Social disparities in diabetes care: a general population study in Denmark

<p><b>Objective:</b> We investigated the association between socioeconomic factors and the attainment of treatment goals and pharmacotherapy in patients with type 2 diabetes in Denmark.</p> <p><b>Design:</b> A cross-sectional population study.</p> <p><b>Setting:</b> The municipality of Naestved, Denmark.</p> <p><b>Subjects:</b> We studied 907 patients with type 2 diabetes identified from a random sample of 21,205 Danish citizens.</p> <p><b>Main outcome measures:</b> The proportion of patients who were not achieving goals for diabetes care based on their HbA_1c, LDL-cholesterol, blood pressure, and lifestyle, and the proportion of patients who were treated with antihypertensive and cholesterol- and glucose-lowering medication.</p> <p><b>Methods:</b> We investigated the association of the socioeconomic factors such as age, gender, education, occupation, income, and civil status and attainment of treatment goals and pharmacotherapy in logistic regression analyses. We investigated effect modification of cardiovascular disease and kidney disease.</p> <p><b>Results:</b> Middle age (40–65 years), low education level (i.e. basic schooling), and low household income (i.e. less than 21,400 € per year) were associated with nonattainment of goals for diabetes care. The association of socioeconomic factors with attainment of individual treatment goals varied. Patients with low socioeconomic status were more often obese, physically inactive, smoking, and had elevated blood pressure. Socioeconomic factors were not associated with treatment goals for hyperglycemia. Socioeconomic factors were inconsistently associated with pharmacotherapy. There was no difference in contacts to general practitioners according to SES.</p> <p><b>Conclusions:</b> In a country with free access to health care, the socioeconomic factors such as middle age, low education, and low income were associated with nonattainment of goals for diabetes care.KEY POINTS</p><p>Middle age, low education, and low income were associated with nonattainment of goals for diabetes care, especially for lifestyle goals.</p><p>Patients with low socioeconomic status were more often obese, physically inactive, smoking, and had elevated blood pressure.</p><p>Association of socioeconomic factors with pharmacotherapy was inconsistent.</p><p></p> <p>Middle age, low education, and low income were associated with nonattainment of goals for diabetes care, especially for lifestyle goals.</p> <p>Patients with low socioeconomic status were more often obese, physically inactive, smoking, and had elevated blood pressure.</p> <p>Association of socioeconomic factors with pharmacotherapy was inconsistent.</p

Additional file 5: of Socio-demographic determinants and effect of structured personal diabetes care: a 19-year follow-up of the randomized controlled study diabetes Care in General Practice (DCGP)

Table S4. The effect of structured personal care on behavioral, clinical, process of care and biochemical variables according to residence (Rural vs. Urban) (DOCX 37 kb

Additional file 3: of Socio-demographic determinants and effect of structured personal diabetes care: a 19-year follow-up of the randomized controlled study diabetes Care in General Practice (DCGP)

Table S2. The effect of structured personal care on behavioral, clinical, process of care and biochemical variables according to civil status (DOCX 37 kb

Additional file 1: of Socio-demographic determinants and effect of structured personal diabetes care: a 19-year follow-up of the randomized controlled study diabetes Care in General Practice (DCGP)

Definition of clinical outcomes in the 19-year registry-based monitoring of the DCGP study, any diabetes related endpoint (list). (DOCX 76 kb

Model calibration.

<p>A) Plateaus to the left show the amount of hematopoietic stem cells <i>x</i>₀ (upper plateau) and that for MPN stem cells <i>y</i>₀ (lower plateau) whereas the plateaus to the right show the amount of hematopoietic stem cells <i>x</i>₀(lower plateau) and MPN mature cells <i>y</i>₀ (upper plateau). B) Plateaus to the left show the amount of hematopoietic mature cells <i>x</i>₁ (upper plateau) and that for MPN mature cells <i>y</i>₁ (lower plateau) whereas the plateaus to the right show the amount of hematopoietic mature cells <i>x</i>₁ (lower plateau) and MPN mature cells <i>y</i>₁ (upper plateau). The yellow and purple boxes show our data used for calibrating (and validating) the model with further details in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183620#pone.0183620.s001" target="_blank">S1 Appendix</a>. Yellow boxes show our “no MPN cancer values”, and purple boxes show our “full blown” MPN values in the advanced myelofibrosis stage. Yellow position marker shows the number of hematopoietic stem cells as used by Dingli & Michor [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183620#pone.0183620.ref092" target="_blank">92</a>], and black position markers show the number of cells as used by Gentry et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183620#pone.0183620.ref086" target="_blank">86</a>].</p

Investigation of increased inflammatory load at various onsets and durations.

