Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction.

Motivated by improving the prediction of the human immunodeficiency virus (HIV) suppression status using electronic health records (EHR) data, we propose a functional multivariable logistic regression model, which accounts for the longitudinal binary process and continuous process simultaneously. Specifically, the longitudinal measurements for either binary or continuous variables are modeled by functional principal components analysis, and their corresponding functional principal component scores are used to build a logistic regression model for prediction. The longitudinal binary data are linked to underlying Gaussian processes. The estimation is done using penalized spline for the longitudinal continuous and binary data. Group-lasso is used to select longitudinal processes, and the multivariate functional principal components analysis is proposed to revise functional principal component scores with the correlation. The method is evaluated via comprehensive simulation studies and then applied to predict viral suppression using EHR data for people living with HIV in South Carolina.

Geographic Disparities in Availability of General and Specialized Pediatricians in the US and Prevalence of Childhood Neurodevelopmental Disorders.

General pediatricians and those specialized in developmental-behavioral and neurodevelopmental disabilities support children with neurodevelopmental disorders, such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). We identified substantial geographic disparities in pediatrician availability (eg, urban > rural areas), as well as regions with low pediatrician access but high ASD/ADHD prevalence estimates (eg, the US Southeast).

A Hypothesis Test for Detecting Spatial Patterns in Categorical Areal Data.

The vast growth of spatial datasets in recent decades has fueled the development of many statistical methods for detecting spatial patterns. Two of the most commonly studied spatial patterns are clustering, loosely defined as datapoints with similar attributes existing close together, and dispersion, loosely defined as the semi-regular placement of datapoints with similar attributes. In this work, we develop a hypothesis test to detect spatial clustering or dispersion at specific distances in categorical areal data. Such data consists of a set of spatial regions whose boundaries are fixed and known (e.g., counties) associated with a categorical random variable (e.g. whether the county is rural, micropolitan, or metropolitan). We propose a method to extend the positive area proportion function (developed for detecting spatial clustering in binary areal data) to the categorical case. This proposal, referred to as the categorical positive areal proportion function test, can detect various spatial patterns, including homogeneous clusters, heterogeneous clusters, and dispersion. Our approach is the first method capable of distinguishing between different types of clustering in categorical areal data. After validating our method using an extensive simulation study, we use the categorical positive area proportion function test to detect spatial patterns in Boulder County, Colorado USA biological, agricultural, built and open conservation easements.

Comparison of the characteristics of the population eligible for lung cancer screening under 2013 and population newly eligible under 2021 US Preventive Services Task Force recommendations.

PURPOSE: In 2021, the United States Preventive Services Task Force (USPSTF) revised their 2013 recommendations for lung cancer screening eligibility by lowering the pack-year history from 30+ to 20+ pack-years and the recommended age from 55 to 50 years. Simulation studies suggest that Black persons and females will benefit most from these changes, but it is unclear how the revised USPSTF recommendations will impact geographic, health-related, and other sociodemographic characteristics of those eligible. METHODS: This cross-sectional study employed data from the 2017-2020 Behavioral Risk Factor Surveillance System surveys from 23 states to compare age, gender, race, marital, sexual orientation, education, employment, comorbidity, vaccination, region, and rurality characteristics of the eligible population according to the original 2013 USPSTF recommendations with the revised 2021 USPSTF recommendations using chi-squared tests. This study compared those originally eligible to those newly eligible using the BRFSS raking-dervived weighting variable. RESULTS: There were 30,190 study participants. The results of this study found that eligibility increased by 62.4% due to the revised recommendations. We found that the recommendation changes increased the proportion of eligible females (50.1% vs 44.1%), Black persons (9.2% vs 6.6%), Hispanic persons (4.4% vs 2.7%), persons aged 55-64 (55.8% vs 52.6%), urban-dwellers(88.3% vs 85.9%), unmarried (3.4% vs 2.5%) and never married (10.4% vs 6.6%) persons, as well as non-retirees (76.5% vs 56.1%) Respondents without comorbidities and COPD also increased. CONCLUSION: It is estimated that the revision of the lung cancer screening recommendations decreased eligibility disparities in sex, race, ethnicity, marital status, respiratory comorbidities, and vaccination status. Research will be necessary to estimate whether uptake patterns subsequently follow the expanded eligibility patterns.

