Medication intensification in diabetes in rural primary care: a cluster-randomised effectiveness trial

OBJECTIVE: To determine the effectiveness of a provider-based intervention to improve medication intensification among patients with diabetes. DESIGN: Effectiveness cluster-randomised trial. Baseline and follow-up cross-sections of diabetes physicians\u27 patients. SETTING: Eleven U.S. Southeastern states, 2006-2008. PARTICIPANTS: 205 Rural primary care physicians, 95 completed the study. INTERVENTION: Multicomponent interactive intervention including web-based continuing medical education (CME), performance feedback and quality improvement tools. PRIMARY OUTCOME MEASURES: Medication intensification, a dose increase of an existing medication or the addition of a new class of medication for glucose, blood pressure and lipids control on any of the three most recent office visits. RESULTS: Of 364 physicians attempting to register, 102 were randomised to the intervention and 103 to the control arms; 95 physicians (intervention, n=48; control, n=47) provided data on their 1182 of their patients at baseline (intervention, n=715; control, n=467) and 945 patients at follow-up (intervention, n=479; control, n=466). For A1c control, medication intensification increased in both groups (intervention, pre 26.4% vs post 32.6%, p=0.022; control, pre 24.8% vs post 31.1%, p=0.033) (intervention, adjusted OR (AOR) 1.37; 95% CI 1.06 to 1.76; control, AOR 1.41 (95% CI 1.06 to 1.89)); however, we observed no incremental benefit solely due to the intervention (group-by-time interaction, p=0.948). Among patients with the worst glucose control (A1c \u3e9%), intensification increased in both groups (intervention, pre 34.8% vs post 62.5%, p=0.002; control, pre 35.7% vs post 61.4%, p=0.008). CONCLUSIONS: A wide-reach, low-intensity, web-based interactive multicomponent intervention had no significant incremental effect on medication intensification for control of glucose, blood pressure or lipids for patients with diabetes of physicians practising in the rural Southeastern USA. TRIAL REGISTRATION: NCT00403091

Multiple uncontrolled conditions and blood pressure medication intensification: an observational study

Abstract Background Multiple uncontrolled medical conditions may act as competing demands for clinical decision making. We hypothesized that multiple uncontrolled cardiovascular risk factors would decrease blood pressure (BP) medication intensification among uncontrolled hypertensive patients. Methods We observed 946 encounters at two VA primary care clinics from May through August 2006. After each encounter, clinicians recorded BP medication intensification (BP medication was added or titrated). Demographic, clinical, and laboratory information were collected from the medical record. We examined BP medication intensification by presence and control of diabetes and/or hyperlipidemia. 'Uncontrolled' was defined as hemoglobin A1c ≥ for diabetes, BP ≥ 140/90 mmHg (≥ 130/80 mmHg if diabetes present) for hypertension, and low density lipoprotein cholesterol (LDL-c) ≥ 130 mg/dl (≥ 100 mg/dl if diabetes present) for hyperlipidemia. Hierarchical regression models accounted for patient clustering and adjusted medication intensification for age, systolic BP, and number of medications. Results Among 387 patients with uncontrolled hypertension, 51.4% had diabetes (25.3% were uncontrolled) and 73.4% had hyperlipidemia (22.7% were uncontrolled). The BP medication intensification rate was 34.9% overall, but higher in individuals with uncontrolled diabetes and uncontrolled hyperlipidemia: 52.8% overall and 70.6% if systolic BP ≥ 10 mmHg above goal. Intensification rates were lowest if diabetes or hyperlipidemia were controlled, lower than if diabetes or hyperlipidemia were not present. Multivariable adjustment yielded similar results. Conclusions The presence of uncontrolled diabetes and hyperlipidemia was associated with more guideline-concordant hypertension care, particularly if BP was far from goal. Efforts to understand and improve BP medication intensification in patients with controlled diabetes and/or hyperlipidemia are warranted.http://deepblue.lib.umich.edu/bitstream/2027.42/78266/1/1748-5908-5-55.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78266/2/1748-5908-5-55.pdfPeer Reviewe

Estimating and Reporting on the Quality of Inpatient Stroke Care by Veterans Health Administration Medical Centers

Background—Reporting of quality indicators (QIs) in Veterans Health Administration Medical Centers is complicated by estimation error caused by small numbers of eligible patients per facility. We applied multilevel modeling and empirical Bayes (EB) estimation in addressing this issue in performance reporting of stroke care quality in the Medical Centers. Methods and Results—We studied a retrospective cohort of 3812 veterans admitted to 106 Medical Centers with ischemic stroke during fiscal year 2007. The median number of study patients per facility was 34 (range, 12–105). Inpatient stroke care quality was measured with 13 evidence-based QIs. Eligible patients could either pass or fail each indicator. Multilevel modeling of a patient's pass/fail on individual QIs was used to produce facility-level EB-estimated QI pass rates and confidence intervals. The EB estimation reduced interfacility variation in QI rates. Small facilities and those with exceptionally high or low rates were most affected. We recommended 8 of the 13 QIs for performance reporting: dysphagia screening, National Institutes of Health Stroke Scale documentation, early ambulation, fall risk assessment, pressure ulcer risk assessment, Functional Independence Measure documentation, lipid management, and deep vein thrombosis prophylaxis. These QIs displayed sufficient variation across facilities, had room for improvement, and identified sites with performance that was significantly above or below the population average. The remaining 5 QIs were not recommended because of too few eligible patients or high pass rates with little variation. Conclusions—Considerations of statistical uncertainty should inform the choice of QIs and their application to performance reporting

