the Life-Course

Budget analysis entities often cannot capture the full downstream impacts of investments in prevention services, programs, and interventions. This study describes and applies an approach to synthesizing existing literature to more fully account for these effects. This study reviewed meta-analyses in PubMed published between Jan 1, 2010 and Dec 31, 2019. The initial search included meta-analyses on the association between health risk factors, including maternal behavioral health, intimate partner violence, child maltreatment, depression, and obesity, with a later health condition. Through a snowball sampling-type approach, the endpoints of the meta-analyses identified became search terms for a subsequent search, until each health risk was connected to one of the ten costliest health conditions. These results were synthesized to create a path model connecting the health risks to the high-cost health conditions in a cascade. Thirty-seven meta-analyses were included. They connected early-life health risk factors with six high-cost health conditions: hypertension, diabetes, asthma and chronic obstructive pulmonary disorder, mental disorders, heart conditions, and trauma-related disorders. If confounders could be controlled for and causality inferred, the cascading associations could be used to more fully account for downstream impacts of preventive interventions. This would support budget analysis entities to better include potential savings from investments in chronic disease prevention and promote greater implementation at scale.

Spatial difference-in-differences analysis of smart city pilot policy and industrial pollution reduction: the mediating role of S&T fiscal expenditure

Smart city has become one of the most important tool to achieve digital transformation and intelligent development. However, the impacts of smart city pilots (SCP) on different industrial pollution have yet to be tested, and the mechanisms of SCP affect industrial pollution could be richer. In this paper, we construct a spatial difference-in-difference model for 2004-2019 by mapping SCP policy to Chinese city data to systematically quantify the impact and its potential mechanisms of digital transformation on industrial pollution. Our results show that the SCP policy achieves industrial pollution reduction targets, on average, wastewater and SO2 emissions decreased by 6.4% and 6.5%, respectively. Cities with SCP policy have more industrial pollution compared to cities without SCP policy, implying a beggar-thy-neighbor effect of SCP policy. Furthermore, significant regional disparities come to light; SCP policy in the Pearl River Delta exceeds other regions such as the Yangtze River Delta and Jing-Jin-Ji city cluster in terms of realizing the impact of industrial pollution reduction. Importantly, mechanism analysis indicated that the SCP reduced industrial pollution was partially mediated by government S&T fiscal expenditure.

Estimating actual SARS-CoV-2 infections from secondary data

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

The impact of cigarette prices on smoking onset and cessation: evidence from Vietnam

BACKGROUND

Vietnam is a country with very high smoking rates among men. According to a Global Adult Tobacco Survey (GATS) conducted in 2015, the daily smoking prevalence among Vietnamese men was 39%.

METHODS

We used data from the 2010 and 2015 Vietnamese GATSs and cigarette price data from General Statistics Office of Vietnam. Since smoking prevalence is low among women, we only considered men. Using discrete-time hazard models, we estimated the effect of cigarette prices on smoking onset and cessation. Sensitivity analyses are conducted using different model specifications.

RESULTS

We find that higher cigarette prices reduce the probability of smoking onset. A 1% increase in the cigarette price reduces the hazard of smoking onset by 1.2% (95% CI -2.12% to -0.28%). This suggests that increases in tobacco taxation, which translate to price increases, can reduce smoking onset. We did not find evidence that cigarette prices impact smoking cessation among men in Vietnam.

CONCLUSION

Vietnam should continue to increase excise taxes on tobacco products to reduce smoking onset. Since smokers are resilient to excise tax increases, other tobacco control policies, such as smoke-free areas and tobacco advertisement bans, should be better enforced to encourage people to quit. Other policies not yet implemented, such as plain packaging of tobacco products, may also encourage smokers to quit.

Analyzing health outcomes measured as bounded counts

This paper assesses analytical strategies that respect the bounded-count nature of health outcomes encountered often in empirical applications. Absent in the literature is a comprehensive discussion and critique of strategies for analyzing and understanding such data. The paper's goal is to provide an in-depth consideration of prominent issues arising in and strategies for undertaking such analyses, emphasizing the merits and limitations of various analytical tools empirical researchers may contemplate. Three main topics are covered. First, bounded-count health outcomes' measurement properties are reviewed and their implications assessed. Second, issues arising when bounded-count outcomes are the objects of concern in evaluations are described. Third, the (conditional) probability and moment structures of bounded-count outcomes are derived and corresponding specification and estimation strategies presented with particular attention to partial effects. Many questions may be asked of such data in health research and a researcher's choice of analytical method is often consequential.

Vaccination coverage against COVID-19 among rural population in Haryana, India: A cross-sectional study

BACKGROUND

Conducting a study in rural pre-dominant areas will help to understand the penetration of the vaccination campaign during the COVID-19 health crisis. This study aimed to investigate vaccination coverage against COVID-19 among the rural adult population in India and to identify factors associated with vaccination coverage.

METHODS

A population-based cross-sectional study was conducted among the rural population in one district of north India from January to February 2023. A semi-structured questionnaire was designed on the SurveyMonkey digital platform for interviewing the participants, which consisted of questions related to socio-demographic profile, health problems, vaccination status, types of vaccine, re-infection after vaccination, and functional difficulties. The data regarding infection with COVID-19 was collected based on self-reported positive testing for SARS-CoV 2 on RT-PCR.

