1 Load Data

The CSV files for the 2013 cohort were downloaded from the NBER collection of Birth Cohort Linked Birth and Infant Death Data of the National Vital Statistics System of the National Center for Health Statistics, on the NBER website.

linked_deaths <- read_csv("../data/linkco2013us_num.csv.zip")
linked_deaths
linked_births <- read_csv("../data/linkco2013us_den.csv.zip")
linked_births

Each row corresponds to a newborn. When the newborn died, an identified with a unique ID (column idnumber) is provided in both tables. A lot of columns from both files contain the same information. We will only keep some of those columns.

1.1 Deaths

First, let us focus on the subsample of deaths, only on the following variables.

deaths <- 
  linked_deaths %>% 
  select(idnumber, d_restatus, hospd, weekdayd, dthyr, dthmon,
         dob_yy, dob_mm, dob_wk, aged)

Warning

Some variables, specific to the deaths file contain only NA:

stoccfipd: “State of Occurrence (FIPS) - Death”,
cntocfipd: “County of Occurrence (FIPS) of Death”,
stresfipd: “State of Residence (FIPS) - Death”,
drcnty: “State of Residence Death Recode”,
cntyrfpd: “County of Residence (FIPS) - Death”,
cntrsppd: “Population Size of County of Residence of Death”,

We therefore do not keep these.

Let us encode the values for categorical variables:

Show the R codes

deaths <- 
  deaths %>% 
  mutate(
    d_restatus = factor(
      d_restatus, 
      levels = c(1:4), 
      labels = c("Residents", "Intrastate Nonresidents", 
                 "Interstate or Interterritory Nonresidents", "Foreign Residents")
    ),
    hospd = factor(
      hospd,
      levels = c(1:7, 9),
      labels = c("Hospital, clinic or Medical Center - Inpatient",
                 "Hospital, clinic or Medical Center - ER",
                 "Hospital, clinic or Medical Center - Dead on Arrival",
                 "Decedent's home",
                 "Hospice facility",
                 "Nursing home/long term care",
                 "Other",
                 "Unknown"
      ),
    ),
    weekdayd = factor(
      weekdayd, 
      levels = c(1:7, 9),
      labels = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday",
                 "Friday", "Saturday", "Unknown")
    ),
    dthmon = factor(dthmon, levels = 1:12),
    dob_mm = factor(dob_mm, levels = 1:12),
    dob_wk = factor(
      dob_wk,
      levels = c(1:7, 9),
      labels = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday",
                 "Friday", "Saturday", "Unknown")
    )
  )

The description of those columns can be added to our tibble, using the set_variable_labels() function from {labelled}.

library(labelled)

Show the R codes

deaths <- 
  deaths %>% 
  labelled::set_variable_labels(
    d_restatus = "Death Resident Status",
    hospd = "Place of Death and Decedent's Status",
    weekdayd = "Day of Week of Death",
    dthyr = "Year of Death",
    dthmon = "Month of Death",
    dob_yy = "Birth Year",
    dob_mm = "Birth Month",
    dob_wk = "Birth Weekday",
    aged = "Age at Death in Days"
  )

Let us save this tibble which contains only the information that we are interested in.

save(deaths, file = "../data/deaths.RData")

Here is an overview of the first rows:

deaths

# A tibble: 23,159 × 10
   idnumber d_restatus     hospd weekd…¹ dthyr dthmon dob_yy dob_mm dob_wk  aged
      <dbl> <fct>          <fct> <fct>   <dbl> <fct>   <dbl> <fct>  <fct>  <dbl>
 1        1 Residents      Hosp… Thursd…  2013 2        2013 1      Thurs…    56
 2        2 Intrastate No… Hosp… Friday   2013 1        2013 1      Sunday    12
 3        3 Residents      Hosp… Saturd…  2013 1        2013 1      Satur…     0
 4        4 Residents      Hosp… Sunday   2013 1        2013 1      Sunday     0
 5        5 Intrastate No… Unkn… Tuesday  2013 1        2013 1      Monday     1
 6        6 Residents      Hosp… Sunday   2013 1        2013 1      Sunday     0
 7        7 Residents      Hosp… Sunday   2013 1        2013 1      Monday     6
 8        8 Residents      Hosp… Friday   2013 1        2013 1      Wedne…     9
 9        9 Residents      Hosp… Monday   2013 7        2013 1      Wedne…   180
10       10 Residents      Hosp… Tuesday  2013 1        2013 1      Tuesd…     0
# … with 23,149 more rows, and abbreviated variable name ¹weekdayd

1.2 Births

Now let us turn to the births dataset. As for the deaths dataset, let us recode the values and add some labels.

births <- 
  linked_births %>% 
  select(
    # Mother's characteristics
    mager41, mbrace, mracerec, mar, meduc,
    # Father's characteristics
    fagecomb, fbrace, fracerec,
    # Child's characteristics
    idnumber, bfacil, bfacil3, restatus, sex, dbwt,
    # Pregnancy
    lbo, tbo, precare_rec, uprevis, wtgain, cig_1, cig_2, cig_3, cig_rec,
    dlmp_mm, dlmp_yy, estgest, combgest,
    # Risk Factor
    rf_diab, rf_gest, rf_phyp, rf_ghyp, rf_eclam, rf_ppterm,
    rf_ppoutc, rf_cesar,
    # Obstetric Procedures
    op_cerv, op_tocol, op_ecvs, op_ecvf, uop_induc, uop_tocol,
    # Onset of Labor
    on_ruptr, on_abrup, on_prolg,
    # Charact. of Labor and Delivery
    ld_induct, ld_augment, ld_nvrtx, ld_steroids, ld_antibio, 
    ld_chorio, ld_mecon, ld_fintol, ld_anesth,
    # Complications of Labor and Delivery
    uld_meco, uld_precip, uld_breech,
    # Method of Delivery, Delivery
    md_present, md_route, md_trial, rdmeth_rec, dmeth_rec, 
    attend, apgar5, dplural,
    # Abnormal Conditions of the Newborn
    ab_vent, ab_vent6, ab_nicu, ab_surfac, ab_antibio, ab_seiz, ab_inj,
    # Congenital Anomalies of the Newborn
    ca_anen, ca_menin, ca_heart, ca_hernia, ca_ompha, ca_gastro, 
    ca_limb, ca_cleftlp, ca_cleft, ca_downs, ca_chrom, ca_hypos,
    # Death
    manner
  )

Recoding the observations, following the guide:

Show the R codes

births <- 
  births %>% 
  mutate(
    mager41 = factor(
      mager41,
      levels = c(12:50),
      labels = c("10-12", 13:49, "50-64"),
    ),
    bfacil = factor(
      bfacil,
      levels = c(1:7, 9),
      labels = c(
        "Hospital",
        "Freestanding Birth Center",
        "Home (intended)",
        "Home (not intended)",
        "Home (unknown if intended)",
        "Clinic / Doctor’s Office",
        "Other",
        "Unknown"
      )
    ),
    bfacil3 = factor(
      bfacil3,
      levels = c(1,2,3),
      labels = c("Hospital", "Not in Hospital", "Unknown or Not Stated")
    ),
    restatus = factor(
      restatus,
      levels = c(1:4), 
      labels = c("Residents", "Intrastate Nonresidents", 
                 "Interstate or Interterritory Nonresidents", "Foreign Residents")
    ),
    mbrace = factor(
      mbrace,
      levels = c(1:14, 21:24),
      labels = c(
        "White – single race",
        "Black – single race",
        "American Indian – single race",
        "Asian Indian – single race",
        "Chinese – single race",
        "Filipino – single race",
        "Japanese – single race",
        "Korean – single race",
        "Vietnamese – single race",
        "Other Asian – single race",
        "Hawaiian – single race",
        "Guamanian – single race",
        "Samoan – single race",
        "Other Pacific Islander – single race",
        "White – bridged multiple race",
        "Black – bridged multiple race",
        "American Indian & Alaskan Native – bridged multiple race",
        "Asian / Pacific Islander – bridged multiple race"
      )
    ),
    mracerec = factor(
      mracerec,
      levels = c(0, 1:4),
      labels = c(
        "Other (not classified as White or Black)",
        "White",
        "Black",
        "American Indian / Alaskan Native ",
        "Asian / Pacific Islander"
      )
      ),
    mar = factor(
      mar,
      levels = c(1,2, 3, 9),
      labels = c("Yes", "No", "Unmarried parents not living together",
                 "Unknown or not Stated")
    ),
    meduc = factor(
      meduc,
      levels = c(1:9),
      labels = c(
        "8th grade or less",
        "9th through 12th grade with no diploma",
        "High school graduate or GED completed",
        "Some college credit, but not a degree",
        "Associate degree (AA,AS)",
        "Bachelor’s degree (BA, AB, BS)",
        "Master’s degree (MA, MS, MEng, MEd, MSW, MBA)",
        "Doctorate (PhD, EdD) or Professional Degree (MD, DDS, DVM, LLB, JD)",
        "Unknown"
      )
    ),
    fagecomb = ifelse(fagecomb == 99, NA, fagecomb),
    fbrace = factor(
      fbrace,
      levels = c(1:14, 21:24, 99),
      labels = c(
        "White – single race",
        "Black – single race",
        "American Indian – single race",
        "Asian Indian – single race",
        "Chinese – single race",
        "Filipino – single race",
        "Japanese – single race",
        "Korean – single race",
        "Vietnamese – single race",
        "Other Asian – single race",
        "Hawaiian – single race",
        "Guamanian – single race",
        "Samoan – single race",
        "Other Pacific Islander – single race",
        "White – bridged multiple race",
        "Black – bridged multiple race",
        "American Indian & Alaskan Native – bridged multiple race",
        "Asian / Pacific Islander – bridged multiple race",
        "Unknown or not stated, also includes states not reporting multiple race"
      )
    ),
    fracerec = factor(
      fracerec,
      levels = c(0:4, 9),
      labels = c(
       "Other (not classified as White or Black)",
       "White",
       "Black",
       "American Indian / Alaskan Native",
       "Asian / Pacific Islander",
       "Unknown or not stated"
      ),
    ),
    lbo = ifelse(lbo == 9, NA, lbo), # careful: 8: 8+
    tbo = ifelse(tbo == 9, NA, tbo), # careful: 8: 8+
    precare_rec = factor(
      precare_rec,
      levels = c(1:5),
      labels = c(
        "1st to 3rd month",
        "4th to 6th month",
        "7th to final month",
        "No prenatal care",
        "Unknown or not stated"
      )
    ),
    uprevis = ifelse(uprevis == 99, NA, uprevis),
    wtgain = ifelse(wtgain == 99, NA, wtgain), # careful: 98: 98+
    cig_1 = ifelse(cig_1 == 99, NA, cig_1), # careful: 98: 98+
    cig_2 = ifelse(cig_2 == 99, NA, cig_1), # careful: 98: 98+
    cig_3 = ifelse(cig_3 == 99, NA, cig_1), # careful: 98: 98+
    cig_rec = factor(
      cig_rec,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    rf_diab = factor(
      rf_diab,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    rf_gest = factor(
      rf_gest,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    rf_phyp = factor(
      rf_phyp,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    rf_ghyp = factor(
      rf_ghyp,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    rf_eclam = factor(
      rf_eclam,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    rf_ppterm = factor(
      rf_ppterm,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    rf_ppoutc = factor(
      rf_ppoutc,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    rf_cesar = factor(
      rf_cesar,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    op_cerv = factor(
      op_cerv,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    op_tocol = factor(
      op_tocol,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    op_ecvs = factor(
      op_ecvs,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    op_ecvf = factor(
      op_ecvf,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    uop_induc = factor(
      uop_induc,
      levels = c(1,2,8,9),
      labels = c("Yes", "No", "Not on certificate", "Unknown or not stated")
    ),
    uop_tocol = factor(
      uop_tocol,
      levels = c(1,2,8,9),
      labels = c("Yes", "No", "Not on certificate", "Unknown or not stated")
    ),
    on_ruptr = factor(
      on_ruptr,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    on_abrup = factor(
      on_abrup,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    on_prolg = factor(
      on_prolg,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    ld_induct = factor(
      ld_induct,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    ld_augment = factor(
      ld_augment,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    ld_nvrtx = factor(
      ld_nvrtx,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    ld_steroids = factor(
      ld_steroids,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    ld_antibio = factor(
      ld_antibio,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    ld_chorio = factor(
      ld_chorio,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    ld_mecon = factor(
      ld_mecon,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    ld_fintol = factor(
      ld_fintol,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    ld_anesth = factor(
      ld_anesth,
      levels = c("Y", "N", "U"),
      labels = c("Yes", "No", "Unknown or not stated")
    ),
    uld_meco = factor(
      uld_meco,
      levels = c(1,2,8,9),
      labels = c("Yes", "No", "Not on certificate", "Unknown or not stated")
    ),
    uld_precip = factor(
      uld_precip,
      levels = c(1,2,8,9),
      labels = c("Yes", "No", "Not on certificate", "Unknown or not stated")
    ),
    uld_breech = factor(
      uld_breech,
      levels = c(1,2,8,9),
      labels = c("Yes", "No", "Not on certificate", "Unknown or not stated")
    ),
    md_present = factor(
      md_present,
      levels = c(1,2,3,9),
      labels = c(
        "Cephalic",
        "Breech",
        "Other",
        "Unknown or not stated"
      )
    ),
    md_route = factor(
      md_route,
      levels = c(1,2,3,4,9),
      labels = c(
        "Spontaneous",
        "Forceps",
        "Vacuum",
        "Cesarean",
        "Unknown or not stated"
      )
    ),
    md_trial = factor(
      md_trial,
      levels = c("Y", "N", "X", "U"),
      labels = c(
        "Yes", "No",
        "Not applicable ", "Unknown or not stated"
      )
    ),
    rdmeth_rec = factor(
      rdmeth_rec,
      levels = c(1:6, 9),
      labels = c(
        "Vaginal",
        "Vaginal after previous c-section",
        "Primary C-section",
        "Repeat C-section",
        "Vaginal (unknown if previous c-section)",
        "C-section (unknown if previous c-section)",
        "Not stated"
      )
    ),
    dmeth_rec = factor(
      dmeth_rec,
      levels = c(1,2,9),
      labels = c("Vaginal", "C-Section", "Unknown")
    ),
    attend = factor(
      attend,
      levels = c(1:5, 9),
      labels = c(
        "Doctor of Medicine",
        "Doctor of Osteopathy",
        "Certified Nurse Midwife",
        "Other Midwife",
        "Other",
        "Unknown or not stated"
      )
    ),
    apgar5 = ifelse(apgar5 == 99, yes = NA, no = apgar5),
    dplural = factor(
      dplural,
      levels = c(1:5),
      labels = c(
        "Single",
        "Twin",
        "Triplet",
        "Quadruplet",
        "Quintuplet or higher"
      )
    ),
    sex = factor(
      sex,
      levels = c("M", "F"),
      labels = c("Male", "Female")
    ),
    dlmp_mm = ifelse(dlmp_mm == 99, NA, dlmp_mm),
    dlmp_yy = ifelse(dlmp_yy == 9999, NA, dlmp_yy),
    estgest = ifelse(estgest == 99, yes = NA, estgest),
    combgest = ifelse(combgest == 99, NA, combgest),
    dbwt = ifelse(dbwt == 9999, NA, dbwt),
    ab_vent = factor(
      ab_vent,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, Complication reported",
        "No Complication reported",
        "Unknown or not stated"
      )
    ),
    ab_vent6 = factor(
      ab_vent6,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, Complication reported",
        "No Complication reported",
        "Unknown or not stated"
      )
    ),
    ab_nicu = factor(
      ab_nicu,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, Complication reported",
        "No Complication reported",
        "Unknown or not stated"
      )
    ),
    ab_surfac = factor(
      ab_surfac,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, Complication reported",
        "No Complication reported",
        "Unknown or not stated"
      )
    ),
    ab_antibio = factor(
      ab_antibio,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, Complication reported",
        "No Complication reported",
        "Unknown or not stated"
      )
    ),
    ab_seiz = factor(
      ab_seiz,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, Complication reported",
        "No Complication reported",
        "Unknown or not stated"
      )
    ),
    ab_inj = factor(
      ab_inj,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, Complication reported",
        "No Complication reported",
        "Unknown or not stated"
      )
    ),
    ca_anen = factor(
      ca_anen,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_menin = factor(
      ca_menin,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_heart = factor(
      ca_heart,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_hernia = factor(
      ca_hernia,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_ompha = factor(
      ca_ompha,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_gastro = factor(
      ca_gastro,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_limb = factor(
      ca_limb,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_cleftlp = factor(
      ca_cleftlp,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_cleft = factor(
      ca_cleft,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_downs = factor(
      ca_downs,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_chrom = factor(
      ca_chrom,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    ca_hypos = factor(
      ca_hypos,
      levels = c("Y", "N", "U"),
      labels = c(
        "Yes, anomaly reported",
        "No, anomaly not reported",
        "Unknown"
      )
    ),
    manner = factor(
      manner,
      levels = c(1:7),
      labels = c(
        "Accident",
        "Suicide",
        "Homicide",
        "Pending investigation",
        "Could not determine",
        "Self-inflicted",
        "Natural"
      )
    )
  )

