Background. Preexposure prophylaxis (PrEP) is effective for preventing human immunodeficiency virus (HIV) infection among men who have sex with men (MSM) within trial settings. Population impact will depend on clinical indications for PrEP initiation, coverage levels, and drug adherence. No modeling studies have estimated the impact of clinical practice guidelines for PrEP issued by the Centers for Disease Control and Prevention (CDC).Methods. Mathematical models of HIV transmission among MSM were used to estimate the percentage of infections averted (PIA) and the number needed to treat (NNT) under behavioral indications of the CDC's PrEP guidelines. We modeled the contribution of these indications while varying treatment coverage and adherence.Results. At 40% coverage of indicated MSM over the next decade, application of CDC guidelines would avert 1162 infections per 100 000 person-years, 33.0% of expected infections. The predicted NNT for the guidelines would be 25. Increasing coverage and adherence jointly raise the PIA, but reductions to the NNT were associated with better adherence only.Conclusions. Implementation of CDC PrEP guidelines would result in strong and sustained reductions in HIV incidence among MSM in the United States. The guidelines strike a good balance between epidemiological impact (PIA) and efficiency (NNT) at plausible scale-up levels. Adherence counseling could maximize public health investment in PrEP by decreasing the NNT.
Vaccine coverage is routinely used as a performance indicator for immunization programs both at local and global levels. For many national immunization programs, there are challenges with accurately estimating vaccination coverage based on available data sources, however an increasing number of low- and middle-income countries (LMICs) have begun implementing electronic immunization registries to replace health facilities’ paper-based tools and aggregate reporting systems. These systems allow for more efficient capture and use of routinely reported individual-level data that can be used to calculate dose-specific and cohort vaccination coverage, replacing the commonly used aggregate routine health information system data. With these individual-level data immunization programs have the opportunity to redefine performance measures to enhance programmatic decision-making at all levels of the health system. In this commentary, we discuss how measures for assessing vaccination status and program performance can be redefined and recalculated using these data when generated at the health facility level and the implications of the use and availability of electronic individual-level data.
Background: As technology has become cheaper and more accessible, health programs are adopting digital health interventions (DHI) to improve the provision of and demand for health services. These interventions are complex and require strong coordination and support across different health system levels and government departments, and they need significant capacities in technology and information to be properly implemented. Electronic immunization registries (EIRs) are types of DHI used to capture, store, access, and share individual-level, longitudinal health information in digitized records. The BID Initiative worked in partnership with the governments of Tanzania and Zambia to introduce an EIR at the sub-national level in both countries within 5 years as part of a multi-component complex intervention package focusing on data use capacity-building. Methods: We aimed to gather and describe learnings from the BID experience by conducting a framework-based mixed methods study to describe perceptions of factors that influenced scale-up of the EIR. Data were collected through key informant interviews, a desk review, EIRs, and health management information systems. We described how implementation of the EIRs fulfilled domains described in our conceptual framework and used cases to illustrate the relationships and relative influence of domains for scale-up and adoption of the EIR. Results: We found that there was no single factor that seemed to influence the introduction or sustained adoption of the EIR as many of the factors were interrelated. For EIR introduction, strong strategic engagement among partners was important, while EIR adoption was influenced by adequate staffing at facilities, training, use of data for supervision, internet and electricity connectivity, and community sensitization. Conclusions: Organizations deploying DHIs in the future should consider how best to adapt their intervention to the existing ecosystem, including human resources and organizational capacity, as well as the changing technological landscape during planning and implementation.
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