FINDINGS

A total of 3700 eligible individuals were enumerated for the survey, out of which 2954 (79.8%) were interviewed. The infection rate of past COVID-19 infection, based on self-report of testing positive, was 6.2% (95%CI: 5.3-7.1). Covishield vaccine was received by most participants (81.3%, 2380) followed by Covaxin (12.3%, 361) and Pfizer manufactured vaccine (0.03,1). The coverage for first, second, and booster doses of the vaccine was 98.2% (2902), 94.8% (2802), and 10.7% (315) respectively. The risk of reinfection at 12 months or more among participants with two doses of vaccine was 1.6% (46/2802, 95%CI: 1.2-2.1). The coverage among those with severe functional difficulties was lesser as compared to those with some or no difficulties.

INTERPRETATION

Vaccination coverage against COVID-19 in rural Haryana, India is not dependent on factors like gender or occupation but is dependent on age and education. Although the full and partial vaccination coverage is high, the booster dose coverage is poor. In addition, the presence of severe disability was significantly associated with reduced vaccination coverage.

Effects of Face Mask Mandates on COVID-19 Transmission in 51 Countries: Retrospective Event Study

BACKGROUND

The question of the utility of face masks in preventing acute respiratory infections has received renewed attention during the COVID-19 pandemic. However, given the inconclusive evidence from existing randomized controlled trials, evidence based on real-world data with high external validity is missing.

OBJECTIVE

To add real-world evidence, this study aims to examine whether mask mandates in 51 countries and mask recommendations in 10 countries increased self-reported face mask use and reduced SARS-CoV-2 reproduction numbers and COVID-19 case growth rates.

METHODS

We applied an event study approach to data pooled from four sources: (1) country-level information on self-reported mask use was obtained from the COVID-19 Trends and Impact Survey, (2) data from the Oxford COVID-19 Government Response Tracker provided information on face mask mandates and recommendations and any other nonpharmacological interventions implemented, (3) mobility indicators from Google's Community Mobility Reports were also included, and (4) SARS-CoV-2 reproduction numbers and COVID-19 case growth rates were retrieved from the Our World in Data-COVID-19 data set.

RESULTS

Mandates increased mask use by 8.81 percentage points (P=.006) on average, and SARS-CoV-2 reproduction numbers declined on average by -0.31 units (P=.008). Although no significant average effect of mask mandates was observed for growth rates of COVID-19 cases (-0.98 percentage points; P=.56), the results indicate incremental effects on days 26 (-1.76 percentage points; P=.04), 27 (-1.89 percentage points; P=.05), 29 (-1.78 percentage points; P=.04), and 30 (-2.14 percentage points; P=.02) after mandate implementation. For self-reported face mask use and reproduction numbers, incremental effects are seen 6 and 13 days after mandate implementation. Both incremental effects persist for >30 days. Furthermore, mask recommendations increased self-reported mask use on average (5.84 percentage points; P<.001). However, there were no effects of recommendations on SARS-CoV-2 reproduction numbers or COVID-19 case growth rates (-0.06 units; P=.70 and -2.45 percentage points; P=.59). Single incremental effects on self-reported mask use were observed on days 11 (3.96 percentage points; P=.04), 13 (3.77 percentage points; P=.04) and 25 to 27 (4.20 percentage points; P=.048 and 5.91 percentage points; P=.01) after recommendation. Recommendations also affected reproduction numbers on days 0 (-0.07 units; P=.03) and 1 (-0.07 units; P=.03) and between days 21 (-0.09 units; P=.04) and 28 (-0.11 units; P=.05) and case growth rates between days 1 and 4 (-1.60 percentage points; P=.03 and -2.19 percentage points; P=.03) and on day 23 (-2.83 percentage points; P=.05) after publication.

CONCLUSIONS

Contrary to recommendations, mask mandates can be used as an effective measure to reduce SARS-CoV-2 reproduction numbers. However, mandates alone are not sufficient to reduce growth rates of COVID-19 cases. Our study adds external validity to the existing randomized controlled trials on the effectiveness of face masks to reduce the spread of SARS-CoV-2.

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period.

METHODS

22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.

FINDINGS

Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.

INTERPRETATION

Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic.

FUNDING

Bill & Melinda Gates Foundation.

Participation in organised sport to improve and prevent adverse developmental trajectories of at-risk youth: A systematic review

Background

Healthy after-school activities such as participation in organised sport have been shown to serve as important resources for reducing school failure and other problem/high-risk behaviour. It remains to be established to what extent organised sport participation has positive impacts on young people in unstable life circumstances.

Objectives

What are the effects of organised sport on risk behaviour, personal, emotional and social skills of young people, who either have experienced or are at-risk of experiencing an adverse outcome?

Search Methods

The database searches were carried out in March 2023 and other sources were searched in May 2023. We searched to identify both published and unpublished literature.

Selection Criteria

The intervention was participation in leisure time organised sport. Young people between 6 and 18 years of age, who either have experienced or are at-risk of experiencing an adverse outcome were eligible. Primary outcomes were problem/high-risk behaviour and a secondary outcomes social and emotional outcomes. Studies that used a control group were eligible for. Studies that utilised qualitative approaches were not.

Data Collection and Analysis

The number of potentially relevant studies was 43,716. Thirteen studies met the inclusion criteria. Only seven studies could be used in the data synthesis. Five studies were judged to have a critical risk of bias and were excluded from the meta-analysis. One study did not report data that enabled the calculation of effect sizes and standard errors. Meta-analyses were conducted on each conceptual outcome separately. All analyses were inverse variance weighted using random effects statistical models.

