Health Care Avoidance Among Rural Populations: Results From a Nationally Representative Survey

BACKGROUND: Previous research suggests that certain populations, including rural residents, exhibit health care avoidant behaviors more frequently than other groups. Additionally, health care avoidance is related to sociodemographics, attitudes, social expectations, ability to pay for care, and prior experiences with providers. However, previous studies have been limited to specific geographic areas, particular health conditions, or by analytic methods. METHODS: The 2008 Health Information Trends Survey (HINTS) was used to estimate the magnitude of health care avoidance nationally and, while controlling for confounding factors, identify groups of people in the United States who are more likely to avoid health care. Chi-square procedures tested the statistical significance (P \u3c .05) of bivariate relationships. Multivariable analysis was conducted through a weighted multiple logistic regression with backward selection. RESULTS: For 6,714 respondents, bivariate analyses revealed differences (P \u3c .05) in health care avoidance for multiple factors. However, multiple regression reduced the set of significant factors (P \u3c .05) to rural residence (OR = 1.69), male sex (OR = 1.24), younger age (18-34 years OR = 2.34; 35-49 years OR = 2.10), lack of health insurance (OR = 1.43), lack of confidence in personal health care (OR = 2.24), lack of regular provider (OR = 1.49), little trust in physicians (OR = 1.34), and poor provider rapport (OR = 0.94). CONCLUSION: The results of this study will help public health practitioners develop programs and initiatives targeted and tailored to specific groups, particularly rural populations, which seek to address avoidant behavior, thereby reducing the likelihood of adverse health outcomes

Distributed usability evaluation of the Pennsylvania Cancer Atlas

<p>Abstract</p> <p>Background</p> <p>The Pennsylvania Cancer Atlas (PA-CA) is an interactive online atlas to help policy-makers, program managers, and epidemiologists with tasks related to cancer prevention and control. The PA-CA includes maps, graphs, tables, that are dynamically linked to support data exploration and decision-making with spatio-temporal cancer data. Our Atlas development process follows a user-centered design approach. To assess the usability of the initial versions of the PA-CA, we developed and applied a novel strategy for soliciting user feedback through multiple distributed focus groups and surveys. Our process of acquiring user feedback leverages an online web application (e-Delphi). In this paper we describe the PA-CA, detail how we have adapted e-Delphi web application to support usability and utility evaluation of the PA-CA, and present the results of our evaluation.</p> <p>Results</p> <p>We report results from four sets of users. Each group provided structured individual and group assessments of the PA-CA as well as input on the kinds of users and applications for which it is best suited. Overall reactions to the PA-CA are quite positive. Participants did, however, provide a range of useful suggestions. Key suggestions focused on improving interaction functions, enhancing methods of temporal analysis, addressing data issues, and providing additional data displays and help functions. These suggestions were incorporated in each design and implementation iteration for the PA-CA and used to inform a set of web-atlas design principles.</p> <p>Conclusion</p> <p>For the Atlas, we find that a design that utilizes linked map, graph, and table views is understandable to and perceived to be useful by the target audience of cancer prevention and control professionals. However, it is clear that considerable variation in experience using maps and graphics exists and for those with less experience, integrated tutorials and help features are needed. In relation to our usability assessment strategy, we find that our distributed, web-based method for soliciting user input is generally effective. Advantages include the ability to gather information from users distributed in time and space and the relative anonymity of the participants while disadvantages include less control over when and how often participants provide input and challenges for obtaining rich input.</p

Family Members\u27 Influence on Family Meal Vegetable Choices

Objective—Characterize the process of family vegetable selection (especially cruciferous, deep orange, and dark green leafy vegetables); demonstrate the usefulness of Exchange Theory (how family norms and past experiences interact with rewards and costs) for interpreting the data. Design—Eight focus groups, two with each segment (men/women vegetable-likers/dislikers based on a screening form). Participants completed a vegetable intake form. Setting—Rural Appalachian Pennsylvania. Participants—61 low-income, married/cohabiting men (n=28) and women (n=33). Analysis—Thematic analysis within Exchange Theory framework for qualitative data. Descriptive analysis, t-tests and chi-square tests for quantitative data. Results—Exchange Theory proved useful for understanding that regardless of sex or vegetable liker/ disliker status, meal preparers see more costs than rewards to serving vegetables. Past experience plus expectations of food preparer role and of deference to family member preferences supported a family norm of serving only vegetables acceptable to everyone. Emphasized vegetables are largely ignored due to unfamiliarity; family norms prevented experimentation and learning through exposure. Conclusions and Implications—Interventions to increase vegetable consumption of this audience could 1) alter family norms about vegetables served, 2) change perceptions of past experiences, 3) reduce social and personal costs of serving vegetables and 4) increase tangible and social rewards of serving vegetables

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality

<p>Abstract</p> <p>Background</p> <p>Kulldorff's spatial scan statistic and its software implementation – SaTScan – are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S.</p> <p>Results</p> <p>We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results.</p> <p>Conclusion</p> <p>The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales.</p> <p>Method</p> <p>We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit.</p

Diabetes status and being up-to-date on colorectal cancer screening, 2012 Behavioral Risk Factor Surveillance System

INTRODUCTION: Although screening rates for colorectal cancer are increasing, 22 million Americans are not up-to-date with recommendations. People with diabetes are an important and rapidly growing group at increased risk for colorectal cancer. Screening status and predictors of being up-to-date on screening are largely unknown in this population. METHODS: This study used logistic regression modeling and data from the 2012 Behavioral Risk Factor Surveillance System to examine the association between diabetes and colorectal cancer screening predictors with being up-to-date on colorectal cancer screening according to criteria of the US Preventive Services Task Force for adults aged 50 or older. State prevalence rates of up-to-date colorectal cancer screening were also calculated and mapped. RESULTS: The prevalence of being up-to-date with colorectal cancer screening for all respondents aged 50 or older was 65.6%; for respondents with diabetes, the rate was 69.2%. Respondents with diabetes were 22% more likely to be up-to-date on colorectal cancer screening than those without diabetes. Among those with diabetes, having a routine checkup within the previous year significantly increased the odds of being up-to-date on colorectal cancer screening (odds ratio, 1.90). Other factors such as age, income, education, race/ethnicity, insurance status, and history of cancer were also associated with up-to-date status. CONCLUSION: Regardless of diabetes status, people who had a routine checkup within the past year were more likely to be up-to-date than people who had not. Among people with diabetes, the duration between routine checkups may be of greater importance than the frequency of diabetes-related doctor visits. Continued efforts should be made to ensure that routine care visits occur regularly to address the preventive health needs of patients with and patients without diabetes