Identifying multilevel predictors of behavioral outcomes like park use: A comparison of conditional and marginal modeling approaches.

This study compared marginal and conditional modeling approaches for identifying individual, park and neighborhood park use predictors. Data were derived from the ParkIndex study, which occurred in 128 block groups in Brooklyn (New York), Seattle (Washington), Raleigh (North Carolina), and Greenville (South Carolina). Survey respondents (n = 320) indicated parks within one half-mile of their block group used within the past month. Parks (n = 263) were audited using the Community Park Audit Tool. Measures were collected at the individual (park visitation, physical activity, sociodemographic characteristics), park (distance, quality, size), and block group (park count, population density, age structure, racial composition, walkability) levels. Generalized linear mixed models and generalized estimating equations were used. Ten-fold cross validation compared predictive performance of models. Conditional and marginal models identified common park use predictors: participant race, participant education, distance to parks, park quality, and population >65yrs. Additionally, the conditional mode identified park size as a park use predictor. The conditional model exhibited superior predictive value compared to the marginal model, and they exhibited similar generalizability. Future research should consider conditional and marginal approaches for analyzing health behavior data and employ cross-validation techniques to identify instances where marginal models display superior or comparable performance.

Sleep disorders and cancer incidence: examining duration and severity of diagnosis among veterans.

Introduction: Sleep disruption affects biological processes that facilitate carcinogenesis. This retrospective cohort study used de-identified data from the Veterans Administration (VA) electronic medical record system to test the hypothesis that patients with diagnosed sleep disorders had an increased risk of prostate, breast, colorectal, or other cancers (1999-2010, N=663,869). This study builds upon existing evidence by examining whether patients with more severe or longer-duration diagnoses were at a greater risk of these cancers relative to those with a less severe or shorter duration sleep disorder. Methods: Incident cancer cases were identified in the VA Tumor Registry and sleep disorders were defined by International Classification of Sleep Disorder codes. Analyses were performed using extended Cox regression with sleep disorder diagnosis as a time-varying covariate. Results: Sleep disorders were present among 56,055 eligible patients (8% of the study population); sleep apnea (46%) and insomnia (40%) were the most common diagnoses. There were 18,181 cancer diagnoses (41% prostate, 12% colorectal, 1% female breast, 46% other). The hazard ratio (HR) for a cancer diagnosis was 1.45 (95% confidence interval [CI]: 1.37, 1.54) among those with any sleep disorder, after adjustment for age, sex, state of residence, and marital status. Risks increased with increasing sleep disorder duration (short [<1-2 years] HR: 1.04 [CI: 1.03-1.06], medium [>2-5 years] 1.23 [1.16-1.32]; long [>5-12 years] 1.52 [1.34-1.73]). Risks also increased with increasing sleep disorder severity using cumulative sleep disorder treatments as a surrogate exposure; African Americans with more severe disorders had greater risks relative to those with fewer treatments and other race groups. Results among patients with only sleep apnea, insomnia, or another sleep disorder were similar to those for all sleep disorders combined. Discussion: The findings are consistent with other studies indicating that sleep disruption is a cancer risk factor. Optimal sleep and appropriate sleep disorder management are modifiable risk factors that may facilitate cancer prevention.

Are the Risk of Generalizability Biases Generalizable? A Meta-Epidemiological Study.

