Data quality shortcomings with the US HIV/AIDS surveillance system

Access to Federally Qualified Health Centers and HIV Outcomes in the U.S. South

INTRODUCTION

Federally Qualified Health Centers may increase access to HIV prevention, care, and treatment for at-risk populations.

METHODS

A pooled cross section of ZIP Code Tabulation Areas from cites in the U.S. South with high HIV diagnoses were used to examine Federally Qualified Health Center density and indicators of HIV epidemic control. The explanatory variable was Federally Qualified Health Center density-number of Federally Qualified Health Centers in a ZIP Code Tabulation Areas' Primary Care Service Area per low-income population-high versus medium/low (2019). Outcomes were 5-year (2015-2019 or 2014-2018) (1) number of new HIV diagnoses, (2) percentage late diagnosis, (3) percentage linked to care, and (4) percentage virally suppressed, which was assessed over 1 year (2018 or 2019). Multiple linear regression was used to examine the relationship, including ZIP Code Tabulation Area-level sociodemographic and city-level HIV funding variables, with state-fixed effects, and data analysis was completed in 2022-2023. Sensitivity analyses included (1) examining ZIP Code Tabulation Areas with fewer non-Federally Qualified Health Center primary care providers, (2) controlling for county-level primary care provider density, (3) excluding the highest HIV prevalence ZIP Code Tabulation Areas, and (4) excluding Florida ZIP Code Tabulation Areas.

RESULTS

High-density ZIP Code Tabulation Areas had a lower percentage of late diagnosis and virally suppressed, a higher percentage linked to care, and no differences in new HIV diagnoses (p<0.05). In adjusted analysis, high density was associated with a greater number of new diagnoses (number or percentage=5.65; 95% CI=2.81, 8.49), lower percentage of late diagnosis (-3.71%; 95% CI= -5.99, -1.42), higher percentage linked to care (2.13%; 95% CI=0.20, 4.06), and higher percentage virally suppressed (1.87%; 95% CI=0.53, 2.74) than medium/low density.

CONCLUSIONS

Results suggest that access to Federally Qualified Health Centers may benefit community-level HIV epidemic indicators.

Adopting Data to Care to Identify and Address Gaps in Services for Children and Adolescents Living With HIV in Mozambique

BACKGROUND

The Data to Care (D2C) strategy uses multiple sources of complementary data on HIV clients and related services to identify individuals with gaps in HIV treatment. Although D2C has been widely used in the United States, there is no evidence on its use in other settings, such as countries most affected by the epidemic.

STRATEGY IMPLEMENTATION

The D2C strategy was implemented within the context of a project that provided community-based support to children and adolescents living with HIV (C/ALHIV) in Mozambique. A data tracking tool and a standard operating procedure manual for local partner community organizations and health care facilities were developed to support the effort. Project staff met with local project implementing partners to discuss and coordinate the intervention in pilot health facilities.

STRATEGY PILOTING

The project initiated a pilot D2C intervention in 2019, working with 14 health facilities across 5 additional districts within 1 province. COVida project data were compared with clinical data from facilities serving C/ALHIV. The D2C intervention identified gaps in HIV treatment for a substantial number of C/ALHIV, and targeted support services were provided to address those gaps. Viral load (VL) monitoring was added in March 2020. Before the intervention, 71% of C/ALHIV reported to be on HIV treatment by their caregivers were documented as on treatment in health facilities. Support interventions targeted those not on treatment, and this proportion increased to 96% within 1 year of implementation. Additionally, 12 months later, the proportion of C/ALHIV with a documented VL test increased from 52% to 72%.

CONCLUSION

Introducing the D2C pilot intervention was associated with substantial improvements in HIV treatment for C/ALHIV, including increased linkage to and continuity in treatment and increased VL testing. D2C may be a useful approach to improve health outcomes for C/ALHIV in settings outside of the United States.

Surveillance of communicable diseases using social media: A systematic review

BACKGROUND

Communicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media.

OBJECTIVE

To conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases.

METHODOLOGY

Broad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

Twenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation.

CONCLUSION

Text mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.

Effects of marriage equality legislation on sexual health of the US population

Even though prior research has investigated the relationship between same-sex partnership recognition policies and health outcomes, the impact of same-sex marriage laws on sexually transmitted infections has not received much attention. Using state-level panel data from 2000 to 2019, I show that marriage equality legislation decreases the spread of (shorter-term) syphilis infections and of (longer-term) human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) infections among the general population. Event study analyses correcting for non-staggered treatment implementation confirm these negative effects, but also suggest that standard difference-in-differences models understate the impact of the legislation by up to 8% points. Further analysis supports that these legislation effects operate through three mechanisms: increasing social tolerance, strengthening relationship commitment, and expanding health care access and coverage for HIV/AIDS prevention and treatment. Disaggregating the results by sexual behavior reveals that legal access to same-sex marriage leads to sizable decreases in AIDS rates among men who have sex with men (MSM) (the most at-risk population for an infection). Even though there is economically significant evidence that the legislation improves sexual health of the heterosexual population due to increased utilization of preventive sexual health care, the legislation does not have a direct impact on infection rates for the non-MSM population.

