Rashid Njai

Relationships Between Housing and Food Insecurity, Frequent Mental Distress, and Insufficient Sleep Among Adults in 12 US States, 2009

Introduction Housing insecurity and food insecurity may be psychological stressors associated with insufficient sleep. Frequent mental distress may mediate the relationships between these variables. The objective of this study was to examine the relationships between housing insecurity and food insecurity, frequent mental distress, and insufficient sleep. Methods We analyzed data from the 2009 Behavioral Risk Factor Surveillance System in 12 states. Housing insecurity and food insecurity were defined as being worried or stressed “sometimes,” “usually,” or “always” during the previous 12 months about having enough money to pay rent or mortgage or to buy nutritious meals. Results Of 68,111 respondents, 26.4% reported frequent insufficient sleep, 28.5% reported housing insecurity, 19.3% reported food insecurity, and 10.8% reported frequent mental distress. The prevalence of frequent insufficient sleep was significantly greater among those who reported housing insecurity (37.7% vs 21.6%) or food insecurity (41.1% vs 22.9%) than among those who did not. The prevalence of frequent mental distress was also significantly greater among those reporting housing insecurity (20.1% vs 6.8%) and food insecurity (23.5% vs 7.7%) than those who did not. The association between housing insecurity or food insecurity and frequent insufficient sleep remained significant after adjustment for other sociodemographic variables and frequent mental distress. Conclusion Sleep health and mental health are embedded in the social context. Research is needed to assess whether interventions that reduce housing insecurity and food insecurity will also improve sleep health and mental health.

Food Insecurity and Self-Reported Hypertension Among Hispanic, Black, and White Adults in 12 States, Behavioral Risk Factor Surveillance System, 2009

Food insecurity is positively linked to risk of hypertension; however, it is not known whether this relationship persists after adjustment for socioeconomic position (SEP). We examined the association between food insecurity and self-reported hypertension among adults aged 35 or older (N = 58,677) in 12 states that asked the food insecurity question in their 2009 Behavioral Risk Factor Surveillance System questionnaire. After adjusting for SEP, hypertension was more common among adults reporting food insecurity (adjusted prevalence ratio, 1.27; 95% confidence interval, 1.19–1.36). Our study found a positive relationship between food insecurity and hypertension after adjusting for SEP and other characteristics.

Housing Insecurity and the Association With Health Outcomes and Unhealthy Behaviors, Washington State, 2011

Few studies of associations between housing and health have focused on housing insecurity and health risk behaviors and outcomes. We measured the association between housing insecurity and selected health risk behaviors and outcomes, adjusted for socioeconomic measures, among 8,415 respondents to the 2011 Washington State Behavioral Risk Factor Surveillance System. Housing insecure respondents were about twice as likely as those who were not housing insecure to report poor or fair health status or delay doctor visits because of costs. This analysis supports a call to action among public health practitioners who address disparities to focus on social determinants of health risk behaviors and outcomes.

Models for count data with an application to Healthy Days measures: are you driving in screws with a hammer?

Introduction Count data are often collected in chronic disease research, and sometimes these data have a skewed distribution. The number of unhealthy days reported in the Behavioral Risk Factor Surveillance System (BRFSS) is an example of such data: most respondents report zero days. Studies have either categorized the Healthy Days measure or used linear regression models. We used alternative regression models for these count data and examined the effect on statistical inference. Methods Using responses from participants aged 35 years or older from 12 states that included a homeownership question in their 2009 BRFSS, we compared 5 multivariate regression models — logistic, linear, Poisson, negative binomial, and zero-inflated negative binomial — with respect to 1) how well the modeled data fit the observed data and 2) how model selections affect inferences. Results Most respondents (66.8%) reported zero mentally unhealthy days. The distribution was highly skewed (variance = 58.7, mean = 3.3 d). Zero-inflated negative binomial regression provided the best-fitting model, followed by negative binomial regression. A significant independent association between homeownership and number of mentally unhealthy days was not found in the logistic, linear, or Poisson regression model but was found in the negative binomial model. The zero-inflated negative binomial model showed that homeowners were 24% more likely than nonowners to have excess zero mentally unhealthy days (adjusted odds ratio, 1.24; 95% confidence interval, 1.08–1.43), but it did not show an association between homeownership and the number of unhealthy days. Conclusion Our comparison of regression models indicates the importance of examining data distribution and selecting models with appropriate assumptions. Otherwise, statistical inferences might be misleading.