Main Results

Two studies were from Canada, one from Australia, and the remaining from the USA. The timespan of the interventions was 23 years, from 1995 to 2018. The median number of participants analysed was 316, and the median number of controls was 452. A number of primary outcomes were reported but each in a single study only. Concerning secondary outcomes, two studies reported the effect on overall psychosocial adjustment at post-intervention. The standardised mean difference was 0.70 (95% CI 0.28-1.11). There was a small amount of heterogeneity. Three studies reported on depressive symptoms at 0-3 years follow-up. The standardised mean difference was 0.02 (95% CI -0.01 to 0.06). There was no heterogeneity between the three studies. In addition, a number of other secondary outcomes were reported each in a single study only.

Authors' Conclusions

There were too few studies included in the meta-analyses in order for us to draw any conclusion. The dominance of Northern America clearly limiting the generalisability of the findings. The majority of the studies were not considered to be of overall high quality and the process of excluding studies with critical risk of bias from the meta-analysis applied in this review left us with only 7 of a total of 13 possible studies to synthesise. Further, because too few studies reported results on the same type of outcome, at most three studies could be combined in a particular meta-analysis and no meta-analysis could be performed on any of the primary outcomes.

On the short-term impact of pollution: The effect of PM 2.5 on emergency room visits

In this paper, we study the short-term effect of fine particulate matter (PM 2.5) exposure on respiratory emergency room (ER) visits in Chile, a middle-income country with high levels of air pollution. To instrument for PM 2.5, we use wind speed at different altitudes (pressure levels). Unlike previous papers, our data allow us to study the impact of high pollution levels across all age groups. We find that a 1 μg per cubic meter (μg/m3 ) increase in PM 2.5 exposure for 1 day increases ER visits for respiratory illness by 0.36%. The effect is positive and significant for all age groups. Furthermore, the coefficients on government environmental alerts suggest that avoidance behavior becomes increasingly significant across all age groups as restrictions become more severe.

The (temporary) Covid-19 baby bust in Mexico

In this paper, we investigate whether fertility and newborn health changed during the Covid-19 pandemic in Mexico. We use national administrative data and an event-study design to examine the impact of the Covid-19 pandemic on fertility and newborn health characteristics. Our findings suggest that Mexico's fertility declined temporarily as measured by conceptions that likely occurred during the stay-at-home order. Initially, the general fertility rate fell by 11-12 per cent but quickly rebounded and returned close to its original levels by the end of 2021. Newborn health also deteriorated during the pandemic. Instances of low birthweight and prematurity substantially increased, with both remaining elevated over the entire pandemic period.

Estimating hidden relationships in dynamical systems: Discovering drivers of infection rates of COVID-19

Discovering causal influences among internal variables is a fundamental goal of complex systems research. This paper presents a framework for uncovering hidden relationships from limited time-series data by combining methods from nonlinear estimation and information theory. The approach is based on two sequential steps: first, we reconstruct a more complete state of the underlying dynamical system, and second, we calculate mutual information between pairs of internal state variables to detail causal dependencies. Equipped with time-series data related to the spread of COVID-19 from the past three years, we apply this approach to identify the drivers of falling and rising infections during the three main waves of infection in the Chicago metropolitan region. The unscented Kalman filter nonlinear estimation algorithm is implemented on an established epidemiological model of COVID-19, which we refine to include isolation, masking, loss of immunity, and stochastic transition rates. Through the systematic study of mutual information between infection rate and various stochastic parameters, we find that increased mobility, decreased mask use, and loss of immunity post sickness played a key role in rising infections, while falling infections were controlled by masking and isolation.

County-Level Mandates Were Generally Effective At Slowing COVID-19 Transmission

Throughout the COVID-19 pandemic in the US, counties adopted numerous nonpharmaceutical interventions, such as mask mandates and stay-at-home orders, to slow COVID-19 transmission and prevent hospitals from reaching full capacity. Early evidence has been mixed about whether these interventions are effective. However, most studies only covered the early waves of COVID-19 and did not account for county-level variation in the adoption and repeal of such policies. Using daily county-level data from the Centers for Disease Control and Prevention, we evaluated the joint impact of bans on large gatherings, stay-at-home orders, mask mandates, and bar and restaurant closures on slowing COVID-19 transmission during waves 1-4 of the pandemic in the US (March 1, 2020-June 30, 2021). Our survival analysis showed that these interventions were generally effective at slowing COVID-19 transmission during this period. The mitigating effect was particularly strong during waves 2 and 3 and less substantial during waves 1 and 4. We also found strong evidence of the overall protective effect of mask mandates and, to a lesser degree, anticongregation policies. These study findings provide crucial evidence for public health officials to reference for support when using nonpharmaceutical interventions to flatten the curve of future waves of COVID-19 or other infectious disease outbreaks.

Multi-feature SEIR model for epidemic analysis and vaccine prioritization

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

Parameter estimation in behavioral epidemic models with endogenous societal risk-response

Behavioral epidemic models incorporating endogenous societal risk-response, where changes in risk perceptions prompt adjustments in contact rates, are crucial for predicting pandemic trajectories. Accurate parameter estimation in these models is vital for validation and precise projections. However, few studies have examined the problem of identifiability in models where disease and behavior parameters must be jointly estimated. To address this gap, we conduct simulation experiments to assess the effect on parameter estimation accuracy of a) delayed risk response, b) neglecting behavioral response in model structure, and c) integrating disease and public behavior data. Our findings reveal systematic biases in estimating behavior parameters even with comprehensive and accurate disease data and a well-structured simulation model when data are limited to the first wave. This is due to the significant delay between evolving risks and societal reactions, corresponding to the duration of a pandemic wave. Moreover, we demonstrate that conventional SEIR models, which disregard behavioral changes, may fit well in the early stages of a pandemic but exhibit significant errors after the initial peak. Furthermore, early on, relatively small data samples of public behavior, such as mobility, can significantly improve estimation accuracy. However, the marginal benefits decline as the pandemic progresses. These results highlight the challenges associated with the joint estimation of disease and behavior parameters in a behavioral epidemic model.

