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The purpose of this session. To introduce the practice dataset (MUSP) To examine distributions To perform a preliminary analysis: know your data!. The MUSP Cohort M ater- U niversity S tudy of P regnancy.
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The purpose of this session • To introduce the practice dataset (MUSP) • To examine distributions • To perform a preliminary analysis: know your data!
The MUSP CohortMater-University Study of Pregnancy • 8556 pregnant women recruited at first antenatal visit 1981-1984 at the Mater Mother’s Hospital, Brisbane • Follow-up of mother-child sets
The MUSP cohort • Follow-ups (mother and child) at delivery + 3 days, 6 months, 5 years, 14 years, 21 years (ongoing) • Current cohort of approximately 5000 • Some repeated pregnancies, multiple births
Information collected • Socio-demographic information, health behaviour • Mental health, stressors, family functioning, relationships • Biological data: pregnancy, delivery history • Physical, developmental assessment of child • Child behaviour • Health outcomes (mother and child)
Data structures • Individual ID number (codea) • Longitudinal record codea a1 a2 a3…b1 b2 b3…c1 c2 c3…. • Restructure for ‘repeated measures’ analysis
Longitudinal file Format: codea a b c1 d1……. c2 d2……. c3 d3……. 1234 1 4 15.2 6….…. 16.6 9……. 18.6 11……. repeated measures Stacked file Time-constant variables Format: codea time a b c d 1234 1 1 4 15.26 1234 2 1 4 16.6 9 1234 3 1 4 18.6 11 Time-dependent variables
Data Sets Longitudinal file: mps.SinglePregnancyMother Used for examining change over time within persons Stacked file: mps.MentalHealth Used for repeated measures analysis
The research questions • Does mother’s mental health change over time? • What factors (time-constant, time-dependent) predict mother’s mental health? • What factors predict change in mother’s mental health?
Mother’s mental health • 14 ordinal items on a five point scale: summed to make score 10 (good) – 50 (bad) • Measured at six times so far: FCV, + 3 days, + 6 months, + 5 years, + 14 years, + 21 years • Missing values and attrition
Possible predictors • Age at First Clinic Visit • Health status: time-dependent • …………
Descriptive analysis • Missing values and attrition • Distribution • Change over time • Correlations over time • Determinants of mental health
Descriptive analysis • Missing values and attrition
Attrition and Missing Values • Attrition high; include only those present at all phases FCV to +14 yearsN =4470 • Missing values elsewhere on particular items, comparatively infrequent.
Descriptive analysis • Distribution (uses stacked file)
Descriptive analysis • Change over time – grouped (uses stacked)
Summary statistics for mental health score (high=poor) Summary statistics for log(mental health score-9)
Poor Mental Health GoodMental Health
Descriptive analysis • Change over time – individual (uses longitudinal)
Poor Mental Health Worsening Improving GoodMental Health
Poor Mental Health Worsening Improving GoodMental Health
Descriptive analysis • Correlations over time
= 0.456 = 0.684
Descriptive analysis • Determinants of mental health
Marital Status at FCV Combine “Other”
Poor Mental Health GoodMental Health
Excluding “missing” and “other” Poor Mental Health Means 2xSEM GoodMental Health
Single mothers poorer mental health at FCV, same at 6m, then widening gap Poor Mental Health GoodMental Health
Descriptive analysis • Determinants of mental health (time-dependent)
Better physical health, better mental health, at all times Poor Mental Health GoodMental Health Physical Health
Better physical health, better mental health, at all times, association stronger at 14 years Poor Mental Health GoodMental Health Physical Health Time
Poor Mental Health Poor Physical Health GoodMental Health Time Excellent Physical Health
The MUSP dataset • The subset available for all phases to 21 years (N = 667)in a ‘stacked’ file: MentalHealth • Outcome: Logged maternal mental health score • Time-constant variables: At FCV: Marital status, Income, Education, Age group, Number of previous pregnancies, Country of birth, Time in Australia • Time-dependent variables: Physical health status