Higher Case Counts Research Articles

Physician and Practice Characteristics Influencing Telemedicine Uptake Among Frontline Clinicians in the Early COVID-19 Pandemic Response: National Survey Study.

Telemedicine expanded rapidly during the COVID-19 pandemic, as key policy changes, financial support, and pandemic fears tipped the balance toward internet-based care. Despite this increased support and benefits to patients and clinicians, telemedicine uptake was variable across clinicians and practices. Little is known regarding physician and institutional characteristics underlying this variability. This study aimed to evaluate factors influencing telemedicine uptake among frontline physicians in the early pandemic response. We surveyed a national stratified sample of frontline clinicians drawn from the American Medical Association Physician Professional Data in June or July 2020. The survey inquired about the first month and most recent month (June 2020) of pandemic telemedicine use; sample data included clinician gender, specialty, census region, and years in practice. Local pandemic conditions were estimated from county-level data on COVID-19 rates at the time of survey response. Data were analyzed in a weighted logistic regression, controlling for county-specific pandemic data, and weighted to account for survey data stratification and nonresponse. Over the first 3-4 months of the pandemic, the proportion of physicians reporting use of telemedicine in >30% of visits increased from 29.2% (70/239) to 35.7% (85/238). Relative to primary care, odds of substantial telemedicine use (>30%) both during the first month of the pandemic and in June 2020 were increased among infectious disease and critical care physicians and decreased among hospitalists and emergency medicine physicians. At least minimal prepandemic telemedicine use (odds ratio [OR] 11.41, 95% CI 1.34-97.04) and a high 2-week moving average of local COVID-19 cases (OR 10.16, 95% CI 2.07-49.97) were also associated with substantial telemedicine use in June 2020. There were no significant differences according to clinician gender, census region, or years in practice. Prepandemic telemedicine use, high local COVID-19 case counts, and clinician specialty were associated with higher levels of substantial telemedicine use during the early pandemic response. These results suggest that telemedicine uptake in the face of the pandemic may have been heavily influenced by the level of perceived threat and the resources available for implementation. Such understanding has important implications for reducing burnout and preparation for future public health emergencies.

Early pandemic recreational fishing patterns across the urban-to-rural gradient in the U.S.

In 2020, the SARS-CoV-2 (COVID-19) pandemic disrupted individual and social behaviors and norms, including outdoor activities. A recreational angling survey of 18,000 licensed anglers from 10 states (AR, CT, FL, IA, MO, NC, SC, TX, UT, WY) was conducted in summer 2020 to characterize recreational fishing trends during the first few months of the pandemic. The study presented here builds off this survey by combining the survey data with county-level human population density and spring 2020 per capita COVID-19 cases to understand how anglers responded to the pandemic along the urban-to-rural continuum. Specifically, we wanted to know if population density or COVID-19 cases per capita influenced angler-reported 1) changes in license sales, 2) number of fishing trips, and 3) motivation for fishing. Overall results suggest that per capita COVID-19 cases were more influential in driving angler behavior than population density in the early pandemic (01 March 2020–31 May 2020). At the onset of the pandemic, high COVID-19 case counts were associated with an uptick in recreational angling activity. In counties with greater COVID-19 case counts, there was greater angler recruitment (i.e., attraction of new individuals to recreational fishing) and earlier license purchases. Anglers aged ≥40 years and earning ≥$50,000 living in areas of greater per capita COVID-19 cases also went on more fishing trips than they typically would. Angler motivations varied across gradients of population density and per capita COVID-19 cases: anglers living in areas of higher population densities were more likely to report fishing for stress relief, sport, and competition among friends, and anglers living in areas of higher per capita COVID-19 cases were more likely to report fishing for sport and because they had free time and less likely to report fishing for food. Management efforts can focus on retaining and reactivating pandemic anglers.

