Felicia Part II Analysis

Data Management

Table I: Summary of the complete dataset

Show the code

df_complete %>% 
  summarytools::dfSummary(graph.col = T, labels.col = F) %>% 
  summarytools::stview(method = "render")

Data Frame Summary

df_complete

Dimensions: 418 x 62
Duplicates: 0

1

studyid [numeric]

Mean (sd) : 211.6 (123)
min ≤ med ≤ max:
1 ≤ 210.5 ≤ 425
IQR (CV) : 212.5 (0.6)

418 distinct values

418 (100.0%)

0 (0.0%)

2

date_of_birth [character]

1. 43965
2. 44014
3. 43815
4. 43816
5. 43858
6. 43905
7. 43997
8. 44013
9. 44019
10. 44024
[ 335 others ]

4	(	1.0%	)
4	(	1.0%	)
3	(	0.7%	)
3	(	0.7%	)
3	(	0.7%	)
3	(	0.7%	)
3	(	0.7%	)
3	(	0.7%	)
3	(	0.7%	)
3	(	0.7%	)
386	(	92.3%	)

418 (100.0%)

0 (0.0%)

3

age_mths [numeric]

Mean (sd) : 21 (14.8)
min ≤ med ≤ max:
6 ≤ 15 ≤ 59
IQR (CV) : 19.8 (0.7)

52 distinct values

418 (100.0%)

0 (0.0%)

4

sex [factor]

1. Female
2. Male

193	(	46.3%	)
224	(	53.7%	)

417 (99.8%)

1 (0.2%)

5

bodytemp [numeric]

Mean (sd) : 36.3 (0.3)
min ≤ med ≤ max:
35.2 ≤ 36.3 ≤ 37.4
IQR (CV) : 0.4 (0)

20 distinct values

418 (100.0%)

0 (0.0%)

6

weight_kg [numeric]

Mean (sd) : 10.9 (3.4)
min ≤ med ≤ max:
5.4 ≤ 9.9 ≤ 22.7
IQR (CV) : 4.4 (0.3)

125 distinct values

417 (99.8%)

1 (0.2%)

7

height_cm [numeric]

Mean (sd) : 82.5 (12.7)
min ≤ med ≤ max:
60 ≤ 80 ≤ 122
IQR (CV) : 18 (0.2)

81 distinct values

418 (100.0%)

0 (0.0%)

8

muac [numeric]

Mean (sd) : 13.9 (1.3)
min ≤ med ≤ max:
11 ≤ 13.8 ≤ 19.6
IQR (CV) : 1.8 (0.1)

58 distinct values

418 (100.0%)

0 (0.0%)

9

m_age [numeric]

Mean (sd) : 29.3 (6.9)
min ≤ med ≤ max:
15 ≤ 29 ≤ 54
IQR (CV) : 11 (0.2)

33 distinct values

418 (100.0%)

0 (0.0%)

10

m_maristat [character]

1. Co-habitation
2. Divorced
3. Married
4. Single
5. Widow

203	(	48.6%	)
10	(	2.4%	)
164	(	39.2%	)
39	(	9.3%	)
2	(	0.5%	)

418 (100.0%)

0 (0.0%)

11

m_educ [factor]

1. None
2. Primary
3. Jhs
4. Shs
5. Tertiary

38	(	9.1%	)
42	(	10.0%	)
233	(	55.7%	)
68	(	16.3%	)
37	(	8.9%	)

418 (100.0%)

0 (0.0%)

12

m_occup [character]

1. Business woman
2. Farmer
3. Government worker
4. Other
5. Petty trader
6. Private teacher
7. Student

10	(	2.4%	)
39	(	9.4%	)
37	(	8.9%	)
183	(	43.9%	)
145	(	34.8%	)
2	(	0.5%	)
1	(	0.2%	)

417 (99.8%)

1 (0.2%)

13

m_occup_other [character]

1. Unemployed
2. Seamstress
3. Hair dresser
4. Apprentice
5. Hairdresser
6. Student
7. Decorater
8. Caterer
9. Cleaner
10. Cook
[ 4 others ]

89	(	48.1%	)
30	(	16.2%	)
24	(	13.0%	)
21	(	11.4%	)
6	(	3.2%	)
6	(	3.2%	)
2	(	1.1%	)
1	(	0.5%	)
1	(	0.5%	)
1	(	0.5%	)
4	(	2.2%	)

185 (44.3%)

233 (55.7%)

14

m_income [numeric]

Mean (sd) : 257.1 (396.2)
min ≤ med ≤ max:
0 ≤ 100 ≤ 2000
IQR (CV) : 300 (1.5)

22 distinct values

417 (99.8%)

1 (0.2%)

15

f_age [numeric]

Mean (sd) : 34.5 (7.8)
min ≤ med ≤ max:
18 ≤ 35 ≤ 60
IQR (CV) : 11 (0.2)

39 distinct values

418 (100.0%)

0 (0.0%)

16

f_maristat [character]

1. Co-habitation
2. Divorced
3. Married
4. Single

205	(	49.4%	)
9	(	2.2%	)
163	(	39.3%	)
38	(	9.2%	)

415 (99.3%)

3 (0.7%)

17

f_educ [character]

1. JHS
2. None
3. Primary
4. SHS
5. Tertiary

151	(	36.1%	)
32	(	7.7%	)
44	(	10.5%	)
120	(	28.7%	)
71	(	17.0%	)

418 (100.0%)

0 (0.0%)

18

f_occup [character]

1. Business man
2. Business woman
3. farmer
4. Farmer
5. Government worker
6. o works with water compan
7. Other
8. petty trader
9. Petty trader
10. Student

16	(	3.8%	)
1	(	0.2%	)
10	(	2.4%	)
58	(	13.9%	)
65	(	15.6%	)
1	(	0.2%	)
235	(	56.5%	)
7	(	1.7%	)
22	(	5.3%	)
1	(	0.2%	)

416 (99.5%)

2 (0.5%)

