Infectious Disease

The model identifies variables associated with missed health care visits for HIV

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A point-of-care model made up of 13 variables and available on a web-based calculator was able to identify predictors of absent HIV health care visits, according to a recent study.

Specific variables included prior visit compliance, age, employment status, and black race.

April C. Pettit

“There are many factors that go beyond those at the individual patient level that contribute to participation in visiting health care providers. In addition, some of these factors are at an individual level such as gender and race [are] not really individual biological factors, but larger social constructs ”. April C. Pettit, MD, Associate Professor of Infectious Diseases and Epidemiology at Vanderbilt University Medical Center, Healio said.

“Previous predictive modeling studies have not considered factors beyond the individual level. Therefore, our aim was to develop and validate a model that also includes factors at the health system, community and structural level, with the hope that these variables will be taken into account in a model with a higher predictive value for missed visits from HIV health care providers, ”Pettit said.

Pettit and colleagues developed predictive models for missed visits to people living with HIV with more than one control visit to the Center for AIDS Research’s Integrated Clinical Systems Network between 2010 and 2016.

The 13 variables included in the model were age, CD4 + number, percentage of postcode table area living in poverty (ZCTA), percentage of black ZCTA, percentage of unemployed ZCTA, HIV viral load, number of patients treated in the clinic, ratio of patients to Providers in the clinic, proportion of a state’s federal AIDS drug assistance program budget allocated, insurance status, number of providers in the clinic, compliance with last visit, and race / ethnicity. Researchers developed models that indicate the likelihood that a patient will miss their next visit to an HIV health care provider before a visit, so that providers have time to intervene if necessary.

Data from 382,432 visits among 20,807 people living with HIV were included in the study. According to the study, the strongest predictors were at the individual level (adherence to previous visit, age, CD4 + number) and at the community level (proportion in poverty, unemployment and black race).

Based on these findings, Pettit said the model has a good ability to predict missing visits from HIV health care providers, similar to previous models. Pettit said the model could be used both in HIV clinics as part of routine care and in potential future research studies to evaluate interventions to improve attendance at appointments for health care providers.

Pettit said that once a patient is determined to be at risk of missing a follow-up visit, the first step is to investigate what is already happening in the clinic to prevent patients from missing out on visits to health care providers. An evidence-based intervention should then be identified to improve adherence to visits such as posters, brochures, and reminders of personal phone calls and to determine what resources are available for delivering these interventions.

“Compliance with visits to HIV health care providers is influenced by factors at many levels, including those outside the individual,” Pettit said. “By identifying the people who are most at risk of missing visits, resources can be directed towards those who are most likely to benefit.”

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