Spatial and temporal variations of screening for breast and colorectal cancer in the United States, 2008 to 2012

Geographical and spatial variations in bowel cancer screening participation, Australia, 2015-2020

BACKGROUND

Participation in bowel cancer screening programs remains poor in many countries. Knowledge of geographical variation in participation rates may help design targeted interventions to improve uptake. This study describes small-area and broad geographical patterns in bowel screening participation in Australia between 2015-2020.

METHODS

Publicly available population-level participation data for Australia's National Bowel Cancer Screening Program (NBCSP) were modelled using generalized linear models to quantify screening patterns by remoteness and area-level disadvantage. Bayesian spatial models were used to obtain smoothed estimates of participation across 2,247 small areas during 2019-2020 compared to the national average, and during 2015-2016 and 2017-2018 for comparison. Spatial heterogeneity was assessed using the maximized excess events test.

RESULTS

Overall, screening participation rates was around 44% over the three time-periods. Participation was consistently lower in remote or disadvantaged areas, although heterogeneity was evident within these broad categories. There was strong evidence of spatial differences in participation over all three periods, with little change in patterns between time periods. If the spatial variation was reduced (so low participation areas were increased to the 80th centile), an extra 250,000 screens (4% of total) would have been conducted during 2019-2020.

CONCLUSIONS

Despite having a well-structured evidence-based government funded national bowel cancer screening program, the substantial spatial variation in participation rates highlights the importance of accounting for the unique characteristics of specific geographical regions and their inhabitants. Identifying the reasons for geographical disparities could inform interventions to achieve more equitable access and a higher overall bowel screening uptake.

Trends in the Impact of Medicaid Expansion on the Use of Clinical Preventive Services

INTRODUCTION

This study aims to evaluate the trends in the impact of Medicaid expansion on the use of selected recommended clinical preventive services and examine the differences in use by income level over time.

METHODS

The data were obtained from the 2011-2019 Behavioral Risk Factor Surveillance System and were analyzed in 2021. This study conducted a difference-in-differences analysis of the association between Medicaid expansion and the use of 5 clinical preventive services, including colon/breast/cervical cancer screenings, HIV testing, and influenza vaccination. Annual percentage change was applied to assess the trends in the impact of Medicaid expansion on the use of clinical preventive services.

RESULTS

The use of all the 5 clinical preventive services varied over time. In almost every year, the use of 4 clinical preventive services (all but HIV testing) among Medicaid expansion states was higher than that among the nonexpansion states. People with lower income used 4 clinical preventive services (all but HIV testing) less frequently than those with higher income, regardless of their residence in expansion or nonexpansion groups. Among the lower-income group, the use of 5 clinical preventive services increased after Medicaid expansion almost every year, with the use of colon cancer screening and HIV testing reaching statistical significance and the impact of Medicaid expansion in the use of each clinical preventive service kept stable from 2014 to 2019.

CONCLUSIONS

These findings provide evidence that Medicaid expansion may be associated with sustainably increased use of the selected recommended clinical preventive services among the lower-income population and that Medicaid expansion to reduce financial barriers may be an effective strategy to improve population health.

Differences in Breast Cancer Screening Practices by Diabetes Status and Race/Ethnicity in the United States

Introduction: Socioeconomic and health-related factors, including comorbid illness, may affect mammography screening rates and subsequently contribute to breast cancer outcomes. We explored the association between diabetes and mammography screening, and whether this association varied between racial, ethnic, and geographic groups. Methods: Cross-sectional data from the 2012, 2014, 2016, and 2018 Behavioral Risk Factor Surveillance System were used to fit logistic regression models assessing the association between diabetes and up-to-date mammography screening in 497,600 women, aged 50-74 years. Participants were considered exposed if they responded "yes" to "(Ever told) you have diabetes?" and up to date on screening if they responded "yes" to having a mammogram within the past 2 years. Models were adjusted for age, health status, socioeconomic, and access variables. Results: The majority of participants were White (79.6%), non-Hispanic (88.9%), and up to date on screening (78.8%). Overall, 16.8% reported having diabetes. In fully adjusted models, White women with diabetes were 12% more likely to be up to date on screening (odds ratio [OR]: 1.12, 95% confidence interval [CI]: 1.06-1.19) than those without diabetes. Black/African American women and those of Hispanic ethnicity with diabetes were more likely to report being up to date with mammography (OR_Black: 1.28, 95% CI: 1.12-1.45; OR_Hispanic: 1.19, 95% CI: 1.13-1.24) than those without. Patterns were similar across geographic regions. Conclusions: Women of ages 50-74 years with diabetes were more likely to be up to date on screening than women without diabetes. Chronic disease management may represent an opportunity to address cancer screening.

