Andreas D. Meid

Is Polypharmacy Associated with Frailty in Older People? Results From the ESTHER Cohort Study

To investigate the relationship between polypharmacy and frailty.

Impact of an electronic clinical decision support system on workflow in antenatal care: the QUALMAT eCDSS in rural health care facilities in Ghana and Tanzania

Background The implementation of new technology can interrupt established workflows in health care settings. The Quality of Maternal Care (QUALMAT) project has introduced an electronic clinical decision support system (eCDSS) for antenatal care (ANC) and delivery in rural primary health care facilities in Africa. Objective This study was carried out to investigate the influence of the QUALMAT eCDSS on the workflow of health care workers in rural primary health care facilities in Ghana and Tanzania. Design A direct observation, time-and-motion study on ANC processes was conducted using a structured data sheet with predefined major task categories. The duration and sequence of tasks performed during ANC visits were observed, and changes after the implementation of the eCDSS were analyzed. Results In 24 QUALMAT study sites, 214 observations of ANC visits (144 in Ghana, 70 in Tanzania) were carried out at baseline and 148 observations (104 in Ghana, 44 in Tanzania) after the software was implemented in 12 of those sites. The median time spent combined for all centers in both countries to provide ANC at baseline was 6.5 min [interquartile range (IQR) =4.0-10.6]. Although the time spent on ANC increased in Tanzania and Ghana after the eCDSS implementation as compared to baseline, overall there was no significant increase in time used for ANC activities (0.51 min, p=0.06 in Ghana; and 0.54 min, p=0.26 in Tanzania) as compared to the control sites without the eCDSS. The percentage of medical history taking in women who had subsequent examinations increased after eCDSS implementation from 58.2% (39/67) to 95.3% (61/64) p<0.001 in Ghana but not in Tanzania [from 65.4% (17/26) to 71.4% (15/21) p=0.70]. Conclusions The QUALMAT eCDSS does not increase the time needed for ANC but partly streamlined workflow at sites in Ghana, showing the potential of such a system to influence quality of care positively.Background The implementation of new technology can interrupt established workflows in health care settings. The Quality of Maternal Care (QUALMAT) project has introduced an electronic clinical decision support system (eCDSS) for antenatal care (ANC) and delivery in rural primary health care facilities in Africa. Objective This study was carried out to investigate the influence of the QUALMAT eCDSS on the workflow of health care workers in rural primary health care facilities in Ghana and Tanzania. Design A direct observation, time-and-motion study on ANC processes was conducted using a structured data sheet with predefined major task categories. The duration and sequence of tasks performed during ANC visits were observed, and changes after the implementation of the eCDSS were analyzed. Results In 24 QUALMAT study sites, 214 observations of ANC visits (144 in Ghana, 70 in Tanzania) were carried out at baseline and 148 observations (104 in Ghana, 44 in Tanzania) after the software was implemented in 12 of those sites. The median time spent combined for all centers in both countries to provide ANC at baseline was 6.5 min [interquartile range (IQR) =4.0–10.6]. Although the time spent on ANC increased in Tanzania and Ghana after the eCDSS implementation as compared to baseline, overall there was no significant increase in time used for ANC activities (0.51 min, p=0.06 in Ghana; and 0.54 min, p=0.26 in Tanzania) as compared to the control sites without the eCDSS. The percentage of medical history taking in women who had subsequent examinations increased after eCDSS implementation from 58.2% (39/67) to 95.3% (61/64) p<0.001 in Ghana but not in Tanzania [from 65.4% (17/26) to 71.4% (15/21) p=0.70]. Conclusions The QUALMAT eCDSS does not increase the time needed for ANC but partly streamlined workflow at sites in Ghana, showing the potential of such a system to influence quality of care positively.

Medication Underuse in Aging Outpatients with Cardiovascular Disease: Prevalence, Determinants, and Outcomes in a Prospective Cohort Study

Background Cardiovascular disease is a leading cause of death in older people, and the impact of being exposed or not exposed to preventive cardiovascular medicines is accordingly high. Underutilization of beneficial drugs is common, but prevalence estimates differ across settings, knowledge on predictors is limited, and clinical consequences are rarely investigated. Methods Using data from a prospective population-based cohort study, we assessed the prevalence, determinants, and outcomes of medication underuse based on cardiovascular criteria from Screening Tool To Alert to Right Treatment (START). Results Medication underuse was present in 69.1% of 1454 included participants (mean age 71.1 ± 6.1 years) and was significantly associated with frailty (odds ratio: 2.11 [95% confidence interval: 1.24–3.63]), body mass index (1.03 [1.01–1.07] per kg/m2), and inversely with the number of prescribed drugs (0.84 [0.79–0.88] per drug). Using this information for adjustment in a follow-up evaluation (mean follow-up time 2.24 years) on cardiovascular and competing outcomes, we found no association of medication underuse with cardiovascular events (fatal and non-fatal) (hazard ratio: 1.00 [0.65–1.56]), but observed a significant association of medication underuse with competing deaths from non-cardiovascular causes (2.52 [1.01–6.30]). Conclusion Medication underuse was associated with frailty and adverse non-cardiovascular clinical outcomes. This may suggest that cardiovascular drugs were withheld because of serious co-morbidity or that concurrent illness can preclude benefit from cardiovascular prevention. In the latter case, adapted prescribing criteria should be developed and evaluated in those patients.