Background: Preliminary studies (e.g., pilot/feasibility studies) can result in misleading evidence that an intervention is ready to be evaluated in a large-scale trial when it is not. Risk of Generalizability Biases (RGBs, a set of external validity biases) represent study features that influence estimates of effectiveness, often inflating estimates in preliminary studies which are not replicated in larger-scale trials. While RGBs have been empirically established in interventions targeting obesity, the extent to which RGBs generalize to other health areas is unknown. Understanding the relevance of RGBs across health behavior intervention research can inform organized efforts to reduce their prevalence. Purpose: The purpose of our study was to examine whether RGBs generalize outside of obesity-related interventions. Methods: A systematic review identified health behavior interventions across four behaviors unrelated to obesity that follow a similar intervention development framework of preliminary studies informing larger-scale trials (i.e., tobacco use disorder, alcohol use disorder, interpersonal violence, and behaviors related to increased sexually transmitted infections). To be included, published interventions had to be tested in a preliminary study followed by testing in a larger trial (the two studies thus comprising a study pair). We extracted health-related outcomes and coded the presence/absence of RGBs. We used meta-regression models to estimate the impact of RGBs on the change in standardized mean difference (ΔSMD) between the preliminary study and larger trial. Results: We identified sixty-nine study pairs, of which forty-seven were eligible for inclusion in the analysis (k = 156 effects), with RGBs identified for each behavior. For pairs where the RGB was present in the preliminary study but removed in the larger trial the treatment effect decreased by an average of ΔSMD=-0.38 (range - 0.69 to -0.21). This provides evidence of larger drop in effectiveness for studies containing RGBs relative to study pairs with no RGBs present (treatment effect decreased by an average of ΔSMD =-0.24, range - 0.19 to -0.27). Conclusion: RGBs may be associated with higher effect estimates across diverse areas of health intervention research. These findings suggest commonalities shared across health behavior intervention fields may facilitate introduction of RGBs within preliminary studies, rather than RGBs being isolated to a single health behavior field.

Clustering Patterns of 24-Hour Physical Activity in Children 6-36 Months Old.

PURPOSE: To determine 24-hour physical activity (PA) clusters in children 6-36 months of age, factors associated with the clusters, and their agreement across time. METHOD: A longitudinal study followed 150 infants from South Carolina up to 36 months of age. Measures included 24-hour PA and demographic data. Functional clustering was used to obtain the clusters. The association between cluster membership and infant/parent characteristics was examined by Kruskal-Wallis and chi-squared tests. Concordance was measured with the kappa coefficient and percent agreement. RESULTS: At each follow-up, 3 clusters were optimal, identified as late activity (cluster 1), high activity (cluster 2), and medium activity (cluster 3). The defining feature of the late activity cluster was that their physical activity (PA) activity was shifted to later in the day versus children in clusters 2 and 3. At 6 months, the clusters were associated with race (<0.001), crawling (0.043), other children in the household (0.043), and mother's education (0.004); at 12 months with race (0.029), childcare (<0.001), and education (<0.001); and at 36 months with other children in the household (0.019). Clusters showed moderate agreement (kappa = .41 [.25 to .57], agreement = 61% [49% to 72%]) between 6 and 12 months and, at 36 months, showed no agreement with either 6 or 12 months. CONCLUSION: Twenty-four-hour PA can be clustered into medium, high, and late PA. Further research is needed into the consequences of late sleeping in children at this age. Clusters are associated with household and childcare factors, and cluster membership is dynamic across time.

Trends in maternal opioid use: Statewide differences by sociodemographic characteristics in Florida from 2000 to 2019.

BACKGROUND: Maternal opioid use (MOU) remains a public health concern. Studies have demonstrated significant increases in MOU, but estimates using ICD-10-CM or stratified by sociodemographic variables are limited. OBJECTIVES: Using a statewide, population-based dataset of Florida resident deliveries from 2000 to 2019, we examined the trend of MOU by age, race/ethnicity, education level, and insurance. METHODS: Florida administrative data was used to conduct a retrospective cohort study. MOU was identified using opioid-related hospital discharge diagnoses documented prenatally or at delivery. Maternal sociodemographic variables were obtained from Florida vital statistics. Joinpoint regression was used to identify statistically significant changes in the trends overall and stratified by sociodemographic variables. Results are presented as annual percentage changes (APC) and 95% confidence intervals. RESULTS: Our sample included over 3.6 million Florida resident mothers; of which, MOU was identified in 1% (n = 22,828) of the sample. From 2000 to 2019, MOU increased over ten-fold from 8.7 to 94.7 per 10,000 live birth deliveries. MOU increased significantly from 2000 to 2011 (APC: 32.8; 95% CI: 29.4, 36.2), remained stable from 2011 to 2016, and decreased significantly from 2016 to 2019 (APC: 3.9; 95% CI: -6.6, -1.0). However, from 2016 to 2019, MOU increased among non-Hispanic Black mothers (APC: 9.2; 95% CI: 7.5, 11.0), and those ages 30-34 (APC: 2.9; 95% CI: 1.2, 4.6) and 35-39 (APC: 6.4; 95% CI: 4.3, 8.4). CONCLUSIONS: Accurate prevalence estimates of MOU by sociodemographic factors are necessary to fully understand prevalence trends, describe the burden among sub-populations, and develop targeted interventions.

