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Cross-Jurisdictional Data Exchange Impact on the Estimation of the HIV Population Living in the District of Columbia: Evaluation Study.

ABSTRACT: Accurate HIV surveillance data are essential to monitor trends to help end the HIV epidemic. Owing to strict policies around data security and confidentiality, HIV surveillance data have not been routinely shared across jurisdictions except a biannual case-by-case review process to identify and remove duplicate cases (Routine Interstate Duplicate Review, RIDR). HIV surveillance estimates for the District of Columbia (DC) are complicated by migration and care seeking throughout the metropolitan area, which includes Maryland and Virginia. To address gaps in HIV surveillance data, health departments of DC, Maryland, and Virginia have established HIV surveillance data sharing agreements. Although the Black Box (a privacy data integration tool external to the health departments) facilitates the secure exchange of data between DC, Maryland, and Virginia, its previous iterations were limited by the frequency and scope of information exchanged. The health departments of DC, Maryland, and Virginia engaged in data sharing to further improve HIV surveillance estimates.This study assessed the impact of cross-jurisdictional data sharing on the estimation of people living with HIV in DC and reduction of cases in the RIDR process.Data sharing agreements established in 2014 allowed for the exchange of HIV case information (eg, current residential address) and laboratory information (eg, test types, result dates, and results) from the enhanced HIV/AIDS Reporting System (eHARS). Regular data exchanges began in 2017. The participating jurisdictions transferred data (via secure file transfer protocol) for individuals having a residential address in a partnering jurisdiction at the time of HIV diagnosis or evidence of receiving HIV-related services at a facility located in a partnering jurisdiction. The DC Department of Health compared the data received to DC eHARS and imported updated data that matched existing cases. Evaluation of changes in current residential address and HIV prevalence was conducted by comparing data before and after HIV surveillance data exchanges.After the HIV surveillance data exchange, an average of 396 fewer cases were estimated to be living in DC each year from 2012 to 2016. Among cases with a residential status change, 66.4% (1316/1982) had relocated to Maryland and 19.8% (392/1982) to Virginia; majority of these had relocated to counties bordering DC. Relocation in and out of DC differed by mode of transmission, race and ethnicity, age group, and gender. After data exchange, the volume of HIV cases needing RIDR decreased by 74% for DC-Maryland and 81% for DC-Virginia.HIV surveillance data exchange between the public health departments of DC, Maryland, and Virginia reduced the number of cases misclassified as DC residents and reduced the number of cases needing RIDR. Continued data exchanges will enhance the ability of DC Department of Health to monitor the local HIV epidemic.

SUBMITTER: Hamp AD

PROVIDER: S-EPMC6111149 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Cross-Jurisdictional Data Exchange Impact on the Estimation of the HIV Population Living in the District of Columbia: Evaluation Study.

Hamp Auntre D AD Doshi Rupali K RK Lum Garret R GR Allston Adam A

JMIR public health and surveillance 20180813 3

<h4>Background</h4>Accurate HIV surveillance data are essential to monitor trends to help end the HIV epidemic. Owing to strict policies around data security and confidentiality, HIV surveillance data have not been routinely shared across jurisdictions except a biannual case-by-case review process to identify and remove duplicate cases (Routine Interstate Duplicate Review, RIDR). HIV surveillance estimates for the District of Columbia (DC) are complicated by migration and care seeking throughout ...[more]