The economic impact of international travel measures used during the COVID-19 pandemic: a scoping review

INTRODUCTION

Assessment of the use of travel measures during COVID-19 has focused on their effectiveness in achieving public health objectives. However, the prolonged use of highly varied and frequently changing measures by governments, and their unintended consequences caused, has been controversial. This has led to a call for coordinated decision-making focused on risk-based approaches, which requires better understanding of the broader impacts of international travel measures (ITMs) on individuals and societies.

METHODS

Our scoping review investigates the literature on the economic impact of COVID-19 ITMs. We searched health, social science and COVID-19-specific databases for empirical studies preprinted or published between 1 January 2020 and 31 October 2023. Evidence was charted using a narrative approach and included jurisdiction of study, ITMs studied, study design, outcome categories, and main findings.

RESULTS

Twenty-six studies met the inclusion criteria and were included for data extraction. Twelve of them focused on the international travel restrictions implemented in early 2020. Limited attention was given to measures such as entry/exit screening and vaccination requirements. Eight studies focused on high-income countries, 6 on low-income and middle-income countries and 10 studies were comparative although did not select countries by income. Economic outcomes assessed included financial markets (n=13), economic growth (n=4), economic activities (n=1), performance of industries central to international travel (n=9), household-level economic status (n=3) and consumer behaviour (n=1). Empirical methods employed included linear regression (n=17), mathematical modelling (n=3) and mixed strategies (n=6).

CONCLUSION

Existing studies have begun to provide evidence of the wide-ranging economic impacts resulting from ITMs. However, the small body of research combined with difficulties in isolating the effects of such measures and limitations in available data mean that it is challenging to draw general and robust conclusions. Future research using rigorous empirical methods and high-quality data is needed on this topic.

Long-term exposure to wildfire smoke PM2.5 and mortality in the contiguous United States

Despite the growing evidence on the health effects of short-term exposure to wildfire smoke fine particles (PM2.5), the impacts of long-term wildfire smoke PM2.5 exposure remain unclear. We investigated the association between long-term exposure to wildfire smoke PM2.5 and all-cause mortality and mortality from a wide range of specific causes in all 3,108 counties in the contiguous U.S., 2007-2020. Monthly county-level mortality data were collected from the National Center for Health Statistics. Wildfire smoke PM2.5 concentration was derived from a 10×10 km2 resolution spatiotemporal model. Controlling for non-smoke PM2.5, air temperature, and unmeasured spatial and temporal confounders, we found a non-linear association between 12-month moving average concentration of smoke PM2.5 and monthly all-cause mortality rate. Relative to a month with the long-term smoke PM2.5 exposure below 0.1 μg/m3, all-cause mortality increased by 0.40-1.54 and 3.65 deaths per 100,000 people per month when the 12-month moving average of PM2.5 concentration was of 0.1-5 and 5+ μg/m3, respectively. Cardiovascular, ischemic heart disease, digestive, endocrine, diabetes, mental, suicide, and chronic kidney disease mortality were all found to be associated with long-term wildfire smoke PM2.5 exposure. Smoke PM2.5 contributed to approximately 30,180 all-cause deaths/year (95% CI: 21,449, 38,910) in the contiguous U.S. Higher smoke PM2.5-related increases in mortality rates were found for people aged 65 above and racial minority populations. Positive interaction effects with extreme heat were also observed. Our study identified the detrimental effects of long-term exposure to wildfire smoke PM2.5 on a wide range of mortality outcomes, underscoring the need for public health actions and communication to prepare communities and individuals to mitigate smoke exposure.

Causal relationships of metabolites with allergic diseases: a trans-ethnic Mendelian randomization study

BACKGROUND

Allergic diseases exert a considerable impact on global health, thus necessitating investigations into their etiology and pathophysiology for devising effective prevention and treatment strategies. This study employs a Mendelian randomization (MR) analysis and meta-analysis to identify metabolite targets potentially associated with allergic diseases.

METHODS

A two-sample MR analysis was conducted to explore potential causal relationships between circulating and urinary metabolites and allergic diseases. Exposures were derived from a genome-wide association study (GWAS) of 486 circulating metabolites and a GWAS of 55 targeted urinary metabolites. Outcome data for allergic diseases, including atopic dermatitis (AD), allergic rhinitis (AR), and asthma, were obtained from the FinnGen biobank in Europe (cohort 1) and the Biobank Japan in Asia (cohort 2). MR results from both cohorts were combined using a meta-analysis.

RESULTS

MR analysis identified 50 circulating metabolites and 6 urinary metabolites in cohort 1 and 54 circulating metabolites and 2 urinary metabolites in cohort 2 as potentially causally related to allergic diseases. A meta-analysis of the MR results revealed stearoylcarnitine (OR 8.654; 95% CI 4.399-17.025; P = 4.06E-10) and 1-arachidonoylglycerophosphoinositol (OR 2.178; 95% CI 1.388-3.419; P = 7.15E-04) as the most reliable causal circulating metabolites for asthma and AR, respectively. Further, histidine (OR 0.734; 95% CI: 0.594-0.907; P = 0.004), tyrosine (OR 0.601; 95% CI: 0.380-0.952; P = 0.030), and alanine (OR 0.280; 95% CI: 0.125-0.628; P = 0.002) emerged as urinary metabolites with the greatest protective effects against asthma, AD, and AR, respectively.