“We were leery of going”: qualitatively exploring Canadian international retirement migrants’ travel-related decisions during the COVID-19 pandemic

BackgroundInternational retirement migration, which is the seasonal or permanent relocation of older people to another country, has grown in popularity in recent years. These retirees are motivated by the promise of warmer winter climates that are conducive to participating in health-promoting recreational and social activities. Ease of cross-border travel facilitates this transnational practice when undertaken seasonally. However, border closures and other travel-related measures put in place to manage the spread of COVID-19, disrupted travel, including for older Canadians who typically winter in the United States (US). During the 2020-21 winter season, for example, Canadians were advised not to engage in non-essential international travel and the land border between Canada and the US was closed to all but essential travellers. Nonetheless, retirement migration remained a significant draw for many Canadian retirees. Here, we qualitatively explore the factors that Canadian international retirement migrants considered when deciding whether or not to travel to the US for the 2020-21 winter during the COVID-19 pandemic.MethodsGuided by case study methodology, semi-structured interviews were conducted with 31 Canadian international retirement migrants who had wintered in the US prior to the COVID-19 pandemic and were in the US at the outset of the pandemic in late winter 2020. Interviews were transcribed verbatim and thematically analyzed to decipher what factors were most important to their travel-related decision-making during the pandemic. We structure the thematic results around four factors previously identified to motivate older people to become international retirement migrants and thus inform decision-making: the destination, the people, the cost, and the movement.ResultsThe previously identified factors that motivate older people to participate in international retirement migration include: the destination (e.g., climate and amenities), the people (e.g., social networks), the cost (e.g., health insurance and living costs), and the movement (e.g., ease of travel). These factors informed how international retirement migrants made decisions to travel abroad or not in the 2020-21 winter season. For example, destination-based factors included a lack of public health measures and high case counts, people-based factors comprised of less opportunities to engage in social activities, cost-based factors involved maintaining property investments and the lack of COVID-19 treatment coverage in available travel health insurance plans, and movement-based factors included challenges in ease of access when travel was viewed as essential or non-essential. These factors disincentivized or motivated international retirement migrants to travel abroad in the 2020-21 winter season during the COVID-19 pandemic.ConclusionsThe results of this study support the need to create tailored decision-support tools for international retirement migrants to make informed travel-related decisions during crisis events so as to protect their health and wellbeing. More research is needed to explore perceptions of risk, especially health risks, among international retirement migrants and how they differently affect their travel-related decisions.

COVID-19 activity risk calculator as a gamified public health intervention tool

The Coronavirus disease 2019 (COVID-19) pandemic, caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has impacted over 200 countries leading to hospitalizations and deaths of millions of people. Public health interventions, such as risk estimators, can reduce the spread of pandemics and epidemics through influencing behavior, which impacts risk of exposure and infection. Current publicly available COVID-19 risk estimation tools have had variable effectiveness during the pandemic due to their dependency on rapidly evolving factors such as community transmission levels and variants. There has also been confusion surrounding certain personal protective strategies such as risk reduction by mask-wearing and vaccination. In order to create a simple easy-to-use tool for estimating different individual risks associated with carrying out daily-life activity, we developed COVID-19 Activity Risk Calculator (CovARC). CovARC is a gamified public health intervention as users can ”play with” how different risks associated with COVID-19 can change depending on several different factors when carrying out routine daily activities. Empowering the public to make informed, data-driven decisions about safely engaging in activities may help to reduce COVID-19 levels in the community. In this study, we demonstrate a streamlined, scalable and accurate COVID-19 risk calculation system. Our study also demonstrates the quantitative impact of vaccination and mask-wearing during periods of high case counts. Validation of this impact could inform and support policy decisions regarding case thresholds for mask mandates, and other public health interventions.

Using Social Vulnerability Indices to Predict Priority Areas for Prevention of Sudden Unexpected Infant Death in Cook County, IL: Cross-Sectional Study.