Predictors of Autism Spectrum Disorder and ADHD: Results from the National Survey of Children's Health.

BACKGROUND: Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two of the most common neurodevelopmental disorders with comorbidity rates of up to 70%. Population-based studies show differential rates of ADHD and ASD diagnosis based on sociodemographic variables. However, no studies to date have examined the role of sociodemographic factors on the likelihood of receiving an ADHD, ASD, or comorbid ASD + ADHD diagnosis in a large, nationally representative sample. OBJECTIVE: This study aims to examine the impact of sociodemographic factors on the odds of experiencing ASD-only, ADHD-only, or both diagnoses for children in the United States. METHODS: Using a mixed effects multinomial logistic modeling approach and data from the 2016-2018 National Survey of Children's Health, we estimated the association between sociodemographic variables and the log odds of being in each diagnostic group. RESULTS: Sociodemographic variables were differentially related to the three diagnostic groups: ASD-only, ADHD-only, and ASD + ADHD. Compared to girls, boys experienced higher odds of all three diagnosis categories. White children had higher odds of having an ADHD-only or ASD + ADHD diagnosis compared to non-Hispanic (NH) Black, NH multiple/other race, and Hispanic children. Odds ratios for levels of parent education, household income, and birth characteristics showed varying trends across diagnostic groups. CONCLUSIONS: Overall, our findings point to unique sets of risk factors differentially associated ASD and ADHD, with lower income standing out as an important factor associated with receiving a diagnosis of ASD + ADHD.

Factors Associated with Children's Physical Activity During Youth Soccer Practices.

Purpose: To identify practice and social contextual factors that associate with physical activity (PA) levels of children during their participation in a youth soccer program. Methods: Twenty-seven youth soccer teams serving children ages 6-11 years participated. Research staff directly observed and recorded PA intensity and practice and social contextual factors using momentary time-sampling procedures. Each team was observed for 1 practice, during which approximately 6 children were each observed for twenty 30-s observation blocks (10-s observation, 20-s recording). In total, children were observed for 3,102 intervals. Multilevel logistic regression analyses were conducted to describe associations between PA intensity and practice and social contexts. Interaction terms were introduced into the models to determine if the associations differed across girls-only, boys-only, and coed teams. Results: A total of 158 children were observed across the 27 teams. Children were more likely to engage in moderate or vigorous PA while performing fitness (Odds Ratio [OR], 9.9, 95% CI = 5.34-18.04), game (OR, 4.0, 95% CI = 2.88-5.66), warm-up (OR, 2.8, 95% CI = 1.85-4.11), and drill (OR, 1.9, 95% CI = 1.41-2.67) activities compared to tactic/instructional activities. The associations between PA intensity levels and practice and social contexts did not differ across girls-only, boys-only, and coed teams. Conclusions: Fitness activities and full-team game play were associated with higher PA intensity levels during children's participation in youth soccer practices. Youth sport practice protocols can be modified to increase children's physical activity.

Scientists' perception of pilot study quality was influenced by statistical significance and study design.

