Comparative impact assessment of COVID-19 policy interventions in five South Asian countries using reported and estimated unreported death counts during 2020-2021.

There has been raging discussion and debate around the quality of COVID death data in South Asia. According to WHO, of the 5.5 million reported COVID-19 deaths from 2020-2021, 0.57 million (10%) were contributed by five low and middle income countries (LMIC) countries in the Global South: India, Pakistan, Bangladesh, Sri Lanka and Nepal. However, a number of excess death estimates show that the actual death toll from COVID-19 is significantly higher than the reported number of deaths. For example, the IHME and WHO both project around 14.9 million total deaths, of which 4.5-5.5 million were attributed to these five countries in 2020-2021. We focus our gaze on the COVID-19 performance of these five countries where 23.5% of the world population lives in 2020 and 2021, via a counterfactual lens and ask, to what extent the mortality of one LMIC would have been affected if it adopted the pandemic policies of another, similar country? We use a Bayesian semi-mechanistic model developed by Mishra et al. (2021) to compare both the reported and estimated total death tolls by permuting the time-varying reproduction number (Rt) across these countries over a similar time period. Our analysis shows that, in the first half of 2021, mortality in India in terms of reported deaths could have been reduced to 96 and 102 deaths per million compared to actual 170 reported deaths per million had it adopted the policies of Nepal and Pakistan respectively. In terms of total deaths, India could have averted 481 and 466 deaths per million had it adopted the policies of Bangladesh and Pakistan. On the other hand, India had a lower number of reported COVID-19 deaths per million (48 deaths per million) and a lower estimated total deaths per million (80 deaths per million) in the second half of 2021, and LMICs other than Pakistan would have lower reported mortality had they followed India's strategy. The gap between the reported and estimated total deaths highlights the varying level and extent of under-reporting of deaths across the subcontinent, and that model estimates are contingent on accuracy of the death data. Our analysis shows the importance of timely public health intervention and vaccines for lowering mortality and the need for better coverage infrastructure for the death registration system in LMICs.

Ecological Momentary Assessments and Passive Sensing in the Prediction of Short-Term Suicidal Ideation in Young Adults.

Importance: Advancements in technology, including mobile-based ecological momentary assessments (EMAs) and passive sensing, have immense potential to identify short-term suicide risk. However, the extent to which EMA and passive data, particularly in combination, have utility in detecting short-term risk in everyday life remains poorly understood. Objective: To examine whether and what combinations of self-reported EMA and sensor-based assessments identify next-day suicidal ideation. Design, Setting, and Participants: In this intensive longitudinal prognostic study, participants completed EMAs 4 times daily and wore a sensor wristband (Fitbit Charge 3) for 8 weeks. Multilevel machine learning methods, including penalized generalized estimating equations and classification and regression trees (CARTs) with repeated 5-fold cross-validation, were used to optimize prediction of next-day suicidal ideation based on time-varying features from EMAs (affective, cognitive, behavioral risk factors) and sensor data (sleep, activity, heart rate). Young adult patients who visited an emergency department with recent suicidal ideation and/or suicide attempt were recruited. Identified via electronic health record screening, eligible individuals were contacted remotely to complete enrollment procedures. Participants (aged 18 to 25 years) completed 14 708 EMA observations (64.4% adherence) and wore a sensor wristband approximately half the time (55.6% adherence). Data were collected between June 2020 and July 2021. Statistical analysis was performed from January to March 2023. Main Outcomes and Measures: The outcome was presence of next-day suicidal ideation. Results: Among 102 enrolled participants, 83 (81.4%) were female; 6 (5.9%) were Asian, 5 (4.9%) were Black or African American, 9 (8.8%) were more than 1 race, and 76 (74.5%) were White; mean (SD) age was 20.9 (2.1) years. The best-performing model incorporated features from EMAs and showed good predictive accuracy (mean [SE] cross-validated area under the receiver operating characteristic curve [AUC], 0.84 [0.02]), whereas the model that incorporated features from sensor data alone showed poor prediction (mean [SE] cross-validated AUC, 0.56 [0.02]). Sensor-based features did not improve prediction when combined with EMAs. Suicidal ideation-related features were the strongest predictors of next-day ideation. When suicidal ideation features were excluded, an alternative EMA model had acceptable predictive accuracy (mean [SE] cross-validated AUC, 0.76 [0.02]). Both EMA models included features at different timescales reflecting within-day, end-of-day, and time-varying cumulative effects. Conclusions and Relevance: In this prognostic study, self-reported risk factors showed utility in identifying near-term suicidal thoughts. Best-performing models required self-reported information, derived from EMAs, whereas sensor-based data had negligible predictive accuracy. These results may have implications for developing decision algorithms identifying near-term suicidal thoughts to guide risk monitoring and intervention delivery in everyday life.

Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience.

India experienced a massive surge in SARS-CoV-2 infections and deaths during April to June 2021 despite having controlled the epidemic relatively well during 2020. Using counterfactual predictions from epidemiological disease transmission models, we produce evidence in support of how strengthening public health interventions early would have helped control transmission in the country and significantly reduced mortality during the second wave, even without harsh lockdowns. We argue that enhanced surveillance at district, state, and national levels and constant assessment of risk associated with increased transmission are critical for future pandemic responsiveness. Building on our retrospective analysis, we provide a tiered data-driven framework for timely escalation of future interventions as a tool for policy-makers.

Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review.

BACKGROUND: This study aims to examine the worldwide prevalence of post-coronavirus disease 2019 (COVID-19) condition, through a systematic review and meta-analysis. METHODS: PubMed, Embase, and iSearch were searched on July 5, 2021 with verification extending to March 13, 2022. Using a random-effects framework with DerSimonian-Laird estimator, we meta-analyzed post-COVID-19 condition prevalence at 28+ days from infection. RESULTS: Fifty studies were included, and 41 were meta-analyzed. Global estimated pooled prevalence of post-COVID-19 condition was 0.43 (95% confidence interval [CI], .39-.46). Hospitalized and nonhospitalized patients had estimates of 0.54 (95% CI, .44-.63) and 0.34 (95% CI, .25-.46), respectively. Regional prevalence estimates were Asia (0.51; 95% CI, .37-.65), Europe (0.44; 95% CI, .32-.56), and United States of America (0.31; 95% CI, .21-.43). Global prevalence for 30, 60, 90, and 120 days after infection were estimated to be 0.37 (95% CI, .26-.49), 0.25 (95% CI, .15-.38), 0.32 (95% CI, .14-.57), and 0.49 (95% CI, .40-.59), respectively. Fatigue was the most common symptom reported with a prevalence of 0.23 (95% CI, .17-.30), followed by memory problems (0.14; 95% CI, .10-.19). CONCLUSIONS: This study finds post-COVID-19 condition prevalence is substantial; the health effects of COVID-19 seem to be prolonged and can exert stress on the healthcare system.

Meta-analysis of nationwide SARS-CoV-2 infection fatality rates in India.

There has been much discussion and debate around underreporting of deaths in India in media articles and in the scientific literature. In this brief report, we aim to meta-analyze the available/inferred estimates of infection fatality rates for SARS-CoV-2 in India based on the existent literature. These estimates account for uncaptured deaths and infections. We consider empirical excess death estimates based on all-cause mortality data as well as disease transmission-based estimates that rely on assumptions regarding infection transmission and ascertainment rates in India. Through an initial systematic review (Zimmermann et al., 2021) that followed PRISMA guidelines and comprised a search of databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv, and SSRN for preprints (accessed through iSearch) on July 3, 2021, we further extended the search verification through May 26, 2022. The screening process yielded 15 studies qualitatively analyzed, of which 9 studies with 11 quantitative estimates were included in the meta-analysis. Using a random effects meta-analysis framework, we obtain a pooled estimate of nationwide infection fatality rate (defined as the ratio of estimated deaths over estimated infections) and a corresponding confidence interval. Death underreporting from excess deaths studies varies by a factor of 6.1-13.0 with nationwide cumulative excess deaths ranging from 2.6-6.3 million, whereas the underreporting from disease transmission-based studies varies by a factor of 3.5-7.3 with SARS-CoV-2 related nationwide estimated total deaths ranging from 1.4-3.4 million, through June 2021 with some estimates extending to 31 December 2021. Underreporting of infections was found previously (Zimmermann et al., 2021) to be 24.9 (relying on the latest 4th nationwide serosurvey from 14 June-6 July 2021 prior to launch of the vaccination program). Conservatively, by considering the lower values of these available estimates, we infer that approximately 95% of infections and 71% of deaths were not accounted for in the reported figures in India. Nationwide pooled infection fatality rate estimate for India is 0.51% (95% confidence interval [CI]: 0.45%- 0.58%). We often tend to compare countries across the world in terms of total reported cases and deaths. Although the US has the highest number of reported cumulative deaths globally, after accounting for underreporting, India appears to have the highest number of cumulative total deaths (reported + unreported). However, the large number of estimated infections in India leads to a lower infection fatality rate estimate than the US, which in part is due to the younger population in India. We emphasize that the age-structure of different countries must be taken into consideration while making such comparisons. More granular data are needed to examine heterogeneities across various demographic groups to identify at-risk and underserved populations with high COVID mortality; the hope is that such disaggregated mortality data will soon be made available for India.

In utero exposure to poly- and perfluoroalkyl substances (PFASs) and subsequent breast cancer.

We tested the hypothesis that maternal perinatal serum levels of poly and perfluoroalkyl substances (PFASs) predict risk for breast cancer in daughters in a 54-year follow-up of 9300 daughters born 1959-1967 in the Child Health and Development Studies pregnancy cohort. Total cholesterol and PFASs were measured in archived maternal perinatal serum for 102 daughter breast cancer cases diagnosed by age 52, and 310 controls matched on birth year and blood draw trimester. High maternal N-ethyl-perfluorooctane sulfonamido acetic acid (EtFOSAA), a precursor of perfluorooctane sulfonic acid (PFOS), in combination with high maternal total cholesterol predicted a 3.6-fold increased risk of breast cancer (pinteraction<0.05). Conversely, maternal PFOS was associated with decreased daughters' breast cancer risk. Predictions were robust to alternative modeling and independent of other maternal factors. Future generations continue to be exposed to ubiquitous, persistent PFASs. These findings are relevant to breast cancer prevention if confirmed experimentally and where possible, in additional epidemiology studies of internal doses of PFASs and other chemical mixtures especially during vulnerable windows in early life.