Rationale and design of the Multicenter Medication Reconciliation Quality Improvement Study (MARQUIS)

Background: Unresolved medication discrepancies during hospitalization can contribute to adverse drug events, resulting in patient harm. Discrepancies can be reduced by performing medication reconciliation; however, effective implementation of medication reconciliation has proven to be challenging. The goals of the Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS) are to operationalize best practices for inpatient medication reconciliation, test their effect on potentially harmful unintentional medication discrepancies, and understand barriers and facilitators of successful implementation. Methods: Six U.S. hospitals are participating in this quality improvement mentored implementation study. Each hospital has collected baseline data on the primary outcome: the number of potentially harmful unintentional medication discrepancies per patient, as determined by a trained on-site pharmacist taking a “gold standard” medication history. With the guidance of their mentors, each site has also begun to implement one or more of 11 best practices to improve medication reconciliation. To understand the effect of the implemented interventions on hospital staff and culture, we are performing mixed methods program evaluation including surveys, interviews, and focus groups of front line staff and hospital leaders. Discussion At baseline the number of unintentional medication discrepancies in admission and discharge orders per patient varies by site from 2.35 to 4.67 (mean=3.35). Most discrepancies are due to history errors (mean 2.12 per patient) as opposed to reconciliation errors (mean 1.23 per patient). Potentially harmful medication discrepancies averages 0.45 per patient and varies by site from 0.13 to 0.82 per patient. We discuss several barriers to implementation encountered thus far. In the end, we anticipate that MARQUIS tools and lessons learned have the potential to decrease medication discrepancies and improve patient outcomes. Trial registration Clinicaltrials.gov identifier NCT0133706

Determinants of health after hospital discharge: rationale and design of the Vanderbilt Inpatient Cohort Study (VICS)

BACKGROUND: The period following hospital discharge is a vulnerable time for patients when errors and poorly coordinated care are common. Suboptimal care transitions for patients admitted with cardiovascular conditions can contribute to readmission and other adverse health outcomes. Little research has examined the role of health literacy and other social determinants of health in predicting post-discharge outcomes. METHODS: The Vanderbilt Inpatient Cohort Study (VICS), funded by the National Institutes of Health, is a prospective longitudinal study of 3,000 patients hospitalized with acute coronary syndromes or acute decompensated heart failure. Enrollment began in October 2011 and is planned through October 2015. During hospitalization, a set of validated demographic, cognitive, psychological, social, behavioral, and functional measures are administered, and health status and comorbidities are assessed. Patients are interviewed by phone during the first week after discharge to assess the quality of hospital discharge, communication, and initial medication management. At approximately 30 and 90 days post-discharge, interviewers collect additional data on medication adherence, social support, functional status, quality of life, and health care utilization. Mortality will be determined with up to 3.5 years follow-up. Statistical models will examine hypothesized relationships of health literacy and other social determinants on medication management, functional status, quality of life, utilization, and mortality. In this paper, we describe recruitment, eligibility, follow-up, data collection, and analysis plans for VICS, as well as characteristics of the accruing patient cohort. DISCUSSION: This research will enhance understanding of how health literacy and other patient factors affect the quality of care transitions and outcomes after hospitalization. Findings will help inform the design of interventions to improve care transitions and post-discharge outcomes

Microbial diversity and community composition of caecal microbiota in commercial and indigenous Indian chickens determined using 16s rDNA amplicon sequencing

Different alpha diversity indices for each sample of university farm data estimated using QIIME for primer pair P2 data. OTUs were clustered at > 97% similarity. (XLSX 12 kb

Extensive microbial and functional diversity within the chicken cecal microbiome

Chickens are major source of food and protein worldwide. Feed conversion and the health of chickens relies on the largely unexplored complex microbial community that inhabits the chicken gut, including the ceca. We have carried out deep microbial community profiling of the microbiota in twenty cecal samples via 16S rRNA gene sequences and an in-depth metagenomics analysis of a single cecal microbiota. We recovered 699 phylotypes, over half of which appear to represent previously unknown species. We obtained 648,251 environmental gene tags (EGTs), the majority of which represent new species. These were binned into over two-dozen draft genomes, which included Campylobacter jejuni and Helicobacter pullorum. We found numerous polysaccharide- and oligosaccharide-degrading enzymes encoding within the metagenome, some of which appeared to be part of polysaccharide utilization systems with genetic evidence for the co-ordination of polysaccharide degradation with sugar transport and utilization. The cecal metagenome encodes several fermentation pathways leading to the production of short-chain fatty acids, including some with novel features. We found a dozen uptake hydrogenases encoded in the metagenome and speculate that these provide major hydrogen sinks within this microbial community and might explain the high abundance of several genera within this microbiome, including Campylobacter, Helicobacter and Megamonas