Statistics
HbA1c diabetes, diabetes
REGRESSION, DIABETES, CVD
Descriptive statistics were used to compare response rates to the overall survey, consent rates for providing each type of blood sample and the provision of ‘good’ quality DBS samples by mode of interview. The population characteristics of participants who gave consent to give a venous blood sample, consent to a DBS blood sample, and provided a good DBS sample, by actual mode of interview, were compared using the Wilcoxon rank sum test for continuous variables and Pearson's Chi-squared test for categorical variables.To calculate venous equivalent-DBS values, for those participants with both samples, venous results were regressed on DBS raw sample values using Deming regression. This was carried out using the mcr R packageTo investigate how closely the raw DBS measures match those taken from venous blood or serum, where both measures were taken from the same participant, simple linear regression was used to compare venous or serum values with—venous-equivalent DBS values (Supplementary Materials Bland–Altman plots (Supplementary Material DBS and venous sample characteristics were investigated to understand if these impacted the agreement of raw DBS measures to venous-equivalence; analysis of variance was used to measure differences between a base model that included only the DBS values regressed on venous values, against another model where for each indicator of blood sample quality an interaction term between DBS and the covariate in question was included (Supplementary Material Cut-offs were used to estimate CVD risk in the venous-equivalent DBS measures; for HbA1c diabetes risk was defined as greater than 6.5%, high cholesterol at a threshold of 5 mmol/l, and high CRP at 10 mg/l. Pearson's Chi-squared tests were used to compare the proportion of participants’ with diagnosed diabetes and participants’ analyte levels that were above clinical cut-offs between the self-collected DBS samples and venous bloods collected by nurses. These analyses were carried out unweighted and weighted to adjust for the complex sample design and likelihood of being included in the Innovation Panel