County-Level Factors That Influenced the Trajectory of COVID-19 Incidence in the New York City Area.

More than a century of research has shown that sociodemographic conditions affect infectious disease transmission. In the late spring and early summer of 2020, reports of the effects of sociodemographic variables on the spread of COVID-19 were used in the media with minimal scientific proof attached. With new cases of COVID-19 surging in the United States at that time, it became essential to better understand how the spread of COVID-19 was varying across all segments of the population. We used hierarchical exponential growth curve modeling techniques to examine whether community socioeconomic characteristics uniquely influence the incidence of reported COVID-19 cases in the urban built environment. We show that as of July 19, 2020, confirmed coronavirus infections in New York City and surrounding areas-one of the early epicenters of the COVID-19 pandemic in the United States-were concentrated along demographic and socioeconomic lines. Furthermore, our data provides evidence that after the onset of the pandemic, timely enactment of physical distancing measures such as school closures was essential to limiting the extent of the coronavirus spread in the population. We conclude that in a pandemic, public health authorities must impose physical distancing measures early on as well as consider community-level factors that associate with a greater risk of viral transmission.

Decomposing Differences in Coronavirus disease 2019-related Case-Fatality Rates across Seventeen Nations.

As of 1 November 2020, estimated case-fatality rates associated with coronavirus disease 2019 are not uniformly patterned across the world and differ substantially in magnitude. Given the global spatial heterogeneity in case-fatality rates, we applied the Blinder-Oaxaca regression decomposition technique to identify how putative sociodemographic, structural, and environmental sources influence variation in case-fatality rates. We show that compositional and associational differences in country-level risk factors explain a substantial proportion of the coronavirus disease 2019-related case-fatality rate gap across nations. Asian countries fair better vis-à-vis case-fatality rate differences mainly due to variation in returns to sociodemographic, structural, and environmental sources among their citizens, relative to those who share similar attributes but live in Europe or North America. The variation in case-fatality rate is driven by Asian populations being better able to buffer the harmful effects of the very risk factors purported to exacerbate the risk of coronavirus disease 2019-related death. The dire circumstances in which we find ourselves demand better understanding of how preexisting conditions across countries contribute to observed disparities in case-fatality rates.

Surgical Patients' Hospital Experience Scores: Neighborhood Context Conceptual Framework.

Objective: Through geocoding the physical residential address included in the electronic medical record to the census tract level, we present a novel model for concomitant examination of individual patient-related and residential context-related factors that are associated with patient-reported experience scores. Summary Background Data: When assessing patient experience in the surgical setting, researchers need to examine the potential influence of neighborhood-level characteristics on patient experience-of-care ratings. Methods: We geocoded the residential address included in the electronic medical record (EMR) from a tertiary care facility to the census tract level of Orange County, CA. We then linked each individual record to the matching census tract and use hierarchical regression analyses to test the impact of distinct neighborhood conditions on patient experience. This approach allows us to estimate how each neighborhood characteristic uniquely influences Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores. Results: Individuals residing in communities characterized by high levels of socioeconomic disadvantage have the highest experience ratings. Accounting for individual patient's characteristics such as age, gender, race/ethnicity, primary language spoken at home, length of stay, and average pain levels during their hospital stay, neighborhood-level characteristics such as proportions of people receiving public assistance influence the ratings of hospital experience (0.01, P < 0.05) independent of, and beyond, these individual-level factors. Conclusions: This manuscript is an example of how geocoding could be used to analyze surgical patient experience scores. In this analysis, we have shown that neighborhood-level characteristics influence the ratings of hospital experience independent of, and beyond, individual-level factors.

Depression and body mass index, differences by education: Evidence from a population-based study of adult women in the U.S. Buffalo-Niagara region.

The relationship between obesity and depression is well described. However, the evidence linking depression and body mass index (BMI) across the broad range of body size is less consistent. We examined the association between depressive symptoms and BMI in a sample of adult women in the Buffalo-Niagara region between 1997 and 2001. Using logistic regression, we investigated whether increased weight status beyond normal-weight was associated with a higher prevalence of depressive symptoms, and if educational attainment modified the association between obesity and depression. There was a trend for increased weight status to be associated with higher depressive symptoms (obese II/III, OR 1.57, 95% CI 1.03-2.41), whereas higher education was associated with lower odds of depressive symptoms, in an adjusted model including BMI (more than 12 but less than 16 years, OR 0.70, 95% CI 0.49-0.98; 16 or more years of education, OR 0.61, 95% CI 0.40-0.93). The association of being obese I with depressive symptoms was different for more educated (OR 2.15, 95% CI 1.27-3.62) compared to less educated women (OR 0.90, 95% CI 0.50-1.62); the sample was larger for the more educated women and reached statistical significance. There were no differences in the association for obese II/III women in strata of education. There was evidence of risk-difference heterogeneity (0.88, 95% CI 0.84-0.93). In this population-based sample of women in western New York state, increased weight was negligibly associated with depressive symptoms. The association of being obese I with depressive symptoms was different for more compared to less educated women.

