Anemia Manuscript

Author

Dr Samuel Blay Nguah

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 < 8) ~ "Moderate", 
            (age_months >= 1 & hb < 9) ~ "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 >= 5 & mcv > 85 ~ "Macrocytic", 
            age_months >= 6 & mcv >= 73  ~ "Normocytic",
            age_months >= 6 & mcv < 73  ~ "Microcytic",
            age_months >= 1 & mcv > 108 ~ "Macrocytic", 
            age_months >= 1 & mcv >= 74  ~ "Normocytic",
            age_months >= 1 & mcv < 74  ~ "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",
            age_months >= 1 & rdw_cv > 16.5 ~ "High", 
            age_months >= 1 & rdw_cv >= 11.0  ~ "Normal",
            age_months >= 1 & rdw_cv < 11.0  ~ "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),
        malnutrition = case_when(
            str_detect(history, "MALNUTRITION") ~ "Yes", 
            str_detect(history_other, "MALNUTRITION") ~ "Yes", 
            str_detect(discharge_diagnosis_upper, "MALNUTRITION") ~ "Yes", 
            TRUE ~ "No"),
        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)), 
        age_cat = case_when(
            (age_months >= 1 & age_months <= 5) ~ "3 to 5 months", 
            (age_months >= 6 & age_months <= 23) ~ "6 to 23 months", 
            (age_months >= 24 & age_months <= 59) ~ "24 to 59 months", 
            (age_months >= 60 & age_months <= 143) ~ "5 to 11 years", 
            (age_months >= 144 & age_months <= 179 & gender == "Female") ~ 
                "12 to 14 years(girls)", 
            (age_months >= 144 & age_months <= 179 & gender == "Male") ~ 
                "12 to 14 years(boys)", 
            (age_months >= 180 & gender == "Female") ~ 
                ">= 15 years (girls)",
            (age_months >= 180 & gender == "Male") ~ 
                ">= 15 years (boys)") %>% 
            factor(levels = c(
                "3 to 5 months", "6 to 23 months", "24 to 59 months", 
                "5 to 11 years", "12 to 14 years(girls)", 
                "12 to 14 years(boys)",">= 15 years (girls)", 
                ">= 15 years (boys)")), 
        age_cat_2 = case_when(
            (age_months >= 6 & age_months <= 59) ~ "6 to 59 months", 
            (age_months < 6) ~ "< 6 months", 
            (age_months > 59) ~ "> 59 months"), 
        exbf_4mths_older_chn = case_when(
            age_months <= 48 ~ exbf_4mths_older_chn ))
Joining with `by = join_by(study_id)`
Code 
labelled::var_label(df_anemia_ready) <- 
    list(
        anemia ="Anemia Present", 
        rdw_cat = "Categorised RDW", 
        mcv_cat = "categorised MCV", 
        died = "Patient Died", 
        rbc_transfused = "Transfused Blood", 
        immun_status = "Immunity Status",
        deworm_6mths_2 = "Dewormed in the past 6 months", 
        rdt_positive = "RDT Positive", 
        mps_positive = "Malaria parasites positive", 
        mal_prev_use = "Use of Malatria prevention method", 
        mal_prev_repellent = "Prevention: Repellent", 
        mal_prev_clothes = "Prevention: Clothes",  
        mal_prev_spray_coil = "Prevention: Spray or Coil",
        mal_prev_itn = "Prevention: ITN", 
        bld_loss_3mths_2 = "Blood loss (past 2 months)", 
        age_months = "Age (Months)", 
        dura_hosp_stay = "Duration of Hospital Stay", 
        picu_required = "PICU admision required", 
        txn_required = "Transfusion required", 
        iron_supp_3mths = "Iron supp. past 3 months", 
        exbf_4mths_older_chn = 
            "Exclusive Breastfeeding (4 months) in older child", 
        eat_iron_veges = "Eats iron containing vegetables", 
        family_income = "Family Income",  
        paternal_education = "Father's educational level",
        maternal_education = "Mother's educational level", 
        residence = "Residence type",  
        gender = "Sex", 
        bld_film_done= "Blood film done", 
        bf_ida = "Blood Film: IDA", 
        bf_scd = "Blood Film: SCD", 
        bf_infection = "Blood Film: Infection", 
        bf_liver = "Blood Film: Liver pathology", 
        bf_renal = "Blood Film: Renal pathology", 
        bf_malignancy = "Blood Film: Malignancy", 
        bf_hemolytic = "Blood Film: Hemolysis", 
        bf_malaria = "Blood Film: Malaria", 
        bf_inflamation = "Blood Film: Inflamation", 
        ida = "Iron Defecienvy Anemia", 
        malaria = "Malaria", 
        scd = "Sicle cell Disease", 
        g6pd = "G6PD",
        anemia_of_malig = "Anemia of malignacy", 
        anemia_of_chr_dx = "Anemia of Chronic Disease",
        diag_bronchiolitis = "Diagnosis: Bronchiolitis",
        diag_gastroenteritis = "Diagnosis: Gastroenteritis", 
        diag_Liver_pathology = "Diagnosis: Liver Pathology",
        diag_nephropathy = "Diagnosis: Nephropathy", 
        diag_malaria = "Diagnosis: malaria",
        diag_sepsis = "Diagnosis: Sepsis",
        diag_urti = "Diagnosis: URTI",
        diag_lrti = "Diagnosis: LRTI", 
        diag_cardiac = "Diagnosis: Cardiopathy",
        diag_malignancy = "Diagnosis: malignancy", 
        diag_others = "Diagnosis: Others", 
        diag_cns = "Diagnosis: CNS",
        diag_poison = "Diagnosis: Poisons",
        diag_dka = "Diagnosis: DKA", 
        diag_rbc_abnormalities = "Diagnosis: Hemoglobinopathies",
        diag_hiv = "Diagnosis: HIV", 
        diag_surgical = "Diagnosis: Surgical", 
        age_cat = "Categorised Age", 
        age_cat_2 = "Categorised Age (6-59)", 
        malnutrition = "Malnutrition")

