Exploring the role of the patient–physician relationship on insulin adherence and clinical outcomes in type 2 diabetes: Insights from the MOSAIc study

BackgroundThe 2‐year prospective MOSAIc (Multinational Observational Study assessing Insulin use: understanding the challenges associated with progression of therapy) study is investigating whether patient‐, physician‐, and health system‐related factors affect outcomes in patients with type 2 diabetes (T2D). This baseline subanalysis investigated how aspects of the patient–physician relationship are associated with diabetes‐related distress, insulin adherence, and glycemic control.MethodsPatients with T2D taking insulin for ≥3 months were recruited at primary care and specialty practice sites in 18 countries. Physicians provided usual care. Clinical history and most recent HbA1c values were collected; patients were surveyed regarding their perception of physician interactions, diabetes‐related distress level, and insulin adherence.ResultsThe analysis population comprised 4341 patients. Four (of six) domains showed a significant relationship with total diabetes‐related distress (P < 0.01). Poor insulin adherence was associated with greater diabetes‐related distress (adjusted odds ratio [aOR] 1.14; 95% confidence interval [CI] 1.06–1.22), higher Discrimination (aOR 1.13; 95% CI 1.02–1.27) and Hurried Communication (aOR 1.35; 95% CI 1.20–1.53) scores, and a lower Explained Results score (aOR 0.86; 95% CI 0.77–0.97). Poor insulin adherence was associated with a 0.43% increase in HbA1c, whereas a 1‐unit increase in total diabetes‐related distress and Hurried Communication scores was associated with a 0.171% and 0.145% increase in HbA1c, respectively.ConclusionsPatients distressed about living with T2D, and dissatisfied with aspects of their interactions with physicians, exhibited poor insulin adherence. Perceived physician inattention and lack of engagement (and diabetes‐related distress) directly affect insulin adherence and glycemic control.背景为期2年的前瞻性MOSAIc(Multinational Observational Study assessing Insulin use: understanding the challenges associated with progression of therapy,评估胰岛素使用情况的多国观察性研究:了解治疗进展带来的挑战)研究调查了患者‐、医生‐、医疗卫生系统‐相关因素是否会对2型糖尿病患者的临床结局产生影响。这项基线亚组分析调查了患者‐医生关系对糖尿病相关的不适、胰岛素依从性以及血糖控制可造成何种影响。方法在18个国家的初级保健以及专业医疗机构中招募胰岛素使用时间≥ 3个月的2型糖尿病患者。医生提供了常规的医疗护理。收集临床病史以及最近的HbA1c值;调查患者对医患之间关系的看法、与糖尿病相关的不适程度以及胰岛素依从性。结果分析人群包含了4341名患者。(在6个领域中)有4个方面与总的糖尿病相关不适之间具有显著的相关性(P < 0.01)。胰岛素依从性差与较高的糖尿病相关不适(校正过的优势比[aOR]为1.14;95%置信区间[CI]为1.06‐1.22)、较高的歧视(aOR为1.13;95% CI为1.02‐1.27)和仓促沟通(aOR为1.35;95% CI为1.20‐1.53)得分以及更低的解释病情得分(aOR为0.86;95% CI为0.77‐0.97)相关。胰岛素依从性差可导致HbA1c升高0.43%,然而总的糖尿病相关不适以及仓促沟通得分每增加1个单位就可以导致HbA1c分别升高0.171%与0.145%。结论患者感到苦恼的是2型糖尿病影响到了他们的生活,对于与医生的交流感到不够满意,而且表现为胰岛素依从性差。患者觉得医生不关心自己、缺乏交流(以及糖尿病本身造成的相关痛苦)会直接影响到胰岛素依从性以及血糖控制。HighlightsPatient perceptions of the quality of their interactions with their physicians have a significant association with total diabetes‐related distress. Diabetes‐related distress and patient–physician interactions have a significant independent association with insulin adherence and HbA1c level.This study delineates specific aspects of the patient–physician interaction that are linked to diabetes‐related distress, insulin adherence behavior, and glycemic control.Path analysis showing associations between patient–physician interactions, diabetes‐related distress, insulin adherence, and HbA1c level. The model is not adjusted for baseline covariates and shows only those factors with at least one significant interaction. Parameter coefficients in the path analysis are shown.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137500/1/jdb12443.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137500/2/jdb12443_am.pd

