Unrepresentative big surveys significantly overestimated US vaccine uptake

Vaccine effectiveness against emerging COVID-19 variants using digital health data

BACKGROUND

Participatory surveillance of self-reported symptoms and vaccination status can be used to supplement traditional public health surveillance and provide insights into vaccine effectiveness and changes in the symptoms produced by an infectious disease. The University of Maryland COVID Trends and Impact Survey provides an example of participatory surveillance that leveraged Facebook's active user base to provide self-reported symptom and vaccination data in near real-time.

METHODS

Here, we develop a methodology for identifying changes in vaccine effectiveness and COVID-19 symptomatology using the University of Maryland COVID Trends and Impact Survey data from three middle-income countries (Guatemala, Mexico, and South Africa). We implement conditional logistic regression to develop estimates of vaccine effectiveness conditioned on the prevalence of various definitions of self-reported COVID-like illness in lieu of confirmed diagnostic test results.

RESULTS

We highlight a reduction in vaccine effectiveness during Omicron-dominated waves of infections when compared to periods dominated by the Delta variant (median change across COVID-like illness definitions: -0.40, IQR[-0.45, -0.35]. Further, we identify a shift in COVID-19 symptomatology towards upper respiratory type symptoms (i.e., cough and sore throat) during Omicron periods of infections. Stratifying COVID-like illness by the National Institutes of Health's (NIH) description of mild and severe COVID-19 symptoms reveals a similar level of vaccine protection across different levels of COVID-19 severity during the Omicron period.

CONCLUSIONS

Participatory surveillance data alongside methodologies described in this study are particularly useful for resource-constrained settings where diagnostic testing results may be delayed or limited.

REFORMS: Consensus-based Recommendations for Machine-learning-based Science

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear recommendations for conducting and reporting ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist (recommendations for machine-learning-based science). It consists of 32 questions and a paired set of guidelines. REFORMS was developed on the basis of a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility.

Media exposure related to COVID-19 is associated with worse mental health consequences in the United States compared to Italy

BACKGROUND

Prolonged media exposure after collective crises is widely shown to have adverse effects on people's mental health. Do these effects show variation across different countries? In the present study, we compared the link between media exposure related to COVID-19 and mental health-related outcomes in the United States and Italy, two countries with high levels of early COVID-19 prevalence.

METHOD

Participants matched on age and gender in the United States (n = 415) and Italy (n = 442) completed assessments of media exposure, stress, anxiety, COVID-19 worry, and other variables shortly after the first wave of infections in 2020.

RESULTS

COVID-19 related media exposure predicted higher levels of stress, anxiety, and COVID-19 worry, net of the effects of neuroticism, political identification, and demographics. Moreover, COVID-19 related media exposure interacted with country to predict more stress and COVID-19 worry in the United States than in Italy.

CONCLUSIONS

Findings are among the first to document cross-national differences in the association of media exposure with mental health outcomes.

Guaranteed Cash Incentives Boosted COVID-19 Vaccinations Of Young Adults: Evidence From West Virginia

Guaranteed small cash incentives were widely employed by policy makers during the COVID-19 vaccination campaign, but the impact of these programs has been largely understudied. We were the first to exploit a statewide natural experiment of one such program implemented in West Virginia in 2021 that provided a $100 incentive to fully vaccinated adults ages 16-35. Using individual-level data from the Census Bureau's Household Pulse Survey, we isolated the policy effect through a difference-in-discontinuities design that exploited the discontinuity in incentive eligibility at age thirty-five. We found that the $100 incentive was associated with a robust increase in the proportion of people ever vaccinated against COVID-19 and the proportion who completed or intended to complete the primary series of COVID-19 vaccines. The policy effects were also likely to be more pronounced among people with low incomes, those who were unemployed, and those with no prior COVID-19 infection. The guaranteed cash incentive program may have created more equitable access to vaccines for disadvantaged populations. Additional outreach may also be needed, especially to unvaccinated people with prior COVID-19 infections.

Adaptive Time-Location Sampling for COMPASS: A SARS-CoV-2 Prevalence Study in Fifteen Diverse Communities in the United States

BACKGROUND

COVID-19 has placed a disproportionate burden on underserved racial and ethnic groups, community members working in essential industries, those living in areas of high population density, and those reliant on in-person services such as transportation. The goal of this study was to estimate the cross-sectional prevalence of SARS-CoV-2 (active SARS-CoV-2 or prior SARS-CoV-2 infection) in children and adults attending public venues in 15 sociodemographically diverse communities in the United States and to develop a statistical design that could be rigorously implemented amidst unpredictable stay-at-home COVID-19 guidelines.

METHODS

We used time-location sampling with complex sampling involving stratification, clustering of units, and unequal probabilities of selection to recruit individuals from selected communities. We safely conducted informed consent, specimen collection, and face-to-face interviews outside of public venues immediately following recruitment.

RESULTS

We developed an innovative sampling design that adapted to constraints such as closure of venues, changing infection hotspots, and uncertain policies. We updated both the sampling frame and the selection probabilities over time using information acquired from prior weeks. We created site-specific survey weights that adjusted sampling probabilities for nonresponse and calibrated to county-level margins on age and sex at birth.

CONCLUSIONS

Although the study itself was specific to COVID-19, the strategies presented in this article could serve as a case study that can be adapted for performing population-level inferences in similar settings and could help inform rapid and effective responses to future global public health challenges.

Vaccine rhetoric on social media and COVID-19 vaccine uptake rates: A triangulation using self-reported vaccine acceptance

The primary goal of this study is to examine the association between vaccine rhetoric on Twitter and the public's uptake rates of COVID-19 vaccines in the United States, compared to the extent of an association between self-reported vaccine acceptance and the CDC's uptake rates. We downloaded vaccine-related posts on Twitter in real-time daily for 13 months, from October 2021 to September 2022, collecting over half a billion tweets. A previously validated deep-learning algorithm was then applied to (1) filter out irrelevant tweets and (2) group the remaining relevant tweets into pro-, anti-, and neutral vaccine sentiments. Our results indicate that the tweet counts (combining all three sentiments) were significantly correlated with the uptake rates of all stages of COVID-19 shots (p < 0.01). The self-reported level of vaccine acceptance was not correlated with any of the stages of COVID-19 shots (p > 0.05) but with the daily new infection counts. These results suggest that although social media posts on vaccines may not represent the public's opinions, they are aligned with the public's behaviors of accepting vaccines, which is an essential step for developing interventions to increase the uptake rates. In contrast, self-reported vaccine acceptance represents the public's opinions, but these were not correlated with the behaviors of accepting vaccines. These outcomes provide empirical support for the validity of social media analytics for gauging the public's vaccination behaviors and understanding a nuanced perspective of the public's vaccine sentiment for health emergencies.

Population Neuroscience: Understanding Concepts of Generalizability and Transportability and Their Application to Improving the Public's Health

In population neuroscience, samples are not often selected with equal or known probability from an underlying population of interest; in other words, samples are not often formally representative of a specified underlying population. This chapter provides an overview of an epidemiological approach to considering the implications of selective participation on the value of our results for population health. We discuss definitions of generalizability and transportability, given the growing recognition that generalizability and transportability are central for interpreting data that are aiming to be population-based. We provide evidence that differences in the prevalence of effect measure modifiers between a study sample and a target population will lead to a lack of generalizability and transportability. We provide an example of an association between a poly-genetic risk score and depression, showing how an internally valid association can differ based on the prevalence of effect measure modifiers. We show that when estimating associations, inferences from a study sample to a population can depend on clearly defining a target population. Given that representative sampling from explicitly defined target populations may not be feasible or realistic in many situations, especially given the sample sizes needed for statistical power for many exposures of interest (and especially when interactions are being tested), researchers should be well versed in tools available to enhance the interpretability of samples regarding target populations.