The incidence of sudden unexpected infant death (SUID) in the United States has persisted at roughly the same level since the mid-2000s, despite intensive prevention efforts around safe sleep. Disparities in outcomes across racial and socioeconomic lines also persist. These disparities are reflected in the spatial distribution of cases across neighborhoods. Strategies for prevention should be targeted precisely in space and time to further reduce SUID and correct disparities. We sought to aid neighborhood-level prevention efforts by characterizing communities where SUID occurred in Cook County, IL, from 2015 to 2019 and predicting where it would occur in 2021-2025 using a semiautomated, reproducible workflow based on open-source software and data. This cross-sectional retrospective study queried geocoded medical examiner data from 2015-2019 to identify SUID cases in Cook County, IL, and aggregated them to "communities" as the unit of analysis. We compared demographic factors in communities affected by SUID versus those unaffected using Wilcoxon rank sum statistical testing. We used social vulnerability indicators from 2014 to train a negative binomial prediction model for SUID case counts in each given community for 2015-2019. We applied indicators from 2020 to the trained model to make predictions for 2021-2025. Validation of our query of medical examiner data produced 325 finalized cases with a sensitivity of 95% (95% CI 93%-97%) and a specificity of 98% (95% CI 94%-100%). Case counts at the community level ranged from a minimum of 0 to a maximum of 17. A map of SUID case counts showed clusters of communities in the south and west regions of the county. All communities with the highest case counts were located within Chicago city limits. Communities affected by SUID exhibited lower median proportions of non-Hispanic White residents at 17% versus 60% (P<.001) and higher median proportions of non-Hispanic Black residents at 32% versus 3% (P<.001). Our predictive model showed moderate accuracy when assessed on the training data (Nagelkerke R2=70.2% and RMSE=17.49). It predicted Austin (17 cases), Englewood (14 cases), Auburn Gresham (12 cases), Chicago Lawn (12 cases), and South Shore (11 cases) would have the largest case counts between 2021 and 2025. Sharp racial and socioeconomic disparities in SUID incidence persisted within Cook County from 2015 to 2019. Our predictive model and maps identify precise regions within the county for local health departments to target for intervention. Other jurisdictions can adapt our coding workflows and data sources to predict which of their own communities will be most affected by SUID.

Spatial Analysis of Greenspace and Mental Health in North Carolina: Consideration of Rural and Urban Communities.

Greenspace positively impacts mental health. Previous research has focused on the greenspace-mental health relationship in urban areas. Yet, little work has looked at rural areas despite rural communities reporting similar rates of poor mental health outcomes and higher rates of suicide mortality compared with urban areas. This ecological research study examined the following research questions: (1) Do public and/or private greenspaces affect the spatial distribution of mental health outcomes in North Carolina? (2) Does this relationship change with rurality? Emergency department data for 6 mental health conditions and suicide mortality data from 2009 to 2018 were included in this analysis. Spatial error and ordinary least squares regressions were used to examine the influence of public and private greenspace quantity on mental health in rural and urban communities. Results suggest greenspace benefits mental health in rural and urban communities. The strength of this relationship varies with urbanity and between public and private greenspaces, suggesting a more complex causal relationship. Given the high case counts and often lower density of mental health care facilities in rural areas, focusing attention on low-cost mental health interventions, such as greenspace, is important when considering rural mental health care.

Barrier measures implemented in French maternity hospitals during the COVID-19 pandemic: A cross-sectional survey

ObjectivesThe objectives of this survey were 1) to describe the changes over time of barrier measures in maternity units, specifically, co-parent visits and women wearing masks in birth rooms, and 2) to identify potential institutional determinants of these barrier measures. DesignWe used an online questionnaire to conduct a descriptive cross-sectional survey from May to July 2021. SettingAll districts in mainland France. ParticipantsMidwife supervisor of each maternity unit. MeasurementsPrimary outcomes were “banning of visits” in the postnatal department during the first lockdown (March–May 2020), and “mandated mask-wearing in birth rooms” during the survey period (May–July 2021); the independent variables were maternity unit characteristics and location in a crisis area. Co-parent visits were considered only during the first lockdown as they were mostly allowed afterwards, and the wearing of masks was studied only during the survey period, as masks were unavailable for the population during the first lockdown. ResultsWe obtained 343 responses, i.e., 75.2% of French maternity units. Visits to the postnatal department were forbidden in 39.3% of the maternity units during the first lockdown and in none during the study period. Maternity hospitals with neonatal intensive care units were the most likely to ban co-parent hospital visits (adjusted OR 2.34 [1.12; 4.96]). However, those were the maternity units least likely to encourage or require women to wear masks while pushing (adjusted OR, 0.31; 95% confidence interval [CI], 0.11–0.77). Maternity units in crisis areas (i.e., with very high case counts) during the first lockdown banned visits significantly more often (adjusted OR, 1.68; 95% CI, 1.05–2.70). Key conclusionsOur study showed that barrier measures evolved during the course of the pandemic but remained extremely variable between facilities. Implications for practiceMaternity units implemented drastic barrier measures at the beginning of the pandemic but were able to adapt these measures over time. It is now time to learn from this experience to ensure that women and infants are no longer harmed by these measures.

