Data Dictionary & Use Cases • mindthegap

Introduction

This vignette serves as a data dictionary for the UNAIDS 2022 Estimates Data. It will cover the data structure and variables, as well as provide an overview of the indicators across the "HIV Estimates" tab and the "HIV Test & Treat" tab. Finally, it will touch briefly on common use cases and which data tab to access for those analytics.

library(mindthegap)
library(knitr)
library(dplyr)
library(tidyr)
library(tibble)
library(knitr)
library(kableExtra)
library(glamr)

UNAIDS Clean Data Structure

Let’s first take a look at the data structure, using the HIV Estimates tab as an example.

df_pct <- pull_unaids(data_type = "HIV Estimates", pepfar_only = TRUE)
knitr::kable(head(df_pct, n = 6L), format = "html")

year	iso	country	region	indicator	age	sex	estimate	lower_bound	upper_bound	estimate_flag	sheet	indic_type	pepfar	achv_95_plhiv	achv_95_relative	epi_ratio_2023	epi_control
1990	KHM	Cambodia	Asia and the Pacific	Number AIDS Related Deaths	All	All	100	100	100	TRUE	HIV Estimates	Integer	TRUE	FALSE	FALSE	0.7608939	TRUE
1990	KHM	Cambodia	Asia and the Pacific	Number AIDS Related Deaths	0-14	All	100	100	100	TRUE	HIV Estimates	Integer	TRUE	FALSE	FALSE	0.7608939	TRUE
1990	KHM	Cambodia	Asia and the Pacific	Number AIDS Related Deaths	15+	All	100	100	100	TRUE	HIV Estimates	Integer	TRUE	FALSE	FALSE	0.7608939	TRUE
1990	KHM	Cambodia	Asia and the Pacific	Number PLHIV	0-14	All	100	100	100	TRUE	HIV Estimates	Integer	TRUE	FALSE	FALSE	0.7608939	TRUE
1990	KHM	Cambodia	Asia and the Pacific	Number PLHIV	15+	Female	200	200	200	TRUE	HIV Estimates	Integer	TRUE	FALSE	FALSE	0.7608939	TRUE
1990	KHM	Cambodia	Asia and the Pacific	Number PLHIV	15+	All	500	500	500	TRUE	HIV Estimates	Integer	TRUE	FALSE	FALSE	0.7608939	TRUE

Within each of the UNAIDS datasets, there are disaggregates by age, sex, and indic_type. Let’s take a look at the disaggregates in the "HIV Estimates" tab.

Indic_type/Age/Sex Count Table from the `HIV Estimates` tab
indicator	indic_type	age	sex	n
Deaths averted by ART	Integer	All	All	1732
Infections averted by PMTCT	Integer	0-14	All	1596
Number AIDS Related Deaths	Integer	0-14	All	1870
Number AIDS Related Deaths	Integer	15+	All	1870
Number AIDS Related Deaths	Integer	All	All	1870
Number New HIV Infections	Integer	0-14	All	1870
Number New HIV Infections	Integer	15+	All	1870
Number New HIV Infections	Integer	All	All	1870
Number PLHIV	Integer	0-14	All	1870
Number PLHIV	Integer	15+	All	1870
Number PLHIV	Integer	15+	Female	1870
Number PLHIV	Integer	All	All	1870
Number PMTCT Needing ART	Integer	All	Female	1870
Percent Incidence	Percent	15-49	All	1870
Percent Incidence	Percent	All	All	1870
Percent Prevalence	Percent	15-24	Female	1870
Percent Prevalence	Percent	15-24	Male	1870
Percent Prevalence	Percent	15-49	All	1870
Total deaths to HIV Population	Integer	All	All	1732
Total deaths to HIV Population	Integer	All	Female	1732
Total deaths to HIV Population	Integer	All	Male	1732

As you can see, there are multiple different disaggregates, especially across indicators. For instance, if you were looking at Percent Incidence (a percent indicator), there are age disaggregates for ages 15-49 and all ages, but no disaggregates for sex. If you were looking at Percent Prevalence, there are sex disaggregates for young adults (15-24), but no sex disaggregates for the age group 15-49.

As such, it is important to filter for the age/sex/indicator type disaggregates that are you interested in before diving further into your analytics.

HIV Estimates Indicators and Use Cases

The HIV Estimates data covers epidemiological indicators, such as HIV prevalence/incidence, new infections, AIDS-related death, etc. There are 2 percent indicators (Percent Prevalence and Percent Incidence) and 7 integer indicators (Number PLHIV, AIDS Related Deaths, Total Deaths to HIV Population, Number PMTCT Needing ART, Number New HIV Infections, Deaths averted by ART, and Infections averted by PMTCT).

To pull the HIV Estimates indicators for PEPFAR countries, use pull_unaids(data_type = "HIV Estimates", pepfar_only = TRUE).