PMID: 30104182

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Project description:ObjectivesAs people living with HIV (PLWH) live longer, morbidity and mortality from non-AIDS comorbidities have emerged as major concerns. Our objective was to compare prevalence trends and age at diagnosis of nine chronic age-associated comorbidities between individuals living with and without HIV.Design and settingThis population-based cohort study used longitudinal cohort data from all diagnosed antiretroviral-treated PLWH and 1:4 age-sex-matched HIV-negative individuals in British Columbia, Canada.ParticipantsThe study included 8031 antiretroviral-treated PLWH and 32 124 HIV-negative controls (median age 40 years, 82% men). Eligible participants were ≥19 years old and followed for ≥1 year during 2000 to 2012.Primary and secondary outcome measuresThe presence of non-AIDS-defining cancers, diabetes, osteoarthritis, hypertension, Alzheimer's and/or non-HIV-related dementia, cardiovascular, kidney, liver and lung diseases were identified from provincial administrative databases. Beta regression assessed annual age-sex-standardised prevalence trends and Kruskal-Wallis tests compared the age at diagnosis of comorbidities stratified by rate of healthcare encounters.ResultsAcross study period, the prevalence of all chronic age-associated comorbidities, except hypertension, were higher among PLWH compared with their community-based HIV-negative counterparts; as much as 10 times higher for liver diseases (25.3% vs 2.1%, p value<0.0001). On stratification by healthcare encounter rates, PLWH experienced most chronic age-associated significantly earlier than HIV-negative controls, as early as 21 years earlier for Alzheimer's and/or dementia.ConclusionsPLWH experienced higher prevalence and earlier age at diagnosis of non-AIDS comorbidities than their HIV-negative controls. These results stress the need for optimised screening for comorbidities at earlier ages among PLWH, and a comprehensive HIV care model that integrates prevention and treatment of chronic age-associated conditions. Additionally, the robust methodology developed in this study, which addresses concerns on the use of administrative health data to measure prevalence and incidence, is reproducible to other settings.

Project description:BackgroundThe HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need, therefore, for studies to estimate HIV prevalence at appropriate levels to improve HIV-related planning and resource allocation.MethodsWe estimated the district-level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay-Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts.ResultsThe results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 32% of the districts (n = 24) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates.ConclusionsHIV prevalence in Zambian is highest in districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence. The variations in the burden of HIV across districts in Zambia point to the need for a differentiated approach in HIV programming within the country. HIV resources need to be prioritized toward districts with high population mobility.

Project description:BackgroundThe U.S. Ending the HIV epidemic (EHE) plan aims to reduce annual HIV incidence by 90% by 2030, by first focusing interventions on 57 regions (EHE jurisdictions) that contributed to more than 50% of annual HIV diagnoses. Mathematical models that project HIV incidence evaluate the impact of interventions and inform intervention decisions. However, current models are either national level, which do not consider jurisdictional heterogeneity, or independent jurisdiction-specific, which do not consider cross jurisdictional interactions. Data suggests that a significant proportion of persons have sexual partnerships outside their own jurisdiction. However, the sensitivity of these jurisdictional interactions on model outcomes and intervention decisions hasn't been studied.MethodsWe developed an ordinary differential equations based compartmental model to generate national-level projections of HIV in the U.S., through dynamic simulations of 96 epidemiological sub-models representing 54 EHE and 42 non-EHE jurisdictions. A Bernoulli equation modeled HIV-transmissions using a mixing matrix to simulate sexual partnerships within and outside jurisdictions. To evaluate sensitivity of jurisdictional interactions on model outputs, we analyzed 16 scenarios, combinations of a) proportion of sexual partnerships mixing outside jurisdiction: no-mixing, low-level-mixing-within-state, high-level-mixing-within-state, or high-level-mixing-within-and-outside-state; b) jurisdictional heterogeneity in care and demographics: homogenous or heterogeneous; and c) intervention assumptions for 2019-2030: baseline or EHE-plan (diagnose, treat, and prevent).ResultsChange in incidence in mixing compared to no-mixing scenarios varied by EHE and non-EHE jurisdictions and aggregation-level. When assuming jurisdictional heterogeneity and baseline-intervention, the change in aggregated incidence ranged from - 2 to 0% for EHE and 5 to 21% for non-EHE, but within each jurisdiction it ranged from - 31 to 46% for EHE and - 18 to 109% for non-EHE. Thus, incidence estimates were sensitive to jurisdictional mixing more at the jurisdictional level. As a result, jurisdiction-specific HIV-testing intervals inferred from the model to achieve the EHE-plan were also sensitive, e.g., when no-mixing scenarios suggested testing every 1 year (or 3 years), the three mixing-levels suggested testing every 0.8 to 1.2 years, 0.6 to 1.5 years, and 0.6 to 1.5 years, respectively (or 2.6 to 3.5 years, 2 to 4.8 years, and 2.2 to 4.1 years, respectively). Similar patterns were observed when assuming jurisdictional homogeneity, however, change in incidence in mixing compared to no-mixing scenarios were high even in aggregated incidence.ConclusionsAccounting jurisdictional mixing and heterogeneity could improve model-based analyses.