Then, let us add some labels to each column.

Show the R codes

births <- 
  births %>% 
  labelled::set_variable_labels(
    # Child's characteristics
    bfacil = "Birth Place",
    bfacil3 = "Birth Place",
    restatus ="Resident Status",
    sex = "Sex of Infant",
    dbwt = "Birth Weight (in Grams)",
    # Death of the newborn
    manner = "Manner of Death",
    # Mother's characteristics
    mager41 = "Mother's Age",
    mbrace = "Mother's Bridged Race",
    mracerec = "Mother's Race",
    mar ="Mother's Marital Status",
    meduc = "Mother's Education",
    # Father's characteristics
    fagecomb = "Father's Combined Age",
    fbrace = "Father's Bridged Race",
    fracerec = "Father's Race",
    # Pregnancy
    lbo = "Live Birth Order",
    tbo = "Total Birth Order",
    precare_rec = "Month Prenatal Care Began",
    uprevis = "Number of Prenatal Visits",
    wtgain = "Weight Gain",
    cig_1 = "Cigarettes 1st Trimester",
    cig_2 = "Cigarettes 2nd Trimester",
    cig_3 = "Cigarettes 3rd Trimester",
    cig_rec = "Smokes cigarettes",
    dlmp_mm = "Last Normal Menses: Month",
    dlmp_yy = "Last Normal Menses: Year",
    estgest = "Obstetric/Clinical Gestation Est.",
    combgest = "Gestation – Detail in Weeks",
    # Risk factor
    rf_diab = "Risk Factor: Prepregnancy Diabetes",
    rf_gest = "Risk Factor: Gestational Diabetes",
    rf_phyp = "Risk Factor: Prepregnancy Hypertension",
    rf_ghyp = "Risk Factor: Gestational Hypertension",
    rf_eclam = "Risk Factor: Hypertension Eclampsia",
    rf_ppterm = "Risk Factor: Previous Preterm Birth",
    rf_ppoutc = "Risk Factor: Poor Pregnancy Outcome",
    rf_cesar = "Risk Factor: Previous Cesarean Deliveries",
    # Obstetric procedures
    op_cerv =  "Obstetric Procedures: Cervical Cerclage",
    op_tocol = "Obstetric Procedures: Tocolysis",
    op_ecvs = "Obstetric Procedures: Successful External Cephalic",
    op_ecvf = "Obstetric Procedures: Failed External Cephalic",
    uop_induc = "Obstetric Procedures: Induction of labor",
    uop_tocol = "Obstetric Procedures: Tocolysis",
    on_ruptr = "Onset of Labor: Premature Rupture of Membrane",
    on_abrup = "Onset of Labor: Abruptio placenta",
    on_prolg = "Onset of Labor: Prolonged Labor",
    # Labor
    ld_induct = "Charact. of Labor and Delivery: Induction of Labor",
    ld_augment = "Charact. of Labor and Delivery: Augmentation of Labor",
    ld_nvrtx = "Charact. of Labor and Delivery: Non-Vertex Presentation",
    ld_steroids = "Charact. of Labor and Delivery: Steroids",
    ld_antibio = "Charact. of Labor and Delivery: Antibiotics",
    ld_chorio = "Charact. of Labor and Delivery: Chorioamnionitis",
    ld_mecon = "Charact. of Labor and Delivery: Meconium Staining",
    ld_fintol = "Charact. of Labor and Delivery: Fetal Intolerance",
    ld_anesth = "Charact. of Labor and Delivery: Anesthesia",
    uld_meco = "Complications of Labor and Delivery: Meconium",
    uld_precip = "Complications of Labor and Delivery: Precipitous labor",
    uld_breech = "Complications of Labor and Delivery: Breech",
    md_present = "Method of Delivery: Fetal Presentation",
    md_route = "Method of Delivery: Final Route and Method of Delivery",
    md_trial = "Method of Delivery: Trial of Labor Attempted",
    rdmeth_rec = "Delivery Method",
    dmeth_rec = "Delivery Method",
    attend = "Attendant",
    apgar5 = "Five Minute Apgar Score",
    dplural = "Plurality",
    # Abnormal Conditions and anomalies of the newborn
    ab_vent = "Abnormal Conditions of the Newborn: Assisted Ventilation",
    ab_vent6 = "Abnormal Conditions of the Newborn: Assisted Ventilation >6hrs",
    ab_nicu = "Abnormal Conditions of the Newborn: Admission to NICU",
    ab_surfac = "Abnormal Conditions of the Newborn: Surfactant",
    ab_antibio = "Abnormal Conditions of the Newborn: Antibiotics",
    ab_seiz = "Abnormal Conditions of the Newborn: Seizures",
    ab_inj = "Abnormal Conditions of the Newborn: Birth Injury",
    #
    ca_anen = "Congenital Anomalies of the Newborn: Anencephaly",
    ca_menin = "Congenital Anomalies of the Newborn: Meningomyelocele/Spina Bifida",
    ca_heart = "Congenital Anomalies of the Newborn: Cyanotic Congenital Heart Disease",
    ca_hernia = "Congenital Anomalies of the Newborn: Congenital Diaphragmatic Hernia",
    ca_ompha = "Congenital Anomalies of the Newborn: Omphalocele",
    ca_gastro = "Congenital Anomalies of the Newborn: Gastroschisis",
    ca_limb = "Congenital Anomalies of the Newborn: Limb Reduction Deficit",
    ca_cleftlp = "Congenital Anomalies of the Newborn: Cleft Lip w/ or w/o Cleft Palate",
    ca_cleft = "Congenital Anomalies of the Newborn: Cleft Palate Alone",
    ca_downs = "Congenital Anomalies of the Newborn: Downs Syndrome",
    ca_chrom = "Congenital Anomalies of the Newborn: Suspected Chromosonal Disorder",
    ca_hypos = "Congenital Anomalies of the Newborn: Hypospadias"
  )

And let us save the data:

save(births, file = "../data/births.RData")

Warning

The father’s education is missing from the data.

Here is an extract from the first rows of the births data:

births

# A tibble: 3,940,764 × 84
   mager41 mbrace      mrace…¹ mar   meduc fagec…² fbrace frace…³ idnum…⁴ bfacil
   <fct>   <fct>       <fct>   <fct> <fct>   <dbl> <fct>  <fct>     <dbl> <fct> 
 1 22      White – si… "White" Yes   High…      23 Unkno… Unknow…      NA Hospi…
 2 25      White – br… "White" Yes   Some…      27 White… White        NA Hospi…
 3 25      White – si… "White" Yes   Bach…      25 White… White        NA Hospi…
 4 30      White – si… "White" Yes   Bach…      31 White… White        NA Hospi…
 5 23      American I… "Ameri… No    9th …      30 Ameri… Americ…      NA Hospi…
 6 32      White – br… "White" No    High…      32 Ameri… Americ…      NA Hospi…
 7 23      American I… "Ameri… No    Some…      26 Ameri… Americ…      NA Hospi…
 8 21      White – si… "White" Yes   Some…      21 White… White        NA Hospi…
 9 31      White – si… "White" Yes   Bach…      35 White… White        NA Frees…
10 25      White – si… "White" Yes   Some…      32 White… White        NA Hospi…
# … with 3,940,754 more rows, 74 more variables: bfacil3 <fct>, restatus <fct>,
#   sex <fct>, dbwt <dbl>, lbo <dbl>, tbo <dbl>, precare_rec <fct>,
#   uprevis <dbl>, wtgain <dbl>, cig_1 <dbl>, cig_2 <dbl>, cig_3 <dbl>,
#   cig_rec <fct>, dlmp_mm <dbl>, dlmp_yy <dbl>, estgest <dbl>, combgest <dbl>,
#   rf_diab <fct>, rf_gest <fct>, rf_phyp <fct>, rf_ghyp <fct>, rf_eclam <fct>,
#   rf_ppterm <fct>, rf_ppoutc <fct>, rf_cesar <fct>, op_cerv <fct>,
#   op_tocol <fct>, op_ecvs <fct>, op_ecvf <fct>, uop_induc <fct>, …

2 Descriptive statistics

df <- 
  births %>% 
  left_join(deaths)

We can quickly produce some descriptive statistics from those data, using the tbl_summary() function from {gtsummary}.

library(gtsummary)
get_table_desc_stat <- function(df, variables){
  df %>% 
  select(!!variables) %>% 
  tbl_summary(
    # by = ,
    type = all_continuous() ~ "continuous2",
    statistic = list(
      all_continuous() ~ c("{mean} ({sd})", "{median} ({p25}, {p75})"),
      all_categorical() ~ "{n} ({p}%)"),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 0
    ),
    missing_text = "Missing value"
  ) %>% 
  modify_header(label ~ "**Variable**") %>% 
  add_stat_label(
    label = list(
      all_continuous() ~ c("Mean (Std)", "Median (IQR)"),
      all_categorical() ~ "n (%)"
    )
  )
}