Spatial, Sociodemographic, and Weather Analysis of the Zika Virus Outbreak: U.S. Virgin Islands, January 2016-January 2018.

Background: The first Zika virus outbreak in U.S. Virgin Islands identified 1031 confirmed noncongenital Zika disease (n = 967) and infection (n = 64) cases during January 2016-January 2018; most cases (89%) occurred during July-December 2016. Methods and Results: The epidemic followed a continued point-source outbreak pattern. Evaluation of sociodemographic risk factors revealed that estates with higher unemployment, more houses connected to the public water system, and more newly built houses were significantly less likely to have Zika virus disease and infection cases. Increased temperature was associated with higher case counts, which suggests a seasonal association of this outbreak. Conclusion: Vector surveillance and control measures are needed to prevent future outbreaks.

Spatio-Temporal Clustering of Adolescent Bereavement in the United States During the Extended Response to COVID-19: A Follow-Up Study

PurposeThis follow-up study investigated the spatio-temporal clustering of adolescent bereavement during the extended response to COVID-19 from October 2020–January 2022 in the continental United States. MethodsDeidentified and anonymized bereavement data from Crisis Text Line (CTL), a text-based crisis intervention service, and SaTScan cluster analysis were used to identify space-time clustering of bereavement among adolescents, aged 24 years and less, during the COVID-19 pandemic. ResultsClustering of bereavement conversations occurred during waves of high COVID-19 case and death counts, with the highest risk occurring in the Southeastern United States during the fall of 2020 (relative risk: 5.86, confidence interval: 3.48–8.24). Of the CTL texters who shared their demographic information, Indigenous American, Black, male, and female adolescents were more likely to seek help for bereavement when compared to the other CTL users. DiscussionFindings show an increased need for bereavement counseling resources during periods of high COVID-19 cases and deaths.

A statewide, cross-sectional evaluation of the knowledge and level of concern of rabies among South Carolina residents.

Animal rabies cases have increased steadily in South Carolina (SC) for the past decade. An understanding of the population's awareness and understanding of the disease is needed to tailor public health interventions. A marketing list-serv of SC residents' email addresses was used to recruit anonymous respondents for a Knowledge Attitudes and Practices (KAP) electronic survey. A total 516 South Carolinians completed the 31-question survey. Quantile regression and a Pearson's correlation evaluated potential associations between respondent's rabies knowledge and their attitudes and practices. Knowledge was assessed on topics of rabies biology, state animal case counts and rabies pet-related laws. Level of concern and level of knowledge were positively correlated. Additionally, statewide hotspot analysis revealed geographic areas warranting targeted public health interventions; counties with low public concern juxtapositioned with high animal rabies case counts. This study demonstrates the utility of statewide KAPs to gauge populations rabies perception and related preventative actions to tailor appropriate educational programs to limit human-animal rabies exposures.

