Early Life Experiences and Trajectories of Cognitive

Early Life Experiences and Trajectoriesof Cognitive DevelopmentBenjamin J. J. McCormick, DPhil,a Laura E. Caulfield, PhD,b Stephanie A. Richard, PhD,a Laura Pendergast, PhD,c

Jessica C. Seidman, PhD,a Angelina Maphula, PhD,d Beena Koshy, MD,e Ladislaus Blacy, BS,f Reeba Roshan, MD,e

Baitun Nahar, PhD,g Rita Shrestha, PhD,h Muneera Rasheed, MS,i Erling Svensen, PhD,j Zeba Rasmussen, MD,a

Rebecca J. Scharf, MD,k Sayma Haque, PhD,g Reinaldo Oria, PhD,l Laura E. Murray-Kolb, PhD,m MAL-ED NETWORK INVESTIGATORS

abstractBACKGROUND: Multiple factors constrain the trajectories of child cognitive development, but thedrivers that differentiate the trajectories are unknown. We examine how multiple early lifeexperiences differentiate patterns of cognitive development over the first 5 years of life inlow-and middle-income settings.

METHODS: Cognitive development of 835 children from the Etiology, Risk Factors, andInteractions of Enteric Infections and Malnutrition and the Consequences for Child Health andDevelopment (MAL-ED) multisite observational cohort study was assessed at 6, 15, 24 (BayleyScales of Infant and Toddler Development), and 60 months (Wechsler Preschool and PrimaryScale of Intelligence). Markers of socioeconomic status, infection, illness, dietary intake andstatus, anthropometry, and maternal factors were also assessed. Trajectories of developmentwere determined by latent class-mixed models, and factors associated with class membershipwere examined by discriminant analysis.

RESULTS: Five trajectory groups of cognitive development are described. The variables that bestdiscriminated between trajectories included presence of stimulating and learning resources inthe home, emotional or verbal responsivity of caregiver and the safety of the homeenvironment (especially at 24 and 60 months), proportion of days (0–24 months) for whichthe child had diarrhea, acute lower respiratory infection, fever or vomiting, maternalreasoning ability, mean nutrient densities of zinc and phytate, and total energy fromcomplementary foods (9–24 months).

CONCLUSIONS: A supporting and nurturing environment was the variable most stronglydifferentiating the most and least preferable trajectories of cognitive development. In addition,a higher quality diet promoted cognitive development while prolonged illness was indicativeof less favorable patterns of development.

WHAT’S KNOWN ON THIS SUBJECT: The Nurturing CareFramework highlights the importance of a stimulating andnurturing environment. Most studies of childhood cognitivedevelopment are characterized by cross-sectional analyses at keyages (e.g., school readiness or the first 1000 days) to identifydrivers.

WHAT THIS STUDY ADDS: In our longitudinal approach from 6 to60 months of age, we identified early life factors includinga stimulating environment, maternal education, illness, and infantand young child feeding that differentiate patterns of cognitivedevelopment across low- and middle-income settings.

To cite: McCormick BJ J, Caulfield LE, Richard SA, et al.Early Life Experiences and Trajectories of CognitiveDevelopment. Pediatrics. 2020;146(3):e20193660

aFogarty International Center, National Institutes of Health, Bethesda, Maryland; bJohns Hopkins University,Baltimore, Maryland; cTemple University, Philadelphia, Pennsylvania; dUniversity of Venda, Thohoyandou, SouthAfrica; eChristian Medical College, Vellore, India; fHaydom Lutheran Hospital, Haydom, Tanzania; gicddr,b, Dhaka,Bangladesh; hTribhuvan University, Kathmandu, Nepal; iAga Khan University, Karachi, Pakistan; jUniversity ofBergen, Bergen, Norway; kUniversity of Virginia, Charlottesville, Virginia; lFederal University of Ceara, Fortaleza,Brazil; and mThe Pennsylvania State University, University Park, Pennsylvania

Data (with the exception of the cognitive development variables) are available from https://clinepidb.org/ce/app/after registration and approval of proposed use.

