Two doses of the BNT162b2 vaccine were associated mainly with low-grade local adverse effects that lasted 2 days or less and afforded nearly 50% protection against omicron infection and symptomatic ...illness, which was lower than that seen against delta. Greater protection in the youngest group was noted.
This study assesses the attributable impact of adherence to oral glucose medications as a risk factor for poor glycemic control in population subgroups of a large general population, using an ...objective medication adherence measure.
Using electronic health records data, adherence to diabetes medications over a two-year period was calculated by prescription-based Medication Possession Ratios for adults with diabetes diagnosed before January 1, 2010. Glycemic control was determined by the HbA1c test closest to the last drug prescription during 2010-2012. Poor control was defined as HbA1c>75 mmol/mol (9.0%). Medication adherence was categorized as "good" (>80%), "moderate" (50-80%), or "poor" (<50%). Logistic regression models assessed the role medication adherence plays in the association between disease duration, age, and poor glycemic control. We calculated the change in the attributable fraction of glucose control if the non-adherent diabetic medication population would become adherent by age-groups.
Among 228,846 diabetes patients treated by oral antiglycemic medication, 46.4% had good, 28.8% had moderate, and 24.8% had poor adherence. Good adherence rates increased with increasing disease duration, while glycemic control became worse. There was a strong inverse association between adherence level and poor control (OR = 2.50; CI = 2.43-2.58), and adherence was a significant mediator between age and poor control.
A large portion of the diabetes population is reported to have poor adherence to oral diabetes medications, which is strongly associated with poor glycemic control in all disease durations. While poor adherence does not mediate the poorer glycemic control seen in patients with longer-standing disease, it is a significant mediator of poor glycemic control among younger diabetes patients. A greater fraction of poorly controlled younger patients, compared to older patients, could be prevented if at least 80% adherence to their medications was achieved. Therefore, our results suggest that interventions to improve adherence should focus on this younger sub-group.
IMPORTANCE: International guidelines recommend treatment with statins for patients with preexisting ischemic heart disease to prevent additional cardiovascular events but differ regarding target ...levels of low-density lipoprotein cholesterol (LDL-C). Trial data on this question are inconclusive and observational data are lacking. OBJECTIVE: To assess the relationship between levels of LDL-C achieved with statin treatment and cardiovascular events in adherent patients with preexisting ischemic heart disease. DESIGN, SETTING, AND PARTICIPANTS: Population-based observational cohort study from 2009 to 2013 using data from a health care organization in Israel covering more than 4.3 million members. Included patients had ischemic heart disease, were aged 30 to 84 years, were treated with statins, and were at least 80% adherent to treatment or, in a sensitivity analysis, at least 50% adherent. Patients with active cancer or metabolic abnormalities were excluded. EXPOSURES: Index LDL-C was defined as the first achieved serum LDL-C measure after at least 1 year of statin treatment, grouped as low (≤70.0 mg/dL), moderate (70.1-100.0 mg/dL), or high (100.1-130.0 mg/dL). MAIN OUTCOMES AND MEASURES: Major adverse cardiac events included acute myocardial infarction, unstable angina, stroke, angioplasty, bypass surgery, or all-cause mortality. The hazard ratio of adverse outcomes was estimated using 2 Cox proportional hazards models with low vs moderate and moderate vs high LDL-C, adjusted for confounders and further tested using propensity score matching analysis. RESULTS: The cohort with at least 80% adherence included 31 619 patients, for whom the mean (SD) age was 67.3 (9.8) years. Of this population, 27% were female and 29% had low, 53% moderate, and 18% high LDL-C when taking statin treatment. Overall, there were 9035 patients who had an adverse outcome during a mean 1.6 years of follow-up (6.7 per 1000 persons per year). The adjusted incidence of adverse outcomes was not different between low and moderate LDL-C (hazard ratio HR, 1.02; 95% CI, 0.97-1.07; P = .54), but it was lower with moderate vs high LDL-C (HR, 0.89; 95% CI, 0.84-0.94; P < .001). Among 54 884 patients with at least 50% statin adherence, the adjusted HR was 1.06 (95% CI, 1.02-1.10; P = .001) in the low vs moderate groups and 0.87 (95% CI, 0.84-0.91; P = .001) in the moderate vs high groups. CONCLUSIONS AND RELEVANCE: Patients with LDL-C levels of 70 to 100 mg/dL taking statins had lower risk of adverse cardiac outcomes compared with those with LDL-C levels between 100 and 130 mg/dL, but no additional benefit was gained by achieving LDL-C of 70 mg/dL or less. These population-based data do not support treatment guidelines recommending very low target LDL-C levels for all patients with preexisting heart disease.
