A presentation by Linda Adair as part of the Comparability of Measurement Instruments Across Ages and Contexts panel discussion at the International Symposium on Cohort and Longitudinal Studies in Developing Contexts, UNICEF Office of Research - Innocenti, Florence, Italy 13-15 October 2014

1. Challenges in harmonization and
development of measures among the
COHORTS studies
Linda Adair
University of North Carolina at Chapel Hill

2. Birth cohort publications
2
Source: www.worldmapper.org
WWW.GOPUBMED.COM (2009)
WWW.WORLDMAPPER.ORG

3. History of COHORTS
• Organized by Cesar Victora as a writing group for the Lancet Maternal and Child
Undernutrition Series
• Motivated by lack of DOHaD studies based on birth cohorts in low and middle
income countries
• Brought together the Principal Investigators of birth cohorts followed to late
adolescence or adulthood

4. Theme for analyses:
Early life influences on later health and
human capital formation
• Rationale for the selected outcomes
– Cardiometabolic disease outcomes
• Diabetes and hypertension now contribute substantially to the burden
of disease in low and middle income countries
– Body composition --- especial central body fat --- is strongly associated with
diabetes and CVD risk and may mediate the relationship of early life nutritional
status with CM disease risk
– Fasting blood glucose levels and impaired fasting glucose are important precursors
to type II diabetes
– Systolic and diastolic blood pressure predict later hypertension and CVD
– Blood pressure, glucose, and body composition can be reliably
measured in large population-based studies, and were available
for a large portion of COHORTS participants
– Schooling and height are important measures of human capital:
Potential conflicting effect of early weight gain on these
outcomes relative to CM disease risk must be considered in
LMIC

5. © 2009 - World Maps
http://www.justmaps.org
Pelotas
INCAP: Guatemala
CLHNS Cebu
New Delhi
Birth to 20

6. Study Characteristics
Study Design Cohort
inception
Participants N with
BW and
adult BMI
Mean age
at follow-up
Pelotas
Brazil
Prospective
cohort
1982 All infants born in the city’s maternity
hospitals (>99% of all births) during
1982. All social classes included.
4,148 22.7
INCAP
Nutrition
Trial Cohort
Guatemala
Community
trial
1969-77 Children <7 y in 1969 & those born
1969-77 enrolled, participants in an
intervention trial of a high-energy and
protein supplement in 4 rural villages.
544 29.6
New Delhi
Birth Cohort
Study
Prospective
cohort
1969-72 Infants born to married women from a
defined area of Delhi.
Primarily middle-class
1,583 29.1
Cebu
Longitudinal
Health &
Nutrition
Survey
Prospective
cohort
1983-84 One yr birth cohort from 33 randomly
selected communities of Metro Cebu;
75% urban. All social classes included.
2,001 21.3
Birth to 20
South
Africa
Prospective
cohort
1990 Infants from a delimited urban area
(Soweto, Johannesburg)
Predominantly poor, black sample.
1,567 15.6, age 18
data added
later

7. All sites exemplify discordance
of birth and adult weight status
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
LBW Stunted (LAZ<-2) BMI>25 BMI>30
Males Females Males Females Males Females Males Females Males Females
Brazil Guatemala Delhi Cebu Soweto
Proportion

8. Identifying directly comparable
data
• Identify essential core data needed for the
analysis
– Birth characteristics
– Anthropometrics through infancy and childhood
– Adult outcomes
– Covariates to adjust for potential confounders
• Establish inclusion criteria

9. Challenges and solutions: timing of
measurements
• Birth: All sites had birth weight, but South Africa and
Brazil lacked birth length
• Frequency of child measures varied by site
– bi-monthly vs every 3 months, vs annual during the first 2
yr
– variable ages at follow-up from 2 yr to adulthood
– some sites followed subsamples at specific age
Solutions
• Use WHO Z-scores to minimize age differences
• Define phases rather than specific ages only
– “mid-childhood”= age 4-5 yr, except for Cebu, where
midchildhood = age 8 yr

10. Challenges and solutions: diverse methods for
measuring common outcomes
• Body composition
– Estimated from DXA (South Africa)
– equations based on anthropometry (New Delhi, Cebu, Guatemala)
– BIA (Pelotas)
Solutions
– Used site and sex-specific Z-scores of lean and fat mass for analysis
• Glucose
– Fasting venous blood
– Fasting capillary/finger stick
– Random non-fasting
Solutions
– Used standard correction factors to harmonize venous and capillary
blood, regression analysis using timing of last meal to estimate
fasting values for Brazil

