Generating data to facilitate more equitable distribution of health resources: an illustration of how local health surveys can identify probable need in mixed socio-economic regions.

OBJECTIVES: This study aimed to (1) encourage allocation of governmental and grant funds to the administration of local area health surveys and (2) illustrate the predictive impact of socio-economic resources on adult health status at the local area level to provide an example of how health surveys can identify residents with the greatest health needs. STUDY DESIGN: Randomly sampled and weight-adjusted regional household health survey (7501 respondents) analyzed with categorical bivariate and multivariate statistics, combined with Census data. Survey sample consists of the lowest, highest, and near highest ranked counties in the County Health Rankings and Roadmaps for Pennsylvania. METHODS: Socio-economic status (SES) is measured regionally with Census data consisting of seven indicators and individually with Health Survey data consisting of five indicators based on poverty level, overall household income, and education. Both of these composite measures are examined jointly for their predictive effects on a validated health status measure using binary logistic regression. RESULTS: Once county-level measures of SES and health status are broken down into smaller areas, better identification of pockets of health need is possible. This was most strongly revealed in an urban county, Philadelphia, which is ranked lowest of 67 counties on health measures in the state of Pennsylvania, yet when broken down into 'neighborhood clusters' contained both the highest- and lowest-ranked local area in a five-county region. Overall, regardless of the SES level of the County subdivision one lives in, a low-SES adult has close to six times greater odds of reporting 'fair or poor health status' than does a high-SES adult. CONCLUSION: Local health survey analysis can lead to a more precise identification of health needs than surveys attempting to cover broad areas. Low-SES communities within counties, and low-SES individuals, regardless of the community they live in, are substantially more likely to experience fair to poor health. This adds urgency to the need to implement and investigate socio-economic interventions, which can hopefully improve health and save healthcare costs. Novel local area research can identify the impact of intervening variables such as race in addition to SES to add more specificity in identifying populations with the greatest health needs.

Neural recruitment after mild traumatic brain injury is task dependent: a meta-analysis.

Individuals with mild traumatic brain injury (TBI) often have deficits in processing speed and working memory (WM) and there is a growing literature using functional imaging studies to document these deficits. However, divergent results from these studies revealed both hypoactivation and hyperactivation of neural resources after injury. We hypothesized that at least part of this variance can be explained by distinct demands between WM tasks. Notably, in this literature some WM tasks use discrete periods of encoding, maintenance, and retrieval, whereas others place continuous demands on WM. The purpose of this meta-analysis is to examine the differences in neural recruitment after mTBI to determine if divergent findings can be explained as a function of task demand and cognitive load. A comprehensive literature review revealed 14 studies using functional magnetic resonance imaging to examine brain activity of individuals with mTBI during working memory tasks. Three of the fourteen studies included reported hypoactivity, five reported hyperactivity, and the remaining six reported both hypoactivity and hyperactivity. Studies were grouped according to task type and submitted to GingerALE maximum likelihood meta-analyses to determine the most consistent brain activation patterns. The primary findings from this meta-analysis suggest that the discrepancy in activation patterns is at least partially attributable to the classification of WM task, with hyperactivation being observed in continuous tasks and hypoactivation being observed during discrete tasks. We anticipate that differential task load expressed in continuous and discrete WM tasks contributes to these differences. Implications for the interpretation of fMRI signals in clinical samples are discussed.