Anemia Manuscript

Importing and cleaning data

Code

df_anemia_ready <- 
    df_anemia %>% 
    janitor::clean_names() %>% 
    rename(
        history = does_the_child_have_a_history_of_the_following_check_all_that_applies,
        history_other = other_medical_history_specify, 
        eat_iron_veges = 
            does_the_child_eat_adequate_iron_rich_foods_regularly_e_g_meat_beans_leafy_vegetables, 
        exbf_4mths_older_chn = was_exclusive_breastfeeding_done_for_at_least_4_months_in_older_children, 
        bld_loss_3mths = recent_blood_loss_past_3_months_before_admission, 
        transfusion_3mths = recent_blood_transfusion_past_3_months_before_admission, 
        deworm_6mths = deworming_within_the_past_6_months_non_applicable_for_children_under_1_year, 
        iron_supp_3mths = iron_supplementation_within_the_past_3_months, 
        adm_clinical_features =clinical_features_at_admission_select_all_that_apply, 
        blood_film_comment = blood_film_comment_check_all_that_applies, 
        anemia_type = type_of_anemia_diagnosed_if_applicable, 
        txn_required = was_blood_transfusion_required,
        picu_required = was_patient_admitted_to_picu, 
        dura_hosp_stay = duration_of_hospital_stay_days, 
        missed_vac_opportunity = in_children_with_missed_immunizations_did_the_child_miss_opportunities_for_immunization_during_previous_admissions_or_opd_consultations, 
        missed_vac_reason = if_missed_vaccination_give_reason_for_incomplete_vaccination_status, 
        missed_vac_opportunity2 = for_children_who_have_missed_some_vaccinations_did_the_child_also_miss_opportunities_for_immunization_during_previous_admissions, 
        hb = hb_g_dl) %>% 
    mutate(
        age_days = interval(
            date_of_birth, date_of_admission) %/% days(1),
        age_months = interval(
            date_of_birth, date_of_admission) %/% months(1),
        age_years = interval(
            date_of_birth, date_of_admission) %/% years(1),
        across(
            c(gender, maternal_education, paternal_education, residence, 
              iron_supp_3mths, exbf_4mths_older_chn, transfusion_3mths, 
              picu_required, txn_required, rdt),
            ~factor(.)), 
        family_income = factor(
            family_income, 
            levels = c("Low", "Middle", "High")), 
        eat_iron_veges = factor(
            eat_iron_veges, 
            levels = c("No", "Yes")), 
        bld_loss_3mths_2 = case_when(
            bld_loss_3mths == "None" ~ "No", 
            TRUE ~ "Yes") %>% factor(), 
        mal_prev_use = case_when(
            malaria_prevention_measures == "None" ~ "No", 
            TRUE ~ "Yes") %>% factor(),
        malaria_prevention_measures = toupper(
            malaria_prevention_measures),
        mal_prev_itn = case_when(
            str_detect(malaria_prevention_measures, "ITN") ~ "Yes", 
            TRUE ~ "No" ) %>% factor(),
        mal_prev_spray_coil = case_when(
            str_detect(malaria_prevention_measures, "COIL") ~ "Yes", 
            TRUE ~ "No" ) %>% factor(),
        mal_prev_clothes = case_when(
            str_detect(malaria_prevention_measures, "CLOTH") ~ "Yes", 
            TRUE ~ "No" ) %>% factor(), 
        mal_prev_repellent = case_when(
            str_detect(malaria_prevention_measures, "REPELLANT") ~ "Yes", 
            TRUE ~ "No") %>% factor(),
        mps_positive = case_when(
            malaria_parasites == "Positive" ~ "Yes", 
            malaria_parasites == "Negative" ~ "No") %>% 
            factor(), 
        rdt_positive = case_when(
            rdt == "Positive" ~ "Yes", 
            rdt == "Negative" ~ "No") %>% 
            factor(),
        deworm_6mths_2 = factor(
            deworm_6mths, levels = c("No", "Yes")),
        immun_status = case_when(
            immunization_status == "Fully vaccinated for age" ~ "Full",
            immunization_status == "Partially vaccinated for age" ~ 
                "Partial",
            immunization_status == "Not vaccinated" ~ "None") %>% 
            factor(levels = c("Full", "Partial", "None")),
        rbc_transfused = case_when(
            txn_required == "Yes" ~ "Yes", TRUE ~ "No") %>% factor(),
        died = case_when(
            outcome_at_discharge == "Died" ~ "Yes", 
            TRUE ~ "No") %>% factor(),
        anemia_cat = case_when(
            (age_years >= 15 & gender == "Male" & hb < 8) ~ "Severe",
            (age_years >= 15 & gender == "Male" & hb < 11) ~ "Moderate", 
            (age_years >= 15 & gender == "Male" & hb < 13) ~ "Mild", 
            (age_years >= 15 & gender == "Male") ~ "None", 
            (age_years >= 15 & gender == "Female" & hb < 8) ~ "Severe",
            (age_years >= 15 & gender == "Female" & hb < 11) ~ "Moderate", 
            (age_years >= 15 & gender == "Female" & hb < 12) ~ "Mild", 
            (age_years >= 15 & gender == "Female") ~ "None", 
            (age_years >= 12 & hb < 8) ~ "Severe",
            (age_years >= 12 & hb < 11) ~ "Moderate", 
            (age_years >= 12 & hb < 12) ~ "Mild", 
            (age_years >= 12) ~ "None", 
            (age_years >= 5 & hb < 8) ~ "Severe",
            (age_years >= 5 & hb < 11) ~ "Moderate", 
            (age_years >= 5 & hb < 11.5) ~ "Mild", 
            (age_years >= 5) ~ "None", 
            (age_months >= 24 & hb < 7) ~ "Severe",
            (age_months >= 24 & hb < 10) ~ "Moderate", 
            (age_months >= 24 & hb < 11) ~ "Mild", 
            (age_months >= 24) ~ "None", 
            (age_months >= 6 & hb < 7) ~ "Severe",
            (age_months >= 6 & hb < 9.5) ~ "Moderate", 
            (age_months >= 6 & hb < 10.5) ~ "Mild", 
            (age_months >= 6) ~ "None", 
            (age_months >= 1 & hb < 7) ~ "Severe",
            (age_months >= 1 & hb < 9) ~ "Moderate", 
            (age_months >= 1 & hb < 9.5) ~ "Mild", 
            (age_months >= 1) ~ "None") %>% 