Figure I

Code 
df_anemia_ready %>% 
    select(starts_with("diag_"), anemia) %>% 
    pivot_longer(cols = starts_with("diag_")) %>% 
    filter(value == "Yes") %>% 
    count(anemia, name, value) %>% 
    arrange(name, n) %>% 
    rename(`Anemia Present` = anemia) %>% 
    mutate(name = factor(
        name,
        levels = c(
            "diag_rbc_abnormalities", 
            "diag_sepsis", 
            "diag_lrti", 
            "diag_malaria", 
            "diag_cns", 
            "diag_nephropathy", 
            "diag_cardiac",  
            "diag_gastroenteritis", 
            "diag_malignancy",
            "diag_others","diag_surgical", "diag_hiv", "diag_poison",
            "diag_urti", "diag_bronchiolitis",
            "diag_dka", "diag_Liver_pathology"),
        labels = c(
            "Haematologic", 
            "Sepsis", 
            "LRTI",
            "Malaria", 
            "CNS", 
            "Nephropathy", 
            "Cardiology", 
            "Gastroenteritis",
            "Oncologic",
            "Others","Surgical", "HIV", "Poison", "URTI",
            "Asthma/Bronchiolitis",
             "DKA", "Hepathopathy"))) %>% 
    ggplot(
        aes(
            x = name, 
            y = n, 
            color = `Anemia Present`, 
            fill = `Anemia Present`))+
    geom_col(position = position_dodge(0.8), width = .70, alpha = .9)+
    labs(x = NULL, y = "Frequency")+
    theme_bw()+
    scale_fill_manual(
        name = NULL, 
        labels = c("Normal", "Anaemia"), 
        values = c("#376996", "#db143b"))+
    scale_color_manual(
        name = NULL, 
        labels = c("Normal", "Anaemia"), 
        values = c("#376996", "#db143b"))+
    theme(
        axis.text.x.bottom = element_text(
            angle = 90, 
            hjust = 1, 
            vjust = 0.5), 
        legend.position = "top")