<p>Blue curve is default parameters corresponding to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183620#pone.0183620.g003" target="_blank">Fig 3</a>, red dotted is a doubling of inflammatory load, full red curve is a doubling of inflammatory load in year 0–20, then back to default level, black dotted curve is inflammatory doubling from year 10, the full black is inflammatory doubling year 10–30. <b>Upper:</b> Increasing inflammatory load has a boosting effect on MPN MC (A) as well as on HMC (B). <b>Lower:</b> Displaying the results in terms of the clinically available quantity, total blood cell count, also shows a boosted effect with increasing inflammatory load (C). The allele burden of JAK2 mutated blood cells similarly shows that increased inflammation increases disease development (D). There is a clear effect of MPN promotion with increasing inflammatory load, earlier onset, and exposure time. Lowering inflammatory load makes disease progression less rapid. Maintaining a doubling (red dotted curve) shifts the allele burden curve to the left by two years. Shortening the exposure time of inflammatory load weakens the disease progression. The inflammation has a fast impact on the total number of blood cells, which typically changes by 25% within the first year after doubling or reducing the inflammatory load by 50%.</p

The conceptual model.

<p>Light gray boxes (symbolized x₀, x₁, y₀, and y₁) illustrate the compartments of the basic model, and the black arrows the rates of the flows between these compartments. Here x₀ denotes the number of HSC, x₁ that of HMS, y₀ that of MPN SC, and y₁ the number of MPN MC. The light blue compartment (symbolized a) contains all dead cells and the light orange compartment (symbolized s) the inflammatory level, i.e. the immune response. Blue arrows from these represent related rates of flows. Red stipulated arrows going from the inflammatory compartment represent effects of the cytokines (or neutrophils when eliminating dead cells) modulating rates of the basic model. Two additional rates (depending on x₀ and y₀) appearing as red stipulated arrows represent the bone marrow niches symbiosis with the stem cells modulating the self-renewal rates. Note, stem cells leaving their respective compartments enter the corresponding mature cell-pools as multiplied by the progenitor amplification factor (A).</p

Model validation.

<p>Cytokines A) IL-1β, B) IL-1RA, C) IL-2R, D) IL-6, E) IL-8, F) IL-10, G) IL-12 and H) C-reactive proteins (CRP) are approximatively linearly correlated with the inflammatory level s. For the specific cytokines, we have from left to right ‘Normal’, ‘PV’, and ‘PMF’ median values (yellow columns) for comparison based on the predicted inflammatory level s (full blue curve) as a function of time after the first insult. I) Similarly, LDH is correlated with and compared to the total rate of dying cells <i>DI</i> = <i>dx</i>₀<i>x</i>₀ + <i>dx</i>₁<i>x</i>₁ + <i>dy</i>₀<i>y</i>₀ + <i>dy</i>₁<i>y</i>₁.</p

Effects of calcium, magnesium, and potassium concentrations on ventricular repolarization in unselected individuals.

Background: Subclinical changes on the electrocardiogram are risk factors for cardiovascular mortality. Recognition and knowledge of electrolyte associations in cardiac electrophysiology are based on only in vitro models and observations in patients with severe medical conditions.Objectives: This study sought to investigate associations between serum electrolyte concentrations and changes in cardiac electrophysiology in the general population.Methods: Summary results collected from 153,014 individuals (54.4% women; mean age 55.1 ± 12.1 years) from 33 studies (of 5 ancestries) were meta-analyzed. Linear regression analyses examining associations between electrolyte concentrations (mmol/l of calcium, potassium, sodium, and magnesium), and electrocardiographic intervals (RR, QT, QRS, JT, and PR intervals) were performed. The study adjusted for potential confounders and also stratified by ancestry, sex, and use of antihypertensive drugs.Results: Lower calcium was associated with longer QT intervals (-11.5 ms; 99.75% confidence interval [CI]: -13.7 to -9.3) and JT duration, with sex-specific effects. In contrast, higher magnesium was associated with longer QT intervals (7.2 ms; 99.75% CI: 1.3 to 13.1) and JT. Lower potassium was associated with longer QT intervals (-2.8 ms; 99.75% CI: -3.5 to -2.0), JT, QRS, and PR durations, but all potassium associations were driven by use of antihypertensive drugs. No physiologically relevant associations were observed for sodium or RR intervals.Conclusions: The study identified physiologically relevant associations between electrolytes and electrocardiographic intervals in a large-scale analysis combining cohorts from different settings. The results provide insights for further cardiac electrophysiology research and could potentially influence clinical practice, especially the association between calcium and QT duration, by which calcium levels at the bottom 2% of the population distribution led to clinically relevant QT prolongation by >5 ms.</p