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Record W2545399815

Factors Associated with Adherence to the HEDIS Quality Measure in Medicaid Patients with Schizophrenia.

2016· article· en· W2545399815 on OpenAlexaff
Marie‐Hélène Lafeuille, C. Frois, Michel Cloutier, Mei Sheng Duh, Patrick Lefèbvre, Jacqueline Pesa, Zoe Clancy, John Fastenau, Mike Durkin

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicineRisperidoneAntipsychoticMedicaidPaliperidone PalmitateSchizophrenia (object-oriented programming)PaliperidonePsychiatryQuetiapineOlanzapineLogistic regressionMedical prescriptionManaged careHealth careCohortInternal medicinePharmacology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Treatment continuity is a major challenge in the long-term management of patients with schizophrenia; poor patient adherence to antipsychotic drugs has been associated with negative clinical outcomes. Long-acting injectable therapies may improve adherence and lessen the risk for psychiatric-related relapse, often leading to rehospitalization and higher healthcare costs. Therefore, understanding the determinants of adherence to antipsychotics is critical in the management of patients with schizophrenia. OBJECTIVE: To assess the impact of baseline patient characteristics on adherence as measured by the Healthcare Effectiveness Data and Information Set (HEDIS) measure of continuity of antipsychotic medications among patients with Medicaid coverage. METHODS: Medicaid healthcare claims data between 2008 and 2011 from 5 states were used to identify patients who were diagnosed with schizophrenia (aged 25-64 years) and received ≥1 antipsychotic prescriptions in baseline year 2010 and in measurement year 2011. The HEDIS continuity of antipsychotic medications (ie, adherence) measure was defined as the proportion of days covered with any antipsychotic medication ≥80% during the measurement year. The 2 cohorts compared paliperidone palmitate with any other antipsychotics, including quetiapine, risperidone, and haloperidol. The baseline-year characteristics were evaluated as potential predictive factors of adherence in the measurement year using multivariate logistic regressions. The regression models incorporated the inverse probability of treatment weights to control for differences in baseline characteristics between the paliperidone palmitate and the other antipsychotics cohort. RESULTS: Among the 12,990 patients who received an antipsychotic during the study period, 48.6% successfully achieved the continuity criteria in the measurement year. After controlling for other covariates, the odds of adherence were improved by adherence at baseline (odds ratio [OR], 9.42; 95% confidence interval [CI], 8.55-10.39). The use of paliperidone palmitate was associated with a 26% increase in the odds of achieving adherence compared with the use of the other antipsychotics studied (OR, 1.26; 95% CI, 1.14-1.39). In addition, female sex (OR, 1.11; 95% CI, 1.01-1.22), age 55 to 64 years (OR, 1.26; 95% CI, 1.09-1.46) versus age 25 to 34 years, Hispanic race (OR, 1.37; 95% CI, 1.05-1.81) versus white race, and an increase of $10,000 in baseline inpatient costs (OR, 1.11; 95% CI, 1.08-1.15) were associated with greater odds of treatment continuity. CONCLUSIONS: In addition to sex, age, and race, the baseline characteristics that were associated with achieving the HEDIS continuity of antipsychotic medication measure included previous-year adherence, inpatient costs, and the use of paliperidone palmitate. These findings offer insight to healthcare plans that cover Medicaid populations on the effects that patient characteristics and treatment types may have on adherence among patients with schizophrenia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.078
GPT teacher head0.286
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2016
Admission routes1
Has abstractyes

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