Assessing Representativeness

# Load required libraries
library(tidyverse)
library(readxl)
library(knitr)
library(kableExtra)
library(gtsummary)
library(patchwork)

## Data loaded successfully!

## Total participants: 3859

## Group distribution:

## 
## Excluded Included     <NA> 
##     3016      843        0

Overview

This report assesses the representativeness of the selected Inflammatory Cytokines cohort relative to the overall cohort (hereafter referred to as the “Inflammatory Cytokines” and “overall” cohorts).

Tables and plots are presented to assess the frequency, distribution, etc. for each variable.

Assess representativeness according to demographic/baseline variable set: - Outlined in the “Variable Category” column of the data dictionary sheet in the data set.

Summary of Results

We find the distributions for most “primary” variables broadly similar between the Inflammatory Cytokines and overall cohorts, with some notable differences:

Primary Variables: - Maternal age at birth differs significantly (p=0.003), with the Inflammatory Cytokines cohort being slightly older (mean 32.3 vs 31.8 years) - Infant birth characteristics show significant differences: - Infant weight is significantly lower in the Inflammatory Cytokines cohort (mean 3321g vs 3369g, p=0.016) - Infant length is significantly shorter (mean 50.0cm vs 50.3cm, p=0.004) - Ethnic origin shows a significant difference (p=0.001), though overall distributions remain similar - Twin births are significantly over-represented in the Inflammatory Cytokines cohort (4.7% vs 3.2%, p=0.006) - Other demographic variables (gender, maternal BMI, diabetes, mental health, asthma) are well-balanced

Sample Availability: - The Inflammatory Cytokines cohort shows significantly higher engagement across virtually all questionnaire measures (all p<0.001) - 61.1% vs 48.1% completed ASQ at 4 months (p<0.001) - 84.8% vs 65.6% completed ASQ at 1 year (p<0.001) - 52.9% vs 30.4% completed ASQ at 3 years (p<0.001) - 13.4% vs 6.4% completed ASQ at 5 years (p<0.001)

Key Differences: - Child age differs significantly, with Inflammatory Cytokines children being older (mean 4.5 vs 3.8 years, p<0.001) - DASS mental health scores show mixed patterns: - Fewer “Normal” domains at 18 weeks in the Inflammatory Cytokines cohort (p=0.001) - More detailed anxiety and stress profiles at 18 weeks show better mental health (p<0.001) - 36-week scores show some differences in anxiety patterns (p=0.032)

Clinical Outcomes: - BMI at 1 year is significantly lower in the Inflammatory Cytokines cohort (16.8 vs 17.0, p=0.023) - BMI at 3 years is significantly lower (15.7 vs 15.9, p=0.016) - SPT results at 3 years show some differences in food and airborne allergies (p=0.039 and p=0.020 respectively)

Data & Methods

DEIDENTIFIED Full Participant list Perron - Final dataset July 2025.xlsx
- Contains data and data dictionary.
After some cleaning and the assignment of variable names, we get the following dimensions (rows, columns):

dim(dat)

## [1] 3859   88

We assess the variable similarity between Inflammatory Cytokines cohort (N = 843) and overall cohort (N = 3859) using:

Summary tabulations
Distributional plots
Simple statistical tests
- Note: the p-values presented can be interpreted with a grain of salt. Often in cases with large sample sizes, non-meaningful differences (in reality) return “significant” p-values.

Variable Breakdown

There are ~79 candidate variables that can be used to assess the similarity between the Inflammatory Cytokines and overall cohorts. - These variables are broadly “sample availability”, “child/maternal demographic”, “child/maternal characteristics”.

Firstly, let’s select just a handful candidate variables with the aim of getting an overall “snapshot” of the similarity between the sub-cohort and overall cohort. - For example, we want to ensure the sub-cohort is not entirely female, born in a single year, of a single ethnic origin, etc.

