HIV self-testing positivity rate and linkage to confirmatory testing and care: a telephone survey in Côte d’Ivoire, Mali and Senegal

Author

Arsène Kouassi Kra et al

Published

April 24, 2024

Code

# Loading packages
library(tidyverse)
library(gtsummary)
library(labelled)
library(scales)
library(ggrepel)
library(RColorBrewer)
library(utils)
library(patchwork)
library(stats)

# Data import
data <- readr::read_csv("data.csv", show_col_types = FALSE)

Table 1. Self-reported test result and the reported number of lines among participants of phase 1, and positivity rates according to different hypotheses

Code

# Creating "test_result_number_lines_reported" variable
data <- data |>
  mutate(
    test_result_number_lines_reported =
      case_when(
        (HIVST_reported_lines == "2 lines" & HIVST_reported_result == "reactive") ~ "2 lines / reactive",
        (HIVST_reported_lines == "2 lines" & HIVST_reported_result == "non reactive") ~ "2 lines / non-reactive",
        (HIVST_reported_lines == "2 lines" & (HIVST_reported_result == "DK" | HIVST_reported_result == "R")) ~ "2 lines / DK-R",
        (HIVST_reported_lines == "1 line" & HIVST_reported_result == "reactive") ~ "1 line / reactive",
        (HIVST_reported_lines == "1 line" & HIVST_reported_result == "non reactive") ~ "1 line / non-reactive",
        (HIVST_reported_lines == "1 line" & (HIVST_reported_result == "DK" | HIVST_reported_result == "R")) ~ "1 line / DK-R", (HIVST_reported_lines == "0 line" & HIVST_reported_result == "non reactive") ~ "0 line / non-reactive", (HIVST_reported_lines == "0 line" & HIVST_reported_result == "reactive") ~ "0 line / reactive", (HIVST_reported_lines == "0 line" & (HIVST_reported_result == "DK" | HIVST_reported_result == "R")) ~ "0 line / DK-R", ((HIVST_reported_lines == "0 line" | HIVST_reported_lines == "1 line") & HIVST_reported_result == "invalid") ~ "0-1 line / invalid",
        (HIVST_reported_lines == "2 lines" & HIVST_reported_result == "invalid") ~ "2 lines / invalid",
        ((HIVST_reported_lines == "DK" | HIVST_reported_lines == "R") & HIVST_reported_result == "invalid") ~ "DK-R / invalid",
        ((HIVST_reported_lines == "DK" | HIVST_reported_lines == "R") & HIVST_reported_result == "reactive") ~ "DK-R / reactive",
        ((HIVST_reported_lines == "DK" | HIVST_reported_lines == "R") & HIVST_reported_result == "non reactive") ~ "DK-R / non-reactive",
        ((HIVST_reported_lines == "DK" | HIVST_reported_lines == "R") & (HIVST_reported_result == "DK" | HIVST_reported_result == "R")) ~ "DK-R / DK-R"
      )
  ) |>
  set_variable_labels(
    test_result_number_lines_reported = "Reported number of lines / self-interpreted HIVST result"
  )

data$test_result_number_lines_reported <- data$test_result_number_lines_reported |>
  fct_relevel(
    "2 lines / reactive",
    "1 line / non-reactive",
    "0-1 line / invalid",
    "1 line / reactive",
    "2 lines / non-reactive", "0 line / non-reactive",
    "0 line / DK-R", "1 line / DK-R", "2 lines / DK-R", "DK-R / reactive",
    "DK-R / DK-R", "DK-R / non-reactive"
  )

# Generating Table 1
tbl1 <-
  data |>
  tbl_summary(
    include = test_result_number_lines_reported,
    label = test_result_number_lines_reported ~ ""
  ) |>
  modify_header(label = "**Phase 1 participants**") |>
  as_gt() |>
  gt::tab_row_group(
    label = gt::md("*Partial reponse (P)*"),
    rows = 8:13
  ) |>
  gt::tab_row_group(
    label = gt::md("*Inconsistant reponse (I)*"),
    rows = 5:7
  ) |>
  gt::tab_row_group(
    label = gt::md("*Consistant reponse (C)*"),
    rows = 2:4
  )

tbl1

Phase 1 participants	N = 2,615¹
Consistant reponse (C)
2 lines / reactive	50 (1.9%)
1 line / non-reactive	2,292 (88%)
0-1 line / invalid	4 (0.2%)
Inconsistant reponse (I)
1 line / reactive	10 (0.4%)
2 lines / non-reactive	35 (1.3%)
0 line / non-reactive	3 (0.1%)
Partial reponse (P)
0 line / DK-R	1 (<0.1%)
1 line / DK-R	117 (4.5%)
2 lines / DK-R	29 (1.1%)
DK-R / reactive	2 (<0.1%)
DK-R / DK-R	28 (1.1%)
DK-R / non-reactive	44 (1.7%)


¹ n (%)

Figure 3. Positivity rates based on self-interpreted HIVST results or the reported number of visible lines, by distribution channel, gender and country, among participants of the first survey phase in Côte d’Ivoire, Mali, and Senegal (2021).

Code

# Creating "key population profiles" variable
data <- data |>
  mutate(
    key_population_profile = interaction(sex, delivery_channel_grouped) |>
      fct_recode() |>
      fct_relevel(
        "man.MSM-based channels", "woman.MSM-based channels", "man.FSW-based channels",
        "woman.FSW-based channels", "man.Other delivery channels", "woman.Other delivery channels"
      )
  ) |> 
  set_variable_labels(key_population_profile = "Key population profile")