19

if_other_to_above [character]

1. maison
2. commercial driver
3. mechanic
4. carpenter
5. driver
6. unemployed
7. Business man
8. Commercial driver
9. painter
10. student
[ 37 others ]

46	(	19.5%	)
33	(	14.0%	)
17	(	7.2%	)
15	(	6.4%	)
12	(	5.1%	)
9	(	3.8%	)
7	(	3.0%	)
7	(	3.0%	)
7	(	3.0%	)
7	(	3.0%	)
76	(	32.2%	)

236 (56.5%)

182 (43.5%)

20

f_income [numeric]

Mean (sd) : 660.6 (638.5)
min ≤ med ≤ max:
0 ≤ 400 ≤ 3000
IQR (CV) : 800 (1)

24 distinct values

417 (99.8%)

1 (0.2%)

21

m_siblings [numeric]

Mean (sd) : 1.8 (1.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 7
IQR (CV) : 3 (0.9)

0	:	114	(	27.3%	)
1	:	97	(	23.2%	)
2	:	88	(	21.1%	)
3	:	52	(	12.4%	)
4	:	34	(	8.1%	)
5	:	24	(	5.7%	)
6	:	7	(	1.7%	)
7	:	2	(	0.5%	)

418 (100.0%)

0 (0.0%)

22

f_siblings [numeric]

Mean (sd) : 2.1 (2.2)
min ≤ med ≤ max:
0 ≤ 2 ≤ 14
IQR (CV) : 3 (1)

13 distinct values

418 (100.0%)

0 (0.0%)

23

mal_prev_measure [character]

1. Clean environment
2. ITN
3. Mosquito repellent
4. Mosquito spray
5. none
6. Other
7. screened house
8. Traditional herbs

7	(	1.7%	)
294	(	70.5%	)
6	(	1.4%	)
32	(	7.7%	)
2	(	0.5%	)
73	(	17.5%	)
2	(	0.5%	)
1	(	0.2%	)

417 (99.8%)

1 (0.2%)

24

if_other_to_above2 [character]

1. ceiling fan
2. Mosquito coil
3. Mosquito repellent
4. Mosquito spray
5. None

4	(	5.1%	)
57	(	73.1%	)
1	(	1.3%	)
2	(	2.6%	)
14	(	17.9%	)

78 (18.7%)

340 (81.3%)

25

do_you_own_itn [character]

1. No
2. Yes

106	(	25.5%	)
309	(	74.5%	)

415 (99.3%)

3 (0.7%)

26

itn_previous_night [character]

1. No
2. Yes

195	(	46.7%	)
223	(	53.3%	)

418 (100.0%)

0 (0.0%)

27

itn_previous_night_2 [character]

1. No
2. Yes

203	(	48.6%	)
215	(	51.4%	)

418 (100.0%)

0 (0.0%)

28

antimal_2weeks [character]

1. No
2. Yes

354	(	85.3%	)
61	(	14.7%	)

415 (99.3%)

3 (0.7%)

29

if_yes_which_antimalarial [character]

1. Arthemeter/Lumefantrine
2. Don’t Know
3. Herbal Preparation

39	(	65.0%	)
20	(	33.3%	)
1	(	1.7%	)

60 (14.4%)

358 (85.6%)

30

if_other_to_above3 [character]

1. Arthemeter/ lumefantrine
2. Don~t know

1	(	50.0%	)
1	(	50.0%	)

2 (0.5%)

416 (99.5%)

31

m_maristat2 [factor]

1. Co-habitation
2. Married
3. Single

203	(	48.6%	)
164	(	39.2%	)
51	(	12.2%	)

418 (100.0%)

0 (0.0%)

32

m_educ2 [ordered, factor]

1. None
2. Primary
3. Secondary
4. Tertiary

38	(	9.1%	)
275	(	65.8%	)
68	(	16.3%	)
37	(	8.9%	)

418 (100.0%)

0 (0.0%)

33

f_maristat2 [factor]

1. Co-habitation
2. Married
3. Single

205	(	49.4%	)
163	(	39.3%	)
47	(	11.3%	)

415 (99.3%)

3 (0.7%)

34

f_educ2 [ordered, factor]

1. None
2. Primary
3. Secondary
4. Tertiary

32	(	7.7%	)
195	(	46.7%	)
120	(	28.7%	)
71	(	17.0%	)

418 (100.0%)

0 (0.0%)

35

m_occup_2 [character]

1. Salaried worker
2. Self-Enployed
3. Unemployed

37	(	8.9%	)
264	(	63.3%	)
116	(	27.8%	)

417 (99.8%)

1 (0.2%)

36

mal_prev_measure_2 [character]

1. ITN
2. Mosquito coil
3. Mosquito repellent
4. Mosquito spray
5. None
6. Others

294	(	70.3%	)
55	(	13.2%	)
7	(	1.7%	)
33	(	7.9%	)
15	(	3.6%	)
14	(	3.3%	)

418 (100.0%)

0 (0.0%)

37

bfaz [numeric]

Mean (sd) : -0.5 (1.4)
min ≤ med ≤ max:
-8.7 ≤ -0.4 ≤ 3.8
IQR (CV) : 1.6 (-2.9)

278 distinct values

416 (99.5%)

2 (0.5%)

38

wfhz [numeric]

Mean (sd) : -0.4 (1.3)
min ≤ med ≤ max:
-8.1 ≤ -0.4 ≤ 3.9
IQR (CV) : 1.5 (-3.2)

252 distinct values

415 (99.3%)

3 (0.7%)

39

wfaz [numeric]

Mean (sd) : -0.2 (1.4)
min ≤ med ≤ max:
-6.1 ≤ -0.2 ≤ 3.4
IQR (CV) : 1.7 (-6.7)

282 distinct values

416 (99.5%)

2 (0.5%)

40

hfaz [numeric]

Mean (sd) : 0.3 (2.2)
min ≤ med ≤ max:
-5.6 ≤ -0.1 ≤ 9.2
IQR (CV) : 2.6 (6.9)