“Primary” variables (amended to include the 19 variables outlined) - Gender of child - Maternal age at birth - Maternal pre-pregnancy weight - Maternal pre-pregnancy height - Maternal pre-pregnancy BMI - Infant weight - Infant length - Infant BMI at birth - Infant ethnic origin - Indigenous status of baby - Vaginal or C-section birth - Maternal gestational diabetes status - Maternal Type 2 Diabetes - Maternal mental health diagnosis (Depression, Anxiety disorder, Bipolar, Schizophrenia, OCD, Anorexia Nervous, Specific Phobias, Behavioural Disorders) - Individual disorder breakdown: Each mental health condition is now analyzed separately, allowing participants with multiple conditions to be counted in each relevant category - Any mental health diagnosis: Overall indicator of any mental health condition - Depression: Depressive disorders - Anxiety: Anxiety disorders
- Bipolar: Bipolar affective disorder - OCD: Obsessive-compulsive disorder (includes various spellings) - Anorexia: Anorexia nervosa - Behavioural: Behavioural disorders - Maternal Asthma - Number of 18wk DASS domains with “Severe” or “Extremely Severe” - Number of 18wk DASS domains with “Normal” - Number of 36wk DASS domains with “Severe” or “Extremely Severe” - Number of 36wk DASS domains with “Normal”

Sample availability variables - Availability of maternal/child urine/blood/stool samples (20 weeks, 2 months, 6 months, 12 months, 3 years) - ASQ completion (4 month, 9 month, 1 year, 3 year, 5 year) - Early Connors assigned and completed - REDCap questionnaires assigned and completed - Availability of MNS data - Total questionnaires completed

Outcome variables - 1yr child wheeze - 1 year BMI - 1 year Ferritin results - 1 year count of positive SPT wheals - 1 year any positive food SPT wheals - 1 year any positive airborne/enviro SPT wheals - 3 year count of positive SPT wheals - 3 year any positive food SPT wheals - 3 year any positive airborne/enviro SPT wheals - 3 year BMI - 3 year wheeze - 3 year asthma - 3 year ferritin - 5 year BMI - 5 year asthma - 5 year Ferritin - 5 year any positive food SPT wheals - 5 year any positive airborne/enviro SPT wheals - 3 year count of Connors domains equal to, or above 65 - 3 year count of other clinical indicators parent reported as “3” highest

Remaining variables - Variables not contained in the primary nor sample availability variable set.

1) Primary Variables

Broadly, the distribution “primary” variable set between the Inflammatory Cytokines and overall cohorts is similar.

Notes - Child sex is well-balanced between groups (48.9% vs 47.9% female, p=0.554) - Maternal age at birth differs significantly, with the Inflammatory Cytokines cohort being older (mean 32.3 vs 31.8 years, p=0.003) - Maternal pre-pregnancy characteristics (weight, height, BMI) are well-balanced between groups (all p>0.08) - Infant birth characteristics show significant differences: - Infant weight is significantly lower in the Inflammatory Cytokines cohort (3321g vs 3369g, p=0.016) - Infant length is significantly shorter (50.0cm vs 50.3cm, p=0.004) - Infant BMI shows no significant difference (p=0.508) - Ethnic origin shows a significant difference (p=0.001), with some variation in category distributions - Indigenous status is well-balanced (p=1.000) - Birth type, maternal gestational diabetes, and Type 2 diabetes proportions are well-balanced (all p>0.1) - Mental health diagnosis patterns: - Any mental health diagnosis is well-balanced between groups (14.9% vs 14.5%, p=0.744) - Individual disorders show similar distributions across both cohorts: - Depression: 8.4% vs 7.8% (p=0.511) - Anxiety: 10.8% vs 10.9% (p=0.932) - Bipolar, OCD, anorexia, and behavioural disorders are rare in both groups - Maternal asthma is well-balanced (9.0% vs 8.1%, p=0.244) - DASS mental health scores show complex patterns: - 18-week severe domains: Fewer in Inflammatory Cytokines cohort (p=0.004) - 18-week normal domains: Fewer in Inflammatory Cytokines cohort (p=0.001) - 36-week scores: No significant differences in domain counts - Detailed DASS categories: Show better anxiety (p<0.001) and stress (p<0.001) profiles at 18 weeks, with some differences in anxiety at 36 weeks (p=0.032)