Percent Indicators
`Percent Prevalence`	HIV Prevalence
`Percent Incidence`	HIV Incidence (per 1,000 uninfected population)
Integer Indicators
`Number PLHIV`	Estimated number people living with HIV
`Number AIDS Related Deaths`	AIDS Related Deaths
`Total deaths to HIV Population`	Estimated number of deaths from all causes among people living with HIV
`Number PMTCT Needing ART`	Pregnant women needing antiretrovirals for preventing mother-to-child transmission
`Number New HIV Infections`	New HIV Infections
`Deaths averted by ART`	Deaths averted by antiretroviral treatment
`Infections averted by PMTCT`	Infections averted by preventing mother-to-child transmission

Use Case #1: Epidemic Control Curves

PEPFAR defines HIV epidemic control as the “point at which the total number of new HIV infections falls below the total number of deaths from all causes among HIV-infected individuals, with both declining.” The integer indicators in the HIV Estimates data can be used to analyze and plot these trends.

To assess progress towards this definition of epidemic control, we will use the "HIV Estimates" data:

df_est <- pull_unaids(data_type = "HIV Estimates", pepfar_only = TRUE)


Integer Indicators
`PLHIV`	Estimated people living with HIV
`Number AIDS Related Deaths`	AIDS Related Deaths
`Total deaths to HIV Population`	Estimated number of deaths from all causes among people living with HIV
`Number PMTCT Needing ART`	Pregnant women needing antiretrovirals for preventing mother-to-child transmission
`Number New HIV Infections`	New HIV Infections
`Deaths averted by ART`	Deaths averted by antiretroviral treatment
`Infections averted by PMTCT`	Infections averted by preventing mother-to-child transmission

To create the epidemic control curves, you will need 2 indicators from the HIV Estimates dataset:
- Number New HIV Infections
- Total deaths to HIV Population

For more information on how to create these plots, see the article Epidemic Control Plots.

Use Case #2: Incidence/Prevalence Curves

To plot HIV Incidence and Prevalence curves with confidence intervals, we will use the percent indicators in the "HIV Estimates" data:

df_est <- pull_unaids(data_type = "HIV Estimates", pepfar_only = TRUE) %>% 
  filter(indic_type == "Percent")


Percent Indicators
`Percent Prevalence`	HIV Prevalence
`Percent Incidence`	HIV Incidence (per 1,000 uninfected population)

To create the incidence/prevalence curves, you will need 2 percent indicators from the HIV Estimates tab:
- Percent Prevalence
- Percent Incidence

HIV Test & Treat Indicators & Use Cases

The HIV Test & Treat Data covers indicators in the clinical cascade, that can be used to track progress to the UNAIDS 95-95-95 targets. There are 6 percent indicators and 5 integer indicators.

To pull the HIV Test & Treat indicators for PEPFAR countries, use pull_unaids(data_type = "HIV Test & Treat", pepfar_only = TRUE).


Percent Indicators
`Percent Known Status of PLHIV`	Among PLHIV, Percent who known their HIV status
`Percent on ART of PLHIV`	Among PLHIV, Percent on ART
`Percent on ART with Known Status`	Among people who know their HIV status, Percent of ART
`Percent VLS of PLHIV`	Among PLHIV, Percent with Suppressed Viral Load
`Percent VLS on ART`	Among people on ART, Percent with Suppressed Viral Load
`Percent PMTCT on ART`	Estimated Percentage of Women Living with HIV on ART for preventing mother-to-child transmission
Integer Indicators
`Number Known Status of PLHIV`	Number who know their HIV status
`Number on ART of PLHIV`	Number on ART
`Number VLS of PLHIV`	Number with Suppressed Viral Load
`Number PMTCT Needing ART`	Number of Pregnant women needing antiretrovirals for preventing mother-to-child transmission
`Number PMTCT on ART`	Estimated Number of Women Living with HIV on ART for preventing mother-to-child transmission

Use Case #3: Progress to 95-95-95 Targets

The percent indicators in the HIV Test & Treat data can be used to assess progress to the UNAIDS 95-95-95 targets that are set to be achieved by 2030, 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. These can also be used to assess progress to the 90-90-90 goals, which were set to be achieved in 2020.

To assess progress to 95-95-95 targets, we will use the percent indicators in the "HIV Test & Treat" data:

df_tt <- pull_unaids(data_type = "HIV Test & Treat", pepfar_only = TRUE) %>%
  filter(indic_type == "Percent")


Percent Indicators
`Percent Known Status of PLHIV`	Among PLHIV, Percent who known their HIV status
`Percent on ART of PLHIV`	Among PLHIV, Percent on ART
`Percent on ART with Known Status`	Among people who know their HIV status, Percent of ART
`Percent VLS of PLHIV`	Among PLHIV, Percent with Suppressed Viral Load
`Percent VLS on ART`	Among people on ART, Percent with Suppressed Viral Load
`Percent PMTCT on ART`	Estimated Percentage of Women Living with HIV on ART for preventing mother-to-child transmission

As you may notice, there are multiple test & treat percent indicators for the clinical cascade. This is because there are 2 types of metrics that can be used to calculate progress to 95-95-95 targets - the relative base/conditional denominator or the PLHIV base/denominator.

If you are using the relative base to track 95-95-95 progress, use the following percent 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 percent 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

For more information about the distinction between relative/PLHIV base and how to assess 95-95-95 progress, see the article 95-95-95 Target Progress: Relative Base vs. PLHIV Base.