Loss and representativeness in a 53 year follow up of a national birth cohort (The 1946 Birth cohort) Dr Gita Mishra MRC National Survey of Health and Development Department of Epidemiology and Public Health Royal Free Hospital, University College Medical School, London

Acknowledgments MRC NSHD Team members Professor Michael Wadsworth (Study director) Professor Diana Kuh Dr Marcus Richards Dr Rebecca Hardy Suzie Butterworth Stephanie Black Rachel Cooper Imran Shah Warren Hilder

Benefits of the longitudinal or life course design Known sequence and chronology of development and ageing, and of exposures Near contemporaneous data collections –only short period of recall Ability to describe the diversity of underlying pathways to later health outcomes

The costs of the design in a long-running study The fixed sample structure Each data collection is informed by contemporary scientific ideas and methods Risk over time of increased sample loss and the consequent potential for bias

MRC National Survey of Health and Development (MRC NSHD) Aims Originally the study aimed to address 2 specific issues in the years before the establishment of the NHS –Reasons for falling fertility –Effectiveness of obstetrics and midwifery on premature births, infant mortality, and on promotion of health of mother’s and infants Has evolved into a life course study investigating pathways to physical and cognitive ageing

MRC National Survey of Health & Development 1946 birth cohort Birth Registrations 3rd - 9th March 1946 (N=16,695) Population of the maternity survey (N=13,687) Selection of follow-up sample of all single born, legitimate children of fathers in non-manual and agricultural employment and 1 in 4 of all other single born legitimate children (N=5,362) Study of cohort first born offspring at ages 4 years and 8 years (N=2,205) Study of women’s health, annually, at ages years (N= 1,572) MRC National Survey of Health & Development 1946 birth cohort Birth Registrations 1 week 1946 (N=16,695) Population of the maternity survey 1 data collection at birth (N=13,687) Selection of follow-up sample of all single born, married women with husbands in non-manual and agricultural employment and 1 in 4 of all other comparable births (N=5,362) 20 data collections from age 2 to 53 years Study of cohort first born offspring at ages 4 years and 8 years (N=2,205) Study of women’s health, annually, at ages years (N= 1,572) 8 data collections

Maternity study of all births in 1 week in England, Wales & Scotland in 1946 Follow-up of a class-stratified sample (N=5362) In infancy at 2 & 4 years During school years at 6, 7, 8, 9, 10, 11, 13, 15 years In early adulthood at 19, 20, 22, 23, 25, 26, 31 years In middle adulthood at 36, 43, 53 years

Years Cohort ages National policy problems 1946Birth Costs of maternity, reason for falling fertility yearsSocial class differences in maternal and child mortality and morbidity. Value of health visitors’ work yearsIncreasing the national level of educational attainment. The ‘waste of talent’ problem yearsOutcomes of education in terms of occupational choice and skills. Delinquency years onwardsAgeing processes, self care of health, receptivity to health promotion. Maintaining the study’s momentum

Sources of information on sample loss By age 53 years Unavoidable losses –Death (n= %) –Emigration (n= %) –Living abroad(n= %) Avoidable or potentially avoidable –Permanent refusal (n= %) * only 28 new cases –Temporary refusal for this data collection only (first classified at age 43 y) (n= %) –Failure to trace (n= %)

Data collections & contacts with the sample (n=5362) YearAge in Years Respond- ent Data Collector Target Sample % achieve MotherHV499395% Mother & child SN or SD or HV & T % All CMsP, HV, I485878% Mothers of first born RN178394% All CMsRN483886% All CMsRN482687% WomenP84-90% All CMsRN (83%) CM cohort member, HV health visitor, SN school nurse, SD school doctors, T teacher, I interview, RN research nurse, P postal

Attrition The greatest overall attrition occurred in early adult years (16-31 years) –Cohort member could, for the first time, choose whether to respond –5 out of the 7 data collections were by postal questionnaire –Name and address changes were particularly frequent –There may have been an adverse effect on response due to blurring of focus of the study aims during this period

Attrition con’t The 3 later data collections (at ages 36, 43, and 53 years) have focussed strongly on health and obtained higher response rates than those earlier years –Clear re-focus on health –The employment of research nurses to collect data –Introduction of summary feed-back of findings with a birthday card –A clear explanation of the study’s aims in letters requesting each data collection

Birthday cards –Birthday cards were introduced at age 16 years to encourage response after leaving school, which requests notification of changes of name and/or address –They have been continued ever since but now include details of recent work, with references to recent publications

60 th Birthday

Sample characteristics of avoidable losses (refusals or failure to trace) Raised risk on avoidable loss were found in key variables Childhood 1.Shortness at age 4 years 2.Experience of serious illness 3.Late achievement of bladder control 4.Childhood social class of crowding 5.Paternal manual social class, low cognitive test scores 6.Low parental interest in education 7.Teachers’ ratings during adolescence of frequent problems with discipline, disobedience and aggression Adulthood 1.Adult social circumstances of low educational attainment 2.Manual social class employment, 3.Not owning the home at 26 years 4.Not belonging to clubs or association 5.Being obese at 36 years was also associated with avoidable loss from the study at age 53 years

Attrition from avoidable causes by quartiles of educational and cognitive score at 8 years

Missing data and multiple imputation The importance of checking completeness is strongly emphasised at the nurse training sessions 73 % of those who provided data at 53 years, were also successfully contacted on 17 or more of the 20 data collections Only 7% of them had taken part in 10 or fewer previous data collections Multiple imputation is now used in analysis together with sensitivity analysis to deal with missing items/contacts –Growth and breast cancer risks –Diaries of alcohol consumption

Representativeness Representation is important not only for extrapolation, but also for estimating true prevalence, and for maintaining policy relevance There are some limitations on the representativeness of this sample –Selection predated the major immigration flows –Excludes births out of wedlock –Excludes multiple births Nevertheless it remains representative in most respects of the native population born in the early post war years

Sample Representativeness: Comparison of the weighted sample at age 53 years with 1991(50-54 yrs) census data Census yrs NSHD Census Ages 45 and up Census 16+ yrs

Sample Representativeness: Comparison of the weighted sample at age 53 years with 1991(50-54%) census data MalesFemales

Conclusions I A high rate of contact can be maintained Data collection with direct contact, such as home visits by a research nurse Provide information about the work of the study to the study members –Introduction of summary feed-back of findings with the birthday card and website –A clear explanation of the study’s aims in a letter requesting each data collection

Conclusions II The responding sample continues in most respects to be representative of the national population of a similar age Consistency of response over the study’s 20 data collections has been high. The size of the sample responding in adulthood is adequate for the study of the major costly diseases and for the study of functional ageing and its precursors. Although the problems inherent in the prospective design are unavoidable they are not, in the study described, a barrier to scientific and policy value

References Wadsworth MEJ et al. JECH 1992; 46: Wadsworth MEJ et al. Soc Sci Med 2003; 57: Wadsworth MEJ et al. IJE 2006; 35:49-54 Longford NT et al. JRSSA 2000; 163: De Stavola et al. AJE 2004; 159:

Reasons for non-contact with the cohort at all data collections from the whole population