CONCLUSIONS

Imbalances in numerous circulating and urinary metabolites may be implicated in the development and progression of allergic diseases. These findings have significant implications for the development of targeted strategies for the prevention and treatment of allergic diseases.

Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable Mendelian randomization to GLP1R gene region

Phenotypic heterogeneity at genomic loci encoding drug targets can be exploited by multivariable Mendelian randomization to provide insight into the pathways by which pharmacological interventions may affect disease risk. However, statistical inference in such investigations may be poor if overdispersion heterogeneity in measured genetic associations is unaccounted for. In this work, we first develop conditional F statistics for dimension-reduced genetic associations that enable more accurate measurement of phenotypic heterogeneity. We then develop a novel extension for two-sample multivariable Mendelian randomization that accounts for overdispersion heterogeneity in dimension-reduced genetic associations. Our empirical focus is to use genetic variants in the GLP1R gene region to understand the mechanism by which GLP1R agonism affects coronary artery disease (CAD) risk. Colocalization analyses indicate that distinct variants in the GLP1R gene region are associated with body mass index and type 2 diabetes (T2D). Multivariable Mendelian randomization analyses that were corrected for overdispersion heterogeneity suggest that bodyweight lowering rather than T2D liability lowering effects of GLP1R agonism are more likely contributing to reduced CAD risk. Tissue-specific analyses prioritized brain tissue as the most likely to be relevant for CAD risk, of the tissues considered. We hope the multivariable Mendelian randomization approach illustrated here is widely applicable to better understand mechanisms linking drug targets to diseases outcomes, and hence to guide drug development efforts.

The demand for online grocery shopping: COVID-induced changes in grocery shopping behavior of Canadian consumers

The COVID-19 pandemic has had a lasting impact on many economies around the globe. One area where significant changes have been documented is consumer behavior. A questionnaire survey was carried out to understand the impact of COVID-19 on grocery purchase behavior of Canadian consumers and evaluate the permanence of these effects. With a focus on online grocery shopping, this work integrates multiple existing theories of consumer behavior to explore the influence of different factors on consumers' adoption of online mode of grocery shopping during the pandemic and their intentions to continue the use of this mode in the post-pandemic world. A total of more than 600 usable survey responses were analyzed using statistical analysis and a Logit econometrics technique. The results reveal that 72% of the survey participants had to alter their grocery shopping habits as a result of the COVID-19 pandemic; 63% of these consumers claim that the changes that occurred would prevail in the future, with no return to the "pre-COVID normal". The results also show that the pandemic resulted in significant proliferation of online grocery shopping among Canadian consumers. Further, the findings show that the important factors that explain adoption of online grocery shopping and the shift towards higher reliance on online grocery purchases in the future include the perceived threat of COVID, pre-COVID shopping habits, socio-demographic characteristics, and the variables that capture technological opportunities and abilities.

Birth weight influences cardiac structure, function, and disease risk: evidence of a causal association

BACKGROUND AND AIMS

Low birth weight is a common pregnancy complication, which has been associated with higher risk of cardiometabolic disease in later life. Prior Mendelian randomization (MR) studies exploring this question do not distinguish the mechanistic contributions of variants that directly influence birth weight through the foetal genome (direct foetal effects), vs. variants influencing birth weight indirectly by causing an adverse intrauterine environment (indirect maternal effects). In this study, MR was used to assess whether birth weight, independent of intrauterine influences, is associated with cardiovascular disease risk and measures of adverse cardiac structure and function.

METHODS

Uncorrelated (r2 < .001), genome-wide significant (P < 5 × 10-8) single nucleotide polymorphisms were extracted from genome-wide association studies summary statistics for birth weight overall, and after isolating direct foetal effects only. Inverse-variance weighted MR was utilized for analyses on outcomes of atrial fibrillation, coronary artery disease, heart failure, ischaemic stroke, and 16 measures of cardiac structure and function. Multiple comparisons were accounted for by Benjamini-Hochberg correction.

RESULTS

Lower genetically-predicted birth weight, isolating direct foetal effects only, was associated with an increased risk of coronary artery disease (odds ratio 1.21, 95% confidence interval 1.06-1.37; P = .031), smaller chamber volumes, and lower stroke volume, but higher contractility.

CONCLUSIONS

The results of this study support a causal role of low birth weight in cardiovascular disease, even after accounting for the influence of the intrauterine environment. This suggests that individuals with a low birth weight may benefit from early targeted cardiovascular disease prevention strategies, independent of whether this was linked to an adverse intrauterine environment during gestation.

Handling dependent samples in meta-analytic structural equation models: A Wishart-based approach

We present an approach to meta-analytic structural equation models that relies on hierarchical modeling of sample covariance matrices under the assumption that the matrices are Wishart. The approach handles the commonplace fixed- and random-effects meta-analytic SEMs, and solves the problem of dependent covariance matrices where more than one covariance matrix is obtained from a single study or study author. The ability of the approach to adequately recover parameters is examined via a simulation study. The approach is implemented in the bayesianmasem R package and a demonstration shows applications of the model.

Age-specific transmission dynamics of SARS-CoV-2 during the first 2 years of the pandemic

During its first 2 years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. In this article, we aimed to develop an inference-based modeling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents, and adults over the seven waves of four variants (wild-type, Alpha, Delta, and Omicron BA.1) during the first 2 years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. However, the ratio of hospitalizations to infections decreased with each subsequent variant in all age categories. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either reinfections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.

