Linking cohort data with administrative health data to develop a new hypertension prediction model to aid precision health approach

Introduction Hypertension is a common medical condition, affecting 1 in 5 Canadians, and is a major risk factor for heart attack, stroke, and kidney disease. Predicting the risk of developing incident hypertension may help to inform targeted preventive strategies. Objectives and Approach Identification of major risk factors and incorporation into a multivariable model for risk stratification may help to identify individuals who are at highest risk for developing incident hypertension and would potentially benefit most from intervention. The goal of the proposed research is to develop a robust hypertension prediction model for the general population using the Alberta Tomorrow Project (ATP) cohort data linked with Alberta’s administrative health data. ATP is Alberta's largest population health cohort, contains baseline data on socio-demographic characteristic, personal and family history of disease, medication use, lifestyle and health behavior, environmental exposures, physical measures and bio samples. Results Alberta’s administrative health data additionally provides information on health care utilization, enrollment, drugs, physician services, and hospital services. A prediction model for hypertension will be developed using logistic regression where information on candidate variables for the model will be gathered from ATP data and outcome (incident hypertension) will be ascertained from administrative health data (physicians/practitioner claim data and hospital discharge abstract data). Lacking follow-up information in current ATP data has laid the foundation of linking the two data sources through an anonymous unique person identifier (e.g. PHN) that will eventually provide follow-up information on ATP participants who are free of hypertension at baseline developed the disease as well as information on other potential variables. Conclusion/Implications The proposed prediction model will help to identify individuals at highest risk for developing hypertension and those who may benefit most from targeted healthy behavioral interventions and/or treatment. Such identification of high risk people may help prevent hypertension as well as the continuing costly cycle of managing hypertension and its complications

Application of geographically weighted regression for assessing spatial non-stationarity

Linear regression is a commonly used method of statistical analysis. However, it is not able to capture any spatial variations that may exist in the relationship between explanatory and response variables. We will study geographically weighted regression, which is a local regression method that can account for spatial non-stationarity that may exist. We will describe the model, estimation and hypothesis testing, both in theory and in simulation studies. We will also apply the method to analyze data collected on housing prices in the Boston metropolitan area

Serious adverse drug events in patients presenting to emergency departments and admitted to hospitals in Newfoundland and Labrador

The primary objective of this research was to examine the extent of adverse drug events (ADEs) in three age-related subgroups of patients (children aged ≤17 years, adults aged ≥18 years and elderly aged ≥65 years) either presenting to emergency departments (EDs) or admitted to hospitals in the Canadian province of Newfoundland and Labrador (NL). As secondary objectives, this research classified ADEs according to severity and preventability (wherever possible) and identified patients' demographic and clinical characteristics that can predict occurrence of ADEs. -- This dissertation research was comprised of three empirical studies, each of which led to a manuscript for publication. The first and second studies used retrospective reviews of patients' ED charts to determine prevalence, severity, and preventability of ADEs among children and adults presenting to EDs. The third study used a population-based retrospective cohort design over a 12-year period to detect adverse drug reactions (ADRs) using diagnosis codes in the hospital discharge abstract. The aim of this study was to determine the incidence of ADRs among elderly hospitalized patients and to assess patient-related risk of ADRs. -- We found that 2.1% (95% CI: 1.6-2.6) of pediatric ED visits and 2.4% (95% CI: 1.8-3.0) of adult ED visits were due to serious ADEs, of which 20% and 29%, respectively, were considered preventable. In the cohort of elderly hospitalized patients, the incidence of ADRs was 15.2 per 1,000 person-years (95% CI: 14.8-15.7). Children with and without ADE-related ED visits were similar with respect to mean age and mean number of medications, whereas adults with ADE-related ED visits were older, prescribed more medications and had a higher number of comorbidities compared to their non-ADE counterparts. In elderly hospitalized patients, comorbidity from chronic diseases and the severity of patient's underlying illness, rather than advancing age and sex, increased the likelihood of recurrent events. The drug classes associated with or implicated to ADEs were dissimilar among the three age-related subgroups of patients. -- By comprising the findings of the three studies together, we concluded that an ADE prevention strategy should be targeted at patient-specific physiologic and functional characteristics, and high-risk medications, as opposed to focusing individual's chronological age

Factors associated with mode of colorectal cancer detection and time to diagnosis: a population level study

Abstract Background Although it is well-known that early detection of colorectal cancer (CRC) is important for optimal patient survival, the relationship of patient and health system factors with delayed diagnosis are unclear. The purpose of this study was to identify the demographic, clinical and healthcare factors related to mode of CRC detection and length of the diagnostic interval. Methods All residents of Alberta, Canada diagnosed with first-ever incident CRC in years 2004–2010 were identified from the Alberta Cancer Registry. Population-based administrative health datasets, including hospital discharge abstract, ambulatory care classification system and physician billing data, were used to identify healthcare services related to CRC diagnosis. The time to diagnosis was defined as the time from the first CRC-related healthcare visit to the date of CRC diagnosis. Mode of CRC detection was classified into three groups: urgent, screen-detected and symptomatic. Quantile regression was performed to assess factors associated with time to diagnosis. Results 9626 patients were included in the study; 25% of patients presented as urgent, 32% were screen-detected and 43% were symptomatic. The median time to diagnosis for urgent, screen-detected and symptomatic patients were 6 days (interquartile range (IQR) 2–14 days), 74 days (IQR 36–183 days), 84 days (IQR 39–223 days), respectively. Time to diagnosis was greater than 6 months for 27% of non-urgent patients. Healthcare factors had the largest impact on time to diagnosis: 3 or more visits to a GP increased the median by 140 days whereas 2 or more visits to a GI-specialist increased it by 108 days compared to 0–1 visits to a GP or GI-specialist, respectively. Conclusion A large proportion of CRC patients required urgent work-up or had to wait more than 6 months for diagnosis. Actions are needed to reduce the frequency of urgent presentation as well as improve the timeliness of diagnosis. Findings suggest a need to improve coordination of care across multiple providers