Consumption responses to an unconditional child allowance in the United States

The COVID-19 pandemic put families in the United States under financial stress. The federal government's largest response in 2021 was the American Rescue Plan Act, which temporarily expanded the Child Tax Credit (CTC) into a large, unconditional child allowance providing monthly payments to families with children. This study investigates consumption responses to the CTC expansion using anonymized mobile-location data and debit/credit card data that track visits and spending at 1.3 million establishments across US counties. For identification, we exploit variation in the size of households' income gains due to the CTC across counties in a difference-in-differences framework spanning January 2021 to May 2022. Counties benefiting most from the CTC expansion experienced larger increases in visits to childcare centres and health- and personal-care establishments, and increased visits to and spending per transaction at grocery and general stores. These findings suggest that the CTC expansion increased household consumption and spending on children.

Expansion of Child Tax Credits and Mental Health of Parents With Low Income in 2021

Importance

The 2021 Child Tax Credit (CTC) expansion, as part of the American Rescue Plan Act, offered financial relief through generous monthly tax benefits to families with children amid the COVID-19 pandemic. In light of heightened concerns about mental health during the pandemic, the expanded CTC may have alleviated parental mental health challenges, especially within families with low income.

Objective

To investigate the association between the 2021 CTC expansion and mental health among parents with low income as measured by depression and anxiety symptoms.

Design, Setting, and Participants

This repeated cross-sectional study used data from the Household Pulse Survey covering April 14, 2021, to January 10, 2022, in the US. A difference-in-difference-in-differences estimator combined with propensity score matching was used to estimate the association of the expanded CTC with mental health symptoms among households with income less than $35 000.

Exposure

The monthly payment of expanded CTC from July 15 to December 15, 2021.

Main Outcomes and Measures

Parental mental health was measured by analyzing depression and anxiety symptoms using logistic regression.

Results

The weighted sample comprised 546 366 adults (mean [SD] age, 43.02 [14.54] years; 52.9% female). The most common education level was high school or less (36.0%), the highest frequency of household income distribution was $50 000 to $74 999 (16.1%), and the majority of the sample was employed (67.3%). The weighted mean (SD) number of children in the household was 0.92 (1.18). For the full sample, receiving expanded CTC benefits was associated with lower odds of experiencing anxiety symptoms (odds ratio, 0.730; 95% CI, 0.598-0.890). Subgroup analyses indicated that the positive associations of the policy with anxiety symptoms were particularly pronounced among the female, working-age (17-60 years), non-Hispanic White, and higher education groups. However, the policy expansion had no association with depression.

Conclusions and Relevance

These findings may provide valuable evidence for policy makers to consider when deliberating on the possibility of making the CTC expansion permanent or transforming it into a universal program.

Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey

BACKGROUND

The potential for selection bias in nonrepresentative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision making. We developed an approach to bias adjust county-level COVID-19 vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey.

DESIGN

We developed a multistep regression framework to adjust for selection bias in predicted county-level vaccination coverage plateaus. Our approach included poststratification to the American Community Survey, adjusting for differences in observed covariates, and secondary normalization to an unbiased reference indicator. As a case study, we prospectively applied this framework to predict county-level long-run vaccination coverage among children ages 5 to 11 y. We evaluated our approach against an interim observed measure of 3-mo coverage for children ages 5 to 11 y and used long-term coverage estimates to monitor equity in the pace of vaccination scale up.

RESULTS

Our predictions suggested a low ceiling on long-term national vaccination coverage (46%), detected substantial geographic heterogeneity (ranging from 11% to 91% across counties in the United States), and highlighted widespread disparities in the pace of scale up in the 3 mo following Emergency Use Authorization of COVID-19 vaccination for 5- to 11-y-olds.

LIMITATIONS

We relied on historical relationships between vaccination hesitancy and observed coverage, which may not capture rapid changes in the COVID-19 policy and epidemiologic landscape.

CONCLUSIONS

Our analysis demonstrates an approach to leverage differing strengths of multiple sources of information to produce estimates on the time scale and geographic scale necessary for proactive decision making.

IMPLICATIONS

Designing integrated health measurement systems that combine sources with different advantages across the spectrum of timeliness, spatial resolution, and representativeness can maximize the benefits of data collection relative to costs.

HIGHLIGHTS

The COVID-19 pandemic catalyzed massive survey data collection efforts that prioritized timeliness and sample size over population representativeness.The potential for selection bias in these large-scale, low-cost, nonrepresentative data has led to questions about their utility for population health measurement.We developed a multistep regression framework to bias adjust county-level vaccination coverage predictions from the largest public health survey conducted in the United States to date: the US COVID-19 Trends and Impact Survey.Our study demonstrates the value of leveraging differing strengths of multiple data sources to generate estimates on the time scale and geographic scale necessary for proactive public health decision making.

Transformation of social relationships in COVID-19 America: Remote communication may amplify political echo chambers

The COVID-19 pandemic, with millions of Americans compelled to stay home and work remotely, presented an opportunity to explore the dynamics of social relationships in a predominantly remote world. Using the 1972-2022 General Social Surveys, we found that the pandemic significantly disrupted the patterns of social gatherings with family, friends, and neighbors but only momentarily. Drawing from the nationwide ego-network surveys of 41,033 Americans from 2020 to 2022, we found that the size and composition of core networks remained stable, although political homophily increased among nonkin relationships compared to previous surveys between 1985 and 2016. Critically, heightened remote communication during the initial phase of the pandemic was associated with increased interaction with the same partisans, although political homophily decreased during the later phase of the pandemic when in-person contacts increased. These results underscore the crucial role of social institutions and social gatherings in promoting spontaneous encounters with diverse political backgrounds.

Slow data public health

Surveillance and research data, despite their massive production, often fail to inform evidence-based and rigorous data-driven health decision-making. In the age of infodemic, as revealed by the COVID-19 pandemic, providing useful information for decision-making requires more than getting more data. Data of dubious quality and reliability waste resources and create data-genic public health damages. We call therefore for a slow data public health, which means focusing, first, on the identification of specific information needs and, second, on the dissemination of information in a way that informs decision-making, rather than devoting massive resources to data collection and analysis. A slow data public health prioritizes better data, ideally population-based, over more data and aims to be timely rather than deceptively fast. Applied by independent institutions with expertise in epidemiology and surveillance methods, it allows a thoughtful and timely public health response, based on high-quality data fostering trustworthiness.

Pregnant women's attitudes and behaviours towards antenatal vaccination against Influenza and COVID-19 in the Liverpool City Region, United Kingdom: Cross-sectional survey

Objectives

Influenza poses a serious health risk to pregnant women and their babies. Despite this risk, influenza vaccine uptake in pregnant women in the UK is less than 50%. Little is known about how COVID-19 affects pregnant women, but its management may affect attitudes and behaviours towards vaccination in pregnancy. The study objectives were to establish attitudes and knowledge of pregnant women towards influenza disease and influenza vaccination and to compare these to attitudes and knowledge about COVID-19 and COVID-19 vaccination.

Design

A cross-sectional survey was conducted using an online questionnaire distributed through local advertisement and social media outlets. Information was sought on attitudes and knowledge of influenza and COVID-19 and their respective vaccines.

Participants and setting

Pregnant women residing in Liverpool City Region, UK.