285 distinct values

417 (99.8%)

1 (0.2%)

41

mfaz [numeric]

Mean (sd) : -0.9 (1)
min ≤ med ≤ max:
-3.6 ≤ -0.9 ≤ 3.8
IQR (CV) : 1.2 (-1.1)

226 distinct values

417 (99.8%)

1 (0.2%)

42

wfaz_cat [factor]

1. Normal
2. Underweight

377	(	90.6%	)
39	(	9.4%	)

416 (99.5%)

2 (0.5%)

43

hfaz_cat [factor]

1. Normal
2. Stunted

375	(	89.9%	)
42	(	10.1%	)

417 (99.8%)

1 (0.2%)

44

mfaz_cat [factor]

1. Normal
2. Low MUAC

367	(	88.0%	)
50	(	12.0%	)

417 (99.8%)

1 (0.2%)

45

wfhz_cat [factor]

1. Normal
2. Malnourished

380	(	91.6%	)
35	(	8.4%	)

415 (99.3%)

3 (0.7%)

46

bfaz_cat [factor]

1. Normal
2. Low BMI

376	(	90.4%	)
40	(	9.6%	)

416 (99.5%)

2 (0.5%)

47

hgb [numeric]

Mean (sd) : 10.9 (1.3)
min ≤ med ≤ max:
6.7 ≤ 10.9 ≤ 15.2
IQR (CV) : 1.6 (0.1)

64 distinct values

417 (99.8%)

1 (0.2%)

48

rbc [numeric]

Mean (sd) : 4.5 (0.5)
min ≤ med ≤ max:
2.1 ≤ 4.5 ≤ 6.2
IQR (CV) : 0.6 (0.1)

168 distinct values

417 (99.8%)

1 (0.2%)

49

mcv [numeric]

Mean (sd) : 73.9 (6.7)
min ≤ med ≤ max:
52.7 ≤ 74.8 ≤ 89.3
IQR (CV) : 9 (0.1)

201 distinct values

416 (99.5%)

2 (0.5%)

50

mch [numeric]

Mean (sd) : 24.2 (2.6)
min ≤ med ≤ max:
15.4 ≤ 24.6 ≤ 35.4
IQR (CV) : 3.3 (0.1)

107 distinct values

416 (99.5%)

2 (0.5%)

51

bf [character]

1. NO MPS SEEN
2. Pf, t, 127
3. Pf, t, 1305
4. Pf, t, 165
5. Pf, t, 204
6. Pf, t, 273
7. Pf, t, 2873
8. Pf, t, 3135
9. Pf, t, 3751
10. Pf, t, 7204
[ 3 others ]

406	(	97.1%	)
1	(	0.2%	)
1	(	0.2%	)
1	(	0.2%	)
1	(	0.2%	)
1	(	0.2%	)
1	(	0.2%	)
1	(	0.2%	)
1	(	0.2%	)
1	(	0.2%	)
3	(	0.7%	)

418 (100.0%)

0 (0.0%)

52

g6pd [character]

1. Full Enzymatic Defect
2. Normal Enzymatic Activity
3. Partial Enzymatic Activit
4. Partial Enzymatic Defect

37	(	8.9%	)
365	(	87.3%	)
1	(	0.2%	)
15	(	3.6%	)

418 (100.0%)

0 (0.0%)

53

sick [character]

1. Negative
2. Positive

384	(	91.9%	)
34	(	8.1%	)

418 (100.0%)

0 (0.0%)

54

hb_elect [character]

1. AS
2. Not Done
3. S
4. SC

27	(	6.5%	)
385	(	92.1%	)
2	(	0.5%	)
4	(	1.0%	)

418 (100.0%)

0 (0.0%)

55

mps_count [numeric]

Mean (sd) : 59.5 (516)
min ≤ med ≤ max:
0 ≤ 0 ≤ 7204
IQR (CV) : 0 (8.7)

13 distinct values

418 (100.0%)

0 (0.0%)

56

mps_present [character]

1. No
2. Yes

406	(	97.1%	)
12	(	2.9%	)

418 (100.0%)

0 (0.0%)

57

scd [factor]

1. No
2. Yes

412	(	98.6%	)
6	(	1.4%	)

418 (100.0%)

0 (0.0%)

58

g6pd_defect [factor]

1. Normal
2. Partial
3. Full

365	(	87.3%	)
16	(	3.8%	)
37	(	8.9%	)

418 (100.0%)

0 (0.0%)

59

anemia_cat2 [ordered, factor]

1. None
2. Mild
3. Moderate
4. Severe

206	(	49.4%	)
121	(	29.0%	)
87	(	20.9%	)
3	(	0.7%	)

417 (99.8%)

1 (0.2%)

60

anemia_cat [factor]

1. No
2. Yes

206	(	49.4%	)
211	(	50.6%	)

417 (99.8%)

1 (0.2%)

61

stool [character]

1. Entamoeba histolytica
2. Intestinal flagellate
3. No specimen
4. Normal
5. Pus cells
6. Yeast cells present

1	(	0.2%	)
3	(	0.7%	)
195	(	46.7%	)
207	(	49.5%	)
1	(	0.2%	)
11	(	2.6%	)

418 (100.0%)

0 (0.0%)

62

urine [character]

1. Blood with RBCs
2. Blood without RBCs
3. Elevated Bilirubin
4. No specimen
5. Normal

2	(	0.5%	)
6	(	1.4%	)
6	(	1.4%	)
209	(	50.0%	)
195	(	46.7%	)

418 (100.0%)

0 (0.0%)