Sample Size Calculation in Clinical Studies: Some Common Scenarios

Determining the optimal sample size is crucial for any scientific investigation. An optimal sample size provides adequate power to detect statistical significant difference between the comparison groups in a study and allows the researcher to control for the risk of reporting a false-negative finding (Type II error). A study with too large a sample is harder to conduct, expensive, time consuming and may expose an unnecessarily large number of subjects to potentially harmful or futile interventions. On the other hand, if the sample size is too small, a best conducted study may fail to answer a research question due to lack of sufficient power. To draw a valid and accurate conclusion, an appropriate sample size must be determined prior to start of any study. This paper covers the essentials in calculating sample size for some common study designs. Formulae along with some worked examples were demonstrated for potential applied health researchers. Although maximum power is desirable, this is not always possible given the resources available for a study. Researchers often needs to choose a sample size that makes a balance between what is desirable and what is feasible

Evaluating the Completness of Physician Billing Claims: A Proof-of-Concept Study

ABSTRACT Objectives An increasing number of physicians are remunerated by alternative forms of payment, instead of conventional fee-for-service (FFS) payments. Changes in physician remuneration methods can to influence the completeness of physician billing claims databases, because physicians on alternative payments may not consistently complete billing records. However, there is no established technique to estimate the magnitude of data loss. This proof-of-concept study estimated completeness of physician claims by comparing them with prescription drug records. We applied the method to estimate completeness of non-fee-for-service (NFFS) and FFS physician claims data over time in Manitoba, Canada. Approach Our method uses information on the date of patient initiation of a new prescription medication, payment method of the prescribing physician, and presence/absence of a physician billing claim prior to the medication initiation date. A billing claim within 7 days of the medication initiation date was defined as a captured claim; if there was no claim in this observation window, it was classified as missed. Our method was applied to annual patient cohorts who initiated a common prescription medication (i.e., anti-hypertensives) between fiscal years 1998/99 and 2012/13. A sensitivity analysis used a 21-day observation window to identify captured/missing claims. Multivariable hierarchical logistic regression models tested patient and prescriber characteristics associated with missing claims. Results The cohort consisted of 274, 462 individuals with a new anti-hypertensive prescription medication. A total of 9.2% of the cohort had a NFFS prescribing physician in 1998/99; this increased to 20.2% in 2012/13 (linear trend p-value < .0001). The percentage of NFFS prescribers almost doubled, from 10.0% to 17.8%. The percentage of the annual cohorts with a FFS prescribing physician and a missing claim remained close to 13.0%. However, the percentage of the annual cohorts with a NFFS prescribing physician and a missing claim increased from 15.6% to 23.3% (linear trend p-value < .0001), and was always higher than the FFS percentage. Patient age, sex, and comorbidity and physician specialty and practice location were associated with the odds of a missing claim. Conclusion The percentage of missing claims was higher for patients with NFFS than FFS prescribing physicians, demonstrating the impact of physician remuneration on database completeness. The trend of greater data loss in later than earlier years suggests that completeness of physician billing claims data may be decreasing. Our method can be applied across jurisdictions to compare the impact of physician payment methods on data quality

Incidence of Pregnancy-Associated Cancer in Two Canadian Provinces: A Population-Based Study

Pregnancy-associated cancer—that is diagnosed in pregnancy or within 365 days after delivery—is increasingly common as cancer therapy evolves and survivorship increases. This study assessed the incidence and temporal trends of pregnancy-associated cancer in Alberta and Ontario—together accounting for 50% of Canada’s entire population. Linked data from the two provincial cancer registries and health administrative data were used to ascertain new diagnoses of cancer, livebirths, stillbirths and induced abortions among women aged 18–50 years, from 2003 to 2015. The annual crude incidence rate (IR) was calculated as the number of women with a pregnancy-associated cancer per 100,000 deliveries. A nonparametric test for trend assessed for any temporal trends. In Alberta, the crude IR of pregnancy-associated cancer was 156.2 per 100,000 deliveries (95% CI 145.8–166.7), and in Ontario, the IR was 149.4 per 100,000 deliveries (95% CI 143.3–155.4). While no statistically significant temporal trend in the IR of pregnancy-associated cancer was seen in Alberta, there was a rise in Ontario (p = 0.01). Pregnancy-associated cancer is common enough to warrant more detailed research on maternal, pregnancy and child outcomes, especially as cancer therapies continue to evolve.Medicine, Faculty ofNon UBCObstetrics and Gynaecology, Department ofReviewedFacult