Drs McCormick, Richard, and Seidman along with Profs Caulfield and Murray-Kolb designed and ranthe analyses, then led the manuscript drafting; Prof Pendergast and Drs Maphula, Koshy, Roshan,Nahar, Shrestha, Haque, Oria, and Svensen, Mr Blacy, and Mrs Rasheed collected, curated, andprocessed cognitive data; Drs Rasmussen and Scharf contributed to the interpretation of results;and all authors contributed to the editing and approval of the manuscript.

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Children living in poverty aredisproportionately likely to haveconstrained development.1 Multiplefactors inhibit development includingrepeated infections and illness, lowintake of a nutritious diet, and a lessnurturing family environment.Particularly sensitive windows ofdevelopment have been highlighted;2

however, studies in which researchersexamine characteristics oflongitudinal patterns of infantdevelopment in low- and middle-income countries are needed. Suchcontinued follow-up allows for betterinterpretation of infant assessments,which have low-to-moderatepredictive power.3

Considerable attention has beendevoted to the first 1000 days of lifeas a critical period in childdevelopment. However, more recentwork has argued that the second1000 days may be more influential inestablishing a platform for cognitivedevelopment.4 Along with others, wehave previously shown thata supportive and stimulating homeenvironment is positively associatedwith development at 5 years of life,whereas enteropathogen exposurefrom birth to 2 years, mediated bysymptomatic illness, negativelyimpacted cognitive scores at 2 yearsbut not at 5 years.5,6 This supportsthe argument that fosteringa stimulating and nurturingenvironment, as laid out in theNurturing Care Framework,7 isbeneficial throughout the pre-schoolyears. In contrast, reducing exposureto enteropathogens is likely to bebeneficial in the short-term but mayhave a lesser impact on shaping theoverall pattern of development.

In this study, we examine groups oftrajectories of early cognitivedevelopment using 4 time pointsfrom 6 to 60 months of age in theEtiology, Risk Factors, andInteractions of Enteric Infections andMalnutrition and the Consequencesfor Child Health and Development(MAL-ED) cohort of children who

were sampled intensely over the first2 years of life. Building on our earlierwork, which described the effects ofvariables on the mean of the studypopulation, we now evaluate if thereare distinct trajectories of cognitivedevelopment that can be differentiallyidentified by early life factors. Withthis work, therefore, we leveragelongitudinal observations of cognitivetrajectories to characterizesubpopulations who are most at riskand identify early life adversityfactors on which to intervene.

METHODS

Study Design

The MAL-ED study wasa multidisciplinary prospectivecommunity-based observational birthcohort study at 8 low- and middle-income sites8: Dhaka, Bangladesh;Fortaleza, Brazil; Vellore, India;Bhaktapur, Nepal; Loreto, Peru;Naushero Feroze, Pakistan; Venda,South Africa; and Haydom, Tanzania.Sites were selected because ofhistorically high rates of malnutritionand diarrheal disease. Each siterecruited $200 singleton childrenwithin the first 17 days of life whowere managed until 24 months ofage. Additional funding allowedfollow-up at 5 years. Children wereeligible for enrollment if they had nohealth problems at birth, weighed.1500 g, their mother was.16 years old, and the familyplanned to stay in the study area for.6 months. The study design hasbeen published,8 and brief details areprovided below. Each site obtainedinstitutional ethical approval andwritten consent from caregivers forthe original and follow-up studies.

Cognitive Development

A modified version of the BayleyScales of Infant and ToddlerDevelopment, Third Edition (BSID-III)was administered at 6, 15, and 24months. Adaptations includedchanges to enhance cultural relevance

following best practices9: redrawingpictures (or replacing photographs)to locally relevant equivalent imagesand dropping culturally inappropriateitems to ensure that the test wasappropriate in each population.Pooling data between sites wassupported by detailed psychometricanalysis (eg, exploratory andconfirmatory factor analyses, multipleindicator multiple cause modeling),refining items to just those that wereconsistent and reliable across allsites.10 In this article, the cognitivesubscale is used.11

At 60 months, the Wechsler Preschooland Primary Scale of Intelligence,Third Edition (WPPSI-III) was used toassess cognitive function, again withculturally appropriate adaptations.Detailed psychometric analyses wereconducted as before and yieldeda fluid reasoning factor composed ofitems from the block design, matrixreasoning, and picture completionsubscales that was consistent andreliable across all MAL-ED sites.12

For analyses, we required threeassessments per child, including theWPPSI-III at 606 2 months. Rigorousquality control and assurance meanteliminating data from the Tanzanian(issues with standardizedadministration) and Peruvian(WPPSI-III data unavailable) sites.Additionally, the psychometricanalysis of the 24-month BSID-IIIshowed that the Nepalese data wereinconsistent with the data from othersites and could not be pooled(although valid within-site),10 hencethese data were also excludedbecause dropping the 24-monthobservation for all Nepalese childrenwould introduce a systematic bias.