2.1 Raw data

Show the R codes

childrens_characteristics <- c("sex", "dbwt", "bfacil", "bfacil3", "restatus")

get_table_desc_stat(df, childrens_characteristics)

Variable	N = 3,940,764
Sex of Infant, n (%)
Male	2,017,374 (51%)
Female	1,923,390 (49%)
Birth Weight (in Grams)
Mean (Std)	3,270.78 (593.63)
Median (IQR)	3,316.00 (2,977.00, 3,630.00)
Missing value	646
Birth Place, n (%)
Hospital	3,510,218 (98%)
Freestanding Birth Center	17,531 (0%)
Home (intended)	25,499 (1%)
Home (not intended)	3,693 (0%)
Home (unknown if intended)	4,552 (0%)
Clinic / Doctor’s Office	355 (0%)
Other	2,456 (0%)
Unknown	113 (0%)
Missing value	376,347
Birth Place, n (%)
Hospital	3,883,310 (99%)
Not in Hospital	57,331 (1%)
Unknown or Not Stated	123 (0%)
Resident Status, n (%)
Residents	2,847,673 (72%)
Intrastate Nonresidents	999,320 (25%)
Interstate or Interterritory Nonresidents	85,188 (2%)
Foreign Residents	8,583 (0%)

Show the R codes

death_variables <- c("d_restatus", "hospd", "weekdayd", "dthyr", "dthmon",
                      "dob_yy", "dob_mm", "dob_wk", "aged", "manner")

get_table_desc_stat(df, death_variables)

Variable	N = 3,940,764
Death Resident Status, n (%)
Residents	14,430 (62%)
Intrastate Nonresidents	7,508 (32%)
Interstate or Interterritory Nonresidents	1,176 (5%)
Foreign Residents	45 (0%)
Missing value	3,917,605
Place of Death and Decedent's Status, n (%)
Hospital, clinic or Medical Center - Inpatient	17,943 (77%)
Hospital, clinic or Medical Center - ER	2,907 (13%)
Hospital, clinic or Medical Center - Dead on Arrival	283 (1%)
Decedent's home	1,586 (7%)
Hospice facility	54 (0%)
Nursing home/long term care	24 (0%)
Other	323 (1%)
Unknown	39 (0%)
Missing value	3,917,605
Day of Week of Death, n (%)
Sunday	3,223 (14%)
Monday	3,120 (13%)
Tuesday	3,249 (14%)
Wednesday	3,469 (15%)
Thursday	3,376 (15%)
Friday	3,391 (15%)
Saturday	3,331 (14%)
Unknown	0 (0%)
Missing value	3,917,605
Year of Death, n (%)
2013	20,487 (88%)
2014	2,672 (12%)
Missing value	3,917,605
Month of Death, n (%)
1	1,897 (8%)
2	1,799 (8%)
3	1,973 (9%)
4	1,920 (8%)
5	2,014 (9%)
6	1,977 (9%)
7	1,930 (8%)
8	1,937 (8%)
9	1,946 (8%)
10	2,019 (9%)
11	1,816 (8%)
12	1,931 (8%)
Missing value	3,917,605
Birth Year, n (%)
2013	23,159 (100%)
Missing value	3,917,605
Birth Month, n (%)
1	1,951 (8%)
2	1,740 (8%)
3	1,897 (8%)
4	1,853 (8%)
5	2,030 (9%)
6	1,971 (9%)
7	1,991 (9%)
8	2,010 (9%)
9	1,995 (9%)
10	2,032 (9%)
11	1,795 (8%)
12	1,894 (8%)
Missing value	3,917,605
Birth Weekday, n (%)
Sunday	2,733 (12%)
Monday	3,316 (14%)
Tuesday	3,667 (16%)
Wednesday	3,578 (15%)
Thursday	3,511 (15%)
Friday	3,474 (15%)
Saturday	2,880 (12%)
Unknown	0 (0%)
Missing value	3,917,605
Age at Death in Days
Mean (Std)	41.56 (72.56)
Median (IQR)	3.00 (0.00, 52.00)
Missing value	3,917,605
Manner of Death, n (%)
Accident	1,205 (6%)
Suicide	0 (0%)
Homicide	261 (1%)
Pending investigation	301 (2%)
Could not determine	2,017 (10%)
Self-inflicted	0 (0%)
Natural	15,622 (81%)
Missing value	3,921,358

Show the R codes

mother_variables <- c("mager41", "mbrace", "mracerec", "mar", "meduc")
get_table_desc_stat(df, mother_variables)

Variable	N = 3,940,764
Mother's Age, n (%)
10-12	71 (0%)
13	459 (0%)
14	2,569 (0%)
15	9,439 (0%)
16	22,596 (1%)
17	42,956 (1%)
18	76,261 (2%)
19	122,346 (3%)
20	151,182 (4%)
21	167,417 (4%)
22	185,362 (5%)
23	194,103 (5%)
24	200,099 (5%)
25	208,091 (5%)
26	215,347 (5%)
27	227,545 (6%)
28	234,861 (6%)
29	237,526 (6%)
30	237,377 (6%)
31	231,394 (6%)
32	212,709 (5%)
33	191,239 (5%)
34	166,761 (4%)
35	143,151 (4%)
36	118,161 (3%)
37	93,346 (2%)
38	73,923 (2%)
39	56,507 (1%)
40	41,758 (1%)
41	29,721 (1%)
42	19,821 (1%)
43	11,936 (0%)
44	6,502 (0%)
45	3,701 (0%)
46	1,887 (0%)
47	977 (0%)
48	587 (0%)
49	387 (0%)
50-64	689 (0%)
Mother's Bridged Race, n (%)
White – single race	2,689,736 (75%)
Black – single race	556,437 (15%)
American Indian – single race	33,908 (1%)
Asian Indian – single race	56,790 (2%)
Chinese – single race	49,453 (1%)
Filipino – single race	32,191 (1%)
Japanese – single race	7,108 (0%)
Korean – single race	14,910 (0%)
Vietnamese – single race	19,687 (1%)
Other Asian – single race	41,740 (1%)
Hawaiian – single race	1,136 (0%)
Guamanian – single race	1,282 (0%)
Samoan – single race	2,285 (0%)
Other Pacific Islander – single race	6,022 (0%)
White – bridged multiple race	38,302 (1%)
Black – bridged multiple race	24,363 (1%)
American Indian & Alaskan Native – bridged multiple race	5,243 (0%)
Asian / Pacific Islander – bridged multiple race	14,306 (0%)
Missing value	345,865
Mother's Race, n (%)
Other (not classified as White or Black)	0 (0%)
White	2,993,686 (76%)
Black	635,120 (16%)
American Indian / Alaskan Native	46,011 (1%)
Asian / Pacific Islander	265,947 (7%)
Mother's Marital Status, n (%)
Yes	2,342,660 (59%)
No	1,598,104 (41%)
Unmarried parents not living together	0 (0%)
Unknown or not Stated	0 (0%)
Mother's Education, n (%)
8th grade or less	136,701 (4%)
9th through 12th grade with no diploma	421,293 (12%)
High school graduate or GED completed	879,956 (25%)
Some college credit, but not a degree	753,056 (21%)
Associate degree (AA,AS)	280,660 (8%)
Bachelor’s degree (BA, AB, BS)	669,170 (19%)
Master’s degree (MA, MS, MEng, MEd, MSW, MBA)	297,054 (8%)
Doctorate (PhD, EdD) or Professional Degree (MD, DDS, DVM, LLB, JD)	84,707 (2%)
Unknown	41,820 (1%)
Missing value	376,347

Show the R codes

father_variables <- c("fagecomb", "fbrace", "fracerec")
get_table_desc_stat(df, father_variables)

Variable	N = 3,940,764
Father's Combined Age
Mean (Std)	31.09 (6.89)
Median (IQR)	31.00 (26.00, 35.00)
Missing value	826,291
Father's Bridged Race, n (%)
White – single race	2,210,002 (61%)
Black – single race	418,722 (12%)
American Indian – single race	22,558 (1%)
Asian Indian – single race	55,700 (2%)
Chinese – single race	42,055 (1%)
Filipino – single race	21,498 (1%)
Japanese – single race	4,108 (0%)
Korean – single race	11,221 (0%)
Vietnamese – single race	15,867 (0%)
Other Asian – single race	33,597 (1%)
Hawaiian – single race	941 (0%)
Guamanian – single race	983 (0%)
Samoan – single race	2,325 (0%)
Other Pacific Islander – single race	4,458 (0%)
White – bridged multiple race	28,772 (1%)
Black – bridged multiple race	14,438 (0%)
American Indian & Alaskan Native – bridged multiple race	7,617 (0%)
Asian / Pacific Islander – bridged multiple race	12,986 (0%)
Unknown or not stated, also includes states not reporting multiple race	687,051 (19%)
Missing value	345,865
Father's Race, n (%)
Other (not classified as White or Black)	0 (0%)
White	2,466,993 (63%)
Black	476,535 (12%)
American Indian / Alaskan Native	35,143 (1%)
Asian / Pacific Islander	222,529 (6%)
Unknown or not stated	739,564 (19%)

Show the R codes

pregnancy_variables <- c("lbo", "tbo", "precare_rec", "uprevis", "wtgain",
                         "cig_1", "cig_2", "cig_3", "cig_rec", 
                         "dlmp_mm", "dlmp_yy", "estgest", "combgest")
get_table_desc_stat(df, pregnancy_variables)

Variable	N = 3,940,764
Live Birth Order, n (%)
1	1,550,114 (40%)
2	1,246,847 (32%)
3	654,946 (17%)
4	276,936 (7%)
5	108,168 (3%)
6	44,188 (1%)
7	20,301 (1%)
8	20,732 (1%)
Missing value	18,532
Total Birth Order, n (%)
1	1,272,200 (33%)
2	1,108,655 (28%)
3	714,320 (18%)
4	392,389 (10%)
5	202,062 (5%)
6	99,603 (3%)
7	51,311 (1%)
8	61,099 (2%)
Missing value	39,125
Month Prenatal Care Began, n (%)
1st to 3rd month	2,539,077 (71%)
4th to 6th month	672,306 (19%)
7th to final month	158,902 (4%)
No prenatal care	51,392 (1%)
Unknown or not stated	142,740 (4%)
Missing value	376,347
Number of Prenatal Visits
Mean (Std)	11.27 (4.04)
Median (IQR)	12.00 (9.00, 13.00)
Missing value	118,625
Weight Gain
Mean (Std)	30.33 (14.92)
Median (IQR)	30.00 (20.00, 39.00)
Missing value	190,653
Cigarettes 1st Trimester
Mean (Std)	0.88 (3.79)
Median (IQR)	0.00 (0.00, 0.00)
Missing value	549,170
Cigarettes 2nd Trimester
Mean (Std)	0.88 (3.79)
Median (IQR)	0.00 (0.00, 0.00)
Missing value	550,029
Cigarettes 3rd Trimester
Mean (Std)	0.88 (3.78)
Median (IQR)	0.00 (0.00, 0.00)
Missing value	550,272
Smokes cigarettes, n (%)
Yes	287,250 (8%)
No	3,104,885 (87%)
Unknown or not stated	172,282 (5%)
Missing value	376,347
Last Normal Menses: Month
Mean (Std)	6.60 (3.49)
Median (IQR)	7.00 (4.00, 10.00)
Missing value	195,745
Last Normal Menses: Year, n (%)
2011	1,455 (0%)
2012	2,810,941 (75%)
2013	940,736 (25%)
Missing value	187,632
Obstetric/Clinical Gestation Est.
Mean (Std)	38.51 (2.14)
Median (IQR)	39.00 (38.00, 40.00)
Missing value	7,483
Gestation – Detail in Weeks
Mean (Std)	38.65 (2.50)
Median (IQR)	39.00 (38.00, 40.00)
Missing value	3,695

Show the R codes

risk_factor_variables <- c("rf_diab", "rf_gest", "rf_phyp", "rf_ghyp", 
                           "rf_eclam", "rf_ppterm", "rf_ppoutc", "rf_cesar")
get_table_desc_stat(df, risk_factor_variables)

Variable	N = 3,940,764
Risk Factor: Prepregnancy Diabetes, n (%)
Yes	26,896 (1%)
No	3,529,191 (99%)
Unknown or not stated	8,330 (0%)
Missing value	376,347
Risk Factor: Gestational Diabetes, n (%)
Yes	187,064 (5%)
No	3,369,023 (95%)
Unknown or not stated	8,330 (0%)
Missing value	376,347
Risk Factor: Prepregnancy Hypertension, n (%)
Yes	54,198 (2%)
No	3,501,889 (98%)
Unknown or not stated	8,330 (0%)
Missing value	376,347
Risk Factor: Gestational Hypertension, n (%)
Yes	172,919 (5%)
No	3,383,168 (95%)
Unknown or not stated	8,330 (0%)
Missing value	376,347
Risk Factor: Hypertension Eclampsia, n (%)
Yes	8,148 (0%)
No	3,547,939 (100%)
Unknown or not stated	8,330 (0%)
Missing value	376,347
Risk Factor: Previous Preterm Birth, n (%)
Yes	92,331 (3%)
No	3,463,756 (97%)
Unknown or not stated	8,330 (0%)
Missing value	376,347
Risk Factor: Poor Pregnancy Outcome, n (%)
Yes	78,310 (2%)
No	3,477,777 (98%)
Unknown or not stated	8,330 (0%)
Missing value	376,347
Risk Factor: Previous Cesarean Deliveries, n (%)
Yes	523,128 (15%)
No	3,032,959 (85%)
Unknown or not stated	8,330 (0%)
Missing value	376,347

