James H. Stark

Seroprevalence Following the Second Wave of Pandemic 2009 H1N1 Influenza.

BACKGROUND In April 2009, a new pandemic strain of influenza infected thousands of persons in Mexico and the United States and spread rapidly worldwide. During the ensuing summer months, cases ebbed in the Northern Hemisphere while the Southern Hemisphere experienced a typical influenza season dominated by the novel strain. In the fall, a second wave of pandemic H1N1 swept through the United States, peaking in most parts of the country by mid October and returning to baseline levels by early December. The objective was to determine the seroprevalence of antibodies against the pandemic 2009 H1N1 influenza strain by decade of birth among Pittsburgh-area residents. METHODS AND FINDINGS Anonymous blood samples were obtained from clinical laboratories and categorized by decade of birth from 1920-2009. Using hemagglutination-inhibition assays, approximately 100 samples per decade (n= 846) were tested from blood samples drawn on hospital and clinic patients in mid-November and early December 2009. Age specific seroprevalences against pandemic H1N1 (A/California/7/2009) were measured and compared to seroprevalences against H1N1 strains that had previously circulated in the population in 2007, 1957, and 1918. (A/Brisbane/59/2007, A/Denver/1/1957, and A/South Carolina/1/1918). Stored serum samples from healthy, young adults from 2008 were used as a control group (n=100). Seroprevalences against pandemic 2009 H1N1 influenza varied by age group, with children age 10-19 years having the highest seroprevalence (45%), and persons age 70-79 years having the lowest (5%). The baseline seroprevalence among control samples from 18-24 year-olds was 6%. Overall seroprevalence against pandemic H1N1 across all age groups was approximately 21%. CONCLUSIONS After the peak of the second wave of 2009 H1N1, HAI seroprevalence results suggest that 21% of persons in the Pittsburgh area had become infected and developed immunity. Extrapolating to the entire US population, we estimate that at least 63 million persons became infected in 2009. As was observed among clinical cases, this sero-epidemiological study revealed highest infection rates among school-age children.

Seroprevalence following the second wave of Pandemic 2009 H1N1 influenza in Pittsburgh, PA, USA.

Background In April 2009, a new pandemic strain of influenza infected thousands of persons in Mexico and the United States and spread rapidly worldwide. During the ensuing summer months, cases ebbed in the Northern Hemisphere while the Southern Hemisphere experienced a typical influenza season dominated by the novel strain. In the fall, a second wave of pandemic H1N1 swept through the United States, peaking in most parts of the country by mid October and returning to baseline levels by early December. The objective was to determine the seroprevalence of antibodies against the pandemic 2009 H1N1 influenza strain by decade of birth among Pittsburgh-area residents. Methods and Findings Anonymous blood samples were obtained from clinical laboratories and categorized by decade of birth from 1920–2009. Using hemagglutination-inhibition assays, approximately 100 samples per decade (n = 846) were tested from blood samples drawn on hospital and clinic patients in mid-November and early December 2009. Age specific seroprevalences against pandemic H1N1 (A/California/7/2009) were measured and compared to seroprevalences against H1N1 strains that had previously circulated in the population in 2007, 1957, and 1918. (A/Brisbane/59/2007, A/Denver/1/1957, and A/South Carolina/1/1918). Stored serum samples from healthy, young adults from 2008 were used as a control group (n = 100). Seroprevalences against pandemic 2009 H1N1 influenza varied by age group, with children age 10–19 years having the highest seroprevalence (45%), and persons age 70–79 years having the lowest (5%). The baseline seroprevalence among control samples from 18–24 year-olds was 6%. Overall seroprevalence against pandemic H1N1 across all age groups was approximately 21%. Conclusions After the peak of the second wave of 2009 H1N1, HAI seroprevalence results suggest that 21% of persons in the Pittsburgh area had become infected and developed immunity. Extrapolating to the entire US population, we estimate that at least 63 million persons became infected in 2009. As was observed among clinical cases, this sero-epidemiological study revealed highest infection rates among school-age children.

Protecting health care workers: a pandemic simulation based on Allegheny County

Please cite this paper as: Cooley et al. (2010) Protecting health care workers: a pandemic simulation based on Allegheny County. Influenza and Other Respiratory Viruses 4(2), 61–72.

