1,721,003 research outputs found
Supply-side solutions targeting demand-side characteristics: causal effects of a chronic disease management program on adherence and health outcomes
Full text linkWe estimate the effects of a chronic disease management program (CDMP) which adapts various supply-side interventions to specific demand-side conditions (disease-staging) for patients with chronic kidney disease (CKD). Using a unique dataset on the entire population of the Emilia–Romagna region of Italy with hospital-diagnosed CKD, we estimate the causal effects of the CDMP on adherence indicators and health outcomes. As CKD is a progressive disease with clearly-defined disease stages and a treatment regimen that can be titrated by disease severity, we calculate dynamic, severity-specific, indicators of adherence as well as several long-term health outcomes. Our empirical work produces statistically significant and sizeable causal effects on many adherence and health outcome indicators across all CKD patients. More interestingly, we show that the CDMP produces larger effects on patients with early-stage CKD, which is at odds with some of the literature on CDMP that advocates intensifying interventions for high-cost (or late-stage) patients. Our results suggest that it may be more efficient to target early-stage patients to slow the deterioration of their health capital. The results contribute to a small, recent literature in health economics that focuses on the marginal effectiveness of CDMPs after controlling either for supply- or demand-side sources of heterogeneity
Designing feasible and effective health plan payments in countries with data availability constraints
No full textRisk equalization schemes, which transfer money to/from insurers that have above/below average risks, are a fundamental tool in regulated health insurance markets in many countries. Risk sharing (the transfer of some responsibility for costs from a plan to the regulator or the overall insurance market), are an additional method of insulating insurers who attract higher-than-average risks. This paper proposes, implements and quantifies incorporating risk sharing within a risk equalization scheme that can be applied in a data-poor context. Using Chile's private health insurance market as case study, we show that modest amount of risk sharing greatly improves fit even in simple demographic-based risk equalization. Expanding the model's formula to include morbidity-based adjustors and risk sharing redirects compensations at insurer level and reduces opportunity to engage in profitable risk selection at the group level. Our emphasis on feasibility may make alternatives proposed attractive to countries facing data-availability constraints
Efficacy and safety of 24-week pramipexole augmentation in patients with treatment resistant depression. A retrospective cohort study
No full textPramipexole is a dopamine agonist with potential antidepressant, neuroprotective, antioxidant and anti-inflammatory activity. In the present study we investigated the 24 weeks effect and safety of traditional AD augmentation with pramipexole for treatment-resistant depression. The study includes 116 patients, 37 (32%) with bipolar disorders and 79 (68%) with major depressive disorder, who failed to respond to at least 2 ADs trials of different classes and that were treated with AD augmented with pramipexole. Mood stabilizers and/or second-generation antipsychotics were added in patients with bipolar or mixed depression. Exclusion criteria were psychotic depression, rapid cycling bipolar course and previous unsuccessful treatment with pramipexole. After 24 weeks of pramipexole augmentation (median max dose 1.05 mg/day, IQR 0.72–1.08) 74.1% of patients responded (≥50% reduction of baseline Hamilton Depression Rating Scale21 total score) and 66.4% remitted (Hamilton Depression Rating Scale21 total score < 7). Global Assessment of Functioning score significantly increase from 53 (50–60) at baseline to 80 (71–81) at 24 weeks (Wilcoxon signed rank test = 8.174, p < 0.001]. Ten patients (8.6%) dropped out (8 due to side effects and 2 for lack of efficacy) and 1 experienced an induced hypomanic switch. No patient committed a suicide attempt, had suicidal ideation, needed hospitalization, reported lethargy, gambling, hypersexuality and compulsive shopping. The limitations of the study are the observational design, the lack of a control group, the inclusion of outpatients only, the unblinded outcomes assessment, and the flexibility of the add-on schedule. The findings of the present study showed that off-label use of pramipexole as augmentation of traditional AD is an effective and safe 24 weeks treatment of resistant unipolar and bipolar depression. These results need confirmation from randomized clinical trials on larger samples
Comparing risk adjustment estimation methods under data availability constraints
