Data analysis helps track patients in the HIV care continuum

Ending AIDS as a public health threat by 2030 is feasible if high-HIV-burden cities around the world fast-track their AIDS responses.

In the United States alone, there are currently 1.2 million people living with HIV/AIDS. And while only a few decades ago, an HIV/AIDS diagnosis spelled imminent decline, HIV is no longer a death sentence. Today, a person diagnosed with HIV at age 20 – if started promptly on antiretroviral therapies – is expected to live a normal life span, with a highly preserved quality of life. Achieving viral suppression both improves individual health and greatly reduces the risk of transmitting the virus to a partner. Nationally, too few people living with HIV are retained in care or have achieved viral suppression; a problem disproportionately affecting youth.

The public health cost of going without care cannot be overstated: According to a 2015 study by the Centers for Disease Control, 92 percent of new HIV infections (as of 2009) were attributable to people living with HIV who were not in medical care.  

For optimal public health outcomes, optimize the care continuum

Why are so few patients being served? As might be assumed from the statistics, many people living with HIV today face barriers to treatment; they drop off the HIV care continuum – that is, the “dashboard” model of care from diagnosis to viral suppression – at every stage. Lapses in care can be brought on by any number of co-occurring conditions including substance use and mental health disorders, homelessness, and chronic conditions, making engagement in HIV care and adherence to treatment more challenging. Physical barriers to care, such as access to transportation, still prohibit many from accessing medical services, particularly in rural areas.

In 2010, President Barack Obama announced the National HIV/AIDS Strategy (NHAS), making increased access to care for people living with HIV a major national priority. The Obama Administration’s subsequent HIV Care Continuum Initiative announced in 2013 called for increased work to address drop-offs in HIV care, underscoring the idea that in order to achieve optimal public health outcomes, more people living with HIV needed to remain in care.

In 2014, a joint United Nations Program on HIV/AIDS released new 90-90-90 treatment targets by 2020. The 90-90-90 targets are significant in that they are the first targets that explicitly focus on improving the HIV care continuum from diagnosis to viral suppression. Updated NHAS targets for treatment and care are:

1. Increase the percentage of people living with HIV who know their status to at least 90 percent.
2. Increase the percentage of newly diagnosed persons linked to HIV medical care within one month of diagnosis to at least 85 percent.
3. Increase the percentage of persons with diagnosed HIV infection who are retained in HIV medical care to at least 90 percent.
4. Increase the percentage of persons with diagnosed HIV infection who are virally suppressed to at least 80 percent.

This targeted treatment strategy is fast being instituted by health departments across the country. The goal of this approach? To use HIV surveillance data to improve disease control interventions.  

Colorado uses data analysis to reach out to its at-risk population

The State of Colorado is doing its part to help the 13,000 people in the state now living with HIV. In order to remain healthy, people with HIV need to obtain regular medical care throughout their lifetimes.  To get lapsed patients back into care, the Colorado Department of Public Health and Environment (CDPHE) is piecing together fractured electronic medical data from multiple providers and geographies. Doing so will enable them to identify and locate people living with HIV who need treatment.

For Elaine Daniloff, Epidemiologist and Data Scientist at the CDPHE, the fight against the HIV/AIDS epidemic is personal. She was working as a laboratory research assistant when AIDS first hit the United States in the 1980s and recalls seeing many people within the LGBT community – of which she is also a part – lose their lives to the virus.

Daniloff now works tirelessly through the CDPHE to help people living with HIV/AIDS. To do so, she relies on data. “My job,” she says, “is to characterize the community of people living with HIV so that we increase access to care, improve health outcomes and reduce new HIV infections.”

JMP^® is ‘indispensable’ in ensuring the quality of electronic laboratory report data

Daniloff and other data scientists working in public health rely on data from electronic laboratory reporting (ELR) systems. And though ELR has become the mainstay of reducing the HIV/AIDS epidemic worldwide, Daniloff says, “there are gaps all the time – we didn’t receive a report, for example, or we didn’t know we received it. Or we have duplicates. Data quality is a big issue for public health. And that black box… that’s where JMP comes in.”

JMP, Daniloff says, “has been absolutely indispensable with data cleaning, quality control, reporting and visualization. No matter how difficult or time consuming, I always find a way using JMP to link records from different data systems, work with dates and calculate time elapsed for intervention outcomes.”

Daniloff uses JMP to match patient data from the state’s case-based surveillance system with three goals: the quality assessment of ELR lab tests, record linkage and deduplication. For record matching and deduplication, she says the Tables features in JMP are critical – Summary, Subset, Sort, Stack, Split, Join, Update, Concatenate and Missing Data Pattern. Daniloff also makes use of Database Open Table and Query Builder, and creates visualizations to communicate her findings to stakeholders at every stage of the care continuum.

After identifying individuals living with HIV/AIDS in Colorado who have fallen out of care, Daniloff alerts state social workers. Fortunately, she says, between the state, health care providers and insurance stakeholders, there are resources to help people get the medical therapies they need. And Daniloff’s analyses don’t end there: she also uses ELR data to study the UN treatment targets, and examines patient outcomes among those who transition between HIV care providers in Colorado.

“The public health focus on viral suppression is the most important measure because it encompasses antiretroviral therapy’s treatment and preventative benefits,” Daniloff says. “Viral suppression allows people living with HIV to remain healthy and significantly reduces (or almost eliminates) the chance of HIV transmission between partners and (between mothers and) children. In the future, I hope to use JMP to create models to estimate HIV transmission among different risk groups in Colorado.”