'They don't think I can do it': Experiences of self-advocates, employment specialists, and employers on employment of adults with intellectual disability.

BACKGROUND: A multi-phase Canadian study was conducted as part of a large-scale community and academic research partnership focused on understanding and improving the employment experiences of people with intellectual disabilities. METHOD: This multi-method study utilized a sequential approach, using findings from qualitative interviews (n = 28) to inform an online survey (n = 149). Participants were invited to share their experiences with paid employment or with persons with intellectual disabilities. RESULTS: Thematic analysis of data across interview and survey findings resulted in six themes: (1) assumptions and attitudes, (2) knowledge and awareness, (3) accessibility of processes, (4) use of accommodations, (5) workplace relationships, and (6) supports and resources. CONCLUSIONS: A holistic and systemic approach has the potential to improve inclusive employment experiences of people with intellectual disabilities. Action is needed mainly at the policy and employer level to reduce barriers and improve on facilitating measures reinforced by the themes shared in this study.

Spatial and temporal clustering of patients hospitalized with laboratory-confirmed influenza in the United States.

BACKGROUND: Timing of influenza spread across the United States is dependent on factors including local and national travel patterns and climate. Local epidemic intensity may be influenced by social, economic and demographic patterns. Data are needed to better explain how local socioeconomic factors influence both the timing and intensity of influenza seasons to result in national patterns. METHODS: To determine the spatial and temporal impacts of socioeconomics on influenza hospitalization burden and timing, we used population-based laboratory-confirmed influenza hospitalization surveillance data from the CDC-sponsored Influenza Hospitalization Surveillance Network (FluSurv-NET) at up to 14 sites from the 2009/2010 through 2013/2014 seasons (n = 35,493 hospitalizations). We used a spatial scan statistic and spatiotemporal wavelet analysis, to compare temporal patterns of influenza spread between counties and across the country. RESULTS: There were 56 spatial clusters identified in the unadjusted scan statistic analysis using data from the 2010/2011 through the 2013/2014 seasons, with relative risks (RRs) ranging from 0.09 to 4.20. After adjustment for socioeconomic factors, there were five clusters identified with RRs ranging from 0.21 to 1.20. In the wavelet analysis, most sites were in phase synchrony with one another for most years, except for the H1N1 pandemic year (2009-2010), wherein most sites had differential epidemic timing from the referent site in Georgia. CONCLUSIONS: Socioeconomic factors strongly impact local influenza hospitalization burden. Influenza phase synchrony varies by year and by socioeconomics, but is less influenced by socioeconomics than is disease burden.

Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples.

BACKGROUND: Infectious disease forecasting aims to predict characteristics of both seasonal epidemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts. MAIN BODY: For forecasts to be fully integrated into public health decision-making, federal, state, and local officials must understand how forecasts were made, how to interpret forecasts, and how well the forecasts have performed in the past. Since the 2013-14 influenza season, the Influenza Division at the Centers for Disease Control and Prevention (CDC) has hosted collaborative challenges to forecast the timing, intensity, and short-term trajectory of influenza-like illness in the United States. Additional efforts to advance forecasting science have included influenza initiatives focused on state-level and hospitalization forecasts, as well as other infectious diseases. Using CDC influenza forecasting challenges as an example, this paper provides an overview of infectious disease forecasting; applications of forecasting to public health; and current work to develop best practices for forecast methodology, applications, and communication. CONCLUSIONS: These efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science.

Social determinants of influenza hospitalization in the United States.

BACKGROUND: Influenza hospitalizations result in substantial morbidity and mortality each year. Little is known about the association between influenza hospitalization and census tract-based socioeconomic determinants beyond the effect of individual factors. OBJECTIVE: To evaluate whether census tract-based determinants such as poverty and household crowding would contribute significantly to the risk of influenza hospitalization above and beyond individual-level determinants. METHODS: We analyzed 33 515 laboratory-confirmed influenza-associated hospitalizations that occurred during the 2009-2010 through 2013-2014 influenza seasons using a population-based surveillance system at 14 sites across the United States. RESULTS: Using a multilevel regression model, we found that individual factors were associated with influenza hospitalization with the highest adjusted odds ratio (AOR) of 9.20 (95% CI 8.72-9.70) for those ≥65 vs 5-17 years old. African Americans had an AOR of 1.67 (95% CI 1.60-1.73) compared to Whites, and Hispanics had an AOR of 1.21 (95% CI 1.16-1.26) compared to non-Hispanics. Among census tract-based determinants, those living in a tract with ≥20% vs <5% of persons living below poverty had an AOR of 1.31 (95% CI 1.16-1.47), those living in a tract with ≥5% vs <5% of persons living in crowded conditions had an AOR of 1.17 (95% CI 1.11-1.23), and those living in a tract with ≥40% vs <5% female heads of household had an AOR of 1.32 (95% CI 1.25-1.40). CONCLUSION: Census tract-based determinants account for 11% of the variability in influenza hospitalization.