Determinants of CognitiveTrajectories

Our aim with these analyses was toidentify factors from early life thatdistinguished membership in thecognitive trajectory groups. Candidatevariables were drawn from anunderlying conceptual model that

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was used to identify factorsinfluencing development at the 24-and 60-month assessments.5,6 Briefly,58 variables describing illness(personal prevalence of diarrhea,fever, vomiting, and acute lowerrespiratory infection [ALRI] between0 and 24 months)13 andenteropathogen exposure (averagenumber of pathogens detected permonthly [first year] or quarterly[second year] nondiarrheal stoolsamples and additionally by categoryof pathogen),14 complementaryfeeding intake (nutrient densities [per1000 kcal] from monthly 24-hourrecalls, 9–24 months),15 nutrientstatus (mean plasma concentration ofhemoglobin, zinc, retinol, ferritin, andtransferrin receptor from 3 blooddraws: 7, 15, 24 months), biomarkersof gut function (mean monthly orquarterly concentration in the firstand second year respectively ofmyeloperoxidase, neopterin, and a-1-antitrypsin),16 size at birth (weight-for-age z score at enrollment), weightgain velocity (from enrollment to2 months old), socioeconomic status(a count of specified householdassets, mean monthly householdincome, years of maternal educationand whether the child hadschooling),17 and the HomeObservation for Measurement of theEnvironment (HOME) Inventory18 at6, 24, and 60 months were subjectedto psychometric analyses (seeSupplemental Information fordetails).19 At the 6- and 24-monthassessments, 3 factors were identifiedthat described the environmentalsafety of the home, child cleanliness,and the emotional and verbalresponsivity of the caregiver. The 60-month HOME psychometricassessment resulted in a single factorcharacterizing the support forlearning that was present in thehousehold. Maternal reasoning wasassessed using the Raven ProgressiveMatrices20 when the child wasbetween 6 and 8 months old, anda single factor was supported inpsychometric analyses. Maternal

depressive symptoms were evaluatedat 1, 6, 15, 24, and 60 months byusing the Self-ReportingQuestionnaire-2021; a single factorwas reported in the psychometricanalyses,22 but data from Brazil couldnot be pooled with the other sites forthe 1- to 24-month time points,whereas all sites could be pooled atthe 60-month time point. For theseanalyses, we chose to include the 60-month assessment because it wascorrelated (0.4–0.6) with the earliertime points and was useful acrossall sites.

Statistical Analysis

Cognitive assessments at each of the4 ages were converted into T scoresto eliminate differences in the scale ofeach assessment.

Groups of children with similartrajectories were identified usinglatent class-mixed models. Latentclass models identify heterogeneoussubgroups23 rather than fit a meancurve to the population. Each groupcan therefore respond differently tothe same suite of exposures andconsequently follow a different path.Cognitive scores were modeled asa function of age, and theseparameters were allowed to vary bytrajectory group. A random interceptwas included for the site of each child,which was considered a structuralrather than explanatory factor. Theoptimum number of classes wasdetermined statistically by using theAkaike information criterion.Trajectories were then reviewedvisually, and those with few childrenthat were similar (in shape and meanT score values) were combined tosimplify the interpretation.

Discriminant analysis was then usedto identify variables that differentiatebetween the latent classes. Lineardiscriminant analysis with shrinkageand correlation-adjusted Student t(CAT) scores was used to account forcorrelation between independentvariables.24 Each variable was usedindividually to discriminate between

trajectory groups and the agreementbetween the latent classes and thosepredicted by the discriminantanalysis were compared with Cohenk. Variables were then filtered usingthe univariate k statistic, retainingthose with k$ 0.1. Retained variableswere entered into a multivariablediscriminant analysis (both altogetherand sequentially), and the sum of thesquared CAT scores gives a summaryof the relative importance for eachvariable.25 Missing values for 0.5% ofthe exposure data were imputed(using predictive mean matchingfrom multiple imputation chainequations and 10 imputations, seeSupplemental Information). Analyseswere performed in R 3.6.0 (RFoundation for Statistical Computing,Vienna, Austria).