Challenges associated with insulin therapy progression among patients with type 2 diabetes: Latin American MOSAIc study baseline data

Background: Poor glycemic control in patients with type 2 diabetes is commonly recorded worldwide; Latin America (LA) is not an exception. Barriers to intensifying insulin therapy and which barriers are most likely to negatively impact outcomes are not completely known. The objective was to identify barriers to insulin progression in individuals with type 2 diabetes mellitus (T2DM) in LA countries (Mexico, Brazil, and Argentina). Methods: MOSAIc is a multinational, non-interventional, prospective, observational study aiming to identify the patient-, physician-, and healthcare-based factors affecting insulin intensification. Eligible patients were ≥18 years, had T2DM, and were treated with insulin for ≥3 months with/without oral antidiabetic drugs (OADs). Demographic, clinical, and psychosocial data were collected at baseline and regular intervals during the 24-month follow-up period. This paper however, focuses on baseline data analysis. The association between glycated hemoglobin (HbA1c) and selected covariates was assessed. Results: A trend toward a higher level of HbA1c was observed in the LA versus non-LA population (8.40 ± 2.79 versus 8.18 ± 2.28; p ≤ 0.069). Significant differences were observed in clinical parameters, treatment patterns, and patient-reported outcomes in LA compared with the rest of the cohorts and between Mexico, Brazil, and Argentina. Higher number of insulin injections and lower number of OADs were used, whereas a lower level of knowledge and a higher level of diabetes-related distress were reported in LA. Covariates associated with HbA1c levels included age (-0.0129; p < 0.0001), number of OADs (0.0835; p = 0.0264), higher education level (-0.2261; p = 0.0101), healthy diet (-0.0555; p = 0.0083), self-monitoring blood glucose (-0.0512; p = 0.0033), hurried communication style in the process of care (0.1295; p = 0.0208), number of insulin injections (0.1616; p = 0.0088), adherence (-0.1939; p ≤ 0.0104), and not filling insulin prescription due to associated cost (0.2651; p = 0.0198). Conclusion: MOSAIc baseline data showed that insulin intensification in LA is not optimal and identified several conditions that significantly affect attaining appropriate HbA1c values. Tailored public health strategies, including education, should be developed to overcome such barriers.Centro de Endocrinología Experimental y Aplicad

Heart failure outcomes according to heart rate and effects of empagliflozin in patients of the EMPEROR-Preserved trial

Aims Empagliflozin reduces cardiovascular death (CVD) or heart failure hospitalization (HHF) in patients with heart failure and preserved ejection fraction (HFpEF). Treatment effects and safety in relation to resting heart rate (RHR) have not been studied. Methods and results The interplay of RHR and empagliflozin effects in EMPEROR-Preserved was evaluated. We grouped patients (n = 5988) according to their baseline RHR (75 bpm [n = 1736]) and explored the influence of RHR on CVD or HHF (primary outcome) and its components in sinus rhythm or atrial fibrillation/flutter (AF) and adverse events. We studied the efficacy of empagliflozin across the RHR spectrum. Compared to placebo, empagliflozin did not change heart rate over time. The primary outcome (p for trend = 0.0004) and its components CVD (p trend = 0.0002), first HHF (p for trend = 0.0099) and all-cause death (p <  0.0001) increased with RHR only in sinus rhythm but not AF. The risk increase with RHR was similar in patients with heart failure and mildly reduced ejection fraction (left ventricular ejection fraction [LVEF] 40–49%) and HFpEF (LVEF ≥50%). Baseline RHR had no influence on the effect of empagliflozin on the primary outcomes (p for trend = 0.20), first HHF (p for trend = 0.49). There were no clinically relevant differences in adverse events between empagliflozin and placebo across the RHR groups. Conclusion Resting heart rate associates with outcomes only in sinus rhythm but not in AF. Empagliflozin reduced outcomes over the entire RHR spectrum without increase of adverse events

An average/deprivation/inequality (ADI) analysis of chronic disease outcomes and risk factors in Argentina