# Recoding variable "key population profiles"

data <- data |>
  mutate(
    key_population_profile = key_population_profile |>
      fct_recode(
        "man\nMSM-\nbased\nchannels" = "man.MSM-based channels",
        "woman\nMSM-\nbased\nchannels" = "woman.MSM-based channels",
        "man\nFSW-\n based\nchannels" = "man.FSW-based channels",
        "woman\nFSW-\nbased\nchannels" = "woman.FSW-based channels",
        "man\nother\nchannels" = "man.Other delivery channels",
        "woman\nother\nchannels" = "woman.Other delivery channels"
      )
  ) |>
  set_variable_labels(key_population_profile = "key population profiles")

dh1 <- data |>
  group_by(key_population_profile, country) |>
  summarise(
    complete = "Based on self-interpreted test results",
    n = sum(HIVST_reported_result %in% c("reactive")),
    N = sum(!HIVST_reported_result %in% c("DK", "R")),
    Lowest = "Based on self-interpreted test results",
    n1 = sum(HIVST_reported_result %in% c("reactive")),
    N1 = n(),
    Highest = "Based on self-interpreted test results",
    n2 = sum(HIVST_reported_result %in% c("reactive", "DK", "R")),
    N2 = n()
  )

dh2 <- data |>
  group_by(key_population_profile, country) |>
  summarise(
    complete = "Based on the reported number of lines",
    n = sum(HIVST_reported_lines %in% c("2 lines")),
    N = sum(!HIVST_reported_result %in% c("DK", "R")),
    Lowest = "Based on the reported number of lines",
    n1 = sum(HIVST_reported_lines %in% c("2 lines")),
    N1 = n(),
    Highest = "Based on the reported number of lines",
    n2 = sum(HIVST_reported_lines %in% c("2 lines", "DK", "R")),
    N2 = n()
  )

dh0 <- bind_rows(dh1, dh2) |>
  mutate(
    complete = factor(complete, levels = c("Based on self-interpreted test results", "Based on the reported number of lines")),
    positivity_rate = n / N,
    label = scales::percent(positivity_rate, suffix = "", accuracy = .1),
    lowest = factor(Lowest, levels = c("reported result", "reported lines")),
    lowest_possible_rate = n1 / N1,
    label1 = scales::percent(lowest_possible_rate, suffix = "", accuracy = .1),
    Highest = factor(Highest, levels = c("reported result", "reported lines")),
    highest_possible_rate = n2 / N2,
    label2 = scales::percent(highest_possible_rate, suffix = "", accuracy = .1)
  ) |>
  mutate(
    ylabel = highest_possible_rate + .01
  )

# Dropping cells with denominator below 25
to_drop <- dh0$N < 25
to_drop <- dh0$N1 < 25
to_drop <- dh0$N2 < 25
dh0$positivity_rate[to_drop] <- NA
dh0$lowest_possible_rate[to_drop] <- NA
dh0$highest_possible_rate[to_drop] <- NA
dh0$label[to_drop] <- ""
dh0$label[to_drop] <- "*"
dh0$ylabel[to_drop] <- 0

fig3 <-
  dh0 |>
  ggplot() +
  aes(
    x = key_population_profile,
    y = positivity_rate,
    ymin = lowest_possible_rate,
    ymax = highest_possible_rate,
    color = complete,
    shape = complete,
  ) +
  geom_errorbar(
    position = position_dodge(width = 0.9),
    color = "#555555",
    width = .2
  ) +
  geom_point(
    stat = "identity",
    position = position_dodge(width = 0.9),
    size = 2.5
  ) +
  geom_text(
    mapping = aes(label = label, y = NULL),
    y = -0.01,
    position = position_dodge(width = 0.9),
    color = "black",
    size = 3,
    vjust = 1,
    face = "bold"
  ) +
  facet_grid(cols = vars(country)) +
  labs(title = "", x = "", y = "") +
  ggtitle("") +
  scale_y_continuous(labels = scales::percent, limits = c(-0.01, .25)) +
  scale_colour_manual(values = c("#DD3D2D", "#1B7837")) +
  theme_classic() +
  theme(
    panel.grid.major.y = element_line(colour = "#DDDDDD", linetype = "dotted"),
    legend.title = element_blank(),
    legend.position = "bottom"
  )
fig3

Figure 4. Elements for the flow chart of the participant selection process for Phase 2 of the survey

Code

n_eligible_for_phase_2 <- data |>
  filter((HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines")) |>
  nrow()

n_eligible_agreed_recontacted <- data |>
  filter((HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines") & (recontact_phase2 == "yes")) |>
  nrow()

n_eligible_refusal_recontacted <- data |>
  filter((HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines") & (recontact_phase2 == "no")) |>
  nrow()

n_unreachable_time_phase2 <- data |>
  filter((HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines") & (recontact_phase2 == "yes") & (status_phase2 == "unavaible" | is.na(status_phase2))) |>
  nrow()

n_successfully_recontacted <- data |>
  filter((HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines") & (recontact_phase2 == "yes") & (status_phase2 != "unavaible" & !is.na(status_phase2))) |>
  nrow()

n_refused_partcipate <- data |>
  filter((HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines") & (recontact_phase2 == "yes") & (status_phase2 != "unavaible" & !is.na(status_phase2)) & status_phase2 == "refusal") |>
  nrow()

n_accepted_partcipate <- data |>
  filter((HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines") & (recontact_phase2 == "yes") & (status_phase2 != "unavaible" & !is.na(status_phase2)) & status_phase2 != "refusal") |>
  nrow()

n_disconneted <- data |>
  filter((HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines") & (recontact_phase2 == "yes") & (status_phase2 != "unavaible" & !is.na(status_phase2)) & (status_phase2 != "refusal" & status_phase2 == "disconnected")) |>
  nrow()

n_dropped <- data |>
  filter((HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines") & (recontact_phase2 == "yes") & (status_phase2 != "unavaible" & !is.na(status_phase2)) & status_phase2 != "refusal" & status_phase2 == "dropped out before the end") |>
  nrow()

n_completed_quest_phase2 <- data |>
  filter((HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines") & (recontact_phase2 == "yes") & status_phase2 == "questionnaires completed") |>
  nrow()

126 participants who reported a reactive test and/or two lines in phase 1
120 participants who agreed to be recalled several months later
6 participants refusal to be recalled several months later
24 unreachable at the time of phase 2
96 successfully recontacted for phase 2
7 refused to participate
89 accepted to participate in phase 2 survey
1 disconnected and unreachable
10 dropped out before the end
78 completed questionnaire phase 2

Table 2. Linkage to confirmatory testing, proportion being confirmed HIV positive and treatment initiation, by reported number of lines and self-interpreted HIVST result among phase 2 eligible participants who completed their questionnaire.