Background. Streptococcus pneumoniae contributes considerably to the burden of pneumonia and invasive pneumococcal disease (IPD), with the effectiveness of the 23-valent pneumococcal polysaccharide ...vaccine (PPSV23) for preventing all-cause pneumonia still undetermined. The aim of this study was to control for common biases and confounders associated with previous observational studies and to assess PPSV23 vaccine effectiveness in preventing IPD and the most resource-intensive type of community-acquired pneumonia, hospital-treated pneumonia (HTP). Methods. This was a retrospective case-control study nested in a population-based cohort, with age-, sex-, and risk-matched controls as the base case. Demographic information, laboratory data, and diagnoses were extracted from the chronic disease registry and from inpatient and outpatient records in the Clalit Health Services database. Vaccine effectiveness for PPSV23 was assessed using multivariable conditional logistic regression. Subgroup, sensitivity, and secondary analyses were conducted to validate findings. Results. A total of 470 070 individuals aged ≥65 years were members of Clalit Health Services during the study period (1 January 2007 through 31 December 2010). The case cohort consisted of 212 participants with IPD and 23 441 with HTP. The adjusted association between vaccination and IPD was protective (odds ratio OR, 0.58; 95% confidence interval CI, .41–.81), whereas there was no demonstrated protective effect between vaccination and HTP (OR, 1.01; 95% CI, .97–1.04). The sensitivity analysis and all but 1 subgroup analysis provided consistent results to the base case. Conclusions. The PPSV23 vaccine is effective against the most severe invasive forms of pneumococcal disease, but the lack of effectiveness of PPSV23 in protecting against all-cause HTP should be considered for future vaccine policies.
Objective To directly compare the performance and externally validate the three most studied prediction tools for osteoporotic fractures—QFracture, FRAX, and Garvan—using data from electronic health ...records.Design Retrospective cohort study.Setting Payer provider healthcare organisation in Israel.Participants 1 054 815 members aged 50 to 90 years for comparison between tools and cohorts of different age ranges, corresponding to those in each tools’ development study, for tool specific external validation.Main outcome measure First diagnosis of a major osteoporotic fracture (for QFracture and FRAX tools) and hip fractures (for all three tools) recorded in electronic health records from 2010 to 2014. Observed fracture rates were compared to probabilities predicted retrospectively as of 2010.Results The observed five year hip fracture rate was 2.7% and the rate for major osteoporotic fractures was 7.7%. The areas under the receiver operating curve (AUC) for hip fracture prediction were 82.7% for QFracture, 81.5% for FRAX, and 77.8% for Garvan. For major osteoporotic fractures, AUCs were 71.2% for QFracture and 71.4% for FRAX. All the tools underestimated the fracture risk, but the average observed to predicted ratios and the calibration slopes of FRAX were closest to 1. Tool specific validation analyses yielded hip fracture prediction AUCs of 88.0% for QFracture (among those aged 30-100 years), 81.5% for FRAX (50-90 years), and 71.2% for Garvan (60-95 years).Conclusions Both QFracture and FRAX had high discriminatory power for hip fracture prediction, with QFracture performing slightly better. This performance gap was more pronounced in previous studies, likely because of broader age inclusion criteria for QFracture validations. The simpler FRAX performed almost as well as QFracture for hip fracture prediction, and may have advantages if some of the input data required for QFracture are not available. However, both tools require calibration before implementation.
Disease-specific guidelines are not aligned with multimorbidity care complexity. Meeting all guideline-recommended care for multimorbid patients has been estimated but not demonstrated across ...multiple guidelines.
Measure guideline-concordant care for patients with multimorbidity; assess in what types of care and by whom (clinician or patient) deviation from guidelines occurs and evaluate whether patient characteristics are associated with concordance.
A retrospective cohort study of care received over 1 year, conducted across 11 primary care clinics within the context of multimorbidity-focused care management program. Patients were aged 45+ years with more than two common chronic conditions and were sampled based on either being new (≤6 months) or veteran to the program (≥1 year).
Three guideline concordance measures were calculated for each patient out of 44 potential guideline-recommended care processes for nine chronic conditions: overall score; referral score (proportion of guideline-recommended care referred) and patient-only score (proportion of referred care completed by patients). Guideline concordance was stratified by care type.
4386 care processes evaluated among 204 patients, mean age = 72.3 years (standard deviation = 9.7). Overall, 79.2% of care was guideline concordant, 87.6% was referred according to guidelines and patients followed 91.4% of referred care. Guideline-concordant care varied across care types. Age, morbidity burden and whether patients were new or veteran to the program were associated with guideline concordance.
