20  :orange_book: Malaria case study - Part 1

20.1 Introduction

20.1.1 Overview

These pages will demonstrate how to use Quarto to data from Tanzania.

20.1.2 Learning objectives

  • Apply what you have learnt on Day 1 on real data

20.2 Getting started

20.2.1 Access the Quarto template

Download the Quarto template used for this case study (add link) using GitHub.

Please review previous sections on Quarto, data import and manipulation.

20.2.2 Install packages

```{r}
install.packages("ggplot2")
install.packages("ggthemes")
install.packages("networkD3")
install.packages("apyramid")
```
```{r}
library(openxlsx)
library(tidyverse)
library(skimr)
library(DataExplorer)
library(gtsummary)
library(finalfit)
library(ggplot2)
library(ggthemes)
library(networkD3) # For alluvial/Sankey diagrams
```

20.2.3 Import the data

Import the dataset and store it into a dataframe called df. Display the first 5 rows and columns child_ID, CTX_month, CTX_district, SDC_age_in_months

Tip

Refer to Section 14.2

```{r}
# Write your code here
```
```{r}
stata_df <- haven::read_dta("./data/dataset2.dta")
```
```{r}
#| df-print: kable
stata_df %>%
  head(5) %>%
  dplyr::select(child_ID,
                CTX_month,
                CTX_district,
                SDC_age_in_months) %>%
  knitr::kable()
```
child_ID CTX_month CTX_district SDC_age_in_months
1 0 1 10
2 0 1 6
3 0 1 6
4 0 1 11
5 1 1 21 
```{r}
df <- openxlsx::read.xlsx("./data/dataset2.xlsx")
```
```{r}
#| df-print: kable
df %>%
  head(5) %>%
  dplyr::select(child_ID,
                CTX_month,
                CTX_district,
                SDC_age_in_months) %>%
  knitr::kable()
```
child_ID CTX_month CTX_district SDC_age_in_months
1 7 Kaliua 10
2 7 Kaliua 6
3 7 Kaliua 6
4 7 Kaliua 11
5 8 Kaliua 21

20.3 Population characteristics

20.3.1 Codebook

Variable Coding
SDC_age_in_months
SDC_sex 1: male
2: female
98: unknown
CLIN_fever 0: no
1: yes
98: not sure
CLIN_fever_onset
CLIN_cough 0: no
1: yes
98: not sure
CLIN_diarrhoea 0: no
1: yes
98: not sure
RX_preconsult_antibiotics
RX_preconsult_antimalarials
CTX_district Kaliua
Sengerema
Tanga
CTX_area Urban
rural
CTX_facility_type Dispensary
Health centre

20.3.2 Structure of the data

Examine the structure of the data, including variable names, labels.

Tip
  • Stata: use the codebook command
  • R: use the skim function from the skimr package
```{r}
# Write your code here
```
```{r}
RStata::stata("codebook SDC_age_in_months SDC_sex CLIN_fever CLIN_fever_onset CLIN_diarrhoea CLIN_cough RX_preconsult_antibiotics RX_preconsult_antimalarials CTX_district CTX_area CTX_facility_type",
              data.in = stata_df)
```
. codebook SDC_age_in_months SDC_sex CLIN_fever CLIN_fever_onset CLIN_diarrhoea
>  CLIN_cough RX_preconsult_antibiotics RX_preconsult_antimalarials CTX_distric
> t CTX_area CTX_facility_type

-------------------------------------------------------------------------------
SDC_age_in_months                                             SDC_age_in_months
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,59]                        Units: 1
         Unique values: 60                        Missing .: 0/10,308

                  Mean: 18.7498
             Std. dev.: 14.8998

           Percentiles:     10%       25%       50%       75%       90%
                              3         7        15        27        43

-------------------------------------------------------------------------------
SDC_sex                                                                 SDC_sex
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 4/10,308

            Tabulation: Freq.  Value
                        5,229  0
                        5,075  1
                            4  .