Code

data <- data |>
  set_variable_labels(
    country = "Country",
    age_group = "Age group",
    marital_status = "Marital status",
    educational_level = "Educational level",
    first_time_tester = "First time tester"
  )


# Selection of individuals eligible for phase 2 who completed their phase 2 questionnaire
phase2 <- data |>
  filter(
    HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines",
    recontact_phase2 == "yes",
    status_phase2 == "questionnaires completed"
  ) |>
  mutate(all = "ALL")
phase2$test_result_number_lines_reported <- droplevels(phase2$test_result_number_lines_reported)

var_label(phase2$test_result_number_lines_reported) <- "Reported number of lines /self-interpreted result"
var_label(phase2$all) <- "Overall"

# Computing the numbers of individuals who completed phase 2 questionnaire
tbl_completed <-
  phase2 |>
  tbl_summary(
    include = c(all, test_result_number_lines_reported),
    statistic = ~"{n}"
  ) |>
  modify_header(stat_0 = "**n**") |>
  modify_footnote(update = everything() ~ NA)

# Computing the proportion who linked to confirmatory testing
tbl_linked <-
  phase2 |>
  tbl_summary(
    by = confirmatory_test,
    include = c(all, test_result_number_lines_reported),
    percent = "row",
    digits = ~0
  ) |>
  add_ci(style_fun = ~ label_percent(accuracy = 1, suffix = "")) |>
  modify_column_hide(c(stat_1, ci_stat_1)) |>
  modify_header(stat_2 ~ "**n (%)**") |>
  modify_footnote(update = everything() ~ NA)

# Computing the proportion who were confirmed HIV-positive
tbl_confirmed <-
  phase2 |>
  filter(confirmatory_test == "yes") |>
  mutate(confirmed = confirmatory_test_result == "positive") |>
  tbl_summary(
    by = confirmed,
    include = c(all, test_result_number_lines_reported),
    percent = "row",
    digits = ~0
  ) |>
  add_ci(style_fun = ~ label_percent(accuracy = 1, suffix = "")) |>
  modify_column_hide(c(stat_1, ci_stat_1)) |>
  modify_header(stat_2 ~ "**n (%)**") |>
  modify_footnote(update = everything() ~ NA)

# Computing the proportion who initiated ART
tbl_initiated <-
  phase2 |>
  filter(confirmatory_test_result == "positive") |>
  mutate(initiated = consulted_health_prof == "yes") |>
  mutate(test_result_number_lines_reported = fct_drop(test_result_number_lines_reported)) |>
  tbl_summary(
    by = initiated,
    include = c(all, test_result_number_lines_reported),
    percent = "row",
    digits = ~0
  ) |>
  add_ci(style_fun = ~ label_percent(accuracy = 1, suffix = "")) |>
  modify_column_hide(c(stat_1, ci_stat_1)) |>
  modify_header(stat_2 ~ "**n (%)**") |>
  modify_footnote(update = everything() ~ NA)

# Merging tables
tbl_merge(
  list(tbl_completed, tbl_linked, tbl_confirmed, tbl_initiated),
  tab_spanner = c(
    "**Completed phase 2**",
    "**Linked to confirmatory testing**",
    "**Confirmed HIV positive**",
    "**Initiated ART**"
  )
) %>%
  bold_labels()

Characteristic	Completed phase 2	Linked to confirmatory testing		Confirmed HIV positive		Initiated ART
Characteristic	n	n (%)	95% CI¹	n (%)	95% CI¹	n (%)	95% CI¹
Overall
ALL	78	34 (44%)	33%, 55%	19 (56%)	38%, 72%	18 (95%)	72%, 100%
Reported number of lines /self-interpreted result
2 lines / reactive	27	15 (56%)	36%, 74%	12 (80%)	51%, 95%	12 (100%)	70%, 100%
1 line / reactive	7	1 (14%)	1%, 58%	0 (0%)	0%, 95%
2 lines / non-reactive	25	9 (36%)	19%, 57%	3 (33%)	9%, 69%	3 (100%)	31%, 100%
2 lines / DK-R	18	8 (44%)	22%, 69%	4 (50%)	22%, 78%	3 (75%)	22%, 99%
DK-R / reactive	1	1 (100%)	5%, 100%	0 (0%)	0%, 95%
¹ CI = Confidence Interval

Table S1a. Factors associated (logistic regression) with positivity rate based on the reported number of visible lines among participants of the first survey phase in Côte d’Ivoire, Mali, and Senegal (2021)

Code

data$HIVST_reported_lines_status <- 
  data$HIVST_reported_lines %>%
  fct_recode(
    NULL = "0 line",
    "non reactive" = "1 line",
    "reactive" = "2 lines",
    NULL = "DK",
    NULL = "R"
  )

modele_reported_lines <- glm(
  HIVST_reported_lines_status ~ key_population_profile + country + age_group + marital_status + educational_level + first_time_tester,
  data = data,
  family = binomial
)

modele_reported_lines %>%
  tbl_regression(intercept = TRUE, exponentiate = TRUE) %>%
  add_global_p() %>%
  add_q(method = "bonferroni") %>%
  bold_labels()

Characteristic	OR¹	95% CI¹	p-value	q-value²
(Intercept)	0.04	0.01, 0.10	<0.001	<0.001
key population profiles			0.5	>0.9
man MSM- based channels	—	—
woman MSM- based channels	1.14	0.38, 2.73
man FSW- based channels	0.90	0.54, 1.49
woman FSW- based channels	0.69	0.40, 1.17
man other channels	0.46	0.13, 1.29
woman other channels	0.40	0.06, 1.49
Country			0.3	>0.9
Côte d'Ivoire	—	—
Mali	1.22	0.80, 1.88
Senegal	1.79	0.84, 3.59
Age group			0.079	0.6
24 years or less	—	—
25-34 years	1.56	1.02, 2.42
35 years or more	1.78	0.92, 3.34
Marital status			0.2	>0.9
divorced / separated / widowed	—	—
living with partner / married	0.48	0.21, 1.22
single	0.64	0.28, 1.60
Educational level			0.2	>0.9
higher	—	—
none / primary	1.49	0.78, 2.86
secondary	1.56	0.95, 2.64
First time tester			0.083	0.6
no	—	—
yes	1.44	0.95, 2.16
¹ OR = Odds Ratio, CI = Confidence Interval
² Bonferroni correction for multiple testing

Table S1b. Factors associated (logistic regression) with positivity rate based on self-reported HIVST, among participants of the first survey phase in Côte d’Ivoire, Mali, and Senegal (2021).

