In USAID-OHA-SI/mindthegap: Mind the Gap

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Introduction

In 2020, UNAIDS updated their Fast Track targets (90-90-90) for 2024 to become the 95-95-95 targets which are set to be achieved by 2030 - 95% of PLHIV know their status, 95% of those who know their status are accessing treatment, and 95% of those who are accessing treatment are virally suppressed.

Importantly, there are two types of metrics to measure 95-95-95 target progress by: the PLHIV base and the relative/conditional denominator base. This vignette will outline the key differences between the two metrics, which UNAIDS indicators to use for each, and how to interpret the different parameters.

Load dependencies

In addition to mindthegap make sure you have glitr installed. This does not need to be loaded into your session, but is needed to run the plots. Dependent packages are typically installed from CRAN, but since our packages are on rOpenSci, they are not installed.

install.packages('glitr', repos = c('https://usaid-oha-si.r-universe.dev', 'https://cran.r-project.org/'))

library(mindthegap)

What's the difference between the relative base and the PLHIV base?

The relative base or conditional denominator (based on the targets) is important for countries and programs to know where to focus their response. This metric provides values on a sliding scale, where 95% of PLHIV know their status, 95% of those who know their status are accessing treatment, and 95% of those who are accessing treatment are virally suppressed.

The PLHIV base/denominator (testing and treatment cascade) is best used for comparing countries and for knowing country-specific progress towards curbing their HIV epidemic. With PLHIV as the base for the 3 95's, the targets become 95% of PLHIV known their status, 90% of PLHIV are accessing treatment, and 86% of PLHIV are virally suppressed.

Both are useful, but for different purposes (see visual below).

knitr::include_graphics("unaids-95s.png", dpi = 150, error = FALSE)

How to use UNAIDS Data to Assess 95's Progress

To assess progress to 95-95-95 targets, we can use the indicators available from EDMS.

If you are using the relative base to track 95-95-95 progress, use the following indicators from the "HIV Test & Treat" data tab.
- Percent Known Status of PLHIV: Percent of PLHIV who known their HIV status
- Percent on ART with Known Status: Among those who know their status, percent on ART
- Percent VLS on ART: Among those who are on ART, percent virally suppressed
If you are using the PLHIV base, use the following indicators from the "HIV Test & Treat" data tab.
- Percent Known Status of PLHIV: Percent of PLHIV who known their HIV status
- Percent on ART of PLHIV: Among PLHVIV, percent on ART
- Percent VLS of PLHIV: Among PLHIV, percent virally suppressed

The package has a built in function, tab_95s, to generate this as a datatable for you.

Let's take a deep dive into Namibia's progress to the 95's using the PLHIV base.

df_unaids <- load_unaids()

v_plhiv <- tab_95s(df_unaids, "Namibia", denom = "PLHIV", grp = "All")
v_plhiv

d_plhiv <- v_plhiv$`_data`

d_plhiv <- d_plhiv %>% 
  dplyr::mutate(clean = stringr::str_extract(indicator, "Status|ART|VLS"),
                text = dplyr::case_match(clean,
                                                    "Status" ~ "know their status",
                                                    "ART" ~ "are accessing treatment",
                                                    "VLS" ~ "are virally suppressed"),
                ind = stringr::str_glue("{estimate}% of PLHIV {text}")) 

stats_plhiv <- d_plhiv%>% 
  dplyr::pull() %>% 
  paste(collapse = "; ")

As we can see, using the PLHIV base, Namibia reached one of the 95's - r stats_plhiv.

What happens if we use the relative base?

v_relative <- tab_95s(df_unaids, "Namibia", denom = "Relative", grp = "All")
v_relative

d_relative <- v_relative$`_data`

df_plhiv_alt <- d_plhiv %>% 
  dplyr::select(clean, est_plhiv = estimate) %>% 
  dplyr::mutate(est_plhiv = stringr::str_glue("{est_plhiv}%"))

stats_relative <- d_relative %>% 
  dplyr::mutate(clean = stringr::str_extract(indicator, "Status|ART|VLS"),
                text = dplyr::case_match(clean,
                                                    "Status" ~ "of PLHIV know their status",
                                                    "ART" ~ "**who know their status** are accessing treatment",
                                                    "VLS" ~ "**who are on treatment** are virally suppressed")) %>% 
  dplyr::select(clean, text, estimate) %>% 
  # dplyr::left_join(df_plhiv_alt, by = "clean") %>% 
  # dplyr::mutate(rel_base_val = lag(est_plhiv)) %>% 
  dplyr::mutate(ind = stringr::str_glue("{estimate}% {text}")) %>% 
  dplyr::pull() %>% 
  paste(collapse = "; ")

Using the relative base, we can see - r stats_relative. As such, Namibia meets the 2nd and 3rd 95 with the relative base, but does not reach the 1st 95.

As a result of these differences between the two metrics, the number of PEPFAR countries that have reached all of the 95's can vary depending on the denominator selected. If we look at the results from 2022, we can see these differences at play.

cntry_epi_plhiv <- df_unaids %>% 
  dplyr::filter(year == max(year)-1,
                age == "All",
                sex == "All",
                achv_95_plhiv == TRUE
                ) %>% 
  dplyr::distinct(country) %>% 
  dplyr::arrange() %>% 
  dplyr::pull()



cntry_epi_relative <- df_unaids %>% 
  dplyr::filter(year == max(year)-1,
                age == "All",
                sex == "All",
                achv_95_relative == TRUE
                ) %>% 
  dplyr::distinct(country) %>% 
  dplyr::arrange() %>% 
  dplyr::pull()