Reduction in the Incidence of Influenza A But Not Influenza B Associated With Use of Hand Sanitizer and Cough Hygiene in Schools A Randomized Controlled Trial

Background: Laboratory-based evidence is lacking regarding the efficacy of nonpharmaceutical interventions (NPIs) such as alcohol-based hand sanitizer and respiratory hygiene to reduce the spread of influenza. Methods: The Pittsburgh Influenza Prevention Project was a cluster-randomized trial conducted in 10 elementary schools in Pittsburgh, PA, during the 2007 to 2008 influenza season. Children in 5 intervention schools received training in hand and respiratory hygiene, and were provided and encouraged to use hand sanitizer regularly. Children in 5 schools acted as controls. Children with influenza-like illness were tested for influenza A and B by reverse-transcriptase polymerase chain reaction. Results: A total of 3360 children participated in this study. Using reverse-transcriptase polymerase chain reaction, 54 cases of influenza A and 50 cases of influenza B were detected. We found no significant effect of the intervention on the primary study outcome of all laboratory-confirmed influenza cases (incidence rate ratio [IRR]: 0.81; 95% confidence interval [CI]: 0.54, 1.23). However, we did find statistically significant differences in protocol-specified ancillary outcomes. Children in intervention schools had significantly fewer laboratory-confirmed influenza A infections than children in control schools, with an adjusted IRR of 0.48 (95% CI: 0.26, 0.87). Total absent episodes were also significantly lower among the intervention group than among the control group; adjusted IRR 0.74 (95% CI: 0.56, 0.97). Conclusions: NPIs (respiratory hygiene education and the regular use of hand sanitizer) did not reduce total laboratory-confirmed influenza. However, the interventions did reduce school total absence episodes by 26% and laboratory-confirmed influenza A infections by 52%. Our results suggest that NPIs can be an important adjunct to influenza vaccination programs to reduce the number of influenza A infections among children.

Neighbourhood food environments and body mass index among New York City adults

Background Studies evaluating the impact of the neighbourhood food environment on obesity have summarised the density or proximity of individual food outlets. Though informative, there is a need to consider the role of the entire food environment; however, few measures of whole system attributes have been developed. New variables measuring the food environment were derived and used to study the association with body mass index (BMI). Methods Individual data on BMI and sociodemographic characteristics were collected from 48 482 respondents of the 2002–2006 community health survey in New York City and linked to residential zip code-level characteristics. The food environment of each zip code was described in terms of the diversity of outlets (number of types of outlets present in a zip code), the density of outlets (outlets/km2) and the proportion of outlets classified as BMI-unhealthy (eg, fast food, bodegas). Results Results of the cross-sectional, multilevel analyses revealed an inverse association between BMI and food outlet density (−0.32 BMI units across the IQR, 95% CI −0.45 to −0.20), a positive association between BMI and the proportion of BMI-unhealthy food outlets (0.26 BMI units per IQR, 95% CI 0.09 to 0.43) and no association with outlet diversity. The association between BMI and the proportion of BMI-unhealthy food outlets was stronger in lower (<median for % poverty) poverty zip codes than in high-poverty zip codes. Conclusions These results support a more nuanced assessment of the impact of the food environment and its association with obesity.

The Effects of Changes in Physical Fitness on Academic Performance Among New York City Youth

PURPOSE To evaluate whether a change in fitness is associated with academic outcomes in New York City (NYC) middle-school students using longitudinal data and to evaluate whether this relationship is modified by student household poverty. METHODS This was a longitudinal study of 83,111 New York City middle-school students enrolled between 2006-2007 and 2011-2012. Fitness was measured as a composite percentile based on three fitness tests and categorized based on change from the previous year. The effect of the fitness change level on academic outcomes, measured as a composite percentile based on state standardized mathematics and English Language Arts test scores, was estimated using a multilevel growth model. Models were stratified by sex, and additional models were tested stratified by student household poverty. RESULTS For both girls and boys, a substantial increase in fitness from the previous year resulted in a greater improvement in academic ranking than was seen in the reference group (girls: .36 greater percentile point improvement, 95% confidence interval: .09-.63; boys: .38 greater percentile point improvement, 95% confidence interval: .09-.66). A substantial decrease in fitness was associated with a decrease in academics in both boys and girls. Effects of fitness on academics were stronger in high-poverty boys and girls than in low-poverty boys and girls. CONCLUSIONS Academic rankings improved for boys and girls who increased their fitness level by >20 percentile points compared to other students. Opportunities for increased physical fitness may be important to support academic performance.