RESULTS

Cognitive data were included from835 children from 5 sites(Bangladesh, India, Pakistan, Brazil,and South Africa). The childrenrepresented 75% to 98% of those re-enrolled at 5 years. Descriptive childand family characteristics areprovided in Table 1. There wereconsiderable differences across sitesin maternal education that rangedfrom a median of 11 years (Brazil andSouth Africa) to 2 years (Pakistan),mean monthly household income(US$349 in Brazil and US$72 inIndia), enrollment weight-for-age zscore (0.04 in Brazil and 21.29 inPakistan), duration of exclusivebreastfeeding (110 days inBangladesh but only 12.5 days inPakistan), and proportion of the first2 years of life when the motherreported symptoms of child illness(most respiratory) that was muchhigher in Pakistan (32%) than othersites (6% averaged across all othersites).

Trajectories

Six latent trajectory groups werefound; however, 2 had visually similarprofiles and few children in each (n =

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61 and 80); they both increased from6 months, then declined from 24 to60 months, and differed only in themean 15-month BSID-III. Therefore,we combined these 2 groups, leaving5 trajectories (Fig 1): children withconsistently “high” scores throughout;children with “increasing” scores;children with intermediate scoresthat had an “early” decline or “late”decline; and children withconsistently “low” scores. The samplesize differed considerably betweengroups, with 2 groups (high and low)accounting for 61% (506 of 835) ofthe sample.

Differentiating Trajectory Groups

Discriminant analysis scores for the58 variables are shown in theSupplemental Information and thosewith some discriminatory power (k$

0.1) are shown in Fig 2. Thecumulative k statistic, indicating theaccuracy of the discriminant analysis,plateaued at 8 variables: 3 aspects ofthe home environment(environmental safety, support forlearning, and the caregiver’semotional and verbal responsivity),maternal reasoning ability, theproportion of days (0–24 months) forwhich the child had symptoms ofillness, the mean nutrient densities ofzinc and phytate, and the total energyfrom complementary foods (all from9 to 24 months).

Additional variables had diminishingdiscriminating power. Notably, thenext ranking variables also described

the home environment from earliertimes points, maternal characteristics(both education and depressivescores), and additional nutrientdensities from non–breast milk foods.The mean subclinical pathogendetection rate was also relativelyhighly ranked. Factors such as childsex, anemia, weight-for-age atenrollment, and household assets andincome were found to have littlediscriminatory power (SupplementalFig 4).

The 17 factors with univariatediscriminatory power (Fig 2) were“fairly accurate” (Cohen k, 0.34; 95%confidence interval, 0.29–0.39)(Table 2) in determining membershipin the 5 trajectories from the latentclass-mixed model. Their individualcontribution to overall classificationaccuracy is shown in Fig 2. The 2largest trajectory groups were mostaccurately classified (.60% correctin the case of both high and low), andthe smaller clusters were lessaccurate (39%–47% correct). Thedistributions of each of these factorsis compared in Table 3.

Radar plots contrast the profiles ofthe 8 most-discriminatory factorsinfluencing membership in eachgroup (Fig 3). Stimulating andnurturing aspects of the homeenvironment and maternal reasoningability were strongly positivelyassociated with the consistently highor increasing trajectories and stronglynegatively correlated with the earlyand low groups. The late group had

positive correlations with earlier (24month) descriptions of a safeenvironment and maternal reasoningability but a null correlation with thelater (60 month) assessment ofsupport for learning.

Aspects of complementary feedingalso influenced membership acrosstrajectories. Greater energy intakewas positively associated with thehigh group and negatively associatedwith the late and low groups. Thecombination of zinc and phytatedensities is informative fordifferentiating the various groups.Higher zinc density and lower phytatedensity are associated with theincreasing group, but the reverse istrue for the early group. Greater zincand phytate densities are associatedwith the late group but are negativelyassociated with the low group.

