Download Free Guidelines For Effective Use Of Data From Hiv Surveillance Systems Book in PDF and EPUB Free Download. You can read online Guidelines For Effective Use Of Data From Hiv Surveillance Systems and write the review.

Recency assays use one or more biomarkers to identify whether HIV infection in a person is recent (usually within a year or less) or longstanding. Recency assays have been used to estimate incidence in representative cross-sectional surveys and in epidemiological studies to better understand the patterns and distributions of new and longstanding HIV infections. This technical guidance outlines best practices regarding the appropriate use of HIV recency assays for surveillance purposes and updates 2011 technical guidance from the World Health Organization (WHO) and the Joint United Nations Programme on HIV/AIDS (UNAIDS) on the use of HIV recency assays.
BACKGROUND: Global surveillance of human immunodeficiency virus (HIV) and sexually transmitted infections (STI) is a joint effort of the World Health Organization (WHO) and the Joint United Nations Programme on HIV/ AIDS (UNAIDS). The UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance, initiated in November 1996, is the main coordination and implementation mechanism for UNAIDS and WHO to compile the best information available and to improve the quality of data needed for informed decision-making and planning at the national, regional and global levels. INTRODUCTION: What is second generation surveillance? First generation surveillance relied solely on data on AIDS cases and some sentinel studies on HIV prevalence. In 2000, a new strategy named second generation surveillance (SGS) was promoted to tailor surveillance systems to the epidemic state of a country. More specifically, the strategy proposed the following: 1. to concentrate strategic information resources where they would yield information that is useful in reducing the spread of HIV and in providing care for those affected;2. to concentrate data collection in key populations at higher risk of HIV exposure, such as populations with high levels of risk behaviour that places them at increased risk or young people at the start of their sexual lives;3. to compare information on HIV prevalence and on the behaviours that spread the infection to build up an informative picture of changes in the epidemic over time;4. to make the best use of other sources of information, such as communicable disease surveillance and reproductive health surveys, to increase understanding of the HIV epidemic and the behaviours that spread it.
As the HIV/AIDS epidemic imposes an ever-larger burden globally, surveillance for HIV becomes more critical in order to understand the trends of the epidemic and make sound decisions on how best to respond to it. This is especially true in low- and middle-income countries, which account for a disproportionate share of new and long-standing infections. To help countries focus their surveillance activities in the context of their epidemic state (low-level, concentrated or generalized), the World Health Organization (WHO) and the Joint United Nations Programme on HIV/AIDS (UNAIDS) have developed a conceptual framework to improve HIV surveillance, known as Second Generation HIV Surveillance (SGS)1 Guidelines for SGS suggest approaches to make better use of data so that the response to the HIV epidemic can be enhanced. As serosurveillance is an important component of most HIV surveillance activities, an understanding of current HIV testing technologies is important. In the context of SGS, these guidelines suggest methods for selecting, evaluating and implementing HIV testing technologies and strategies based on a country's laboratory infrastructure and surveillance needs. The guidelines provide recommendations for specimen selection, collection, storage and testing, and for the selection and evaluation of appropriate HIV testing strategies and technologies to meet surveillance objectives. Quality assurance issues are also addressed.
In the United States (U.S.), the purpose of HIV surveillance and related data systems is changing. Evolving programmatic priorities and complicated care needs of an aging population has led to greater demand for timely, accurate, and detailed data. My dissertation evaluates three prominent data systems: (1) National HIV Surveillance System (NHSS), (2) Medical Monitoring Project (MMP), and (3) CFAR Network of Integrated Clinical Systems (CNICS). Identification and management of data limitations were goals underlying each aim. My first aim evaluates the burden of diabetes, chronic kidney disease, and hypertension in MMP, a nationally representative sample, and CNICS, a clinical cohort. We encountered and addressed the following challenges: selection bias, missing data, non-standardized case definitions, and dissimilar patient populations. After using a standardized analytic approach, MMP and CNICS yielded similar sub-group specific prevalence estimates. Both data sources suggest considerable disease burden among older adults in HIV care. My second aim used NHSS and US census data to project the demographic composition of the U.S. population of people living with diagnosed HIV (PLWDH) through 2045. The model developed for this aim projects that the US PLWDH population will continue to grow in absolute size and will increasingly be comprised of racial/ethnic minorities; the number of PLWDH 55 years and older is projected to more than double between 2013 and 2045. My final aim used King County HIV surveillance data to explore the origins of NHSS data and how surveillance data is used for HIV control interventions. We discovered that the number of in-migrants with HIV is increasing concurrently with a decrease in the number of new diagnoses; that 12% of cases reported to CDC as newly diagnosed had evidence of a prior HIV diagnosis; and integration of patient care and HIV control activities improved key program metrics. In conclusion, existing data systems to monitor the U.S. PLWDH population have limitations, some of which can be addressed through statistical adjustment and some can only be resolved through adaptation of the data system's design. As demands on HIV care programs are projected to grow, the programmatic utility of HIV surveillance systems should be enhanced.
