Operationalizing a Data to Care Strategy in Michigan Through Cross-Agency Collaborations.

BACKGROUND: For persons with HIV infection (PWH), viral load suppression is essential to maintaining health and reducing the likelihood of HIV transmission. Data to Care (D2C) is an important strategy for improving HIV outcomes but may be resource-intensive to execute. SETTING: In 2016, Michigan joined the HIV Health Improvement Affinity Group to strengthen D2C partnerships between its Medicaid and HIV program. Goals included establishing routine data sharing, matching data sources to understand health outcomes, and collaborating to turn data into action. METHODS: Michigan established data use agreements to assess gaps in care for PWH enrolled in Medicaid. The HIV Surveillance Program used Link Plus to match surveillance records on PWH to Medicaid's active beneficiary file to identify PWH who were beneficiaries as of December 31, 2015. RESULTS: Matching the 2,300,877 Michigan Medicaid beneficiaries with the 15,845 PWH in HIV surveillance yielded 4822 matched PWH enrolled in Medicaid in 2015. Of Medicaid beneficiaries with HIV, 597 had no evidence of receiving HIV care, representing 20% of all Michigan residents with HIV and not in care in 2015. CONCLUSION: D2C is an effective strategy for improving HIV care continuum outcomes but can be relatively inefficient if implementation models rely solely on public health infrastructure. Through the HIV Health Improvement Affinity Group, Michigan's Medicaid and HIV programs leveraged their combined data assets to evaluate and improve care quality and outcomes for PWH on Medicaid. Partnerships between Medicaid and public health offer attractive mechanisms for potentially increasing efficiency and effectiveness of D2C investments.

Estimating Effects of HIV Sequencing Data Completeness on Transmission Network Patterns and Detection of Growing HIV Transmission Clusters.

HIV nucleotide sequence data can identify clusters of persons with genetically similar strains suggesting transmission. We simulated the effect of lowered data completeness, defined by the percent of persons with diagnosed HIV with a reported sequence, on transmission patterns and detection of growing HIV transmission clusters. We analyzed HIV surveillance data for persons with HIV diagnosed during 2008-2014 who resided in Michigan or Washington. We calculated genetic distances, constructed the inferred transmission network for each jurisdiction, and compared transmission network characteristics and detection of growing transmission clusters in the full dataset with artificially reduced datasets. Simulating lower levels of completeness resulted in decreased percentages of persons linked to a cluster from high completeness (full dataset) to low completeness (5%) (Michigan: 54%-18%; Washington, 46%-16%). Patterns of transmission between certain populations remained robust as data completeness level was reduced. As data completeness was artificially decreased, sensitivity of cluster detection substantially diminished in both states. In Michigan, sensitivity decreased from 100% with the full dataset, to 62% at 50% completeness and 21% at 25% completeness. In Washington, sensitivity decreased from 100% with the full dataset, to 71% at 50% completeness and 29% at 25% completeness. Lower sequence data completeness limits the ability to detect clusters that may benefit from investigation; however, inferences can be made about transmission patterns even with low data completeness, given sufficient numbers. Data completeness should be prioritized, as lack of or delays in detection of transmission clusters could result in additional infections.

HIV-1 transmission during early infection in men who have sex with men: a phylodynamic analysis.

BACKGROUND: Conventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. We conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates by stage of infection. METHODS AND FINDINGS: We analyzed 662 HIV-1 subtype B sequences collected between October 14, 2004, and February 24, 2012, from MSM in the Detroit metropolitan area, Michigan. These sequences were cross-referenced with a database of 30,200 patients diagnosed with HIV infection in the state of Michigan, which includes clinical information that is informative about the recency of infection at the time of diagnosis. These data were analyzed using recently developed population genetic methods that have enabled the estimation of transmission rates from the population-level genetic diversity of the virus. We found that genetic data are highly informative about HIV donors in ways that standard surveillance data are not. Genetic data are especially informative about the stage of infection of donors at the point of transmission. We estimate that 44.7% (95% CI, 42.2%-46.4%) of transmissions occur during the first year of infection. CONCLUSIONS: In this study, almost half of transmissions occurred within the first year of HIV infection in MSM. Our conclusions may be sensitive to un-modeled intra-host evolutionary dynamics, un-modeled sexual risk behavior, and uncertainty in the stage of infected hosts at the time of sampling. The intensity of transmission during early infection may have significance for public health interventions based on early treatment of newly diagnosed individuals.