Body silhouettes as a tool to reflect obesity in the past

<div><p>Life course data on obesity may enrich the quality of epidemiologic studies analysing health consequences of obesity. However, achieving such data may require substantial resources.</p><p>We investigated the use of body silhouettes in adults as a tool to reflect obesity in the past. We used large population-based samples to analyse to what extent self-reported body silhouettes correlated with the previously measured (9–23 years) body mass index (BMI) from both measured (European Community Respiratory Health Survey, N = 3 041) and self-reported (Respiratory Health In Northern Europe study, N = 3 410) height and weight. We calculated Spearman correlation between BMI and body silhouettes and ROC-curve analyses for identifying obesity (BMI ≥30) at ages 30 and 45 years. Spearman correlations between measured BMI age 30 (±2y) or 45 (±2y) and body silhouettes in women and men were between 0.62–0.66 and correlations for self-reported BMI were between 0.58–0.70. The area under the curve for identification of obesity at age 30 using body silhouettes <i>vs</i> previously measured BMI at age 30 (±2y) was 0.92 (95% CI 0.87, 0.97) and 0.85 (95% CI 0.75, 0.95) in women and men, respectively; for previously self-reported BMI, 0.92 (95% CI 0.88, 0.95) and 0.90 (95% CI 0.85, 0.96). Our study suggests that body silhouettes are a useful epidemiological tool, enabling retrospective differentiation of obesity and non-obesity in adult women and men.</p></div

Additional file 1: of Diabetes knowledge in nursing homes and home-based care services: a validation study of the Michigan Diabetes Knowledge Test adapted for use among nursing personnel

The adapted Michigan Diabetes Knowledge Test. (DOC 519 kb

Additional file 1: Table A1. of Ethnic inequalities in acute myocardial infarction and stroke rates in Norway 1994–2009: a nationwide cohort study (CVDNOR)

Regions and countries of birth. Norwegian residents aged 35–64, 1994–2009. Table A2. Age standardized AMI event rates per 100 000 person-years, subjects aged 35–89 years, CVDNOR 1994–2009. Table A3. Age standardized stroke event rates per 100 000 person-years, subjects aged 35–89 years, CVDNOR 1994–2009. The additional tables provide supplementary information to the article. Table A1 lists all the countries within each region. Table A2 and A3 respectively show AMI and stroke event rates for a wider age group than the one we focused on in the article. (PDF 1020 kb

Relative and absolute inequalities in AMI incidence according to level of education by gender and age group: a CVDNOR project.

<p>Abbreviations:</p><p>AMI, Acute myocardial infarction.</p><p>AASIR, Average age-standardised incident rate between 2001 and 2009.</p><p>IRR, Incidence rate ratio from Poisson regression.</p><p>RII, relative index of inequality. Ratio between rates at the upper 100th- and lower 0^th %-end of the education scale.</p><p>SII = slope index of inequality, absolute difference in rate per 100 000 between the upper 100^th %- and lower 0^th %-end of the education scale.</p><p>IRR, RII and SII are adjusted for age and calendar year in each gender- and age strata. Total model also adjusted for gender.</p><p>*Person-years for the total population at risk of getting an incident AMI.</p><p>Relative and absolute inequalities in AMI incidence according to level of education by gender and age group: a CVDNOR project.</p

Incident AMIs in Norway 2001–2009 by level of education: a CVDNOR project.

<p>*AMI  =  Acute myocardial infarction;</p><p>**CHD  =  Coronary Heart Disease.</p><p>Incident AMIs in Norway 2001–2009 by level of education: a CVDNOR project.</p

Time-trends in relative index of inequality (RII) and slope index of inequality (SII): a CVDNOR project.

<p>Estimates are displayed by gender and age group. Upper panel: Men and women aged 35–69 years. Lower panel: Men and women aged 70–94 years. The y- axis for the RII is given on the left hand side and the y-axis for the SII is given on the right hand side.</p

Time-trends in age-standardised AMI incidence rates by level of education: a CVDNOR project.

<p>Rates are shown in four gender- and age groups. Upper panel: Men and women aged 35–69 years. Lower panel: Men and women aged 70–94 years. Arrows indicate significant log-linear trend.</p

Additional file 1 of High number of hypoglycaemic episodes identified by CGM among home-dwelling older people with diabetes: an observational study in Norway

Additional file 1. Clinical data for the total 56 participants with diabetes (≥65 years) receiving home care, and divided into subgroups of participants with no hypoglycaemic episode and participants with one or more hypoglycaemic episodes during the study period

Severity and duration of diabetic foot ulcer (DFU) before seeking care as predictors of healing time: A retrospective cohort study

<div><p>Objectives</p><p>To investigate whether A) duration of ulcer before start of treatment in specialist health care, and B) severity of ulcer according to University of Texas classification system (UT) at start of treatment (baseline), are independent predictors of healing time.</p><p>Methods</p><p>This retrospective cohort study, based on electronic medical record data, included 105 patients from two outpatient clinics in Western Norway with a new diabetic foot ulcer during 2009–2011. The associations of duration of ulcer and ulcer severity with healing time were assessed using cumulative incidence curves and subdistribution hazard ratio estimated using competing risk regression with adjustment for potential confounders.</p><p>Results</p><p>Of the 105 participants, 45.7% achieved ulcer healing, 36.2% underwent amputations, 9.5% died before ulcer healing and 8.5% were lost to follow-up. Patients who were referred to specialist health care by a general practitioner ≥ 52 days after ulcer onset had a 58% (SHR 0.42, CI 0.18–0.98) decreased healing rate compared to patients who were referred earlier, in the adjusted model. High severity (grade 2/3, stage C/D) according to the UT classification system was associated with a decreased healing rate compared to low severity (grade1, stage A/B or grade 2, stage A) with SHR (95% CI) equal to 0.14 (0.05–0.43) after adjustment for referral time and other potential confounders.</p><p>Conclusion</p><p>Early detection and referral by both the patient and general practitioner are crucial for optimal foot ulcer healing. Ulcer grade and severity are also important predictors for healing time, and early screening to assess the severity and initiation of prompt treatment is important.</p></div

Subdistribution hazard regression model to calculate the association between duration of ulcer, severity of ulcer and healing time.

<p>Subdistribution hazard regression model to calculate the association between duration of ulcer, severity of ulcer and healing time.</p