Knowledge, Attitudes, and Practices of Nonpharmaceutical Interventions following School Dismissals during the 2009 Influenza A H1N1 Pandemic in Michigan, United States

Background Many schools throughout the United States reported an increase in dismissals due to the 2009 influenza A H1N1 pandemic (pH1N1). During the fall months of 2009, more than 567 school dismissals were reported from the state of Michigan. In December 2009, the Michigan Department of Community Health, in collaboration with the United States Centers for Disease Control and Prevention, conducted a survey to describe the knowledge, attitudes, and practices (KAPs) of households with school-aged children and classroom teachers regarding the recommended use of nonpharmaceutical interventions (NPIs) to slow the spread of influenza. Methods A random sample of eight elementary schools (kindergarten through 5th grade) was selected from each of the eight public health preparedness regions in the state. Within each selected school, a single classroom was randomly identified from each grade (K-5), and household caregivers of the classroom students and their respective teachers were asked to participate in the survey. Results In total, 26% (2,188/8,280) of household caregivers and 45% (163/360) of teachers from 48 schools (of the 64 sampled) responded to the survey. Of the 48 participating schools, 27% (13) experienced a school dismissal during the 2009 fall term. Eighty-seven percent (1,806/2,082) of caregivers and 80% (122/152) of teachers thought that the 2009 influenza A H1N1 pandemic was severe, and >90% of both groups indicated that they told their children/students to use NPIs, such as washing hands more often and covering coughs with tissues, to prevent infection with influenza. Conclusions Knowledge and instruction on the use of NPIs appeared to be high among household caregivers and teachers responding to the survey. Nevertheless, public health officials should continue to explain the public health rationale for NPIs to reduce pandemic influenza. Ensuring this information is communicated to household caregivers and teachers through trusted sources is essential.

Does Perceived Neighborhood Walkability and Safety Mediate the Association Between Education and Meeting Physical Activity Guidelines

The role of neighborhood walkability and safety in mediating the association between education and physical activity has not been quantified. We used data from the 2010 and 2012 Communities Putting Prevention to Work Behavioral Risk Factor Surveillance System and structural equation modeling to estimate how much of the effect of education level on physical activity was mediated by perceived neighborhood walkability and safety. Neighborhood walkability accounts for 11.3% and neighborhood safety accounts for 6.8% of the effect. A modest proportion of the important association between education and physical activity is mediated by perceived neighborhood walkability and safety, suggesting that interventions focused on enhancing walkability and safety could reduce the disparity in physical activity associated with education level.

Summarizing health-related quality of life (HRQOL): development and testing of a one-factor model

BackgroundHealth-related quality of life (HRQOL) is a multi-dimensional concept commonly used to examine the impact of health status on quality of life. HRQOL is often measured by four core questions that asked about general health status and number of unhealthy days in the Behavioral Risk Factor Surveillance System (BRFSS). Use of these measures individually, however, may not provide a cohesive picture of overall HRQOL. To address this concern, this study developed and tested a method for combining these four measures into a summary score.MethodsExploratory and confirmatory factor analyses were performed using BRFSS 2013 data to determine potential numerical relationships among the four HRQOL items. We also examined the stability of our proposed one-factor model over time by using BRFSS 2001–2010 and BRFSS 2011–2013 data sets.ResultsBoth exploratory factor analysis and goodness of fit tests supported the notion that one summary factor could capture overall HRQOL. Confirmatory factor analysis indicated acceptable goodness of fit of this model. The predicted factor score showed good validity with all of the four HRQOL items. In addition, use of the one-factor model showed stability, with no changes being detected from 2001 to 2013.ConclusionInstead of using four individual items to measure HRQOL, it is feasible to study overall HRQOL via factor analysis with one underlying construct. The resulting summary score of HRQOL may be used for health evaluation, subgroup comparison, trend monitoring, and risk factor identification.

Disparities among 2009 Pandemic Influenza A (H1N1) Hospital Admissions: A Mixed Methods Analysis – Illinois, April–December 2009

During late April 2009, the first cases of 2009 pandemic influenza A (H1N1) (pH1N1) in Illinois were reported. On-going, sustained local transmission resulted in an estimated 500,000 infected persons. We conducted a mixed method analysis using both quantitative (surveillance) and qualitative (interview) data; surveillance data was used to analyze demographic distribution of hospitalized cases and follow-up interview data was used to assess health seeking behavior. Invitations to participate in a telephone interview were sent to 120 randomly selected Illinois residents that were hospitalized during April–December 2009. During April–December 2009, 2,824 pH1N1 hospitalizations occurred in Illinois hospitals; median age (interquartile range) at admission was 24 (range: 6–49) years. Hospitalization rates/100,000 persons for blacks and Hispanics, regardless of age or sex were 2–3 times greater than for whites (blacks, 36/100,000 (95% Confidence Interval ([95% CI], 33–39)); Hispanics, 35/100,000 [95%CI,32–37] (; whites, 13/100,000[95%CI, 12–14); p<0.001). Mortality rates were higher for blacks (0.9/100,000; p<0.09) and Hispanics (1/100,000; p<0.04) when compared with the mortality rates for whites (0.6/100,000). Of 33 interview respondents, 31 (94%) stated that they had heard of pH1N1 before being hospitalized, and 24 (73%) did not believed they were at risk for pH1N1. On average, respondents reported experiencing symptoms for 2 days (range: 1–7) before seeking medical care. When asked how to prevent pH1N1 infection in the future, the most common responses were getting vaccinated and practicing hand hygiene. Blacks and Hispanics in Illinois experienced disproportionate pH1N1 hospitalization and mortality rates. Public health education and outreach efforts in preparation for future influenza pandemics should include prevention messaging focused on perception of risk, and ensure community wide access to prevention messages and practices.