Dried Blood Spot Technique for the Monitoring of Ambrisentan, Bosentan, Sildenafil, and Tadalafil in Patients with Pulmonary Arterial Hypertension

Endothelin receptor antagonists (ERA) and phosphodiesterase 5 inhibitors (PDE5I) are long-term therapeutics for the treatment of pulmonary arterial hypertension (PAH). Their interindividual pharmacokinetic variability is remarkably large, and despite the seriousness of the disease, nonadherence is occurring. Therefore, methods to monitor sufficient circulating drug levels are essential. The objectives of this study were to develop and validate dried blood spot (DBS) assays for the quantification of ambrisentan, bosentan, sildenafil, tadalafil, and their main metabolites. We also quantified the influence of different hematocrit levels and assessed the correlation of simultaneously taken capillary whole blood (DBS) and venous plasma samples. The aliquot punches were extracted by liquid/liquid extraction followed by liquid chromatography/tandem mass spectrometry (LC/MS/MS) quantification methods. All assays fulfilled the requirements of the FDA and EMA guidelines for assay validation with a lower limit of quantification of 2.5 ng/mL for the ERAs, 5 ng/mL for sildenafil, and 10 ng/mL for tadalafil. All analytes were stable for at least 147 days when stored on DBS filter paper cards at room temperature in the dark. Due to poor distribution into erythrocytes, drug concentrations in DBS were always lower than in plasma, resulting in conversion factors of 1.58 for ambrisentan and sildenafil and 1.52 for bosentan and tadalafil.

Comparative evaluation of methods approximating drug prescription durations in claims data: modeling, simulation, and application to real data.

The purpose of this study was to compare the predictive accuracy of different methods suggested for approximation of drug prescription durations in claims data.

Health Service Use, Costs, and Adverse Events Associated with Potentially Inappropriate Medication in Old Age in Germany: Retrospective Matched Cohort Study

BackgroundDrug-related problems are an important healthcare safety concern in the growing population of older people. Prescription of potentially inappropriate medication (PIM) is one aspect of this concern that is considered to increase the risk of adverse health outcomes.ObjectiveThe aim of the Health Economics of Potentially Inappropriate Medication (HEPIME) study was to analyze the association between the prescription of PIMs according to the German PRISCUS list and healthcare utilization, healthcare costs, and the occurrence of adverse events in old age.MethodsInsurants of a large German health insurance company aged 65+ years were included in a retrospective matched cohort study. A total of 3,953,423 individuals with no exposure to PIM in 2011 were matched to 521,644 exposed individuals and compared in terms of outpatient healthcare utilization, healthcare costs, and the occurrence of adverse events in outpatient, hospital, and rehabilitation sectors during a 12-month follow-up.ResultsOn average, individuals in the exposed group had additional 143 [95% confidence interval (CI) 140–146] daily defined doses of pharmaceuticals and 4.5 (95% CI 4.4–4.6) days in hospital. Mean annual total healthcare costs per individual in the exposed group exceeded those in the non-exposed group by €2321 (95% CI 2269–2372), resulting mainly from differences in hospitalization costs of €1718 (95% CI 1678–1759). Odds ratios for the occurrence of adverse events in the exposed group were 1.32 (95% CI 1.32–1.34) in the outpatient sector, 1.76 (95% CI 1.73–1.79) in the hospital sector, and 1.82 (95% CI 1.76–1.89) in the rehabilitation sector.ConclusionsIncreased healthcare utilization and costs as well as an increased probability for adverse events in individuals exposed to PIM demonstrate the health economic relevance of PIM prescriptions. Whether avoiding PIM listed on the PRISCUS list may potentially improve the quality and efficiency of healthcare is currently unknown.