            factor(levels = c("None", "Mild", "Moderate", "Severe")), 
        anemia = case_when(
            anemia_cat == "None" ~ "No", 
            anemia_cat %in% c("Mild", "Moderate", "Severe") ~ "Yes") %>% 
            factor(),
        mcv_cat = case_when(
            age_years > 12 & mcv > 96 ~ "Macrocytic", 
            age_years > 12 & mcv >= 80  ~ "Normocytic",
            age_years > 12 & mcv < 80  ~ "Microcytic",
            age_years > 6 & mcv > 90 ~ "Macrocytic", 
            age_years > 6 & mcv >= 78  ~ "Normocytic",
            age_years > 6 & mcv < 78  ~ "Microcytic",
            age_years >= 2 & mcv > 86 ~ "Macrocytic", 
            age_years >= 2 & mcv >= 75  ~ "Normocytic",
            age_years >= 2 & mcv < 75  ~ "Microcytic",
            age_months >= 6 & mcv > 85 ~ "Macrocytic", 
            age_months >= 6 & mcv >= 73  ~ "Normocytic",
            age_months >= 6 & mcv < 73  ~ "Microcytic") %>% 
            factor(levels = c("Microcytic", "Normocytic", "Macrocytic")),
        rdw_cat = case_when(
            (gender == "Male" & age_years > 12 & rdw_cv > 13.5) ~ "High", 
            (gender == "Male" & age_years > 12 & rdw_cv >= 10.9) ~ "Normal",
            (gender == "Male" & age_years > 12 & rdw_cv < 10.9)  ~ "Low",
            (gender == "Female" & age_years > 12 & rdw_cv > 14.6) ~ "High", 
            (gender == "Female" & age_years > 12 & rdw_cv >= 11.9)  ~ 
                "Normal",
            (gender == "Female" & age_years > 12 & rdw_cv < 11.9)  ~ "Low",
            age_years > 6 & rdw_cv > 13.8 ~ "High", 
            age_years > 6 & rdw_cv >= 11.9  ~ "Normal",
            age_years > 6 & rdw_cv < 11.9  ~ "Low",
            age_years >= 2 & rdw_cv > 14.6 ~ "High", 
            age_years >= 2 & rdw_cv >= 12.0  ~ "Normal",
            age_years >= 2 & rdw_cv < 12.0  ~ "Low",
            age_months >= 6 & rdw_cv > 15.6 ~ "High", 
            age_months >= 6 & rdw_cv >= 12.3  ~ "Normal",
            age_months >= 6 & rdw_cv < 12.3  ~ "Low") %>% 
            factor(levels = c("Low", "Normal", "High")) %>% 
            fct_recode("Normal" = "Low"),
        bld_film_done = case_when(
            is.na(blood_film_comment) ~ "No", 
            TRUE ~ "Yes") %>% factor(),
        bf_ida = case_when(
            str_detect(
                blood_film_comment, "Iron deficiency anemia") ~ "Yes",
            TRUE ~ "No") %>% factor(), 
        bf_scd = case_when(
            str_detect(
                blood_film_comment, "Sickle") ~ "Yes", 
            str_detect(blood_film_comment, "SCD") ~ "Yes",
            TRUE ~ "No") %>% factor(),
        bf_infection = case_when(
            str_detect(
                blood_film_comment, "Infection") ~ "Yes",
            TRUE ~ "No") %>% factor(),
        bf_liver = case_when(
            str_detect(
                blood_film_comment, "Liver") ~ "Yes",
            TRUE ~ "No") %>% factor(),
        bf_renal = case_when(
            str_detect(
                blood_film_comment, "Renal") ~ "Yes",
            TRUE ~ "No") %>% factor(), 
        bf_malignancy = case_when(
            str_detect(
                blood_film_comment, "ALL") ~ "Yes",
            str_detect(
                blood_film_comment, "MALIGNANCY") ~ "Yes",
            TRUE ~ "No") %>% factor(),
        bf_hemolytic = case_when(
            str_detect(
                blood_film_comment, "Hemolytic") ~ "Yes",
            TRUE ~ "No") %>% factor(),
        bf_malaria = case_when(
            str_detect(
                blood_film_comment, "Malaria") ~ "Yes",
            TRUE ~ "No") %>% factor(),
        bf_inflamation = case_when(
            str_detect(
                blood_film_comment, "INFLA") ~ "Yes",
            TRUE ~ "No") %>% factor(), 
        ida = case_when(
            bf_ida == "Yes" ~ "Yes",
            (hb < 10 & rdw_cv > 15 & mcv_cat == "Microcytic") ~ "Yes",
            TRUE ~ "No"),
        malaria = case_when(
            rdt_positive == "Yes" ~ "Yes", 
            mps_positive == "Yes" ~ "Yes",
            bf_malaria == "Yes" ~ "Yes",
            TRUE ~ "No"), 
        history = toupper(history),
        blood_film_comment = toupper(blood_film_comment),
        discharge_diagnosis_list = toupper(discharge_diagnosis_list),
        discharge_diagnosis_upper = toupper(discharge_diagnosis_upper),
        scd = case_when(
            str_detect(history, "SICKLE CELL DISEASE") ~ "Yes", 
            str_detect(history, "SCD") ~ "Yes", 
            str_detect(history_other, "SICKLE CELL DISEASE") ~ "Yes", 
            str_detect(history_other, "SCD") ~ "Yes", 
            str_detect(blood_film_comment, "SICKLE CELL DISEASE") ~ "Yes", 
            str_detect(blood_film_comment, "SCD") ~ "Yes", 
            str_detect(discharge_diagnosis_list, 
                       "SICKLE CELL DISEASE") ~ "Yes", 
            str_detect(discharge_diagnosis_list, "SCD") ~ "Yes", 
            str_detect(discharge_diagnosis_upper, 
                       "SICKLE CELL DISEASE") ~ "Yes", 
            str_detect(discharge_diagnosis_upper, "SCD") ~ "Yes",
            str_detect(anemia_type, "Sickle cell anemia") ~ "Yes",
            TRUE ~ "No"),
        g6pd = case_when(
            str_detect(discharge_diagnosis_list, "G6PD") ~ "Yes", 
            str_detect(discharge_diagnosis_upper, "G6PD") ~ "Yes", 
            str_detect(other_laboratory_findings, "G6PD") ~ "Yes", 
            str_detect(additional_comments, "G6PD") ~ "Yes", 
            TRUE ~ "No"), 
        anemia_of_malig = case_when(
        str_detect(blood_film_comment, "MALIGN") ~ "Yes", 
            TRUE ~ "No"),
        anemia_of_chr_dx = case_when(
            str_detect(blood_film_comment, "CHRONIC") ~ "Yes", 
            TRUE ~ "No"))%>%
    select(!c(column_42, column_43, timestamp))  %>% 
    full_join(df_anemia_diag) %>% 
    mutate(
        across(starts_with("diag_"), ~ifelse(is.na(.x), "No", .x)))

