Reliability of the American Community Survey for Unintentional Drowning and Submersion Injury Surveillance: A Comprehensive Assessment of 10 Socioeconomic Indicators Derived From the 2006-2013 Annual and Multi-Year Data Cycles

BACKGROUND: Our objective was to evaluate the reliability and predictability of ten socioeconomic indicators obtained from the 2006-2013 annual and multi-year ACS data cycles for unintentional drowning and submersion injury surveillance. METHODS: Each indicator was evaluated using its margin of error and coefficient of variation. For the multi-year data cycles we calculated the frequency that estimates for the same geographic areas from consecutive surveys were statistically significantly different. Relative risk estimates of drowning-related deaths were constructed using the National Center for Health Statistics compressed mortality file. All analyses were derived using census counties. RESULTS: Five of the ten socioeconomic indicators derived from the annual and multi-year data cycles produced high reliability CV estimates for at least 85 % of all US counties. On average, differences in socioeconomic characteristics for the same geographic areas for consecutive 3- and 5-year data cycles were unlikely to be caused by sampling error in only 17 % (5-89 %) and 21 % (5-93 %) of all counties. No indicator produced statistically significant relative risk estimates across all data cycles and survey years. CONCLUSIONS: The reliability of the annual and multi-year county-level ACS data cycles varies by census indicator. More than 75 % of the differences in estimates between consecutive multi-year surveys are likely to have occurred as a result of sampling error, suggesting that researchers should be judicious when interpreting overlapping survey data as reflective of real changes in socioeconomic conditions. Although no indicator predicted disparities in drowning-related injury mortality across all data cycles and years, further studies are needed to determine if these associations remain consistent at different geographic scales and for injury morbidity

Using GIS-based methods of multicriteria analysis to construct socio-economic deprivation indices

<p>Abstract</p> <p>Background</p> <p>Over the past several decades researchers have produced substantial evidence of a social gradient in a variety of health outcomes, rising from systematic differences in income, education, employment conditions, and family dynamics within the population. Social gradients in health are measured using deprivation indices, which are typically constructed from aggregated socio-economic data taken from the national census – a technique which dates back at least until the early 1970's. The primary method of index construction over the last decade has been a Principal Component Analysis. Seldom are the indices constructed from survey-based data sources due to the inherent difficulty in validating the subjectivity of the response scores. We argue that this very subjectivity can uncover spatial distributions of local health outcomes. Moreover, indication of neighbourhood socio-economic status may go underrepresented when weighted without expert opinion. In this paper we propose the use of geographic information science (GIS) for constructing the index. We employ a GIS-based Order Weighted Average (OWA) Multicriteria Analysis (MCA) as a technique to validate deprivation indices that are constructed using more qualitative data sources. Both OWA and traditional MCA are well known and used methodologies in spatial analysis but have had little application in social epidemiology.</p> <p>Results</p> <p>A survey of British Columbia's Medical Health Officers (MHOs) was used to populate the MCA-based index. Seven variables were selected and weighted based on the survey results. OWA variable weights assign both local and global weights to the index variables using a sliding scale, producing a range of variable scenarios. The local weights also provide leverage for controlling the level of uncertainty in the MHO response scores. This is distinct from traditional deprivation indices in that the weighting is simultaneously dictated by the original respondent scores and the value of the variables in the dataset.</p> <p>Conclusion</p> <p>OWA-based MCA is a sensitive instrument that permits incorporation of expert opinion in quantifying socio-economic gradients in health status. OWA applies both subjective and objective weights to the index variables, thus providing a more rational means of incorporating survey results into spatial analysis.</p

Census-Based Socioeconomic Indicators for Monitoring Injury Causes in the USA: A Review

BACKGROUND: Unlike the UK or New Zealand, there is no standard set of census variables in the USA for characterising socioeconomic (SES, socioeconomic status) inequalities in health outcomes, including injury. We systematically reviewed existing US studies to identify conceptual and methodological strengths and limitations of current approaches to determine those most suitable for research and surveillance. METHODS: We searched seven electronic databases to identify census variables proposed in the peer-reviewed literature to monitor injury risk. Inclusion criteria were that numerator data were derived from hospital, trauma or vital statistics registries and that exposure variables included census SES constructs. RESULTS: From 33 eligible studies, we identified 70 different census constructs for monitoring injury risk. Of these, fewer than half were replicated by other studies or against other causes, making the majority of studies non-comparable. When evaluated for a statistically significant relationship with a cause of injury, 74% of all constructs were predictive of injury risk when assessed in pairwise comparisons, whereas 98% of all constructs were significant when aggregated into composite indices. Fewer than 30% of studies selected SES constructs based on known associations with injury risk. CONCLUSIONS: There is heterogeneity in the conceptual and methodological approaches for using census data for monitoring injury risk as well as in the recommendations as to how these constructs can be used for injury prevention. We recommend four priority areas for research to facilitate a more unified approach towards use of the census for monitoring socioeconomic inequalities in injury risk

Modelling Optimal Location for Pre-Hospital Helicopter Emergency Medical Services