OBJECTIVES: Preliminary studies play a key role in developing large-scale interventions but may be held to higher or lower scientific standards during the peer review process because of their preliminary study status. STUDY DESIGN AND SETTING: Abstracts from 5 published obesity prevention preliminary studies were systematically modified to generate 16 variations of each abstract. Variations differed by 4 factors: sample size (n = 20 vs. n = 150), statistical significance (P < 0.05 vs. P > 0.05), study design (single group vs. randomized 2 groups), and preliminary study status (presence/absence of pilot language). Using an online survey, behavioral scientists were provided with a randomly selected variation of each of the 5 abstracts and blinded to the existence of other variations. Respondents rated each abstract on aspects of study quality. RESULTS: Behavioral scientists (n = 271, 79.7% female, median age 34 years) completed 1,355 abstract ratings. Preliminary study status was not associated with perceived study quality. Statistically significant effects were rated as more scientifically significant, rigorous, innovative, clearly written, warranted further testing, and had more meaningful results. Randomized designs were rated as more rigorous, innovative, and meaningful. CONCLUSION: Findings suggest reviewers place a greater value on statistically significant findings and randomized control design and may overlook other important study characteristics.

County-Level Prevalence Estimates of Autism Spectrum Disorder in Children in the United States.

Prevalence estimates of autism spectrum disorder (ASD) point to geographic and socioeconomic disparities in identification and diagnosis. Estimating national prevalence rates can limit understanding of local disparities, especially in rural areas where disproportionately higher rates of poverty and decreased healthcare access exist. Using a small area estimation approach from the 2016-2018 National Survey of Children's Health (N = 70,913), we identified geographic differences in ASD prevalence, ranging from 4.38% in the Mid-Atlantic to 2.71% in the West South-Central region. Cluster analyses revealed "hot spots" in parts of the Southeast, East coast, and Northeast. This geographic clustering of prevalence estimates suggests that local or state-level differences in policies, service accessibility, and sociodemographics may play an important role in identification and diagnosis of ASD.County-Level Prevalence Estimates of Autism Spectrum Disorder in Children in the United States.

Neighborhood Socioeconomic Status, Green Space, and Walkability and Risk for Falls Among Postmenopausal Women: The Women's Health Initiative.

PURPOSE: This study estimated associations between neighborhood socioeconomic status (NSES), walkability, green space, and incident falls among postmenopausal women and evaluated modifiers of these associations, including study arm, race and ethnicity, baseline household income, baseline walking, age at enrollment, baseline low physical functioning, baseline fall history, climate region, and urban-rural residence. METHODS: The Women's Health Initiative recruited a national sample of postmenopausal women (50-79 years) across 40 U.S. clinical centers and conducted yearly assessments from 1993 to 2005 (n = 161,808). Women reporting a history of hip fracture or walking limitations were excluded, yielding a final sample of 157,583 participants. Falling was reported annually. NSES (income/wealth, education, occupation), walkability (population density, diversity of land cover, nearby high-traffic roadways), and green space (exposure to vegetation) were calculated annually and categorized into tertiles (low, intermediate, high). Generalized estimating equations assessed longitudinal relationships. RESULTS: NSES was associated with falling before adjustment (high vs. low, odds ratio, 1.01; 95% confidence interval, 1.00-1.01). Walkability was significantly associated with falls after adjustment (high vs. low, odds ratio, 0.99; 95% confidence interval, 0.98-0.99). Green space was not associated with falling before or after adjustment. Study arm, race and ethnicity, household income, age, low physical functioning, fall history, and climate region modified the relationship between NSES and falling. Race and ethnicity, age, fall history, and climate region modified relationships between walkability and green space and falling. CONCLUSIONS: Our results did not show strong associations of NSES, walkability, or green space with falling. Future research should incorporate granular environmental measures that may directly relate to physical activity and outdoor engagement.

Longitudinal associations between sleep and weight status in infants and toddlers.

