Dataset Information

The CITIES Project: Understanding the Health of Underrepresented Populations in Ohio.

ABSTRACT: BACKGROUND:Ohio, the catchment area of The Ohio State University Comprehensive Cancer Center (OSUCCC), includes diverse populations with different cancer profiles. As part of the National Cancer Institute (NCI)-funded initiative to conduct population health assessments in cancer center catchment areas, the OSUCCC surveyed residents, focusing on factors contributing to cancer disparities in Ohio populations. METHODS:Two sampling strategies were used: (i) probability sampling of mailing lists and (ii) convenience sampling at community events, coupled with phone/in-person/web surveys. Survey items were chosen along multilevel framework constructs, used in concert with other funded NCI-Designated Cancer Centers. Multivariable logistic regression models investigated predictors associated with health behaviors, cancer beliefs, knowledge, and screening. RESULTS:The sample of 1,005 respondents were white (46.6%), African American (24.7%), Hispanic (13.7%), Somali (7.6%), and Asian (7.5%). A total of 216 respondents were Appalachian. Variations in cancer attitudes, knowledge, and behaviors were noted by racial/ethnic and geographic group. Multivariable models identified individuals with less financial security as less likely to exercise or be within guidelines for screening, but more likely to smoke and have a poor diet. At the community-level, measures of poverty were highest in Appalachia, whereas children in female-headed households were greater in urban minority areas. CONCLUSIONS:This population health assessment reinforced the diversity of the OSUCCC catchment area. These populations are ripe for implementation science strategies, focusing in communities and clinics that serve vulnerable populations. IMPACT:Understanding attitudes, knowledge, and behaviors of this population can assist tailoring outreach and research strategies to lessen the cancer burden.

SUBMITTER: Paskett ED

PROVIDER: S-EPMC6724198 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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The CITIES Project: Understanding the Health of Underrepresented Populations in Ohio.

Paskett Electra D ED Young Gregory S GS Bernardo Brittany M BM Washington Chasity C DeGraffinreid Cecilia R CR Fisher James L JL Huerta Timothy R TR

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20181030 3

<h4>Background</h4>Ohio, the catchment area of The Ohio State University Comprehensive Cancer Center (OSUCCC), includes diverse populations with different cancer profiles. As part of the National Cancer Institute (NCI)-funded initiative to conduct population health assessments in cancer center catchment areas, the OSUCCC surveyed residents, focusing on factors contributing to cancer disparities in Ohio populations.<h4>Methods</h4>Two sampling strategies were used: (i) probability sampling of mai ...[more]

PMID: 30377208

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Project description:BackgroundIn recent years, there have been a growing number of studies on spatial inequalities in health covering a variety of scales, from small areas to metropolitan areas or regions, and for various health outcomes. However, few investigations have compared health status between cities with a view to gaining a better understanding of the relationships between such inequalities and the social, economic and physical characteristics. This paper focuses on disparities in respiratory health among the 55 largest French cities. The aim is to explore the relationships between inter-urban health patterns, city characteristics and regional context, and to determine how far a city's health status relates to the features observed on different geographical scales.MethodsWe used health data describing hospitalizations for Chronic Obstructive Pulmonary Disease (COPD) as a proxy for respiratory health, and the total number of hospitalizations (overall) as a proxy for general health. This last indicator was used as a benchmark. A large set of indicators relating to socioeconomic, physical and amenity aspects of the cities (urban units) was also constructed. Data were analyzed using linear correlations and multiple linear regression models.ResultsThe results suggest that socioeconomic characteristics are major discriminators for inequalities in respiratory health status among urban units. Indeed, once combined to socioeconomic characteristics, only a climate indicator remained significant among the physical indicators. It appeared that the pollution indicators which were significantly correlated with COPD hospitalization rates loosed significance when associated to the socio-economic indicators in a multiple regression. The analysis showed that among the socio-economic indicators, an employment indicator derived at the regional scale, and two indicators reflecting the unequal intra-urban spatial distribution of population according to their education, were the most efficient to describe differences in the respiratory health status of urban units.ConclusionIn order to design effective urban policies, it is essential to gain a better understanding of the differences among cities in their entirety, rather than solely differences across small urban areas or individuals.

Project description:BackgroundIncreasing urban bicycling has established net benefits for human and environmental health. Questions remain about which policies are needed and in what order, to achieve an increase in cycling while avoiding negative consequences. Novel ways of considering cycling policy are needed, bringing together expertise across policy, community and research to develop a shared understanding of the dynamically complex cycling system. In this paper we use a collaborative learning process to develop a dynamic causal model of urban cycling to develop consensus about the nature and order of policies needed in different cycling contexts to optimise outcomes.MethodsWe used participatory system dynamics modelling to develop causal loop diagrams (CLDs) of cycling in three contrasting contexts: Auckland, London and Nijmegen. We combined qualitative interviews and workshops to develop the CLDs. We used the three CLDs to compare and contrast influences on cycling at different points on a "cycling trajectory" and drew out policy insights.ResultsThe three CLDs consisted of feedback loops dynamically influencing cycling, with significant overlap between the three diagrams. Common reinforcing patterns emerged: growing numbers of people cycling lifts political will to improve the environment; cycling safety in numbers drives further growth; and more cycling can lead to normalisation across the population. By contrast, limits to growth varied as cycling increases. In Auckland and London, real and perceived danger was considered the main limit, with added barriers to normalisation in London. Cycling congestion and "market saturation" were important in the Netherlands.ConclusionsA generalisable, dynamic causal theory for urban cycling enables a more ordered set of policy recommendations for different cities on a cycling trajectory. Participation meant the collective knowledge of cycling stakeholders was represented and triangulated with research evidence. Extending this research to further cities, especially in low-middle income countries, would enhance generalizability of the CLDs.

Dataset Information

The CITIES Project: Understanding the Health of Underrepresented Populations in Ohio.

Publications

The CITIES Project: Understanding the Health of Underrepresented Populations in Ohio.

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