Background: Increasing the range and scope of early activation/auto launch helicopter emergencymedical services (HEMS) may alleviate unnecessary injury mortality that disproportionately affectsrural populations. To date, attempts to develop a quantitative framework for the optimal locationof HEMS facilities have been absent.Methods: Our analysis used five years of critical care data from tertiary health care facilities, spatialdata on origin of transport and accurate road travel time catchments for tertiary centres. Alocation optimization model was developed to identify where the expansion of HEMS would coverthe greatest population among those currently underserved. The protocol was developed usinggeographic information systems (GIS) to measure populations, distances and accessibility toservices.Results: Our model determined Royal Inland Hospital (RIH) was the optimal site for an expandedHEMS – based on denominator population, distance to services and historical usage patterns.Conclusion: GIS based protocols for location of emergency medical resources can providesupportive evidence for allocation decisions – especially when resources are limited. In this study,we were able to demonstrate conclusively that a logical choice exists for location of additionalHEMS. This protocol could be extended to location analysis for other emergency and healthservices

Reliability of the American Community Survey Estimates of Risk-Adjusted Readmission Rankings for Hospitals Before and After Peer Group Stratification

Importance Since the transition to the American Community Survey, data uncertainty has complicated its use for policy making and research, despite the ongoing need to identify disparities in health care outcomes. The US Centers for Medicare & Medicaid Services’ new, stratified payment adjustment method for its Hospital Readmissions Reduction Program may be able to reduce the reliance on data linkages to socioeconomic survey estimates. Objective To determine whether there are differences in the reliability of socioeconomically risk-adjusted hospital readmission rates among hospitals that serve a disproportionate share of low-income populations after stratifying hospitals into peer group–based classification groups. Design, Setting, and Participants This cross-sectional study uses data from the 2014 New York State Health Cost and Utilization Project State Inpatient Database for 96 278 hospital admissions for acute myocardial infarction, pneumonia, and congestive heart failure. The analysis included patients aged 18 years and older who were not transferred to another hospital, who were discharged alive, who did not leave the hospital against medical advice, and who were discharged before December 2014. Main Outcomes and Measures The main outcomes were 30-day hospital readmissions after acute myocardial infarction, pneumonia, and congestive heart failure assessed using hierarchical logistic regression. Results The mean (SD) age of the patients was 69.6 (16.0) years for the safety-net hospitals and 74.9 (14.7) years for the non–safety-net hospitals; 9382 (48.8%) and 7003 (48.5%) patients, respectively, were female. For safety net designations, 20% (3 of 15) of all evaluations concealed and distorted differences in risk, with factors such as poverty failing to identify similar risk of acute myocardial infarction readmission until unreliable estimates were excluded from the analysis (OR, 1.23 [95% CI, 1.00-1.52], P = .02; vs OR, 1.17 [95% CI, 0.94-1.46], P = .15). By comparison, 2 of the 60 models (3%) for the peer group–based classification altered the association between socioeconomic status and readmission risk, concealing similarities in congestive heart failure readmission when adjusted using high school completion rates (OR, 1.27 [95% CI 1.02-1.58], P = .04; vs OR, 1.23 [95% CI, 0.98-1.53], P = .06) and distorting similarities in pneumonia readmissions when accounting for the proportion of lone-parent families (OR, 1.27 [95% CI, 0.98-1.66], P = .07; vs OR, 1.35 [95% CI, 1.02-1.80], P = .04) between the lowest and highest socioeconomic status hospitals in quartile 1. Conclusions and Relevance There was greater precision in socioeconomic adjusted readmission estimates when hospitals were stratified into the new payment adjustment criteria compared with safety net designations. A contributing factor for improved reliability of American Community Survey estimates under the new payment criteria was the merging of patients from low-income neighborhoods with greater homogeneity in survey estimates into groupings similar to those for higher-income patients, whose neighborhoods often exhibit greater estimate variability. Additional efforts are needed to explore the effect of measurement error on American Community Survey–adjusted readmissions using the new peer group–based classification methods

AN ANALYSIS OF INDIVIDUALS’ PERCEPTION TOWARDS TEAM GRIT

Throughout a regular season, there is no doubt that sports teams experience setbacks. Overcoming these setbacks and remaining mentally and physically focused on the championship requires grit; not only in each individual, but the team as a whole. In order to analyze grit at a team or organizational level, this study will focus on analyzing individual's perceptions of their own team's grit. It will examine three NCAA Division One Athletic teams in the Mid-Atlantic region of the United States. The teams include a mix of men's and women's varsity sports, in order to gain diversity of perspectives. The focus groups use an interview guide for all three sports teams. Data analysis will occur using a variety of qualitative coding techniques that will be grouped into themes for each case and across all three cases to answer our research question. This study will further our understanding of team grit by gathering individuals' perceptions of their team dynamics, which will contribute to the research of grit at a higher level of analysis.Lieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyApproved for public release. Distribution is unlimited