No full textThe Italian National Healthcare Service relies on per capita allocation for healthcare funds, despite having a highly detailed and wide range of data to potentially build a complex risk-adjustment formula. However, heterogeneity in data availability limits the development of a national model. This paper implements and ealuates machine learning (ML) and standard risk-adjustment models on different data scenarios that a Region or Country may face, to optimize information with the most predictive model. We show that ML achieves a small but generally statistically insignificant improvement of adjusted R2 and mean squared error with fine data granularity compared to linear regression, while in coarse granularity and poor range of variables scenario no differences were observed. The advantage of ML algorithms is greater in the coarse granularity and fair/rich range of variables set and limited with fine granularity scenarios. The inclusion of detailed morbidity- and pharmacy-based adjustors generally increases fit, although the trade-off of creating adverse economic incentives must be considered
Developing and validating an algorithm to identify incident chronic dialysis patients using administrative data
Full text linkBackground: Administrative healthcare databases are widespread and are often standardized with regard to their content and data coding, thus they can be used also as data sources for surveillance and epidemiological research. Chronic dialysis requires patients to frequently access hospital and clinic services, causing a heavy burden to healthcare providers. This also means that these patients are routinely tracked on administrative databases, yet very few case definitions for their identification are currently available. The aim of this study was to develop two algorithms derived from administrative data for identifying incident chronic dialysis patients and test their validity compared to the reference standard of the regional dialysis registry. Methods: The algorithms are based on data retrieved from hospital discharge records (HDR) and ambulatory specialty visits (ASV) to identify incident chronic dialysis patients in an Italian region. Subjects are included if they have at least one event in the HDR or ASV databases based on the ICD9-CM dialysis-related diagnosis or procedure codes in the study period. Exclusion criteria comprise non-residents, prevalent cases, or patients undergoing temporary dialysis, and are evaluated only on ASV data by the first algorithm, on both ASV and HDR data by the second algorithm. We validated the algorithms against the Emilia-Romagna regional dialysis registry by searching for incident patients in 2014 and performed sensitivity analyses by modifying the criteria to define temporary dialysis. Results: Algorithm 1 identified 680 patients and algorithm 2 identified 676 initiating dialysis in 2014, compared to 625 patients included in the regional dialysis registry. Sensitivity for the two algorithms was respectively 90.8 and 88.4%, positive predictive value 84.0 and 82.0%, and percentage agreement was 77.4 and 74.1%. Conclusions: Algorithms relying on retrieval of administrative records have high sensitivity and positive predictive value for the identification of incident chronic dialysis patients. Algorithm 1, which showed the higher accuracy and has a simpler case definition, can be used in place of regional dialysis registries when they are not present or sufficiently developed in a region, or to improve the accuracy and timeliness of existing registries
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Modified-Chronic Disease Score (M-CDS): Predicting the individual risk of death using drug prescriptions
Full text linkBackground Estimating the morbidity of a population is strategic for health systems to improve healthcare. In recent years administrative databases have been increasingly used to predict health outcomes. In 1992, Von Korff proposed a Chronic Disease Score (CDS) to predict 1-year mortality by only using drug prescription data. Because pharmacotherapy underwent many changes over the last 3 decades, the original version of the CDS has limitations. The aim of this paper is to report on the development of the modified version of the CDS. Methods The modified CDS (M-CDS) was developed using 33 variables (from drug prescriptions within two-year before 01/01/2018) to predict one-year mortality in Bologna residents aged ≥50 years. The population was split into training and testing sets for internal validation. Score weights were estimated in the training set using Cox regression model with LASSO procedure for variables selection. The external validation was carried out on the Imola population. The predictive ability of M-CDS was assessed using ROC analysis and compared with that of the Charlson Comorbidity Index (CCI), that is based on hospital data only, and of the Multisource Comorbidity Score (MCS), which uses hospital and pharmaceutical data. Results The predictive ability of M-CDS was similar in the training and testing sets (AUC 95% CI: Training [0.760-0.770] vs. testing [0.750-0.772]) and in the external population (Imola AUC 95% CI [0.756-0.781]). M-CDS was significantly better than CCI (M-CDS AUC = 0.761, 95% CI [0.750-0.772] vs. CCI-AUC = 0.696, 95% CI [0.681-0.711]). No significant difference was found between M-CDS and MCS (MCS AUC = 0.762, 95% CI [0.749-0.775]). Conclusions M-CDS, using only drug prescriptions, has a better performance than the CCI score in predicting 1-year mortality, and is not inferior to the multisource comorbidity score. M-CDS can be used for population risk stratification, for risk-adjustment in association studies and to predict the individual risk of death
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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