# Primary continuous variables
primary_continuous <- list(
  list("gender_child", "Child sex assigned at birth"),
  list("maternal_age_birth", "Maternal Age (Birth)"),
  list("maternal_prepreg_weight", "Maternal pre-pregnancy weight"),
  list("maternal_prepreg_height", "Maternal Pre-pregnancy height"),
  list("maternal_prepreg_bmi_calc", "Maternal Pre-pregnancy BMI (Calc.)"),
  list("infant_weight", "Infant Weight"),
  list("infant_length", "Infant Length at birth"),
  list("infant_bmi_calc", "Infant BMI at birth (Calc.)"),
  list("ethnic_origin", "Ethnic Origin"),
  list("indigenous_status", "Indigenous Status of Baby"),
  list("birth_type_derived", "Vaginal or C section birth (Deriv.)"),
  list("maternal_gest_diabetes_derived", "Maternal Gestational Diabetes? (Deriv.)"),
  list("maternal_diabetes_t2_derived", "Maternal Type 2 Diabetes? (Deriv.)"),
  list("mh_any", "Any Maternal Mental Health Diagnosis"),
  list("mh_depression", "Maternal Depression"),
  list("mh_anxiety", "Maternal Anxiety Disorder"),
  list("mh_bipolar", "Maternal Bipolar Disorder"),
  list("mh_ocd", "Maternal OCD"),
  list("mh_anorexia", "Maternal Anorexia"),
  list("mh_behavioural", "Maternal Behavioural Disorders"),
  list("maternal_asthma_derived", "Maternal Asthma? (Deriv.)"),
  list("dass21_18w_severe_count", "Number of 18wk DASS domains with \"Severe\" or \"Extremely Severe\""),
  list("dass21_18w_normal_count", "Number of 18wk DASS domains with \"Normal\""),
  list("dass21_36w_severe_count", "Number of 36wk DASS domains with \"Severe\" or \"Extremely Severe\""),
  list("dass21_36w_normal_count", "Number of 36wk DASS domains with \"Normal\"")
)

for(var_info in primary_continuous) {
  var_name <- var_info[[1]]
  title <- var_info[[2]]
  
  if(var_name %in% numeric_vars) {
    analyze_continuous(dat, var_name, title)
  } else {
    analyze_categorical(dat, var_name, title)
  }
}

Child sex assigned at birth

**P-value:** 0.554 (Chi-squared test)
Characteristic	Included N = 843	Overall N = 3859
Female	412 (48.9%)	1849 (47.9%)
Male	431 (51.1%)	2010 (52.1%)
Total	843 (100.0%)	3859 (100.0%)

Maternal Age (Birth)

**P-value:** 0.003 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	843	32.3 (17, 49)	32 (30, 35)
Overall	3859	31.8 (17, 50)	32 (29, 35)

Unknown: Included = 0 , Overall = 0

Maternal pre-pregnancy weight

**P-value:** 0.849 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	800	70.4 (42, 129)	68 (60, 78)
Overall	3633	70.5 (38, 134)	68 (60, 79)

Unknown: Included = 43 , Overall = 226

Maternal Pre-pregnancy height

**P-value:** 0.089 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	809	1.7 (1.5, 1.9)	1.7 (1.6, 1.7)
Overall	3698	1.7 (1.4, 1.9)	1.6 (1.6, 1.7)

Unknown: Included = 34 , Overall = 161

Maternal Pre-pregnancy BMI (Calc.)

**P-value:** 0.610 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	796	25.6 (15.4, 45.2)	24.5 (21.8, 28.4)
Overall	3623	25.7 (14.7, 47.3)	24.7 (21.9, 28.6)

Unknown: Included = 47 , Overall = 236

Infant Weight

**P-value:** 0.016 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	768	3321 (1475, 5100)	3340 (3050, 3624.5)
Overall	3578	3369.2 (1095, 5410)	3390 (3062.8, 3695)

Unknown: Included = 75 , Overall = 281

Infant Length at birth

**P-value:** 0.004 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	768	50 (34, 60)	50 (49, 52)
Overall	3574	50.3 (31, 60)	50 (49, 52)

Unknown: Included = 75 , Overall = 285

Infant BMI at birth (Calc.)