Results

Of the 237 respondents, 73.8% reported receiving an influenza vaccine. Over half (56.5%) perceived themselves to be at risk from influenza, 70.5% believed that if they got influenza, their baby would get ill, and 64.6% believed getting influenza could hurt their baby, 60.3% believed that the influenza vaccine would prevent their baby from getting ill, and 70.8% believed it would protect their baby. Only 32.9% of respondents stated they would receive the COVID-19 vaccine if it were available to them. However, 80.2% stated they would receive a COVID-19 vaccine if they were not pregnant. Most of the women stated that they would accept a vaccine if recommended to them by healthcare professionals.

Conclusions

Acceptance of the influenza and COVID-19 vaccines during pregnancy seems to be more related to the safety of the baby rather than the mother. Women perceived their child to be more at risk than themselves. Information about influenza and COVID-19 vaccine safety as well as healthcare provider recommendations play an important role in vaccine uptake in pregnant women.

What is cooperative breeding in mammals and birds? Removing definitional barriers for comparative research

Cooperative breeding (i.e. when alloparents care for the offspring of other group members) has been studied for nearly a century. Yet, inconsistent definitions of this breeding system still hamper comparative research. Here, we identify two major inconsistencies, discuss their consequences and propose a way forward. First, some researchers restrict the term 'cooperative breeding' to species with non-breeding alloparents. We show that such restrictive definitions lack distinct quantitative criteria to define non-breeding alloparents. This ambiguity, we argue, reflects the reproductive-sharing continuum among cooperatively breeding species. We therefore suggest that cooperative breeding should not be restricted to the few species with extreme reproductive skew and should be defined independent of the reproductive status of alloparents. Second, definitions rarely specify the type, extent and prevalence of alloparental care required to classify species as cooperative breeders. We thus analysed published data to propose qualitative and quantitative criteria for alloparental care. We conclude by proposing the following operational definition: cooperative breeding is a reproductive system where >5% of broods/litters in at least one population receive species-typical parental care and conspecifics provide proactive alloparental care that fulfils >5% of at least one type of the offspring's needs. This operational definition is designed to increase comparability across species and disciplines while allowing to study the intriguing phenomenon of cooperative breeding as a behaviour with multiple dimensions.

Public awareness of and opinions on the use of mathematical transmission modelling to inform public health policy in the United Kingdom

Mathematical modelling is used to inform public health policy, particularly so during the COVID-19 pandemic. As the public are key stakeholders, understanding the public perceptions of these tools is vital. To complement our previous study on the science-policy interface, novel survey data were collected via an online panel ('representative' sample) and social media ('non-probability' sample). Many questions were asked twice, in reference to the period 'prior to' (retrospectively) and 'during' the COVID-19 pandemic. Respondents reported being increasingly aware of modelling in informing policy during the pandemic, with higher levels of awareness among social media respondents. Modelling informing policy was perceived as more reliable during the pandemic than in reference to the pre-pandemic period in both samples. Trust in government public health advice remained high within both samples but was lower during the pandemic in comparison with the (retrospective) pre-pandemic period. The decay in trust was greater among social media respondents. Many respondents explicitly made the distinction that their trust was reserved for 'scientists' and not 'politicians'. Almost all respondents believed governments have responsibility for communicating modelling to the public. These results provide a reminder of the skewed conclusions that could be drawn from non-representative samples.

Disparities in child and adolescent COVID-19 vaccination coverage and parental intent toward vaccinations for their children and adolescents

INTRODUCTION

Despite recommendations for COVID-19 primary series completion and booster doses for children and adolescents, coverage has been less than optimal, particularly in some subpopulations. This study explored disparities in childhood/adolescent COVID-19 vaccination, parental intent to vaccinate their children and adolescents, and reasons for non-vaccination in the US.

METHODS

Using the U.S. Census Bureau's Household Pulse Survey (HPS), we analyzed households with children aged <18 years using data collected from September 14 to November 14, 2022 (n = 44,929). Child and adolescent COVID-19 vaccination coverage (≥1 dose, completed primary series, and booster vaccination) and parental intentions toward vaccination were assessed by sociodemographic characteristics. Factors associated with child and adolescent vaccination coverage were examined using multivariable regression models. Reasons for non-vaccination were assessed overall, by the child's age group and respondent's age group.

RESULTS

Overall, approximately half (50.1%) of children aged < 18 years were vaccinated against COVID-19 (≥1 dose). Completed primary series vaccination was 44.2% among all children aged <18 years. By age group, completed primary series was 13.2% among children <5 years, 43.9% among children 5-11 years, and 63.3% among adolescents 12-17 years. Booster vaccination among those who completed the primary series was 39.1% among children 5-11 years and 55.3% among adolescents 12-17 years. Vaccination coverage differed by race/ethnicity, educational attainment, household income, region, parental COVID-19 vaccination status, prior COVID-19 diagnosis, child's age group, and parental age group. Parental reluctance was highest for children aged <5 years (46.8%). Main reasons for non-vaccination among reluctant parents were concerns about side effects (53.3%), lack of trust in COVID-19 vaccines (48.7%), and the belief that children do not need a COVID-19 vaccine (38.8%).

CONCLUSION

Disparities in COVID-19 vaccination coverage among children and adolescents continue to exist. Further efforts are needed to increase COVID-19 primary series and booster vaccination and parental confidence in vaccines.

How well do surveys on adherence to pandemic policies assess actual behaviour: Measurement properties of the Dutch COVID-19 adherence to prevention advice survey (CAPAS)

BACKGROUND

Survey data on adherence to COVID-19 prevention measures have often been used to inform policy makers and public health professionals. Although behavioural survey data are often considered to suffer from biases, there is a lack of studies critically examining the validity, reliability and responsiveness of population-survey data on behaviour throughout the COVID-19 pandemic.

AIM

We studied the measurement properties of the COVID-19 Adherence to Prevention Advice Survey (CAPAS), a novel questionnaire implemented in a repeated cross-sectional (i.e., 'Trend') Study and a Cohort Study in the Netherlands during the COVID-19 pandemic.

METHODS

The CAPAS is a novel questionnaire developed in March 2020, with the aim to assess social activity and adherence to COVID-19 prevention measures. Items were formulated to minimise social desirability and aid memory retrieval. Based on the COSMIN framework, we selected the most suitable test for each behavioural question. We investigated criterion validity of vaccination, testing behaviour and mobility by comparing (aggregate) trends of self-reported behaviour to trends in objective data. Responsiveness of mobility and ventilation behaviour was assessed by studying whether self-reported behaviour changed following contextual (e.g., policy) changes. Test-retest reliability of hygienic behaviour, wearing face masks, ventilation behaviour and social distancing was examined during a period in which the context was stable.

RESULTS

Overall, aggregate trends in self-reported behaviour closely corresponded to trends in external objective data. Self-reported behaviours were responsive to contextual changes and test-retest reliabilities were adequate. For infrequent behaviours reliability improved when measures were dichotomised. We were able to examine national representativeness for vaccination, which suggested a modest overestimation of on average 3.7%.

CONCLUSIONS

This study supports the suitability of using carefully designed, self-reported surveys (and the CAPAS specifically) to study changes in protective behaviours in a dynamic context.

Effects of an online tailored decision aid to promote informed decision making about maternal pertussis vaccination in the Netherlands: A randomized controlled trial

INTRODUCTION

In 2019, maternal pertussis vaccination (MPV) during pregnancy was introduced in the Netherlands. New interventions to promote informed decision making (IDM) about vaccinations are highly needed, especially for new vaccinations. Decision aids (DAs) have the potential to support IDM. This study evaluates the effects of an online DA on IDM and MPV uptake.

