Analyzing the Relationship Between Socioeconomic Deprivation and Outpatient Medicare Part D Fluoroquinolone Claim Rates in Texas

INTRODUCTION: Only a few studies have assessed the relationship between deprivation and excessive antibiotic use. In Texas, antimicrobial prescription rates are particularly high compared with the rest of the US. This study analyzed the association between local area socioeconomic deprivation and providers\u27 fluoroquinolone claim rates among beneficiaries 65 years and older in Texas. METHODS: This ecological study utilized provider- and area-level data from Medicare Part D Prescribers and the Social Deprivation Index (SDI) repositories. Negative binomial regression models were employed to evaluate the relationship between provider- and area-level characteristics (prescriber\u27s gender, specialty, rural-urban community area, beneficiaries\u27 demographics, area-level population, and SDI) and fluoroquinolone claim rates per 1,000 beneficiaries. RESULTS: A total of 11,996 providers were included. SDI (IRR 0.98, 95% CI 0.97-0.99) and male providers (IRR 0.96, 95% CI 0.94-0.99) were inversely associated with claim rates. In contrast, several factors were associated with higher claim rates, including non-metropolitan areas (1.04, 95% CI 1.00-1.09), and practices with a high proportion of male (IRR 1.12, 95% CI 1.10-1.14), Black (IRR 1.05, 95% CI 1.03-1.07), or Medicaid beneficiaries (IRR 1.15, 95% CI 1.12-1.17). Effect modification was observed between SDI and rurality, with higher SDI in non-metropolitan areas associated with higher claim rates, whereas SDI in metropolitan areas was inversely related to claim rates. CONCLUSION: Lower fluoroquinolone claim rates were observed among Texas Medicare providers in metropolitan areas with higher SDI. Conversely, higher rates were observed in rural areas with higher SDI. More studies are needed to understand the underlying causes of this variation and develop effective stewardship interventions

Variations in cardiovascular disease under-diagnosis in England: national cross-sectional spatial analysis

BACKGROUND: There is under-diagnosis of cardiovascular disease (CVD) in the English population, despite financial incentives to encourage general practices to register new cases. We compared the modelled (expected) and diagnosed (observed) prevalence of three cardiovascular conditions- coronary heart disease (CHD), hypertension and stroke- at local level, their geographical variation, and population and healthcare predictors which might influence diagnosis. METHODS: Cross-sectional observational study in all English local authorities (351) and general practices (8,372) comparing model-based expected prevalence with diagnosed prevalence on practice disease registers. Spatial analyses were used to identify geographic clusters and variation in regression relationships. RESULTS: A total of 9,682,176 patients were on practice CHD, stroke and transient ischaemic attack, and hypertension registers. There was wide spatial variation in observed: expected prevalence ratios for all three diseases, with less than five per cent of expected cases diagnosed in some areas. London and the surrounding area showed statistically significant discrepancies in observed: expected prevalence ratios, with observed prevalence much lower than the epidemiological models predicted. The addition of general practitioner supply as a variable yielded stronger regression results for all three conditions. CONCLUSIONS: Despite almost universal access to free primary healthcare, there may be significant and highly variable under-diagnosis of CVD across England, which can be partially explained by persistent inequity in GP supply. Disease management studies should consider the possible impact of under-diagnosis on population health outcomes. Compared to classical regression modelling, spatial analytic techniques can provide additional information on risk factors for under-diagnosis, and can suggest where healthcare resources may be most needed

Interactive map communication: pilot study of the visual perceptions and preferences of public health practitioners