-------------------------------------------------------------------------------
CLIN_fever                                                           CLIN_fever
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,2]                         Units: 1
         Unique values: 3                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                        3,068  0
                        7,225  1
                           15  2

-------------------------------------------------------------------------------
CLIN_fever_onset                                               CLIN_fever_onset
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,14]                        Units: 1
         Unique values: 15                        Missing .: 3,083/10,308

                  Mean: 2.50145
             Std. dev.: 1.93099

           Percentiles:     10%       25%       50%       75%       90%
                              1         1         2         3         4

-------------------------------------------------------------------------------
CLIN_diarrhoea                                                   CLIN_diarrhoea
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,2]                         Units: 1
         Unique values: 3                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                        7,982  0
                        2,306  1
                           20  2

-------------------------------------------------------------------------------
CLIN_cough                                                           CLIN_cough
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,2]                         Units: 1
         Unique values: 3                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                        4,658  0
                        5,635  1
                           15  2

-------------------------------------------------------------------------------
RX_preconsult_antibiotics                             RX_preconsult_antibiotics
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                        8,573  0
                        1,735  1

-------------------------------------------------------------------------------
RX_preconsult_antimalarials                         RX_preconsult_antimalarials
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                        9,866  0
                          442  1

-------------------------------------------------------------------------------
CTX_district                                                       CTX_district
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [1,3]                         Units: 1
         Unique values: 3                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                        2,429  1
                        2,703  2
                        5,176  3

-------------------------------------------------------------------------------
CTX_area                                                               CTX_area
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [1,2]                         Units: 1
         Unique values: 2                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                        4,088  1
                        6,220  2

-------------------------------------------------------------------------------
CTX_facility_type                                             CTX_facility_type
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [1,2]                         Units: 1
         Unique values: 2                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                        5,599  1
                        4,709  2
```{r}
df %>%
  skim(SDC_age_in_months,
       SDC_sex,
       CLIN_fever,
       CLIN_fever_onset,
       CLIN_diarrhoea,
       CLIN_cough,
       RX_preconsult_antibiotics,
       RX_preconsult_antimalarials,
       CTX_district,
       CTX_area,
       CTX_facility_type)
```
Data summary
Name Piped data
Number of rows 10308
Number of columns 39
_______________________
Column type frequency:
character 3
numeric 8
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
CTX_district 0 1 5 9 0 3 0
CTX_area 0 1 5 5 0 2 0
CTX_facility_type 0 1 10 13 0 2 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
SDC_age_in_months 0 1.0 18.75 14.90 0 7 15 27 59 ▇▆▃▂▁
SDC_sex 4 1.0 1.49 0.50 1 1 1 2 2 ▇▁▁▁▇
CLIN_fever 0 1.0 0.84 3.74 0 0 1 1 98 ▇▁▁▁▁
CLIN_fever_onset 3083 0.7 2.50 1.93 0 1 2 3 14 ▇▅▁▁▁
CLIN_diarrhoea 0 1.0 0.41 4.32 0 0 0 0 98 ▇▁▁▁▁
CLIN_cough 0 1.0 0.69 3.75 0 0 1 1 98 ▇▁▁▁▁
RX_preconsult_antibiotics 0 1.0 0.17 0.37 0 0 0 0 1 ▇▁▁▁▂
RX_preconsult_antimalarials 0 1.0 0.04 0.20 0 0 0 0 1 ▇▁▁▁▁ 
```{r}
df <- df %>%
  tibble::remove_rownames() %>%
  tibble::column_to_rownames(var="child_ID") %>% 
  dplyr::mutate(across(c(SDC_sex, 
                         CLIN_fever,
                         CLIN_cough,
                         CLIN_diarrhoea,
                         RX_preconsult_antibiotics,
                         RX_preconsult_antimalarials,
                         CTX_district,
                         CTX_area,
                         CTX_facility_type),
                       factor))
```

20.3.2.1 Identify missing values

Identify missing values in each variable

DataExplorer::plot_missing(df,
                           geom_label_args = list(size = 2, label.padding = unit(0.2, "lines")))

DataExplorer::plot_bar(df %>% 
                         dplyr::select(-CTX_facility_ID),
                       by = "CTX_district")