Age-Dependent Impact of Medication Underuse and Strategies for Improvement

Background: Medication underuse is common in aging populations and, because of the growing risk for competing deaths, the benefit of preventive medicines gradually vanishes with advancing age, thus limiting their success. Objective: To estimate the optimum time of initiation of the secondary prevention of cardiovascular events, we examined the impact of appropriate pharmacotherapy for different starting ages at which it is implemented. Methods: In the competing risk framework, we obtained the populations life course from life tables, combined it with effect estimates quantifying the real-world effectiveness of secondary prevention, and compared the outcome of patients not receiving appropriate treatment (underuse) with those receiving preventive medicines that have demonstrated a reduction in the transition to serious cardiovascular events (START criteria). Starting at the age of 55 years, the population proportions of the distinct states of the framework were calculated for each year of chronological age in subgroups of appropriate treatment and underuse. These proportions were used over a follow-up period to estimate measures of treatment effectiveness and risks of underuse. Results: Despite increasing relative effectiveness with advancing age, benefits measured by patient-relevant endpoints, such as life years gained (LYG) or gained quality-adjusted life years (QALYs), markedly dropped after the starting age of 75 years, but even at an initiation age of 85 years, QALYs gained exceeded 1 year. Conclusion: Interventions targeting medication underuse may achieve considerable benefits at any stage of later life, while the benefit is probably largest if appropriate treatment is started before 75 years.

Refining estimates of prescription durations by using observed covariates in pharmacoepidemiologic databases: Necessary refinements to stimulate alternative approaches

Claims data are a valuable information source of drug use but often only provides dispensing dates of drug packages, so that true drug exposure durations of the individual patient must be extrapolated. This is, however, rather important whenever quantitative and temporal drug exposure is of relevance for pharmacoepidemiological research. We read the paper by Støvring and colleagues with interest, addressing the timely topic of accurate drug exposure estimation. Methodologically, the authors previously described the waiting time distribution (WTD) to derive prescription durations from a distributional pattern in the total sample (i.e., aggregated data). Now, the authors introduce the reverse WTD as the distribution of times from each patients last prescription within a time window to the end of the time window. Distribution percentiles at a certain cutoff can be used to derive the typical prescription duration. Although this approach is largely independent of (clinical) assumptions and well suited for regular long‐term therapies, flexible treatment regimens and random noise introduced by the reimbursement system will likely limit any “one‐size‐fits‐all” approach. We therefore appreciate a refinement of their original approach based on aggregated prescription patterns and are convinced of the basic idea of exploiting covariate information in parametric models to predict prescription durations. This could expand and enrich alternative approaches that more directly relate to individual drug use patterns (e.g., COV or PRE2DUP), where such covariate information could be used to predict administered doses. Hence, stimulated by the work of Støvring, we performed a simulation study based on our previously established framework aiming to demonstrate the potential for improvement for the alternative COV approach. One of their fundamental findings is that prescription durations strongly depended on the prescribed number of pills. Although this seems trivial, it questions the appropriateness of (previous) approaches ignoring this information at the same time. Conceptually, we are yet convinced that the derived dose rather than the duration itself should be predicted, unless good reasons for stable prescription durations exist (e.g., fixed administration schemes in chronic treatments with good adherence). This assumption already does not hold if, for example, reimbursement and prescription drug policies force to prescribe smaller (i.e., cheaper) package sizes towards the end of a quarter (as

Association of preventable adverse drug events with inpatients' length of stay—A propensity‐matched cohort study

Using clinical administrative data (CAD) of inpatients, we aimed to identify ICD‐10 codes coding for potentially preventable inhospital adverse drug events (ADE) that affect the length of hospital stay (LOS) and thus patient well‐being and cost.

How can we define and analyse drug exposure more precisely to improve the prediction of hospitalizations in longitudinal (claims) data

BackgroundRisk prediction models can be powerful tools to support clinical decision-making, to help targeting interventions, and, thus, to improve clinical and economic outcomes, provided that model performance is good and sensitivity and specificity are well balanced. Drug utilization as a potential risk factor for unplanned hospitalizations has recently emerged as a meaningful predictor variable in such models. Drug treatment is a rather unstable (i.e. time-dependent) phenomenon and most drug-induced events are concentration-dependent and therefore individual drug exposure will likely modulate the risk. This especially applies to longitudinal monitoring of appropriate drug treatment within claims data as another promising application for prediction models.Methods and ResultsTo guide future research towards this direction, we firstly reviewed current risk prediction models for unplanned hospitalizations that explicitly included information on drug utilization and were surprised to find that these models rarely attempted to consider dose and frequent modulators of drug clearance such as interactions with co-medication or co-morbidities. As another example, they often presumed class effects where in fact, differences between active moieties were well established. In addition, the study designs and statistical risk analysis disregarded the fact that medication and risk modulators and, thus, adverse events can vary over time. In a simulation study, we therefore evaluated the potential benefit of time-dependent Cox models over standard binary regression approaches with a fixed follow-up period.ConclusionsLongitudinal drug information could be utilized much more efficiently both by precisely estimating individual drug exposure and by applying more refined statistical methodology to account for time-dependent drug utilization patterns.