Joining with `by = join_by(study_id)`

Display data

Code

# df_anemia_ready %>% 
#     summarytools::dfSummary(graph.col = F)

Table 1

Code

df_anemia_ready %>% 
    select(
        anemia, rdw_cat, mcv_cat, died, rbc_transfused, immun_status,
        deworm_6mths_2, rdt_positive, mps_positive, mal_prev_use, 
        mal_prev_repellent, mal_prev_clothes,  mal_prev_spray_coil,
        mal_prev_itn, bld_loss_3mths_2, age_months, dura_hosp_stay, 
        picu_required, txn_required, iron_supp_3mths, exbf_4mths_older_chn, 
        eat_iron_veges, family_income,  paternal_education,
        maternal_education, residence,  gender, bld_film_done, bf_ida, 
        bf_scd, bf_infection, bf_liver, bf_renal, bf_malignancy, 
        bf_hemolytic, bf_malaria, bf_inflamation, ida, malaria, scd, 
        g6pd, anemia_of_malig:diag_surgical) %>% 
    gtsummary::tbl_summary(
        by = anemia, 
        digits = list(gtsummary::all_categorical() ~ c(0,1))) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols() ~ "**Anemia Present**") %>% 
    gtsummary::add_overall(last = T) %>%
    gtsummary::add_p(
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)) %>%
    gtsummary::bold_labels() %>%
    gtsummary::bold_p()

