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

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

Bibliographic record

VenueSexually Transmitted Diseases · 2013
Typeletter
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Mental Health
KeywordsMedicaidMedicineFamily medicineTest (biology)Medical diagnosisDiagnosis codePregnancyQuarter (Canadian coin)Health careGerontologyPopulationEnvironmental health

Abstract

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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]

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.560
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.343
Teacher spread0.292 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations3
Published2013
Admission routes1
Has abstractyes

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