**P-value:** 0.508 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	768	13.2 (7.6, 30.8)	13.2 (12.3, 14.1)
Overall	3574	13.3 (7.6, 32.3)	13.2 (12.3, 14.2)

Unknown: Included = 75 , Overall = 285

Ethnic Origin

**P-value:** <0.001 (Fisher’s exact test)
Characteristic	Included N = 843	Overall N = 3859
Unknown	78 (9.3%)	285 (7.4%)
1	662 (78.5%)	2951 (76.5%)
3	34 (4%)	177 (4.6%)
4	8 (0.9%)	116 (3%)
5	2 (0.2%)	18 (0.5%)
7	1 (0.1%)	12 (0.3%)
8	58 (6.9%)	288 (7.5%)
10	0 (0%)	8 (0.2%)
6	0 (0%)	4 (0.1%)
Total	843 (100.0%)	3859 (100.0%)

Indigenous Status of Baby

**P-value:** 1.000 (Fisher’s exact test)
Characteristic	Included N = 843	Overall N = 3859
Unknown	76 (9%)	283 (7.3%)
1	2 (0.2%)	12 (0.3%)
4	765 (90.7%)	3563 (92.3%)
2	0 (0%)	1 (0%)
Total	843 (100.0%)	3859 (100.0%)

Vaginal or C section birth (Deriv.)

**P-value:** 0.104 (Fisher’s exact test)
Characteristic	Included N = 843	Overall N = 3859
Unknown	75 (8.9%)	281 (7.3%)
Caesarean Elective	229 (27.2%)	964 (25%)
Caesarean Emergency	159 (18.9%)	804 (20.8%)
Vaginal	380 (45.1%)	1810 (46.9%)
Total	843 (100.0%)	3859 (100.0%)

Maternal Gestational Diabetes? (Deriv.)

**P-value:** 0.433 (Chi-squared test)
Characteristic	Included N = 843	Overall N = 3859
Unknown	75 (8.9%)	281 (7.3%)
FALSE	708 (84%)	3271 (84.8%)
TRUE	60 (7.1%)	307 (8%)
Total	843 (100.0%)	3859 (100.0%)

Maternal Type 2 Diabetes? (Deriv.)

**P-value:** 0.615 (Fisher’s exact test)
Characteristic	Included N = 843	Overall N = 3859
Unknown	75 (8.9%)	281 (7.3%)
FALSE	766 (90.9%)	3572 (92.6%)
TRUE	2 (0.2%)	6 (0.2%)
Total	843 (100.0%)	3859 (100.0%)

Any Maternal Mental Health Diagnosis

**P-value:** 0.744 (Chi-squared test)
Characteristic	Included N = 843	Overall N = 3859
FALSE	717 (85.1%)	3298 (85.5%)
TRUE	126 (14.9%)	561 (14.5%)
Total	843 (100.0%)	3859 (100.0%)

Maternal Depression

**P-value:** 0.511 (Chi-squared test)
Characteristic	Included N = 843	Overall N = 3859
FALSE	772 (91.6%)	3557 (92.2%)
TRUE	71 (8.4%)	302 (7.8%)
Total	843 (100.0%)	3859 (100.0%)

Maternal Anxiety Disorder

**P-value:** 0.932 (Chi-squared test)
Characteristic	Included N = 843	Overall N = 3859
FALSE	752 (89.2%)	3437 (89.1%)
TRUE	91 (10.8%)	422 (10.9%)
Total	843 (100.0%)	3859 (100.0%)

Maternal Bipolar Disorder

**P-value:** 0.747 (Fisher’s exact test)
Characteristic	Included N = 843	Overall N = 3859
FALSE	841 (99.8%)	3845 (99.6%)
TRUE	2 (0.2%)	14 (0.4%)
Total	843 (100.0%)	3859 (100.0%)

Maternal OCD

**P-value:** 0.583 (Fisher’s exact test)
Characteristic	Included N = 843	Overall N = 3859
FALSE	843 (100%)	3855 (99.9%)
TRUE	0 (0%)	4 (0.1%)
Total	843 (100.0%)	3859 (100.0%)

Maternal Anorexia

**P-value:** 0.542 (Chi-squared test)
Characteristic	Included N = 843	Overall N = 3859
FALSE	838 (99.4%)	3843 (99.6%)
TRUE	5 (0.6%)	16 (0.4%)
Total	843 (100.0%)	3859 (100.0%)

Maternal Behavioural Disorders

**P-value:** 0.701 (Fisher’s exact test)
Characteristic	Included N = 843	Overall N = 3859
FALSE	842 (99.9%)	3849 (99.7%)
TRUE	1 (0.1%)	10 (0.3%)
Total	843 (100.0%)	3859 (100.0%)

Maternal Asthma? (Deriv.)