Characteristic	Anemia Present		Overall N = 318¹	p-value²
Characteristic	No N = 103¹	Yes N = 215¹	Overall N = 318¹	p-value²
rdw_cat				<0.001
Normal	46 (51.1%)	37 (18.2%)	83 (28.3%)
High	44 (48.9%)	166 (81.8%)	210 (71.7%)
Unknown	13	12	25
mcv_cat				0.217
Microcytic	56 (62.2%)	126 (62.1%)	182 (62.1%)
Normocytic	33 (36.7%)	66 (32.5%)	99 (33.8%)
Macrocytic	1 (1.1%)	11 (5.4%)	12 (4.1%)
Unknown	13	12	25
died	8 (7.8%)	22 (10.2%)	30 (9.4%)	0.481
rbc_transfused	2 (1.9%)	74 (34.4%)	76 (23.9%)	<0.001
immun_status				0.065
Full	77 (74.8%)	163 (75.8%)	240 (75.5%)
Partial	23 (22.3%)	52 (24.2%)	75 (23.6%)
None	3 (2.9%)	0 (0.0%)	3 (0.9%)
deworm_6mths_2	47 (57.3%)	101 (56.1%)	148 (56.5%)	0.855
Unknown	21	35	56
rdt_positive	3 (2.9%)	41 (19.1%)	44 (13.8%)	<0.001
mps_positive	1 (1.3%)	27 (15.5%)	28 (11.1%)	<0.001
Unknown	25	41	66
mal_prev_use	79 (76.7%)	164 (76.3%)	243 (76.4%)	0.934
mal_prev_repellent	2 (1.9%)	0 (0.0%)	2 (0.6%)	0.104
mal_prev_clothes	1 (1.0%)	1 (0.5%)	2 (0.6%)	0.544
mal_prev_spray_coil	28 (27.2%)	61 (28.4%)	89 (28.0%)	0.825
mal_prev_itn	49 (47.6%)	105 (48.8%)	154 (48.4%)	0.833
bld_loss_3mths_2	6 (5.8%)	31 (14.4%)	37 (11.6%)	0.025
age_months	24 (11, 70)	45 (15, 105)	38 (14, 93)	0.002
dura_hosp_stay	5 (2, 8)	6 (3, 11)	6 (3, 10)	0.024
picu_required	3 (2.9%)	10 (4.7%)	13 (4.1%)	0.559
txn_required				<0.001
FACTOR 8 CONCENTRATE	0 (0.0%)	1 (0.5%)	1 (0.3%)
No	101 (98.1%)	138 (64.2%)	239 (75.2%)
PLATELETS	0 (0.0%)	2 (0.9%)	2 (0.6%)
Yes	2 (1.9%)	74 (34.4%)	76 (23.9%)
iron_supp_3mths	48 (46.6%)	77 (35.8%)	125 (39.3%)	0.065
exbf_4mths_older_chn	67 (65.0%)	116 (54.0%)	183 (57.5%)	0.061
eat_iron_veges	62 (68.9%)	104 (51.2%)	166 (56.7%)	0.005
Unknown	13	12	25
family_income				0.007
Low	59 (57.3%)	158 (73.5%)	217 (68.2%)
Middle	40 (38.8%)	48 (22.3%)	88 (27.7%)
High	4 (3.9%)	9 (4.2%)	13 (4.1%)
paternal_education				0.147
None	9 (8.7%)	35 (16.3%)	44 (13.8%)
Primary	31 (30.1%)	65 (30.2%)	96 (30.2%)
Secondary	36 (35.0%)	77 (35.8%)	113 (35.5%)
Tertiary	27 (26.2%)	38 (17.7%)	65 (20.4%)
maternal_education				0.597
None	8 (7.8%)	24 (11.2%)	32 (10.1%)
Primary	41 (39.8%)	89 (41.4%)	130 (40.9%)
Secondary	33 (32.0%)	69 (32.1%)	102 (32.1%)
Tertiary	21 (20.4%)	33 (15.3%)	54 (17.0%)
residence				0.209
Rural	36 (35.0%)	91 (42.3%)	127 (39.9%)
Urban	67 (65.0%)	124 (57.7%)	191 (60.1%)
gender				0.744
Female	48 (46.6%)	96 (44.7%)	144 (45.3%)
Male	55 (53.4%)	119 (55.3%)	174 (54.7%)
bld_film_done	4 (3.9%)	106 (49.3%)	110 (34.6%)	<0.001
bf_ida	2 (1.9%)	52 (24.2%)	54 (17.0%)	<0.001
bf_scd	0 (0.0%)	26 (12.1%)	26 (8.2%)	<0.001
bf_infection	3 (2.9%)	51 (23.7%)	54 (17.0%)	<0.001
bf_liver	1 (1.0%)	14 (6.5%)	15 (4.7%)	0.043
bf_renal	1 (1.0%)	12 (5.6%)	13 (4.1%)	0.068
bf_malignancy	0 (0.0%)	7 (3.3%)	7 (2.2%)	0.101
bf_hemolytic	1 (1.0%)	10 (4.7%)	11 (3.5%)	0.112
bf_malaria	0 (0.0%)	2 (0.9%)	2 (0.6%)	>0.999
bf_inflamation	1 (1.0%)	8 (3.7%)	9 (2.8%)	0.280
ida	2 (1.9%)	94 (43.7%)	96 (30.2%)	<0.001
malaria	3 (2.9%)	43 (20.0%)	46 (14.5%)	<0.001
scd	2 (1.9%)	41 (19.1%)	43 (13.5%)	<0.001
g6pd	0 (0.0%)	10 (4.7%)	10 (3.1%)	0.034
anemia_of_malig	0 (0.0%)	4 (1.9%)	4 (1.3%)	0.309
anemia_of_chr_dx	0 (0.0%)	2 (0.9%)	2 (0.6%)	>0.999
diag_appendicitis	1 (1.0%)	2 (0.9%)	3 (0.9%)	>0.999
diag_asthma	2 (1.9%)	0 (0.0%)	2 (0.6%)	0.104
diag_bronchiolitis	3 (2.9%)	2 (0.9%)	5 (1.6%)	0.334
diag_gastroenteritis	16 (15.5%)	10 (4.7%)	26 (8.2%)	<0.001
diag_Liver_pathology	0 (0.0%)	7 (3.3%)	7 (2.2%)	0.101
diag_nephropathy	2 (1.9%)	17 (7.9%)	19 (6.0%)	0.036
diag_malaria	1 (1.0%)	22 (10.2%)	23 (7.2%)	0.003
diag_sepsis	5 (4.9%)	35 (16.3%)	40 (12.6%)	0.004
diag_urti	4 (3.9%)	3 (1.4%)	7 (2.2%)	0.219
diag_lrti	5 (4.9%)	26 (12.1%)	31 (9.7%)	0.042
diag_cardiac	17 (16.5%)	4 (1.9%)	21 (6.6%)	<0.001
diag_malignancy	5 (4.9%)	14 (6.5%)	19 (6.0%)	0.560
diag_others	11 (10.7%)	14 (6.5%)	25 (7.9%)	0.196
diag_cns	13 (12.6%)	19 (8.8%)	32 (10.1%)	0.294
diag_poison	3 (2.9%)	6 (2.8%)	9 (2.8%)	>0.999
diag_dka	1 (1.0%)	2 (0.9%)	3 (0.9%)	>0.999
diag_g6pd	0 (0.0%)	2 (0.9%)	2 (0.6%)	>0.999
diag_rbc_abnormalities	3 (2.9%)	38 (17.7%)	41 (12.9%)	<0.001
diag_hiv	1 (1.0%)	8 (3.7%)	9 (2.8%)	0.280
diag_surgical	11 (10.7%)	8 (3.7%)	19 (6.0%)	0.014
¹ n (%); Median (Q1, Q3)
² Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum test