Show the R codes

obstetric_procedures_variables <- c("op_cerv", "op_tocol", "op_ecvs", "op_ecvf",
                                    "uop_induc", "uop_tocol", "on_ruptr", 
                                    "on_abrup", "on_prolg")
get_table_desc_stat(df, obstetric_procedures_variables)

Variable	N = 3,940,764
Obstetric Procedures: Cervical Cerclage, n (%)
Yes	10,900 (0%)
No	3,542,733 (99%)
Unknown or not stated	10,784 (0%)
Missing value	376,347
Obstetric Procedures: Tocolysis, n (%)
Yes	33,727 (1%)
No	3,519,906 (99%)
Unknown or not stated	10,784 (0%)
Missing value	376,347
Obstetric Procedures: Successful External Cephalic, n (%)
Yes	4,790 (0%)
No	3,548,843 (100%)
Unknown or not stated	10,784 (0%)
Missing value	376,347
Obstetric Procedures: Failed External Cephalic, n (%)
Yes	4,070 (0%)
No	3,549,563 (100%)
Unknown or not stated	10,784 (0%)
Missing value	376,347
Obstetric Procedures: Induction of labor, n (%)
Yes	904,437 (23%)
No	3,031,183 (77%)
Not on certificate	0 (0%)
Unknown or not stated	5,144 (0%)
Obstetric Procedures: Tocolysis, n (%)
Yes	37,355 (1%)
No	3,892,312 (99%)
Not on certificate	0 (0%)
Unknown or not stated	11,097 (0%)
Onset of Labor: Premature Rupture of Membrane, n (%)
Yes	123,588 (3%)
No	3,429,618 (96%)
Unknown or not stated	11,211 (0%)
Missing value	376,347
Onset of Labor: Abruptio placenta, n (%)
Yes	110,247 (3%)
No	3,442,959 (97%)
Unknown or not stated	11,211 (0%)
Missing value	376,347
Onset of Labor: Prolonged Labor, n (%)
Yes	47,198 (1%)
No	3,506,008 (98%)
Unknown or not stated	11,211 (0%)
Missing value	376,347

Show the R codes

labor_variables <- 
  c("ld_induct", "ld_augment", "ld_nvrtx", "ld_steroids", "ld_antibio", 
    "ld_chorio", "ld_mecon", "ld_fintol", "ld_anesth", "uld_meco", 
    "uld_precip", "uld_breech", "md_present", "md_route", "md_trial", 
    "rdmeth_rec", "dmeth_rec", "attend", "apgar5", "dplural")
get_table_desc_stat(df, labor_variables)

Variable	N = 3,940,764
Charact. of Labor and Delivery: Induction of Labor, n (%)
Yes	825,322 (23%)
No	2,734,264 (77%)
Unknown or not stated	4,831 (0%)
Missing value	376,347
Charact. of Labor and Delivery: Augmentation of Labor, n (%)
Yes	716,538 (20%)
No	2,843,048 (80%)
Unknown or not stated	4,831 (0%)
Missing value	376,347
Charact. of Labor and Delivery: Non-Vertex Presentation, n (%)
Yes	0 (NA%)
No	0 (NA%)
Unknown or not stated	0 (NA%)
Missing value	3,940,764
Charact. of Labor and Delivery: Steroids, n (%)
Yes	52,190 (1%)
No	3,507,396 (98%)
Unknown or not stated	4,831 (0%)
Missing value	376,347
Charact. of Labor and Delivery: Antibiotics, n (%)
Yes	775,645 (22%)
No	2,783,941 (78%)
Unknown or not stated	4,831 (0%)
Missing value	376,347
Charact. of Labor and Delivery: Chorioamnionitis, n (%)
Yes	46,183 (1%)
No	3,513,403 (99%)
Unknown or not stated	4,831 (0%)
Missing value	376,347
Charact. of Labor and Delivery: Meconium Staining, n (%)
Yes	184,992 (5%)
No	3,374,594 (95%)
Unknown or not stated	4,831 (0%)
Missing value	376,347
Charact. of Labor and Delivery: Fetal Intolerance, n (%)
Yes	160,446 (5%)
No	3,399,140 (95%)
Unknown or not stated	4,831 (0%)
Missing value	376,347
Charact. of Labor and Delivery: Anesthesia, n (%)
Yes	2,548,929 (72%)
No	1,010,657 (28%)
Unknown or not stated	4,831 (0%)
Missing value	376,347
Complications of Labor and Delivery: Meconium, n (%)
Yes	204,711 (5%)
No	3,730,794 (95%)
Not on certificate	0 (0%)
Unknown or not stated	5,259 (0%)
Complications of Labor and Delivery: Precipitous labor, n (%)
Yes	119,166 (3%)
No	3,809,959 (97%)
Not on certificate	0 (0%)
Unknown or not stated	11,639 (0%)
Complications of Labor and Delivery: Breech, n (%)
Yes	221,560 (6%)
No	3,615,311 (92%)
Not on certificate	0 (0%)
Unknown or not stated	103,893 (3%)
Method of Delivery: Fetal Presentation, n (%)
Cephalic	3,253,574 (91%)
Breech	131,733 (4%)
Other	75,645 (2%)
Unknown or not stated	103,465 (3%)
Missing value	376,347
Method of Delivery: Final Route and Method of Delivery, n (%)
Spontaneous	2,281,509 (64%)
Forceps	21,220 (1%)
Vacuum	97,440 (3%)
Cesarean	1,161,670 (33%)
Unknown or not stated	2,578 (0%)
Missing value	376,347
Method of Delivery: Trial of Labor Attempted, n (%)
Yes	301,959 (8%)
No	838,556 (24%)
Not applicable	2,400,169 (67%)
Unknown or not stated	23,733 (1%)
Missing value	376,347
Delivery Method, n (%)
Vaginal	2,339,254 (66%)
Vaginal after previous c-section	55,320 (2%)
Primary C-section	692,015 (19%)
Repeat C-section	467,728 (13%)
Vaginal (unknown if previous c-section)	5,595 (0%)
C-section (unknown if previous c-section)	1,927 (0%)
Not stated	2,578 (0%)
Missing value	376,347
Delivery Method, n (%)
Vaginal	2,648,618 (67%)
C-Section	1,287,178 (33%)
Unknown	4,968 (0%)
Attendant, n (%)
Doctor of Medicine	3,316,572 (84%)
Doctor of Osteopathy	244,057 (6%)
Certified Nurse Midwife	321,236 (8%)
Other Midwife	29,119 (1%)
Other	27,377 (1%)
Unknown or not stated	2,403 (0%)
Five Minute Apgar Score
Mean (Std)	8.79 (0.83)
Median (IQR)	9.00 (9.00, 9.00)
Missing value	17,008
Plurality, n (%)
Single	3,803,447 (97%)
Twin	132,589 (3%)
Triplet	4,392 (0%)
Quadruplet	270 (0%)
Quintuplet or higher	66 (0%)

Show the R codes

conditions_newborn_variable <- 
  c("ab_vent", "ab_vent6", "ab_nicu", "ab_surfac", "ab_antibio", "ab_seiz", 
    "ab_inj", "ca_anen", "ca_menin", "ca_heart", "ca_hernia", "ca_ompha", 
    "ca_gastro", "ca_limb", "ca_cleftlp", "ca_cleft", "ca_downs", 
    "ca_chrom", "ca_hypos")
get_table_desc_stat(df, conditions_newborn_variable)

Variable	N = 3,940,764
Abnormal Conditions of the Newborn: Assisted Ventilation, n (%)
Yes, Complication reported	118,090 (3%)
No Complication reported	3,436,435 (96%)
Unknown or not stated	9,892 (0%)
Missing value	376,347
Abnormal Conditions of the Newborn: Assisted Ventilation >6hrs, n (%)
Yes, Complication reported	37,317 (1%)
No Complication reported	3,517,208 (99%)
Unknown or not stated	9,892 (0%)
Missing value	376,347
Abnormal Conditions of the Newborn: Admission to NICU, n (%)
Yes, Complication reported	283,207 (8%)
No Complication reported	3,271,318 (92%)
Unknown or not stated	9,892 (0%)
Missing value	376,347
Abnormal Conditions of the Newborn: Surfactant, n (%)
Yes, Complication reported	14,339 (0%)
No Complication reported	3,540,186 (99%)
Unknown or not stated	9,892 (0%)
Missing value	376,347
Abnormal Conditions of the Newborn: Antibiotics, n (%)
Yes, Complication reported	2,071 (0%)
No Complication reported	3,552,454 (100%)
Unknown or not stated	9,892 (0%)
Missing value	376,347
Abnormal Conditions of the Newborn: Seizures, n (%)
Yes, Complication reported	0 (NA%)
No Complication reported	0 (NA%)
Unknown or not stated	0 (NA%)
Missing value	3,940,764
Abnormal Conditions of the Newborn: Birth Injury, n (%)
Yes, Complication reported	0 (NA%)
No Complication reported	0 (NA%)
Unknown or not stated	0 (NA%)
Missing value	3,940,764
Congenital Anomalies of the Newborn: Anencephaly, n (%)
Yes, anomaly reported	379 (0%)
No, anomaly not reported	3,550,054 (100%)
Unknown	13,984 (0%)
Missing value	376,347
Congenital Anomalies of the Newborn: Meningomyelocele/Spina Bifida, n (%)
Yes, anomaly reported	575 (0%)
No, anomaly not reported	3,549,858 (100%)
Unknown	13,984 (0%)
Missing value	376,347
Congenital Anomalies of the Newborn: Cyanotic Congenital Heart Disease, n (%)
Yes, anomaly reported	3,047 (0%)
No, anomaly not reported	3,547,386 (100%)
Unknown	13,984 (0%)
Missing value	376,347
Congenital Anomalies of the Newborn: Congenital Diaphragmatic Hernia, n (%)
Yes, anomaly reported	463 (0%)
No, anomaly not reported	3,549,970 (100%)
Unknown	13,984 (0%)
Missing value	376,347
Congenital Anomalies of the Newborn: Omphalocele, n (%)
Yes, anomaly reported	376 (0%)
No, anomaly not reported	3,550,057 (100%)
Unknown	13,984 (0%)
Missing value	376,347
Congenital Anomalies of the Newborn: Gastroschisis, n (%)
Yes, anomaly reported	993 (0%)
No, anomaly not reported	3,549,440 (100%)
Unknown	13,984 (0%)
Missing value	376,347
Congenital Anomalies of the Newborn: Limb Reduction Deficit, n (%)
Yes, anomaly reported	471 (0%)
No, anomaly not reported	3,549,962 (100%)
Unknown	13,984 (0%)
Missing value	376,347
Congenital Anomalies of the Newborn: Cleft Lip w/ or w/o Cleft Palate, n (%)
Yes, anomaly reported	1,834 (0%)
No, anomaly not reported	3,548,599 (100%)
Unknown	13,984 (0%)
Missing value	376,347
Congenital Anomalies of the Newborn: Cleft Palate Alone, n (%)
Yes, anomaly reported	847 (0%)
No, anomaly not reported	3,549,586 (100%)
Unknown	13,984 (0%)
Missing value	376,347
Congenital Anomalies of the Newborn: Downs Syndrome, n (%)
Yes, anomaly reported	0 (0%)
No, anomaly not reported	3,548,582 (100%)
Unknown	13,984 (0%)
Missing value	378,198
Congenital Anomalies of the Newborn: Suspected Chromosonal Disorder, n (%)
Yes, anomaly reported	0 (0%)
No, anomaly not reported	3,549,154 (100%)
Unknown	13,984 (0%)
Missing value	377,626
Congenital Anomalies of the Newborn: Hypospadias, n (%)
Yes, anomaly reported	2,104 (0%)
No, anomaly not reported	3,548,329 (100%)
Unknown	13,984 (0%)
Missing value	376,347

2.2 Recoding some variables

Let us recode some variables, based on what was observed on the descriptive statistics table. Some variables are discarded, because they correspond to multiply defined information. For example, the race of the mother is provided through multiple variables: mbrace and mracerec. The former gives a much finer decomposition, but the number of observation is too limited for some categories.