Add the following two new variables to data frame df

Variable Coding
SDC_age_category <2 months
2-11 months
12-23 months
24-35 months
36-47 months
48-59 months
CLIN_fever_onset_category <2 days
2-3 days
4-6 days
β‰₯7 days
Tip
  • Stata: use the gen command
  • R: use the mutate and case_when functions from the dplyr package
```{r}
# Write your code here
```
```{r}
df <- df %>%
  dplyr::mutate(
    SDC_age_category = dplyr::case_when(
      SDC_age_in_months < 2 ~ "<2 months",
      SDC_age_in_months >= 2 & SDC_age_in_months < 12 ~ "02-11 months",
      SDC_age_in_months >= 12 & SDC_age_in_months < 24 ~ "12-23 months",
      SDC_age_in_months >= 24 & SDC_age_in_months < 36 ~ "24-35 months",
      SDC_age_in_months >= 36 & SDC_age_in_months < 48 ~ "36-47 months",
      SDC_age_in_months >= 48 & SDC_age_in_months < 60 ~ "48-59 months",
      TRUE ~ ""
    )
  ) %>% 
  dplyr::mutate(
    CLIN_fever_onset_category = dplyr::case_when(
      CLIN_fever_onset < 2 ~ "<2 days",
      CLIN_fever_onset >= 2 & CLIN_fever_onset < 4 ~ "2-3 days",
      CLIN_fever_onset >= 4 & CLIN_fever_onset < 7 ~ "4-6 days",
      CLIN_fever_onset >= 7 ~ ">= 7 days",
      TRUE ~ ""
    )
  )
```

Display descriptive statistics for the following population characteristics:

Tip
  • Stata
  • R: use the tbl_summary function from the gtsummary package
```{r}
# Write your code here
```
```{r}
RStata::stata('tabulate SDC_age_category
              tabulate SDC_sex
              tabulate CLIN_fever
              tabulate CLIN_fever_onset_category
              tabulate CLIN_diarrhoea
              tabulate CLIN_cough
              tabulate RX_preconsult_antibiotics
              tabulate RX_preconsult_antimalarials
              tabulate CTX_district
              tabulate CTX_area
              tabulate CTX_facility_type',
              data.in = stata_df)
```
. tabulate SDC_age_category
variable SDC_age_category not found
r(111);
.               tabulate SDC_sex
.               tabulate CLIN_fever
.               tabulate CLIN_fever_onset_category
.               tabulate CLIN_diarrhoea
.               tabulate CLIN_cough
.               tabulate RX_preconsult_antibiotics
.               tabulate RX_preconsult_antimalarials
.               tabulate CTX_district
.               tabulate CTX_area
.               tabulate CTX_facility_type
```{r}
df %>%
  dplyr::select(SDC_age_category,
                SDC_sex,
                CLIN_fever,
                CLIN_fever_onset_category,
                CLIN_diarrhoea,
                CLIN_cough,
                RX_preconsult_antibiotics,
                RX_preconsult_antimalarials,
                CTX_district,
                CTX_area,
                CTX_facility_type) %>%
  gtsummary::tbl_summary(missing_text = "(Missing)")
```
Characteristic N = 10,3081
SDC_age_category
    <2 months 597 (5.8%)
    02-11 months 3,576 (35%)
    12-23 months 2,947 (29%)
    24-35 months 1,529 (15%)
    36-47 months 980 (9.5%)
    48-59 months 679 (6.6%)
SDC_sex
    1 5,229 (51%)
    2 5,075 (49%)
    (Missing) 4
CLIN_fever
    0 3,068 (30%)
    1 7,225 (70%)
    98 15 (0.1%)
CLIN_fever_onset_category
     3,083 (30%)
    <2 days 1,998 (19%)
    >= 7 days 343 (3.3%)
    2-3 days 4,386 (43%)
    4-6 days 498 (4.8%)
CLIN_diarrhoea
    0 7,982 (77%)
    1 2,306 (22%)
    98 20 (0.2%)
CLIN_cough
    0 4,658 (45%)
    1 5,635 (55%)
    98 15 (0.1%)
RX_preconsult_antibiotics
    0 8,573 (83%)
    1 1,735 (17%)
RX_preconsult_antimalarials
    0 9,866 (96%)
    1 442 (4.3%)
CTX_district
    Kaliua 2,429 (24%)
    Sengerema 2,703 (26%)
    Tanga 5,176 (50%)
CTX_area
    rural 4,088 (40%)
    urban 6,220 (60%)
CTX_facility_type
    dispensary 5,599 (54%)
    health center 4,709 (46%)
1 n (%)

20.4 Healthcare provider actions

20.4.1 Codebook

  • Temperature measured
    • Fever measured
  • Fever (temp or history)
  • Malaria test
  • Any severe diagnosis
  • Malaria diagnosis
  • Malaria treatment
  • Referral
Variable Coding
MEAS_temperature
TEST_malaria_result 0: negative
1: positive
2: indeterminate
95: unreadable result
98: not sure
DX_malaria 0: no
1: yes
RX_antimalarials 0: no
1: yes
MGMT_referral_src_caregiver
MGMT_referral_src_registry

20.4.2 Structure of the data

Examine the structure of the data, including variable names, labels.

