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[NM-99] Return all indicators for input time series #456

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Dec 4, 2024
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[NM-99] Return all indicators for input time series #456

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7d0f80e
Return all indicators for input time series
r-ash Dec 3, 2024
f7f8e03
Improve strings for new ANC indicators
r-ash Dec 4, 2024
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2 changes: 1 addition & 1 deletion DESCRIPTION
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@@ -1,6 +1,6 @@
Package: naomi
Title: Naomi Model for Subnational HIV Estimates
Version: 2.10.2
Version: 2.10.3
Authors@R:
person(given = "Jeff",
family = "Eaton",
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4 changes: 4 additions & 0 deletions NEWS.md
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# naomi 2.10.3

* Return `anc_already_art`, `anc_status`, `anc_art_among_known` and `anc_total_pos` indicators from ANC input time series data.
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What is anc_status? Is that the total denominator? I usually use anc_hiv_status for that. Doesn't matter too much


# naomi 2.10.2

* Add ANC testing outputs to T4 projection period for including in PEPFAR datapack output.
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7 changes: 1 addition & 6 deletions R/input-time-series.R
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Expand Up @@ -503,12 +503,7 @@ prepare_input_time_series_anc <- function(anc, shape) {

anc_plot_data_long <- anc_long |>
dplyr::mutate(!!!mutate_exprs) |>
dplyr::select(area_id, area_name, area_level, area_level_label, parent_area_id,
area_sort_order, age_group, time_period, year, quarter,
calendar_quarter, anc_clients, anc_tested, anc_tested_pos,
anc_prevalence, anc_known_pos, anc_known_neg,
anc_art_coverage, births_facility, births_clients_ratio,
area_hierarchy) |>
dplyr::select(-sex) |>
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Is there any risk that anc_long has other columns dangling around, or are the columns of that table rigidly specified by an earlier step?

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So the indicators are specified by mutate_exprs. So not selecting specific columns here means if we add any more then they should come straight through. Are you worried about some new non-indicator column though?

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Double checked, no that shouldn't happen. We read the anc file in and specify the columns we want to read. So that should filter out any unwanted stuff. This should just mean all the columns we create during the aggregation get returned automatically

tidyr::pivot_longer(cols = c(dplyr::starts_with("anc"), "births_facility", "births_clients_ratio"),
names_to = "plot",
values_to = "value") |>
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4 changes: 4 additions & 0 deletions inst/metadata/time_series_plot_metadata.csv
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Expand Up @@ -13,6 +13,10 @@ anc_tested,t_(ANC_TESTED),t_(ANC_TESTED_DESC),,"""0,0"""
anc_tested_pos,t_(ANC_TESTED_POS),t_(ANC_TESTED_POS_DESC),,"""0,0"""
anc_known_pos,t_(ANC_KNOWN_POS),t_(ANC_KNOWN_POS_DESC),,"""0,0"""
anc_known_neg,t_(ANC_KNOWN_NEG),t_(ANC_KNOWN_NEG_DESC),,"""0,0"""
anc_already_art,t_(ANC_ALREADY_ART),t_(ANC_ALREADY_ART_DESC),,"""0,0"""
anc_total_pos,t_(ANC_TOTAL_POS),t_(ANC_TOTAL_POS_DESC),,"""0,0"""
anc_status,t_(ANC_STATUS),t_(ANC_STATUS_DESC),,"""0,0"""
anc_art_among_known,t_(ANC_ART_AMONG_KNOWN),t_(ANC_ART_AMONG_KNOWN_DESC),,"""0,0"""
births_clients_ratio,t_(BIRTHS_CLIENTS_RATIO),t_(BIRTHS_CLIENTS_RATIO_DESC),,"""0%"""
births_facility,t_(BIRTHS_FACILITY),t_(BIRTHS_FACILITY_DESC),,"""0,0"""
art_new_total,t_(ART_NEW_TOTAL),t_(ART_NEW_TOTAL_DESC),,"""0,0"""
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9 changes: 8 additions & 1 deletion inst/traduire/en-translation.json
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Expand Up @@ -276,5 +276,12 @@
"DOWNLOAD_AGYW_DESCRIPTION": "Naomi AGYW tool uploaded from Naomi web app",
"NUMBER_ON_ART": "Number on ART",
"NUMBER_ON_ART_DESC": "Number on ART description",
"POPULATION_PROPORTION": "Population proportion"
"POPULATION_PROPORTION": "Population proportion",
"ANC_TOTAL_POS": "Anc total pos",
"ANC_TOTAL_POS_DESC": "Anc total pos desc",
"ANC_ALREADY_ART_DESC": "ANC already ART desc",
"ANC_STATUS": "ANC status",
"ANC_STATUS_DESC": "ANC status desc",
"ANC_ART_AMONG_KNOWN": "ANC ART among known",
"ANC_ART_AMONG_KNOWN_DESC": "ANC ART among known desc"
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"ANC total HIV positive"
"Total HIV positive women at ANC (known positive and tested positive)"
"Women already on lifelong ART before first ANC visit during this pregnancy"
"ANC HIV status ascertained"
"Number of women with HIV status ascertained during ANC (either known or tested at least once)"
"ANC ART coverage among known positive" this might be too long?
"Proportion of women known HIV positive at first ANC visit who were already on ART"

