Access to paid sick days (PSD) differs by workplace size, race/ethnicity, gender, and income in the United States. It is not known to what extent decisions to stay home from work when sick with infectious illnesses such as influenza depend on PSD access, and whether access impacts certain demographic groups more than others. We examined demographic and workplace characteristics (including access to PSD) associated with employees’ decisions to stay home from work for their own or a child’s illness. Linking the 2009 Medical Expenditure Panel Survey (MEPS) consolidated data file to the medical conditions file, we used multivariate Poisson regression models with robust variance estimates to identify factors associated with missed work for an employee’s own or a child’s illness/injury, influenza-like-illness (ILI), and influenza. Controlling for gender, race/ethnicity, education, and income, access to PSD was associated with a higher probability of staying home for an employee’s own illness/injury, ILI, or influenza, and for a child’s illness/injury. Hispanic ethnicity was associated with a lower prevalence of staying home for the employee’s own or a child’s illness compared to non-Hispanic Whites. Access to PSD was associated with a significantly greater increase in the probability of staying home among Hispanics than among non-Hispanic Whites. Women had a significantly higher probability of staying home for their child’s illness compared to men, suggesting that women remain the primary caregivers for ill children. Our results indicate that PSD access is important to encourage employees to stay home from work when sick with ILI or influenza. Also, PSD access may be important to enable stay-at-home behavior among Hispanics. We conclude that access to PSD is likely to reduce the spread of disease in workplaces by increasing the rate at which sick employees stay home from work, and reduce the economic burden of staying home on minorities, women, and families.

BackgroundIn New Haven County, CT (NHC), influenza hospitalization rates have been shown to increase with census tract poverty in multiple influenza seasons. Though multiple factors have been hypothesized to cause these inequalities, including population structure, differential vaccine uptake, and differential access to healthcare, the impact of each in generating observed inequalities remains unknown. We can design interventions targeting factors with the greatest explanatory power if we quantify the proportion of observed inequalities that hypothesized factors are able to generate. Here, we ask if population structure is sufficient to generate the observed area-level inequalities in NHC. To our knowledge, this is the first use of simulation models to examine the causes of differential poverty-related influenza rates.MethodsUsing agent-based models with a census-informed, realistic representation of household size, age-structure, population density in NHC census tracts, and contact rates in workplaces, schools, households, and neighborhoods, we measured poverty-related differential influenza attack rates over the course of an epidemic with a 23 % overall clinical attack rate. We examined the role of asthma prevalence rates as well as individual contact rates and infection susceptibility in generating observed area-level influenza inequalities.ResultsSimulated attack rates (AR) among adults increased with census tract poverty level (F = 30.5; P < 0.001) in an epidemic caused by a virus similar to A (H1N1) pdm09. We detected a steeper, earlier influenza rate increase in high-poverty census tracts—a finding that we corroborate with a temporal analysis of NHC surveillance data during the 2009 H1N1 pandemic. The ratio of the simulated adult AR in the highest- to lowest-poverty tracts was 33 % of the ratio observed in surveillance data. Increasing individual contact rates in the neighborhood did not increase simulated area-level inequalities. When we modified individual susceptibility such that it was inversely proportional to household income, inequalities in AR between high- and low-poverty census tracts were comparable to those observed in reality.DiscussionTo our knowledge, this is the first study to use simulations to probe the causes of observed inequalities in influenza disease patterns. Knowledge of the causes and their relative explanatory power will allow us to design interventions that have the greatest impact on reducing inequalities.ConclusionDifferential exposure due to population structure in our realistic simulation model explains a third of the observed inequality. Differential susceptibility to disease due to prevailing chronic conditions, vaccine uptake, and smoking should be considered in future models in order to quantify the role of additional factors in generating influenza inequalities.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-015-2284-2) contains supplementary material, which is available to authorized users.

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BACKGROUND:The first methodologic step needed to compare pediatric health outcomes at children's hospitals (CHs) and non-children's hospitals (NCHs) is to classify hospitals into CH and NCH categories. However, there are currently no standardized or validated methods for classifying hospitals. The purpose of this study was to describe a novel and reproducible hospital classification methodology.METHODS: By using data from the 2015 American Hospital Association survey, 4464 hospitals were classified into 4 categories (tiers A-D) on the basis of self-reported presence of pediatric services. Tier A included hospitals that only provided care to children. Tier B included hospitals that had key pediatric services, including pediatric emergency departments, PICUs, and NICUs. Tier C included hospitals that provided limited pediatric services. Tier D hospitals provided no key pediatric services. Classifications were then validated by using publicly available data on hospital membership in various pediatric programs as well as Health Care Cost Institute claims data.RESULTS: Fifty-one hospitals were classified as tier A, 228 as tier B, 1721 as tier C, and 1728 as tier D. The majority of tier A hospitals were members of the Children's Hospital Association, Children's Oncology Group, and National Surgical Quality Improvement Program-Pediatric. By using claims data, the percentage of admissions that were pediatric was highest in tier A (88.9%), followed by tiers B (10.9%), C (3.9%), and D (3.9%). CONCLUSIONS:Using American Hospital Association survey data is a feasible and valid method for classifying hospitals into CH and NCH categories by using a reproducible multitiered system.

Patient-centered frameworks are an effective way to engage patients in treatment plans, strengthen adherence behaviors, and improve disease outcomes. These frameworks can also be applied in the design of mobile technology disease management applications. However, the utilization of these frameworks is rare and frequently overlooked in existing colorectal mobile health (mHealth) applications. The purpose of this study was to utilize a patient-centered framework to facilitate the development of a valid, appropriate, and feasible mHealth tool for pediatric patients and their caregivers. To inform application design and production, in-depth interviews were conducted with pediatric patients and their caregivers to capture management experiences, application preferences, and barriers and facilitators to application use. Patient ages ranged from 3 to 16. Six caregivers and 2 adolescent patients participated in the interviews. Patients and caregivers reported various management styles and desired an application that is not only user-friendly and customizable, but also able to facilitate communication and information sharing with other patients, caregivers, and providers. Older patients also wanted the application to give them more independence in managing their disease. Employing patient-centered frameworks is context-specific, but holds much promise at the intersection of mobile technology and healthcare. By incorporating pediatric patient experiences and viewpoints, we identified important components for inclusion in a mHealth surgical colorectal disease management application. Patients and caregivers wanted a mHealth application that was unique to their needs and easy to use. They suggested that the application include treatment tracking, note taking, and provider communication features.