BACKGROUND: The limited research assessing relationships between sleep duration and weight status in infants and toddlers relies primarily on parent-reported sleep and cross-sectional studies. OBJECTIVES: Examine whether average sleep duration and changes in sleep duration among 6-24-month-old children were associated with weight-for-length z-scores, and whether these associations varied by race/ethnicity, socioeconomic status and sex. METHODS: Data were collected when children were approximately 6, 12, 18 and 24 months old (N = 116). Sleep duration was measured using actigraphy. Weight-for-length z-scores were calculated using children's height and weight. Physical activity was assessed using accelerometry. Diet was assessed using a feeding frequency questionnaire. Demographic characteristics included sex, race/ethnicity and socioeconomic status. Separate associations of between- and within-person changes in sleep duration were estimated with weight-for-length z-score treated as the outcome variable in linear mixed model analyses. Additional models were assessed that included interactions between sleep and demographic characteristics. RESULTS: At time points where children slept longer at night compared to their own average, their weight-for-length z-score was lower. This relationship was attenuated by physical activity levels. CONCLUSIONS: Increasing sleep duration can improve weight status outcomes in very young children who have low physical activity levels.

The role of sleep and heart rate variability in metabolic syndrome: evidence from the Midlife in the United States study.

STUDY OBJECTIVES: Poor sleep and autonomic dysregulation can both disrupt metabolic processes. This study examined the individual and combined effects of poor sleep and reduced heart rate variability (HRV) on metabolic syndrome among 966 participants in the Midlife in the United States II (MIDUS II) study. METHODS: Self-reported sleep was assessed using the Pittsburgh Sleep Quality Index (PSQI). HRV was acquired from 11-minute resting heart rate recordings. Spearman correlations, general linear regression, and logistic regression models were used to examine the study hypotheses. RESULTS: Poor sleep quality was associated with metabolic syndrome when global PSQI scores were evaluated as a continuous (odds ratio [OR]: 1.07, 95% confidence interval [CI]: 1.03 to 1.11) or categorical measure (cutoff > 5, OR: 1.58, 95% CI: 1.19 to 2.10), after adjustment for confounding. There also was an association between reduced HRV and metabolic syndrome (ln [HF-HRV] OR: 0.89, 95% CI: 0.80 to 0.99; ln [LF-HRV] OR: 0.82, 95% CI: 0.72 to 0.92; ln [SDRR] OR: 0.59, 95% CI: 0.43 to 0.79; ln [RMSSD] OR: 0.75, 95% CI: 0.60 to 0.94). When the combined effects of poor sleep and low HRV were examined, the association with metabolic syndrome was further strengthened relative to those with normal sleep and HRV. CONCLUSIONS: To the best of the author's knowledge, this is the first study to suggest a combined effect of poor sleep and low HRV on the odds of metabolic syndrome.

Associations between the Dietary Inflammatory Index and Sleep Metrics in the Energy Balance Study (EBS).

(1) Background: Sleep, a physiological necessity, has strong inflammatory underpinnings. Diet is a strong moderator of systemic inflammation. This study explored the associations between the Dietary Inflammatory Index (DII®) and sleep duration, timing, and quality from the Energy Balance Study (EBS). (2) Methods: The EBS (n = 427) prospectively explored energy intake, expenditure, and body composition. Sleep was measured using BodyMedia's SenseWear® armband. DII scores were calculated from three unannounced dietary recalls (baseline, 1-, 2-, and 3-years). The DII was analyzed continuously and categorically (very anti-, moderately anti-, neutral, and pro-inflammatory). Linear mixed-effects models estimated the DII score impact on sleep parameters. (3) Results: Compared with the very anti-inflammatory category, the pro-inflammatory category was more likely to be female (58% vs. 39%, p = 0.02) and African American (27% vs. 3%, p < 0.01). For every one-unit increase in the change in DII score (i.e., diets became more pro-inflammatory), wake-after-sleep-onset (WASO) increased (ßChange = 1.00, p = 0.01), sleep efficiency decreased (ßChange = −0.16, p < 0.05), and bedtime (ßChange = 1.86, p = 0.04) and waketime became later (ßChange = 1.90, p < 0.05). Associations between bedtime and the DII were stronger among African Americans (ßChange = 6.05, p < 0.01) than European Americans (ßChange = 0.52, p = 0.64). (4) Conclusions: Future studies should address worsening sleep quality from inflammatory diets, leading to negative health outcomes, and explore potential demographic differences.

County-level prevalence estimates of ADHD in children in the United States.