11. Challenges and solutions: common
concept, different measures
• Examples include:
– Socioeconomic status: Income, wealth, education, social class
• Quality of health care
• Maternal autonomy
• Urbanicity of residence
Solutions:
• Identification of concepts
• Inventory of data
• Assessment of comparability
• Construction of working variables

12. Concept inventories
Thematic category
Bt20
(South Africa)
Pelotas
(Brazil) Guatemala
CLHNS
(Phillipines)
New Delhi
(India)
Income and SES
Maternal employment status P P P P P
Partner employment status P P
Paternal schooling P P P P P
Maternal schooling P P P P P
Family income P P P P P
SES (measured differentially
across studies, through Grants.
Assets, wealth index) P P P P P

13. COHORTS: Codebook example for creating comparable variables
Variable Site N Verbatim Source Question Precise derivation of
composite variable
Child age at
collection
Other child ages at
which equivalent
variable is available
Coding
categories &
description
c3mtscho
Pelotas 5,906
Demography file (number of
years of completed
education)
N/A Birth 2 Years
years of
completed
education
Cebu 3,080
What is the highest grade
have you (mother)
completed?
Grade converted to
years of schooling
Mother
asked during
pregnancy
8 ,11, 15, 18, and 21
yrs
South
Africa
2,932
Demography file (number of
years of completed
education)
Categorical, recoded
to continuous using
midpoints of each
category.
No formal educ = 0;
gr1-gr5=2.5;
gr6-7=6.5;
gr8-10=9;
gr11-12=11.5; post-high
school
education=14
0-2
3, 7, 10, 12, 13, 14,
15, 17, 18 Yrs
Delhi 5,454
Person card (Married
Women): 3 – (18) Education
(1- Illiterate, 2- Primary, 3-
Middle, 4- Matric, 5- College,
6- Literate)
Years of completed
education coded as
midpoint:
Illiterate – 0 yr
Primary – 3 yrs
Middle – 7 yrs
Matric – 10 yrs
College – 12 yrs
Before child
birth
Guatemala 2,169 Census file maternal
education variables (number
of years of completed
education)
Birth

14. Challenges and solutions: Which
references or standards?
• WHO growth standards for child Z-scores
– No controversies
• International Diabetes foundation or WHO definitions
for overweight and obesity, hypertension, impaired
fasting glucose and diabetes, and central obesity
– Proposed alternate cutpoints for Asians?
– Youngest participants have low risk
Solutions
• Use IDF pre-HTN + HTN, dysglycemia + diabetes
• waist to height ratio
• WHO BMI cutpoints for weight status definitions

15. Data analysis
• Meta-regression or pooled data?
• Testing for heterogeneity: site and sex
differences
– What to compare: size, sign of coefficients,
confidence intervals, statistical significance?
– When to stratify
• Initial COHORTS analysis used
metaregression

16. Adult height according to
length/age at 2 yr
1 Z-score at age 2 y = 3.2 cm taller adult
Victora et al. Lancet 2008
Victora, Adair et al, Lancet 2008 Adjusted for several confounding variables

17. Achieved schooling according to length-for-
age at 2 yr
1 Z-score at age 2 y = half a year more schooling
VVictora et al. Lancet 2008 Adjusted for several confounding variables

18. Meta-analysis of birth weight and adult BMI
Females
Males
-3 0 3
Mean change in BMI per unit change in birth weight (kg)
Brazil
Guatemala
India
Philippines
South Africa
Brazil
Guatemala
India
Philippines
South Africa
Combined
VVictora et al. Lancet 2008

19. Data Analysis: pooled data
• Pooled data from 5 sites: maximizes sample size
for analysis (~7500-8000)
• Use regression models to examine associations
of early exposures with later outcomes
• Evaluate heterogeneity by site & sex
– Include site and sex indicator variables
– Test interactions of key exposures with
site and sex
• Evaluate confounding by SES