Local Spatial and Temporal Processes of Influenza in Pennsylvania, USA: 2003–2009

Background Influenza is a contagious respiratory disease responsible for annual seasonal epidemics in temperate climates. An understanding of how influenza spreads geographically and temporally within regions could result in improved public health prevention programs. The purpose of this study was to summarize the spatial and temporal spread of influenza using data obtained from the Pennsylvania Department of Healths influenza surveillance system. Methodology and Findings We evaluated the spatial and temporal patterns of laboratory-confirmed influenza cases in Pennsylvania, United States from six influenza seasons (2003–2009). Using a test of spatial autocorrelation, local clusters of elevated risk were identified in the South Central region of the state. Multivariable logistic regression indicated that lower monthly precipitation levels during the influenza season (OR = 0.52, 95% CI: 0.28, 0.94), fewer residents over age 64 (OR = 0.27, 95% CI: 0.10, 0.73) and fewer residents with more than a high school education (OR = 0.76, 95% CI: 0.61, 0.95) were significantly associated with membership in this cluster. In addition, time series analysis revealed a temporal lag in the peak timing of the influenza B epidemic compared to the influenza A epidemic. Conclusions These findings illustrate a distinct spatial cluster of cases in the South Central region of Pennsylvania. Further examination of the regional transmission dynamics within these clusters may be useful in planning public health influenza prevention programs.

Local Variations in Spatial Synchrony of Influenza Epidemics

Background Understanding the mechanism of influenza spread across multiple geographic scales is not complete. While the mechanism of dissemination across regions and states of the United States has been described, understanding the determinants of dissemination between counties has not been elucidated. The paucity of high resolution spatial-temporal influenza incidence data to evaluate disease structure is often not available. Methodology and Findings We report on the underlying relationship between the spread of influenza and human movement between counties of one state. Significant synchrony in the timing of epidemics exists across the entire state and decay with distance (regional correlation = 62%). Synchrony as a function of population size display evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations is a stronger predictor of influenza spread than adult movement to and from workplaces suggesting that non-routine and leisure travel drive local epidemics. Conclusions These findings highlight the complex nature of influenza spread across multiple geographic scales.

Household transmission of influenza A and B in a school-based study of non-pharmaceutical interventions

The effect of school-based non-pharmaceutical interventions (NPIs) on influenza A and B transmission in childrens households has not been estimated in published literature. We use data from a large school-based cluster randomized trial of improved hand and respiratory hygiene measures to explore the secondary transmission of influenza A and B in households of laboratory confirmed influenza cases. Data were taken from the Pittsburgh Influenza Prevention Project, a cluster-randomized trial of NPIs conducted in ten Pittsburgh, PA elementary schools during the 2007-2008 influenza season. We estimated two measures of influenza transmissibility in households; the susceptible infectious transmission probability, using variants of the Reed-Frost chain binomial model, and the secondary attack rate. We identified predictors of ILI using a logistic generalized estimating equation model. We estimate the secondary attack rates in intervention households to be 0.26 (95% confidence interval (CI) 0.19-0.34) compared to 0.30 (95% CI 0.23-0.38) in control households. Race and age were significant risk factors for secondary ILI acquisition in this study. We found no significant differences between the transmission probabilities for infectious individuals in intervention (0.19, 95% CI 0.14-0.25), and control households (0.22, 95% CI 0.16-0.29). Similarly, estimates for secondary attack rates and transmission probabilities for households with confirmed influenza A (0.31 and 0.22) were not significantly different from estimates from households with confirmed influenza B (0.25 and 0.20). While influenza A and B are thought to have different transmission characteristics, we find no significant differences in their transmissibility within households. Though our results suggest a potential effect, we found no statistically significant effect of school-based non-pharmaceutical interventions on transmission in symptomatic childrens homes.

Relationship between recreational resources in the school neighborhood and changes in fitness in New York City public school students

Physical fitness in children has many beneficial effects, including the maintenance of a healthy weight. The built environment may influence youths’ physical fitness by encouraging physical activity. This paper assessed whether higher density of parks, playgrounds, and sports facilities around a school is related to improvements in fitness in middle school boys and girls. Fitness scores and other student covariates collected as part of NYC FITNESSGRAM between the 2006–2007 and 2010–2011 school years were linked with school neighborhood data on characteristics of the built environment for NYC public school students in grades 6–8. Data were analyzed in 2015. Medium, but not high, density of recreational resources in the area surrounding a school was associated with greater annual improvements in fitness for both boys and girls. This association appeared to be driven mainly by the presence of parks. Findings for sports facilities and playgrounds were inconsistent. Overall, few associations were observed between recreational resources near a school and changes in student fitness. Future studies of school influences on student fitness should consider the influence of school resources and the home neighborhood.