Project description:BackgroundMulti-assay algorithms (MAAs) are used to estimate population-level HIV incidence and identify individuals with recent infection. Many MAAs use low viral load (VL) as a biomarker for long-term infection. This could impact incidence estimates in settings with high rates of early HIV treatment initiation. We evaluated the performance of two MAAs that do not include VL.MethodsSamples were collected from 219 seroconverters (infected < 1 year) and 4376 non-seroconverters (infected > 1 year) in the HPTN 071 (PopART) trial; 28.8% of seroconverter samples and 73.2% of non-seroconverter samples had VLs ≤ 400 copies/mL. Samples were tested with the Limiting Antigen Avidity assay (LAg) and JHU BioRad-Avidity assays. Antibody reactivity to two HIV peptides was measured using the MSD U-PLEX assay. Two MAAs were evaluated that do not include VL: a MAA that includes the LAg-Avidity assay and BioRad-Avidity assay (LAg + BR) and a MAA that includes the LAg-Avidity assay and two peptide biomarkers (LAg + PepPair). Performance of these MAAs was compared to a widely used MAA that includes LAg and VL (LAg + VL).ResultsThe incidence estimate for LAg + VL (1.29%, 95% CI: 0.97-1.62) was close to the observed longitudinal incidence (1.34% 95% CI: 1.17-1.53). The incidence estimates for the other two MAAs were higher (LAg + BR: 2.56%, 95% CI 2.01-3.11; LAg + PepPair: 2.84%, 95% CI: 1.36-4.32). LAg + BR and LAg + PepPair also misclassified more individuals infected > 2 years as recently infected than LAg + VL (1.2% [42/3483 and 1.5% [51/3483], respectively, vs. 0.2% [6/3483]). LAg + BR classified more seroconverters as recently infected than LAg + VL or LAg + PepPair (80 vs. 58 and 50, respectively) and identified ~ 25% of virally suppressed seroconverters as recently infected.ConclusionsThe LAg + VL MAA produced a cross-sectional incidence estimate that was closer to the longitudinal estimate than two MAAs that did not include VL. The LAg + BR MAA classified the greatest number of individual seroconverters as recently infected but had a higher false recent rate.

Project description:BackgroundThe evolving proportion of the population considered immunologically naive versus primed for more efficient immune memory response to SARS-CoV-2 has implications for risk assessment. We sought to chronicle vaccine- and infection-induced seroprevalence across the first 7 waves of the COVID-19 pandemic in British Columbia, Canada.MethodsDuring 8 cross-sectional serosurveys conducted between March 2020 and August 2022, we obtained anonymized residual sera from children and adults who attended an outpatient laboratory network in the Lower Mainland (Greater Vancouver and Fraser Valley). We used at least 3 immunoassays per serosurvey to detect SARS-CoV-2 spike and nucleocapsid antibodies. We assessed any seroprevalence (vaccineor infection-induced, or both), defined by positivity on any 2 assays, and infection-induced seroprevalence, also defined by dual-assay positivity but requiring both antinucleocapsid and antispike detection. We used estimates of infection-induced seroprevalence to explore underascertainment of infections by surveillance case reports.ResultsBy January 2021, we estimated that any seroprevalence remained less than 5%, increasing with vaccine rollout to 56% by May-June 2021, 83% by September-October 2021 and 95% by March 2022. Infection-induced seroprevalence remained less than 15% through September-October 2021, increasing across Omicron waves to 42% by March 2022 and 61% by July-August 2022. By August 2022, 70%-80% of children younger than 20 years and 60%-70% of adults aged 20-59 years had been infected, but fewer than half of adults aged 60 years and older had been infected. Compared with estimates of infection-induced seroprevalence, surveillance case reports underestimated infections 12-fold between September 2021 and March 2022 and 92-fold between March 2022 and August 2022.InterpretationBy August 2022, most children and adults younger than 60 years had evidence of both SARS-CoV-2 vaccination and infection. As previous evidence suggests that a history of both exposures may induce stronger, more durable hybrid immunity than either exposure alone, older adults - who have the lowest infection rates but highest risk of severe outcomes - continue to warrant prioritized vaccination.

Dataset Information

Cross-Jurisdictional Data Exchange Impact on the Estimation of the HIV Population Living in the District of Columbia: Evaluation Study.

Publications

Cross-Jurisdictional Data Exchange Impact on the Estimation of the HIV Population Living in the District of Columbia: Evaluation Study.

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