df <- 
  df %>% 
  select(
    -bfacil, -hospd, -mbrace, -fbrace, -tbo, -ld_nvrtx, -rdmeth_rec, 
    -ab_seiz, -ab_inj) %>% 
  filter(!is.na(bfacil3)) %>% 
  filter(is.na(manner) | manner %in% c("Could not determine", "Natural")) %>% 
  mutate(
    d_restatus = fct_recode(
      d_restatus, 
      "Other" = "Interstate or Interterritory Nonresidents", 
      "Other" = "Foreign Residents"
    ),
    precare_rec = replace_na(precare_rec, "Unknown or not stated"),
    cig_rec = replace_na(cig_rec, "Unknown or not stated"),
    op_cerv = replace_na(op_cerv, "Unknown or not stated"),
    op_tocol = replace_na(op_tocol, "Unknown or not stated"),
    op_ecvs = replace_na(op_ecvs, "Unknown or not stated"),
    op_ecvf = replace_na(op_ecvf, "Unknown or not stated"),
    uop_tocol = replace_na(uop_tocol, "Unknown or not stated"),
    on_ruptr = replace_na(on_ruptr, "Unknown or not stated"),
    on_abrup = replace_na(on_abrup, "Unknown or not stated"),
    on_prolg = replace_na(on_prolg, "Unknown or not stated"),
    ld_induct = replace_na(ld_induct, "Unknown or not stated"),
    ld_augment = replace_na(ld_augment, "Unknown or not stated"),
    ld_steroids = replace_na(ld_steroids, "Unknown or not stated"),
    ld_antibio = replace_na(ld_antibio, "Unknown or not stated"),
    ld_chorio = replace_na(ld_chorio, "Unknown or not stated"),
    ld_mecon = replace_na(ld_mecon, "Unknown or not stated"),
    ld_fintol = replace_na(ld_fintol, "Unknown or not stated"),
    ld_anesth = replace_na(ld_anesth, "Unknown or not stated"),
    uld_meco = replace_na(uld_meco, "Unknown or not stated"),
    uld_precip = replace_na(uld_precip, "Unknown or not stated"),
    uld_breech = replace_na(uld_breech, "Unknown or not stated"),
    md_present = replace_na(md_present, "Unknown or not stated"),
    md_route = replace_na(md_route, "Unknown or not stated"),
    md_trial = replace_na(md_trial, "Unknown or not stated"),
    dmeth_rec = replace_na(dmeth_rec, "Unknown"),
    attend = replace_na(attend, "Unknown or not stated"),
    dplural = fct_recode(
      dplural,
      "Quadruplet or higher" = "Quadruplet",
      "Quadruplet or higher" = "Quintuplet or higher"
    ),
    ab_vent = replace_na(ab_vent, "Unknown or not stated"),
    ab_vent6 = replace_na(ab_vent6, "Unknown or not stated"),
    ab_nicu = replace_na(ab_nicu, "Unknown or not stated"),
    ab_surfac = replace_na(ab_surfac, "Unknown or not stated"),
    ab_antibio = replace_na(ab_antibio, "Unknown or not stated"),
    ca_anen = replace_na(ca_anen, "Unknown"),
    ca_menin = replace_na(ca_menin, "Unknown"),
    ca_heart = replace_na(ca_heart, "Unknown"),
    ca_hernia = replace_na(ca_hernia, "Unknown"),
    ca_ompha = replace_na(ca_ompha, "Unknown"),
    ca_gastro = replace_na(ca_gastro, "Unknown"),
    ca_limb = replace_na(ca_limb, "Unknown"),
    ca_cleftlp = replace_na(ca_cleftlp, "Unknown"),
    ca_cleft = replace_na(ca_cleft, "Unknown"),
    ca_downs = replace_na(ca_downs, "Unknown"),
    ca_chrom = replace_na(ca_chrom, "Unknown"),
    ca_hypos = replace_na(ca_hypos, "Unknown")
  )

Let us also create three dummy variables to state whether the mother smoked during each trimester of her pregnancy.

df <- 
  df %>% 
  mutate(
    cig_1_d = case_when(
      cig_1 == 0 ~ "No",
      cig_1 > 0 ~ "True",
      is.na(cig_1) ~ "Unknown or not stated"
    ),
    cig_2_d = case_when(
      cig_2 == 0 ~ "No",
      cig_2 > 0 ~ "True",
      is.na(cig_2) ~ "Unknown or not stated"
    ),
    cig_3_d = case_when(
      cig_3 == 0 ~ "No",
      cig_3 > 0 ~ "True",
      is.na(cig_3) ~ "Unknown or not stated"
    ),
    cig_1_d = factor(cig_1_d, levels = c("Yes", "No", "Unknown or not stated")),
    cig_2_d = factor(cig_2_d, levels = c("Yes", "No", "Unknown or not stated")),
    cig_3_d = factor(cig_3_d, levels = c("Yes", "No", "Unknown or not stated"))
  )

Let us add some labels for these three dummies.

df <- 
  df %>% 
  labelled::set_variable_labels(
    cig_1_d = "Smoked cigarettes 1st Trimester",
    cig_2_d = "Smoked cigarettes 2nd Trimester",
    cig_3_d = "Smoked cigarettes 3rd Trimester"
  )

And the, let us save the data:

save(df, file = "../data/df_2013.rda")

2.3 Grouping according to smoking

Let us provide the same kind descriptive statistics as above, but this time by splitting the data according to whether the mother smokes or not.

get_table_desc_stat_smoke <- function(df, variables){
  df %>% 
    select(!!variables, cig_rec) %>% 
    tbl_summary(
      by = cig_rec,
      type = all_continuous() ~ "continuous2",
      statistic = list(
        all_continuous() ~ c("{mean} ({sd})", "{median} ({p25}, {p75})"),
        all_categorical() ~ "{n} ({p}%)"),
      digits = list(
        all_continuous() ~ 2,
        all_categorical() ~ 0
      ),
      missing_text = "Missing value"
    ) %>% 
    add_p() %>% 
    add_overall(col_label = "Whole sample") %>% 
    modify_header(label ~ "**Variable**") %>% 
    modify_spanning_header(c("stat_1", "stat_2") ~ "**Mother is a smoker**") %>% 
    add_stat_label(
      label = list(
        all_continuous() ~ c("Mean (Std)", "Median (IQR)"),
        all_categorical() ~ "n (%)"
      )
    )
}

Show the R codes

childrens_characteristics <- c("sex", "dbwt", "bfacil3", "restatus")

get_table_desc_stat_smoke(df, childrens_characteristics)

Variable	Whole sample	Mother is a smoker		Unknown or not stated, N = 548,314	p-value¹
Variable	Whole sample	Yes, N = 286,824	No, N = 3,103,859	Unknown or not stated, N = 548,314	p-value¹
Sex of Infant, n (%)					0.4
Male	2,016,365 (51%)	147,124 (51%)	1,588,849 (51%)	280,392 (51%)
Female	1,922,632 (49%)	139,700 (49%)	1,515,010 (49%)	267,922 (49%)
Birth Weight (in Grams)					<0.001
Mean (Std)	3,270.92 (593.50)	3,096.68 (600.91)	3,288.66 (588.60)	3,261.68 (602.18)
Median (IQR)	3,316.00 (2,977.00, 3,630.00)	3,146.00 (2,790.00, 3,469.00)	3,325.00 (2,997.00, 3,657.00)	3,310.00 (2,965.00, 3,629.00)
Missing value	642	22	508	112
Birth Place, n (%)					<0.001
Hospital	3,881,580 (99%)	285,364 (99%)	3,053,119 (98%)	543,097 (99%)
Not in Hospital	57,295 (1%)	1,450 (1%)	50,684 (2%)	5,161 (1%)
Unknown or Not Stated	122 (0%)	10 (0%)	56 (0%)	56 (0%)
Resident Status, n (%)					<0.001
Residents	2,846,402 (72%)	193,302 (67%)	2,261,285 (73%)	391,815 (71%)
Intrastate Nonresidents	998,869 (25%)	86,150 (30%)	770,291 (25%)	142,428 (26%)
Interstate or Interterritory Nonresidents	85,144 (2%)	7,327 (3%)	65,016 (2%)	12,801 (2%)
Foreign Residents	8,582 (0%)	45 (0%)	7,267 (0%)	1,270 (0%)
¹ Pearson's Chi-squared test; Kruskal-Wallis rank sum test

Show the R codes

death_variables <- c("d_restatus", "weekdayd", "dthyr", "dthmon",
                      "dob_yy", "dob_mm", "dob_wk", "aged", "manner")

get_table_desc_stat_smoke(df, death_variables)

Variable	Whole sample	Mother is a smoker		Unknown or not stated, N = 548,314	p-value¹
Variable	Whole sample	Yes, N = 286,824	No, N = 3,103,859	Unknown or not stated, N = 548,314	p-value¹
Death Resident Status, n (%)					0.010
Residents	13,044 (61%)	1,478 (60%)	9,524 (61%)	2,042 (61%)
Intrastate Nonresidents	7,204 (34%)	880 (36%)	5,234 (34%)	1,090 (33%)
Other	1,144 (5%)	112 (5%)	822 (5%)	210 (6%)
Missing value	3,917,605	284,354	3,088,279	544,972
Day of Week of Death, n (%)
Sunday	2,992 (14%)	372 (15%)	2,132 (14%)	488 (15%)
Monday	2,876 (13%)	326 (13%)	2,086 (13%)	464 (14%)
Tuesday	3,025 (14%)	345 (14%)	2,227 (14%)	453 (14%)
Wednesday	3,232 (15%)	346 (14%)	2,422 (16%)	464 (14%)
Thursday	3,122 (15%)	378 (15%)	2,255 (14%)	489 (15%)
Friday	3,105 (15%)	334 (14%)	2,275 (15%)	496 (15%)
Saturday	3,040 (14%)	369 (15%)	2,183 (14%)	488 (15%)
Unknown	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Missing value	3,917,605	284,354	3,088,279	544,972
Year of Death, n (%)					<0.001
2013	19,246 (90%)	2,127 (86%)	14,143 (91%)	2,976 (89%)
2014	2,146 (10%)	343 (14%)	1,437 (9%)	366 (11%)
Missing value	3,917,605	284,354	3,088,279	544,972
Month of Death, n (%)					0.5
1	1,762 (8%)	220 (9%)	1,261 (8%)	281 (8%)
2	1,670 (8%)	196 (8%)	1,225 (8%)	249 (7%)
3	1,832 (9%)	217 (9%)	1,320 (8%)	295 (9%)
4	1,772 (8%)	196 (8%)	1,289 (8%)	287 (9%)
5	1,867 (9%)	213 (9%)	1,366 (9%)	288 (9%)
6	1,827 (9%)	210 (9%)	1,317 (8%)	300 (9%)
7	1,798 (8%)	185 (7%)	1,314 (8%)	299 (9%)
8	1,781 (8%)	182 (7%)	1,327 (9%)	272 (8%)
9	1,808 (8%)	229 (9%)	1,308 (8%)	271 (8%)
10	1,858 (9%)	206 (8%)	1,388 (9%)	264 (8%)
11	1,638 (8%)	190 (8%)	1,182 (8%)	266 (8%)
12	1,779 (8%)	226 (9%)	1,283 (8%)	270 (8%)
Missing value	3,917,605	284,354	3,088,279	544,972
Birth Year, n (%)
2013	21,392 (100%)	2,470 (100%)	15,580 (100%)	3,342 (100%)
Missing value	3,917,605	284,354	3,088,279	544,972
Birth Month, n (%)					>0.9
1	1,796 (8%)	217 (9%)	1,290 (8%)	289 (9%)
2	1,602 (7%)	178 (7%)	1,207 (8%)	217 (6%)
3	1,752 (8%)	199 (8%)	1,281 (8%)	272 (8%)
4	1,727 (8%)	192 (8%)	1,264 (8%)	271 (8%)
5	1,890 (9%)	222 (9%)	1,370 (9%)	298 (9%)
6	1,831 (9%)	202 (8%)	1,333 (9%)	296 (9%)
7	1,830 (9%)	203 (8%)	1,321 (8%)	306 (9%)
8	1,848 (9%)	207 (8%)	1,363 (9%)	278 (8%)
9	1,819 (9%)	218 (9%)	1,321 (8%)	280 (8%)
10	1,888 (9%)	216 (9%)	1,380 (9%)	292 (9%)
11	1,661 (8%)	205 (8%)	1,185 (8%)	271 (8%)
12	1,748 (8%)	211 (9%)	1,265 (8%)	272 (8%)
Missing value	3,917,605	284,354	3,088,279	544,972
Birth Weekday, n (%)
Sunday	2,555 (12%)	295 (12%)	1,861 (12%)	399 (12%)
Monday	3,062 (14%)	343 (14%)	2,246 (14%)	473 (14%)
Tuesday	3,363 (16%)	427 (17%)	2,428 (16%)	508 (15%)
Wednesday	3,292 (15%)	366 (15%)	2,392 (15%)	534 (16%)
Thursday	3,224 (15%)	345 (14%)	2,372 (15%)	507 (15%)
Friday	3,198 (15%)	381 (15%)	2,331 (15%)	486 (15%)
Saturday	2,698 (13%)	313 (13%)	1,950 (13%)	435 (13%)
Unknown	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Missing value	3,917,605	284,354	3,088,279	544,972
Age at Death in Days					<0.001
Mean (Std)	35.66 (67.92)	47.95 (72.18)	33.50 (66.79)	36.65 (68.92)
Median (IQR)	2.00 (0.00, 37.00)	8.00 (0.00, 72.75)	1.00 (0.00, 30.00)	2.00 (0.00, 38.00)
Missing value	3,917,605	284,354	3,088,279	544,972
Manner of Death, n (%)
Accident	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Suicide	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Homicide	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Pending investigation	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Could not determine	2,017 (11%)	521 (23%)	1,113 (9%)	383 (13%)
Self-inflicted	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Natural	15,622 (89%)	1,712 (77%)	11,392 (91%)	2,518 (87%)
Missing value	3,921,358	284,591	3,091,354	545,413
¹ Pearson's Chi-squared test; Kruskal-Wallis rank sum test