Assumption-checking rather than (just) testing: The importance of visualization and effect size in statistical diagnostics

Statistical methods generally have assumptions (e.g., normality in linear regression models). Violations of these assumptions can cause various issues, like statistical errors and biased estimates, whose impact can range from inconsequential to critical. Accordingly, it is important to check these assumptions, but this is often done in a flawed way. Here, I first present a prevalent but problematic approach to diagnostics-testing assumptions using null hypothesis significance tests (e.g., the Shapiro-Wilk test of normality). Then, I consolidate and illustrate the issues with this approach, primarily using simulations. These issues include statistical errors (i.e., false positives, especially with large samples, and false negatives, especially with small samples), false binarity, limited descriptiveness, misinterpretation (e.g., of p-value as an effect size), and potential testing failure due to unmet test assumptions. Finally, I synthesize the implications of these issues for statistical diagnostics, and provide practical recommendations for improving such diagnostics. Key recommendations include maintaining awareness of the issues with assumption tests (while recognizing they can be useful), using appropriate combinations of diagnostic methods (including visualization and effect sizes) while recognizing their limitations, and distinguishing between testing and checking assumptions. Additional recommendations include judging assumption violations as a complex spectrum (rather than a simplistic binary), using programmatic tools that increase replicability and decrease researcher degrees of freedom, and sharing the material and rationale involved in the diagnostics.

The Impact of Education on Fertility During the Chinese Reform Era (1980-2018): Changes Across Birth Cohorts and Interaction with Fertility Policies

We examined the influence of education on fertility decisions in contemporary China, drawing upon theoretical insights that emphasise the role of social institutions, gender relations, and life course dynamics in shaping family behaviour. This led us to propose a set of hypotheses that explain the differential effect of education on each parity. We used information on female cohorts born between 1960 and 1989, coming from the China Family Panel Studies for 2010-2018. We applied event history models with both independent and simultaneous equations models to account for selection and endogeneity effects. The results point to a substantial contribution of the increased educational attainment in the population in the fertility decline and current low levels of fertility, beyond the role of fertility policies. Consistent with our hypotheses, the results show that woman's educational attainment has a strong negative effect on the hazard of bearing a second or third child. Male partner's educational attainment also has a negative effect on the hazard of transition to a second or third birth, yet with a weaker intensity. We also found that the negative effect of education on second birth rates significantly declines across birth cohorts. The results show little educational differentials in the probability of bearing a first child, while the better educated postpone first births. Moreover, the effect of fertility policies, measured at the individual level, gradually increases with the level of education.

Twitter social mobility data reveal demographic variations in social distancing practices during the COVID-19 pandemic

The COVID-19 pandemic demonstrated the importance of social distancing practices to stem the spread of the virus. However, compliance with public health guidelines was mixed. Understanding what factors are associated with differences in compliance can improve public health messaging since messages could be targeted and tailored to different population segments. We utilize Twitter data on social mobility during COVID-19 to reveal which populations practiced social distancing and what factors correlated with this practice. We analyze correlations between demographic and political affiliation with reductions in physical mobility measured by public geolocation tweets. We find significant differences in mobility reduction between these groups in the United States. We observe that males, Asian and Latinx individuals, older individuals, Democrats, and people from higher population density states exhibited larger reductions in movement. Furthermore, our study also unveils meaningful insights into the interactions between different groups. We hope these findings will provide evidence to support public health policy-making.

The health consequences of returning to work after retirement: Evidence from a Japanese longitudinal survey

Prolonged and active old age provides individuals with more chances to work again after full retirement. Returning to work is an increasingly common form of the retirement process and influences the sustainability of social security systems. Previous studies show a beneficial relationship between returning to work and health; however, little is known about the causal effect of returning to work on health. This study used data from men and women aged 50 and older (11,991 individuals) in the Longitudinal Survey of Middle-aged and Older Adults, conducted annually from 2005 to 2019 in Japan. The effects of three types of labor force transitions (continued work, full retirement, and return to work) on physical and mental health were examined. To obtain the causal effects, an instrumental variable approach was used by exploiting the Japanese pension reform and labor market settings as instruments. Compared with full retirement, returning to work showed significantly worse mental health but no significant difference in physical health. The negative effect of returning to work was pronounced among men, former nonmanual workers, and low-wealth individuals. Contrary to the findings in previous studies, insignificant or detrimental effects of returning to work were found in this study. The rigorous causal analysis adds new evidence to the literature. The findings provide important implications for labor and health policy in aging societies.

A genome-wide comprehensive analysis of nucleosome positioning in yeast

In eukaryotic cells, the one-dimensional DNA molecules need to be tightly packaged into the spatially constraining nucleus. Folding is achieved on its lowest level by wrapping the DNA around nucleosomes. Their arrangement regulates other nuclear processes, such as transcription and DNA repair. Despite strong efforts to study nucleosome positioning using Next Generation Sequencing (NGS) data, the mechanism of their collective arrangement along the gene body remains poorly understood. Here, we classify nucleosome distributions of protein-coding genes in Saccharomyces cerevisiae according to their profile similarity and analyse their differences using functional Principal Component Analysis. By decomposing the NGS signals into their main descriptive functions, we compared wild type and chromatin remodeler-deficient strains, keeping position-specific details preserved whilst considering the nucleosome arrangement as a whole. A correlation analysis with other genomic properties, such as gene size and length of the upstream Nucleosome Depleted Region (NDR), identified key factors that influence the nucleosome distribution. We reveal that the RSC chromatin remodeler-which is responsible for NDR maintenance-is indispensable for decoupling nucleosome arrangement within the gene from positioning outside, which interfere in rsc8-depleted conditions. Moreover, nucleosome profiles in chd1Δ strains displayed a clear correlation with RNA polymerase II presence, whereas wild type cells did not indicate a noticeable interdependence. We propose that RSC is pivotal for global nucleosome organisation, whilst Chd1 plays a key role for maintaining local arrangement.