Code 
ggsave("Anemia at PEU/Figures/Figure I.jpg", width = 6, height = 4)

Table I

Code 
table_one_new <- 
    df_anemia_ready %>%
    select(anemia, age_months, gender, residence, maternal_education,
           maternal_education, family_income, exbf_4mths_older_chn,
           eat_iron_veges, bld_loss_3mths_2, mal_prev_use, 
           iron_supp_3mths, deworm_6mths_2, scd, g6pd, ida, 
           diag_malignancy, diag_sepsis,  malnutrition, 
           rdt_positive, diag_lrti, 
           picu_required, rbc_transfused, dura_hosp_stay, died, 
           age_cat_2) %>% 
    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()

table_one_new
Characteristic
Anemia Present
Overall
N = 3181		 p-value2
No
N = 1061		 Yes
N = 2121
Age (Months) 23 (10, 68) 47 (16, 106) 38 (14, 93) <0.001
Sex


0.633
    Female 50 (47.2%) 94 (44.3%) 144 (45.3%)
    Male 56 (52.8%) 118 (55.7%) 174 (54.7%)
Residence type


0.293
    Rural 38 (35.8%) 89 (42.0%) 127 (39.9%)
    Urban 68 (64.2%) 123 (58.0%) 191 (60.1%)
Mother's educational level


0.653
    None 10 (9.4%) 22 (10.4%) 32 (10.1%)
    Primary 41 (38.7%) 89 (42.0%) 130 (40.9%)
    Secondary 33 (31.1%) 69 (32.5%) 102 (32.1%)
    Tertiary 22 (20.8%) 32 (15.1%) 54 (17.0%)
Family Income


0.019
    Low 62 (58.5%) 155 (73.1%) 217 (68.2%)
    Middle 40 (37.7%) 48 (22.6%) 88 (27.7%)
    High 4 (3.8%) 9 (4.2%) 13 (4.1%)
Exclusive Breastfeeding (4 months) in older child 47 (66.2%) 59 (54.6%) 106 (59.2%) 0.123
    Unknown 35 104 139
Eats iron containing vegetables 62 (68.9%) 104 (51.2%) 166 (56.7%) 0.005
    Unknown 16 9 25
Blood loss (past 2 months) 6 (5.7%) 31 (14.6%) 37 (11.6%) 0.019
Use of Malatria prevention method 82 (77.4%) 161 (75.9%) 243 (76.4%) 0.779
Iron supp. past 3 months 49 (46.2%) 76 (35.8%) 125 (39.3%) 0.074
Dewormed in the past 6 months 47 (57.3%) 101 (56.1%) 148 (56.5%) 0.855
    Unknown 24 32 56
Sicle cell Disease 2 (1.9%) 41 (19.3%) 43 (13.5%) <0.001
G6PD 0 (0.0%) 10 (4.7%) 10 (3.1%) 0.034
Iron Defecienvy Anemia 3 (2.8%) 94 (44.3%) 97 (30.5%) <0.001
Diagnosis: malignancy 5 (4.7%) 14 (6.6%) 19 (6.0%) 0.503
Diagnosis: Sepsis 7 (6.6%) 33 (15.6%) 40 (12.6%) 0.023
Malnutrition 15 (14.2%) 34 (16.0%) 49 (15.4%) 0.660
RDT Positive 3 (2.8%) 41 (19.3%) 44 (13.8%) <0.001
Diagnosis: LRTI 5 (4.7%) 26 (12.3%) 31 (9.7%) 0.032
PICU admision required 3 (2.8%) 10 (4.7%) 13 (4.1%) 0.555
Transfused Blood 3 (2.8%) 73 (34.4%) 76 (23.9%) <0.001
Duration of Hospital Stay 5 (2, 8) 6 (3, 11) 6 (3, 10) 0.041
Patient Died 9 (8.5%) 21 (9.9%) 30 (9.4%) 0.684
Categorised Age (6-59)