Using big data to gauge effectiveness of breast cancer awareness month

Breast Cancer Awareness Month (BCAM) has been used for decades to increase awareness and screening for breast cancer, but its geographic reach and effectiveness is difficult to judge. Using Internet Search Interest (ISI) could allow for better evaluation of BCAM effects. Using Google Trends, we evaluated the ISI for "breast cancer" and "mammogram" for each state and metropolitan area from 2006 to 2019. The ISI represents population level Google internet searches relative to the highest number of searches for the United States over a given period, with a max number of 100. The ISI for each term in October (BCAM) was compared against all other months during this period, across states and across major metropolitan regions. ISI was 2.34 times higher (95% Confidence Interval [CI]: 2.10-2.61, P < .001) in BCAM than the average for all other months combined. Geographically categorized data revealed that there were significant differences in the ISI for "breast cancer" and for "mammogram" among the 50 states, and among major metropolitan areas (P < .001for each). ISI suggests that BCAM is effective at increasing breast cancer related internet searches, with significant heterogeneity across states and metro areas. Google Trends is a publicly available free tool that can be used to assess penetrance of awareness campaigns in a time sensitive and location specific manner for future targeting of populations with low breast cancer awareness. Future research is needed to assess relationships between preventive outcomes and ISI scores.

Spatial Insights for Understanding Colorectal Cancer Screening in Disproportionately Affected Populations, Central Texas, 2019

INTRODUCTION

Colorectal cancer (CRC) screening can reduce morbidity and mortality; however, important disparities exist in CRC uptake. Our study examines the associations of distance to care and frequency of using primary care and screening.

METHODS

To examine the distribution of screening geographically and according to several demographic features, we used individual patient-level data, dated September 30, 2018, from a large urban safety-net health system in Central Texas. We used spatial cluster analysis and logistic regression adjusted for age, race, sex, socioeconomic status, and health insurance status.

RESULTS

We obtained screening status data for 13,079 age-eligible patients from the health system's electronic medical records. Of those eligible, 55.1% were female, and 55.9% identified as Hispanic. Mean age was 58.1 years. Patients residing more than 20 miles from one of the system's primary care clinics was associated with lower screening rates (odds ratio [OR], 0.63; 95% CI, 0.43-0.93). Patients with higher screening rates included those who had a greater number of primary care-related (nonspecialty) visits within 1 year (OR, 6.90; 95% CI, 6.04-7.88) and those who were part of the county-level medical assistance program (OR, 1.61; 95% CI, 1.40-1.84). Spatial analysis identified an area where the level of CRC screening was particularly low.

CONCLUSION

Distance to primary care and use of primary care were associated with screening. Priorities in targeted interventions should include identifying and inviting patients with limited care engagements.

What are the contextual risk factors for low colorectal cancer screening uptake in El Paso County, Texas? Spatial cross-sectional analysis

OBJECTIVE

This study examines the geographical and socioeconomic factors associated with uptake of colorectal cancer (CRC) screening (colonoscopies or faecal immunochemical test (FIT) testing).

DESIGN

Secondary data analysis.

SETTING

The Against Colorectal Cancer in our Community (ACCION) programme was implemented in El Paso County, Texas, to increase screening rates among the uninsured and underinsured.

PARTICIPANTS

We successfully geocoded 5777 who were offered a free colonoscopy or FIT testing kit.

PRIMARY OUTCOME MEASURE

Census-tract CRC screening uptake average.

RESULTS

Medicare recipient mortality (β=0.409, p-value=0.049) and % 65 years or older (β=-0.577, p value=0.000) were significant census tract contextual factors that were associated with the prevalence of CRC screening uptake in the geographically weighted Poisson regression. Neither Latino ethnicity nor immigrant concentration were significant predictors of CRC screening uptake in the ACCION programme. Hot spot analysis demonstrated that there was a significant low-value cluster in South Central El Paso. There was a similar hot spot for % 65 years or older in this same area, suggesting that uptake was lowest in an area that had the highest concentration of older adults.

CONCLUSION

The results from this study revealed not only feasibility of hot spot analysis but also its utility in geographically tracking successful CRC screening uptake in cancer prevention and intervention programmes.

Factors associated with mammography use: A side-by-side comparison of results from two national surveys

BACKGROUND

Mammography use is affected by multiple factors that may change as public health interventions are implemented. We examined two nationally representative, population-based surveys to seek consensus and identify inconsistencies in factors associated with mammography use in the entirety of the US population, and by black and white subgroups.

METHODS

Self-reported mammography use in the past year was extracted for 12 639 and 169 116 women aged 40-74 years from the 2016 National Health Interview Survey (NHIS) and the 2016 Behavioral Risk Factor Surveillance System (BRFSS), respectively. We applied a random forest algorithm to identify the risk factors of mammography use and used a subset of them in multivariable survey logistic regressions to examine their associations with mammography use, reporting predictive margins and effect sizes.

RESULTS

The weighted prevalence of past year mammography use was comparable across surveys: 54.31% overall, 54.50% in white, and 61.57% in black in NHIS and 53.24% overall, 56.97% in white, and 62.11% in black in BRFSS. Overall, mammography use was positively associated with black race, older age, higher income, and having health insurance, while negatively associated with having three or more children at home and residing in the Western region of the US. Overweight and moderate obesity were significantly associated with increased mammography use among black women (NHIS), while severe obesity was significantly associated with decreased mammography use among white women (BRFSS).

CONCLUSION

We found higher mammography use among black women than white women, a change in the historical trend. We also identified high parity as a risk factor for mammography use, which suggests a potential subpopulation to target with interventions aimed at increasing mammography use.

Bivariate Spatial Pattern between Smoking Prevalence and Lung Cancer Screening in US Counties

Objectives: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) has been a reimbursable preventive service covered by Medicare since 2015. Geographic disparities in the access to LDCT providers may contribute to the low uptake of LCS. We evaluated LDCT service availability for older adults in the United States (US) based on Medicare claims data and explored its ecological correlation with smoking prevalence. Materials and Methods: We identified providers who provided at least 11 LDCT services in 2016 using the Medicare Provider Utilization and Payment Data: Physician and Other Supplier Public Use File. We constructed a 30-mile Euclidian distance buffer around each provider’s location to estimate individual LDCT coverage areas. We then mapped the county-level density of LDCT providers and the county-level prevalence of current daily cigarette smoking in a bivariate choropleth map. Results: Approximately 1/5 of census tracts had no LDCT providers within 30 miles and 46% of counties had no LDCT services. At the county level, the median LDCT density was 0.5 (interquartile range (IQR): 0–5.3) providers per 1000 Medicare fee-for-service beneficiaries, and cigarette smoking prevalence was 17.5% (IQR: 15.2–19.8%). High LDCT service availability was most concentrated in the northeast US, revealing a misalignment with areas of high current smoking prevalence, which tended to be in the central and southern US. Conclusions: Our maps highlight areas in need for enhanced workforce and capacity building aimed at reducing disparities in the access and utilization of LDCT services among older adults in the US.

Assessing and Explaining Geographic Variations in Mammography Screening Participation and Breast Cancer Incidence

Investigating geographic variations in mammography screening participation and breast cancer incidence help improve prevention strategies to reduce the burden of breast cancer. This study examined the suitability of health insurance claims data for assessing and explaining geographic variations in mammography screening participation and breast cancer incidence at the district level. Based on screening unit data (1,181,212 mammography screening events), cancer registry data (13,241 incident breast cancer cases) and claims data (147,325 mammography screening events; 1,778 incident breast cancer cases), screening unit and claims-based standardized participation ratios (SPR) of mammography screening as well as cancer registry and claims-based standardized incidence ratios (SIR) of breast cancer between 2011 and 2014 were estimated for the 46 districts of the German federal state of Lower Saxony. Bland-Altman analyses were performed to benchmark claims-based SPR and SIR against screening unit and cancer registry data. Determinants of district-level variations were investigated at the individual and contextual level using claims-based multilevel logistic regression analysis. In claims and benchmark data, SPR showed considerable variations and SIR hardly any. Claims-based estimates were between 0.13 below and 0.14 above (SPR), and between 0.36 below and 0.36 above (SIR) the benchmark. Given the limited suitability of health insurance claims data for assessing geographic variations in breast cancer incidence, only mammography screening participation was investigated in the multilevel analysis. At the individual level, 10 of 31 Elixhauser comorbidities were negatively and 11 positively associated with mammography screening participation. Age and comorbidities did not contribute to the explanation of geographic variations. At the contextual level, unemployment rate was negatively and the proportion of employees with an academic degree positively associated with mammography screening participation. Unemployment, income, education, foreign population and type of district explained 58.5% of geographic variations. Future studies should combine health insurance claims data with individual data on socioeconomic characteristics, lifestyle factors, psychological factors, quality of life and health literacy as well as contextual data on socioeconomic characteristics and accessibility of mammography screening. This would allow a comprehensive investigation of geographic variations in mammography screening participation and help to further improve prevention strategies for reducing the burden of breast cancer.

Mapping Variation in Breast Cancer Screening: Where to Intervene?

Small geographic areas with lower mammography screening participation rates may reflect gaps in screening efforts. Our objective was to use spatial analyses to understand disparities in mammography screening use and to identify factors to increase its uptake in areas that need it in Lyon metropolitan area, France. Data for screened women between the ages of 50 and 74 were analyzed. Census blocks of screened and non screened women were extracted from the mammography screening programme 2015-2016 dataset. We used spatial regression models, within a generalized additive framework to determine clusters of census blocks with significantly higher prevalence of non-participation of mammography screening. Smoothed risk maps were crude and adjusted on the following covariates: deprivation index and opportunistic screening. Among 178,002 women aged 50 to 74, 49.9% received mammography screening. As hypothesized, women living in highly deprived census blocks had lower participation rates compared to less deprived blocks, 45.2% vs. 51.4% p < 0.001. Spatial analyses identified four clusters, one located in an urban area and three in suburban areas. Moreover, depending on the location of the cluster, the influence came from different variables. Knowing the impact of site-specific risk factors seems to be important for implementing an appropriate prevention intervention.

Spatially varying effects of predictors for the survival prediction of nonmetastatic colorectal Cancer

Background

An increasing number of studies have identified spatial differences in colorectal cancer survival. However, little is known about the spatially varying effects of predictors in survival prediction modeling studies of colorectal cancer that have focused on estimating the absolute survival risk for patients from a wide range of populations. This study aimed to demonstrate the spatially varying effects of predictors of survival for nonmetastatic colorectal cancer patients.

Methods

Patients diagnosed with nonmetastatic colorectal cancer from 2004 to 2013 who were followed up through the end of 2013 were extracted from the Surveillance Epidemiology End Results registry (Patients: 128061). The log-rank test and the restricted mean survival time were used to evaluate survival outcome differences among spatial clusters corresponding to a widely used clinical predictor: stage determined by AJCC 7th edition staging system. The heterogeneity test, which is used in meta-analyses, revealed the spatially varying effects of single predictors. Then, considering the above predictors in a standard survival prediction model based on spatially clustered data, the spatially varying coefficients of these models revealed that some covariate effects may not be constant across the geographic regions of the study. Then, two types of survival prediction models (a statistical model and a machine learning model) were built; these models considered the predictors and enabled survival prediction for patients from a wide range of geographic regions.

Results

Based on univariate and multivariate analysis, some prognostic factors, such as “TNM stage”, “tumor size” and “age at diagnosis,” have significant spatially varying effects among different regions. When considering these spatially varying effects, machine learning models have fewer assumption constraints (such as proportional hazard assumptions) and better predictive performance compared with statistical models. Upon comparing the concordance indexes of these two models, the machine learning model was found to be more accurate (0.898[0.895,0.902]) than the statistical model (0.732 [0.726, 0.738]).

Conclusions

Based on this study, it’s recommended that the spatially varying effect of predictors should be considered when building survival prediction models involving large-scale and multicenter research data. Machine learning models that are not limited by the requirement of a statistical hypothesis are promising alternative models.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4985-2) contains supplementary material, which is available to authorized users.

Multilevel Regression for Small-Area Estimation of Mammography Use in the United States, 2014

BACKGROUND

The U.S. Preventive Services Task Force recommends biennial screening mammography for average-risk women aged 50-74 years. County-level information on population measures of mammography use can inform targeted intervention to reduce geographic disparities in mammography use. County-level estimates for mammography use nationwide are rarely presented.

METHODS

We used data from the 2014 Behavioral Risk Factor Surveillance System (BRFSS; n = 130,289 women), linked it to the American Community Survey poverty data, and fitted multilevel logistic regression models with two outcomes: mammography within the past 2 years (up-to-date), and most recent mammography 5 or more years ago or never (rarely/never). We poststratified the data with U.S. Census population counts to run Monte Carlo simulations. We generated county-level estimates nationally and by urban-rural county classifications. County-level prevalence estimates were aggregated into state and national estimates. We validated internal consistency between our model-based state-specific estimates and urban-rural estimates with BRFSS direct estimates using Spearman correlation coefficients and mean absolute differences.

RESULTS

Correlation coefficients were 0.94 or larger. Mean absolute differences for the two outcomes ranged from 0.79 to 1.03. Although 78.45% (95% confidence interval, 77.95%-78.92%) of women nationally were up-to-date with mammography, more than half of the states had counties with >15% of women rarely/never using a mammogram, many in rural areas.

CONCLUSIONS

We provided estimates for all U.S. counties and identified marked variations in mammography use. Many states and counties were far from the 2020 target (81.1%).

IMPACT

Our results suggest a need for planning and resource allocation on a local level to increase mammography uptake.