Tip
  • Stata: use the codebook command
  • R: use the skim function from the skimr package
```{r}
# Write your code here
```
RStata::stata("codebook MEAS_temperature TEST_malaria_result TEST_malaria_result DX_malaria RX_antimalarials MGMT_referral_src_caregiver MGMT_referral_src_registry",
              data.in = stata_df)
. codebook MEAS_temperature TEST_malaria_result TEST_malaria_result DX_malaria 
> RX_antimalarials MGMT_referral_src_caregiver MGMT_referral_src_registry

-------------------------------------------------------------------------------
MEAS_temperature                                               MEAS_temperature
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [34.5,42.5]                   Units: .1
         Unique values: 16                        Missing .: 9,271/10,308

                  Mean: 37.0781
             Std. dev.: .977139

           Percentiles:     10%       25%       50%       75%       90%
                             36      36.5        37      37.5      38.5

-------------------------------------------------------------------------------
TEST_malaria_result                                         TEST_malaria_result
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,98]                        Units: 1
         Unique values: 5                         Missing .: 4,550/10,308

            Tabulation: Freq.  Value
                        4,665  0
                        1,032  1
                            1  2
                            3  95
                           57  98
                        4,550  .

-------------------------------------------------------------------------------
TEST_malaria_result                                         TEST_malaria_result
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,98]                        Units: 1
         Unique values: 5                         Missing .: 4,550/10,308

            Tabulation: Freq.  Value
                        4,665  0
                        1,032  1
                            1  2
                            3  95
                           57  98
                        4,550  .

-------------------------------------------------------------------------------
DX_malaria                                                           DX_malaria
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                        8,508  0
                        1,800  1

-------------------------------------------------------------------------------
RX_antimalarials                                               RX_antimalarials
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                        9,018  0
                        1,290  1

-------------------------------------------------------------------------------
MGMT_referral_src_caregiver                         MGMT_referral_src_caregiver
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,98]                        Units: 1
         Unique values: 4                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                       10,122  0
                          164  1
                            9  97
                           13  98

-------------------------------------------------------------------------------
MGMT_referral_src_registry                           MGMT_referral_src_registry
-------------------------------------------------------------------------------

                  Type: Numeric (double)

                 Range: [0,1]                         Units: 1
         Unique values: 2                         Missing .: 0/10,308

            Tabulation: Freq.  Value
                       10,194  0
                          114  1
df %>%
  skimr::skim(MEAS_temperature,
              TEST_malaria_result,
              TEST_malaria_result,
              DX_malaria,
              RX_antimalarials,
              MGMT_referral_src_caregiver,
              MGMT_referral_src_registry)
Data summary
Name Piped data
Number of rows 10308
Number of columns 40
_______________________
Column type frequency:
numeric 6
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
MEAS_temperature 9271 0.10 37.08 0.98 34.5 36.5 37 37.5 42.5 ▃▇▃▁▁
TEST_malaria_result 4550 0.56 1.20 9.93 0.0 0.0 0 0.0 98.0 ▇▁▁▁▁
DX_malaria 0 1.00 0.17 0.38 0.0 0.0 0 0.0 1.0 ▇▁▁▁▂
RX_antimalarials 0 1.00 0.13 0.33 0.0 0.0 0 0.0 1.0 ▇▁▁▁▁
MGMT_referral_src_caregiver 0 1.00 0.22 4.50 0.0 0.0 0 0.0 98.0 ▇▁▁▁▁
MGMT_referral_src_registry 0 1.00 0.01 0.10 0.0 0.0 0 0.0 1.0 ▇▁▁▁▁

Add the following two new variables to data frame df

  • MEAS_fever
  • Fever (temp or history)
Tip
  • Stata: use the gen command
  • R: use the mutate function from the dplyr package
```{r}
# Write your code here
```
```{r}
df <- df %>%
  dplyr::mutate(CALC_temperature_measured = !is.na(MEAS_temperature)) %>%
  dplyr::mutate(CALC_fever = MEAS_temperature >= 37.5) %>%
  dplyr::mutate(CALC_fever_or_temp = (CLIN_fever == 1) | (CALC_fever == 1))
```

Display descriptive statistics for the following healthcare provider actions:

