Hispanic health in the USA: a scoping review of the literature

Hispanics are the largest minority group in the USA. They contribute to the economy, cultural diversity, and health of the nation. Assessing their health status and health needs is key to inform health policy formulation and program implementation. To this end, we conducted a scoping review of the literature and national statistics on Hispanic health in the USA using a modified social-ecological framework that includes social determinants of health, health disparities, risk factors, and health services, as they shape the leading causes of morbidity and mortality. These social, environmental, and biological forces have modified the epidemiologic profile of Hispanics in the USA, with cancer being the leading cause of mortality, followed by cardiovascular diseases and unintentional injuries. Implementation of the Affordable Care Act has resulted in improved access to health services for Hispanics, but challenges remain due to limited cultural sensitivity, health literacy, and a shortage of Hispanic health care providers. Acculturation barriers and underinsured or uninsured status remain as major obstacles to health care access. Advantageous health outcomes from the “Hispanic Mortality Paradox” and the “Latina Birth Outcomes Paradox” persist, but health gains may be offset in the future by increasing rates of obesity and diabetes. Recommendations focus on the adoption of the Health in All Policies framework, expanding access to health care, developing cultural sensitivity in the health care workforce, and generating and disseminating research findings on Hispanic health

Bias and imprecision in posture percentile variables estimated from short exposure samples

BACKGROUND: Upper arm postures are believed to be an important risk determinant for musculoskeletal disorder development in the neck and shoulders. The 10th and 90th percentiles of the angular elevation distribution have been reported in many studies as measures of neutral and extreme postural exposures, and variation has been quantified by the 10th-90th percentile range. Further, the 50th percentile is commonly reported as a measure of "average" exposure. These four variables have been estimated using samples of observed or directly measured postures, typically using sampling durations between 5 and 120 min. METHODS: The present study examined the statistical properties of estimated full-shift values of the 10th, 50th and 90th percentile and the 10th-90th percentile range of right upper arm elevation obtained from samples of seven different durations, ranging from 5 to 240 min. The sampling strategies were realized by simulation, using a parent data set of 73 full-shift, continuous inclinometer recordings among hairdressers. For each shift, sampling duration and exposure variable, the mean, standard deviation and sample dispersion limits (2.5% and 97.5%) of all possible sample estimates obtained at one minute intervals were calculated and compared to the true full-shift exposure value. RESULTS: Estimates of the 10th percentile proved to be upward biased with limited sampling, and those of the 90th percentile and the percentile range, downward biased. The 50th percentile was also slightly upwards biased. For all variables, bias was more severe with shorter sampling durations, and it correlated significantly with the true full-shift value for the 10th and 90th percentiles and the percentile range. As expected, shorter samples led to decreased precision of the estimate; sample standard deviations correlated strongly with true full-shift exposure values. CONCLUSIONS: The documented risk of pronounced bias and low precision of percentile estimates obtained from short posture samples presents a concern in ergonomics research and practice, and suggests that alternative, unbiased exposure variables should be considered if data collection resources are restricted

Accuracy and precision of variance components in occupational posture recordings : a simulation study of different data collection strategies

Background: Information on exposure variability, expressed as exposure variance components, is of vital use in occupational epidemiology, including informed risk control and efficient study design. While accurate and precise estimates of the variance components are desirable in such cases, very little research has been devoted to understanding the performance of data sampling strategies designed specifically to determine the size and structure of exposure variability. The aim of this study was to investigate the accuracy and precision of estimators of between-subjects, between-days and within-day variance components obtained by sampling strategies differing with respect to number of subjects, total sampling time per subject, number of days per subject and the size of individual sampling periods. Methods: Minute-by-minute values of average elevation, percentage time above 90 degrees and percentage time below 15 degrees were calculated in a data set consisting of measurements of right upper arm elevation during four full shifts from each of 23 car mechanics. Based on this parent data, bootstrapping was used to simulate sampling with 80 different combinations of the number of subjects (10, 20), total sampling time per subject (60, 120, 240, 480 minutes), number of days per subject (2, 4), and size of sampling periods (blocks) within days (1, 15, 60, 240 minutes). Accuracy (absence of bias) and precision (prediction intervals) of the variance component estimators were assessed for each simulated sampling strategy. Results: Sampling in small blocks within days resulted in essentially unbiased variance components. For a specific total sampling time per subject, and in particular if this time was small, increasing the block size resulted in an increasing bias, primarily of the between-days and the within-days variance components. Prediction intervals were in general wide, and even more so at larger block sizes. Distributing sampling time across more days gave in general more precise variance component estimates, but also reduced accuracy in some cases. Conclusions: Variance components estimated from small samples of exposure data within working days may be both inaccurate and imprecise, in particular if sampling is laid out in large consecutive time blocks. In order to estimate variance components with a satisfying accuracy and precision, for instance for arriving at trustworthy power calculations in a planned intervention study, larger samples of data will be required than for estimating an exposure mean value with a corresponding certainty