Code

data$HIVST_reported_result_status <- 
  data$HIVST_reported_result %>%
  fct_recode(
    NULL = "DK",
    NULL = "invalid",
    NULL = "R"
  )

modele_reported_result <- glm(
  HIVST_reported_result_status ~ key_population_profile + country + age_group + marital_status + educational_level + first_time_tester,
  data = data,
  family = binomial
)

modele_reported_result %>%
  tbl_regression(intercept = TRUE, exponentiate = TRUE) %>%
  add_global_p() %>%
  add_q(method = "bonferroni") %>%
  bold_labels()

Characteristic	OR¹	95% CI¹	p-value	q-value²
(Intercept)	0.01	0.00, 0.06	<0.001	<0.001
key population profiles			0.13	>0.9
man MSM- based channels	—	—
woman MSM- based channels	0.28	0.02, 1.35
man FSW- based channels	0.42	0.19, 0.86
woman FSW- based channels	0.55	0.28, 1.05
man other channels	0.25	0.01, 1.40
woman other channels	0.42	0.02, 2.42
Country			0.2	>0.9
Côte d'Ivoire	—	—
Mali	1.48	0.85, 2.62
Senegal	0.69	0.15, 2.31
Age group			0.3	>0.9
24 years or less	—	—
25-34 years	1.57	0.89, 2.79
35 years or more	1.35	0.48, 3.39
Marital status			0.5	>0.9
divorced / separated / widowed	—	—
living with partner / married	0.53	0.18, 1.98
single	0.48	0.16, 1.79
Educational level			0.002	0.014
higher	—	—
none / primary	4.00	1.44, 12.9
secondary	4.12	1.76, 12.1
First time tester			0.10	0.7
no	—	—
yes	1.58	0.91, 2.78
¹ OR = Odds Ratio, CI = Confidence Interval
² Bonferroni correction for multiple testing

Table S2a. Positivity rates based on self-interpreted HIVST result, by key population profiles and country, among phase 1 participants

Code

# Creation of a table of the lowest possible positivity rates


tblS2a_low <- data |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = key_population_profile,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_result", value = c("reactive")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)


# Creation of a table of positivity rates based on complete responses

tblS2a_central <- data |>
  filter(HIVST_reported_result != "DK", HIVST_reported_result != "R") |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = key_population_profile,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_result", value = c("reactive")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)


# Creation of a table of the highest possible positivity rates

tblS2a_high <- data |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = key_population_profile,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_result", value = c("reactive", "DK", "R")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)


# Merging the different tables
tblS2a <- tbl_stack(
  list(tblS2a_low, tblS2a_central, tblS2a_high),
  group_header = c(
    "Lowest possible rate: positivity rate based on self reported test result and DK-R are considered not reactive",
    "Complete responses: positivity rate based on self reported test result and DK-R are excluded",
    "Highest possible rate: positivity rate based on self reported test result and DK-R are considered reactive"
  )
)

tblS2a |>
  modify_header(label ~ "**Variable**") |>
  modify_spanning_header(
    c("stat_1", "stat_2") ~ "**MSM-based channels**",
    c("stat_3", "stat_4") ~ "**FSW-based channels**",
    c("stat_5", "stat_6") ~ "**Others delivery channels**",
    stat_0 ~ "**Total**"
  ) |>
  modify_header(
    stat_1 ~ "**Man**", stat_2 ~ "**Woman**",
    stat_3 ~ "**Man**", stat_4 ~ "**Woman**",
    stat_5 ~ "**Man**", stat_6 ~ "**Woman**",
    stat_0 ~ " "
  ) |>
  modify_footnote(update = everything() ~ NA)

Variable	Total	MSM-based channels		FSW-based channels		Others delivery channels
Variable		Man	Woman	Man	Woman	Man	Woman
Lowest possible rate: positivity rate based on self reported test result and DK-R are considered not reactive
Overall	2.4% (62/2,615)	3.2% (32/997)	1.0% (1/103)	1.6% (10/620)	2.5% (17/685)	0.7% (1/137)	1.4% (1/73)
Country
Côte d'Ivoire	1.8% (25/1,390)	2.5% (16/650)	1.4% (1/73)	1.5% (5/339)	1.2% (3/245)	0% (0/60)	0% (0/23)
Mali	3.5% (34/984)	4.6% (14/306)	0% (0/29)	1.9% (5/269)	3.9% (14/360)	9.1% (1/11)	0% (0/9)
Senegal	1.2% (3/241)	4.9% (2/41)	0% (0/1)	0% (0/12)	0% (0/80)	0% (0/66)	2.4% (1/41)
Complete responses: positivity rate based on self reported test result and DK-R are excluded
Overall	2.5% (62/2,440)	3.4% (32/931)	1.0% (1/101)	1.7% (10/579)	2.7% (17/631)	0.8% (1/130)	1.5% (1/68)
Country
Côte d'Ivoire	2.0% (25/1,279)	2.7% (16/597)	1.4% (1/71)	1.6% (5/311)	1.4% (3/221)	0% (0/58)	0% (0/21)
Mali	3.6% (34/952)	4.7% (14/301)	0% (0/29)	1.9% (5/257)	4.1% (14/345)	9.1% (1/11)	0% (0/9)
Senegal	1.4% (3/209)	6.1% (2/33)	0% (0/1)	0% (0/11)	0% (0/65)	0% (0/61)	2.6% (1/38)
Highest possible rate: positivity rate based on self reported test result and DK-R are considered reactive
Overall	9.1% (237/2,615)	9.8% (98/997)	2.9% (3/103)	8.2% (51/620)	10% (71/685)	5.8% (8/137)	8.2% (6/73)
Country
Côte d'Ivoire	9.8% (136/1,390)	11% (69/650)	4.1% (3/73)	9.7% (33/339)	11% (27/245)	3.3% (2/60)	8.7% (2/23)
Mali	6.7% (66/984)	6.2% (19/306)	0% (0/29)	6.3% (17/269)	8.1% (29/360)	9.1% (1/11)	0% (0/9)
Senegal	15% (35/241)	24% (10/41)	0% (0/1)	8.3% (1/12)	19% (15/80)	7.6% (5/66)	9.8% (4/41)

Table S2b. Positivity rates based on the reported number of lines, by key population profiles and country, among phase 1 participants

Code

# Creation of a table of the lowest possible positivity rates

tblS2b_low <- data |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = key_population_profile,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_lines", value = c("2 lines")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)


# Creation of a table of positivity rates based on complete responses

tblS2b_central <- data |>
  filter(HIVST_reported_lines != "DK", HIVST_reported_lines != "R") |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = key_population_profile,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_lines", value = c("2 lines")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)