A goal of CDC's National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) is to strengthen collaborative work across disease areas and integrate services that are provided by state and local programs* for prevention of HIV/AIDS, viral hepatitis, other sexually transmitted diseases (STDs), and tuberculosis (TB). A major barrier to achieving this goal is the lack of standardized data security and confidentiality procedures, which has often been cited as an obstacle for programs seeking to maximize use of data for public health action and provide integrated and comprehensive services. Maintaining confidentiality and security of public health data is a priority across all public health programs. However, policies vary and although disease-specific standards exist for CDC-funded HIV programs, similarly comprehensive CDC standards are lacking for viral hepatitis, STD, and TB prevention programs. Successful implementation of common data protections in state and local health departments with integrated programs suggest implementation of common data security and confidentiality policies is both reasonable and feasible. These programs have benefited from enhanced successful collaborations citing increased completeness of key data elements, collaborative analyses, and gains in program efficiencies as important benefits. Despite the potential benefits, however, policies have not been consistently implemented and the absence of common standards is frequently cited as impeding data sharing and use. Adoption of common practices for securing and protecting data will provide a critical foundation and be increasingly important for ensuring the appropriate sharing and use of data as programs begin to modify policies and increasingly use data for public health action. This document recommends standards for all NCHHSTP programs that, when adopted, will facilitate the secure collection, storage, and use of data while maintaining confidentiality. Designed to support the most desirable practices for enabling secure use of surveillance data for public health action and ensuring implementation of comprehensive evidence-based prevention services, the standards are based on 10 guiding principles that provide the foundation for the collection, storage, and use of these public health data. They address five areas: program policies and responsibilities, data collection and use, data sharing and release, physical security, and electronic data security. Intended for use by state and local health department disease programs to inform the development of policies and procedures, the standards are intentionally broad to allow for differences in public health activities and response across disease programs. The standards, and the guiding principles from which they are derived, are meant to serve as the foundation for more detailed policy development by programs and as a basis for determining if and where improvements are needed. The process includes seven main steps: designating an overall responsible party; performing a standards-based initial assessment of data security and confidentiality protections; developing and maintaining written data security policies and procedures based on assessment findings; developing and implementing training; developing data-sharing plans or agreements as needed; certification of adherence to standards; and performing periodic reviews of policies and procedures. CDC will work with state and local health departments to monitor the implementation of the guidelines and evaluate their impact on securing data, facilitating data use, and increasing program effectiveness.
Continuous engagement in HIV care and treatment is crucial for the health of persons living with HIV (PLWH) and for preventing HIV transmission to others. However, in the United States (US), care engagement, or retention in care, represents the biggest drop off in the HIV care continuum, which maps out the care process from HIV testing and diagnosis, linkage to and retention in HIV care, and ultimately achievement of viral suppression. Many health departments in the US use HIV surveillance data to facilitate HIV care engagement activities, a process known as data to care. While Data to Care programs have had some success, their effectiveness is hindered by the completeness and timeliness of HIV surveillance data. A novel approach to Data to Care uses real-time data exchange between HIV surveillance with external data sources, such as emergency department (ED) and inpatient (IP) hospitalization data and jail booking rosters, to improve the signal of Data to Care investigations, and provide a setting and an opportunity to re-engage PLWH in HIV care. Since real-time data exchange involves linking data sources that don't often have a shared unique person identifier, these programs should also consider the accuracy of the record linkage algorithms they utilize, in order to maximize their reach and efficiency. We investigated the effect of the use of real-time data exchange on HIV care engagement outcomes in two settings: emergency department and inpatient hospitals and in jails. First, we evaluated the impact of an existing ED/hospital-based health information exchange on HIV care outcomes. We compared the proportion of patients that had a viral load test in the 3 months and viral suppression in the 6 months after an alert-eligible ED visit/inpatient admission in the pre-intervention (01/20/13-01/20/15) and post-intervention (07/20/15-07/20/17) periods. To assess whether our pre/post results could be due to secular trends, we compared the difference between patients with an alert-eligible ED visit/IP admission to patients who had a visit outside of the alert window in both the pre-intervention and post-intervention periods. Next, we developed a new automated, real-time data exchange between public health HIV surveillance and county jail data to identify incarcerated PLWH and facilitate post-incarceration HIV care engagement efforts. A team of public health relinkage specialists and jail release planners used this data exchange to guide case conferences