Dataset Information

Longitudinal Analysis of Quality of Diabetes Care and Relational Climate in Primary Care.

ABSTRACT:

Objective

To assess the influence of relational climate on quality of diabetes care.

Data sources/study setting

The study was conducted at the Department of Veterans Affairs (VA). The VA All Employee Survey (AES) was used to measure relational climate. Patient and facility characteristics were gathered from VA administrative datasets.

Study design

Multilevel panel data (2008-2012) with patients nested into clinics.

Data collection/extraction methods

Diabetic patients were identified using ICD-9 codes and assigned to the clinic with the highest frequency of primary care visits. Multiple quality indicators were used, including an all-or-none process measure capturing guideline compliance, the actual number of tests and procedures, and three intermediate continuous outcomes (cholesterol, glycated hemoglobin, and blood pressure).

Principal findings

The study sample included 327,805 patients, 212 primary care clinics, and 101 parent facilities in 2010. Across all study years, there were 1,568,180 observations. Clinics with the highest relational climate were 25 percent more likely to provide guideline-compliant care than those with the lowest relational climate (OR for a 1-unit increase: 1.02, p-value <.001). Among insulin-dependent diabetic veterans, this effect was twice as large. Contrary to that expected, relational climate did not influence intermediate outcomes.

Conclusions

Relational climate is positively associated with tests and procedures provision, but not with intermediate outcomes of diabetes care.

SUBMITTER: Soley-Bori M

PROVIDER: S-EPMC5867181 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Publications

Longitudinal Analysis of Quality of Diabetes Care and Relational Climate in Primary Care.

Soley-Bori Marina M Benzer Justin K JK Burgess James F JF

Health services research 20170310 2

<h4>Objective</h4>To assess the influence of relational climate on quality of diabetes care.<h4>Data sources/study setting</h4>The study was conducted at the Department of Veterans Affairs (VA). The VA All Employee Survey (AES) was used to measure relational climate. Patient and facility characteristics were gathered from VA administrative datasets.<h4>Study design</h4>Multilevel panel data (2008-2012) with patients nested into clinics.<h4>Data collection/extraction methods</h4>Diabetic patients ...[more]

PMID: 28294310

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Project description:BackgroundLack of healthcare access to due to physician shortages is a significant driver of telemedicine expansion in rural areas. Telemedicine is effective for management of chronic conditions such as diabetes but its effectiveness in primary care settings is unknown.ObjectiveTo evaluate differences in diabetes care before and after implementation of a longitudinal virtual primary care program.DesignPropensity score-matched cohort study utilizing difference-in-differences analysis.ParticipantsPatients with diabetes who received care at VA primary care clinics between January 2018 and December 2019 where the Virtual Integrated Multisite Patient Aligned Care Teams (V-IMPACT) program was implemented.ExposurePatient participation in at least one V-IMPACT visit while usual care patients did not participate in V-IMPACT.Main measuresThe primary outcome was change in hemoglobin A1C (HbA1C) and secondary outcomes included change in the proportion of patients meeting diabetes quality indicators: blood pressure control, statin use, angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (ACEi/ARB) use, and annual microalbuminuria testing.Key resultsOur propensity-matched cohort included 9010 patients split evenly between those who participated in V-IMPACT and those who remained in usual in-person care. Among individuals with diabetes who participated in V-IMPACT, the change in mean HbA1C was - 0.055% (95% CI - 0.088 to - 0.022%) while those in usual care had a - 0.047% (95% CI - 0.080 to - 0.014%) change before and after program implementation. We observed a 5.1% (95% CI 2.4 to 7.7%) absolute increase in the proportion prescribed statins in the V-IMPACT group, a 5.3% (95% CI 2.5 to 8.2%) increase prescribed ACE/ARBs, and a 4.6% (95% 1.7 to 7.5%) increase in completed yearly microalbuminuria testing. V-IMPACT was not associated with a significant difference in the proportion with controlled blood pressure at < 140/90 or < 130/90 mmHg thresholds.ConclusionsQuality of diabetes care delivered by a longitudinal virtual primary care model was similar if not better than traditional in-person care.

Project description:BackgroundGreater continuity of care has been associated with lower hospital admissions and patient mortality. This systematic review aims to examine the impact of relational continuity between primary care professionals and older people receiving aged care services, in residential or home care settings, on health care resource use and person-centred outcomes.MethodsSystematic review of five databases, four trial registries and three grey literature sources to October 2020. Included studies (a) aimed to increase relational continuity with a primary care professional, (b) focused on older people receiving aged care services (c) included a comparator and (d) reported outcomes of health care resource use, quality of life, activities of daily living, mortality, falls or satisfaction. Cochrane Collaboration or Joanna Briggs Institute criteria were used to assess risk of bias and GRADE criteria to rate confidence in evidence and conclusions.ResultsHeterogeneity in study cohorts, settings and outcome measurement in the five included studies (one randomised) precluded meta-analysis. None examined relational continuity exclusively with non-physician providers. Higher relational continuity with a primary care physician probably reduces hospital admissions (moderate certainty evidence; high versus low continuity hazard ratio (HR) 0.94; 95% confidence interval (CI) 0.92-0.96, n = 178,686; incidence rate ratio (IRR) 0.99, 95%CI 0.76-1.27, n = 246) and emergency department (ED) presentations (moderate certainty evidence; high versus low continuity HR 0.90, 95%CI 0.89-0.92, n = 178,686; IRR 0.91, 95%CI 0.72-1.15, n = 246) for older community-dwelling aged care recipients. The benefit of providing on-site primary care for relational continuity in residential settings is uncertain (low certainty evidence, 2 studies, n = 2,468 plus 15 care homes); whilst there are probably lower hospitalisations and may be fewer ED presentations, there may also be an increase in reported mortality and falls. The benefit of general practitioners' visits during hospital admission is uncertain (very low certainty evidence, 1 study, n = 335).ConclusionGreater relational continuity with a primary care physician probably reduces hospitalisations and ED presentations for community-dwelling aged care recipients, thus policy initiatives that increase continuity may have cost offsets. Further studies of approaches to increase relational continuity of primary care within aged care, particularly in residential settings, are needed.Review registrationCRD42021215698.