Tip
  • R: use the tbl_summary function from the gtsummary package
```{r}
# Write your code here
```
```{r}
df %>%
  dplyr::select(CALC_temperature_measured,
                CALC_fever,
                CALC_fever_or_temp,
                TEST_malaria_result,
                TEST_malaria_result,
                DX_malaria,
                RX_antimalarials,
                MGMT_referral_src_caregiver,
                MGMT_referral_src_registry) %>%
  gtsummary::tbl_summary(missing_text = "(Missing)")
```
Characteristic N = 10,3081
CALC_temperature_measured 1,037 (10%)
CALC_fever 326 (31%)
    (Missing) 9,271
CALC_fever_or_temp 7,252 (97%)
    (Missing) 2,842
TEST_malaria_result
    0 4,665 (81%)
    1 1,032 (18%)
    2 1 (<0.1%)
    95 3 (<0.1%)
    98 57 (1.0%)
    (Missing) 4,550
DX_malaria 1,800 (17%)
RX_antimalarials 1,290 (13%)
MGMT_referral_src_caregiver
    0 10,122 (98%)
    1 164 (1.6%)
    97 9 (<0.1%)
    98 13 (0.1%)
MGMT_referral_src_registry 114 (1.1%)
1 n (%)

20.5 Number of consultations by facility

Plot the number of consultations by facility in bars, grouped by district.

Tip
  • Stata:
  • R:
```{r}
stata_cmd <- '
graph bar (count) child_ID, over(CTX_facility_ID, axis(off)) over(CTX_district, relabel(1 "Kaliua" 2 "Sengerema" 3 "Tanga")) nofill ytitle(No. consultations) title(No. consultations by facility)
graph export ./images/day02_stata_plot.png, replace
'
RStata::stata(stata_cmd,
              data.in = stata_df)
```
. 
. graph bar (count) child_ID, over(CTX_facility_ID, axis(off)) over(CTX_distric
> t, relabel(1 "Kaliua" 2 "Sengerema" 3 "Tanga")) nofill ytitle(No. consultatio
> ns) title(No. consultations by facility)
. graph export ./images/day02_stata_plot.png, replace
file ./images/day02_stata_plot.png saved as PNG format

```{r}
df %>%
  dplyr::group_by(CTX_district,
                  CTX_facility_ID) %>%
  count() %>% 
  ggplot2::ggplot(aes(x = haven::as_factor(CTX_district),
                      y = n)) + 
  ggplot2::geom_bar(position = position_dodge2(preserve = "single"),
                    stat="identity") +
  ggplot2::labs(x = "", y = "No. consultations") +
  ggthemes::theme_hc()
```

20.6 Fever assessment

  • temp measurement by reported fever; by facility
```{r}
# Write here
```
chisq.test(df$CLIN_fever, df$TEST_malaria_result)

    Pearson's Chi-squared test

data:  df$CLIN_fever and df$TEST_malaria_result
X-squared = 61.449, df = 8, p-value = 2.42e-10
```{r}
df$CTX_facility_ID <- haven::as_factor(df$CTX_facility_ID) 
df %>%
  dplyr::filter(CLIN_fever == 1) %>%
  dplyr::select(MEAS_temperature,
                CTX_facility_ID) %>%
  gtsummary::tbl_summary(by = CTX_facility_ID) %>%
  add_p()
```
Characteristic F8529, N = 1861 F8195, N = 5811 F8793, N = 7801 F7729, N = 1971 F4848, N = 4061 F8444, N = 2091 F9109, N = 3581 F8379, N = 4651 F5145, N = 4901 F6552, N = 2911 F4024, N = 5501 F3366, N = 6031 F7327, N = 2701 F4056, N = 621 F5560, N = 3561 F6813, N = 3231 F0542, N = 6821 F5656, N = 4161 p-value2
MEAS_temperature 37.50 (37.00, 38.00) NA (NA, NA) 39.50 (39.25, 39.75) 37.00 (36.25, 37.50) 36.50 (36.00, 36.88) 37.00 (37.00, 37.00) 38.50 (37.00, 38.50) 37.50 (36.50, 38.25) 37.50 (37.00, 38.75) 37.00 (37.00, 38.00) 37.00 (37.00, 37.50) 38.00 (38.00, 38.50) 38.25 (38.13, 38.38) 39.00 (38.25, 39.50) 37.00 (36.50, 38.00) 37.00 (36.50, 37.50) 37.50 (36.88, 37.75) 36.25 (36.00, 37.00) <0.001
    Unknown 136 581 778 70 380 203 333 422 479 117 531 598 268 59 222 184 670 398
1 Median (IQR)
2 Kruskal-Wallis rank sum test
  • also showing β€˜prevalence’ of fever when of whole clinic vs of those who measure to indicate bias
```{r}
# Write here
```