# Creation of a table of the highest possible positivity rates

tblS2b_high <- data |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = key_population_profile,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_lines", value = c("2 lines", "DK", "R")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)


# Merging of the different tables
tblS2b <- tbl_stack(
  list(tblS2b_low, tblS2b_central, tblS2b_high),
  group_header = c(
    "Lowest possible rate: positivity rate based on self reported number of lines and DK-R are considered not 2 lines",
    "Complete responses: positivity rate based on self reported number of lines and DK-R are excluded",
    "Highest possible rate: positivity rate based on self reported number of lines and DK-R are considered 2 lines"
  )
)

tblS2b |>
  modify_header(label ~ "**Variable**") |>
  modify_spanning_header(
    c("stat_1", "stat_2") ~ "**MSM-based channels**",
    c("stat_3", "stat_4") ~ "**FSW-based channels**",
    c("stat_5", "stat_6") ~ "**Others delivery channels**",
    stat_0 ~ "**Total**"
  ) |>
  modify_header(
    stat_1 ~ "**Man**", stat_2 ~ "**Woman**",
    stat_3 ~ "**Man**", stat_4 ~ "**Woman**",
    stat_5 ~ "**Man**", stat_6 ~ "**Woman**",
    stat_0 ~ " "
  ) |>
  modify_footnote(update = everything() ~ NA)

Variable	Total	MSM-based channels		FSW-based channels		Others delivery channels
Variable		Man	Woman	Man	Woman	Man	Woman
Lowest possible rate: positivity rate based on self reported number of lines and DK-R are considered not 2 lines
Overall	4.4% (114/2,615)	4.7% (47/997)	4.9% (5/103)	4.5% (28/620)	4.1% (28/685)	2.9% (4/137)	2.7% (2/73)
Country
Côte d'Ivoire	3.8% (53/1,390)	4.2% (27/650)	5.5% (4/73)	4.7% (16/339)	2.0% (5/245)	0% (0/60)	4.3% (1/23)
Mali	4.9% (48/984)	4.9% (15/306)	3.4% (1/29)	4.5% (12/269)	5.3% (19/360)	9.1% (1/11)	0% (0/9)
Senegal	5.4% (13/241)	12% (5/41)	0% (0/1)	0% (0/12)	5.0% (4/80)	4.5% (3/66)	2.4% (1/41)
Complete responses: positivity rate based on self reported number of lines and DK-R are excluded
Overall	4.5% (114/2,541)	4.8% (47/977)	4.9% (5/103)	4.6% (28/605)	4.2% (28/660)	3.1% (4/128)	2.9% (2/68)
Country
Côte d'Ivoire	3.9% (53/1,368)	4.2% (27/641)	5.5% (4/73)	4.8% (16/331)	2.1% (5/241)	0% (0/60)	4.5% (1/22)
Mali	5.0% (48/955)	5.0% (15/298)	3.4% (1/29)	4.5% (12/264)	5.5% (19/344)	9.1% (1/11)	0% (0/9)
Senegal	6.0% (13/218)	13% (5/38)	0% (0/1)	0% (0/10)	5.3% (4/75)	5.3% (3/57)	2.7% (1/37)
Highest possible rate: positivity rate based on self reported number of lines and DK-R are considered 2 lines
Overall	7.2% (188/2,615)	6.7% (67/997)	4.9% (5/103)	6.9% (43/620)	7.7% (53/685)	9.5% (13/137)	9.6% (7/73)
Country
Côte d'Ivoire	5.4% (75/1,390)	5.5% (36/650)	5.5% (4/73)	7.1% (24/339)	3.7% (9/245)	0% (0/60)	8.7% (2/23)
Mali	7.8% (77/984)	7.5% (23/306)	3.4% (1/29)	6.3% (17/269)	9.7% (35/360)	9.1% (1/11)	0% (0/9)
Senegal	15% (36/241)	20% (8/41)	0% (0/1)	17% (2/12)	11% (9/80)	18% (12/66)	12% (5/41)

Table S3a. Positivity rates based on self-interpreted HIVST result, by age group and country, among participants of the first survey phase in Côte d’Ivoire, Mali, and Senegal (2021)

Code

# Creation of a table of the lowest possible positivity rates

tblS3a_low <- data |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = age_group,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_result", value = c("reactive")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)


# Creation of a table of positivity rates based on complete responses

tblS3a_central <- data |>
  filter(HIVST_reported_result != "DK", HIVST_reported_result != "R") |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = age_group,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_result", value = c("reactive")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)


# Creation of a table of the highest possible positivity rates

tblS3a_high <- data |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = age_group,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_result", value = c("reactive", "DK", "R")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)

# Merging of the different tables
tblS3a <- tbl_stack(
  list(tblS3a_low, tblS3a_central, tblS3a_high),
  group_header = c(
    "Lowest possible rate: positivity rate based on self reported test result and DK-R are considered not reactive",
    "Complete responses: positivity rate based on self reported test result and DK-R are excluded",
    "Highest possible rate: positivity rate based on self reported test result and DK-R are considered reactive"
  )
)

tblS3a |>
  modify_header(label ~ "")

	Overall, N = 2,615¹	24 years or less, N = 1,164¹	25-34 years, N = 1,063¹	35 years or more, N = 388¹
Lowest possible rate: positivity rate based on self reported test result and DK-R are considered not reactive
Overall	2.4% (62/2,615)	2.2% (26/1,164)	2.7% (29/1,063)	1.8% (7/388)
Country
Côte d'Ivoire	1.8% (25/1,390)	1.7% (11/645)	2.0% (11/553)	1.6% (3/192)
Mali	3.5% (34/984)	3.3% (15/455)	3.9% (16/415)	2.6% (3/114)
Senegal	1.2% (3/241)	0% (0/64)	2.1% (2/95)	1.2% (1/82)
Complete responses: positivity rate based on self reported test result and DK-R are excluded
Overall	2.5% (62/2,440)	2.4% (26/1,099)	2.9% (29/991)	2.0% (7/350)
Country
Côte d'Ivoire	2.0% (25/1,279)	1.8% (11/604)	2.2% (11/506)	1.8% (3/169)
Mali	3.6% (34/952)	3.4% (15/439)	4.0% (16/403)	2.7% (3/110)
Senegal	1.4% (3/209)	0% (0/56)	2.4% (2/82)	1.4% (1/71)
Highest possible rate: positivity rate based on self reported test result and DK-R are considered reactive
Overall	9.1% (237/2,615)	7.8% (91/1,164)	9.5% (101/1,063)	12% (45/388)
Country
Côte d'Ivoire	9.8% (136/1,390)	8.1% (52/645)	10% (58/553)	14% (26/192)
Mali	6.7% (66/984)	6.8% (31/455)	6.7% (28/415)	6.1% (7/114)
Senegal	15% (35/241)	13% (8/64)	16% (15/95)	15% (12/82)
¹ prop% (n/N)