Decomposing trends in adult body mass index, obesity, and morbid obesity, 1971-2012.

Trends in adult obesity have been used to motivate key public health policies in the United States. While these analyses provide important insights into the broad historical contours of the obesity epidemic in the U.S., they shed less light on the proximate mechanisms that have generated these changes and that will ultimately determine the long-term course and pace of change in obesity rates. We used data from the National Health and Nutrition Examination Survey (NHANES), Glenn Firebaugh's linear decomposition technique, and Kitagawa's algebraic decomposition method to decompose change in body mass index (BMI), obesity, and morbid obesity from 1971 through 2012 for adults aged 20+. We partitioned change into that attributable to (1) older, fitter cohorts in the population being replaced by newer, less fit cohorts (intercohort change), and (2) cohort members becoming less fit over time (intracohort change). We found that the rise in mean BMI and rates of obesity and morbid obesity was primarily a consequence of intracohort change driven by variation in the demographic and socioeconomic composition and in the diet of the population over time. Obesity and BMI in the population rose largely because of individual increases in weight status that were broadly distributed across age and cohort groups. Cohort replacement reinforced and amplified intracohort change over the study period, leading to rapid increases in mean BMI and obesity. Because intracohort change has been the central force in the increase in BMI and obesity, successful social, dietary, medical, or policy interventions have the potential to quickly slow or reverse the upward trend in weight status. Our results also imply that policy efforts and health interventions should be broadly targeted at all age groups and birth cohorts because increases in obesity have been widely distributed across all ages and generations.

Association Between Age and Obesity Over Time.

BACKGROUND AND OBJECTIVES: A decline in the prevalence of obesity among 2- to 5-year-olds in the United States was recently reported. This decline may be due to changes in the population composition of children over time or may be a consequence of changes in how strongly individual- or family-level factors are linked to childhood obesity. We applied regression decomposition techniques to identify the sources of the decline. METHODS: We used data from the 2003-2004 and 2011-2012 NHANES restricted to 2- to 5-year-old children and Blinder-Oaxaca regression decomposition techniques to partition the decline in early childhood obesity into 2 components: changes resulting from (1) how demographic, economic, and health characteristics of children have changed over this period (ie, changes in population composition) and (2) changes in how these demographic, economic, and health factors are associated with obesity (ie, changes in associations). RESULTS: The obesity rate was lower in 2011-2012 than it was in 2003-2004 mainly because obesity was strongly and positively associated with age in 2003-2004 (ie, older children were more likely to be obese than younger children) but not in 2011-2012 (ie, older children were not more likely to be obese than younger children). CONCLUSIONS: If the weaker association between age and obesity we observed for this cohort of 2- to 5-year-old children in 2011-2012 persists for subsequent cohorts of young children, the obesity rate for young children will remain at or near the lower rate seen in 2011-2012.

The moderating effect of self-efficacy on normal-weight, overweight, and obese children's math achievement: a longitudinal analysis.

Increased body weight is associated with decreased cognitive function in school-aged children. The role of self-efficacy in shaping the connection between children's educational achievement and obesity-related comorbidities has not been examined to date. Evidence of the predictive ability of self-efficacy in children is demonstrated in cognitive tasks, including math achievement scores. This study examined the relationship between self-efficacy and math achievement in normal weight, overweight, and obese children. I hypothesized that overweight and obese children with higher self-efficacy will be less affected in math achievement than otherwise comparable children with lower self-efficacy. I tested this prediction with multilevel growth modeling techniques using the ECLS-K 1998-1999 survey data, a nationally representative sample of children. Increased self-efficacy moderates the link between body weight and children's math achievement by buffering the risks that increased weight status poses to children's cognitive function. My findings indicate that self-efficacy moderates math outcomes in overweight, but not obese, children.