Household and social characteristics associated with COVID-19 vaccine intent among Latino families in the San Francisco Bay Area

BackgroundLatinos have had higher case counts, hospitalization rates and deaths during the COVID-19 pandemic nationally and in the state of California. Meanwhile, Latino vaccination rates remain lower than those of non-Hispanic Whites. COVID-19 vaccine nonintent, defined as intent to not vaccinate against COVID-19, among Latino individuals continues to be an issue in the state of California.MethodsFamilies from three Latino longitudinal mother–child cohorts previously recruited in the San Francisco Bay Area were surveyed telephonically from February to June 2021 to assess attitudes towards vaccination against COVID-19 and prior vaccination, in general, for themselves and their children. Risk for vaccine nonintent was assessed using the Mann–Whitney rank sum non-parametric test for continuous predictors and chi-squared tests for categorical ones.ResultsThree hundred and nineteen families were surveyed from the Telomere at Birth (TAB), Hispanic Eating and Nutrition (HEN) and Latino Eating and Diabetes Cohort (LEAD). Approximately 36% from TAB and 28% from HEN/LEAD indicated COVID-19 vaccine nonintent for themselves and/or their children. Risk factors for vaccine nonintent included lower maternal age (p = 0.01), concern about vaccine side effects (p < 0.01) and prior history of a household members being infected with SARS-CoV-2 (p < 0.01) and indexes of household crowding including number of people sharing a bathroom (p = 0.048). Vaccine intent was also associated with receiving vaccine input from friends (p = 0.03), family (p < 0.01) and/or coworkers (p = 0.02) compared with those who were not planning on getting vaccinated against COVID-19.ConclusionsLatino families living in crowded living situations who may not have received any COVID-19 advice from family, coworkers or friends are at particular risk for nonintent for vaccinatation against COVID-19. Community-based grassroots or promotor/a based interventions centered on trusted individuals with close community ties and counseling concerning vaccination against COVID-19 could help boost vaccination rates in this population group.

Prescribing of common outpatient antibiotics for respiratory infections in adults amid the COVID-19 pandemic in Brazil

Background: Inappropriate antibiotic use for SARS-CoV-2 infection has the potential to increase the burden of antibiotic resistance. Brazil experienced spread of a new SARS-CoV-2 variant in the fourth quarter (Q4) of 2020, resulting in the highest case counts in Latin America, raising concerns of antibiotic overuse. To better understand antibiotic use during the COVID-19 pandemic, we evaluated prescribing changes in antibiotics commonly used for outpatient respiratory infections (amoxicillin-clavulanate, azithromycin, and levofloxacin or moxifloxacin [AALM]) among adults aged ≥20 years in Brazil in 2020 versus 2019. Methods: We analyzed the IQVIA MIDAS medical data set for AALM prescribing by age group (20–39, 40–59, 60–64, 65–74, ≥75 years), comparing Q4 2020 rates to those in Q4 2019. We estimated crude rate ratios and 95% CIs using prescription number as the numerator (assuming Poisson counts) and age-adjusted population as the denominator. We also determined the most common prescribing specialties for each antibiotic across both time points. Results: Compared to Q4 2019, Q4 2020 azithromycin prescribing increased among all ages, ranging from 90.7% (95% CI, 90.0%–91.4%) in those aged 20–39 years to 927.2% (95% CI, 912.9%–941.7%) in those aged 65–74 years (Fig. 1). Amoxicillin-clavulanate prescribing decreased for most ages, ranging from −78.4% (95% CI, −78.7% to −78.1%) in those aged 60–64 years to −25.8% (95% CI, −26.6% to −25.0%) in those aged 65–74 years. Prescribing of levofloxacin or moxifloxacin decreased for most ages, ranging from −39.1% (95% CI, −39.4% to −38.8%) in those aged 20–39 years to −16.9% (95% CI, −18.1% to −15.7%) in those aged 60–64 years. For those aged ≥75 years, prescribing of amoxicillin-clavulanate and levofloxacin or moxifloxacin increased by 13.2% (95% CI, 11.9%–14.5%) and 43.1% (95% CI, 41.7%–44.5%), respectively. In Q4 2019 and Q4 2020, the 2 most common prescribing specialties for azithromycin were general practice (48%–50% of prescriptions) and gynecology (19%–25%). Compared to Q4 2019, infectious disease specialists in Q4 2020 saw the largest decline in percentage of azithromycin prescriptions (10% to 1%) and surgeons saw the largest increase (0% to 7%). General practitioners were also the most common prescribers of the remaining antibiotics (43%–54%), followed by gynecology for levofloxacin or moxifloxacin (25%–29%) and otolaryngology for amoxicillin-clavulanate (14%–20%). Conclusions: Despite decreases in prescribing of amoxicillin-clavulanate and respiratory fluoroquinolones for most adults, azithromycin prescribing increased dramatically across all adults during the COVID-19 pandemic. Targeting inappropriate outpatient antibiotic use in Brazil, particularly azithromycin prescribing among general practitioners, gynecologists, and surgeons, may be high-yield targets for antibiotic stewardship.Funding: NoneDisclosures: None