Table S3b. Positivity rates based on the reported number of visible lines, by age group and country, among participants of the first survey phase in Côte d’Ivoire, Mali, and Senegal (2021)

Code

# Creation of a table of the lowest possible positivity rates

tblS3b_low <- data |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = age_group,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_lines", value = c("2 lines")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)

# Creation of a table of positivity rates based on complete responses

tblS3b_central <- data |>
  filter(HIVST_reported_lines != "DK", HIVST_reported_lines != "R") |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = age_group,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_lines", value = c("2 lines")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)


# Creation of a table of the highest possible positivity rates

tblS3b_high <- data |>
  to_factor() |>
  tbl_custom_summary(
    include = c(country),
    by = age_group,
    stat_fns = ~ proportion_summary(variable = "HIVST_reported_lines", value = c("2 lines", "DK", "R")),
    statistic = ~"{prop}% ({n}/{N})",
    digits = ~ list(
      function(x) {
        style_percent(x, digits = 0)
      }, 0, 0
    ),
    overall_row = TRUE
  ) |>
  add_overall(last = FALSE)


# Merging of the different tables
tblS3b <- tbl_stack(
  list(tblS3b_low, tblS3b_central, tblS3b_high),
  group_header = c(
    "Lowest possible rate: positivity rate based on self reported number of lines and DK-R are considered not 2 lines",
    "Complete responses: positivity rate based on self reported number of lines and DK-R are excluded",
    "Highest possible rate: positivity rate based on self reported number of lines and DK-R are considered 2 lines"
  )
)


tblS3b |>
  modify_header(label ~ "")

	Overall, N = 2,615¹	24 years or less, N = 1,164¹	25-34 years, N = 1,063¹	35 years or more, N = 388¹
Lowest possible rate: positivity rate based on self reported number of lines and DK-R are considered not 2 lines
Overall	4.4% (114/2,615)	3.7% (43/1,164)	4.9% (52/1,063)	4.9% (19/388)
Country
Côte d'Ivoire	3.8% (53/1,390)	3.1% (20/645)	4.5% (25/553)	4.2% (8/192)
Mali	4.9% (48/984)	4.8% (22/455)	4.8% (20/415)	5.3% (6/114)
Senegal	5.4% (13/241)	1.6% (1/64)	7.4% (7/95)	6.1% (5/82)
Complete responses: positivity rate based on self reported number of lines and DK-R are excluded
Overall	4.5% (114/2,541)	3.8% (43/1,138)	5.0% (52/1,032)	5.1% (19/371)
Country
Côte d'Ivoire	3.9% (53/1,368)	3.1% (20/637)	4.6% (25/546)	4.3% (8/185)
Mali	5.0% (48/955)	4.9% (22/447)	5.0% (20/401)	5.6% (6/107)
Senegal	6.0% (13/218)	1.9% (1/54)	8.2% (7/85)	6.3% (5/79)
Highest possible rate: positivity rate based on self reported number of lines and DK-R are considered 2 lines
Overall	7.2% (188/2,615)	5.9% (69/1,164)	7.8% (83/1,063)	9.3% (36/388)
Country
Côte d'Ivoire	5.4% (75/1,390)	4.3% (28/645)	5.8% (32/553)	7.8% (15/192)
Mali	7.8% (77/984)	6.6% (30/455)	8.2% (34/415)	11% (13/114)
Senegal	15% (36/241)	17% (11/64)	18% (17/95)	9.8% (8/82)
¹ prop% (n/N)

Table S4a. Eligibility and participation in phase 2 survey by sociodemographic characteristics, distribution channel, and HIV testing history (bivariable comparison ).

Code

# Reordoring value labels
data <- data |>
  mutate(
    marital_status = marital_status |>
      fct_relevel(
        "single", "divorced / separated / widowed", "living with partner / married"
      ),
    educational_level = educational_level |>
      fct_relevel(
        "none / primary", "secondary", "higher"
      )
  )


# Improving variable labels
data <- data |>
  set_variable_labels(
    country = "Country",
    key_population_profile = "Sex and distribution channel",
    age_group = "Age group",
    marital_status = "Marital status",
    educational_level = "Educational level",
    first_time_tester = "Firt-time tester"
  )

# Computing Table S4.a
data |>
  tbl_summary(
    include =
      c(
        country, key_population_profile, age_group,
        marital_status, educational_level, first_time_tester
      ),
    by = final_status_phas,
    type = list(c(first_time_tester) ~ "categorical")
  ) |>
  add_overall(TRUE) |>
  add_p(
    test = list(
      key_population_profile ~ "chisq.test",
      country ~ "chisq.test",
      marital_status ~ "chisq.test"
    )
  ) |>
  bold_labels()

