RESUMO
Background: Colorectal cancer (CRC) is a prevalent cause of cancer-related deaths in Michigan, but not all Michigan adults had appropriate CRC screening. Objective: To assess the relationship between rurality and age on CRC screenings to inform how pharmacists could focus their efforts to educate, facilitate, or offer CRC health screenings. Methods: This was a retrospective, cross-sectional study using 2018 Michigan Behavioral Risk Factor Surveillance System (MiBRFSS) survey data. Michigan participants aged ≥ 50 years were included. Outcomes included the utilization of stool-based tests, sigmoidoscopies, colonoscopies, and the most recent CRC screening. Demographic variables included age, sex, income, race/ethnicity, relationship status, education level, employment status, income, rurality, and health insurance. Representative sampling weights were used to adjust for the complex survey design. Descriptive statistics, chi-square, and multivariable logistic regression analyses were conducted. IBM SPSS version 28.0.1.0 was used and an a priori p-value of <0.05 was deemed significant. Results: A weighted total of 3,762,540 participants were included, of which 21.3% (n = 781,907) reported living in a rural area and approximately 70% (n = 2,616,646) were between the ages of 50-69 years old. Most participants reported being White, non-Hispanic (n = 3,104,117, 84.5%), having health insurance (n = 3,619,801, 96.4%), and having a colonoscopy (74.6%, n= 2,620,581). There was no difference based on rurality. Compared to those aged 50-59 years, adults 60-69 years (AOR = 1.97, 95% CI: 1.58,2.45), 70-79 years (AOR = 3.29, 95% CI: 2.40,4.51), and ≥ 80 years (AOR = 2.23, 95% CI: 1.54,3.24) had higher odds of receiving a colonoscopy. Lack of insurance was associated with lower odds of receiving a colonoscopy (AOR = 0.38, 95% CI: 0.23, 0.56). Conclusion: Most participants reported having a CRC screening but efforts to increase CRC screening in Michigan adults aged 50-59 are warranted.
RESUMO
BACKGROUND: Although previous research has highlighted the association between the built environment and individual health, methodological challenges in assessing the built environment remain. In particular, many researchers have demonstrated the high inter-rater reliability of assessing large or objective built environment features and the low inter-rater reliability of assessing small or subjective built environment features using Google Street View. New methods for auditing the built environment must be evaluated to understand if there are alternative tools through which researchers can assess all types of built environment features with high agreement. This paper investigates measures of inter-rater reliability of GigaPan®, a tool that assists with capturing high-definition panoramic images, relative to Google Street View. METHODS: Street segments (n = 614) in Pittsburgh, Pennsylvania in the United States were randomly selected to audit using GigaPan® and Google Street View. Each audit assessed features related to land use, traffic and safety, and public amenities. Inter-rater reliability statistics, including percent agreement, Cohen's kappa, and the prevalence-adjusted bias-adjusted kappa (PABAK) were calculated for 106 street segments that were coded by two, different, human auditors. RESULTS: Most large-scale, objective features (e.g. bus stop presence or stop sign presence) demonstrated at least substantial inter-rater reliability for both methods, but significant differences emerged across finely detailed features (e.g. trash) and features at segment endpoints (e.g. sidewalk continuity). After adjusting for the effects of bias and prevalence, the inter-rater reliability estimates were consistently higher for almost all built environment features across GigaPan® and Google Street View. CONCLUSION: GigaPan® is a reliable, alternative audit tool to Google Street View for studying the built environment. GigaPan® may be particularly well-suited for built environment projects with study settings in areas where Google Street View imagery is nonexistent or updated infrequently. The potential for enhanced, detailed imagery using GigaPan® will be most beneficial in studies in which current, time sensitive data are needed or microscale built environment features would be challenging to see in Google Street View. Furthermore, to better understand the effects of prevalence and bias in future reliability studies, researchers should consider using PABAK to supplement or expand upon Cohen's kappa findings.
