PEPFAR Datapack 2025 -- national outputs

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: naomi
 Title: Naomi Model for Subnational HIV Estimates
-Version: 2.10.7
+Version: 2.10.8
 Authors@R:
     person(given = "Jeff",
            family = "Eaton",

NEWS.md

@@ -1,5 +1,9 @@
-# naomi 2.10.7
+# naomi 2.10.8
+
+* Add national level aggregate to PEPFAR Target Setting Tool CSV. 
+* Remove two-year ahead projection (`*.T2`) indicators from PEPFAR Target Setting Tool output.
 
+# naomi 2.10.7
 * Add example datasets for 28 district, dropping the `District + Metro` level.
   Datasets are saved in `extdata/demo-district28`.
 

R/pepfar-datapack.R

@@ -74,6 +74,10 @@ build_datapack_output <- function(naomi_output, psnu_level, dmppt2_output) {
     warning("PSNU level ", psnu_level, " not included in model outputs.")
   }
 
+  ## PEPFAR Target Setting Tool 2025: select both PSNU level and national aggregates
+  ## Assume that national aggregate is level 0
+  datapack_output_levels <- c(0L, psnu_level)
+
   datapack_indicator_map$calendar_quarter <- naomi_output$meta_period$calendar_quarter[datapack_indicator_map$time]
 
   datapack_indicator_map <- datapack_indicator_map %>%
@@ -120,9 +124,10 @@ build_datapack_output <- function(naomi_output, psnu_level, dmppt2_output) {
 
   dat <- indicators %>%
     dplyr::rename(sex_naomi = sex) %>%
-    dplyr::semi_join(
+    dplyr::inner_join(
       naomi_output$meta_area %>%
-        dplyr::filter(area_level == psnu_level),
+        dplyr::filter(area_level %in% datapack_output_levels) %>%
+        dplyr::select(area_id, area_level),
       by = "area_id"
     ) %>%
     dplyr::left_join(
@@ -142,6 +147,7 @@ build_datapack_output <- function(naomi_output, psnu_level, dmppt2_output) {
     ) %>%
     dplyr::transmute(
       area_id,
+      area_level,
       indicator,
       indicator_sort_order,
       sex_naomi,
@@ -156,7 +162,7 @@ build_datapack_output <- function(naomi_output, psnu_level, dmppt2_output) {
     dplyr::rename(age_sex_rse = rse) %>%
     dplyr::left_join(
       dplyr::filter(dat, age_group %in% c("Y000_999", "Y015_049")) %>%
-        dplyr::select(-indicator_sort_order, -age_group, -sex_naomi, -value) %>%
+        dplyr::select(-area_level, -indicator_sort_order, -age_group, -sex_naomi, -value) %>%
         dplyr::rename(district_rse = rse),
       by = c("area_id", "indicator", "calendar_quarter")
     ) %>%
@@ -165,7 +171,7 @@ build_datapack_output <- function(naomi_output, psnu_level, dmppt2_output) {
         dplyr::select(area_name, area_id),
       by = "area_id"
     ) %>%
-    dplyr::arrange(calendar_quarter, indicator_sort_order, area_id, sex_naomi, age_group)
+    dplyr::arrange(calendar_quarter, indicator_sort_order, area_level, area_id, sex_naomi, age_group)
 
 
   dat$district_rse[is.na(dat$district_rse) & dat$indicator %in% c("circ_new", "circ_ever")] <- 0.0
@@ -249,7 +255,7 @@ datapack_aggregate_1to9 <- function(indicators) {
 
 
   indicators_keep <- c("plhiv", "plhiv_attend", "untreated_plhiv_attend", "infections",
-                       "population", "art_current", "art_current_residents", "aware_plhiv_num")
+                       "population", "art_current", "art_current_residents", "aware_plhiv_num", "aware_plhiv_attend")
 
   indicators1to9 <- indicators %>%
     dplyr::filter(

inst/datapack/datapack_indicator_mapping.csv

@@ -29,8 +29,5 @@ incidence,Incidence_SUBNAT.Rt.T,Incidence_SUBNAT.Rt.T,,FALSE,4
 infections,NEW_INFECTIONS_SUBNAT.T,NEW_INFECTIONS_SUBNAT.T,,TRUE,4
 plhiv,PLHIV_Residents.T,PLHIV_Residents.T,,TRUE,4
 plhiv_attend,PLHIV_Attend.T,PLHIV_Attend.T,,TRUE,4
-infections,NEW_INFECTIONS_SUBNAT.T2,NEW_INFECTIONS_SUBNAT.T2,,TRUE,5
-plhiv,PLHIV_Residents.T2,PLHIV_Residents.T2,,TRUE,5
-plhiv_attend,PLHIV_Attend.T2,PLHIV_Attend.T2,,TRUE,5
 circ_ever,ZayJeEa6pCa,VMMC_TOTALCIRC_SUBNAT.T_1,,TRUE,3
 circ_new,SSun4i7nHlV,VMMC_CIRC_SUBNAT.T_1,,TRUE,3