Characteristic	completed phase 2 questionnaire, N = 78¹	eligible for phase 2 but did not complete the questionnaire, N = 48¹	phase 1 participants not eligible for phase 2, N = 2,489¹	Overall, N = 2,615¹	p-value²
Country					>0.9
Côte d'Ivoire	39 (50%)	23 (48%)	1,328 (53%)	1,390 (53%)
Mali	31 (40%)	20 (42%)	933 (37%)	984 (38%)
Senegal	8 (10%)	5 (10%)	228 (9.2%)	241 (9.2%)
Sex and distribution channel					0.3
man MSM- based channels	35 (45%)	18 (38%)	944 (38%)	997 (38%)
woman MSM- based channels	5 (6.4%)	0 (0%)	98 (3.9%)	103 (3.9%)
man FSW- based channels	22 (28%)	10 (21%)	588 (24%)	620 (24%)
woman FSW- based channels	14 (18%)	16 (33%)	655 (26%)	685 (26%)
man other channels	1 (1.3%)	3 (6.3%)	133 (5.3%)	137 (5.2%)
woman other channels	1 (1.3%)	1 (2.1%)	71 (2.9%)	73 (2.8%)
Age group					0.5
24 years or less	27 (35%)	21 (44%)	1,116 (45%)	1,164 (45%)
25-34 years	38 (49%)	20 (42%)	1,005 (40%)	1,063 (41%)
35 years or more	13 (17%)	7 (15%)	368 (15%)	388 (15%)
Marital status					0.3
single	54 (69%)	32 (67%)	1,675 (67%)	1,761 (67%)
divorced / separated / widowed	6 (7.7%)	2 (4.2%)	89 (3.6%)	97 (3.7%)
living with partner / married	18 (23%)	14 (29%)	725 (29%)	757 (29%)
Educational level					0.079
none / primary	13 (17%)	13 (27%)	477 (19%)	503 (19%)
secondary	50 (64%)	29 (60%)	1,353 (54%)	1,432 (55%)
higher	15 (19%)	6 (13%)	659 (26%)	680 (26%)
Firt-time tester					0.2
no	40 (51%)	25 (52%)	1,472 (59%)	1,537 (59%)
yes	38 (49%)	23 (48%)	1,017 (41%)	1,078 (41%)
¹ n (%)
² Pearson’s Chi-squared test

Table S4b. Eligibility and participation in phase 2 survey by sociodemographic characteristics, distribution channel, and HIV testing history (multivariable multinomial regression model).

Details of the multinomial model

Code

# Calculating the multinomial model

reg <- nnet::multinom(
  final_status_phas ~ country + key_population_profile + age_group + marital_status +
    educational_level + first_time_tester,
  data = data
)

# weights:  48 (30 variable)
initial  value 2872.871135 
iter  10 value 667.083122
iter  20 value 581.210504
iter  30 value 569.730948
iter  40 value 569.541017
final  value 569.540841 
converged

Code

# Model results display

tbl_reg <- reg |>
  tbl_regression(exponentiate = TRUE)

# Displaying the coefficients table in long format

multinom_pivot_wider <- function(x) {
  # check inputs match expectatations
  if (!inherits(x, "tbl_regression") || !inherits(x$model_obj, "multinom")) {
    stop("`x=` must be class 'tbl_regression' summary of a `nnet::multinom()` model.")
  }

  # create tibble of results
  df <- tibble::tibble(outcome_level = unique(x$table_body$groupname_col))
  df$tbl_reg <-
    purrr::map(
      df$outcome_level,
      function(lvl) {
        gtsummary::modify_table_body(
          x,
          ~ dplyr::filter(.x, .data$groupname_col %in% lvl) %>%
            dplyr::ungroup() %>%
            dplyr::select(-.data$groupname_col)
        )
      }
    )



  tbl_merge(df$tbl_reg, tab_spanner = paste0("**", df$outcome_level, "**"))
}

# Computing Table S4.b

tbl_reg |>
  multinom_pivot_wider() |>
  bold_labels()

Characteristic	eligible for phase 2 but did not complete the questionnaire			phase 1 participants not eligible for phase 2
Characteristic	OR¹	95% CI¹	p-value	OR¹	95% CI¹	p-value
Country
Côte d'Ivoire	—	—		—	—
Mali	0.97	0.43, 2.20	>0.9	0.92	0.55, 1.54	0.8
Senegal	0.55	0.13, 2.28	0.4	0.55	0.22, 1.33	0.2
Sex and distribution channel
man MSM- based channels	—	—		—	—
woman MSM- based channels	0.00	0.00, 0.00	<0.001	0.64	0.24, 1.72	0.4
man FSW- based channels	0.95	0.35, 2.53	>0.9	1.09	0.62, 1.95	0.8
woman FSW- based channels	2.34	0.85, 6.42	0.10	2.21	1.10, 4.44	0.025
man other channels	9.32	0.77, 113	0.080	7.99	0.99, 64.2	0.051
woman other channels	3.20	0.16, 63.5	0.4	4.79	0.58, 39.9	0.15
Age group
24 years or less	—	—		—	—
25-34 years	0.55	0.24, 1.27	0.2	0.51	0.30, 0.87	0.013
35 years or more	0.43	0.13, 1.50	0.2	0.44	0.20, 0.96	0.038
Marital status
single	—	—		—	—
divorced / separated / widowed	0.51	0.08, 3.16	0.5	0.49	0.18, 1.35	0.2
living with partner / married	1.33	0.55, 3.20	0.5	1.32	0.74, 2.35	0.3
Educational level
none / primary	—	—		—	—
secondary	0.62	0.24, 1.63	0.3	0.75	0.38, 1.45	0.4
higher	0.45	0.12, 1.67	0.2	1.25	0.55, 2.80	0.6
Firt-time tester
no	—	—		—	—
yes	0.79	0.36, 1.72	0.6	0.66	0.41, 1.08	0.10
¹ OR = Odds Ratio, CI = Confidence Interval

Global p-values used in Table S4

Code

reg |> car::Anova()

Analysis of Deviance Table (Type II tests)

Response: final_status_phas
                       LR Chisq Df Pr(>Chisq)  
country                  1.6352  4    0.80245  
key_population_profile  17.5542 10    0.06297 .
age_group                7.5856  4    0.10799  
marital_status           3.3810  4    0.49620  
educational_level        7.9290  4    0.09421 .
first_time_tester        2.9531  2    0.22842  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table S5. Time between HIVST and confirmatory testing among phase 2 participants who did link to confirmatory testing, by reported number of lines and self-interpreted HIVST result

Code

data <- data |>
  mutate(
    time_to_confirmation = time_to_confirmation |>
      fct_relevel(
        "last than a week after the HIVST", "between 1 and 2 weeks",
        "between 3 and 4 weeks", "between 1 and 2 months after", "3 months later or more"
      )
  )
data <- data |>
  set_variable_labels(
    time_to_confirmation = "Time between HIVST and confirmatory testing `\n` among phase 2 participants who did link to confirmatory testing"
  )

data |>
  filter(
    recontact_phase2 == "yes",
    HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines",
    confirmatory_test_result == "positive",
    confirmatory_test == "yes",
    status_phase2 == "questionnaires completed",
    consulted_health_prof == "yes",
  ) |>
  mutate(
    test_result_number_lines_reported = droplevels(test_result_number_lines_reported)
  ) |>
  tbl_summary(
    include = c(time_to_confirmation),
    by = test_result_number_lines_reported
  ) |>
  add_overall() |> 
  bold_labels()