METHODS

Pregnant individuals, recruited for the randomized controlled trial (RCT), who gave informed consent (N = 1,236) were randomly assigned to the control (N = 650; no information) or intervention condition (N = 586; DA at 18 weeks pregnancy). MPV uptake and IDM were primary outcomes, decisional certainty and psychological determinants of MPV uptake were secondary outcomes. Measures were taken at 18 weeks of pregnancy (baseline) and at 20 weeks of pregnancy (post-test); intervention use was logged. Data were analysed using intention-to-treat analyses, logistic regression, and linear mixed regression models.

RESULTS

Uptake of MPV was high in our sample (92.3 %). No significant effect of the DA condition on MPV uptake was found compared to the control condition. We found that the DA increased IDM (β = 0.24, p < .004) and one of its components level of knowledge about MPV (β = 0.31, p < .004). We also found an increase in decisional certainty (β = 0.24, p < .004), perceived susceptibility (β = 0.24, p < .004), severity of pertussis (β = 0.41, p < .004), and positive affect about MPV (β = 0.15, p < .004). There was a positive association between dose of the intervention and MPV uptake (β = 0.05, p < .004).

DISCUSSION

The DA seemed effective in promoting IDM about and determinants of MPV uptake. No main effect was found on MPV uptake, but MPV uptake was related to the level of exposure to the DA. People with high intentions towards MPV were overrepresented in the sample. However, effects on IDM were consistent among participants with different levels of MPV intention at baseline.

The prevalence of anxiety and depression symptoms (ADS), persistent and chronic ADS among the adult general population and specific subgroups before and during the COVID-19 pandemic until December 2021

BACKGROUND

It is unclear to what extent the prevalence of moderate and severe anxiety and depression symptoms (ADS) is higher during the first 20 months after the COVID-19 outbreak than before the outbreak. The same holds for persistent and chronic ADS among the adult general population and subgroups (such as employed, minorities, young adults, work disabled).

METHODS

Data were extracted from six surveys conducted with the Dutch longitudinal LISS panel, based on a traditional probability sample (N = 3493). Biographic characteristics and ADS (MHI-5 scores) were assessed in March-April 2019, November-December 2019, March-April 2020, November-December 2020, March-April 2021, and November-December 2021. Generalized estimating equations were conducted to examine differences in the prevalence of post-outbreak ADS, persistent and chronic ADS compared to the pre-outbreak prevalence in similar periods. The Benjamini-Hochberg correction for multiple testing was applied.

RESULTS

Among the general population chronic moderate ADS increased significantly but slightly in the period March-April 2020 to March-April 2021 compared to a similar period before the pandemic (11.9 % versus 10.9 %, Odds Ratio = 1.11). In the same period a somewhat larger significant increase in chronic moderate ADS was observed among 19-24 years old respondents (21.4 % versus 16.7 %, Odds Ratio = 1.35). After the Benjamini-Hochberg correction several other differences were no longer significant.

LIMITATIONS

No other mental health problems were assessed.

CONCLUSIONS

The Dutch general population and most of the assessed subgroups were relatively resilient given the limited increase or absence of increases in (persistent and chronic) ADS. However, young adults suffered from an increase of chronic ADS.

Time-sensitive testing pressures and COVID-19 outcomes: are socioeconomic inequalities over the first year of the pandemic explained by selection bias?

BACKGROUND

There are many ways in which selection bias might impact COVID-19 research. Here we focus on selection for receiving a polymerase-chain-reaction (PCR) SARS-CoV-2 test and how known changes to selection pressures over time may bias research into COVID-19 infection.

METHODS

Using UK Biobank (N = 420,231; 55% female; mean age = 66.8 [SD = 8·11]) we estimate the association between socio-economic position (SEP) and (i) being tested for SARS-CoV-2 infection versus not being tested (ii) testing positive for SARS-CoV-2 infection versus testing negative and (iii) testing negative for SARS-CoV-2 infection versus not being tested. We construct four distinct time-periods between March 2020 and March 2021, representing distinct periods of testing pressures and lockdown restrictions and specify both time-stratified and combined models for each outcome. We explore potential selection bias by examining associations with positive and negative control exposures.

RESULTS

The association between more disadvantaged SEP and receiving a SARS-CoV-2 test attenuated over time. Compared to individuals with a degree, individuals whose highest educational qualification was a GCSE or equivalent had an OR of 1·27 (95% CI: 1·18 to 1·37) in March-May 2020 and 1·13 (95% CI: 1.·10 to 1·16) in January-March 2021. The magnitude of the association between educational attainment and testing positive for SARS-CoV-2 infection increased over the same period. For the equivalent comparison, the OR for testing positive increased from 1·25 (95% CI: 1·04 to 1·47), to 1·69 (95% CI: 1·55 to 1·83). We found little evidence of an association between control exposures, and any considered outcome.

CONCLUSIONS

The association between SEP and SARS-CoV-2 testing changed over time, highlighting the potential of time-specific selection pressures to bias analyses of COVID-19. Positive and negative control analyses suggest that changes in the association between SEP and SARS-CoV-2 infection over time likely reflect true increases in socioeconomic inequalities.

Evidence for a reproductive sharing continuum in cooperatively breeding mammals and birds: consequences for comparative research

Extreme reproductive skew occurs when a dominant female/male almost monopolizes reproduction within a group of multiple sexually mature females/males, respectively. It is sometimes considered an additional, restrictive criterion to define cooperative breeding. However, datasets that use this restrictive definition to classify species as cooperative breeders systematically overestimate reproductive skew by including groups in which reproduction cannot be shared by definition (e.g. groups with a single female/male). Here, we review the extent of reproductive sharing in 41 mammal and 37 bird species previously classified as exhibiting alloparental care and extreme reproductive skew, while only considering multi-female or multi-male groups. We demonstrate that in groups where unequal reproduction sharing is possible, extreme reproductive skew occurs in a few species only (11/41 mammal species and 12/37 bird species). These results call for significant changes in datasets that classify species' caring and mating system. To facilitate these changes, we provide an updated dataset on reproductive sharing in 63 cooperatively breeding species. At the conceptual level, our findings suggest that reproductive skew should not be a defining criterion of cooperative breeding and support the definition of cooperative breeding as a care system in which alloparents provide systematic care to other group members' offspring.

Building causal knowledge in behavior genetics without racial/ethnic diversity will result in weak causal knowledge

Behavior genetics often emphasizes methods over the underlying quality of the psychological information to which the methods are applied. A core aspect of this quality is the demographic diversity of the samples. Building causal genetic models based only on European-ancestry samples compromises their generalizability. Behavior genetics researchers must spend additional time and resources diversifying their samples before emphasizing causation.

The COVID-19 pandemic impact on independent and provider-based rural health clinics' operations and cancer prevention and screening provision in the United States

INTRODUCTION

The COVID-19 pandemic has disrupted cancer care, but it is unknown how the pandemic has affected care in Medicare-certified rural health clinics (RHCs) where cancer prevention and screening services are critical for their communities. This study examined how the provision of these cancer services changed pre- and peri-pandemic overall and by RHC type (independent and provider-based).

METHODS

We administered a cross-sectional survey to a stratified random sample of RHCs to assess clinic characteristics, pandemic stressors, and the provision of cancer prevention and control services among RHCs pre- and peri-pandemic. We used McNemar's test and Wilcoxon signed rank tests to assess differences in the provision of cancer prevention and screening services pre- and peri-pandemic by RHC type.

RESULTS

Of the 153 responding RHCs (response rate of 8%), 93 (60.8%) were provider-based and 60 (39.2%) were independent. Both RHC types were similar in their experience of pandemic stressors, though a higher proportion of independent RHCs reported financial concerns and challenges obtaining personal protective equipment. Both types of RHCs provided fewer cancer prevention and screening services peri-pandemic-5.8 to 4.2 for provider-based and 5.3 to 3.5 for independent (P<.05 for both). Across lung, cervical, breast, and colorectal cancer-related services, the proportion of both RHC groups providing services dropped peri-pandemic.