20. Systolic Blood Pressure
6
5
4
3
2
1
0
-1
-2
-3
Weight relative to linear growth Linear growth relative to weight gain
Brazil M
Guatemala M
Delhi M
Cebu M
Soweto M
Brazil F
Guatemala F
Delhi F
Cebu F
Soweto F
pooled
*
Birth 24 m MC Adult 24 m MC Adult
* Significant sex-site heterogeneity
mm Hg
Adair et al Lancet 2013

21. Challenges and solutions:
attrition and missing data
• Attrition rates vary by cohort
• Survey designs led to missing data
– E.g. South Africa and Brazil: not all infants were
measured at age 12 mo.
Solution
• Imputation of some missing values
• Inverse probability weighting

22. COHORTS sample, comparing ALL, those with
BW, BW&adult BMI, BW&adult BMI & most
basic set of CVs (CV4, birth, 24 mo, MC, Adult)
brazil
guatemala
delhi
cebu
soweto
brazil
guatemala
delhi
cebu
soweto
brazil
guatemala
delhi
cebu
soweto
brazil
guatemala
delhi
cebu
soweto
All With BW With BW&adult BMI
With BW&adult BMI
&CV4s
site code Freq. Percent Freq. Percent Freq. Percent Freq. Percent
brazil 5,913 26.65 brazil 5,805 29.2 brazil 4,184 41.59 brazil 3,583 47.05
guatemala 2,392 10.78 guatemala 973 4.89 guatemala 552 5.49 guatemala 301 3.95
delhi 7,530 33.94 delhi 6,809 34.25 delhi 1,424 14.15 delhi 1,326 17.41
cebu 3,080 13.88 cebu 3,029 15.23 cebu 2,001 19.89 cebu 1,887 24.78
soweto 3,273 14.75 soweto 3,267 16.43 soweto 1,900 18.88 soweto 518 6.8
Total 22,188 100 19,883 100 10,061 100 7,615 100
Pie charts show site composition across samples

