Statistical Investigation of the Prevalence and Incidence of High Blood Pressure among Rural Residents of Saskatchewan
Bibliographic record
Abstract
Objectives: The purpose of this study is to investigate the principal risk factors for the incidence and prevalence of high blood pressure (HBP) and longitudinal changes in the prevalence of HBP among rural residents of Saskatchewan. Methods: The Saskatchewan Rural Health Study (SRHS) was a prospective cohort study conducted in the Canadian province of Saskatchewan between 2010 and 2014. A total of 8261 participants (≥ 18 years) in 4624 households answered the questionnaires mailed at baseline, and 4867 participants in 2797 households completed the mailed questionnaires at follow-up. Statistical analyses were implemented using R, SPSS, STATA, and SAS. We conducted descriptive statistics at both baseline and follow-up. The crude prevalence of self-reported doctor-diagnosed HBP for participants was calculated at baseline and at follow-up. Marginal logistic regression models based on generalized estimating equations (GEE) were used to calculate the adjusted prevalence of HBP and to explore factors associated with the prevalence of HBP over four years. By using GEE, we accounted for the effect resulting from clustering (number of individuals within each household) and within-subject correlation resulting from repeated measurements. To understand the change in HBP prevalence over the study period, a comparison was made between the predicted probabilities of the prevalence at baseline and follow-up. Survival analysis techniques using the Cox proportional hazards model were employed to determine the adjusted incidence of HBP and to explore risk factors for the incidence of HBP. Results: The prevalence of HBP was 33.63% at baseline and 41.5% at follow-up. The prevalence of HBP was associated with residence type (farm vs. non-farm, OR = 0.82, 95% Confidence Interval (CI): (0.73, 0.92)), diabetes (OR = 2.87, 95% CI: (2.35, 3.50)), and stroke (OR = 3.66, 95% CI: (2.33, 5.73)). Other variables associated with the prevalence of HBP were education level, exercise duration, alcohol consumption (“once a month” and “4 to 6 times a week”), parental history of HBP, being exposed to insecticides, body mass index (BMI), the number of people living in a household, and history of heart disease and heart attack. In the younger age groups (18-45 years and 46-55 years), we observed significantly higher changes in the prevalence of HBP among those diagnosed with sleep apnea when compared with those who were not. We observed a significantly higher change in HBP prevalence among males compared to females in the income adequacy category of “just enough money at the end of the month" (a higher chance for an increased HBP prevalence for males compared to females). In contrast, we see a similar change for both males and females in the other categories. Over the 2010-2014 period, the crude estimate of the incidence of HBP among rural residents of Saskatchewan was 343/3127 = 0.11; that is, there were 110 new HBP cases per 1,000 people during this time period. Family history of HBP (both parents: (HR = 1.52; 95% CI: (1.05, 2.19)), either mom or dad: HR = 1.28; 95% CI: (0.99, 1.65)), being exposed to pesticides (HR = 1.29; 95% CI: (1, 1.67)), age (46-55 years: HR = 2.52; 95% CI: (1.66, 3.81), 56-65 years: HR = 2.86; 95% CI: (1.86, 4.41), 65 years and older: HR = 5.04; 95% CI: (3.26, 7.79)), BMI (obese: HR = 2.06; 95% CI: (1.48,2.86)), overweight: (HR = 1.39; 95% CI: (1.02, 1.89)), smoking status (current smoker) (HR = 1.45; 95% CI: (0.98, 2.14)), and diabetes (HR = 1.81; 95% CI: (1.2, 2.71)) were associated with HBP incidence. Conclusion: This study revealed how individual and contextual factors, along with comorbidities, impact the prevalence and incidence of HBP in the rural population of Saskatchewan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".