DISCUSSION

The pandemic's impact on independent and provider-based RHCs and their patients was considerable. Going forward, greater resources should be targeted to RHCs-particularly independent RHCs-to ensure their ability to initiate and sustain evidence-based prevention and screening services.

A multi-modal panel dataset to understand the psychological impact of the pandemic

Besides far-reaching public health consequences, the COVID-19 pandemic had a significant psychological impact on people around the world. To gain further insight into this matter, we introduce the Real World Worry Waves Dataset (RW3D). The dataset combines rich open-ended free-text responses with survey data on emotions, significant life events, and psychological stressors in a repeated-measures design in the UK over three years (2020: n = 2441, 2021: n = 1716 and 2022: n = 1152). This paper provides background information on the data collection procedure, the recorded variables, participants' demographics, and higher-order psychological and text-derived variables that emerged from the data. The RW3D is a unique primary data resource that could inspire new research questions on the psychological impact of the pandemic, especially those that connect modalities (here: text data, psychological survey variables and demographics) over time.

Studying the genetics of participation using footprints left on the ascertained genotypes

The trait of participating in a genetic study probably has a genetic component. Identifying this component is difficult as we cannot compare genetic information of participants with nonparticipants directly, the latter being unavailable. Here, we show that alleles that are more common in participants than nonparticipants would be further enriched in genetic segments shared by two related participants. Genome-wide analysis was performed by comparing allele frequencies in shared and not-shared genetic segments of first-degree relative pairs of the UK Biobank. In nonoverlapping samples, a polygenic score constructed from that analysis is significantly associated with educational attainment, body mass index and being invited to a dietary study. The estimated correlation between the genetic components underlying participation in UK Biobank and educational attainment is estimated to be 36.6%-substantial but far from total. Taking participation behaviour into account would improve the analyses of the study data, including those of health traits.

Isolation and Quarantine for Coronavirus Disease 2019 in the United States, 2020-2022

BACKGROUND

Public health programs varied in ability to reach people with coronavirus disease 2019 (COVID-19) and their contacts to encourage separation from others. For both adult case patients with COVID-19 and their contacts, we estimated the impact of contact tracing activities on separation behaviors from January 2020 until March 2022.

METHODS

We used a probability-based panel survey of a nationally representative sample to gather data for estimates and comparisons.

RESULTS

An estimated 64 255 351 adults reported a positive severe acute respiratory syndrome coronavirus 2 test result; 79.6% isolated for ≥5 days, 60.2% isolated for ≥10 days, and 79.2% self-notified contacts. A total of, 24 057 139 (37.7%) completed a case investigation, and 46.2% of them reported contacts to health officials. More adults who completed a case investigation isolated than those who did not complete a case investigation (≥5 days, 82.6% vs 78.2%, respectively; ≥10 days, 69.8% vs 54.8%; both P < .05). A total of 84 946 636 adults were contacts of a COVID-19 case patient. Of these, 73.1% learned of their exposure directly from a case patient; 49.4% quarantined for ≥5 days, 18.7% quarantined for ≥14 days, and 13.5% completed a contact tracing call. More quarantined among those who completed a contact tracing call than among those who did not complete a tracing call (≥5 days, 61.2% vs 48.5%, respectively; ≥14 days, 25.2% vs 18.0%; both P < .05).

CONCLUSIONS

Engagement in contact tracing was positively correlated with isolation and quarantine. However, most adults with COVID-19 isolated and self-notified contacts regardless of whether the public health workforce was able to reach them. Identifying and reaching contacts was challenging and limited the ability to promote quarantining, and testing.

A novel adaptation of spatial interpolation methods to map health attitudes related to COVID-19

BACKGROUND

The COVID-19 pandemic presented substantial challenges to public health stakeholders working to vaccinate populations against the disease, particularly among vaccine hesitant individuals in low- and middle-income countries. Data on the determinants of vaccine hesitancy are scarce, and often available only at the national level. In this paper, our goal is to inform programmatic decision making in support of local vaccine uptake. Our analytical objectives to support this goal are to (1) reliably estimate attitudinal data at the hyperlocal level, and (2) estimate the loss of data heterogeneity among these attitudinal indicators at higher levels of aggregation. With hyperlocal attitudinal data on the determinants of vaccine hesitancy, public health stakeholders can better tailor interventions aimed at increasing uptake sub-nationally, and even down to the individual vaccination site or neighborhood.

METHODS

We estimated attitudinal data on the determinants of vaccine hesitancy as framed by the WHO's Confidence, Complacency, and Convenience ("3Cs") Model of Vaccine Hesitancy using a nationally and regionally representative household survey of 4,922 adults aged 18 and above, collected in February 2022. This custom survey was designed to collect information on attitudes towards COVID-19 and concerns about the COVID-19 vaccine. A machine learning (ML) framework was used to spatially interpolate metrics representative of the 3Cs at a one square kilometer (1km²) resolution using approximately 130 spatial covariates from high-resolution satellite imagery, and 24 covariates from the 2018 Nigeria Demographic and Health Survey (DHS).

RESULTS

Spatial interpolated hyperlocal estimates of the 3Cs captured significant information on attitudes towards COVID-19 and COVID-19 vaccines. The interpolated estimates held increased heterogeneity within each subsequent level of disaggregation, with most variation at the 1km² level.

CONCLUSIONS

Our findings demonstrate that a) attitudinal data can be successfully estimated at the hyperlocal level, and b) the determinants of COVID-19 vaccine hesitancy have large spatial variance that cannot be captured through national surveys alone. Access to community level attitudes toward vaccine safety and efficacy; vaccination access, time, and financial burden; and COVID-19 beliefs and infection concerns presents novel implications for public health practitioners and policymakers seeking to increase COVID-19 vaccine uptake through more customized community-level interventions.

Disparities in mobile phone ownership reflect inequities in access to healthcare

Human movement and population connectivity inform infectious disease management. Remote data, particularly mobile phone usage data, are frequently used to track mobility in outbreak response efforts without measuring representation in target populations. Using a detailed interview instrument, we measure population representation in phone ownership, mobility, and access to healthcare in a highly mobile population with low access to health care in Namibia, a middle-income country. We find that 1) phone ownership is both low and biased by gender, 2) phone ownership is correlated with differences in mobility and access to healthcare, and 3) reception is spatially unequal and scarce in non-urban areas. We demonstrate that mobile phone data do not represent the populations and locations that most need public health improvements. Finally, we show that relying on these data to inform public health decisions can be harmful with the potential to magnify health inequities rather than reducing them. To reduce health inequities, it is critical to integrate multiple data streams with measured, non-overlapping biases to ensure data representativeness for vulnerable populations.

Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns

BACKGROUND

Implementing a lockdown for disease mitigation is a balancing act: Non-pharmaceutical interventions can reduce disease transmission significantly, but interventions also have considerable societal costs. Therefore, decision-makers need near real-time information to calibrate the level of restrictions.

METHODS

We fielded daily surveys in Denmark during the second wave of the COVID-19 pandemic to monitor public response to the announced lockdown. A key question asked respondents to state their number of close contacts within the past 24 hours. Here, we establish a link between survey data, mobility data, and hospitalizations via epidemic modelling of a short time-interval around Denmark's December 2020 lockdown. Using Bayesian analysis, we then evaluate the usefulness of survey responses as a tool to monitor the effects of lockdown and then compare the predictive performance to that of mobility data.