Show the R codes

mother_variables <- c("mager41", "mracerec", "mar", "meduc")
get_table_desc_stat_smoke(df, mother_variables)

Variable	Whole sample	Mother is a smoker		Unknown or not stated, N = 548,314	p-value¹
Variable	Whole sample	Yes, N = 286,824	No, N = 3,103,859	Unknown or not stated, N = 548,314	p-value¹
Mother's Age, n (%)					<0.001
10-12	71 (0%)	0 (0%)	61 (0%)	10 (0%)
13	458 (0%)	9 (0%)	386 (0%)	63 (0%)
14	2,567 (0%)	60 (0%)	2,188 (0%)	319 (0%)
15	9,426 (0%)	331 (0%)	7,866 (0%)	1,229 (0%)
16	22,581 (1%)	1,113 (0%)	18,384 (1%)	3,084 (1%)
17	42,923 (1%)	3,006 (1%)	33,793 (1%)	6,124 (1%)
18	76,187 (2%)	7,277 (3%)	58,088 (2%)	10,822 (2%)
19	122,239 (3%)	13,187 (5%)	91,432 (3%)	17,620 (3%)
20	151,061 (4%)	17,499 (6%)	111,820 (4%)	21,742 (4%)
21	167,280 (4%)	19,788 (7%)	123,190 (4%)	24,302 (4%)
22	185,224 (5%)	21,780 (8%)	136,737 (4%)	26,707 (5%)
23	193,979 (5%)	22,191 (8%)	143,773 (5%)	28,015 (5%)
24	199,972 (5%)	21,055 (7%)	150,686 (5%)	28,231 (5%)
25	207,972 (5%)	20,028 (7%)	158,782 (5%)	29,162 (5%)
26	215,237 (5%)	18,665 (7%)	166,403 (5%)	30,169 (6%)
27	227,443 (6%)	17,354 (6%)	178,969 (6%)	31,120 (6%)
28	234,771 (6%)	16,158 (6%)	186,336 (6%)	32,277 (6%)
29	237,448 (6%)	14,178 (5%)	190,606 (6%)	32,664 (6%)
30	237,305 (6%)	13,161 (5%)	191,389 (6%)	32,755 (6%)
31	231,337 (6%)	11,654 (4%)	187,748 (6%)	31,935 (6%)
32	212,653 (5%)	10,262 (4%)	173,427 (6%)	28,964 (5%)
33	191,198 (5%)	8,662 (3%)	156,639 (5%)	25,897 (5%)
34	166,722 (4%)	7,094 (2%)	136,557 (4%)	23,071 (4%)
35	143,123 (4%)	5,638 (2%)	117,884 (4%)	19,601 (4%)
36	118,137 (3%)	4,502 (2%)	97,566 (3%)	16,069 (3%)
37	93,337 (2%)	3,495 (1%)	76,973 (2%)	12,869 (2%)
38	73,908 (2%)	2,683 (1%)	61,194 (2%)	10,031 (2%)
39	56,495 (1%)	1,979 (1%)	46,844 (2%)	7,672 (1%)
40	41,750 (1%)	1,506 (1%)	34,675 (1%)	5,569 (1%)
41	29,715 (1%)	1,086 (0%)	24,623 (1%)	4,006 (1%)
42	19,816 (1%)	685 (0%)	16,491 (1%)	2,640 (0%)
43	11,933 (0%)	403 (0%)	9,934 (0%)	1,596 (0%)
44	6,501 (0%)	189 (0%)	5,467 (0%)	845 (0%)
45	3,701 (0%)	93 (0%)	3,092 (0%)	516 (0%)
46	1,887 (0%)	27 (0%)	1,575 (0%)	285 (0%)
47	977 (0%)	15 (0%)	832 (0%)	130 (0%)
48	587 (0%)	6 (0%)	502 (0%)	79 (0%)
49	387 (0%)	0 (0%)	336 (0%)	51 (0%)
50-64	689 (0%)	5 (0%)	611 (0%)	73 (0%)
Mother's Race, n (%)
Other (not classified as White or Black)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
White	2,992,542 (76%)	239,932 (84%)	2,348,230 (76%)	404,380 (74%)
Black	634,570 (16%)	37,526 (13%)	501,090 (16%)	95,954 (17%)
American Indian / Alaskan Native	45,974 (1%)	6,734 (2%)	30,805 (1%)	8,435 (2%)
Asian / Pacific Islander	265,911 (7%)	2,632 (1%)	223,734 (7%)	39,545 (7%)
Mother's Marital Status, n (%)
Yes	2,342,150 (59%)	83,748 (29%)	1,940,699 (63%)	317,703 (58%)
No	1,596,847 (41%)	203,076 (71%)	1,163,160 (37%)	230,611 (42%)
Unmarried parents not living together	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Unknown or not Stated	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Mother's Education, n (%)					<0.001
8th grade or less	136,648 (4%)	5,880 (2%)	126,262 (4%)	4,506 (3%)
9th through 12th grade with no diploma	420,930 (12%)	67,193 (23%)	334,434 (11%)	19,303 (11%)
High school graduate or GED completed	879,360 (25%)	114,693 (40%)	717,840 (23%)	46,827 (27%)
Some college credit, but not a degree	752,694 (21%)	71,611 (25%)	641,677 (21%)	39,406 (23%)
Associate degree (AA,AS)	280,582 (8%)	16,205 (6%)	252,219 (8%)	12,158 (7%)
Bachelor’s degree (BA, AB, BS)	669,084 (19%)	8,026 (3%)	631,975 (20%)	29,083 (17%)
Master’s degree (MA, MS, MEng, MEd, MSW, MBA)	297,029 (8%)	1,465 (1%)	281,653 (9%)	13,911 (8%)
Doctorate (PhD, EdD) or Professional Degree (MD, DDS, DVM, LLB, JD)	84,701 (2%)	262 (0%)	81,151 (3%)	3,288 (2%)
Unknown	41,789 (1%)	1,489 (1%)	36,648 (1%)	3,652 (2%)
Missing value	376,180	0	0	376,180
¹ Pearson's Chi-squared test

Show the R codes

father_variables <- c("fagecomb", "fracerec")
get_table_desc_stat_smoke(df, father_variables)

Variable	Whole sample	Mother is a smoker		Unknown or not stated, N = 548,314	p-value¹
Variable	Whole sample	Yes, N = 286,824	No, N = 3,103,859	Unknown or not stated, N = 548,314	p-value¹
Father's Combined Age					<0.001
Mean (Std)	31.09 (6.89)	29.39 (7.04)	31.23 (6.86)	30.91 (6.81)
Median (IQR)	31.00 (26.00, 35.00)	28.00 (24.00, 33.00)	31.00 (26.00, 35.00)	31.00 (26.00, 35.00)
Missing value	825,598	80,392	342,123	403,083
Father's Race, n (%)
Other (not classified as White or Black)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
White	2,466,248 (63%)	161,227 (56%)	1,961,775 (63%)	343,246 (63%)
Black	476,215 (12%)	28,421 (10%)	375,761 (12%)	72,033 (13%)
American Indian / Alaskan Native	35,122 (1%)	4,729 (2%)	24,002 (1%)	6,391 (1%)
Asian / Pacific Islander	222,505 (6%)	1,858 (1%)	186,679 (6%)	33,968 (6%)
Unknown or not stated	738,907 (19%)	90,589 (32%)	555,642 (18%)	92,676 (17%)
¹ Kruskal-Wallis rank sum test

Show the R codes

pregnancy_variables <- c("lbo", "precare_rec", "uprevis", "wtgain",
                         "cig_1", "cig_2", "cig_3", "cig_rec", 
                         "cig_1_d", "cig_2_d", "cig_3_d",
                         "dlmp_mm", "dlmp_yy", "estgest", "combgest")
get_table_desc_stat_smoke(df, pregnancy_variables)

Variable	Whole sample	Mother is a smoker		Unknown or not stated, N = 548,314	p-value¹
Variable	Whole sample	Yes, N = 286,824	No, N = 3,103,859	Unknown or not stated, N = 548,314	p-value¹
Live Birth Order, n (%)					<0.001
1	1,549,617 (40%)	95,054 (33%)	1,239,353 (40%)	215,210 (39%)
2	1,246,311 (32%)	85,290 (30%)	986,084 (32%)	174,937 (32%)
3	654,590 (17%)	56,171 (20%)	507,684 (16%)	90,735 (17%)
4	276,749 (7%)	27,839 (10%)	211,055 (7%)	37,855 (7%)
5	108,082 (3%)	12,173 (4%)	81,057 (3%)	14,852 (3%)
6	44,142 (1%)	4,970 (2%)	32,856 (1%)	6,316 (1%)
7	20,282 (1%)	2,294 (1%)	15,119 (0%)	2,869 (1%)
8	20,712 (1%)	1,977 (1%)	15,786 (1%)	2,949 (1%)
Missing value	18,512	1,056	14,865	2,591
Month Prenatal Care Began, n (%)					<0.001
1st to 3rd month	2,538,197 (64%)	173,111 (60%)	2,254,884 (73%)	110,202 (20%)
4th to 6th month	671,883 (17%)	75,017 (26%)	567,356 (18%)	29,510 (5%)
7th to final month	158,790 (4%)	20,054 (7%)	131,511 (4%)	7,225 (1%)
No prenatal care	51,313 (1%)	8,415 (3%)	39,878 (1%)	3,020 (1%)
Unknown or not stated	518,814 (13%)	10,227 (4%)	110,230 (4%)	398,357 (73%)
Number of Prenatal Visits					<0.001
Mean (Std)	11.27 (4.04)	10.39 (4.49)	11.35 (3.96)	11.26 (4.19)
Median (IQR)	12.00 (9.00, 13.00)	11.00 (8.00, 13.00)	12.00 (9.00, 13.00)	11.00 (9.00, 13.00)
Missing value	118,528	10,074	97,285	11,169
Weight Gain					<0.001
Mean (Std)	30.33 (14.92)	30.55 (17.25)	30.41 (14.80)	29.79 (14.25)
Median (IQR)	30.00 (20.00, 39.00)	30.00 (19.00, 41.00)	30.00 (21.00, 39.00)	30.00 (20.00, 38.00)
Missing value	190,520	13,534	144,864	32,122
Cigarettes 1st Trimester					<0.001
Mean (Std)	0.88 (3.79)	10.45 (8.38)	0.00 (0.00)	NA (NA)
Median (IQR)	0.00 (0.00, 0.00)	10.00 (5.00, 15.00)	0.00 (0.00, 0.00)	NA (NA, NA)
Missing value	548,853	539	0	548,314
Cigarettes 2nd Trimester					<0.001
Mean (Std)	0.88 (3.78)	10.45 (8.37)	0.00 (0.00)	NA (NA)
Median (IQR)	0.00 (0.00, 0.00)	10.00 (5.00, 15.00)	0.00 (0.00, 0.00)	NA (NA, NA)
Missing value	549,711	1,397	0	548,314
Cigarettes 3rd Trimester					<0.001
Mean (Std)	0.88 (3.78)	10.45 (8.37)	0.00 (0.00)	NA (NA)
Median (IQR)	0.00 (0.00, 0.00)	10.00 (5.00, 15.00)	0.00 (0.00, 0.00)	NA (NA, NA)
Missing value	549,950	1,636	0	548,314
Smoked cigarettes 1st Trimester, n (%)
Yes	0 (0%)	0 (0%)	0 (0%)	0 (0%)
No	3,110,782 (85%)	6,923 (93%)	3,103,859 (100%)	0 (0%)
Unknown or not stated	548,853 (15%)	539 (7%)	0 (0%)	548,314 (100%)
Missing value	279,362	279,362	0	0
Smoked cigarettes 2nd Trimester, n (%)
Yes	0 (0%)	0 (0%)	0 (0%)	0 (0%)
No	3,110,782 (85%)	6,923 (83%)	3,103,859 (100%)	0 (0%)
Unknown or not stated	549,711 (15%)	1,397 (17%)	0 (0%)	548,314 (100%)
Missing value	278,504	278,504	0	0
Smoked cigarettes 3rd Trimester, n (%)
Yes	0 (0%)	0 (0%)	0 (0%)	0 (0%)
No	3,110,777 (85%)	6,918 (81%)	3,103,859 (100%)	0 (0%)
Unknown or not stated	549,950 (15%)	1,636 (19%)	0 (0%)	548,314 (100%)
Missing value	278,270	278,270	0	0
Last Normal Menses: Month					<0.001
Mean (Std)	6.60 (3.49)	6.57 (3.48)	6.60 (3.49)	6.62 (3.46)
Median (IQR)	7.00 (4.00, 10.00)	7.00 (4.00, 10.00)	7.00 (4.00, 10.00)	7.00 (4.00, 10.00)
Missing value	195,573	23,950	151,869	19,754
Last Normal Menses: Year, n (%)					<0.001
2011	1,454 (0%)	161 (0%)	1,177 (0%)	116 (0%)
2012	2,809,765 (75%)	196,255 (74%)	2,212,677 (75%)	400,833 (76%)
2013	940,312 (25%)	67,292 (26%)	744,884 (25%)	128,136 (24%)
Missing value	187,466	23,116	145,121	19,229
Obstetric/Clinical Gestation Est.					<0.001
Mean (Std)	38.51 (2.14)	38.27 (2.31)	38.54 (2.11)	38.48 (2.20)
Median (IQR)	39.00 (38.00, 40.00)	39.00 (38.00, 39.00)	39.00 (38.00, 40.00)	39.00 (38.00, 40.00)
Missing value	7,469	935	5,568	966
Gestation – Detail in Weeks					<0.001
Mean (Std)	38.65 (2.50)	38.49 (2.81)	38.66 (2.45)	38.63 (2.61)
Median (IQR)	39.00 (38.00, 40.00)	39.00 (38.00, 40.00)	39.00 (38.00, 40.00)	39.00 (38.00, 40.00)
Missing value	3,685	492	2,611	582
¹ Pearson's Chi-squared test; Kruskal-Wallis rank sum test