<0.001
    < 6 months 16 (15.1%) 9 (4.2%) 25 (7.9%)
    > 59 months 30 (28.3%) 90 (42.5%) 120 (37.7%)
    6 to 59 months 60 (56.6%) 113 (53.3%) 173 (54.4%)
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test
Code 
file.remove("Anemia at PEU/Tables/table_one_new.docx")
[1] TRUE
Code 
table_one_new %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave("Anemia at PEU/Tables/table_one_new.docx")

Table II

Code 
table_two_new <- 
    df_anemia_ready %>% 
    gtsummary::tbl_cross(
        row = age_cat,
        col = anemia_cat,
        missing = "ifany",
        percent = "row",
        digits = c(0,1)) %>% 
    gtsummary::bold_labels()

table_two_new
anemia_cat
Total
None Mild Moderate Severe
Categorised Age




    3 to 5 months 16 (64.0%) 4 (16.0%) 2 (8.0%) 3 (12.0%) 25 (100.0%)
    6 to 23 months 37 (38.9%) 22 (23.2%) 29 (30.5%) 7 (7.4%) 95 (100.0%)
    24 to 59 months 23 (29.5%) 14 (17.9%) 33 (42.3%) 8 (10.3%) 78 (100.0%)
    5 to 11 years 23 (25.6%) 4 (4.4%) 33 (36.7%) 30 (33.3%) 90 (100.0%)
    12 to 14 years(girls) 5 (35.7%) 2 (14.3%) 3 (21.4%) 4 (28.6%) 14 (100.0%)
    12 to 14 years(boys) 2 (16.7%) 1 (8.3%) 7 (58.3%) 2 (16.7%) 12 (100.0%)
    >= 15 years (girls) 0 (0.0%) 0 (0.0%) 3 (75.0%) 1 (25.0%) 4 (100.0%)
    >= 15 years (boys) 0 (NA%) 0 (NA%) 0 (NA%) 0 (NA%) 0 (NA%)
Total 106 (33.3%) 47 (14.8%) 110 (34.6%) 55 (17.3%) 318 (100.0%)
Code 
file.remove("Anemia at PEU/Tables/table_two_new.docx")
[1] TRUE
Code 
table_two_new %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave("Anemia at PEU/Tables/table_two_new.docx")

Table IIIA

Code 
table_threeA_new <- 
    df_anemia_ready %>%
    select(anemia, rdw_cat, mcv_cat) %>% 
    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()

table_threeA_new
Characteristic
Anemia Present
Overall
N = 3181		 p-value2
No
N = 1061		 Yes
N = 2121
Categorised RDW


<0.001
    Normal 53 (50.0%) 40 (18.9%) 93 (29.2%)
    High 53 (50.0%) 172 (81.1%) 225 (70.8%)
categorised MCV


0.180
    Microcytic 67 (63.2%) 128 (60.4%) 195 (61.3%)
    Normocytic 38 (35.8%) 73 (34.4%) 111 (34.9%)
    Macrocytic 1 (0.9%) 11 (5.2%) 12 (3.8%)
1 n (%)
2 Pearson’s Chi-squared test; Fisher’s exact test
Code 
file.remove("Anemia at PEU/Tables/table_threeA_new.docx")
[1] TRUE
Code 
table_threeA_new %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave("Anemia at PEU/Tables/table_threeA_new.docx")