Objectives: To conduct a pilot study into the comprehension and visualisation preferences of geographic information by public health practitioners (PHPs), particularly in the context of interactive, Internet-based atlases. Study design: Structured human-computer interaction interviews. Methods: Seven academia-based PHPs were interviewed as information service users based on a structured questionnaire to assess their understanding of geographic representations of morbidity data, and identify their visualisation preferences in a geographic information systems environment. Results: Awareness of area-based deprivation indices and the Index of Multiple Deprivation 2007 health and disability domain was near-universal. However, novice users of disease maps had difficulties in interpreting data classifications, in understanding supplementary information in the form of box plots and histograms, and in making use of links between interactive tabular and cartographic information. Choices for colour plans when viewing maps showed little agreement between users, although pre-viewing comments showed preferences for red-blue diverging schema. Conclusions: PHPs new to geographic information would benefit from enhanced interpretive support documentation to meet their needs when using Internet-based, interactive public health atlases, which are rarely provided at such sites. Technical, software-related support alone is insufficient. Increased interaction between PHPs and mapmakers would be beneficial to maximise the potential of the current growth in interactive, electronic atlases, and improve geographic information support for public health decision-making and informing the wider public. © 2011 The Royal Society for Public Health

Socio-demographic data sources for monitoring locality health profiles and geographical planning of primary health care in the UK

Aim: the aim of this article is to provide UK-based primary health care research and development workers with a review of the current range of published, aggregated socio-demographic indicators that can be combined with health and health care datasets, for the purposes of monitoring locality health profiles and planning primary health care. Non-UK readers should nevertheless find the review of some relevance to their own national contexts.Background: there is an increasing range of resources available for such purposes and many of these datasets are equally useful outside of geographic work. The 2001 census introduced important changes to what routine data are available, as will the 2011 census. These changes have been paralleled by developments in the availability of socio-demographic indicators and the increasing popularity of geographic information systems. Health data can now be combined with those from socio-demographic more efficiently to produce what are termed value-added datasets.Methods: we review recent and planned developments in key data sources currently available in the UK and examine they can be used to monitor inequalities in primary health care inequalities and their role in the integration of primary health care needs mapping and forecasting with the spatial planning of areas undergoing regeneration.Conclusions: recent and planned developments in the availability of both socio-demographic datasets in tandem with parallel developments in spatial technologies have provided a flexible, potent geographical methodology for primary health care research and development. The current consultation process for the 2011 census provides those involved with primary health care research and development an opportunity to influence future development

Older people's navigation of urban areas as pedestrians: measuring quality of the built environment using oral narratives and virtual routes.

Studies of navigation and walkability of the outdoor built environment are now common. However, few have taken a ‘virtual’ approach and in this study we examine the qualitative oral narratives of forty-eight older people provided whilst they watched film footage of a journey around an unfamiliar, urban landscape, and compare them with quantitative measures of the built environment. Pre-film cognitive/psychological tests were carried out, and the participants filled out a questionnaire covering relevant issues such as feelings about home area and navigational behaviour. From the oral narratives we found that signage as well as the presence of historical and distinctive buildings to be central. There was little evidence that perception of residential (familiar) neighbourhood impacted upon commentary about the unfamiliar space suggesting the findings are generalisable to the wider senior citizen demographic and transferable to other localities. We propose a prototype index for urban landscape navigation from these findings

Association of relative fat mass (RFM) index with diabetes-related mortality and heart disease mortality

Although studies have examined the association of the Relative Fat Mass (RFM, a novel anthropometric index used as a surrogate for whole-body fat percentage) with all-cause mortality, the association of RFM with diabetes-related mortality and heart disease mortality has not been thoroughly investigated. In addition, no study has compared the associations of RFM and waist circumference (a surrogate for intra-abdominal fat) with cause-specific mortality and all-cause mortality. In the present study, we addressed these knowledge gaps. We used data from the US National Health and Nutrition Examination Survey (NHANES) 1999–2018. NHANES III was used for validation. Analyses included 46,535 adults (mean age 46.5 years). During a median follow-up time of 9.7 years, 6,101 participants died (743 from diabetes; 1,514 from heart disease). Compared with BMI and WC, RFM was more strongly associated with diabetes-related mortality in both women and men, adjusting for age, ethnicity, education, and smoking status. All anthropometric measures were similarly strongly associated with heart disease mortality and all-cause mortality. RFM showed greater predictive discrimination of mortality. Similar results were found in NHANES III (n = 14,448). In conclusion, RFM is strongly associated with diabetes-related mortality, heart disease mortality, and all-cause mortality, and outperforms conventional adiposity measures for prediction of mortality