PURPOSE: Attention-deficit/hyperactivity disorder (ADHD) is a common childhood disorder often characterized by long-term impairments in family, academic, and social settings. Measuring the prevalence of ADHD is important as treatment options increase around the U.S. Prevalence data helps inform decisions by care providers, policymakers, and public health officials about allocating resources for ADHD. In addition, measuring geographic variation in prevalence estimates can facilitate hypothesis generation for future analytic work. Most U.S. studies of ADHD prevalence among children focus on national or demographic group rates. METHODS: Using a small area estimation approach and data from the 2016 to 2018 National Survey of Children's Health, we estimated childhood ADHD prevalence estimates at the census regional division, state, and county levels. The sample included approximately 70,000 children aged 5-17 years. RESULTS: The national ADHD rate was estimated to be 12.9% (95% Confidence Interval: 11.5%, 14.4%). Counties in the West South Central, East South Central, New England, and South Atlantic divisions had higher estimated rates of childhood ADHD (55.1%, 53.6%, 49.3%, and 46.2% of the counties had rates of 16% or greater, respectively) compared to counties in the Mountain, Mid Atlantic, West North Central, Pacific, and East North Central divisions (2.1%, 4%, 5.8%, 6.9%, and 11.7% of the counties had rates of 16% or greater, respectively). CONCLUSIONS: These local-level rates are useful for decision-makers to target programs and direct sufficient ADHD resources based on communities' needs.

A Bayesian susceptible-infectious-hospitalized-ventilated-recovered model to predict demand for COVID-19 inpatient care in a large healthcare system.

The COVID-19 pandemic has strained healthcare systems in many parts of the United States. During the early months of the pandemic, there was substantial uncertainty about whether the large number of COVID-19 patients requiring hospitalization would exceed healthcare system capacity. This uncertainty created an urgent need to accurately predict the number of COVID-19 patients that would require inpatient and ventilator care at the local level. As the pandemic progressed, many healthcare systems relied on such predictions to prepare for COVID-19 surges and to make decisions regarding staffing, the discontinuation of elective procedures, and the amount of personal protective equipment (PPE) to purchase. In this work, we develop a Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered (SIHVR) model to predict the burden of COVID-19 at the healthcare system level. The Bayesian SIHVR model provides daily estimates of the number of new COVID-19 patients admitted to inpatient care, the total number of non-ventilated COVID-19 inpatients, and the total number of ventilated COVID-19 patients at the healthcare system level. The model also incorporates county-level data on the number of reported COVID-19 cases, and county-level social distancing metrics, making it locally customizable. The uncertainty in model predictions is quantified with 95% credible intervals. The Bayesian SIHVR model is validated with an extensive simulation study, and then applied to data from two regional healthcare systems in South Carolina. This model can be adapted for other healthcare systems to estimate local resource needs.

Effects of social isolation on quality of life in elderly adults.

Prolonged periods of social isolation are known to have significant negative health consequences and reduce quality of life, an effect that is particularly pronounced in older populations. Despite the known deleterious effects of social isolation, a key component of the response to the COVID-19 pandemic has been the issuance of stay at home and/or shelter in place orders. Relatively little is known about the potential effects these periods of social isolation could have on older adults, and less still is known about potential risk factors or protective factors that modulate these effects. Here, we describe results from a longitudinal study in which we measured quality of life both prior to and immediately following a one-month period of social isolation associated with the issuance and revocation of a shelter in place order (April 6, 2020 through May 4, 2020) in the state of South Carolina. Healthy adult participants (N = 62) between the ages of 60 and 80 who had already completed quality of life questionnaires prior to isolation again completed the questionnaires following a one-month order to shelter in place. Quality of life significantly decreased during the social isolation period, with older participants showing the greatest declines. Participants with higher levels of physical activity and better physical/mental health going into the isolation period tended to show greater decreases in quality of life over time. These results highlight the negative consequences of even short bouts of social isolation for the elderly and suggest that reductions in social contact related to COVID-19 may have significant effects on mental health and emotional well-being, at least among older individuals.