Additional file 2: of Informing implementation of quality improvement in Australian primary care

Feedback regarding research findings - Presentations of the findings where feedback was sought from a wider setting. (DOCX 13 kb

Implications of Cardiovascular Disease Risk Assessment Using the WHO/ISH Risk Prediction Charts in Rural India

<div><p>Cardiovascular disease (CVD) risk in India is currently assessed using the World Health Organization/International Society for Hypertension (WHO/ISH) risk prediction charts since no population-specific models exist. The WHO/ISH risk prediction charts have two versions—one with total cholesterol as a predictor (the high information (HI) model) and the other without (the low information (LI) model). However, information on the WHO/ISH risk prediction charts including guidance on which version to use and when, as well as relative performance of the LI and HI models, is limited. This article aims to, firstly, quantify the relative performance of the LI and HI WHO/ISH risk prediction (for WHO-South East Asian Region D) using data from rural India. Secondly, we propose a pre-screening (simplified) point-of-care (POC) test to identify patients who are likely to benefit from a total cholesterol (TC) test, and subsequently when the LI model is preferential to HI model. Analysis was performed using cross-sectional data from rural Andhra Pradesh collected in 2005 with recorded blood cholesterol measurements (N = 1066). CVD risk was computed using both LI and HI models, and <i>high risk individuals who needed treatment</i>(<i>T</i>_<i>HR</i>) were subsequently identified based on clinical guidelines. Model development for the POC assessment of a TC test was performed through three machine learning techniques: Support Vector Machine (SVM), Regularised Logistic Regression (RLR), and Random Forests (RF) along with a feature selection process. Disagreement in CVD risk predicted by LI and HI WHO/ISH models was 14.5% (n = 155; p<0.01) overall and comprised 36 clinically relevant <i>T</i>_<i>HR</i> patients (31% of patients identified as <i>T</i>_<i>HR</i> by using either model). Using two patient-specific parameters (age, systolic blood pressure), our POC assessment can pre-determine the benefit of TC testing and choose the appropriate risk model (out-of-sample AUCs:RF-0.85,SVM-0.84,RLR:0.82 and maximum sensitivity-98%). The identification of patients benefitting from a TC test for CVD risk stratification can aid planning for resource-allocation and save costs for large-scale screening programmes.</p></div

CVD risk prediction using the LI and HI WHO/ISH risk prediction charts on the chosen subset of APHRI data (N = 1066).

<p>The five CVD risk ranges for whom the predicted risk using LI and HI WHO/ISH risk prediction charts concurred, are colour coded as follows: less than 10% risk in green; 10 to <20% risk in orange; 20 to <30% risk in red; 30 to <40% in light red; and ≥ 40% risk in maroon. Subjects for whom the predictions differed are highlighted in yellow circles with those that can be <i>T</i>_<i>HR</i> having a red highlight.</p

Statistical characteristics of the subpopulation (n = 155) who are likely to benefit from TC testing.

<p>To investigate variables that were different between the subpopulation and chosen subjects (N = 1066), significance testing was performed. The difference was statistically significant (p<0.05) for all gender-stratified variables except male total cholesterol measurements (p = 0.0530; t-test), female diastolic blood pressure levels (p = 0.0672; t-test), female blood glucose measurements (p = 0.1355; t-test), and female smokers (p = 0.7484; Wilcoxon signed-rank test).</p

Governance framework for skilled health worker migration in Nigeria.

Governance framework for skilled health worker migration in Nigeria.</p

Population characteristics from the chosen subset of APHRI (N = 1066).

<p>Statistical significance testing was performed to investigate this chosen subset from the full APHRI cohort (<i>N</i>_<i>a</i> = 4535). Only male smokers were significantly different (p = 0.0095; Wilcoxon signed-rank test). BP indicates blood pressure.</p

Receiver Operating Characteristic Curves for the SVM, RF, and RLR models.

<p>The F1, F2, and F3 scores were computed for every point on the ROC curve from the training dataset and Plot <b>(a)</b> illustrates thresholds where the scores were maximum. The Out of Bag samples during training were used to obtain the ROC curve for RF since the performance on the training data was perfect (AUC 1.00). Plot <b>(b)</b> represents ROC of the test data where thresholds chosen from the training data are marked.</p

Variable importance ranked according to the mean decrease in accuracy using the RF OOB samples.

<p>Variable importance ranked according to the mean decrease in accuracy using the RF OOB samples.</p

Additional file 2: of Examining the use of process evaluations of randomised controlled trials of complex interventions addressing chronic disease in primary health care—a systematic review protocol

Example of search strategy. (PDF 314 kb

Additional file 1: of Improving preventive health care in Aboriginal and Torres Strait Islander primary care settings

Identified preliminary priority evidence-practice gaps [26]. (DOCX 22 kb