Show the R codes

risk_factor_variables <- c("rf_diab", "rf_gest", "rf_phyp", "rf_ghyp", 
                           "rf_eclam", "rf_ppterm", "rf_ppoutc", "rf_cesar")
get_table_desc_stat_smoke(df, risk_factor_variables)

Variable	Whole sample	Mother is a smoker		Unknown or not stated, N = 548,314	p-value¹
Variable	Whole sample	Yes, N = 286,824	No, N = 3,103,859	Unknown or not stated, N = 548,314	p-value¹
Risk Factor: Prepregnancy Diabetes, n (%)					<0.001
Yes	26,874 (1%)	2,638 (1%)	23,033 (1%)	1,203 (1%)
No	3,527,629 (99%)	283,268 (99%)	3,074,720 (99%)	169,641 (99%)
Unknown or not stated	8,314 (0%)	918 (0%)	6,106 (0%)	1,290 (1%)
Missing value	376,180	0	0	376,180
Risk Factor: Gestational Diabetes, n (%)					<0.001
Yes	186,992 (5%)	14,720 (5%)	164,545 (5%)	7,727 (4%)
No	3,367,511 (95%)	271,186 (95%)	2,933,208 (95%)	163,117 (95%)
Unknown or not stated	8,314 (0%)	918 (0%)	6,106 (0%)	1,290 (1%)
Missing value	376,180	0	0	376,180
Risk Factor: Prepregnancy Hypertension, n (%)					<0.001
Yes	54,172 (2%)	5,405 (2%)	46,240 (1%)	2,527 (1%)
No	3,500,331 (98%)	280,501 (98%)	3,051,513 (98%)	168,317 (98%)
Unknown or not stated	8,314 (0%)	918 (0%)	6,106 (0%)	1,290 (1%)
Missing value	376,180	0	0	376,180
Risk Factor: Gestational Hypertension, n (%)					<0.001
Yes	172,825 (5%)	13,624 (5%)	151,296 (5%)	7,905 (5%)
No	3,381,678 (95%)	272,282 (95%)	2,946,457 (95%)	162,939 (95%)
Unknown or not stated	8,314 (0%)	918 (0%)	6,106 (0%)	1,290 (1%)
Missing value	376,180	0	0	376,180
Risk Factor: Hypertension Eclampsia, n (%)					<0.001
Yes	8,138 (0%)	691 (0%)	7,257 (0%)	190 (0%)
No	3,546,365 (100%)	285,215 (99%)	3,090,496 (100%)	170,654 (99%)
Unknown or not stated	8,314 (0%)	918 (0%)	6,106 (0%)	1,290 (1%)
Missing value	376,180	0	0	376,180
Risk Factor: Previous Preterm Birth, n (%)					<0.001
Yes	92,251 (3%)	14,261 (5%)	73,747 (2%)	4,243 (2%)
No	3,462,252 (97%)	271,645 (95%)	3,024,006 (97%)	166,601 (97%)
Unknown or not stated	8,314 (0%)	918 (0%)	6,106 (0%)	1,290 (1%)
Missing value	376,180	0	0	376,180
Risk Factor: Poor Pregnancy Outcome, n (%)					<0.001
Yes	78,255 (2%)	11,227 (4%)	63,498 (2%)	3,530 (2%)
No	3,476,248 (98%)	274,679 (96%)	3,034,255 (98%)	167,314 (97%)
Unknown or not stated	8,314 (0%)	918 (0%)	6,106 (0%)	1,290 (1%)
Missing value	376,180	0	0	376,180
Risk Factor: Previous Cesarean Deliveries, n (%)					<0.001
Yes	522,869 (15%)	45,860 (16%)	453,483 (15%)	23,526 (14%)
No	3,031,634 (85%)	240,046 (84%)	2,644,270 (85%)	147,318 (86%)
Unknown or not stated	8,314 (0%)	918 (0%)	6,106 (0%)	1,290 (1%)
Missing value	376,180	0	0	376,180
¹ Pearson's Chi-squared test

Show the R codes

obstetric_procedures_variables <- c("op_cerv", "op_tocol", "op_ecvs", "op_ecvf",
                                    "uop_induc", "uop_tocol", "on_ruptr", 
                                    "on_abrup", "on_prolg")
get_table_desc_stat_smoke(df, obstetric_procedures_variables)

Variable	Whole sample	Mother is a smoker		Unknown or not stated, N = 548,314	p-value¹
Variable	Whole sample	Yes, N = 286,824	No, N = 3,103,859	Unknown or not stated, N = 548,314	p-value¹
Obstetric Procedures: Cervical Cerclage, n (%)					<0.001
Yes	10,895 (0%)	815 (0%)	9,230 (0%)	850 (0%)
No	3,541,148 (90%)	285,365 (99%)	3,085,945 (99%)	169,838 (31%)
Unknown or not stated	386,954 (10%)	644 (0%)	8,684 (0%)	377,626 (69%)
Obstetric Procedures: Tocolysis, n (%)					<0.001
Yes	33,707 (1%)	4,064 (1%)	28,820 (1%)	823 (0%)
No	3,518,336 (89%)	282,116 (98%)	3,066,355 (99%)	169,865 (31%)
Unknown or not stated	386,954 (10%)	644 (0%)	8,684 (0%)	377,626 (69%)
Obstetric Procedures: Successful External Cephalic, n (%)					<0.001
Yes	4,787 (0%)	366 (0%)	4,265 (0%)	156 (0%)
No	3,547,256 (90%)	285,814 (100%)	3,090,910 (100%)	170,532 (31%)
Unknown or not stated	386,954 (10%)	644 (0%)	8,684 (0%)	377,626 (69%)
Obstetric Procedures: Failed External Cephalic, n (%)					<0.001
Yes	4,069 (0%)	330 (0%)	3,618 (0%)	121 (0%)
No	3,547,974 (90%)	285,850 (100%)	3,091,557 (100%)	170,567 (31%)
Unknown or not stated	386,954 (10%)	644 (0%)	8,684 (0%)	377,626 (69%)
Obstetric Procedures: Induction of labor, n (%)
Yes	904,069 (23%)	77,594 (27%)	712,180 (23%)	114,295 (21%)
No	3,029,795 (77%)	208,841 (73%)	2,388,093 (77%)	432,861 (79%)
Not on certificate	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Unknown or not stated	5,133 (0%)	389 (0%)	3,586 (0%)	1,158 (0%)
Obstetric Procedures: Tocolysis, n (%)
Yes	37,332 (1%)	4,064 (1%)	28,820 (1%)	4,448 (1%)
No	3,890,578 (99%)	282,116 (98%)	3,066,355 (99%)	542,107 (99%)
Not on certificate	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Unknown or not stated	11,087 (0%)	644 (0%)	8,684 (0%)	1,759 (0%)
Onset of Labor: Premature Rupture of Membrane, n (%)					<0.001
Yes	123,507 (3%)	11,401 (4%)	106,288 (3%)	5,818 (1%)
No	3,428,111 (87%)	274,695 (96%)	2,988,702 (96%)	164,714 (30%)
Unknown or not stated	387,379 (10%)	728 (0%)	8,869 (0%)	377,782 (69%)
Onset of Labor: Abruptio placenta, n (%)					<0.001
Yes	110,169 (3%)	10,674 (4%)	91,062 (3%)	8,433 (2%)
No	3,441,449 (87%)	275,422 (96%)	3,003,928 (97%)	162,099 (30%)
Unknown or not stated	387,379 (10%)	728 (0%)	8,869 (0%)	377,782 (69%)
Onset of Labor: Prolonged Labor, n (%)					<0.001
Yes	47,182 (1%)	3,656 (1%)	41,280 (1%)	2,246 (0%)
No	3,504,436 (89%)	282,440 (98%)	3,053,710 (98%)	168,286 (31%)
Unknown or not stated	387,379 (10%)	728 (0%)	8,869 (0%)	377,782 (69%)
¹ Pearson's Chi-squared test

Show the R codes

labor_variables <- 
  c("ld_induct", "ld_augment", "ld_steroids", "ld_antibio", 
    "ld_chorio", "ld_mecon", "ld_fintol", "ld_anesth", "uld_meco", 
    "uld_precip", "uld_breech", "md_present", "md_route", "md_trial", 
    "dmeth_rec", "attend", "apgar5", "dplural")
get_table_desc_stat_smoke(df, labor_variables)