As shown, illness prevalence wasstrongly positively associated withmembership in the low group andmore weakly with membership in theearly group. In contrast, greater timespent ill was negatively associatedwith the high, increasing, and lategroups.

DISCUSSION

We describe 5 clusters depictingdiffering trajectories of developmentin resource-limited settings. Aharmonized protocol with rigorousquality control and extensivepsychometric analyses to supportpooling of cognitive development

TABLE 1 Selected Characteristics of the Analytic Population

Variable Dhaka, Bangladesh Fortaleza, Brazil Vellore, India Naushero Feroze,Pakistan

Venda, SouthAfrica

Sex, n (%), male 89 (51.6) 36 (58.1) 111 (46.4) 93 (51.6) 81 (51.2)First child, n (%) 72 (39.1) 27 (31.4) 68 (33.2) 42 (21.9) 60 (36.1)Enrollment wt-for-age, z score, median (IQR) 21.09 (21.79 to

20.54)0.04 (20.57 to

0.68)21.02 (21.75 to

20.48)21.29 (21.94 to

20.68)20.24 (20.9 to

0.33)Exclusive breastfeeding, d, median (IQR) 110 (66 to 157) 78 (29 to 132) 81 (43 to 113) 12.5 (7 to 15) 26 (15 to 46)Reported child illness,% d, 0–24 mo, median(IQR)

13 (9 to 18) 1 (0 to 2) 9 (6 to 13) 32 (22 to 48) 1 (1 to 2)

Number of household assets, 0–8, median(IQR)

3.3 (2.0 to 4.4) 7.3 (6.8 to 7.5) 3.5 (2.3 to 5.3) 2.5 (1.0 to 4.3) 7.3 (6.3 to 7.8)

Household monthly income, $, median (IQR) 116 (85.9 to 154) 349 (315 to 419) 71.7 (57.5 to 97.9) 143 (88.5 to 219) 232 (164 to 375)Maternal education, y, median (IQR) 5 (2 to 7) 11 (8 to 12) 8 (4 to 9) 2 (0 to 5) 11 (9 to 12)

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assessments across settingspermitted this novel analysis.Because of the rich multidisciplinaryfocus in the study, we were ableto evaluate a wide range ofinfluences on child development

over time. This study representsa unique opportunity to consider thedevelopmental paths of pre-schoolaged children and the role of earlyenvironmental factors influencingthose paths.

The contrasts between lifeexperiences of children in thepositive trajectories of the high andincreasing groups and children in thelow group, a concerning pattern ofdevelopment, are most striking. The

FIGURE 1Profiles of the five latent classes; the bold line shows the mean of each trajectory group. Six classes were identified statistically, but two sparse andsimilar groups were combined into the late group. The number of children in each class is shown above each plot.

FIGURE 2Left: the k agreement between univariate linear discriminant analysis and the latent class model, only showing variables with k $ 0.1. Middle: themultivariable sum of the CAT gives a rank score of the contribution of each variable to the overall discriminant analysis. Right: the cumulative (includedfrom top to bottom) contribution of each variable to the k agreement of the multivariable discriminant analysis model. a Factors derived from HOMEInventory.


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most discriminating factors wereaspects of the home environmentwith higher nurturing associated withthe high and increasing groups andless nurturing associated with thelow group. Our assessment of thehome environment captured theopportunities, constraints, anddemands afforded to these childrenwithin the context of their home

environments. Although there aresocietal differences in parentingbehavior, there is strong evidence thatprovision of certain experiences tochildren within their home context isuniversally related to child adaptivefunctioning and development. Theseexperiences include (1) warmth andresponsiveness, (2) discipline andcontrol, and (3) stimulation and

teaching.26–28 Indeed, accumulatingevidence reveals that parentalparticipation in cognitivelystimulating activities like reading,provision of child-appropriatelearning materials, and sensitive andresponsive interactions betweencaregiver and child are significantcontributors to optimal childdevelopment.29–31 The MAL-ED

TABLE 2 Matrix of Classification Errors by the Latent Class Model and Discriminant Analysis Based on the 17 Variables Shown in Fig 2

Discriminant Analysis Latent Class-Mixed Model

High Increasing Early Late Low

High 198 34 28 65 33Increasing 18 24 0 13 10Early 4 1 11 7 6Late 19 5 16 27 10Low 35 12 57 29 173

Values give the number of children in each trajectory group.