23. Productivity
1: Addo OY, Stein AD, Fall CH, Gigante DP, Guntupalli AM, Horta BL, Kuzawa CW,
Lee N, Norris SA, Osmond C, Prabhakaran P, Richter LM, Sachdev HP, Martorell R;
and on Behalf of the Cohorts Group. Parental childhood growth and offspring
birthweight: Pooled analyses from four birth cohorts in low and middle income
countries. Am J Hum Biol. 2014 Sep 3.
2: Lundeen EA, Stein AD, Adair LS, Behrman JR, Bhargava SK, Dearden KA, Gigante
D, Norris SA, Richter LM, Fall CH, Martorell R, Sachdev HS, Victora CG; COHORTS
Investigators. Height-for-age z scores increase despite increasing height
deficits among children in 5 developing countries. Am J Clin Nutr. 2014
Sep;100(3):821-5.
3: Stein AD, Barros FC, Bhargava SK, Hao W, Horta BL, Lee N, Kuzawa CW, Martorell
R, Ramji S, Stein A, Richter L; Consortium of Health-Orientated Research in
Transitioning Societies (COHORTS) investigators. Birth status, child growth, and
adult outcomes in low- and middle-income countries. J Pediatr. 2013
Dec;163(6):1740-1746.e4.
4: Adair LS, Fall CH, Osmond C, Stein AD, Martorell R, Ramirez-Zea M, Sachdev HS,
Dahly DL, Bas I, Norris SA, Micklesfield L, Hallal P, Victora CG; COHORTS group.
Associations of linear growth and relative weight gain during early life with
adult health and human capital in countries of low and middle income: findings
from five birth cohort studies. Lancet. 2013 Aug 10;382(9891):525-34.
5: Addo OY, Stein AD, Fall CH, Gigante DP, Guntupalli AM, Horta BL, Kuzawa CW,
Lee N, Norris SA, Prabhakaran P, Richter LM, Sachdev HS, Martorell R; Consortium
on Health Orientated Research in Transitional Societies (COHORTS) Group. Maternal
height and child growth patterns. J Pediatr. 2013 Aug;163(2):549-54.
6: Horta BL, Bas A, Bhargava SK, Fall CH, Feranil A, de Kadt J, Martorell R,
Richter LM, Stein AD, Victora CG; COHORTS group. Infant feeding and school
attainment in five cohorts from low- and middle-income countries. PLoS One. 2013
Aug 20;8(8):e71548.
7: Richter LM, Victora CG, Hallal PC, Adair LS, Bhargava SK, Fall CH, Lee N,
Martorell R, Norris SA, Sachdev HS, Stein AD; COHORTS Group. Cohort profile: the
consortium of health-orientated research in transitioning societies. Int J
Epidemiol. 2012 Jun;41(3):621-6.
8: Kuzawa CW, Hallal PC, Adair L, Bhargava SK, Fall CH, Lee N, Norris SA, Osmond
C, Ramirez-Zea M, Sachdev HS, Stein AD, Victora CG; COHORTS Group. Birth weight,
postnatal weight gain, and adult body composition in five low and middle income
countries. Am J Hum Biol. 2012 Jan-Feb;24(1):5-13.
9: Norris SA, Osmond C, Gigante D, Kuzawa CW, Ramakrishnan L, Lee NR, Ramirez-Zea
M, Richter LM, Stein AD, Tandon N, Fall CH; COHORTS Group. Size at birth, weight
gain in infancy and childhood, and adult diabetes risk in five low- or
middle-income country birth cohorts. Diabetes Care. 2012 Jan;35(1):72-9.
10: Fall CH, Borja JB, Osmond C, Richter L, Bhargava SK, Martorell R, Stein AD,
Barros FC, Victora CG; COHORTS group. Infant-feeding patterns and cardiovascular
risk factors in young adulthood: data from five cohorts in low- and middle-income
countries. Int J Epidemiol. 2011 Feb;40(1):47-62.
11: Stein AD, Wang M, Martorell R, Norris SA, Adair LS, Bas I, Sachdev HS,
Bhargava SK, Fall CH, Gigante DP, Victora CG; Cohorts Group. Growth patterns in
early childhood and final attained stature: data from five birth cohorts from
low- and middle-income countries. Am J Hum Biol. 2010 May-Jun;22(3):353-9.
12: Martorell R, Horta BL, Adair LS, Stein AD, Richter L, Fall CH, Bhargava SK,
Biswas SK, Perez L, Barros FC, Victora CG; Consortium on Health Orientated
Research in Transitional Societies Group. Weight gain in the first two years of
life is an important predictor of schooling outcomes in pooled analyses from five
birth cohorts from low- and middle-income countries. J Nutr. 2010
Feb;140(2):348-54.
13: Adair LS, Martorell R, Stein AD, Hallal PC, Sachdev HS, Prabhakaran D, Wills
AK, Norris SA, Dahly DL, Lee NR, Victora CG. Size at birth, weight gain in
infancy and childhood, and adult blood pressure in 5 low- and
middle-income-country cohorts: when does weight gain matter? Am J Clin Nutr. 2009
May;89(5):1383-92.
14: Victora CG, Adair L, Fall C, Hallal PC, Martorell R, Richter L, Sachdev HS;
Maternal and Child Undernutrition Study Group. Maternal and child undernutrition:
consequences for adult health and human capital. Lancet. 2008 Jan
26;371(9609):340-57.

24. Pelotas Cohort Study
Cesar G. Victora, Pedro C. Hallal, Fernando C. Barros, Bernardo L Horta
and Denise P Gigante
(Universidade Federal de Pelotas)
INCAP Nutrition Trial
Reynaldo Martorell, Aryeh D. Stein (Emory University) Manuel Ramirez-
Zea (Institute of Nutrition of Central
America and Panama, Guatemala City )
Cebu Longitudinal Health and Nutrition Survey
Linda S. Adair (UNC Chapel Hill); Judith Borja, Nanette Lee, Isabelita Bas
(Office of Population Studies Foundation, University of San Carlos, Cebu,
Philippines); Darren Dahly ( University of Cork);
Chris Kuzawa and Thom McDade ( Northwestern University)
New Delhi Birth Cohort Study
Santosh K. Bhargava (Sunder Lal Jain Hospital); Harshpal S. Sachdev
(Sitaram Bhartia Institute of Science and Research) ;
Caroline Fall, Clive Osmond (MRC Epidemiology Resource Centre,
University of Southampton,UK)
Birth to Twenty
Linda Richter ( Human Sciences Research Council, Durban, South Africa)
Shane A. Norris, Lisa Mickleford (Developmental Pathways for Health
Research Unit
University of the Witwatersrand, Johannesburg)

25. Funding for COHORT analysis:
Wellcome Trust
Bill and Melinda Gates Foundation