Improving data quality and information utilization at Metema Primary Hospital, Amhara national regional state, Northwest Ethiopia 2018: Capstone project

Quality data for evidence-based decision making become a growing concern globally. Available information needs to be disseminated on time and used for decision making. Therefore, an effective Health Management Information System is essential to make evidence-based decision. This study aimed to measure the change in data quality and information utilization before and after intervention. Facility-based pre-post interventional study design was conducted at Metema hospital from September/2016 to December30/2018. A total of 384 individual medical-records, HMIS registration-books and reports were reviewed. Training, supportive supervision and feedback were intervention packages. About 309 (80.5%) of charts were from outpatient department. Data recording completeness increased from 69.0% to 96.0%, data consistency increased from 84.0% to 99.5% and report timeliness enhanced from 66.0% to 100%. There was a statistically significant difference for data recording completeness between pre and post-intervention results with mean difference of -0.246 (-0.412, -0.081). Also, after the intervention, gap-filling feedback and supportive supervision were given to all departments. In addition, four quality improvement projects were developed at post-intervention phase. The level of data quality and use was improved after the intervention. So, designing and implementing intervention strategies based on the root causes will help to improve data quality and use.

HIV self-testing: A review and analysis to guide HIV prevention policy

HIV self-testing is a relatively new approach to HIV testing that, with increasing licensure in many countries, is an impending intervention. Considering such current or near-future implementation, it is important for HIV prevention policymakers to reflect on if and how to incorporate HIV self-testing. In this paper, using the Impact Fraction Model and the Anderson-May equation, I discuss how HIV self-testing should be an efficacious prevention approach when targeted appropriately. I then review the potential impacts of self-testing on HIV surveillance and discuss possible ethical concerns about such testing. The outcome of this analysis is that, while some issues may arise related to surveillance, and while some ethical concerns can be raised, no empirical evidence substantiates either, suggesting that HIV self-testing is an important strategy if it can be focused on and used by the persons most affected by HIV.

Validation of an Optimized Algorithm for Identifying Persons Living With Diagnosed HIV From New York State Medicaid Data, 2006-2014

Algorithms are regularly used to identify persons living with diagnosed human immunodeficiency virus (HIV) (PLWDH) in Medicaid data. To our knowledge, there are no published reports of an HIV algorithm from Medicaid claims codes that have been compared with an HIV surveillance system to assess its sensitivity, specificity, positive predictive value, and negative predictive value in identifying PLWDH. Therefore, our aims in this study were to 1) develop an algorithm that could identify PLWDH in New York State Medicaid data from 2006-2014 and 2) validate this algorithm using the New York State HIV surveillance system. Classification and regression tree analysis identified 16 nodes that we combined to create a case-finding algorithm with 5 criteria. This algorithm identified 86,930 presumed PLWDH, 88.0% of which were verified by matching to the surveillance system. The algorithm yielded a sensitivity of 94.5%, a specificity of 94.4%, a positive predictive value of 88.0%, and a negative predictive value of 97.6%. This validated algorithm has the potential to improve the utility of Medicaid data for assessing health outcomes and programmatic interventions.

Data and knowledge standards for learning health: A population management example using chronic kidney disease

The widespread creation of learning health care systems (LHSs) will depend upon the use of standards for data and knowledge representation. Standards can facilitate the reuse of approaches for the identification of patient cohorts and the implementation of interventions. Standards also support rapid evaluation and dissemination across organizations. Building upon widely-used models for process improvement, we identify specific LHS activities that will require data and knowledge standards. Using chronic kidney disease (CKD) as an example, we highlight the specific data and knowledge requirements for a disease-specific LHS cycle, and subsequently identify areas where standards specifications, clarification, and tools are needed. The current data standards for CKD population management recommendations were found to be partially ambiguous, leading to barriers in phenotyping, risk identification, patient-centered clinical decision support, patient education needs, and care planning. Robust tools are needed to effectively identify patient health care needs and preferences and to measure outcomes that accurately depict the multiple facets of CKD. This example presents an approach for defining the specific data and knowledge representation standards required to implement condition-specific population health management programs. These standards specifications can be promoted by disease advocacy and professional societies to enable the widespread design, implementation, and evaluation of evidence-based health interventions, and the subsequent dissemination of experience in different settings and populations.