20.7 Malaria tests

  • malaria tests of those with history or measured fever
```{r}
# Write here
```
```{r}
table(df$CALC_fever_or_temp, df$TEST_malaria_result)
```
       
           0    1    2   95   98
  FALSE   70    2    0    1    1
  TRUE  3952  966    1    2   47
chisq.test(df$CALC_fever_or_temp, df$TEST_malaria_result)

    Pearson's Chi-squared test

data:  df$CALC_fever_or_temp and df$TEST_malaria_result
X-squared = 33.92, df = 4, p-value = 7.74e-07
```{r}
df$CTX_facility_ID <- haven::as_factor(df$CTX_facility_ID) 
df %>%
  dplyr::filter(CALC_fever_or_temp == 1) %>%
  dplyr::select(TEST_malaria_result,
                CTX_facility_ID) %>%
  gtsummary::tbl_summary(by = CTX_facility_ID)
```
Characteristic F8529, N = 1901 F8195, N = 5811 F8793, N = 7801 F7729, N = 1971 F4848, N = 4061 F8444, N = 2091 F9109, N = 3581 F8379, N = 4671 F5145, N = 4911 F6552, N = 3011 F4024, N = 5501 F3366, N = 6031 F7327, N = 2701 F4056, N = 621 F5560, N = 3581 F6813, N = 3301 F0542, N = 6831 F5656, N = 4161
TEST_malaria_result

















    0 83 (67%) 151 (33%) 319 (54%) 125 (82%) 285 (75%) 135 (98%) 159 (95%) 371 (93%) 226 (95%) 213 (96%) 221 (97%) 346 (97%) 21 (25%) 32 (57%) 242 (92%) 225 (91%) 511 (90%) 287 (97%)
    1 38 (31%) 300 (66%) 270 (46%) 28 (18%) 95 (25%) 0 (0%) 2 (1.2%) 25 (6.3%) 7 (2.9%) 4 (1.8%) 6 (2.6%) 6 (1.7%) 59 (69%) 20 (36%) 20 (7.6%) 21 (8.5%) 55 (9.7%) 10 (3.4%)
    2 0 (0%) 0 (0%) 1 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    95 1 (0.8%) 0 (0%) 1 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    98 2 (1.6%) 5 (1.1%) 1 (0.2%) 0 (0%) 0 (0%) 3 (2.2%) 6 (3.6%) 2 (0.5%) 6 (2.5%) 6 (2.7%) 0 (0%) 3 (0.8%) 5 (5.9%) 4 (7.1%) 0 (0%) 1 (0.4%) 3 (0.5%) 0 (0%)
    Unknown 66 125 188 44 26 71 191 69 252 78 323 248 185 6 96 83 114 119
1 n (%)

20.8 Malaria treatments

  • malaria diagnoses vs. positive tests vs. treatment.
```{r}
# Write here
```
```{r}
df <- df %>%
  dplyr::mutate(TEST_malaria_positive = 1* (TEST_malaria_result == 1))
table(df$TEST_malaria_result, df$DX_malaria, df$RX_antimalarials) %>% knitr::kable()
```
Var1 Var2 Var3 Freq
0 0 0 3890
1 0 0 15
2 0 0 1
95 0 0 2
98 0 0 31
0 1 0 645
1 1 0 49
2 1 0 0
95 1 0 1
98 1 0 17
0 0 1 99
1 0 1 50
2 0 1 0
95 0 1 0
98 0 1 2
0 1 1 31
1 1 1 918
2 1 1 0
95 1 1 0
98 1 1 7 
chisq.test(df$TEST_malaria_result, df$DX_malaria)

    Pearson's Chi-squared test

data:  df$TEST_malaria_result and df$DX_malaria
X-squared = 2582, df = 4, p-value < 2.2e-16
chisq.test(df$TEST_malaria_result, df$RX_antimalarials)

    Pearson's Chi-squared test

data:  df$TEST_malaria_result and df$RX_antimalarials
X-squared = 4508.8, df = 4, p-value < 2.2e-16
```{r}
# df$CTX_facility_ID <- haven::as_factor(df$CTX_facility_ID) 
# df %>%
#   dplyr::filter(TEST_malaria_positive == 1) %>%
#   dplyr::select(DX_malaria,
#                 RX_antimalarials,
#                 CTX_facility_ID) %>%
#   gtsummary::tbl_summary(by = CTX_facility_ID)
```