Characteristic	Overall, N = 18¹	2 lines / reactive, N = 12¹	2 lines / non-reactive, N = 3¹	2 lines / DK-R, N = 3¹
Time between HIVST and confirmatory testing ` ` among phase 2 participants who did link to confirmatory testing
last than a week after the HIVST	13 (72%)	10 (83%)	0 (0%)	3 (100%)
between 1 and 2 weeks	2 (11%)	0 (0%)	2 (67%)	0 (0%)
between 3 and 4 weeks	1 (5.6%)	1 (8.3%)	0 (0%)	0 (0%)
between 1 and 2 months after	1 (5.6%)	1 (8.3%)	0 (0%)	0 (0%)
3 months later or more	1 (5.6%)	0 (0%)	1 (33%)	0 (0%)
¹ n (%)

Table S6. Main reason for not linking to confirmatory testing among phase 2 participants who did not link to confirmatory testing, by reported number of lines and self-interpreted HIVST result

Code

data <- data |>
  set_variable_labels(
    reason_non_confirmatory_test = "Main reason for not  `\n` linking to confirmatory testing among phase 2  `\n`  participants who did not link to confirmatory testing"
  )

data |>
  filter(
    status_phase2 == "questionnaires completed",
    HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines",
    recontact_phase2 == "yes",
    confirmatory_test == "no"
  ) |>
  mutate(
    test_result_number_lines_reported = droplevels(test_result_number_lines_reported)
  ) |>
  tbl_summary(
    include = c(reason_non_confirmatory_test),
    by = test_result_number_lines_reported,
    sort = reason_non_confirmatory_test ~ "frequency"
  ) |> 
  bold_labels()

Characteristic	2 lines / reactive, N = 12¹	1 line / reactive, N = 6¹	2 lines / non-reactive, N = 16¹	2 lines / DK-R, N = 10¹
Main reason for not ` ` linking to confirmatory testing among phase 2 ` ` participants who did not link to confirmatory testing
my test was non-reactive	7 (58%)	3 (50%)	5 (31%)	5 (50%)
Didn't know he should get a confirmatory test	2 (17%)	2 (33%)	5 (31%)	1 (10%)
Didn't have time	3 (25%)	0 (0%)	3 (19%)	2 (20%)
Fear that the result will be known by others	0 (0%)	0 (0%)	1 (6.3%)	1 (10%)
No reason	0 (0%)	1 (17%)	1 (6.3%)	0 (0%)
Did not know where to take the test	0 (0%)	0 (0%)	1 (6.3%)	0 (0%)
The testing site was too for away	0 (0%)	0 (0%)	0 (0%)	1 (10%)
¹ n (%)

Table S7. Place of confirmatory testing among phase 2 participants who did link to confirmatory testing, by reported number of lines and self-interpreted HIVST result

Code

data <- data |>
  set_variable_labels(
    place_confirmatory_test = "Place of confirmatory testing among phase 2 participants who did link to confirmatory testing"
  )

data |>
  filter(
    recontact_phase2 == "yes",
    HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines",
    status_phase2 == "questionnaires completed",
    confirmatory_test == "yes"
  ) |>
  mutate(
    test_result_number_lines_reported = droplevels(test_result_number_lines_reported)
  ) |>
  tbl_summary(
    include = c(place_confirmatory_test),
    by = test_result_number_lines_reported
  ) |>
  add_overall() |> 
  bold_labels()

Characteristic	Overall, N = 34¹	2 lines / reactive, N = 15¹	1 line / reactive, N = 1¹	2 lines / non-reactive, N = 9¹	2 lines / DK-R, N = 8¹	DK-R / reactive, N = 1¹
Place of confirmatory testing among phase 2 participants who did link to confirmatory testing
Community Clinic/Dedicated Health Center	12 (35%)	3 (20%)	0 (0%)	6 (67%)	3 (38%)	0 (0%)
Health Center/ Hospital /Clinic/ Maternity	22 (65%)	12 (80%)	1 (100%)	3 (33%)	5 (63%)	1 (100%)
¹ n (%)

Table S8. Time between phase 1 and phase 2 interviews among phase 2 participants who did link to confirmatory testing, by reported number of lines and self-interpreted HIVST result

Code

# Computing time between phase 1 and phase 2
data$date_phase1 <- ymd_hms(data$date_phase1)
data$date_phase2 <- ymd_hms(data$date_phase2)
data <-
  data |>
  mutate(
    time_between_phase1_2 = time_length(date_phase2 - date_phase1, "month")
  )
data$time_between_phase1_2 <- cut(data$time_between_phase1_2,
  include.lowest = TRUE,
  right = TRUE,
  dig.lab = 4,
  breaks = c(3, 4, 6, 9),
  labels = c("less than 4 months", "Between 4 and 6 months", "More than 6 months")
)

# labeling the "place_confirmatory_test" variable
data <- data |>
  set_variable_labels(
    time_between_phase1_2 = "Time between phase 1 and phase 2 interviews among phase 2 participants who did link to confirmatory testing"
  )

# Generating the S7 table
data |>
  filter(
    recontact_phase2 == "yes",
    HIVST_reported_result == "reactive" | HIVST_reported_lines == "2 lines",
    status_phase2 == "questionnaires completed"
  ) |>
  mutate(
    test_result_number_lines_reported = droplevels(test_result_number_lines_reported)
  ) |>
  tbl_summary(
    include = c(time_between_phase1_2),
    by = test_result_number_lines_reported
  ) |>
  add_overall() |> 
  bold_labels()

Characteristic	Overall, N = 78¹	2 lines / reactive, N = 27¹	1 line / reactive, N = 7¹	2 lines / non-reactive, N = 25¹	2 lines / DK-R, N = 18¹	DK-R / reactive, N = 1¹
Time between phase 1 and phase 2 interviews among phase 2 participants who did link to confirmatory testing
less than 4 months	8 (10%)	3 (11%)	0 (0%)	4 (16%)	1 (5.6%)	0 (0%)
Between 4 and 6 months	67 (86%)	24 (89%)	5 (71%)	21 (84%)	17 (94%)	0 (0%)
More than 6 months	3 (3.8%)	0 (0%)	2 (29%)	0 (0%)	0 (0%)	1 (100%)
¹ n (%)