Generated by summarytools 1.1.5 (R version 4.6.1)
2026-10-13

Table 1

Show the code

gtsummary::theme_gtsummary_compact()
df_complete %>% 
    gtsummary::tbl_summary(
        include = c(sex, age_mths, bodytemp, wfaz_cat, hfaz_cat, wfhz_cat, 
                    mfaz_cat, bfaz_cat, f_siblings, m_siblings, do_you_own_itn,
                    itn_previous_night, mal_prev_measure_2),
        by = sex,
        type = list(
            m_siblings ~ "continuous",
            c(do_you_own_itn, itn_previous_night) ~ "categorical"
            ),
        digits = list(
            gtsummary::all_categorical() ~ c(0,1),
            c(m_siblings, f_siblings) ~ 0,
            bodytemp ~ 1
            ),
        missing = "no",
        statistic = gtsummary::all_categorical()~ "{n}({p})"
    ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols()~"**Sex of Participant**"
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption("Table 1")

Table 1
Characteristic	Overall N = 417¹	Sex of Participant
Characteristic	Overall N = 417¹	Female N = 193¹	Male N = 224¹
Patient's Age (months)	15 (10, 29)	15 (10, 28)	15 (9, 29)
Body temperature	36.3 (36.1, 36.5)	36.3 (36.1, 36.5)	36.3 (36.1, 36.5)
Weight-for-Age Category
Normal	377(90.6)	183(95.3)	194(86.6)
Underweight	39(9.4)	9(4.7)	30(13.4)
Height-for-Age Category
Normal	375(89.9)	177(91.7)	198(88.4)
Stunted	42(10.1)	16(8.3)	26(11.6)
Weight-for-Height Category
Normal	380(91.6)	182(95.3)	198(88.4)
Malnourished	35(8.4)	9(4.7)	26(11.6)
MUAC-for-Age Catofory
Normal	367(88.0)	175(90.7)	192(85.7)
Low MUAC	50(12.0)	18(9.3)	32(14.3)
BMI-for-Age Category
Normal	376(90.4)	179(93.2)	197(87.9)
Low BMI	40(9.6)	13(6.8)	27(12.1)
Paternal siblings	2 (0, 3)	1 (0, 3)	2 (1, 4)
Maternal siblings	1 (0, 3)	1 (0, 2)	2 (1, 3)
Do you own an ITN
No	106(25.6)	62(32.5)	44(19.7)
Yes	308(74.4)	129(67.5)	179(80.3)
ITN used the previous night
No	195(46.8)	106(54.9)	89(39.7)
Yes	222(53.2)	87(45.1)	135(60.3)
Malaaria preventive measure used
ITN	293(70.3)	122(63.2)	171(76.3)
Mosquito coil	55(13.2)	32(16.6)	23(10.3)
Mosquito repellent	7(1.7)	4(2.1)	3(1.3)
Mosquito spray	33(7.9)	18(9.3)	15(6.7)
None	15(3.6)	9(4.7)	6(2.7)
Others	14(3.4)	8(4.1)	6(2.7)
¹ Median (Q1, Q3); n(%)

Table 2

Show the code

df_complete %>%
    gtsummary::tbl_summary(
        include = c(m_age, m_maristat2, m_educ, m_occup, m_income),
        by = sex,
        digits = list(
            gtsummary::all_categorical() ~ c(0,1)),
        missing = "no",
        statistic = gtsummary::all_categorical()~ "{n}({p})"
    ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols()~"**Sex of Participant**"
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption("Table 2")

Table 2
Characteristic	Overall N = 417¹	Sex of Participant
Characteristic	Overall N = 417¹	Female N = 193¹	Male N = 224¹
Mother's age (yrs)	29 (24, 35)	28 (23, 34)	30 (24, 35)
Mother's Marital Status
Co-habitation	203(48.7)	103(53.4)	100(44.6)
Married	163(39.1)	72(37.3)	91(40.6)
Single	51(12.2)	18(9.3)	33(14.7)
Mother's educational level
None	38(9.1)	14(7.3)	24(10.7)
Primary	42(10.1)	17(8.8)	25(11.2)
Jhs	232(55.6)	117(60.6)	115(51.3)
Shs	68(16.3)	32(16.6)	36(16.1)
Tertiary	37(8.9)	13(6.7)	24(10.7)
Mother's Occupation
Business woman	10(2.4)	5(2.6)	5(2.2)
Farmer	39(9.4)	16(8.3)	23(10.3)
Government worker	37(8.9)	13(6.8)	24(10.7)
Other	183(44.0)	90(46.9)	93(41.5)
Petty trader	144(34.6)	67(34.9)	77(34.4)
Private teacher	2(0.5)	1(0.5)	1(0.4)
Student	1(0.2)	0(0.0)	1(0.4)
Mother's income (GHC)	100 (0, 300)	100 (0, 300)	140 (0, 300)
¹ Median (Q1, Q3); n(%)

Table 3

Show the code

df_complete %>% 
    gtsummary::tbl_summary(
        include = c(f_age, f_maristat2, f_educ, f_occup, f_income),
        by = sex,
        digits = list(
            gtsummary::all_categorical() ~ c(0,1)
            ),
        missing = "no",
        statistic = gtsummary::all_categorical()~ "{n}({p})"
    ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols()~"**Sex of Participant**"
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption("Table 3")

Table 3
Characteristic	Overall N = 417¹	Sex of Participant
Characteristic	Overall N = 417¹	Female N = 193¹	Male N = 224¹
Father's age (years)	35 (29, 40)	32 (29, 39)	35 (29, 40)
Father's marital status
Co-habitation	205(49.5)	103(53.6)	102(45.9)
Married	162(39.1)	73(38.0)	89(40.1)
Single	47(11.4)	16(8.3)	31(14.0)
Father's educational level
JHS	151(36.2)	72(37.3)	79(35.3)
None	32(7.7)	13(6.7)	19(8.5)
Primary	44(10.6)	20(10.4)	24(10.7)
SHS	119(28.5)	59(30.6)	60(26.8)
Tertiary	71(17.0)	29(15.0)	42(18.8)
Father's occupation
Business man	16(3.9)	10(5.2)	6(2.7)
Business woman	1(0.2)	0(0.0)	1(0.4)
Farmer	58(14.0)	23(12.0)	35(15.7)
Government worker	65(15.7)	26(13.5)	39(17.5)
Other	234(56.4)	116(60.4)	118(52.9)
Petty trader	22(5.3)	10(5.2)	12(5.4)
Student	1(0.2)	0(0.0)	1(0.4)
farmer	10(2.4)	4(2.1)	6(2.7)
o works with water company	1(0.2)	1(0.5)	0(0.0)
petty trader	7(1.7)	2(1.0)	5(2.2)
Father's income (GHC)	450 (200, 1,000)	400 (200, 800)	500 (200, 1,000)
¹ Median (Q1, Q3); n(%)