Heat stress morbidity among US military personnel: Daily exposure and lagged response (1998-2019).

Heat stress illnesses represent a rising public health threat; however, associations between environmental heat and observed adverse health outcomes across populations and geographies remain insufficiently elucidated to evaluate risk and develop prevention strategies. In particular, military-relevant large-scale studies of daily heat stress morbidity responses among physically active, working-age adults to various indices of heat have been limited. We evaluated daily means, maximums, minimums, and early morning measures of temperature, heat index, and wet bulb globe temperature (WBGT) indices, assessing their association with 31,642 case-definition heat stroke and heat exhaustion encounters among active duty servicemembers diagnosed at 24 continental US installations from 1998 to 2019. We utilized anonymized encounter data consisting of hospitalizations, ambulatory (out-patient) visits, and reportable events to define heat stress illness cases and select the 24 installations with the highest case counts. We derived daily indices of heat from hourly-scale gridded climate data and applied a case-crossover study design incorporating distributed-lag, nonlinear models with 5days of lag to estimate odds ratios at one-degree increments for each index of heat. All indices exhibited nonlinear odds ratios with short-term lag effects throughout observed temperature ranges. Responses were positive, monotonic, and exponential in nature, except for maximum daily WBGT, minimum daily temperature, temperature at 0600h (local), and WBGT at 0600h (local), which, while generally increasing, showed decreasing risk for the highest heat category days. The risk for a heat stress illness on a day with a maximum WBGT of 32.2°C (90.0 °F) was 1.93 (95% CI, 1.82 - 2.05) times greater than on a day with a maximum WBGT of 28.6°C (83.4 °F). The risk was 2.53 (2.36-2.71) times greater on days with a maximum heat index of 40.6°C (105 °F) compared to 32.8°C (91.0 °F). Our findings suggest that prevention efforts may benefit from including prior-day heat levels in risk assessments, from monitoring temperature and heat index in addition to WBGT, and by promoting control measures and awareness across all heat categories.

Meeting case investigation and contact tracing needs during COVID-19 in Alberta: the development and implementation of the Alberta Health Services Pod Partnership Model.

SettingIn Alberta, a small team of specialized public health experts typically complete case investigation and contact tracing. High COVID-19 case counts and a shortage of trained public health professionals required a rapid and significant adaptation of staffing models to meet the population’s needs.InterventionA tiered, interdisciplinary staffing model, based on those in critical care, was developed, piloted, and implemented in the Alberta Health Services’ Communicable Disease Control department in late 2020 to complete case investigation and contact tracing. The final model included novice, non-regulated professionals divided into pods of four to six investigators, led by an experienced regulated investigator. Team leads oversaw five pods. Communicable disease nurses provided an additional tier of clinical expertise. During the model development, roles and responsibilities of team members were delineated, ratios for supervision were tested, and rapid training was provided.OutcomesThe tiered staffing model began in November 2020 with staff members in two pods. At its peak in early May 2021, 72 pods of 502 non-regulated members, 134 regulated investigators, and 4 communicable disease nurses completed 780–973 case investigations daily, or 40–45% of all positive cases in Alberta. In comparison, the same number of regulated investigators working independently in the traditional staffing model without non-regulated pods completed, on average, 249 case investigations daily.ImplicationsA tiered staffing model can be effective at maximizing the skills of the experienced members of the case investigation team to maintain case investigation and contact tracing activities during a pandemic.

