Results: lung

Author

Vagish Hemmige

Table 1 and Table 2: Summary results in PLWH and without HIV

Table 1: People Living With HIV (PLWH)

This table summarizes the number and proportion of people living with HIV who reside within defined catchment areas of:

Active transplant centers
HIV R+ transplant centers

Catchment areas are defined by:

50-mile radius
60-minute driving time
100-mile radius
200-mile radius

Results are shown for 2017 and 2022.

Access to transplant in PLWH
Characteristic	2017			2022
Characteristic	Numerator	Denominator	Percentage	Numerator	Denominator	Percentage
Active lung center 50 mile radius	687,760	943,972	72.9	755,291	1,062,528	71.1
Active HIV R+ lung center 50 mile radius	224,995	943,972	23.8	496,378	1,062,528	46.7
Active lung center 60 min radius	661,712	943,972	70.1	724,678	1,062,528	68.2
Active HIV R+ lung center 60 min radius	216,602	943,972	22.9	472,089	1,062,528	44.4
Active lung center 100 mile radius	803,324	943,972	85.1	899,219	1,062,528	84.6
Active HIV R+ lung center 100 mile radius	268,761	943,972	28.5	642,032	1,062,528	60.4
Active lung center 200 mile radius	911,686	943,972	96.6	1,024,082	1,062,528	96.4
Active HIV R+ lung center 200 mile radius	470,934	943,972	49.9	878,460	1,062,528	82.7

Table 2: Individuals Without HIV

This table presents the same catchment analysis for individuals without HIV.

It allows direct comparison of geographic access between:

PLWH
Individuals without HIV

Access to transplant in people without HIV
Characteristic	2017			2022
Characteristic	Numerator	Denominator	Percentage	Numerator	Denominator	Percentage
Active lung center 50 mile radius	181,100,239	317,900,212	57.0	187,021,525	327,849,655	57.0
Active lung center 60 min radius	166,197,517	317,900,212	52.3	171,620,447	327,849,655	52.3
Active lung center 100 mile radius	242,944,675	317,900,212	76.4	250,661,797	327,849,655	76.5
Active lung center 200 mile radius	298,567,105	317,900,212	93.9	307,731,863	327,849,655	93.9

Figures

Figures are grouped into three sections.

Compare PLWH and non-HIV

These figures compare geographic access between PLWH and individuals without HIV within each catchment definition.

Each panel corresponds to a distance-based definition:

50 miles
60 minutes
100 miles
200 miles

These figures highlight disparities in access by HIV status.

Compare PLWH Between Years

These figures evaluate changes in geographic access among PLWH between 2017 and 2022.

For each catchment definition, results are shown for:

Active centers
HIV R+ centers

This section highlights temporal changes in transplant center availability.

Beeswarm Plots

In these beeswarm plots, we plot the number of PLWh in each transplant center’s catchment area, classifying centers by whether or not they had performed an HIV R+ lung transplant in 2013-2017 and 2018-2022, respectively.

Each point represents a transplant center, with the distribution shown for each distance definition.

These plots illustrate variability in geographic access across centers rather than only aggregate proportions.

The 50-mile results are below.

The 60-minute results are below.

The 100-mile results are below.

The 200-mile results are below.

