Patient characteristics (means and proportions) at the hospital level.

<p>Patient characteristics (means and proportions) at the hospital level.</p

Sensitivity and specificity for different values of acceptable deviation (δ) and statistical evidence (k).

<p>Sensitivity and specificity for different values of acceptable deviation (δ) and statistical evidence (k).</p

Estimated effects from multiple logistic regression modeling death or dependency at 3 months.

<p>Presented with estimated standard errors (SE), odds ratios (OR) and confidence intervals (CI).</p

Patient characteristics grouped by availibity of three month follow-up.

<p>Patient characteristics grouped by availibity of three month follow-up.</p

The Importance of Integrating Clinical Relevance and Statistical Significance in the Assessment of Quality of Care –Illustrated Using the Swedish Stroke Register - Fig 2

<p>a: Hospital effects (odds ratios) from the logistic regression model (each individual hospital compared to the average over all hospitals). b: Standardized risks (original data) with lines for the benchmark values ((1 + δ) observed population risk) for different values of δ.</p

Are doctors using more preventive medication for cardiovascular disease? A Swedish cross-sectional study

Despite decreasing mortality from cardiovascular disease (CVD), there are persistent inequities in mortality between socioeconomic groups. Primary preventative medications reduce mortality in CVD; thus, inequitable treatments will contribute to unequal outcomes. Physicians might contribute to inequality by prescribing preventative medication for CVD to themselves in a biased manner. To determine whether primary medications for preventing CVD were prescribed inequitably between physicians and non-physicians. This retrospective study retrieved registry data on prescribed medications for all physicians in Sweden aged 45–74 years, during 2013, and for reference non-physician individuals, matched by sex, age, residence, and level of education. The outcome was any medication for preventing CVD, received at least once during 2013. Age and the sex-specific prevalence of myocardial infarction (MI) among physicians and non-physicians were used as a proxy for the need for medication. Thereafter, to limit the analysis to preventative medication, we excluded individuals that were diagnosed with CVD or diabetes. To analyse differences in medication usage between physicians and matched non-physicians, we estimated odds ratios (ORs) with conditional logistic regression and adjusted for need and household income. MI prevalences were 5.7% for men and 2.3% for women, among physicians, and 5.4% for men and 1.8% for women, among non-physicians. We included 25,105 physicians and 44,366 non-physicians. The OR for physicians receiving any CVD preventative medication, compared to non-physicians, was 1.65 (95% confidence interval 1.59–1.72). We found an inequity in prescribed preventative CVD medications, which favoured physicians over non-physicians. Groups with low socioeconomic status have lower rates of using medication that prevents cardiovascular disease, compared to groups with high socioeconomic status. Physicians are responsible for prescribing all medicines to prevent cardiovascular disease; thus, biased prescriptions could have effects on the equality of care in the population. Compared to individuals with equivalent education, physicians had higher rates of using medication that prevents cardiovascular disease. This study highlights the need for systematic population-based evaluation of CVD risk in order to promote equitable CVD outcomes.</p