TABLE 3 Mean (Interquartile Range) of the Observed Data From the 17 Variables That Most Discriminated Between Trajectory Groups, Shown in Order ofMost to Least Discriminatory

Trajectory Group Time,mo

Units High Increasing Early Late Low

Environmental safety* 24 z score 0.4 (0.7 to 0.7)a 0.4 (20.1 to0.7)a

20.3 (21 to 0.7)b 0.4 (20.1 to0.7)a

20.7 (22 to20.1)b

Behaviors and environment to supportlearning*

60 z score 0.40 (0.01 to0.82)a

0.28 (20.1 to0.71)a

20.15 (20.6 to0.21)b

0.11 (20.2 to0.21)c

20.18 (20.4 to0.21)b

Maternal reasoning 6–8 Score (0–45) 31 (24 to 40)a 32 (26 to 41)a 25 (12 to 36)b 28 (18 to 38)a,b 19 (8 to 28)c

Zinc density 9–24 sqrt, g per 1000kcal

2.2 (1.9 to 2.5)a 2.3 (1.9 to 2.9)a 2.1 (1.8 to 2.5)b,d 2.2 (1.9 to2.6)a,d

1.9 (1.7 to 1.9)c

Phytate density 9–24 sqrt, g per 1000kcal

24 (18 to 26)a 19 (17 to 22)b 23 (16 to 34)c 24 (17 to 34)a,c 18 (15 to 19)d

Illness** 0–24 % d 9.3 (1.9 to 12)a 8.5 (1.7 to 13)a 17 (1.2 to 27)b 9 (1.2 to 13)a 25 (9.2 to 37)c

Total energy from complementaryfoods

9–24 kcal/d 790 (600 to970)a

720 (540 to890)a,c

700 (490 to 880)a 650 (380 to880)b,c

580 (360 to 770)b

Emotional–verbal responsiveness* 24 z score 0.3 (0.3 to 0.8)a 0.1 (20.1 to0.8)b,c

20.3 (20.6 to0.3)b,e

0.2 (20.1 to0.8)a,c

20.4 (21 to0.3)d,e

Maternal education — y 7.6 (5 to 11)a,c 8.7 (6 to 12)a 6.2 (0.75 to 10)c 7.4 (5 to 10)a,c 4.1 (0 to 8)b

Environmental safety 6 z score 0.3 (0.6 to 0.6)a 0.2 (20.5 to0.6)a,b

20.1 (20.5 to0.6)b,c

0.2 (20.5 to0.6)a,c

20.5 (22 to0.6)b

Emotional–verbal responsiveness* 6 z score 0.4 (20.1 to1)a

20.1 (20.9 to1)b,c

0 (20.9 to 1)b,c 0.1 (20.5 to 1)b 20.4 (21 to0.2)c

Vitamin A density 9–24 sqrt, g per 1000kcal)

18 (14 to 20)a 20 (14 to 29)b 17 (13 to 20)a,d 17 (12 to 21)b,d 15 (11 to 17)c

Folate density 9–24 sqrt, g per 1000kcal

12 (10 to 14)a,b 12 (10 to 13)a 13 (9.5 to 16)b 13 (9.9 to 16)b 11 (9.5 to 11)c

Nondiarrheal pathogen exposure 0–24 Count 1.1 (0.69 to1.3)a

1.1 (0.82 to 1.3)a 1.2 (0.81 to 1.6)a,b 1 (0.73 to 1.3)a 1.4 (1 to 1.7)b

Vitamin E density 9–24 sqrt, g per 1000kcal

1.6 (1.3 to 1.9)a 1.6 (1.3 to 1.8)a 1.6 (1.2 to 2)a 1.6 (1.3 to 2)a 1.4 (1.2 to 1.6)b

Maternal depressive symptoms 60 Count, 0–18 2.7 (1 to 4)a 3.4 (1 to 5)a,c 4.4 (1 to 7)b,c,d 3.7 (1 to 5)a,d 5 (2 to 7)b,c

Nondiarrheal bacterial pathogenexposure

0–24 Count 0.79 (0.54 to1)a

0.83 (0.59 to 1)a 0.85 (0.6 to 1.1)a,b 0.78 (0.5 to 1)a 0.98 (0.75 to1.2)b

Values within a row with different superscript letters are significantly different (P # .05 with Bonferroni correction tested using Kolmogorov–Smirnov tests). sqrt, square root; —, notapplicable.* HOME z score with higher scores indicating a more stimulating and responsive environment.** Illness defined as the percent of days between 0 and 24 mo with reported diarrhea, fever, vomiting, or ALRI.