Table IIIB

Code 
table_threeB_new <- 
    df_anemia_ready %>%
    mutate(
        anemia = factor(
            anemia, 
            levels = c("No", "Yes"), 
            labels = c("Anemia Absent", "Anemia Present"))) %>% 
  gtsummary::tbl_strata(
    strata = anemia,
    .tbl_fun = ~ .x %>% 
      gtsummary::tbl_cross(
        row = mcv_cat, 
        col = rdw_cat, 
        percent = "cell", 
        missing = "ifany", )) %>%
      gtsummary::bold_labels()

table_threeB_new
Anemia Absent
Anemia Present
Normal High Total Normal High Total
categorised MCV





    Microcytic 27 (25%) 40 (38%) 67 (63%) 20 (9.4%) 108 (51%) 128 (60%)
    Normocytic 26 (25%) 12 (11%) 38 (36%) 18 (8.5%) 55 (26%) 73 (34%)
    Macrocytic 0 (0%) 1 (0.9%) 1 (0.9%) 2 (0.9%) 9 (4.2%) 11 (5.2%)
Total 53 (50%) 53 (50%) 106 (100%) 40 (19%) 172 (81%) 212 (100%)
Code 
file.remove("Anemia at PEU/Tables/table_threeB_new.docx")
[1] TRUE
Code 
table_threeB_new %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave("Anemia at PEU/Tables/table_threeB_new.docx")

Table 4: Uni-variate Regression

Code 
x <- 
    df_anemia_ready %>%
    select(anemia, age_months, gender, residence, maternal_education,
           maternal_education, family_income, exbf_4mths_older_chn,
           eat_iron_veges, malnutrition,
           bld_loss_3mths_2, mal_prev_use, iron_supp_3mths, deworm_6mths_2, 
           scd, g6pd, ida, diag_malignancy, rdt_positive, diag_lrti, 
           diag_sepsis,
           picu_required, rbc_transfused, dura_hosp_stay, died) %>% 
    gtsummary::tbl_uvregression(
        method = glm,
        y = anemia,
        method.args = family(binomial),
        exponentiate = TRUE,
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)) %>% 
    gtsummary::modify_header(
            estimate ~ "**COR**",
            label ~ "**Variable**") %>%
    gtsummary::bold_labels() %>% 
    gtsummary::bold_p()
There was a warning running `tbl_regression()` for variable "g6pd". See message
below.
! glm.fit: fitted probabilities numerically 0 or 1 occurred, glm.fit: fitted
  probabilities numerically 0 or 1 occurred, glm.fit: fitted probabilities
  numerically 0 or 1 occurred, glm.fit: fitted probabilities numerically 0 or 1
  occurred, glm.fit: fitted probabilities numerically 0 or 1 occurred, glm.fit:
  fitted probabilities numerically 0 or 1 occurred, glm.fit: fitted
  probabilities numerically 0 or 1 occurred, glm.fit: fitted probabilities
  numerically 0 or 1 occurred, glm.fit: fitted probabilities numerically 0 or 1
  occurred, glm.fit: fitted probabilities numerically 0 or 1 occurred, and
  collapsing to unique 'x' values

Table 4 - Multivariate

Code 
y <- 
    df_anemia_ready %>% 
    select(anemia, age_months, family_income, 
           eat_iron_veges, bld_loss_3mths_2, iron_supp_3mths, 
           scd, ida, rdt_positive, diag_lrti, diag_sepsis,
           rbc_transfused, dura_hosp_stay) %>%
    glm(anemia ~ ., data = ., family=binomial) %>%
    gtsummary::tbl_regression(
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3),
        exponentiate = T
    ) %>% 
    gtsummary::modify_header(
            estimate ~ "**AOR**",
            label ~ "**Variable**") %>% 
    gtsummary::bold_p() %>% 
    gtsummary::bold_labels()


table_four_new <- 
    gtsummary::tbl_merge(
        tbls = list(x, y), 
        tab_spanner = c("Univariate", "Multivariate"))
The number rows in the tables to be merged do not match, which may result in
rows appearing out of order.
ℹ See `tbl_merge()` (`?gtsummary::tbl_merge()`) help file for details. Use
  `quiet=TRUE` to silence message.
Code 
table_four_new
Variable
Univariate
Multivariate
N COR 95% CI p-value AOR 95% CI p-value
Age (Months) 318 1.01 1.00, 1.01 0.002 1.01 1.00, 1.01 0.179
Sex 318