Table 2

Code

df_anemia_ready %>% 
    gtsummary::tbl_cross(
        row = rdw_cat,
        col = mcv_cat,
        missing = "no",
        percent = "row",
        digits = c(0,1)) %>% 
    gtsummary::bold_labels()

	mcv_cat			Total
	Microcytic	Normocytic	Macrocytic	Total
rdw_cat
Normal	43 (51.8%)	38 (45.8%)	2 (2.4%)	83 (100.0%)
High	139 (66.2%)	61 (29.0%)	10 (4.8%)	210 (100.0%)
Total	182 (62.1%)	99 (33.8%)	12 (4.1%)	293 (100.0%)

Table 3

Code

df_anemia_ready %>% 
    select(
        anemia, rdw_cat, mcv_cat, died, rbc_transfused, immun_status,
        deworm_6mths_2, rdt_positive, mps_positive, mal_prev_use, 
        mal_prev_repellent, mal_prev_clothes,  mal_prev_spray_coil,
        mal_prev_itn, bld_loss_3mths_2, age_months, dura_hosp_stay, 
        picu_required, txn_required, iron_supp_3mths, exbf_4mths_older_chn, 
        eat_iron_veges, family_income,  paternal_education,
        maternal_education, residence,  gender, bld_film_done, bf_ida, 
        bf_scd, bf_infection, bf_liver, bf_renal, bf_malignancy, 
        bf_hemolytic, bf_malaria, bf_inflamation, ida, malaria, scd, 
        g6pd, anemia_of_malig:diag_surgical) %>% 
    gtsummary::tbl_summary(
        by =  rbc_transfused, 
        digits = list(gtsummary::all_categorical() ~ c(0,1))) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols() ~ "**Transfusion Done**") %>% 
    gtsummary::add_overall(last = T) %>%
    gtsummary::add_p(
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)) %>%
    gtsummary::bold_labels() %>%
    gtsummary::bold_p()