RESULTS

We find that, unlike mobility, self-reported contacts decreased significantly in all regions before the nation-wide implementation of non-pharmaceutical interventions and improved predicting future hospitalizations compared to mobility data. A detailed analysis of contact types indicates that contact with friends and strangers outperforms contact with colleagues and family members (outside the household) on the same prediction task.

CONCLUSIONS

Representative surveys thus qualify as a reliable, non-privacy invasive monitoring tool to track the implementation of non-pharmaceutical interventions and study potential transmission paths.

Geospatially-resolved public-health surveillance via wastewater sequencing

Wastewater, which contains everything from pathogens to pollutants, is a geospatially-and temporally-linked microbial fingerprint of a given population. As a result, it can be leveraged for monitoring multiple dimensions of public health across locales and time. Here, we integrate targeted and bulk RNA sequencing (n=1,419 samples) to track the viral, bacterial, and functional content over geospatially distinct areas within Miami Dade County from 2020-2022. First, we used targeted amplicon sequencing (n=966) to track diverse SARS-CoV-2 variants across space and time, and we found a tight correspondence with clinical caseloads from University students (N = 1,503) and Miami-Dade County hospital patients (N = 3,939 patients), as well as an 8-day earlier detection of the Delta variant in wastewater vs. in patients. Additionally, in 453 metatranscriptomic samples, we demonstrate that different wastewater sampling locations have clinically and public-health-relevant microbiota that vary as a function of the size of the human population they represent. Through assembly, alignment-based, and phylogenetic approaches, we also detect multiple clinically important viruses (e.g., norovirus ) and describe geospatial and temporal variation in microbial functional genes that indicate the presence of pollutants. Moreover, we found distinct profiles of antimicrobial resistance (AMR) genes and virulence factors across campus buildings, dorms, and hospitals, with hospital wastewater containing a significant increase in AMR abundance. Overall, this effort lays the groundwork for systematic characterization of wastewater to improve public health decision making and a broad platform to detect emerging pathogens.

Media use and vaccine resistance

Public health requires collective action-the public best addresses health crises when individuals engage in prosocial behaviors. Failure to do so can have dire societal and economic consequences. This was made clear by the disjointed, politicized response to COVID-19 in the United States. Perhaps no aspect of the pandemic exemplified this challenge more than the sizeable percentage of individuals who delayed or refused vaccination. While scholars, practitioners, and the government devised a range of communication strategies to persuade people to get vaccinated, much less attention has been paid to where the unvaccinated could be reached. We address this question using multiple waves of a large national survey as well as various secondary data sets. We find that the vaccine resistant seems to predictably obtain information from conservative media outlets (e.g. Fox News) while the vaccinated congregate around more liberal outlets (e.g. MSNBC). We also find consistent evidence that vaccine-resistant individuals often obtain COVID-19 information from various social media, most notably Facebook, rather than traditional media sources. Importantly, such individuals tend to exhibit low institutional trust. While our results do not suggest a failure of sites such as Facebook's institutional COVID-19 efforts, as the counterfactual of no efforts is unknown, they do highlight an opportunity to reach those who are less likely to take vital actions in the service of public health.

Inference with non-probability samples and survey data integration: a science mapping study

In recent years, survey data integration and inference based on non-probability samples have gained considerable attention. Because large probability-based samples can be cost-prohibitive in many instances, combining a probabilistic survey with auxiliary data is appealing to enhance inferences while reducing the survey costs. Also, as new data sources emerge, such as big data, inference and statistical data integration will face new challenges. This study aims to describe and understand the evolution of this research field over the years with an original approach based on text mining and bibliometric analysis. In order to retrieve the publications of interest (books, journal articles, proceedings, etc.), the Scopus database is considered. A collection of 1023 documents is analyzed. Through the use of such methodologies, it is possible to characterize the literature and identify contemporary research trends as well as potential directions for future investigation. We propose a research agenda along with a discussion of the research gaps which need to be addressed.

Psychological inoculation protects against the social media infodemic

Misinformation can have a profound detrimental impact on populations' wellbeing. In this large UK-based online experiment (n = 2430), we assessed the performance of false tag and inoculation interventions in protecting against different forms of misinformation ('variants'). While previous experiments have used perception- or intention-based outcome measures, we presented participants with real-life misinformation posts in a social media platform simulation and measured their engagement, a more ecologically valid approach. Our pre-registered mixed-effects models indicated that both interventions reduced engagement with misinformation, but inoculation was most effective. However, random differences analysis revealed that the protection conferred by inoculation differed across posts. Moderation analysis indicated that immunity provided by inoculation is robust to variation in individuals' cognitive reflection. This study provides novel evidence on the general effectiveness of inoculation interventions over false tags, social media platforms' current approach. Given inoculation's effect heterogeneity, a concert of interventions will likely be required for future safeguarding efforts.

Categories of Evidence and Methods in Surgical Decision-Making

Surgical decision-making is a continuum of judgments that take place during the preoperative, intraoperative, and postoperative periods. The fundamental, and most challenging, step is determining whether a patient will benefit from an intervention given the dynamic interplay of diagnostic, temporal, environmental, patient-centric, and surgeon-centric factors. The myriad combinations of these considerations generate a wide spectrum of reasonable therapeutic approaches within the standards of care. Although surgeons may seek evidenced-based practices to support their decision-making, threats to the validity of evidence and appropriate application of evidence may influence implementation. Furthermore, a surgeon's conscious and unconscious biases may additionally determine individual practice.

Determinants of people's motivations to approach COVID-19 vaccination centers

This paper presents the results of a survey exploring the determinants of vacinees' confidence in COVID-19 vaccines and their motivations to become vaccinated. At the threatening rise of the highly infectious Omicron variant, in December 2021, we interviewed people in waiting lines of vaccination centers. Our results identify risk-averse and social-distancing-compliant people as showing high confidence in the vaccine, which motivates them to receive it for reasons of protecting themselves and others. By contrast, policy incentives, such as "3G/2G" restrictions, motivate risk-tolerant people who opted for vaccination to get access to public areas. Trusting people who regularly vote are little afraid of vaccines' side effects. Our findings offer insights for policymakers in societies and firms that help to tailor policies promoting vaccination based on people's economic preferences.

Spatiotemporal Trends in Self-Reported Mask-Wearing Behavior in the United States: Analysis of a Large Cross-sectional Survey

BACKGROUND

Face mask wearing has been identified as an effective strategy to prevent the transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance, potentially generating heterogeneities in the local trajectories of COVID-19 in the United States. Although numerous studies have investigated the patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask wearing at fine spatial scales across the United States through different phases of the pandemic.

OBJECTIVE

Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the United States. This information is critical to further assess the effectiveness of masking, evaluate the drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges.

METHODS

We analyzed spatiotemporal masking patterns in over 8 million behavioral survey responses from across the United States, starting in September 2020 through May 2021. We adjusted for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debiased self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county level. Lastly, we evaluated whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data.

RESULTS

We found that county-level masking behavior was spatially heterogeneous along an urban-rural gradient, with mask wearing peaking in winter 2021 and declining sharply through May 2021. Our results identified regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask wearing may be influenced by national guidance and disease prevalence. We validated our bias correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of a small sample size and representation. Self-reported behavior estimates were especially prone to social desirability and nonresponse biases, and our findings demonstrated that these biases can be reduced if individuals are asked to report on community rather than self behaviors.

CONCLUSIONS

Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.

Trajectories of psychological distress over multiple COVID-19 lockdowns in Australia

The impact of the global COVID-19 pandemic, including the indirect effect of policy responses, on psychological distress has been the subject of much research. However, there has been little consideration of how the prevalence of psychological distress changed with the duration and repetition of lockdowns, or the rate of resolution of psychological distress once lockdowns ended. This study describes the trajectories of psychological distress over multiple lockdowns during the first two years of the pandemic across five Australian states for the period May 2020 to December 2021 and examines whether psychological distress trajectories varied as a function of time spent in lockdown, or time since lockdown ended.