--- title: "Results: lung" format: html --- ## Table 1 and Table 2: Summary results in PLWH and without HIV ```{r, include=FALSE} #This code prepares the .qmd environent for analysis organ<-"lung" Results_HIV_gt <- readRDS("../cache/Results_HIV_gt.rds") Results_nonHIV_gt <- readRDS("../cache/Results_nonHIV_gt.rds") source("../R/setup.R") source("../R/functions.R") ``` ### Table 1: People Living With HIV (PLWH) This table summarizes the number and proportion of people living with HIV who reside within defined catchment areas of: - Active transplant centers - HIV R+ transplant centers Catchment areas are defined by: - 50-mile radius - 60-minute driving time - 100-mile radius - 200-mile radius Results are shown for 2017 and 2022. --- ```{r, echo=FALSE} tbl_body <- gt::extract_body(Results_HIV_gt[[organ]]) tbl_body_no_hope <- tbl_body %>% dplyr::filter( !dplyr::if_any( everything(), ~ stringr::str_detect(as.character(.x), stringr::regex("HOPE", ignore_case = TRUE)) ) ) %>% gt::gt() %>% { purrr::reduce( year_list, ~ .x %>% gt::tab_spanner( label = .y, columns = matches(paste0("_", .y, "$")) ), .init = . ) } %>% gt::fmt_number( columns = matches("Numerator_|Denominator_"), decimals = 0, use_seps = TRUE ) %>% gt::fmt_number( columns = matches("Percentage_"), decimals = 1 ) %>% gt::tab_header( title = "Access to transplant in PLWH" ) %>% gt::cols_label_with(~ "Numerator", columns = matches("^Numerator_")) %>% gt::cols_label_with(~ "Denominator", columns = matches("^Denominator_")) %>% gt::cols_label_with(~ "Percentage", columns = matches("^Percentage_")) %>% { purrr::reduce( seq_along(year_list), ~ .x %>% gt::tab_style( style = gt::cell_fill( color = colorRampPalette(c("lightgray", "lightblue"))(length(year_list))[.y] ), locations = gt::cells_body( columns = matches(paste0("_", year_list[.y], "$")) ) ), .init = . ) } %>% { purrr::reduce( seq_len(length(year_list) - 1), ~ .x %>% gt::tab_style( style = gt::cell_borders( sides = "right", weight = gt::px(2), color = "black" ), locations = gt::cells_body( columns = matches(paste0("Percentage_", year_list[.y], "$")) ) ) %>% gt::tab_style( style = gt::cell_borders( sides = "left", weight = gt::px(2), color = "black" ), locations = gt::cells_body( columns = matches(paste0("Numerator_", year_list[.y + 1], "$")) ) ), .init = . ) } tbl_body_no_hope ``` ### Table 2: Individuals Without HIV This table presents the same catchment analysis for individuals without HIV. It allows direct comparison of geographic access between: - PLWH - Individuals without HIV --- ```{r, echo=FALSE} Results_nonHIV_gt[[organ]] ``` ## Figures Figures are grouped into three sections. --- ### Compare PLWH and non-HIV These figures compare geographic access between PLWH and individuals without HIV within each catchment definition. Each panel corresponds to a distance-based definition: - 50 miles - 60 minutes - 100 miles - 200 miles These figures highlight disparities in access by HIV status. --- ```{r, echo=FALSE} list.files( glue::glue("../figures/{organ}"), pattern = "Combined.*\\.png$", full.names = TRUE )%>% str_subset("Active")%>% str_subset("HOPE", negate = TRUE) %>% { .[order(as.numeric(stringr::str_extract(., "\\d+(?=(mile|min))")))] } %>% knitr::include_graphics() ``` --- ### Compare PLWH Between Years These figures evaluate changes in geographic access among PLWH between 2017 and 2022. For each catchment definition, results are shown for: - Active centers - HIV R+ centers This section highlights temporal changes in transplant center availability. --- ```{r, echo=FALSE} list.files( glue::glue("../figures/{organ}"), pattern = "Combined.*\\.png$", full.names = TRUE )%>% str_subset("Active", negate=TRUE)%>% str_subset("HOPE", negate = TRUE) %>% { .[order(as.numeric(stringr::str_extract(., "\\d+(?=(mile|min))")))] } %>% knitr::include_graphics() ``` --- ### Beeswarm Plots In these beeswarm plots, we plot the number of PLWh in each transplant center's catchment area, classifying centers by whether or not they had performed an HIV R+ lung transplant in 2013-2017 and 2018-2022, respectively. Each point represents a transplant center, with the distribution shown for each distance definition. These plots illustrate variability in geographic access across centers rather than only aggregate proportions. The 50-mile results are below. ![](../figures/lung/Beeswarm lung plot 2026-2-14 50mile.png) The 60-minute results are below. ![](../figures/lung/Beeswarm lung plot 2026-2-14 60min.png) The 100-mile results are below. ![](../figures/lung/Beeswarm lung plot 2026-2-14 100mile.png) The 200-mile results are below. ![](../figures/lung/Beeswarm lung plot 2026-2-14 200mile.png)