    Female
— —



    Male
1.12 0.70, 1.79 0.633


Residence type 318





    Rural
— —



    Urban
0.77 0.47, 1.25 0.293


Mother's educational level 318





    None
— —



    Primary
0.99 0.41, 2.23 0.975


    Secondary
0.95 0.39, 2.20 0.907


    Tertiary
0.66 0.26, 1.64 0.380


Family Income 318





    Low
— —
— —
    Middle
0.48 0.29, 0.80 0.005 0.80 0.37, 1.72 0.572
    High
0.90 0.28, 3.42 0.865 0.99 0.17, 5.26 0.987
Exclusive Breastfeeding (4 months) in older child 179





    No
— —



    Yes
0.61 0.33, 1.14 0.125


Eats iron containing vegetables 293





    No
— —
— —
    Yes
0.47 0.28, 0.80 0.005 0.81 0.36, 1.81 0.605
Malnutrition 318





    No
— —



    Yes
1.16 0.61, 2.29 0.661


Blood loss (past 2 months) 318





    No
— —
— —
    Yes
2.85 1.23, 7.80 0.024 1.75 0.57, 5.83 0.338
Use of Malatria prevention method 318





    No
— —



    Yes
0.92 0.52, 1.59 0.779


Iron supp. past 3 months 318





    No
— —
— —
    Yes
0.65 0.40, 1.04 0.075 0.69 0.34, 1.41 0.313
Dewormed in the past 6 months 262





    No
— —



    Yes
0.95 0.56, 1.61 0.855


Sicle cell Disease 318





    No
— —
— —
    Yes
12.5 3.73, 77.5 <0.001 9.70 2.37, 66.7 0.005
G6PD 318





    No
— —



    Yes
8,213,090 0.00,
 0.983


Iron Defecienvy Anemia 318





    No
— —
— —
    Yes
27.4 9.88, 114 <0.001 36.6 10.3, 234 <0.001
Diagnosis: malignancy 318





    No
— —



    Yes
1.43 0.53, 4.52 0.505


RDT Positive 318





    No
— —
— —
    Yes
8.23 2.90, 34.6 <0.001 7.91 2.39, 36.2 0.002
Diagnosis: LRTI 318





    No
— —
— —
    Yes
2.82 1.14, 8.55 0.039 1.92 0.52, 7.38 0.326
Diagnosis: Sepsis 318





    No
— —
— —
    Yes
2.61 1.18, 6.62 0.027 2.30 0.65, 9.03 0.209
PICU admision required 318





    No
— —



    Yes
1.70 0.51, 7.70 0.428


Transfused Blood 318





    No
— —
— —
    Yes
18.0 6.48, 75.0 <0.001 8.65 2.22, 58.0 0.007
Duration of Hospital Stay 318 1.03 1.00, 1.07 0.033 1.01 0.96, 1.06 0.655
Patient Died 318





    No
— —



    Yes
1.18 0.54, 2.82 0.684


Abbreviations: CI = Confidence Interval, OR = Odds Ratio
Code 
file.remove("Anemia at PEU/Tables/table_four_new.docx")
[1] TRUE
Code 
table_four_new %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave("Anemia at PEU/Tables/table_four_new.docx")

