Model identifies variables associated with missing health care visits for HIV
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A point-of-care model, consisting of 13 variables and available via a web-based calculator, was able to identify predictors for missing HIV health care visits, according to a recent study.
Specific variables included prior visit adherence, age, employment status and Black race.
“There are many factors beyond those at the individual patient level contributing to health care provider visit attendance. Further, some of these individual-level factors such as gender and race [are] not truly individual biologic factors but larger social constructs,” April C. Pettit, MD, associate professor of infectious diseases and epidemiology at the Vanderbilt University Medical Center Department of Medicine, told Healio.
“Previous prediction modeling studies have not accounted for factors beyond the individual level, so our objective was to develop and validate a model that also included factors at the health care system, community, and structural levels with the hopes that inclusion of these variables would result in a model with greater predictive value for missing HIV health care provider visits,” Pettit said.
Pettit and colleagues developed prediction models for missed visits among people with HIV with more than one follow-up visit in the Center for AIDS Research Network of Integrated Clinical Systems between 2010 and 2016.
The 13 variables included in the model were age, CD4+ count, proportion of ZIP code tabulation area (ZCTA) living in poverty, proportion of ZCTA who are Black, proportion of ZCTA unemployed, HIV viral load, number of patients in care at the clinic, ratio of patients to providers at the clinic, proportion of a state’s AIDS Drug Assistance Program budget contributed by the state government, insurance status, number of providers at the clinic, adherence to most recent visit and race/ethnicity. The researchers developed models that provided the probability of a patient missing their next HIV health care provider visit before a visit, allowing time for providers to intervene if indicated.
Data from 382,432 visits among 20,807 people with HIV were included in the study. According to the study, the strongest predictors were at the individual (prior visit adherence, age, CD4+ count) and community levels (proportion living in poverty, unemployed, and of Black race).
Based on these findings, Pettit said the model has a good ability to predict missing HIV health care provider visits, similar to previous models. According to Pettit, the model could be used both in HIV clinics as part of routine care, as well as in potential future research studies aimed at evaluating interventions to improve health care provider appointment attendance.
Pettit said that once it is determined that a patient is at risk for missing a follow-up visit, the first step would be to look at what is already happening in the clinic to prevent patients from missing health care provider visits. Then, evidence-based intervention should be identified to improve visit adherence such as posters, brochures and personal phone call reminders as well as to determine what resources are available to deploy these interventions.
“HIV health care provider visit adherence is influenced by factors at many levels, including those beyond the individual level,” Pettit said. “Identifying those at highest risk for missing visits allows for targeting of resources toward those most likely to benefit.”
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