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children live in settings characterizedby poverty, and yet those living ina more stimulating and responsivehome environment were more likelyto be in groups with higher scoringtrajectories. Greater maternalreasoning ability may also lead toa more responsive or nurturingenvironment; here, it isindependently associated withcognitive trajectories in the samepattern as aspects of the nurturinghome environment. As evidenced inthe recent study by Zhu et al,32 thefactors identified in our study asinfluencing early life trajectories canalso differentiate functioning inmidchildhood and adolescence.

Higher socioeconomic status isa frequently reported driver of

cognitive development.33 However, inthese analyses, aspects of wealth(household income or assets) did notdifferentiate trajectories despite thefact that both were associated withcognitive development at specificages.5,6 We posit that ourconsideration of multiple aspects ofthe home environment, more closelyassociated with behavior andengagement over time, displacedinfluences associated withwealth.34,35

Other key factors differentiating thesecontrasting groups related to childhealth and variation in infant feedingpractices. The group with consistentlylow scores was characterized by highrates of illness in the first 2 years oflife, higher rates of enteropathogen

detection, and lower intakes fromcomplementary foods in terms ofenergy and densities of zinc andphytate. Chronic illness duringchildhood is negatively associatedwith cognitive development andconsequently delays schoolreadiness.36 In our study, weexamined the prevalence ofsymptoms of illness in early life thatwere related to cognition in ourearlier analyses at 245 but not 60months.6 Infants were excluded fromthe study if they weighed ,1500 g orhad serious health problems at birth,which ought to have excluded themost vulnerable children who mightbe more prone to illnesses; however,despite this, children in the low grouphad $1 illness symptom for an

FIGURE 3Profiles for each trajectory group showing the CAT comparing each group to the pooled mean for the top 8 discriminating variables. Scores that arecloser to the center indicate a negative association between a given variable and the probability of belonging to a class; scores that are further to theouter ring have a positive association with the probability of belonging to a class. The solid circle indicates a t score of zero, hence no associationbetween the variable and a trajectory class.


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average 25% of their first 2 years oflife. The most common symptomswere respiratory.13 Combined, thenegative roles of illness, infection, andpoor infant feeding practices insettings with less promotion ofdevelopment suggest an identifiablesubgroup of vulnerable infants whoare at risk for constrained cognitivedevelopment.

Higher mean energy from non–breastmilk foods was positively associatedwith the high group, and greater zincand lower phytate densities wereassociated with the increasing group.The zinc and phytate densities alsoinfluenced membership in the earlyand late groups. Our earlier analysesfound that diets richer in B vitaminswere positively associated withcognitive scores at 24 months5 andthat greater intakes of nutrientsconsistent with animal source foodswere positively associated withcognitive scores at 60 months.6

Analyses at 24 months weredetermined by specific hypothesesregarding dietary factors,hemoglobin, and cognitivedevelopment, whereas those at5 years broadly considered dietaryexposures, as we have done here. Inthis data set, the zinc density of thediet is associated with iron, folate,and protein densities, and a lowerphytate density is associated witha more bioavailable trace mineral dietor a diet with more animal foodsources. In this way, our currentfindings are consistent with ourearlier findings.

There were unexpected findings fromthis analysis, the most notable ofwhich was the relatively weakdiscriminatory power of maternaldepressive symptoms, which isa known correlate with childdevelopment, especially throughmother–child interactions.37 In thisstudy, we included maternaldepressive symptoms at 60 months inan effort to capture underlying (notpostpartum) depression, althoughconsiderable development has