Variable	Whole sample	Mother is a smoker		Unknown or not stated, N = 548,314	p-value¹
Variable	Whole sample	Yes, N = 286,824	No, N = 3,103,859	Unknown or not stated, N = 548,314	p-value¹
Charact. of Labor and Delivery: Induction of Labor, n (%)					<0.001
Yes	824,982 (21%)	77,594 (27%)	712,180 (23%)	35,208 (6%)
No	2,733,015 (69%)	208,841 (73%)	2,388,093 (77%)	136,081 (25%)
Unknown or not stated	381,000 (10%)	389 (0%)	3,586 (0%)	377,025 (69%)
Charact. of Labor and Delivery: Augmentation of Labor, n (%)					<0.001
Yes	716,271 (18%)	62,446 (22%)	620,256 (20%)	33,569 (6%)
No	2,841,726 (72%)	223,989 (78%)	2,480,017 (80%)	137,720 (25%)
Unknown or not stated	381,000 (10%)	389 (0%)	3,586 (0%)	377,025 (69%)
Charact. of Labor and Delivery: Steroids, n (%)					<0.001
Yes	52,145 (1%)	6,458 (2%)	43,437 (1%)	2,250 (0%)
No	3,505,852 (89%)	279,977 (98%)	3,056,836 (98%)	169,039 (31%)
Unknown or not stated	381,000 (10%)	389 (0%)	3,586 (0%)	377,025 (69%)
Charact. of Labor and Delivery: Antibiotics, n (%)					<0.001
Yes	775,285 (20%)	71,955 (25%)	669,069 (22%)	34,261 (6%)
No	2,782,712 (71%)	214,480 (75%)	2,431,204 (78%)	137,028 (25%)
Unknown or not stated	381,000 (10%)	389 (0%)	3,586 (0%)	377,025 (69%)
Charact. of Labor and Delivery: Chorioamnionitis, n (%)					<0.001
Yes	46,172 (1%)	2,373 (1%)	42,262 (1%)	1,537 (0%)
No	3,511,825 (89%)	284,062 (99%)	3,058,011 (99%)	169,752 (31%)
Unknown or not stated	381,000 (10%)	389 (0%)	3,586 (0%)	377,025 (69%)
Charact. of Labor and Delivery: Meconium Staining, n (%)					<0.001
Yes	184,920 (5%)	16,822 (6%)	159,631 (5%)	8,467 (2%)
No	3,373,077 (86%)	269,613 (94%)	2,940,642 (95%)	162,822 (30%)
Unknown or not stated	381,000 (10%)	389 (0%)	3,586 (0%)	377,025 (69%)
Charact. of Labor and Delivery: Fetal Intolerance, n (%)					<0.001
Yes	160,372 (4%)	16,456 (6%)	136,328 (4%)	7,588 (1%)
No	3,397,625 (86%)	269,979 (94%)	2,963,945 (95%)	163,701 (30%)
Unknown or not stated	381,000 (10%)	389 (0%)	3,586 (0%)	377,025 (69%)
Charact. of Labor and Delivery: Anesthesia, n (%)					<0.001
Yes	2,547,825 (65%)	211,505 (74%)	2,216,646 (71%)	119,674 (22%)
No	1,010,172 (26%)	74,930 (26%)	883,627 (28%)	51,615 (9%)
Unknown or not stated	381,000 (10%)	389 (0%)	3,586 (0%)	377,025 (69%)
Complications of Labor and Delivery: Meconium, n (%)
Yes	204,630 (5%)	16,822 (6%)	159,631 (5%)	28,177 (5%)
No	3,729,119 (95%)	269,613 (94%)	2,940,642 (95%)	518,864 (95%)
Not on certificate	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Unknown or not stated	5,248 (0%)	389 (0%)	3,586 (0%)	1,273 (0%)
Complications of Labor and Delivery: Precipitous labor, n (%)
Yes	119,085 (3%)	10,674 (4%)	91,062 (3%)	17,349 (3%)
No	3,808,285 (97%)	275,422 (96%)	3,003,928 (97%)	528,935 (96%)
Not on certificate	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Unknown or not stated	11,627 (0%)	728 (0%)	8,869 (0%)	2,030 (0%)
Complications of Labor and Delivery: Breech, n (%)
Yes	221,432 (6%)	16,283 (6%)	181,516 (6%)	23,633 (4%)
No	3,613,736 (92%)	266,362 (93%)	2,843,775 (92%)	503,599 (92%)
Not on certificate	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Unknown or not stated	103,829 (3%)	4,179 (1%)	78,568 (3%)	21,082 (4%)
Method of Delivery: Fetal Presentation, n (%)					<0.001
Cephalic	3,252,160 (83%)	266,362 (93%)	2,843,775 (92%)	142,023 (26%)
Breech	131,667 (3%)	11,368 (4%)	114,056 (4%)	6,243 (1%)
Other	75,589 (2%)	4,915 (2%)	67,460 (2%)	3,214 (1%)
Unknown or not stated	479,581 (12%)	4,179 (1%)	78,568 (3%)	396,834 (72%)
Method of Delivery: Final Route and Method of Delivery, n (%)					<0.001
Spontaneous	2,280,543 (58%)	182,242 (64%)	1,986,844 (64%)	111,457 (20%)
Forceps	21,209 (1%)	1,813 (1%)	18,579 (1%)	817 (0%)
Vacuum	97,401 (2%)	8,170 (3%)	85,132 (3%)	4,099 (1%)
Cesarean	1,161,090 (29%)	94,440 (33%)	1,011,203 (33%)	55,447 (10%)
Unknown or not stated	378,754 (10%)	159 (0%)	2,101 (0%)	376,494 (69%)
Method of Delivery: Trial of Labor Attempted, n (%)					<0.001
Yes	301,828 (8%)	25,765 (9%)	261,416 (8%)	14,647 (3%)
No	838,115 (21%)	67,228 (23%)	730,935 (24%)	39,952 (7%)
Not applicable	2,399,153 (61%)	192,225 (67%)	2,090,555 (67%)	116,373 (21%)
Unknown or not stated	399,901 (10%)	1,606 (1%)	20,953 (1%)	377,342 (69%)
Delivery Method, n (%)					<0.001
Vaginal	2,647,505 (67%)	192,225 (67%)	2,090,555 (67%)	364,725 (67%)
C-Section	1,286,529 (33%)	94,440 (33%)	1,011,203 (33%)	180,886 (33%)
Unknown	4,963 (0%)	159 (0%)	2,101 (0%)	2,703 (0%)
Attendant, n (%)					<0.001
Doctor of Medicine	3,315,042 (84%)	236,528 (82%)	2,622,845 (85%)	455,669 (83%)
Doctor of Osteopathy	243,944 (6%)	25,036 (9%)	169,203 (5%)	49,705 (9%)
Certified Nurse Midwife	321,145 (8%)	22,467 (8%)	260,953 (8%)	37,725 (7%)
Other Midwife	29,116 (1%)	708 (0%)	26,141 (1%)	2,267 (0%)
Other	27,352 (1%)	1,880 (1%)	22,816 (1%)	2,656 (0%)
Unknown or not stated	2,398 (0%)	205 (0%)	1,901 (0%)	292 (0%)
Five Minute Apgar Score					<0.001
Mean (Std)	8.79 (0.83)	8.74 (0.95)	8.79 (0.83)	8.82 (0.79)
Median (IQR)	9.00 (9.00, 9.00)	9.00 (9.00, 9.00)	9.00 (9.00, 9.00)	9.00 (9.00, 9.00)
Missing value	16,979	1,281	14,147	1,551
Plurality, n (%)					<0.001
Single	3,801,760 (97%)	278,646 (97%)	2,995,139 (96%)	527,975 (96%)
Twin	132,510 (3%)	8,025 (3%)	104,889 (3%)	19,596 (4%)
Triplet	4,392 (0%)	148 (0%)	3,533 (0%)	711 (0%)
Quadruplet or higher	335 (0%)	5 (0%)	298 (0%)	32 (0%)
¹ Pearson's Chi-squared test; Kruskal-Wallis rank sum test

Show the R codes

conditions_newborn_variable <- 
  c("ab_vent", "ab_vent6", "ab_nicu", "ab_surfac", "ab_antibio", 
    "ca_anen", "ca_menin", "ca_heart", "ca_hernia", "ca_ompha", 
    "ca_gastro", "ca_limb", "ca_cleftlp", "ca_cleft", "ca_downs", 
    "ca_chrom", "ca_hypos")
get_table_desc_stat_smoke(df, conditions_newborn_variable)

Variable	Whole sample	Mother is a smoker		Unknown or not stated, N = 548,314	p-value¹
Variable	Whole sample	Yes, N = 286,824	No, N = 3,103,859	Unknown or not stated, N = 548,314	p-value¹
Abnormal Conditions of the Newborn: Assisted Ventilation, n (%)					<0.001
Yes, Complication reported	117,969 (3%)	13,618 (5%)	99,463 (3%)	4,888 (1%)
No Complication reported	3,434,968 (87%)	272,386 (95%)	2,996,427 (97%)	166,155 (30%)
Unknown or not stated	386,060 (10%)	820 (0%)	7,969 (0%)	377,271 (69%)
Abnormal Conditions of the Newborn: Assisted Ventilation >6hrs, n (%)					<0.001
Yes, Complication reported	37,266 (1%)	4,671 (2%)	31,221 (1%)	1,374 (0%)
No Complication reported	3,515,671 (89%)	281,333 (98%)	3,064,669 (99%)	169,669 (31%)
Unknown or not stated	386,060 (10%)	820 (0%)	7,969 (0%)	377,271 (69%)
Abnormal Conditions of the Newborn: Admission to NICU, n (%)					<0.001
Yes, Complication reported	282,937 (7%)	29,714 (10%)	240,725 (8%)	12,498 (2%)
No Complication reported	3,270,000 (83%)	256,290 (89%)	2,855,165 (92%)	158,545 (29%)
Unknown or not stated	386,060 (10%)	820 (0%)	7,969 (0%)	377,271 (69%)
Abnormal Conditions of the Newborn: Surfactant, n (%)					<0.001
Yes, Complication reported	14,318 (0%)	1,946 (1%)	11,874 (0%)	498 (0%)
No Complication reported	3,538,619 (90%)	284,058 (99%)	3,084,016 (99%)	170,545 (31%)
Unknown or not stated	386,060 (10%)	820 (0%)	7,969 (0%)	377,271 (69%)
Abnormal Conditions of the Newborn: Antibiotics, n (%)					<0.001
Yes, Complication reported	2,066 (0%)	199 (0%)	1,777 (0%)	90 (0%)
No Complication reported	3,550,871 (90%)	285,805 (100%)	3,094,113 (100%)	170,953 (31%)
Unknown or not stated	386,060 (10%)	820 (0%)	7,969 (0%)	377,271 (69%)
Congenital Anomalies of the Newborn: Anencephaly, n (%)					<0.001
Yes, anomaly reported	379 (0%)	32 (0%)	330 (0%)	17 (0%)
No, anomaly not reported	3,548,468 (90%)	285,826 (100%)	3,092,784 (100%)	169,858 (31%)
Unknown	390,150 (10%)	966 (0%)	10,745 (0%)	378,439 (69%)
Congenital Anomalies of the Newborn: Meningomyelocele/Spina Bifida, n (%)					<0.001
Yes, anomaly reported	574 (0%)	80 (0%)	473 (0%)	21 (0%)
No, anomaly not reported	3,548,273 (90%)	285,778 (100%)	3,092,641 (100%)	169,854 (31%)
Unknown	390,150 (10%)	966 (0%)	10,745 (0%)	378,439 (69%)
Congenital Anomalies of the Newborn: Cyanotic Congenital Heart Disease, n (%)					<0.001
Yes, anomaly reported	3,045 (0%)	241 (0%)	2,759 (0%)	45 (0%)
No, anomaly not reported	3,545,802 (90%)	285,617 (100%)	3,090,355 (100%)	169,830 (31%)
Unknown	390,150 (10%)	966 (0%)	10,745 (0%)	378,439 (69%)
Congenital Anomalies of the Newborn: Congenital Diaphragmatic Hernia, n (%)					<0.001
Yes, anomaly reported	463 (0%)	44 (0%)	410 (0%)	9 (0%)
No, anomaly not reported	3,548,384 (90%)	285,814 (100%)	3,092,704 (100%)	169,866 (31%)
Unknown	390,150 (10%)	966 (0%)	10,745 (0%)	378,439 (69%)
Congenital Anomalies of the Newborn: Omphalocele, n (%)					<0.001
Yes, anomaly reported	375 (0%)	35 (0%)	327 (0%)	13 (0%)
No, anomaly not reported	3,548,472 (90%)	285,823 (100%)	3,092,787 (100%)	169,862 (31%)
Unknown	390,150 (10%)	966 (0%)	10,745 (0%)	378,439 (69%)
Congenital Anomalies of the Newborn: Gastroschisis, n (%)					<0.001
Yes, anomaly reported	992 (0%)	198 (0%)	750 (0%)	44 (0%)
No, anomaly not reported	3,547,855 (90%)	285,660 (100%)	3,092,364 (100%)	169,831 (31%)
Unknown	390,150 (10%)	966 (0%)	10,745 (0%)	378,439 (69%)
Congenital Anomalies of the Newborn: Limb Reduction Deficit, n (%)					<0.001
Yes, anomaly reported	468 (0%)	68 (0%)	386 (0%)	14 (0%)
No, anomaly not reported	3,548,379 (90%)	285,790 (100%)	3,092,728 (100%)	169,861 (31%)
Unknown	390,150 (10%)	966 (0%)	10,745 (0%)	378,439 (69%)
Congenital Anomalies of the Newborn: Cleft Lip w/ or w/o Cleft Palate, n (%)					<0.001
Yes, anomaly reported	1,832 (0%)	259 (0%)	1,499 (0%)	74 (0%)
No, anomaly not reported	3,547,015 (90%)	285,599 (100%)	3,091,615 (100%)	169,801 (31%)
Unknown	390,150 (10%)	966 (0%)	10,745 (0%)	378,439 (69%)
Congenital Anomalies of the Newborn: Cleft Palate Alone, n (%)					<0.001
Yes, anomaly reported	847 (0%)	118 (0%)	694 (0%)	35 (0%)
No, anomaly not reported	3,548,000 (90%)	285,740 (100%)	3,092,420 (100%)	169,840 (31%)
Unknown	390,150 (10%)	966 (0%)	10,745 (0%)	378,439 (69%)
Congenital Anomalies of the Newborn: Downs Syndrome, n (%)
Yes, anomaly reported	0 (0%)	0 (0%)	0 (0%)	0 (0%)
No, anomaly not reported	3,546,998 (90%)	285,715 (100%)	3,091,485 (100%)	169,798 (31%)
Unknown	391,999 (10%)	1,109 (0%)	12,374 (0%)	378,516 (69%)
Congenital Anomalies of the Newborn: Suspected Chromosonal Disorder, n (%)
Yes, anomaly reported	0 (0%)	0 (0%)	0 (0%)	0 (0%)
No, anomaly not reported	3,547,568 (90%)	285,717 (100%)	3,092,011 (100%)	169,840 (31%)
Unknown	391,429 (10%)	1,107 (0%)	11,848 (0%)	378,474 (69%)
Congenital Anomalies of the Newborn: Hypospadias, n (%)					<0.001
Yes, anomaly reported	2,103 (0%)	214 (0%)	1,780 (0%)	109 (0%)
No, anomaly not reported	3,546,744 (90%)	285,644 (100%)	3,091,334 (100%)	169,766 (31%)
Unknown	390,150 (10%)	966 (0%)	10,745 (0%)	378,439 (69%)
¹ Pearson's Chi-squared test