A total of N = 574,306 Australian adults completed Facebook surveys over 611 days (on average 940 participants per day). Trajectories of psychological distress (depression and anxiety) were regressed on lockdown duration and time since lockdown ended. Random effects reflecting the duration of each lockdown were included to account for varying effects on psychological distress associated with lockdown length.

The prevalence of psychological distress was higher during periods of lockdown, more so for longer lockdowns relative to shorter lockdowns. Psychological distress increased rapidly over the first ten weeks of lockdowns spanning at least twelve weeks, though less rapidly for short lockdowns of three weeks or less. Psychological distress levels tended to stabilise, or even decrease, after ten consecutive weeks of lockdown. After lockdown restrictions were lifted, psychological distress rapidly subsided but did not return to pre-lockdown levels within four weeks, although continued to decline afterwards.

In Australia short lockdowns of pre-announced durations were associated with slower rises in psychological distress. Lockdowns may have left some temporary residual population effect, but we cannot discern whether this reflects longer term trends in increasing psychological distress. However, the findings do re-emphasise the resilience of individuals to major life stressors.

COVID-19 vaccination coverage in Egypt: a large-scale national survey - to help achieving vaccination target, March-May, 2022

BACKGROUND

Only 57 countries have vaccinated 70% of their population against COVID-19, most of them in high-income countries, whereas almost one billion people in low-income countries remained unvaccinated. In March-May 2022, Egypt's Ministry of Health and Population (MoHP) conducted a nationwide community-based survey to determine COVID-19 vaccine coverage and people's perceptions of vaccination in order to improve COVID-19 vaccination uptake and confidence among Egyptians, as well as to prioritize interventions.

METHODS

A cross-sectional population-based household survey among Egyptians ≥ 18 years of age was implemented in two phases using a multistage random sampling technique in all of Egypt's 27 governorates. A sample of 18,000 subjects divided into 450 clusters of 20 households each was calculated in proportion to each governorate and the main occupation of the population. Participants were interviewed using a semistructured questionnaire that included demographics, vaccination information from the vaccination card, history of COVID-19 infection, reasons for vaccine refusal among the unvaccinated, and vaccination experience among vaccinated subjects. Vaccination coverage rates were calculated by dividing numbers by the total number of participants. Bivariate and multivariate analyses were performed by comparing the vaccinated and unvaccinated to identify the risk factors for low vaccine uptake.

RESULTS

Overall 18,107 were interviewed, their mean age was 42 ± 16 years and 58.8% were females. Of them, 8,742 (48.3%) had COVID-19 vaccine and 8,020 (44.3%) were fully vaccinated. Factors associated with low vaccination uptake by multivariate analysis included: age groups (18-29 and 30-39) (ORs 2.0 (95% C.I. 1.8-2.2) and 1.3 (95% C.I.1.2-1.4), respectively), residences in urban or frontier governorates (ORs 1.6 (95% C.I. 1.5-1.8) and 1.2 (95% C.I. 1.1-1.4), respectively), housewives and self-employed people (ORs 1.3 (95% C.I. 1.2-1.4) and 1.2 (95% C.I. 1.1-1.4), respectively), married people (ORs 1.3 (95% C.I. 1.2-1.4), and primary and secondary educated (ORs 1.1 (95% C.I. 1.01-1.2) and 1.1(1.04-1.2) respectively). Vaccine hesitancy was due to fear of adverse events (17.5%), mistrust of vaccine (10.2%), concern over safety during pregnancy and lactation (6.9%), and chronic diseases (5.0%).

CONCLUSIONS

Survey identified lower vaccination coverage in Egypt compared to the WHO 70% target. Communication programs targeting the groups with low vaccine uptake are needed to eliminate barriers related to vaccination convenience, side effects, and safety to effectively promote vaccine uptake. Findings from the survey could contribute significantly to vaccination promotion by guiding decision-making efforts on the risky groups and preventing vaccine hesitancy.

We need to talk about nonprobability samples

In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of 'big data', however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance. We explain why the use of nonprobability samples can lead to spurious conclusions, and why seemingly large nonprobability samples can be (effectively) very small. We also review some recent controversies surrounding the use of nonprobability samples in biodiversity monitoring. These points notwithstanding, we argue that nonprobability samples can be useful, provided that their limitations are assessed, mitigated where possible and clearly communicated. Ecologists can learn much from other disciplines on each of these fronts.

Population Immunity to Pre-Omicron and Omicron Severe Acute Respiratory Syndrome Coronavirus 2 Variants in US States and Counties Through 1 December 2021

BACKGROUND

Both severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) vaccination contribute to population-level immunity against SARS-CoV-2. This study estimated the immunological exposure and effective protection against future SARS-CoV-2 infection in each US state and county over 2020-2021 and how this changed with the introduction of the Omicron variant.

METHODS

We used a Bayesian model to synthesize estimates of daily SARS-CoV-2 infections, vaccination data and estimates of the relative rates of vaccination conditional on infection status to estimate the fraction of the population with (1) immunological exposure to SARS-CoV-2 (ever infected with SARS-CoV-2 and/or received ≥1 doses of a COVID-19 vaccine), (2) effective protection against infection, and (3) effective protection against severe disease, for each US state and county from 1 January 2020 to 1 December 2021.

RESULTS

The estimated percentage of the US population with a history of SARS-CoV-2 infection or vaccination as of 1 December 2021 was 88.2% (95% credible interval [CrI], 83.6%-93.5%). Accounting for waning and immune escape, effective protection against the Omicron variant on 1 December 2021 was 21.8% (95% CrI, 20.7%-23.4%) nationally and ranged between 14.4% (13.2%-15.8%; West Virginia) and 26.4% (25.3%-27.8%; Colorado). Effective protection against severe disease from Omicron was 61.2% (95% CrI, 59.1%-64.0%) nationally and ranged between 53.0% (47.3%-60.0%; Vermont) and 65.8% (64.9%-66.7%; Colorado).

CONCLUSIONS

While more than four-fifths of the US population had prior immunological exposure to SARS-CoV-2 via vaccination or infection on 1 December 2021, only a fifth of the population was estimated to have effective protection against infection with the immune-evading Omicron variant.

Thirty-three myths and misconceptions about population data: from data capture and processing to linkage

Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a guarantee for valid inferences. However, population data have characteristics that make them challenging to use. Various assumptions on population coverage and data quality are commonly made, including how such data were captured and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases. Record linkage often implies subtle technical problems, which are easily missed. We discuss a diverse range of myths and misconceptions relevant for anybody capturing, processing, linking, or analysing population data. Remarkably, many of these myths and misconceptions are due to the social nature of data collections and are therefore missed by purely technical accounts of data processing. Many are also not well documented in scientific publications. We conclude with a set of recommendations for using population data.

Digital public health interventions at scale: The impact of social media advertising on beliefs and outcomes related to COVID vaccines

Public health organizations increasingly use social media advertising campaigns in pursuit of public health goals. In this paper, we evaluate the impact of about $40 million of social media advertisements that were run and experimentally tested on Facebook and Instagram, aimed at increasing COVID-19 vaccination rates in the first year of the vaccine roll-out. The 819 randomized experiments in our sample were run by 174 different public health organizations and collectively reached 2.1 billion individuals in 15 languages. We find that these campaigns are, on average, effective at influencing self-reported beliefs-shifting opinions close to 1% at baseline with a cost per influenced person of about $3.41. Combining this result with an estimate of the relationship between survey outcomes and vaccination rates derived from observational data yields an estimated cost per additional vaccination of about $5.68. There is further evidence that campaigns are especially effective at influencing users' knowledge of how to get vaccines. Our results represent, to the best of our knowledge, the largest set of online public health interventions analyzed to date.