table 4

Show the code

df_complete %>% 
    gtsummary::tbl_summary(
        include = c(hgb, anemia_cat, rbc, mcv, mch, g6pd_defect, mps_present, 
                    sick, hb_elect, urine, stool),
        by = sex,
        digits = list(
            gtsummary::all_categorical() ~ c(0,1)
            ),
        missing ="no",
        statistic = gtsummary::all_categorical()~ "{n}({p})"
    ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols()~"**Sex of Participant**"
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption("Table 4")

Table 4
Characteristic	Overall N = 417¹	Sex of Participant
Characteristic	Overall N = 417¹	Female N = 193¹	Male N = 224¹
Hemoglobin (mg/dl)	10.90 (10.10, 11.70)	11.00 (10.20, 11.80)	10.90 (10.00, 11.70)
anemia_cat	211(50.7)	91(47.2)	120(53.8)
RBC count	4.51 (4.20, 4.78)	4.48 (4.20, 4.76)	4.54 (4.21, 4.79)
Mean corpuscular volume	75 (70, 79)	76 (70, 80)	74 (69, 78)
Mean corpuscular hemoglobin	24.60 (22.70, 26.10)	24.90 (23.10, 26.20)	24.30 (22.30, 25.90)
G6PD Defect
Normal	364(87.3)	167(86.5)	197(87.9)
Partial	16(3.8)	12(6.2)	4(1.8)
Full	37(8.9)	14(7.3)	23(10.3)
mps_present	12(2.9)	6(3.1)	6(2.7)
Sickling Status
Negative	383(91.8)	182(94.3)	201(89.7)
Positive	34(8.2)	11(5.7)	23(10.3)
Hemoglobin Electrophoresis
AS	27(6.5)	10(5.2)	17(7.6)
Not Done	384(92.1)	182(94.3)	202(90.2)
S	2(0.5)	1(0.5)	1(0.4)
SC	4(1.0)	0(0.0)	4(1.8)
Urine examination
Blood with RBCs	2(0.5)	1(0.5)	1(0.4)
Blood without RBCs	6(1.4)	3(1.6)	3(1.3)
Elevated Bilirubin	6(1.4)	4(2.1)	2(0.9)
No specimen	208(49.9)	94(48.7)	114(50.9)
Normal	195(46.8)	91(47.2)	104(46.4)
Stool examination
Entamoeba histolytica	1(0.2)	1(0.5)	0(0.0)
Intestinal flagellate	3(0.7)	2(1.0)	1(0.4)
No specimen	194(46.5)	86(44.6)	108(48.2)
Normal	207(49.6)	98(50.8)	109(48.7)
Pus cells	1(0.2)	0(0.0)	1(0.4)
Yeast cells present	11(2.6)	6(3.1)	5(2.2)
¹ Median (Q1, Q3); n(%)

Table 5

Show the code

gtsummary::theme_gtsummary_compact()
df_complete %>% 
    gtsummary::tbl_summary(
        include = c(age_mths, bodytemp, wfaz_cat, hfaz_cat, wfhz_cat, 
                    mfaz_cat, bfaz_cat, f_siblings, m_siblings, do_you_own_itn,
                    itn_previous_night, mal_prev_measure_2, g6pd_defect, 
                    antimal_2weeks ),
        by = mps_present,
        missing ="no",
        type = list(
            m_siblings ~ "continuous",
            c(do_you_own_itn, itn_previous_night) ~ "categorical"
            ),
        digits = list(
            gtsummary::all_categorical() ~ c(0,1),
            c(m_siblings, f_siblings) ~ 0,
            bodytemp ~ 1
            ),
        statistic = gtsummary::all_categorical()~ "{n}({p})"
    ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols()~"**MPs Present**"
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p(pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>% 
    gtsummary::modify_caption("Table 5") |> 
    gtsummary::bold_p()

Table 5
Characteristic	Overall N = 418¹	MPs Present		p-value²
Characteristic	Overall N = 418¹	No N = 406¹	Yes N = 12¹	p-value²
Patient's Age (months)	15 (9, 29)	15 (9, 29)	28 (11, 36)	0.329
Body temperature	36.3 (36.1, 36.5)	36.3 (36.1, 36.5)	36.1 (36.0, 36.4)	0.087
Weight-for-Age Category				0.312
Normal	377(90.6)	367(90.8)	10(83.3)
Underweight	39(9.4)	37(9.2)	2(16.7)
Height-for-Age Category				0.345
Normal	375(89.9)	365(90.1)	10(83.3)
Stunted	42(10.1)	40(9.9)	2(16.7)
Weight-for-Height Category				0.610
Normal	380(91.6)	368(91.3)	12(100.0)
Malnourished	35(8.4)	35(8.7)	0(0.0)
MUAC-for-Age Catofory				0.644
Normal	367(88.0)	357(88.1)	10(83.3)
Low MUAC	50(12.0)	48(11.9)	2(16.7)
BMI-for-Age Category				0.616
Normal	376(90.4)	364(90.1)	12(100.0)
Low BMI	40(9.6)	40(9.9)	0(0.0)
Paternal siblings	2 (0, 3)	1 (0, 3)	3 (2, 5)	0.062
Maternal siblings	1 (0, 3)	1 (0, 3)	3 (1, 3)	0.423
Do you own an ITN				0.512
No	106(25.5)	102(25.3)	4(33.3)
Yes	309(74.5)	301(74.7)	8(66.7)
ITN used the previous night				0.410
No	195(46.7)	188(46.3)	7(58.3)
Yes	223(53.3)	218(53.7)	5(41.7)
Malaaria preventive measure used				0.332
ITN	294(70.3)	286(70.4)	8(66.7)
Mosquito coil	55(13.2)	54(13.3)	1(8.3)
Mosquito repellent	7(1.7)	6(1.5)	1(8.3)
Mosquito spray	33(7.9)	32(7.9)	1(8.3)
None	15(3.6)	14(3.4)	1(8.3)
Others	14(3.3)	14(3.4)	0(0.0)
G6PD Defect				0.433
Normal	365(87.3)	355(87.4)	10(83.3)
Partial	16(3.8)	15(3.7)	1(8.3)
Full	37(8.9)	36(8.9)	1(8.3)
antimal_2weeks	61(14.7)	59(14.6)	2(16.7)	0.692
¹ Median (Q1, Q3); n(%)
² Wilcoxon rank sum test; Fisher’s exact test; Pearson’s Chi-squared test