<p>Abstract</p> <p>Background</p> <p>Recognition of the global economic and epidemiological burden of chronic non-communicable diseases has increased in recent years. However, much of the research on this issue remains focused on individual-level risk factors and neglects the underlying social patterning of risk factors and disease outcomes.</p> <p>Methods</p> <p>Secondary analysis of Argentina's 2005 <it>Encuesta Nacional de Factores de Riesgo </it>(National Risk Factor Survey, <it>N </it>= 41,392) using a novel analytical strategy first proposed by the United Nations Development Programme (UNDP), which we here refer to as the Average/Deprivation/Inequality (ADI) framework. The analysis focuses on two risk factors (unhealthy diet and obesity) and one related disease outcome (diabetes), a notable health concern in Latin America. Logistic regression is used to examine the interplay between socioeconomic and demographic factors. The ADI analysis then uses the results from the logistic regression to identify the most deprived, the best-off, and the difference between the two ideal types.</p> <p>Results</p> <p>Overall, 19.9% of the sample reported being in poor/fair health, 35.3% reported not eating any fruits or vegetables in five days of the week preceding the interview, 14.7% had a BMI of 30 or greater, and 8.5% indicated that a health professional had told them that they have diabetes or high blood pressure. However, significant variation is hidden by these summary measures. Educational attainment displayed the strongest explanatory power throughout the models, followed by household income, with both factors highlighting the social patterning of risk factors and disease outcomes. As educational attainment and household income increase, the probability of poor health, unhealthy diet, obesity, and diabetes decrease. The analyses also point toward important provincial effects and reinforce the notion that both compositional factors (i.e., characteristics of individuals) and contextual factors (i.e., characteristics of places) are important in understanding the social patterning of chronic diseases.</p> <p>Conclusion</p> <p>The application of the ADI framework enables identification of the regions or groups worst-off for each outcome measure under study. This can be used to highlight the variation embedded within national averages; as such, it encourages a social perspective on population health indicators that is particularly attuned to issues of inequity. The ADI framework is an important tool in the evaluation of policies aiming to prevent or control chronic non-communicable diseases.</p

An average/deprivation/inequality (ADI) analysis of chronic disease

Background: Recognition of the global economic and epidemiological burden of chronic noncommunicable diseases has increased in recent years. However, much of the research on this issue remains focused on individual- level risk factors and neglects the underlying social patterning of risk factors and disease outcomes. Methods: Secondary analysis of Argentina\u27s 2005 Encuesta Nacional de Factores de Riesgo (National Risk Factor Survey, N = 41,392) using a novel analytical strategy first proposed by the United Nations Development Programme (UNDP), which we here refer to as the Average / Deprivation / Inequality (ADI) framework. The analysis focuses on two risk factors (unhealthy diet and obesity) and one related disease outcome (diabetes), a notable health concern in Latin America. Logistic regression is used to examine the interplay between socioeconomic and demographic factors. The ADI analysis then uses the results from the logistic regression to identify the most deprived, the best-off, and the difference between the two ideal types. Results: Overall, 19.9% of the sample reported being in poor/fair health, 35.3% reported not eating any fruits or vegetables in five days of the week preceding the interview, 14.7% had a BMI of 30 or greater, and 8.5% indicated that a health professional had told them that they have diabetes or high blood sugar. However, significant variation is hidden by these summary measures. Educational attainment displayed the strongest explanatory power throughout the models, followed by household income, with both factors highlighting the social patterning of risk factors and disease outcomes. As educational attainment and household income increase, the probability of poor health, unhealthy diet, obesity, and diabetes decrease. The analyses also point toward important provincial effects and reinforce the notion that both compositional factors (i.e., characteristics of individuals) and contextual factors (i.e., characteristics of places) are important in understanding the social patterning of chronic diseases. Conclusion: The application of the ADI framework enables identification of the regions or groups worst-off for each outcome measure under study. This can be used to highlight the variation embedded within national averages; as such, it encourages a social perspective on population health indicators that is particularly attuned to issues of inequity. The ADI framework is an important tool in the evaluation of policies aiming to prevent or control chronic non-communicable diseases