Background: We aimed to quantify the excess mortality associated with increased temperature due to climate change in six major Korean cities under Representative Concentration Pathways (RCPs) which are new emission scenarios designed for the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC). Methods: We first examined the association between daily mean temperature and mortality in each during the summertime (June to September) from2001 to 2008. This was done using a generalized linear Poisson model with adjustment for a long-term time trend, relative humidity, air pollutants, and day of the week. We then computed heat-related mortality attributable to future climate change using estimated mortality risks, projected future populations, and temperature increments for both future years 2041-2070 and 2071-2100 under RCP 4.5 and 8.5. We considered effects from added days with high temperatures over thresholds and shifted effects from high to higher temperature. Results: Estimated excess all-cause mortalities for six cities in Korea ranged from 500 (95% CI: 313-703) for 2041-2070 to 2,320 (95% CI: 1430-3281) deaths per year for 2071-2100 under two RCPs. Excess cardiovascular mortality was estimated to range from 192 (95% CI: 41-351) to 896 (95% CI: 185-1694) deaths per year, covering about 38.5% of all-cause excess mortality. Increased rates of heat-related mortality were higher in cities located at relatively lower latitude than cities with higher latitude. Estimated excess mortality under RCP 8.5, a fossil fuel-intensive emission scenario, was more than twice as high compared with RCP 4.5, low to medium emission scenario. Conclusions: Excess mortality due to climate change is expected to be profound in the future showing spatial variation. Efforts to mitigate climate change can cause substantial health benefits via reducing heat-related mortality.
Increasing population levels of cycling has the potential to improve public health by increasing physical activity. As cyclists have begun using smartphone apps to record trips, researchers have used data generated from these apps to monitor cycling levels and evaluate cycling-related interventions. The goal of this research is to assess the extent to which app-using cyclists represent the broader cycling population to inform whether use of app-generated data in bike-infrastructure intervention research may bias effect estimates. Using an intercept survey, we asked 95 cyclists throughout Atlanta, Georgia, USA about their use of GPS-based smartphone apps to record bike rides. We asked respondents to draw their common bike routes, from which we assessed the proportion of ridership captured by app-generated data sources overall and on types of bicycle infrastructure. We measured socio-demographics and bike-riding habits, including cyclist type, ride frequency, and most common ride purpose. Cyclists who used smartphone apps to record their bike rides differed from those who did not across some but not all socio-demographic characteristics and differed in several bike-riding attributes. App users rode more frequently, self-classified as stronger riders, and rode proportionately more for leisure. Although groups had similar infrastructure preferences at the person level, differences appeared at the level of the estimated ride, where, for example, the proportion of ridership captured by an app on protected bike lanes was lower than the overall proportion of ridership captured. A sample calculation illustrated how such differences may induce selection bias in smartphone-data-based research on infrastructure and motor-vehicle-cyclist crash risk. We illustrate in the sample scenario how the bias can be corrected, assuming inverse-probability-of-selection weights can be accurately specified. The presented bias-adjustment method may be useful for future bike-infrastructure research using smartphone-generated data.
Introduction: Despite recommendations for COVID-19 primary series completion and booster doses for children and adolescents, coverage has been less than optimal, particularly in some subpopulations. This study explored disparities in childhood/adolescent COVID-19 vaccination, parental intent to vaccinate their children and adolescents, and reasons for non-vaccination in the US. Methods: Using the U.S. Census Bureau’s Household Pulse Survey (HPS), we analyzed households with children aged <18 years using data collected from September 14 to November 14, 2022 (n = 44,929). Child and adolescent COVID-19 vaccination coverage (≥1 dose, completed primary series, and booster vaccination) and parental intentions toward vaccination were assessed by sociodemographic characteristics. Factors associated with child and adolescent vaccination coverage were examined using multivariable regression models. Reasons for non-vaccination were assessed overall, by the child’s age group and respondent’s age group. Results: Overall, approximately half (50.1%) of children aged < 18 years were vaccinated against COVID-19 (≥1 dose). Completed primary series vaccination was 44.2% among all children aged <18 years. By age group, completed primary series was 13.2% among children <5 years, 43.9% among children 5–11 years, and 63.3% among adolescents 12–17 years. Booster vaccination among those who completed the primary series was 39.1% among children 5–11 years and 55.3% among adolescents 12–17 years. Vaccination coverage differed by race/ethnicity, educational attainment, household income, region, parental COVID-19 vaccination status, prior COVID-19 diagnosis, child’s age group, and parental age group. Parental reluctance was highest for children aged <5 years (46.8%). Main reasons for non-vaccination among reluctant parents were concerns about side effects (53.3%), lack of trust in COVID-19 vaccines (48.7%), and the belief that children do not need a COVID-19 vaccine (38.8%). Conclusion: Disparities in COVID-19 vaccination coverage among children and adolescents continue to exist. Further efforts are needed to increase COVID-19 primary series and booster vaccination and parental confidence in vaccines.
Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed “anchor stream” sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database.
Background: The Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project is a multiagency and multicountry collaboration that was formed to improve micronutrient assessment and to better characterize anemia.Objectives: The aims of the project were to 1) identify factors associated with inflammation, 2) assess the relations between inflammation, malaria infection, and biomarkers of iron and vitamin A status and compare adjustment approaches, and 3) assess risk factors for anemia in preschool children (PSC) and women of reproductive age (WRA).Design: The BRINDA database inclusion criteria included surveys that 1) were conducted after 2004, 2) had target groups of PSC, WRA, or both, and 3) used a similar laboratory methodology for the measurement of ≥1 biomarker of iron [ferritin or soluble transferrin receptor or vitamin A status (retinol-binding protein or retinol)] and ≥1 biomarker of inflammation (α-1-acid glycoprotein or C-reactive protein). Individual data sets were standardized and merged into a BRINDA database comprising 16 nationally and regionally representative surveys from 14 countries. Collectively, the database covered all 6 WHO geographic regions and contained ∼30,000 PSC and 27,000 WRA. Data were analyzed individually and combined with the use of a meta-analysis.Results: The methods that were used to standardize the BRINDA database and the analytic approaches used to address the project's research questions are presented in this article. Three approaches to adjust micronutrient biomarker concentrations in the presence of inflammation and malaria infection are presented, along with an anemia conceptual framework that guided the BRINDA project's anemia analyses.Conclusions: The BRINDA project refines approaches to interpret iron and vitamin A biomarker values in settings of inflammation and malaria infection and suggests the use of a new regression approach as well as proposes an anemia framework to which real-world data can be applied. Findings can inform guidelines and strategies to prevent and control micronutrient deficiencies and anemia globally.
Hemato-immunopoiesis takes place in the adult human bone marrow (BM), which is composed of heterogenous niches with complex architecture that enables tight regulation of homeostatic and stress responses. There is a paucity of representative culture systems that recapitulate the heterogeneous three-dimensional (3D) human BM microenvironment, and that can endogenously produce soluble factors and extracellular matrix that deliver culture fidelity for the study of both normal and abnormal hematopoiesis. Native BM lymphoid populations are also poorly represented in current in vitro and in vivo models, creating challenges for the study and treatment of BM immunopathology. BM organoid models leverage normal 3D organ structure to recreate functional niche microenvironments. Our focus herein is to review the current state-of-the-art on the use of 3D BM organoids, focusing on their capacities to recreate critical quality attributes of the in vivo BM microenvironment for the study of human normal and abnormal hematopoiesis.