about patients who were virally unsuppressed or out-of-care and jointly developed a plan for re-engagement in care and treatment. We compared viral load testing within 3 months and viral suppression within 6 months after release from jail among PLWH released in the post-intervention period (04/01/18-11/01/18) to those released in the pre-intervention period (10/01/16-10/01/17) using Cox proportional hazards models. Finally, we compared the performance of record linkage algorithms commonly used by data exchanges commonly used in public health practice. We compared five deterministic algorithms and two probabilistic record linkage algorithms using simulations and a real-world scenario. We simulated pairs of datasets while varying the number of erroneous fields per record and overlap between these datasets. We matched datasets using each algorithm and calculated their recall (sensitivity) and precision (positive predictive value). In a real-world scenario, HIV and STD surveillance data from King County, WA were matched to identify PLWH who had a syphilis diagnosis. We used manual review to define a gold standard and calculate recall and precision. In our evaluation of an ED/hospital-based health information exchange, patients in the post-intervention period were 1.08 times more likely to have a viral load test within 3 months after an ED visit/IP admission (95% CI: 0.97, 1.20) and 1.50 times more likely to achieve viral suppression within 6 months after an ED visit/IP admission (95% CI: 1.27, 1.76). However, there was a similar pre/post increase in both HIV care engagement (DID: 1.00, 95% CI: 0.84, 1.18) and viral suppression (DID: 1.01, 95% CI: 0.84, 1.20) among patients with visits outside of the alert window. After implementation of a real-time data exchange between HIV surveillance and jail booking data coupled with HIV care coordination between health department and jail release planners, viral load testing within 3 months after release from jail increased by 35% (95% CI: 0.84, 2.18) and viral suppression within 6 months after release from jail increased by 37% (95% CI: 0.82, 2.30), but these differences were not statistically significant. In our simulation study, we found that probabilistic algorithms maintained a high recall at nearly all data quality levels, while being comparable to deterministic algorithms in terms of precision. Deterministic algorithms typically failed to identify matches in scenarios with low data quality. In the real-world scenario, probabilistic algorithms had the lowest trade-off between recall and precision. The results of this dissertation indicate that ED/hospital-based data exchange provides substantial opportunities to interact with PLWH who are poorly engaged in HIV care. However, the observed increase in HIV re-engagement and viral suppression after implementation of this data exchange may reflect secular trends resulting from diverse interventions of which this program was only one. Real-time health information exchange with emergency departments and hospitals can identify PLWH who are inadequately engaged with care and facilitate D2C efforts, but more efforts are needed to improve the effectiveness of reengagement interventions linked to real-time D2C. Implementation of a real-time data exchange between HIV surveillance and jail booking rosters resulted in a trend towards improved post-incarceration HIV care outcomes for incarcerated PLWH who are virally unsuppressed/out-of-care in King County. Real-time data exchange between health departments and county jails is a promising strategy for identifying incarcerated PLWH to support care coordination and improving post-incarceration HIV care engagement. Finally, in our simulation study on record linkage algorithms, we found that probabilistic algorithms maximize the number of true matches identified, while still maintaining high precision. Public health activities that rely on the integration of multiple data sources to target intervention delivery should utilize probabilistic algorithms to reduce gaps in the coverage of interventions and maximize their reach.
The three primary goals of the National HIV/AIDS Strategy are: 1) reducing the number of people who become infected with human immunodeficiency virus (HIV), 2) increasing access to care and optimizing health outcomes for people living with HIV, and 3) reducing HIV-related health disparities. To achieve these goals, _the National HIV/AIDS Strategy's Federal Implementation Plan calls for efforts to support surveillance activities to better characterize HIV among smaller populations such as Asian Americans (AAs) and Native Hawaiians and other Pacific Islanders (NHOPIs), and calls for the Centers for Disease Control and Prevention (CDC) to provide recom- mendations on effective HIV surveillance activities to health departments of states with high concentrations of AA and NHOPI populations. To develop these recommendations, CDC selected 5 states (California, Hawaii, New York, Texas, Washington) and 3 separately funded cities and county within these states(Los Angeles County, New York City and San Francisco) with large numbers of AAs and/or NHOPIs to conduct an assessment of current surveillance practices and identify areas for improvement. As a first step, a literature review was conducted to identify key issues. This was followed by consultations with experts from CDC and other federal agencies, academia, and partner organizations that work with AAs and NHOPIs. Finally, we held assessments_with state HIV case surveillance and Medical Monitoring Project/National HIV Behavioral Surveillance System coordinators to gain insight into issues of data collection, analysis, dissemination and use, and identify areas for improvement. Based on the findings of the assessment and recognizing that some approaches may be applicable in some jurisdictions but not others, we propose recommendations that should be standard practice to improve HIV surveillance among AAs and NHOPIs. We also propose recommended practices that expand on these basic improvements to be implemented where possible.