Characteristic	Transfusion Done		Overall N = 318¹	p-value²
Characteristic	No N = 242¹	Yes N = 76¹	Overall N = 318¹	p-value²
anemia	141 (58.3%)	74 (97.4%)	215 (67.6%)	<0.001
rdw_cat				<0.001
Normal	75 (33.8%)	8 (11.3%)	83 (28.3%)
High	147 (66.2%)	63 (88.7%)	210 (71.7%)
Unknown	20	5	25
mcv_cat				0.118
Microcytic	141 (63.5%)	41 (57.7%)	182 (62.1%)
Normocytic	75 (33.8%)	24 (33.8%)	99 (33.8%)
Macrocytic	6 (2.7%)	6 (8.5%)	12 (4.1%)
Unknown	20	5	25
died	20 (8.3%)	10 (13.2%)	30 (9.4%)	0.203
immun_status				0.330
Full	178 (73.6%)	62 (81.6%)	240 (75.5%)
Partial	61 (25.2%)	14 (18.4%)	75 (23.6%)
None	3 (1.2%)	0 (0.0%)	3 (0.9%)
deworm_6mths_2	115 (59.0%)	33 (49.3%)	148 (56.5%)	0.166
Unknown	47	9	56
rdt_positive	24 (9.9%)	20 (26.3%)	44 (13.8%)	<0.001
mps_positive	18 (9.9%)	10 (14.3%)	28 (11.1%)	0.320
Unknown	60	6	66
mal_prev_use	190 (78.5%)	53 (69.7%)	243 (76.4%)	0.116
mal_prev_repellent	2 (0.8%)	0 (0.0%)	2 (0.6%)	>0.999
mal_prev_clothes	1 (0.4%)	1 (1.3%)	2 (0.6%)	0.421
mal_prev_spray_coil	68 (28.1%)	21 (27.6%)	89 (28.0%)	0.937
mal_prev_itn	122 (50.4%)	32 (42.1%)	154 (48.4%)	0.206
bld_loss_3mths_2	25 (10.3%)	12 (15.8%)	37 (11.6%)	0.195
age_months	35 (12, 84)	55 (21, 116)	38 (14, 93)	0.038
dura_hosp_stay	5 (2, 8)	8 (4, 16)	6 (3, 10)	<0.001
picu_required	8 (3.3%)	5 (6.6%)	13 (4.1%)	0.201
txn_required				<0.001
FACTOR 8 CONCENTRATE	1 (0.4%)	0 (0.0%)	1 (0.3%)
No	239 (98.8%)	0 (0.0%)	239 (75.2%)
PLATELETS	2 (0.8%)	0 (0.0%)	2 (0.6%)
Yes	0 (0.0%)	76 (100.0%)	76 (23.9%)
iron_supp_3mths	98 (40.5%)	27 (35.5%)	125 (39.3%)	0.439
exbf_4mths_older_chn	144 (59.5%)	39 (51.3%)	183 (57.5%)	0.208
eat_iron_veges	134 (60.6%)	32 (44.4%)	166 (56.7%)	0.016
Unknown	21	4	25
family_income				0.019
Low	157 (64.9%)	60 (78.9%)	217 (68.2%)
Middle	76 (31.4%)	12 (15.8%)	88 (27.7%)
High	9 (3.7%)	4 (5.3%)	13 (4.1%)
paternal_education				0.254
None	31 (12.8%)	13 (17.1%)	44 (13.8%)
Primary	68 (28.1%)	28 (36.8%)	96 (30.2%)
Secondary	90 (37.2%)	23 (30.3%)	113 (35.5%)
Tertiary	53 (21.9%)	12 (15.8%)	65 (20.4%)
maternal_education				0.369
None	23 (9.5%)	9 (11.8%)	32 (10.1%)
Primary	96 (39.7%)	34 (44.7%)	130 (40.9%)
Secondary	77 (31.8%)	25 (32.9%)	102 (32.1%)
Tertiary	46 (19.0%)	8 (10.5%)	54 (17.0%)
residence				0.212
Rural	92 (38.0%)	35 (46.1%)	127 (39.9%)
Urban	150 (62.0%)	41 (53.9%)	191 (60.1%)
gender				0.877
Female	109 (45.0%)	35 (46.1%)	144 (45.3%)
Male	133 (55.0%)	41 (53.9%)	174 (54.7%)
bld_film_done	62 (25.6%)	48 (63.2%)	110 (34.6%)	<0.001
bf_ida	39 (16.1%)	15 (19.7%)	54 (17.0%)	0.463
bf_scd	11 (4.5%)	15 (19.7%)	26 (8.2%)	<0.001
bf_infection	28 (11.6%)	26 (34.2%)	54 (17.0%)	<0.001
bf_liver	6 (2.5%)	9 (11.8%)	15 (4.7%)	0.002
bf_renal	6 (2.5%)	7 (9.2%)	13 (4.1%)	0.017
bf_malignancy	4 (1.7%)	3 (3.9%)	7 (2.2%)	0.364
bf_hemolytic	6 (2.5%)	5 (6.6%)	11 (3.5%)	0.141
bf_malaria	1 (0.4%)	1 (1.3%)	2 (0.6%)	0.421
bf_inflamation	4 (1.7%)	5 (6.6%)	9 (2.8%)	0.039
ida	57 (23.6%)	39 (51.3%)	96 (30.2%)	<0.001
malaria	26 (10.7%)	20 (26.3%)	46 (14.5%)	<0.001
scd	23 (9.5%)	20 (26.3%)	43 (13.5%)	<0.001
g6pd	5 (2.1%)	5 (6.6%)	10 (3.1%)	0.063
anemia_of_malig	3 (1.2%)	1 (1.3%)	4 (1.3%)	>0.999
anemia_of_chr_dx	0 (0.0%)	2 (2.6%)	2 (0.6%)	0.057
diag_appendicitis	3 (1.2%)	0 (0.0%)	3 (0.9%)	>0.999
diag_asthma	2 (0.8%)	0 (0.0%)	2 (0.6%)	>0.999
diag_bronchiolitis	5 (2.1%)	0 (0.0%)	5 (1.6%)	0.343
diag_gastroenteritis	26 (10.7%)	0 (0.0%)	26 (8.2%)	0.003
diag_Liver_pathology	4 (1.7%)	3 (3.9%)	7 (2.2%)	0.364
diag_nephropathy	13 (5.4%)	6 (7.9%)	19 (6.0%)	0.412
diag_malaria	14 (5.8%)	9 (11.8%)	23 (7.2%)	0.075
diag_sepsis	24 (9.9%)	16 (21.1%)	40 (12.6%)	0.011
diag_urti	6 (2.5%)	1 (1.3%)	7 (2.2%)	>0.999
diag_lrti	17 (7.0%)	14 (18.4%)	31 (9.7%)	0.003
diag_cardiac	19 (7.9%)	2 (2.6%)	21 (6.6%)	0.110
diag_malignancy	11 (4.5%)	8 (10.5%)	19 (6.0%)	0.091
diag_others	24 (9.9%)	1 (1.3%)	25 (7.9%)	0.015
diag_cns	29 (12.0%)	3 (3.9%)	32 (10.1%)	0.042
diag_poison	9 (3.7%)	0 (0.0%)	9 (2.8%)	0.121
diag_dka	3 (1.2%)	0 (0.0%)	3 (0.9%)	>0.999
diag_g6pd	1 (0.4%)	1 (1.3%)	2 (0.6%)	0.421
diag_rbc_abnormalities	21 (8.7%)	20 (26.3%)	41 (12.9%)	<0.001
diag_hiv	7 (2.9%)	2 (2.6%)	9 (2.8%)	>0.999
diag_surgical	18 (7.4%)	1 (1.3%)	19 (6.0%)	0.053
¹ n (%); Median (Q1, Q3)
² Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum test