**P-value:** 0.244 (Chi-squared test)
Characteristic	Included N = 843	Overall N = 3859
Unknown	75 (8.9%)	281 (7.3%)
FALSE	692 (82.1%)	3264 (84.6%)
TRUE	76 (9%)	314 (8.1%)
Total	843 (100.0%)	3859 (100.0%)

Number of 18wk DASS domains with “Severe” or “Extremely Severe”

**P-value:** 0.004 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	733	0.1 (0, 3)	0 (0, 0)
Overall	2781	0.1 (0, 3)	0 (0, 0)

Unknown: Included = 110 , Overall = 1078

Number of 18wk DASS domains with “Normal”

**P-value:** 0.001 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	733	2.4 (0, 3)	3 (2, 3)
Overall	2781	2.6 (0, 3)	3 (2, 3)

Unknown: Included = 110 , Overall = 1078

Number of 36wk DASS domains with “Severe” or “Extremely Severe”

**P-value:** 0.376 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	400	0.1 (0, 3)	0 (0, 0)
Overall	1570	0.1 (0, 3)	0 (0, 0)

Unknown: Included = 443 , Overall = 2289

Number of 36wk DASS domains with “Normal”

**P-value:** 0.720 (Wilcoxon rank sum test)
Characteristic	N	Mean (Min, Max)	Median (Q1, Q3)
Included	400	2.6 (0, 3)	3 (3, 3)
Overall	1570	2.6 (0, 3)	3 (3, 3)

Unknown: Included = 443 , Overall = 2289

2) Sample Availability

In general, sample availability and engagement in the Inflammatory Cytokines cohort is substantially higher relative to the overall cohort, with significant differences across virtually all measures.

Key findings: - MNS data availability shows a slight difference (91.1% vs 92.7%, p=0.049) - All questionnaire measures show significantly higher engagement (all p<0.001): - ASQ questionnaires assigned: 5.9 vs 5.1 (p<0.001) - ASQ questionnaires completed: 3.6 vs 2.6 (p<0.001) - Total questionnaires completed: 9.3 vs 6.3 (p<0.001) - ASQ completion at specific time points: - 4 months: 61.1% vs 48.1% completed (p<0.001) - 9 months: 58.4% vs 44.5% completed (p<0.001) - 1 year: 84.8% vs 65.6% completed (p<0.001) - 3 years: 52.9% vs 30.4% completed (p<0.001) - 5 years: 13.4% vs 6.4% completed (p<0.001) - Paediatric reviews show higher engagement with some significant differences in early timepoints

# Sample availability variables
sample_vars <- list(
  list("mns_data_available", "MNS Data Available?"),
  list("asq_assigned", "ASQ Questionnaires Assigned"),
  list("asq_completed", "ASQ Questionnaires Completed"),
  list("early_connors_assigned", "Early Connors Assigned"),
  list("early_connors_completed", "Early Connors Completed"),
  list("aes_assigned", "AES Questionnaires Assigned"),
  list("aes_completed", "AES Questionnaires Completed"),
  list("redcap_assigned", "RedCap Questionnaires Assigned"),
  list("redcap_completed", "RedCap Questionnaires completed"),
  list("questionnaires_total_completed", "Total Questionnaires Completed"),
  list("asq_4m_completed", "ASQ 4 Month Completed"),
  list("asq_4m_paed_review", "ASQ 4 Month Review with Paediatrician"),
  list("asq_9m_completed", "ASQ 9 Month Completed"),
  list("asq_9m_paed_review", "ASQ 9 Month Review with Paediatrician"),
  list("asq_1yr_completed", "ASQ 1 Year Completed"),
  list("asq_1yr_paed_review", "ASQ 1 Year Review with Paediatrician"),
  list("asq_3yr_completed", "ASQ 3 Year Completed"),
  list("asq_3yr_paed_review", "ASQ 3 Year Review with Paediatrician"),
  list("asq_5yr_completed", "ASQ 5 Year Completed"),
  list("asq_5yr_paed_review", "ASQ 5 Year Review with Paediatrician"),
  list("asq_paed_review_count", "Number of times ASQ has prompted review with PAED")
)

for(var_info in sample_vars) {
  var_name <- var_info[[1]]
  title <- var_info[[2]]
  
  if(var_name %in% numeric_vars) {
    analyze_continuous(dat, var_name, title)
  } else {
    analyze_categorical(dat, var_name, title)
  }
}