Project description:ImportanceDiabetes management operates under a complex interrelationship between behavioral, social, and economic factors that affect a patient's ability to self-manage and access care.ObjectiveTo examine the association between 2 complementary area-based metrics, area deprivation index (ADI) score and rurality, and optimal diabetes care.Design, setting, and participantsThis cross-sectional study analyzed the electronic health records of patients who were receiving care at any of the 75 Mayo Clinic or Mayo Clinic Health System primary care practices in Minnesota, Iowa, and Wisconsin in 2019. Participants were adults with diabetes aged 18 to 75 years. All data were abstracted and analyzed between June 1 and November 30, 2020.Main outcomes and measuresThe primary outcome was the attainment of all 5 components of the D5 metric of optimal diabetes care: glycemic control (hemoglobin A1c <8.0%), blood pressure (BP) control (systolic BP <140 mm Hg and diastolic BP <90 mm Hg), lipid control (use of statin therapy according to recommended guidelines), aspirin use (for patients with ischemic vascular disease), and no tobacco use. The proportion of patients receiving optimal diabetes care was calculated as a function of block group-level ADI score (a composite measure of 17 US Census indicators) and zip code-level rurality (calculated using Rural-Urban Commuting Area codes). Odds of achieving the D5 metric and its components were assessed using logistic regression that was adjusted for demographic characteristics, coronary artery disease history, and primary care team specialty.ResultsAmong the 31 934 patients included in the study (mean [SD] age, 59 [11.7] years; 17 645 men [55.3%]), 13 138 (41.1%) achieved the D5 metric of optimal diabetes care. Overall, 4090 patients (12.8%) resided in the least deprived quintile (quintile 1) of block groups and 1614 (5.1%) lived in the most deprived quintile (quintile 5), while 9193 patients (28.8%) lived in rural areas and 2299 (7.2%) in highly rural areas. The odds of meeting the D5 metric were lower for individuals residing in quintile 5 vs quintile 1 block groups (odds ratio [OR], 0.72; 95% CI, 0.67-0.78). Patients residing in rural (OR, 0.84; 95% CI, 0.73-0.97) and highly rural (OR, 0.81; 95% CI, 0.72-0.91) zip codes were also less likely to attain the D5 metric compared with those in urban areas.Conclusions and relevanceThis cross-sectional study found that patients living in more deprived and rural areas were significantly less likely to attain high-quality diabetes care compared with those living in less deprived and urban areas. The results call for geographically targeted population health management efforts by health systems, public health agencies, and payers.

Project description:Recent research from a complexity theory perspective suggests that implementation of complex models of care, such as the Chronic Care Model (CCM), requires strong relationships and learning capacities among primary care teams.Our primary aim was to assess the extent to which practice member perceptions of relational coordination and reciprocal learning were associated with the presence of CCM elements in community-based primary care practices.We used baseline measures from a cluster randomized controlled trial testing a practice facilitation intervention to implement the CCM and improve risk factor control for patients with Type 2 diabetes in small primary care practices. Practice members (i.e., physicians, nonphysician providers, and staff) completed baseline assessments, which included the Relational Coordination Scale, Reciprocal Learning Scale, and the Assessment of Chronic Illness Care (ACIC) survey, along with items assessing individual and clinic characteristics. To assess the association between Relational Coordination, Reciprocal Learning, and ACIC, we used a series of hierarchical linear regression models accounting for clustering of individual practice members within clinics and controlling for individual- and practice-level characteristics and tested for mediation effects.A total of 283 practice members from 39 clinics completed baseline measures. Relational Coordination scores were significantly and positively associated with ACIC scores (Model 1). When Reciprocal Learning was added, Relational Coordination remained a significant yet notably attenuated predictor of ACIC (Model 2). The mediation effect was significant (z = 9.3, p < .01); 24% of the association between Relational Coordination and ACIC scores was explained by Reciprocal Learning. Of the individual- and practice-level covariates included in Model 3, only the presence of an electronic medical record was significant; Relational Coordination and Reciprocal Learning remained significant independent predictors of ACIC.Efforts to implement complex models of care should incorporate strategies to strengthen relational coordination and reciprocal learning among team members.

Dataset Information

Longitudinal Analysis of Quality of Diabetes Care and Relational Climate in Primary Care.

Objective

Data sources/study setting

Study design

Data collection/extraction methods

Principal findings

Conclusions

Publications

Longitudinal Analysis of Quality of Diabetes Care and Relational Climate in Primary Care.

Similar Datasets

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