Table 4

Code

df_anemia_ready %>% 
    select(
        anemia, rdw_cat, mcv_cat, died, rbc_transfused, immun_status,
        deworm_6mths_2, rdt_positive, mps_positive, mal_prev_use, 
        mal_prev_repellent, mal_prev_clothes,  mal_prev_spray_coil,
        mal_prev_itn, bld_loss_3mths_2, age_months, dura_hosp_stay, 
        picu_required, txn_required, iron_supp_3mths, exbf_4mths_older_chn, 
        eat_iron_veges, family_income,  paternal_education,
        maternal_education, residence,  gender, bld_film_done, bf_ida, 
        bf_scd, bf_infection, bf_liver, bf_renal, bf_malignancy, 
        bf_hemolytic, bf_malaria, bf_inflamation, ida, malaria, scd, 
        g6pd, anemia_of_malig:diag_surgical) %>% 
    gtsummary::tbl_summary(
        by =  died, 
        digits = list(gtsummary::all_categorical() ~ c(0,1))) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols() ~ "**Died**") %>% 
    gtsummary::add_overall(last = T) %>%
    gtsummary::add_p(
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)) %>%
    gtsummary::bold_labels() %>%
    gtsummary::bold_p()

Characteristic	Died		Overall N = 318¹	p-value²
Characteristic	No N = 288¹	Yes N = 30¹	Overall N = 318¹	p-value²
anemia	193 (67.0%)	22 (73.3%)	215 (67.6%)	0.481
rdw_cat				0.334
Normal	78 (29.1%)	5 (20.0%)	83 (28.3%)
High	190 (70.9%)	20 (80.0%)	210 (71.7%)
Unknown	20	5	25
mcv_cat				0.044
Microcytic	172 (64.2%)	10 (40.0%)	182 (62.1%)
Normocytic	85 (31.7%)	14 (56.0%)	99 (33.8%)
Macrocytic	11 (4.1%)	1 (4.0%)	12 (4.1%)
Unknown	20	5	25
rbc_transfused	66 (22.9%)	10 (33.3%)	76 (23.9%)	0.203
immun_status				0.042
Full	223 (77.4%)	17 (56.7%)	240 (75.5%)
Partial	62 (21.5%)	13 (43.3%)	75 (23.6%)
None	3 (1.0%)	0 (0.0%)	3 (0.9%)
deworm_6mths_2	137 (56.8%)	11 (52.4%)	148 (56.5%)	0.692
Unknown	47	9	56
rdt_positive	41 (14.2%)	3 (10.0%)	44 (13.8%)	0.781
mps_positive	26 (11.5%)	2 (8.0%)	28 (11.1%)	>0.999
Unknown	61	5	66
mal_prev_use	217 (75.3%)	26 (86.7%)	243 (76.4%)	0.165
mal_prev_repellent	2 (0.7%)	0 (0.0%)	2 (0.6%)	>0.999
mal_prev_clothes	2 (0.7%)	0 (0.0%)	2 (0.6%)	>0.999
mal_prev_spray_coil	82 (28.5%)	7 (23.3%)	89 (28.0%)	0.551
mal_prev_itn	135 (46.9%)	19 (63.3%)	154 (48.4%)	0.086
bld_loss_3mths_2	32 (11.1%)	5 (16.7%)	37 (11.6%)	0.369
age_months	42 (14, 94)	18 (8, 45)	38 (14, 93)	0.015
dura_hosp_stay	6 (3, 11)	4 (3, 6)	6 (3, 10)	0.166
picu_required	6 (2.1%)	7 (23.3%)	13 (4.1%)	<0.001
txn_required				0.107
FACTOR 8 CONCENTRATE	1 (0.3%)	0 (0.0%)	1 (0.3%)
No	220 (76.4%)	19 (63.3%)	239 (75.2%)
PLATELETS	1 (0.3%)	1 (3.3%)	2 (0.6%)
Yes	66 (22.9%)	10 (33.3%)	76 (23.9%)
iron_supp_3mths	111 (38.5%)	14 (46.7%)	125 (39.3%)	0.386