Table 6

Show the code

df_complete %>% 
    gtsummary::tbl_summary(
        include = c(m_age, m_maristat2, m_educ, m_occup, m_income),
        by = mps_present,
        digits = list(
            gtsummary::all_categorical() ~ c(0,1)),
        statistic = gtsummary::all_categorical()~ "{n}({p})",
        missing = "no"
    ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols()~"**MPs Present**"
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p(pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>% 
    gtsummary::modify_caption("Table 6")|> 
    gtsummary::bold_p()

Table 6
Characteristic	Overall N = 418¹	MPs Present		p-value²
Characteristic	Overall N = 418¹	No N = 406¹	Yes N = 12¹	p-value²
Mother's age (yrs)	29 (24, 35)	29 (24, 35)	29 (24, 37)	0.565
Mother's Marital Status				0.170
Co-habitation	203(48.6)	194(47.8)	9(75.0)
Married	164(39.2)	162(39.9)	2(16.7)
Single	51(12.2)	50(12.3)	1(8.3)
Mother's educational level				0.862
None	38(9.1)	37(9.1)	1(8.3)
Primary	42(10.0)	41(10.1)	1(8.3)
Jhs	233(55.7)	224(55.2)	9(75.0)
Shs	68(16.3)	67(16.5)	1(8.3)
Tertiary	37(8.9)	37(9.1)	0(0.0)
Mother's Occupation				0.261
Business woman	10(2.4)	10(2.5)	0(0.0)
Farmer	39(9.4)	36(8.9)	3(25.0)
Government worker	37(8.9)	37(9.1)	0(0.0)
Other	183(43.9)	180(44.4)	3(25.0)
Petty trader	145(34.8)	139(34.3)	6(50.0)
Private teacher	2(0.5)	2(0.5)	0(0.0)
Student	1(0.2)	1(0.2)	0(0.0)
Mother's income (GHC)	100 (0, 300)	100 (0, 300)	100 (50, 250)	0.979
¹ Median (Q1, Q3); n(%)
² Wilcoxon rank sum test; Fisher’s exact test

Table 7

Show the code

df_complete %>% 
    gtsummary::tbl_summary(
        include = c(f_age, f_maristat2, f_educ, f_occup, f_income),
        by = mps_present,
        digits = list(
            gtsummary::all_categorical() ~ c(0,1)),
        statistic = gtsummary::all_categorical()~ "{n}({p})",
        missing = "no"
    ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols()~"**MPs Present**"
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>%
    gtsummary::add_p(pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>%
    gtsummary::modify_caption("Table 7")|> 
    gtsummary::bold_p()

Table 7
Characteristic	Overall N = 418¹	MPs Present		p-value²
Characteristic	Overall N = 418¹	No N = 406¹	Yes N = 12¹	p-value²
Father's age (years)	35 (29, 40)	35 (29, 40)	36 (32, 43)	0.359
Father's marital status				0.177
Co-habitation	205(49.4)	196(48.6)	9(75.0)
Married	163(39.3)	161(40.0)	2(16.7)
Single	47(11.3)	46(11.4)	1(8.3)
Father's educational level				0.375
JHS	151(36.1)	144(35.5)	7(58.3)
None	32(7.7)	31(7.6)	1(8.3)
Primary	44(10.5)	43(10.6)	1(8.3)
SHS	120(28.7)	117(28.8)	3(25.0)
Tertiary	71(17.0)	71(17.5)	0(0.0)
Father's occupation				0.414
Business man	16(3.8)	16(4.0)	0(0.0)
Business woman	1(0.2)	1(0.2)	0(0.0)
Farmer	58(13.9)	54(13.4)	4(33.3)
Government worker	65(15.6)	65(16.1)	0(0.0)
Other	235(56.5)	228(56.4)	7(58.3)
Petty trader	22(5.3)	21(5.2)	1(8.3)
Student	1(0.2)	1(0.2)	0(0.0)
farmer	10(2.4)	10(2.5)	0(0.0)
o works with water company	1(0.2)	1(0.2)	0(0.0)
petty trader	7(1.7)	7(1.7)	0(0.0)
Father's income (GHC)	400 (200, 1,000)	500 (200, 1,000)	350 (100, 500)	0.091
¹ Median (Q1, Q3); n(%)
² Wilcoxon rank sum test; Fisher’s exact test