Cluster of SARS-CoV-2 Gamma Variant Infections, Parintins, Brazil, March 2021.

High case counts after the Gamma (P. 1) variant of severe acute respiratory syndrome coronavirus 2 emerged in Brazil raised concerns that previously infected persons might become reinfected. Investigation of a cluster of coronavirus disease cases in Parintins, in the Brazilian Amazon, suggested household transmission but did not identify high rates of reinfection.

Estimating the Effects of Non-Pharmaceutical Interventions and Population Mobility on Daily COVID-19 Cases: Evidence from Ontario.

This study uses coronavirus disease 2019 (COVID-19) case counts and Google mobility data for 12 of Ontario’s largest Public Health Units from Spring 2020 until the end of January 2021 to evaluate the effects of non-pharmaceutical interventions (NPIs; policy restrictions on business operations and social gatherings) and population mobility on daily cases. Instrumental variables (IV) estimation is used to account for potential simultaneity bias, because both daily COVID-19 cases and NPIs are dependent on lagged case numbers. IV estimates based on differences in lag lengths to infer causal estimates imply that the implementation of stricter NPIs and indoor mask mandates are associated with reductions in COVID-19 cases. Moreover, estimates based on Google mobility data suggest that increases in workplace attendance are correlated with higher case counts. Finally, from October 2020 to January 2021, daily Ontario forecasts from Box–Jenkins time-series models are more accurate than official forecasts and forecasts from a susceptible-infected-removed epidemiology model.

Predictors of Nursing Home Covid-19 Cases: a Community Vulnerability Approach

It is well known that the Covid-19 pandemic has placed considerable burden on nursing homes, including from resident, facility, and community perspectives, among others. This study examined facility and community factors that were related to incident Covid-19 cases in nursing home facilities. N=12,473 US nursing homes were included in this study. Data from June 2020 - January 2021 from several publicly available sources were combined to create a dataset that included facility name, size, ownership, mortality rate, Covid case rate, personal protective equipment (PPE) and staff shortages, % white residents, and % Medicaid residents. Community factors included core-based statistical area (CBSA) Covid case rates, urban/rural, CBSA death rates, and the CDC’s Social Vulnerability Index (SVI). Zero-inflated Poisson regression models were used to determine predictors of 8-month Covid case counts, normalized by facility size. Results indicated that higher staff shortages, poorer facility rating, for-profit ownership, proportionally more Medicaid and non-white residents were all significantly associated with higher Covid case rates over 8 months (all P < 0.0001). Significant community level predictors of higher cases included urban setting and higher SVI. PPE shortages was not associated with higher case counts. Of all the factors included, SVI was the strongest predictor of Covid case counts. This large US study assists in determining critical facility and community factors that predict increasing Covid burden in nursing homes. Particularly, SVI is an important factor in determining facility and public health policy, and targeting resources in large scale health crises such as the Covid-19 pandemic.

Spatial Risk Factors for Pillar 1 COVID-19 Excess Cases and Mortality in Rural Eastern England, UK.

Understanding is still developing about spatial risk factors for COVID‐19 infection or mortality. This is a secondary analysis of patient records in a confined area of eastern England, covering persons who tested positive for SARS‐CoV‐2 through end May 2020, including dates of death and residence area. We obtained residence area data on air quality, deprivation levels, care home bed capacity, age distribution, rurality, access to employment centers, and population density. We considered these covariates as risk factors for excess cases and excess deaths in the 28 days after confirmation of positive Covid status relative to the overall case load and death recorded for the study area as a whole. We used the conditional autoregressive Besag—York–Mollie model to investigate the spatial dependency of cases and deaths allowing for a Poisson error structure. Structural equation models were applied to clarify relationships between predictors and outcomes. Excess case counts or excess deaths were both predicted by the percentage of population age 65 years, care home bed capacity and less rurality: older population and more urban areas saw excess cases. Greater deprivation did not correlate with excess case counts but was significantly linked to higher mortality rates after infection. Neither excess cases nor excess deaths were predicted by population density, travel time to local employment centers, or air quality indicators. Only 66% of mortality was explained by locally high case counts. Higher deprivation clearly linked to higher COVID‐19 mortality separate from wider community prevalence and other spatial risk factors.