Mortality and associated factors

Code 
table_four_new <- 
    df_anemia_ready %>%
    select(anemia, age_months, gender, residence, maternal_education,
           maternal_education, family_income, exbf_4mths_older_chn,
           eat_iron_veges, bld_loss_3mths_2, mal_prev_use, 
           iron_supp_3mths, deworm_6mths_2, scd, g6pd, ida, 
           diag_malignancy, diag_sepsis,  malnutrition, 
           rdt_positive, starts_with("daig_"), 
           picu_required, rbc_transfused, dura_hosp_stay, died, 
           age_cat_2) %>% 
    gtsummary::tbl_summary(
        by = died, 
        digits = list(gtsummary::all_categorical() ~ c(0,1))) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols() ~ "**Death**") %>% 
    gtsummary::add_overall(last = T) %>%
    gtsummary::add_p(
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)) %>%
    gtsummary::bold_labels() %>%
    gtsummary::bold_p()

table_four_new
Characteristic
Death
Overall
N = 3181		 p-value2
No
N = 2881		 Yes
N = 301
Anemia Present 191 (66.3%) 21 (70.0%) 212 (66.7%) 0.684
Age (Months) 42 (14, 94) 18 (8, 45) 38 (14, 93) 0.015
Sex


0.822
    Female 131 (45.5%) 13 (43.3%) 144 (45.3%)
    Male 157 (54.5%) 17 (56.7%) 174 (54.7%)
Residence type


0.429
    Rural 113 (39.2%) 14 (46.7%) 127 (39.9%)
    Urban 175 (60.8%) 16 (53.3%) 191 (60.1%)
Mother's educational level


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%)
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%)
Exclusive Breastfeeding (4 months) in older child 95 (60.9%) 11 (47.8%) 106 (59.2%) 0.234
    Unknown 132 7 139
Eats iron containing vegetables 151 (56.3%) 15 (60.0%) 166 (56.7%) 0.724
    Unknown 20 5 25
Blood loss (past 2 months) 32 (11.1%) 5 (16.7%) 37 (11.6%) 0.369
Use of Malatria prevention method 217 (75.3%) 26 (86.7%) 243 (76.4%) 0.165
Iron supp. past 3 months 111 (38.5%) 14 (46.7%) 125 (39.3%) 0.386
Dewormed in the past 6 months 137 (56.8%) 11 (52.4%) 148 (56.5%) 0.692
    Unknown 47 9 56
Sicle cell Disease 42 (14.6%) 1 (3.3%) 43 (13.5%) 0.097
G6PD 10 (3.5%) 0 (0.0%) 10 (3.1%) 0.607
Iron Defecienvy Anemia 87 (30.2%) 10 (33.3%) 97 (30.5%) 0.724
Diagnosis: malignancy 15 (5.2%) 4 (13.3%) 19 (6.0%) 0.091
Diagnosis: Sepsis 31 (10.8%) 9 (30.0%) 40 (12.6%) 0.006
Malnutrition 41 (14.2%) 8 (26.7%) 49 (15.4%) 0.105
RDT Positive 41 (14.2%) 3 (10.0%) 44 (13.8%) 0.781
PICU admision required 6 (2.1%) 7 (23.3%) 13 (4.1%) <0.001
Transfused Blood 66 (22.9%) 10 (33.3%) 76 (23.9%) 0.203
Duration of Hospital Stay 6 (3, 11) 4 (3, 6) 6 (3, 10) 0.166
Categorised Age (6-59)


0.031
    < 6 months 20 (6.9%) 5 (16.7%) 25 (7.9%)
    > 59 months 114 (39.6%) 6 (20.0%) 120 (37.7%)
    6 to 59 months 154 (53.5%) 19 (63.3%) 173 (54.4%)
1 n (%); Median (Q1, Q3)
2 Pearson’s Chi-squared test; Wilcoxon rank sum test; Fisher’s exact test
Code 
file.remove("Anemia at PEU/Tables/table_four_new.docx")
[1] TRUE
Code 
table_four_new %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave("Anemia at PEU/Tables/table_four_new.docx")