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities

Introduction

There is a vast literature on the performance of different short-term forecasting models for country specific COVID-19 cases, but much less research with respect to city level cases. This paper employs daily case counts for 25 Metropolitan Statistical Areas (MSAs) in the U.S. to evaluate the efficacy of a variety of statistical forecasting models with respect to 7 and 28-day ahead predictions.

Methods

This study employed Gradient Boosted Regression Trees (GBRT), Linear Mixed Effects (LME), Susceptible, Infectious, or Recovered (SIR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to generate daily forecasts of COVID-19 cases from November 2020 to March 2021.

Results

Consistent with other research that have employed Machine Learning (ML) based methods, we find that Median Absolute Percentage Error (MAPE) values for both 7-day ahead and 28-day ahead predictions from GBRTs are lower than corresponding values from SIR, Linear Mixed Effects (LME), and Seasonal Autoregressive Integrated Moving Average (SARIMA) specifications for the majority of MSAs during November-December 2020 and January 2021. GBRT and SARIMA models do not offer high-quality predictions for February 2021. However, SARIMA generated MAPE values for 28-day ahead predictions are slightly lower than corresponding GBRT estimates for March 2021.

Discussion

The results of this research demonstrate that basic ML models can lead to relatively accurate forecasts at the local level, which is important for resource allocation decisions and epidemiological surveillance by policymakers.

The optimisation of public health emergency governance: a simulation study based on COVID-19 pandemic control policy

BACKGROUND

The outbreak of the COVID-19 pandemic sparked numerous studies on policy options for managing public health emergencies, especially regarding how to choose the intensity of prevention and control to maintain a balance between economic development and disease prevention.

METHODS

We constructed a cost-benefit model of COVID-19 pandemic prevention and control policies based on an epidemic transmission model. On this basis, numerical simulations were performed for different economies to analyse the dynamic evolution of prevention and control policies. These economies include areas with high control costs, as seen in high-income economies, and areas with relatively low control costs, exhibited in upper-middle-income economies.

RESULTS

The simulation results indicate that, at the outset of the COVID-19 pandemic, both high-and low-cost economies tended to enforce intensive interventions. However, as the virus evolved, particularly in circumstances with relatively rates of reproduction, short incubation periods, short spans of infection and low mortality rates, high-cost economies became inclined to ease restrictions, while low-cost economies took the opposite approach. However, the consideration of additional costs incurred by the non-infected population means that a low-cost economy is likely to lift restrictions as well.

CONCLUSIONS

This study concludes that variations in prevention and control policies among nations with varying income levels stem from variances in virus transmission characteristics, economic development, and control costs. This study can help researchers and policymakers better understand the differences in policy choice among various economies as well as the changing trends of dynamic policy choices, thus providing a certain reference value for the policy direction of global public health emergencies.

Genetically predicted androgenic profiles and adverse cardiac markers: a sex-specific Mendelian randomization study

AIMS

Observational evidence suggests associations between sex hormone levels and heart failure (HF). We used sex-specific genetic variants associated with androgenic sex hormone profiles to investigate the causal relevance of androgenic sex hormone profiles on cardiac structure and function and HF using Mendelian randomization (MR).

METHODS AND RESULTS

Sex-specific uncorrelated genome-wide significant (P < 5 × 10-8 ) variants predicting sex hormone-binding globulin (SHBG), total testosterone, and bioavailable testosterone were extracted from summary statistics of genome-wide association study (GWAS) on 425 097 participants in the UK Biobank. Sex-specific gene-outcome association estimates were computed for left ventricular ejection fraction (LVEF), left ventricular end-diastolic and end-systolic volumes (LVEDV and LVESV, respectively), left ventricular stroke volume (LVSV), cardiac index, and cardiac output in 11 528 female and 14 356 male UK Biobank Imaging Study participants and for incident or prevalent HF in an external cohort of 47 309 cases and 930 014 controls. Inverse-variance weighted MR was the primary analysis method. In females, higher genetically predicted bioavailable testosterone was associated with lower LVEDV [β per nmol/L = -0.11 (-0.19 to -0.03), P = 0.006], lower LVESV [β = -0.09 (-0.17 to -0.01), P = 0.022], lower LVSV [β = -0.11 (-0.18 to -0.03), P = 0.005], lower cardiac output [β = -0.08 (-0.16 to 0.00), P = 0.046], and lower cardiac index [β = -0.08 (-0.16 to -0.01), P = 0.034] and a higher risk of HF [odds ratio 1.10 (1.01-1.19), P = 0.026] on external validation analysis in larger scale, sex-adjusted GWAS data. Higher genetically predicted SHBG was associated with higher LVEDV [β per nmol/L = 0.17 (0.08-0.25), P = 2 × 10-4 ], higher LVESV [β = 0.13 (0.05-0.22), P = 0.003], and higher LVSV [β = 0.18 (0.08-0.28), P = 2 × 10-4 ]. In males, higher genetically predicted total and bioavailable testosterone was associated with lower LVESV [β = -0.07 (-0.12 to -0.02), P = 0.007] and LVEF [β = -0.11 (-0.18 to -0.04), P = 0.003], respectively.

CONCLUSIONS

This study supports a causal effect of pro-androgenic sex hormone profiles in females on adverse markers of left ventricular structure and function typically associated with HF with preserved ejection fraction and with HF. There was weaker evidence of association in males.