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These look good to me

}
9 changes: 8 additions & 1 deletion inst/traduire/fr-translation.json
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Expand Up @@ -273,5 +273,12 @@
"DOWNLOAD_COMPARISON_DESCRIPTION": "Rapport de comparaison Naomi téléchargé à partir de l'application web Naomi",
"NUMBER_ON_ART": "Nombre de personnes sous TARV",
"NUMBER_ON_ART_DESC": "Number on ART description",
"POPULATION_PROPORTION": "Proportion de la population"
"POPULATION_PROPORTION": "Proportion de la population",
"ANC_TOTAL_POS": "Anc total pos",
"ANC_TOTAL_POS_DESC": "Anc total pos desc",
"ANC_ALREADY_ART_DESC": "ANC already ART desc",
"ANC_STATUS": "ANC status",
"ANC_STATUS_DESC": "ANC status desc",
"ANC_ART_AMONG_KNOWN": "ANC ART among known",
"ANC_ART_AMONG_KNOWN_DESC": "ANC ART among known desc"
}
9 changes: 8 additions & 1 deletion inst/traduire/pt-translation.json
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Expand Up @@ -273,5 +273,12 @@
"DOWNLOAD_COMPARISON_DESCRIPTION": "Relatório de comparação Naomi carregado a partir da aplicação web Naomi",
"NUMBER_ON_ART": "Nombre de personnes sous TARV",
"NUMBER_ON_ART_DESC": "Number on ART description",
"POPULATION_PROPORTION": "Proporção da população"
"POPULATION_PROPORTION": "Proporção da população",
"ANC_TOTAL_POS": "Anc total pos",
"ANC_TOTAL_POS_DESC": "Anc total pos desc",
"ANC_ALREADY_ART_DESC": "ANC already ART desc",
"ANC_STATUS": "ANC status",
"ANC_STATUS_DESC": "ANC status desc",
"ANC_ART_AMONG_KNOWN": "ANC ART among known",
"ANC_ART_AMONG_KNOWN_DESC": "ANC ART among known desc"
}
4 changes: 3 additions & 1 deletion tests/testthat/test-input-time-series.R
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Expand Up @@ -454,7 +454,9 @@ test_that("data can be formatted for ANC input time series", {
expect_setequal(unique(data$plot),
c("anc_clients", "anc_tested", "anc_tested_pos",
"anc_prevalence", "anc_known_pos", "anc_known_neg",
"anc_art_coverage", "births_clients_ratio", "births_facility"))
"anc_art_coverage", "births_clients_ratio",
"births_facility", "anc_already_art", "anc_status",
"anc_art_among_known", "anc_total_pos"))

# Time period has correct format
expect_match(as.character(data$time_period), "\\d{4}")
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