exbf_4mths_older_chn	167 (58.0%)	16 (53.3%)	183 (57.5%)	0.624
eat_iron_veges	151 (56.3%)	15 (60.0%)	166 (56.7%)	0.724
Unknown	20	5	25
family_income				0.874
Low	195 (67.7%)	22 (73.3%)	217 (68.2%)
Middle	81 (28.1%)	7 (23.3%)	88 (27.7%)
High	12 (4.2%)	1 (3.3%)	13 (4.1%)
paternal_education				0.971
None	40 (13.9%)	4 (13.3%)	44 (13.8%)
Primary	88 (30.6%)	8 (26.7%)	96 (30.2%)
Secondary	101 (35.1%)	12 (40.0%)	113 (35.5%)
Tertiary	59 (20.5%)	6 (20.0%)	65 (20.4%)
maternal_education				0.434
None	30 (10.4%)	2 (6.7%)	32 (10.1%)
Primary	121 (42.0%)	9 (30.0%)	130 (40.9%)
Secondary	89 (30.9%)	13 (43.3%)	102 (32.1%)
Tertiary	48 (16.7%)	6 (20.0%)	54 (17.0%)
residence				0.429
Rural	113 (39.2%)	14 (46.7%)	127 (39.9%)
Urban	175 (60.8%)	16 (53.3%)	191 (60.1%)
gender				0.822
Female	131 (45.5%)	13 (43.3%)	144 (45.3%)
Male	157 (54.5%)	17 (56.7%)	174 (54.7%)
bld_film_done	101 (35.1%)	9 (30.0%)	110 (34.6%)	0.579
bf_ida	47 (16.3%)	7 (23.3%)	54 (17.0%)	0.330
bf_scd	26 (9.0%)	0 (0.0%)	26 (8.2%)	0.152
bf_infection	48 (16.7%)	6 (20.0%)	54 (17.0%)	0.644
bf_liver	12 (4.2%)	3 (10.0%)	15 (4.7%)	0.159
bf_renal	11 (3.8%)	2 (6.7%)	13 (4.1%)	0.352
bf_malignancy	6 (2.1%)	1 (3.3%)	7 (2.2%)	0.504
bf_hemolytic	9 (3.1%)	2 (6.7%)	11 (3.5%)	0.278
bf_malaria	2 (0.7%)	0 (0.0%)	2 (0.6%)	>0.999
bf_inflamation	9 (3.1%)	0 (0.0%)	9 (2.8%)	>0.999
ida	86 (29.9%)	10 (33.3%)	96 (30.2%)	0.693
malaria	42 (14.6%)	4 (13.3%)	46 (14.5%)	>0.999
scd	42 (14.6%)	1 (3.3%)	43 (13.5%)	0.097
g6pd	10 (3.5%)	0 (0.0%)	10 (3.1%)	0.607
anemia_of_malig	3 (1.0%)	1 (3.3%)	4 (1.3%)	0.329
anemia_of_chr_dx	2 (0.7%)	0 (0.0%)	2 (0.6%)	>0.999
diag_appendicitis	3 (1.0%)	0 (0.0%)	3 (0.9%)	>0.999
diag_asthma	2 (0.7%)	0 (0.0%)	2 (0.6%)	>0.999
diag_bronchiolitis	5 (1.7%)	0 (0.0%)	5 (1.6%)	>0.999
diag_gastroenteritis	26 (9.0%)	0 (0.0%)	26 (8.2%)	0.152
diag_Liver_pathology	3 (1.0%)	4 (13.3%)	7 (2.2%)	0.002
diag_nephropathy	18 (6.3%)	1 (3.3%)	19 (6.0%)	>0.999
diag_malaria	22 (7.6%)	1 (3.3%)	23 (7.2%)	0.709
diag_sepsis	31 (10.8%)	9 (30.0%)	40 (12.6%)	0.006
diag_urti	7 (2.4%)	0 (0.0%)	7 (2.2%)	>0.999
diag_lrti	28 (9.7%)	3 (10.0%)	31 (9.7%)	>0.999
diag_cardiac	20 (6.9%)	1 (3.3%)	21 (6.6%)	0.706
diag_malignancy	15 (5.2%)	4 (13.3%)	19 (6.0%)	0.091
diag_others	22 (7.6%)	3 (10.0%)	25 (7.9%)	0.718
diag_cns	29 (10.1%)	3 (10.0%)	32 (10.1%)	>0.999
diag_poison	9 (3.1%)	0 (0.0%)	9 (2.8%)	>0.999
diag_dka	3 (1.0%)	0 (0.0%)	3 (0.9%)	>0.999
diag_g6pd	2 (0.7%)	0 (0.0%)	2 (0.6%)	>0.999
diag_rbc_abnormalities	40 (13.9%)	1 (3.3%)	41 (12.9%)	0.149
diag_hiv	8 (2.8%)	1 (3.3%)	9 (2.8%)	0.595
diag_surgical	19 (6.6%)	0 (0.0%)	19 (6.0%)	0.235
¹ n (%); Median (Q1, Q3)
² Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum test