Geospatial analysis and participant characteristics associated with colorectal cancer screening participation in Alberta, Canada: a population-based cross-sectional study.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of death in Canada and early detection can prevent deaths through screening. However, CRC screening in Alberta, Canada remains suboptimal and varies by sociodemographic and health system characteristics, as well as geographic location. This study aimed to further the understanding of these participant and health system characteristics associated with CRC screening in Alberta and identify clusters of regions with higher rates of overdue or unscreened individuals. METHODS: We included Albertans aged 52 to 74 as of December 31, 2019 (index date) and we used data from administrative health data sources and linked to the Alberta Colorectal Cancer Screening Program database to determine colorectal cancer screening rates. We used multivariable multinomial logistic regression analysis to investigate the relationship between sociodemographic, health system characteristics and participation in CRC screening. We used optimized Getis-Ord Gi* hot-spot analysis to identify hot and cold-spots in overdue for and no record of CRC screening. RESULTS: We included 919,939 Albertans, of which 65% were currently up to date on their CRC screening, 21% were overdue, and 14% had no record of CRC screening. Compared to Albertans who were currently up to date, those who were in older age groups, those without a usual provider of care, those who were health system non-users, and those living in more deprived areas were more likely to have no record of screening. Areas with high number of Albertans with no record of screening were concentrated in the North and Central zones. CONCLUSIONS: Our study showed important variation in colorectal cancer screening participation across sociodemographic, health system and geographical characteristics and identified areas with higher proportions of individuals who have no record of screening or are under-screened in Alberta, Canada.

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Effective interventions to increase routine childhood immunization coverage in low socioeconomic status communities in developed countries: A systematic review and critical appraisal of peer-reviewed literature.

OBJECTIVES: Childhood immunization coverage rates are known to be disproportionate according to population's socioeconomic status (SES). This systematic review examined and appraised quality of interventions deemed effective to increase routine childhood immunization uptake in low SES populations in developed countries. METHODS: A literature search was conducted using Medline, Embase, CINAHL, EBMR, PsycInfo, PubMed, and Health STAR. We systematically searched and critically appraised articles published between January 1990 and December 2019 using the Effective Public Health Practice Project Quality Assessment tool. This systematic review provides a synthesis of the available evidence for childhood immunization interventions deemed effective for low SES parents or families of children ≤ 5 years of age. SYNTHESIS: The search yielded 3317 records, of which 2975 studies met the inclusion criteria. From the 100 relevant studies, a total of 40 were included. The majority of effective and strongly rated studies synthesized consisted of multi-component interventions. Such interventions addressed access, community-based mobilization, outreach, appointment reminders, education, clinical tracking and incentives, and were language and health literacy appropriate to support low SES parents. Improving access to low SES parents was deemed effective in the vast majority of strongly rated studies. Incorrect contact information of low SES parents due to increased social mobility (i.e. household moves) rendered reminders ineffective, and therefore, updating contact information should be pursued proactively by front-line healthcare providers. In addition, plain language communication with low SES parents regarding immunization was deemed effective in improving immunization uptake. CONCLUSION: Comprehensive multi-component interventions including improved access, appointment reminders, education and precision health communication are effective for addressing health inequities in immunization coverage amongst marginalized populations. Most low SES parents still believe that the benefits of immunization outweigh the risks.