Improved inference for doubly robust estimators of heterogeneous treatment effects

We propose a doubly robust approach to characterizing treatment effect heterogeneity in observational studies. We develop a frequentist inferential procedure that utilizes posterior distributions for both the propensity score and outcome regression models to provide valid inference on the conditional average treatment effect even when high-dimensional or nonparametric models are used. We show that our approach leads to conservative inference in finite samples or under model misspecification and provides a consistent variance estimator when both models are correctly specified. In simulations, we illustrate the utility of these results in difficult settings such as high-dimensional covariate spaces or highly flexible models for the propensity score and outcome regression. Lastly, we analyze environmental exposure data from NHANES to identify how the effects of these exposures vary by subject-level characteristics.

Childhood sunburn and risk of melanoma and non-melanoma skin cancer: a Mendelian randomization study

Previous evidence has suggested that childhood sunburn could be a risk factor for cutaneous malignant melanoma (MM) and non-melanoma skin cancer (NMSC). However, existing observational studies could not reveal the causal associations genetically. This study aimed to investigate whether there was a genetic causal relationship between childhood sunburn and skin cancers. Univariable Mendelian randomization (MR) and Causal Analysis Using Summary Effect analysis was carried out for causal estimates and evaluation for the horizontal pleiotropy. Multivariable MR and the mediation effects analysis were used to test whether the causal associations were mediated by potential confounders. A suggestively significant causal association between childhood sunburn and MM was indicated (OR = 4.74; 95% CI: 1.31-17.19; p = 1.79E-02). Genetically predicted childhood sunburn was significantly associated with increased risk of overall melanoma in situ (MIS) (OR = 4.02; 95% CI: 2.00-8.08; p = 9.40E-05), MIS of face (OR = 18.28; 95% CI: 5.28-63.35; p = 4.59E-06), and MIS of trunk (OR = 7.05; 95% CI: 2.06-24.13; p = 1.88E-03). Similar trends were found for childhood sunburn and NMSC (OR = 8.16; 95% CI: 6.07-10.99; p = 1.53E-20), including both basal cell carcinoma (BCC) (OR = 3.76; 95% CI:2.96-4.77; p = 2.19E-08) and squamous cell carcinoma (SCC) (OR = 7.44; 95% CI: 5.09-10.87; p = 2.19E-08). After adjustment for hair and skin color, facial ageing, vitamin D levels, body mass index, alcohol consumption, and smoking status, childhood sunburn showed an independent association with MIS, MIS of face, MIS of trunk, as well as NMSC, including both BCC and SCC. Mediation analysis showed no significant mediation effect. This study demonstrated a causal relationship between childhood sunburn and the risk of both MM and NMSC, which suggested that enhanced screening and prevention for childhood sunburn could contribute to the early detection and decreased risk of MM and NMSC.

Combining observational and experimental datasets using shrinkage estimators

We consider the problem of combining data from observational and experimental sources to draw causal conclusions. To derive combined estimators with desirable properties, we extend results from the Stein shrinkage literature. Our contributions are threefold. First, we propose a generic procedure for deriving shrinkage estimators in this setting, making use of a generalized unbiased risk estimate. Second, we develop two new estimators, prove finite sample conditions under which they have lower risk than an estimator using only experimental data, and show that each achieves a notion of asymptotic optimality. Third, we draw connections between our approach and results in sensitivity analysis, including proposing a method for evaluating the feasibility of our estimators.

The Role of Infant Health Problems in Constraining Interneighborhood Mobility: Implications for Citywide Employment Networks

Infant health problems are a persistent concern across the United States, disproportionally affecting socioeconomically vulnerable communities. We investigate how inequalities in infant health contribute to differences in interneighborhood commuting mobility and shape neighborhoods' embeddedness in the citywide structure of employment networks in Chicago over a 14-year period. We use the Census Bureau's Longitudinal Employer-Household Dynamics' Origin-Destination Employment Statistics to analyze commuting networks between 2002 and 2015. Results from longitudinal network analyses indicate two main patterns. First, after the Great Recession, a community's infant health problems began to significantly predict isolation from the citywide employment network. Second, pairwise dissimilarity in infant health problems predicts a lower likelihood of mobility ties between communities throughout the entire study period. The findings suggest that infant health problems present a fundamental barrier for communities in equally accessing the full range of jobs and opportunities across the city-compounding existing inequalities.

Homogeneity tests of covariance for high-dimensional functional data with applications to event segmentation

We consider inference problems for high-dimensional (HD) functional data with a dense number of T repeated measurements taken for a large number of p variables from a small number of n experimental units. The spatial and temporal dependence, high dimensionality, and dense number of repeated measurements pose theoretical and computational challenges. This paper has two aims; our first aim is to solve the theoretical and computational challenges in testing equivalence among covariance matrices from HD functional data. The second aim is to provide computationally efficient and tuning-free tools with guaranteed stochastic error control. The weak convergence of the stochastic process formed by the test statistics is established under the "large p, large T, and small n" setting. If the null is rejected, we further show that the locations of the change points can be estimated consistently. The estimator's rate of convergence is shown to depend on the data dimension, sample size, number of repeated measurements, and signal-to-noise ratio. We also show that our proposed computation algorithms can significantly reduce the computation time and are applicable to real-world data with a large number of HD-repeated measurements (e.g., functional magnetic resonance imaging (fMRI) data). Simulation results demonstrate both the finite sample performance and computational effectiveness of our proposed procedures. We observe that the empirical size of the test is well controlled at the nominal level, and the locations of multiple change points can be accurately identified. An application to fMRI data demonstrates that our proposed methods can identify event boundaries in the preface of the television series Sherlock. Code to implement the procedures is available in an R package named TechPhD.