ADAPTIVE TIME LOCATION SAMPLING FOR COMPASS, A SARS-COV-2 PREVALENCE STUDY IN FIFTEEN DIVERSE COMMUNITIES IN THE UNITED STATES

The COVPN 5002 (COMPASS) study aimed to estimate the prevalence of SARS-CoV-2 (active SARS-CoV-2 or prior SARS-CoV-2 infection) in children and adults attending public venues in 15 socio-demographically diverse communities in the United States. To protect against potential challenges in implementing traditional sampling strategies, time-location sampling (TLS) using complex sampling involving stratification, clustering of units, and unequal probabilities of selection was used to recruit individuals from neighborhoods in selected communities. The innovative design adapted to constraints such as closure of venues; changing infection hotspots; and uncertain policies. Recruitment of children and the elderly raised additional challenges in sample selection and implementation. To address these challenges, the TLS design adaptively updated both the sampling frame and the selection probabilities over time using information acquired from prior weeks. Although the study itself was specific to COVID-19, the strategies presented in this paper could serve as a case study that can be adapted for performing rigorous population-level inferences in similar settings and could help inform rapid and effective responses to future global public health challenges.

Why weight? Analytic approaches for large-scale population neuroscience data

Population-based neuroimaging studies that feature complex sampling designs enable researchers to generalize their results more widely. However, several theoretical and analytical questions pose challenges to researchers interested in these data. The following is a resource for researchers interested in using population-based neuroimaging data. We provide an overview of sampling designs and describe the differences between traditional model-based analyses and survey-oriented design-based analyses. To elucidate key concepts, we leverage data from the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), a population-based sample of 11,878 9-10-year-olds in the United States. Analyses revealed modest sociodemographic discrepancies between the target population of 9-10-year-olds in the U.S. and both the recruited ABCD sample and the analytic sample with usable structural and functional imaging data. In evaluating the associations between socioeconomic resources (i.e., constructs that are tightly linked to recruitment biases) and several metrics of brain development, we show that model-based approaches over-estimated the associations of household income and under-estimated the associations of caregiver education with total cortical volume and surface area. Comparable results were found in models predicting neural function during two fMRI task paradigms. We conclude with recommendations for ABCD Study® users and users of population-based neuroimaging cohorts more broadly.

Community risks for SARS-CoV-2 infection among fully vaccinated US adults by rurality: A retrospective cohort study from the National COVID Cohort Collaborative

BACKGROUND

While COVID-19 vaccines reduce adverse outcomes, post-vaccination SARS-CoV-2 infection remains problematic. We sought to identify community factors impacting risk for breakthrough infections (BTI) among fully vaccinated persons by rurality.

METHODS

We conducted a retrospective cohort study of US adults sampled between January 1 and December 20, 2021, from the National COVID Cohort Collaborative (N3C). Using Kaplan-Meier and Cox-Proportional Hazards models adjusted for demographic differences and comorbid conditions, we assessed impact of rurality, county vaccine hesitancy, and county vaccination rates on risk of BTI over 180 days following two mRNA COVID-19 vaccinations between January 1 and September 21, 2021. Additionally, Cox Proportional Hazards models assessed the risk of infection among adults without documented vaccinations. We secondarily assessed the odds of hospitalization and adverse COVID-19 events based on vaccination status using multivariable logistic regression during the study period.

RESULTS

Our study population included 566,128 vaccinated and 1,724,546 adults without documented vaccination. Among vaccinated persons, rurality was associated with an increased risk of BTI (adjusted hazard ratio [aHR] 1.53, 95% confidence interval [CI] 1.42-1.64, for urban-adjacent rural and 1.65, 1.42-1.91, for nonurban-adjacent rural) compared to urban dwellers. Compared to low vaccine-hesitant counties, higher risks of BTI were associated with medium (1.07, 1.02-1.12) and high (1.33, 1.23-1.43) vaccine-hesitant counties. Compared to counties with high vaccination rates, a higher risk of BTI was associated with dwelling in counties with low vaccination rates (1.34, 1.27-1.43) but not medium vaccination rates (1.00, 0.95-1.07). Community factors were also associated with higher odds of SARS-CoV-2 infection among persons without a documented vaccination. Vaccinated persons with SARS-CoV-2 infection during the study period had significantly lower odds of hospitalization and adverse events across all geographic areas and community exposures.

CONCLUSIONS

Our findings suggest that community factors are associated with an increased risk of BTI, particularly in rural areas and counties with high vaccine hesitancy. Communities, such as those in rural and disproportionately vaccine hesitant areas, and certain groups at high risk for adverse breakthrough events, including immunosuppressed/compromised persons, should continue to receive public health focus, targeted interventions, and consistent guidance to help manage community spread as vaccination protection wanes.

Spatiotemporal trends in self-reported mask-wearing behavior in the United States: Analysis of a large cross-sectional survey

Background

Face mask-wearing has been identified as an effective strategy to prevent transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance potentially generating heterogeneities in the local trajectories of COVID-19 in the U.S. While numerous studies have investigated patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask-wearing at fine spatial scales across the U.S. through different phases of the pandemic.

Objective

Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the U.S. This information is critical to further assess the effectiveness of masking, evaluate drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges.

Methods

We analyze spatiotemporal masking patterns in over eight million behavioral survey responses from across the United States starting in September 2020 through May 2021. We adjust for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debias self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county-level. Lastly, we evaluate whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data.

Results

We find that county-level masking behavior is spatially heterogeneous along an urban-rural gradient, with mask-wearing peaking in winter 2021 and declining sharply through May 2021. Our results identify regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask-wearing may be influenced by national guidance and disease prevalence. We validate our bias-correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of small sample size and representation. Self-reported behavior estimates are especially prone to social desirability and non-response biases and our findings demonstrate that these biases can be reduced if individuals are asked to report on community rather than self behaviors.

Conclusions

Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.

The effect of altruism on COVID-19 vaccination rates

BACKGROUND

After the emergence of the first vaccines against the COVID-19, public health authorities have promoted mass vaccination in order to achieve herd immunity and reduce the effects of the disease. Vaccination rates have differed between countries, depending on supply (availability of resources) and demand (altruism and resistance to vaccination) factors.

METHODS

This work considers the hypothesis that individuals' health altruism has been an important factor to explain the different levels of vaccination between countries, using the number of transplants as a proxy for altruism. Taking European Union's countries to remove, as far as possible, supply factors that might affect vaccination, we carry out cross-sectional regressions for the most favorable date of the vaccination process (maximum vaccination speed) and for each month during the vaccination campaign.

RESULTS

Our findings confirm that altruism has affected vaccination rates against the COVID-19. We find a direct relationship between transplants rates (proxy variable) and vaccination rates during periods in which the decision to be vaccinated depended on the individual's choice, without supply restrictions. The results show that other demand factors have worked against vaccination: political polarization and belonging to the group of countries of the former Eastern bloc.

CONCLUSIONS

Altruism is a useful tool to define future vaccination strategies, since it favors the individuals' awareness for vaccination.

Risk Model Development and Validation in Clinical Oncology: Lessons Learned

Reliable risk models can greatly facilitate patient-centered inferences and decisions. Herein we summarize key considerations related to risk modeling in clinical oncology. Often overlooked challenges include data quality, missing data, effective sample size estimation, and selecting the variables to be included in the risk model. The stability and quality of the model should be carefully interrogated with particular emphasis on rigorous internal validation.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.