Table 8

Show the code

gtsummary::theme_gtsummary_compact()
df_complete %>% 
    gtsummary::tbl_summary(
        include = c(age_mths, bodytemp, wfaz_cat, hfaz_cat, wfhz_cat, 
                    mfaz_cat, bfaz_cat, f_siblings, m_siblings, do_you_own_itn,
                    itn_previous_night, mal_prev_measure_2, g6pd_defect, 
                    antimal_2weeks, mps_present),
        by = anemia_cat,
        missing = "no",
        type = list(
            m_siblings ~ "continuous",
            c(do_you_own_itn, itn_previous_night) ~ "categorical"
            ),
        digits = list(
            gtsummary::all_categorical() ~ c(0,1),
            c(m_siblings, f_siblings) ~ 0,
            bodytemp ~ 1
            ),
        statistic = list(
            gtsummary::all_categorical()~ "{n}({p})",
            bodytemp ~ "{mean} ({sd})"
        )
    ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols()~"**Anemia present**"
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p(pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>% 
    gtsummary::modify_caption("Table 8")|> 
    gtsummary::bold_p()

Table 8
Characteristic	Overall N = 417¹	Anemia present		p-value²
Characteristic	Overall N = 417¹	No N = 206¹	Yes N = 211¹	p-value²
Patient's Age (months)	15 (10, 29)	19 (11, 39)	13 (9, 21)	<0.001
Body temperature	36.3 (0.3)	36.4 (0.3)	36.3 (0.3)	0.036
Weight-for-Age Category				0.082
Normal	376(90.6)	190(93.1)	186(88.2)
Underweight	39(9.4)	14(6.9)	25(11.8)
Height-for-Age Category				0.029
Normal	374(89.9)	191(93.2)	183(86.7)
Stunted	42(10.1)	14(6.8)	28(13.3)
Weight-for-Height Category				0.189
Normal	380(91.8)	190(93.6)	190(90.0)
Malnourished	34(8.2)	13(6.4)	21(10.0)
MUAC-for-Age Catofory				0.621
Normal	366(88.0)	182(88.8)	184(87.2)
Low MUAC	50(12.0)	23(11.2)	27(12.8)
BMI-for-Age Category				0.286
Normal	376(90.6)	188(92.2)	188(89.1)
Low BMI	39(9.4)	16(7.8)	23(10.9)
Paternal siblings	2 (0, 3)	1 (0, 3)	2 (0, 4)	0.051
Maternal siblings	1 (0, 3)	1 (0, 3)	2 (1, 3)	0.059
Do you own an ITN				0.694
No	105(25.4)	50(24.5)	55(26.2)
Yes	309(74.6)	154(75.5)	155(73.8)
ITN used the previous night				0.718
No	194(46.5)	94(45.6)	100(47.4)
Yes	223(53.5)	112(54.4)	111(52.6)
Malaaria preventive measure used				0.011
ITN	294(70.5)	142(68.9)	152(72.0)
Mosquito coil	54(12.9)	29(14.1)	25(11.8)
Mosquito repellent	7(1.7)	4(1.9)	3(1.4)
Mosquito spray	33(7.9)	23(11.2)	10(4.7)
None	15(3.6)	2(1.0)	13(6.2)
Others	14(3.4)	6(2.9)	8(3.8)
G6PD Defect				0.805
Normal	364(87.3)	182(88.3)	182(86.3)
Partial	16(3.8)	7(3.4)	9(4.3)
Full	37(8.9)	17(8.3)	20(9.5)
antimal_2weeks	61(14.7)	26(12.7)	35(16.7)	0.244
mps_present	12(2.9)	1(0.5)	11(5.2)	0.004
¹ Median (Q1, Q3); Mean (SD); n(%)
² Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test

Table 9

Show the code

df_complete %>% 
    gtsummary::tbl_summary(
        include = c(m_age, m_maristat2, m_educ, m_occup, m_income),
        by = anemia_cat,
        digits = list(
            gtsummary::all_categorical() ~ c(0,1)),
        statistic = gtsummary::all_categorical()~ "{n}({p})",
        missing = "no"
    ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols()~"**Anemia Present**"
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p(pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>% 
    gtsummary::modify_caption("Table 9")|> 
    gtsummary::bold_p()

Table 9
Characteristic	Overall N = 417¹	Anemia Present		p-value²
Characteristic	Overall N = 417¹	No N = 206¹	Yes N = 211¹	p-value²
Mother's age (yrs)	29 (24, 35)	30 (24, 35)	28 (23, 34)	0.413
Mother's Marital Status				0.546
Co-habitation	203(48.7)	96(46.6)	107(50.7)
Married	163(39.1)	86(41.7)	77(36.5)
Single	51(12.2)	24(11.7)	27(12.8)
Mother's educational level				0.013
None	38(9.1)	20(9.7)	18(8.5)
Primary	42(10.1)	19(9.2)	23(10.9)
Jhs	233(55.9)	102(49.5)	131(62.1)
Shs	68(16.3)	39(18.9)	29(13.7)
Tertiary	36(8.6)	26(12.6)	10(4.7)
Mother's Occupation				0.102
Business woman	10(2.4)	6(2.9)	4(1.9)
Farmer	39(9.4)	13(6.3)	26(12.4)
Government worker	37(8.9)	24(11.7)	13(6.2)
Other	182(43.8)	87(42.2)	95(45.2)
Petty trader	145(34.9)	75(36.4)	70(33.3)
Private teacher	2(0.5)	1(0.5)	1(0.5)
Student	1(0.2)	0(0.0)	1(0.5)
Mother's income (GHC)	100 (0, 300)	120 (0, 300)	100 (0, 300)	0.106
¹ Median (Q1, Q3); n(%)
² Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test

Table 10

Show the code

df_complete %>% 
    gtsummary::tbl_summary(
        include = c(f_age, f_maristat2, f_educ, f_occup, f_income),
        by = anemia_cat,
        digits = list(
            gtsummary::all_categorical() ~ c(0,1)),
        missing = "no",
        statistic = gtsummary::all_categorical()~ "{n}({p})") %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols()~"**Anemia Present**") %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>%
    gtsummary::add_p(pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>%
    gtsummary::modify_caption("Table 10")|> 
    gtsummary::bold_p()

