MétaCan
Menu
Retour à la cohorte
Enregistrement W2065960798 · doi:10.1097/01.olq.0000430802.91969.98

Do Only 21% of HIV-Positive Medicaid Enrollees Link to Treatment? Challenges in Interpreting Medicaid Claims Data

2013· letter· en· W2065960798 sur OpenAlexaboutno aff
Arleen Leibowitz, Katherine A. Desmond

Notice bibliographique

RevueSexually Transmitted Diseases · 2013
Typeletter
Langueen
DomaineMedicine
ThématiqueHIV/AIDS Research and Interventions
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute of Mental Health
Mots-clésMedicaidMedicineFamily medicineTest (biology)Medical diagnosisDiagnosis codePregnancyQuarter (Canadian coin)Health careGerontologyPopulationEnvironmental health

Résumé

récupéré en direct d'OpenAlex

To the Editors In a recent article, Johnston et al.1 found that only 21% of Medicaid enrollees with a new HIV diagnosis code were linked to appropriate care within a year after the HIV test. This finding contrasts with Centers for Disease Control and Prevention estimates based on surveillance data, which indicate that 75% of people with HIV-positive test results link to care within a year. This analysis was based on Medicaid claims, which lack information on laboratory test results; consequently, Johnston et al. inferred a positive test result if the claims contained an HIV diagnosis code on the same day as the test or at a later date. This method likely resulted in including many non–HIV-positive adults in the sample. Comparable with Johnston et al., we selected from Medicaid claims files all individuals with an HIV diagnosis code, initially identifying 14,402 individuals in California with Medicaid, but not also Medicare coverage. Of those, 14% had HIV diagnoses coded only on the same day they were screened for HIV, and there was no evidence of a subsequent diagnosis from a confirmatory test. Of this same-day group, 82% were female and 49% were entitled not to full benefits, but only to services for pregnancy, family planning, breast cancer treatment, or other limited services. One quarter of this group received services for pregnancy. We concluded that many of these enrollees were receiving HIV screening tests in prenatal care or at delivery and did not have HIV disease. Only 1% of those whose diagnoses were recorded only on screening days, with no confirmatory test; had claims for viral loads or CD4 tests; or had claims for antiretroviral medications. Conversely, of those with diagnoses recorded on days other than screening days, 63% had viral load or CD4 tests and 70% had claims for antiretroviral medication. Many of the “HIV diagnoses” in 2007 California Medicaid claims seemed to be “rule-out HIV” diagnoses. Thus, the strategy of Johnston et al. regarding using a same-day HIV diagnosis to identify a new HIV case may have inadvertently included many HIV testers who did not turn out to be HIV infected. Including many Medicaid recipients without confirmed HIV diagnoses may be responsible for the unexpected findings of Johnston et al. that 70% of the HIV-positive Medicaid enrollees were female and that 21% received their HIV test in an inpatient setting.1 If women represent 70% of Medicaid enrollees with HIV, it implies a greater infection rate among women than men because women account for only 59% of the adult Medicaid population. That the infection rate is 3.7 times higher among men than among women casts doubt on the selection criteria used by Johnston et al.2 Including individuals who do not actually have HIV disease when calculating the percentage of HIV-positive individuals who receive appropriate medical follow-up understates the true level of linkage to care and also biases other measures of interest, such as Medicaid expenditures for HIV. The difficulties in identifying new HIV cases in claims data raise serious questions about the conclusion that Medicaid enrollees with HIV are not being linked to effective treatment in a timely way. Arleen A. Leibowitz, PhD Katherine Desmond, MS Department of Public Policy UCLA Luskin School of Public Affairs Los Angeles, CA [email protected]

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,560
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,051
Tête enseignante GPT0,343
Écart entre enseignants0,292 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2013
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueSexually Transmitted DiseasesMême sujetHIV/AIDS Research and InterventionsTravaux en français237 207