Placental morphometry in relation to daughters' percent mammographic breast density at midlife.

Intrauterine and early-life exposures, including intrauterine smoke exposures and infant growth are associated with mammographic breast density (MBD), a strong breast cancer risk factor. We investigated whether placental morphometry, which is affected by intrauterine smoke exposure and also influences infant growth, predicts %MBD at ages 37-47. In 247 daughters in the Child Health and Development Studies, we found that larger placental surface area and placental thickness were associated with lower %MBD (-0.32 per cm2, 95% CI -0.6, -0.05; -37.8 per 0.5 cm, 95% CI= -73.3, -2.3 respectively) independent of mothers' smoking, age, weight, parity and daughters' birthweight and age at mammogram. We also observed a positive interaction between placental surface area and thickness (p < 0.05) such that the highest breast dense area was observed for offspring with the thickest and largest placentas. Factors that impact placental morphometry, in addition to in utero smoke exposure, may influence adult breast architecture and breast cancer risk.

Maternal infection and stress during pregnancy and depressive symptoms in adolescent offspring.

Maternal infection during pregnancy has been linked to increased risk of offspring depression. Additionally, maternal stress during pregnancy has been consistently linked with adverse offspring outcomes associated with depression. Relatedly, stress has been associated with increased risk of infection; however no study has investigated stress-infection interactions during pregnancy and risk for offspring depression. Participants were drawn from the Child Health and Development Studies (CHDS), a prospective, longitudinal study that enrolled pregnant women from 1959 to 1966. Maternal health and birth outcome information were collected, as well as open-ended interviews about worrisome events during pregnancy. The present study included participants from a subsample of women whose offspring (n = 1711) completed self-reports of depressive symptoms during adolescence. Results indicated that maternal infection during only the second trimester was associated with higher scores on adolescent offspring depressive symptoms, while controlling for maternal education at birth, adolescent age, and maternal depressive symptoms at adolescence. Maternal experiences of daily stress during pregnancy moderated this association, such that mothers diagnosed with second trimester infection and who experienced daily stress had offspring with significantly higher depression scores than mothers of adolescents diagnosed with an infection alone. Findings have potential implications for prevention and intervention strategies.

DDT Exposure in Utero and Breast Cancer.

CONTEXT: Currently no direct evidence links in utero dichlorodiphenyltrichloroethane (DDT) exposure to human breast cancer. However, in utero exposure to another xenoestrogen, diethylstilbestrol, predicts an increased breast cancer risk. If this finding extends to DDT, it could have far-reaching consequences. Many women were heavily exposed in utero during widespread DDT use in the 1960s. They are now reaching the age of heightened breast cancer risk. DDT exposure persists and use continues in Africa and Asia without clear knowledge of the consequences for the next generation. HYPOTHESIS: In utero exposure to DDT is associated with an increased risk of breast cancer. DESIGN: This was a case-control study nested in a prospective 54-year follow-up of 9300 daughters in the Child Health and Development Studies pregnancy cohort (n = 118 breast cancer cases, diagnosed by age 52 y and 354 controls matched on birth year). SETTING AND PARTICIPANTS: Kaiser Foundation Health Plan members who received obstetric care in Alameda County, California, from 1959 to 1967, and their adult daughters participated in the study. MAIN OUTCOME MEASURE: Daughters' breast cancer diagnosed by age 52 years as of 2012 was measured. RESULTS: Maternal o,p'-DDT predicted daughters' breast cancer (odds ratio fourth quartile vs first = 3.7, 95% confidence interval 1.5-9.0). Mothers' lipids, weight, race, age, and breast cancer history did not explain the findings. CONCLUSIONS: This prospective human study links measured DDT exposure in utero to risk of breast cancer. Experimental studies are essential to confirm results and discover causal mechanisms. Findings support classification of DDT as an endocrine disruptor, a predictor of breast cancer, and a marker of high risk.

Individual augmentee deployment and newly reported mental health morbidity.

OBJECTIVE: To investigate the association between US Navy individual augmentee (IA) deployers, who may lack the protective effects of unit cohesion and social support, and newly reported mental health. METHODS: Responses from the Millennium Cohort Study questionnaires were examined for 2086 Navy deployers in this prospective exploratory study. Multivariable logistic regression was used to evaluate IA deployment and newly reported mental health symptoms. RESULTS: After adjusting for covariates, IA deployment was not significantly associated with newly reported posttraumatic stress disorder (odds ratio = 1.02; 95% confidence interval: 0.53-1.95) or mental health symptoms (odds ratio = 1.03; 95% confidence interval: 0.66-1.60) compared with non-IA deployment. CONCLUSION: IA deployment was not associated with increased risk for posttraumatic stress disorder or mental health symptoms following deployment. It is likely that social isolation was not highly influential among Navy IAs in this study.