Table 10
Characteristic	Overall N = 417¹	Anemia Present		p-value²
Characteristic	Overall N = 417¹	No N = 206¹	Yes N = 211¹	p-value²
Father's age (years)	35 (29, 40)	35 (29, 40)	34 (29, 40)	0.467
Father's marital status				0.393
Co-habitation	205(49.5)	96(46.8)	109(52.2)
Married	162(39.1)	87(42.4)	75(35.9)
Single	47(11.4)	22(10.7)	25(12.0)
Father's educational level				0.040
JHS	151(36.2)	71(34.5)	80(37.9)
None	32(7.7)	10(4.9)	22(10.4)
Primary	44(10.6)	20(9.7)	24(11.4)
SHS	120(28.8)	61(29.6)	59(28.0)
Tertiary	70(16.8)	44(21.4)	26(12.3)
Father's occupation
Business man	16(3.9)	12(5.8)	4(1.9)
Business woman	1(0.2)	0(0.0)	1(0.5)
Farmer	58(14.0)	24(11.7)	34(16.3)
Government worker	64(15.4)	40(19.4)	24(11.5)
Other	235(56.6)	110(53.4)	125(59.8)
Petty trader	22(5.3)	12(5.8)	10(4.8)
Student	1(0.2)	0(0.0)	1(0.5)
farmer	10(2.4)	3(1.5)	7(3.3)
o works with water company	1(0.2)	1(0.5)	0(0.0)
petty trader	7(1.7)	4(1.9)	3(1.4)
Father's income (GHC)	400 (200, 1,000)	500 (200, 1,000)	300 (200, 800)	0.013
¹ Median (Q1, Q3); n(%)
² Wilcoxon rank sum test; Pearson’s Chi-squared test; NA

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df_complete %>% 
    gtsummary::tbl_summary(
        include = anemia_cat2,
        digits = anemia_cat2 ~ c(0,1), 
        label = list(anemia_cat2 ~ "Anemia Category")) %>% 
    gtsummary::bold_labels()

Characteristic	N = 418¹
Anemia Category
None	206 (49.4%)
Mild	121 (29.0%)
Moderate	87 (20.9%)
Severe	3 (0.7%)
Unknown	1
¹ n (%)

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gtsummary::theme_gtsummary_eda()
df_complete %>%
  gtsummary::tbl_summary(by = mps_present, include = age_mths)

Characteristic	No N = 406	Yes N = 12
Patient's Age (months)
Median (Q1, Q3)	15 (9, 29)	28 (11, 36)
Mean (SD)	21 (15)	26 (15)
Min, Max	6, 59	6, 49

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df_complete %>% 
  gtsummary::tbl_summary(
    by = anemia_cat2, 
    include = c(mps_present, anemia_cat2),
    label = list(
      mps_present ~ "Asymptomatic malaria")) %>% 
  gtsummary::add_overall(last = T) %>% 
  gtsummary::add_p() %>% 
  gtsummary::bold_labels()|> 
    gtsummary::bold_p()

1 missing row in the "anemia_cat2" column has been removed.

Characteristic	None N = 206¹	Mild N = 121¹	Moderate N = 87¹	Severe N = 3¹	Overall N = 417¹	p-value²
Asymptomatic malaria	1 (0.49%)	6 (4.96%)	5 (5.75%)	0 (0%)	12 (2.88%)	0.013
¹ n (%)
² Fisher’s exact test

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gtsummary::reset_gtsummary_theme()
gtsummary::theme_gtsummary_compact()

Setting theme "Compact"

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df_complete %>% 
  gtsummary::tbl_summary(
    by = anemia_cat2, 
    include = c(age_mths, anemia_cat2),
    label = list(
      age_mths ~ "Age in Months")) %>% 
  gtsummary::add_overall(last = T) %>% 
  gtsummary::add_p() %>% 
  gtsummary::bold_labels()|> 
    gtsummary::bold_p()

1 missing row in the "anemia_cat2" column has been removed.

Characteristic	None N = 206¹	Mild N = 121¹	Moderate N = 87¹	Severe N = 3¹	Overall N = 417¹	p-value²
Age in Months	19 (11, 39)	13 (9, 20)	13 (9, 23)	15 (8, 37)	15 (10, 29)	<0.001
¹ Median (Q1, Q3)
² Kruskal-Wallis rank sum test

Logistic regression for anemia

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df_complete %>%
  select(
    age_mths, bodytemp, hfaz_cat, wfhz_cat, mal_prev_measure_2, 
    mps_present, anemia_cat, f_educ, f_income, m_educ) |> 
  gtsummary::tbl_uvregression(
        method = glm,
        y = anemia_cat,
        method.args = list(family = binomial),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()

Characteristic	N	OR	95% CI	p-value
Patient's Age (months)	417	0.96	0.95, 0.98	<0.001
Body temperature	417	0.56	0.30, 1.02	0.058
Height-for-Age Category	416
Normal		—	—
Stunted		2.09	1.08, 4.20	0.032
Weight-for-Height Category	414
Normal		—	—
Malnourished		1.62	0.79, 3.40	0.192
Malaaria preventive measure used	417
ITN		—	—
Mosquito coil		0.81	0.45, 1.44	0.466
Mosquito repellent		0.70	0.14, 3.23	0.645
Mosquito spray		0.41	0.18, 0.86	0.023
None		6.07	1.64, 39.3	0.019
Others		1.25	0.42, 3.87	0.691
mps_present	417
No		—	—
Yes		11.3	2.16, 207	0.021
Father's educational level	417
JHS		—	—
None		1.95	0.89, 4.57	0.107
Primary		1.07	0.54, 2.10	0.855
SHS		0.86	0.53, 1.39	0.533
Tertiary		0.52	0.29, 0.93	0.029
Father's income (GHC)	416	1.00	1.00, 1.00	0.005
Mother's educational level	417
None		—	—
Primary		1.35	0.56, 3.27	0.509
Jhs		1.43	0.72, 2.86	0.311
Shs		0.83	0.37, 1.84	0.639
Tertiary		0.43	0.16, 1.11	0.085
Abbreviations: CI = Confidence Interval, OR = Odds Ratio