Patients with multimorbidity do not receive ~20% of guideline recommendations, mostly due to clinicians not referring care. Determining the types of care for which the greatest deviation from guidelines exists can inform the tailoring of care for multimorbidity patients.
Reasons why care does not conform to single-disease guideline recommendations for multimorbid patients have not been systematically measured in practice. Using a mixed methods approach, we identified ...and quantified types of reasons why care deviates from nine sets of disease guideline recommendations for multimorbid patients. Utilizing a focus group concept mapping technique, we built on a categorization of reasons explaining guideline deviation, and surveyed treating nurses about these reasons for patients’ specific care processes. Directed content analysis was conducted to classify the responses into reasons categories. Of 4,386 guideline-recommended care processes evaluated, 920 were not guideline-concordant (944 reasons). Three broad categories of reasons and 18 specific reasons were identified: Biomedical-related occurred 35.2% of the time, patient personal-related (30.4%), context-related (18.4%), and unknown (16.0%). Patient- and context-related factors are prevalent drivers for guideline deviation in multimorbidity, demonstrating that patient-centered aspects are as much a part of care decisions as biomedical aspects.
ObjectivesTo assess whether the extent of deviation from chronic disease guideline recommendations is more prominent for specific diseases compared with combined-care across multiple conditions among ...multimorbid patients, and to examine reasons for this deviation.DesignA cross-sectional cohort.SettingMultimorbidity care management programme across 11 primary care clinics.PatientsPatients aged 45–95 years with at least two common chronic conditions, sampled according to being new (≤6 months) or veteran (≥1 year) to the programme.Main outcome measuresDeviation from guideline-recommended care was measured for each patient’s relevant conditions, aggregated and stratified across disease groups, calculated as measures of ‘disease-specific’ guideline deviation and ‘combined-care’ (all conditions) guideline deviation for: atrial fibrillation, congestive heart failure, chronic kidney disease, chronic obstructive pulmonary disorder, depression, diabetes, dyslipidaemia, hypertension and ischaemic heart disease. Combined-care deviation was evaluated for its association with specific diseases. Frequencies of previously derived reason types for deviation (biomedical, patient personal and contextual) were reported by nurse care managers, assessed across diseases and evaluated for their association with specific diseases.ResultsAmong 204 patients, disease-specific deviation varied more (from 14.7% to 48.2%) across diseases than combined-care deviation (from 14.7% to 25.6%). Depression and diabetes were significantly associated with more deviation (mean: 6% (95% CI: 2% to 10%) and 5% (95% CI: 2% to 9%), respectively). For some conditions, assessments were among small patient samples. Guideline deviation was often attributed to non-disease-specific reasons, such as physical limitations or care burden, as much as disease-specific reasons, which was reflected in the likelihood for guideline deviation to be due to different types of reasons for some diseases.ConclusionsWhen multimorbid patients are considered in disease groups rather than as ‘whole persons’, as in many quality of care studies, the cross-cutting factors in their care delivery can be missed. The types of reasons more likely to occur for specific diseases may inform improvement strategies.Trial registration numberNCT01811173; Pre-results.
To identify clinically meaningful clusters of patients with similar glycated hemoglobin (HbA1c) trajectories among patients with type 2 diabetes.
A retrospective cohort study using unsupervised ...machine learning clustering methodologies to determine clusters of patients with similar longitudinal HbA1c trajectories. Stability of these clusters was assessed and supervised random forest analysis verified the clusters' reproducibility. Clinical relevance of the clusters was assessed through multivariable analysis, comparing differences in risk for a composite outcome (macrovascular and microvascular outcomes, hypoglycemic events, and all-cause mortality) at HbA1c thresholds for each cluster.
Among 60,423 patients, three clusters of HbA1c trajectories were generated: stable (n = 45,679), descending (n = 6,084), and ascending (n = 8,660) trends, which were reproduced with 99.8% accuracy using a random forest model. In the clinical relevance assessment, HbA1c levels demonstrated a J-shape association with the risk for outcomes. HbA1c level thresholds for minimizing outcomes' risk differed by cluster: 6.0-6.4% for the stable cluster, <8.0% for the descending cluster, and <9.0 for the ascending cluster.
By applying unsupervised machine learning to longitudinal HbA1c trajectories, we have identified clusters of patients who have distinct risk for diabetes-related complications. These clusters can be the basis for developing individualized models to personalize glycemic targets.
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