Further tables from Reveiwers

Code 
table_five_A <- 
  df_anemia_ready %>%
  select(
    anemia_cat, transfusion_3mths, picu_required, died, 
    dura_hosp_stay, rdt_positive, scd, g6pd, ida, 
    diag_sepsis, diag_lrti,diag_malignancy, malnutrition) %>% 
  gtsummary::tbl_summary(
    by = anemia_cat, 
    label = list(transfusion_3mths ~ "Transfusion")) %>% 
  gtsummary::add_overall(last = T) %>%
  gtsummary::bold_labels()

table_five_B <- 
  df_anemia_ready %>%
  select(
    anemia_cat, transfusion_3mths, picu_required, died, 
    dura_hosp_stay, rdt_positive, scd, g6pd, ida, 
    diag_sepsis, diag_lrti,diag_malignancy, malnutrition) %>% 
    gtsummary::tbl_uvregression(
        method = ordinal::clm,
        y = anemia_cat,
        exponentiate = TRUE,
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3), 
        show_single_row = c(
          anemia_cat, transfusion_3mths, picu_required, died, 
          dura_hosp_stay, rdt_positive, scd, g6pd, ida, 
          diag_sepsis, diag_lrti,diag_malignancy, malnutrition), 
         hide_n = TRUE, 
    label = list(transfusion_3mths ~ "Transfusion")) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::bold_p() 

table_five <- 
  gtsummary::tbl_merge(
        tbls = list(table_five_A, table_five_B))

table_five
Characteristic
Table 1
Table 2
None
N = 1061		 Mild
N = 471		 Moderate
N = 1101		 Severe
N = 551		 Overall
N = 3181		 OR 95% CI p-value
Transfusion 4 (3.8%) 3 (6.4%) 9 (8.2%) 9 (16%) 25 (7.9%) 2.82 1.33, 6.11 0.007
PICU admision required 3 (2.8%) 5 (11%) 4 (3.6%) 1 (1.8%) 13 (4.1%) 0.82 0.32, 2.09 0.683
Patient Died 9 (8.5%) 5 (11%) 12 (11%) 4 (7.3%) 30 (9.4%) 1.01 0.52, 1.96 0.979
Duration of Hospital Stay 5 (2, 8) 5 (2, 7) 6 (3, 11) 8 (4, 14) 6 (3, 10) 1.04 1.02, 1.07 <0.001
RDT Positive 3 (2.8%) 6 (13%) 21 (19%) 14 (25%) 44 (14%) 3.52 1.99, 6.32 <0.001
Sicle cell Disease 2 (1.9%) 0 (0%) 21 (19%) 20 (36%) 43 (14%) 7.96 4.28, 15.1 <0.001
G6PD 0 (0%) 1 (2.1%) 6 (5.5%) 3 (5.5%) 10 (3.1%) 3.48 1.19, 10.6 0.023
Iron Defecienvy Anemia 3 (2.8%) 17 (36%) 49 (45%) 28 (51%) 97 (31%) 5.14 3.28, 8.18 <0.001
Diagnosis: Sepsis 7 (6.6%) 9 (19%) 19 (17%) 5 (9.1%) 40 (13%) 1.36 0.77, 2.40 0.291
Diagnosis: LRTI 5 (4.7%) 3 (6.4%) 11 (10%) 12 (22%) 31 (9.7%) 3.26 1.63, 6.63 <0.001
Diagnosis: malignancy 5 (4.7%) 2 (4.3%) 6 (5.5%) 6 (11%) 19 (6.0%) 1.85 0.77, 4.48 0.166
Malnutrition 15 (14%) 6 (13%) 19 (17%) 9 (16%) 49 (15%) 1.19 0.68, 2.07 0.538
1 n (%); Median (Q1, Q3)
Abbreviations: CI = Confidence Interval, OR = Odds Ratio
Code 
file.remove("Anemia at PEU/Tables/table_five.docx")
[1] TRUE
Code 
table_five %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave("Anemia at PEU/Tables/table_five.docx")