A Spatial Analysis of Functional Outcomes and Quality of Life Outcomes After Pediatric Injury

BACKGROUND: Changes in health-related quality of life (HRQoL) are more regularly being monitored during the first year after injury. Monitoring changes in HRQoL using spatial cluster analysis can potentially identify concentrations of geographic areas with injury survivors with similar outcomes, thereby improving how interventions are delivered or in how outcomes are evaluated. METHODS: We used a spatial scan statistic designed for oridinal data to test two different spatial cluster analysis of very low, low, high, and very high HRQoL scores. Our study was based on HRQoL scores returned by children treated for injury at British Columbia Children\u27s Hospital and discharged to the Vancouver Metropolitan Area. Spatial clusters were assessed at 4 time periods - baseline (based on pre-injury health as reported prior to discharge from hospital), and one, four, and twelve months after discharge. Outcome data were measured used the PedsQL™ outcome scale. Outcome values of very low, low, high, and very high HRQoL scores were defined by classifying PedsQL™ scores into quartiles. In the first test, all scores were assessed for clustering without specifying whether the response score was from a baseline or follow-up response. In the second analysis, we built a space-time model to identify whether HRQoL responses could be identified at specific time points. RESULTS: Among all participants, geographic clustering of response scores were observed globally and at specific time periods. In the purely spatial analysis, five significant clusters of \u27very low\u27 PedsQL physical and psychosocial health outcomes were identified within geographic zones ranging in size from 1 to 21 km. A space-time analysis of outcomes identified significant clusters of both \u27very low\u27 and \u27low\u27 outcomes between survey months within zones ranging in size from 3 to 5 km. CONCLUSION: Monitoring patient health outcomes following injury is important for planning and targeting interventions. A common theme in the literature is that future prevention efforts may benefit from identifying those most a risk of developing ongoing problems after injury in effort to target resources to those most in need. Spatial scan statistics are tools that could be applied for identifying concentrations of poor recovery outcomes. By classifying outcomes as a categorical variable, clusters of \u27potentially low\u27 outcomes can also be mapped, thereby identifying populations whose recovery status may decrease

Ghost direction detection and other innovations for Ms. Pac-Man

Ms. Pac-Man was developed in the 1980s, becoming one of the most popular arcade games of its time. It still has a significant following today and has recently attracted the attention of artificial intelligence researchers, in part, due to the fact that the agent must react in real time in order to navigate its way through the maze. This pape

A Two-Stage Cluster Sampling Method Using Gridded Population Data, A GIS, And Google Earthtm Imagery in a Population-Based Mortality Survey in Iraq

Background Mortality estimates can measure and monitor the impacts of conflict on a population, guide humanitarian efforts, and help to better understand the public health impacts of conflict. Vital statistics registration and surveillance systems are rarely functional in conflict settings, posing a challenge of estimating mortality using retrospective population-based surveys. Results We present a two-stage cluster sampling method for application in population-based mortality surveys. The sampling method utilizes gridded population data and a geographic information system (GIS) to select clusters in the first sampling stage and Google Earth TM imagery and sampling grids to select households in the second sampling stage. The sampling method is implemented in a household mortality study in Iraq in 2011. Factors affecting feasibility and methodological quality are described. Conclusion Sampling is a challenge in retrospective population-based mortality studies and alternatives that improve on the conventional approaches are needed. The sampling strategy presented here was designed to generate a representative sample of the Iraqi population while reducing the potential for bias and considering the context specific challenges of the study setting. This sampling strategy, or variations on it, are adaptable and should be considered and tested in other conflict settings

Precision of Provider Licensure Data for Mapping Member Accessibility to Medicaid Managed Care Provider Networks

BACKGROUND: In July 2018, the Centers for Medicare and Medicaid Services (CMS) updated its Medicaid Managed Care (MMC) regulations that govern network and access standards for enrollees. There have been few published studies of whether there is accurate geographic information on primary care providers to monitor network adequacy. METHODS: We analyzed a sample of nurse practitioner (NP) and physician address data registered in the state labor, licensing, and regulation (LLR) boards and the National Provider Index (NPI) using employment location data contained in the patient-centered medical home (PCMH) data file. Our main outcome measures were address discordance (%) at the clinic-level, city, ZIP code, and county spatial extent and the distance, in miles, between employment location and the LLR/NPI address on file. RESULTS: Based on LLR records, address information provided by NPs corresponded to their place of employment in 5% of all cases. NP address information registered in the NPI corresponded to their place of employment in 64% of all cases. Among physicians, the address information provided in the LLR and NPI corresponded to the place of employment in 64 and 72% of all instances. For NPs, the average distance between the PCMH and the LLR address was 21.5 miles. Using the NPI, the distance decreased to 7.4 miles. For physicians, the average distance between the PCMH and the LLR and NPI addresses was 7.2 and 4.3 miles. CONCLUSIONS: Publicly available data to forecast state-wide distributions of the NP workforce for MMC members may not be reliable if done using state licensure board data. Meaningful improvements to correspond with MMC policy changes require collecting and releasing information on place of employment