The impact of lockdown timing on COVID-19 transmission across US counties.

BackgroundMany countries have implemented lockdowns to reduce COVID-19 transmission. However, there is no consensus on the optimal timing of these lockdowns to control community spread of the disease. Here we evaluated the relationship between timing of lockdowns, along with other risk factors, and the growth trajectories of COVID-19 across 3,112 counties in the US.MethodsWe ascertained dates for lockdowns and implementation of various non-pharmaceutical interventions at a county level and merged these data with those of US census and county-specific COVID-19 daily cumulative case counts. We then applied a Functional Principal Component (FPC) analysis on this dataset to generate FPC scores, which were used as a surrogate variable to describe the trajectory of daily cumulative case counts for each county. We used machine learning methods to identify risk factors including the timing of lockdown that significantly influenced the FPC scores.FindingsWe found that the first eigen-function accounted for most (>92%) of the variations in the daily cumulative case counts. The impact of lockdown timing on the total daily case count of a county became significant beginning approximately 7 days prior to that county reporting at least 5 cumulative cases of COVID-19. Delays in lockdown implementation after this date led to a rapid acceleration of COVID-19 spread in the county over the first ~50 days from the date with at least 5 cumulative cases, and higher case counts across the entirety of the follow-up period. Other factors such as total population, median family income, Gini index, median age, and within-county mobility also had a substantial effect. When adjusted for all these factors, the timing of lockdowns was the most significant risk factor associated with the county-specific daily cumulative case counts.InterpretationLockdowns are an effective way of controlling the spread of COVID-19 in communities. Significant delays in lockdown cause a dramatic increase in the cumulative case counts. Thus, the timing of the lockdown relative to the case count is an important consideration in controlling the pandemic in communities.FundingThe study period is from June 2020 to July 2021. Dr. Xuekui Zhang is a Tier 2 Canada Research Chairs (Grant No. 950231363) and funded by Natural Sciences and Engineering Research Council of Canada (Grant No. RGPIN201704722). Dr. Li Xing is funded by Natural Sciences and Engineering Research Council of Canada (Grant Number: RGPIN 202103530). This research was enabled in part by support provided by WestGrid (www.westgrid.ca) and Compute Canada (www.computecanada.ca). The computing resource is provided by Compute Canada Resource Allocation Competitions #3495 (PI: Xuekui Zhang) and #1551 (PI: Li Xing). Dr. Don Sin is a Tier 1 Canada Research Chair in COPD and holds the de Lazzari Family Chair at the Heart Lung Innovation, Vancouver, Canada.

Epidemiologic trends and characteristics of SARS-CoV-2 infections among children in the United States.

To review the epidemiological characteristics and clinical features associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections among children in the United States. In the United States, the majority of SARS-CoV-2 infections in children have been mild illnesses, with those 5-17 years of age having the highest frequency. Specifically, the incidence of SARS-CoV-2 in children is two times higher in adolescents (12-17 years) than younger school-aged children (5-11 years). Despite the higher case counts in older children, 10% of pediatric hospitalizations have been in infants less than one year. In addition, severe respiratory and renal complications, hospitalization, and even death have been documented in children. Clinical manifestations of SARS-CoV-2 infection in children range from asymptomatic to severe respiratory distress, with mild nonspecific symptoms being the most commonly reported. The broad clinical presentation and the frequency of asymptomatic or minimally symptomatic infections in children pose challenges for controlling and detecting SARS-CoV-2. However, severe disease has been noted in children with associated medical complications and death. Thus, additional active surveillance and research is needed to understand the burden children contribute to the SARS-CoV-2 pandemic in the United States.