already occurred. As noted earlier, wechose the latter time point because ofits psychometric properties andcorrelations with earlier assessments.The high univariate association butlower multivariate associationbetween maternal depressivesymptoms and the trajectory groupssuggests that maternal depressioncovaries with other aspects of thenurturing environment, which werecaptured by other included variables.Anemia and micronutrientdeficiencies,38 and gutinflammation39 in the first 2 years oflife, were weakly associated withtrajectory groups. If data wereavailable on micronutrientdeficiencies or gut inflammation after24 months, it is possible that we mayhave found an association. Thistemporal resolution (high in the first2 years but low thereafter) isa limitation of the study, as was theloss of children to follow-up after theoriginal study had ended.Additionally, the rigorousrequirements for inclusion meant thatwe necessarily excluded somechildren and some sites fromanalyses. As children get older, theimportance of theirmicroenvironment diminishes as themesoenvironment becomesincreasingly influential.40 Althoughthe original study design did notcapture this expanding web, thebreadth of domains that weremeasured is unique, and withthese results, we identify earlydrivers of cognitive developmentacross markedly different populationsusing common (and validated)methods.

Even with the breadth of variablescollected, the classification agreementbetween the discriminant analysisand latent classes was at best “fair”(including all variables), and onlythen for the more populous trajectorygroups. The sparser groups (notablythe early and late groups) werepoorly cross-classified, and some16% (12 of 76) from the increasing

group were assigned to the lowgroup. Figure 1 reveals considerablevariability and overlap betweengroups. It is likely that additional orcombined factors were missing fromthese analyses, but the variables thatdid emerge to either constrain orsupport cognitive development fitwithin the existing evidence ofa pattern of vulnerability and thepotential of a nurturing environmentto make a positive difference.Importantly, these results suggestthat positive impacts can be made inthe postnatal period, for example,teaching optimal caregiver behaviorsthrough educational sessions andlearning opportunities with childrenincluding semistructured play andaccess to materials.41–44 With ouranalyses, we point to patterns thatmay help identify subpopulations ofchildren who would benefit the mostfrom interventions and also indicateaspects of the environment on whichto intervene.

CONCLUSIONS

Despite their impoverishedenvironments, some children hadpreferable trajectories of cognitivedevelopment through age 60 months.Such patterns were characterized byfewer illnesses, a more nutrient densediet from complementary foods,having a mother with greaterreasoning ability, and, crucially, livingin a home with a more nurturing andstimulating environment over thepre-school period. Although wecannot provide causal evidence, withour findings, we support the need forstrengthening comprehensiveservices to improve child health,nutrition, and the responsivity of thehome environment to supportcognitive development45 and suggestthat specific early life characteristicscan identify a group of children athigh risk who would benefit fromadditional inputs to support theirdevelopment through the pre-schoolperiod.

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ACKNOWLEDGMENTS

We thank the staff and participants ofthe MAL-ED Network for theirimportant contributions. The findingsand conclusions in this report arethose of the authors and do notnecessarily represent the officialposition of the US National Institutesof Health or Department of Healthand Human Services. MAL-EDNetwork Investigators are as follows:Angel Mendez Acosta, Rosa Rios deBurga, Cesar Banda Chavez, JulianTorres Flores, Maribel ParedesOlotegui, Silvia Rengifo Pinedo, MerySiguas Salas, Dixner Rengifo Trigoso,and Angel Orbe Vasquez (A.B.PRISMA, Iquitos, Peru); ImranAhmed, Didar Alam, Asad Ali, ZulfiqarA Bhutta, Shahida Qureshi, MuneeraRasheed, Sajid Soofi, Ali Turab, andAnita KM Zaidi (Aga Khan University,Karachi, Pakistan); LadapornBodhidatta and Carl J Mason (ArmedForces Research Institute of MedicalSciences, Bangkok, Thailand); SudhirBabji, Anuradha Bose, Ajila T George,Dinesh Hariraju, M. Steffi Jennifer,Sushil John, Shiny Kaki, GagandeepKang, Priyadarshani Karunakaran,Beena Koshy, Robin P Lazarus,Jayaprakash Muliyil, Mohan VenkataRaghava, Sophy Raju, AnupRamachandran, Rakhi Ramadas,Karthikeyan Ramanujam, AnuradhaBose, Reeba Roshan, Srujan L Sharma,Shanmuga Sundaram E, and Rahul JThomas (Christian Medical College,Vellore, India); William K Pan (DukeUniversity, Durham, NC; FogartyInternational Center, NationalInstitutes of Health, Bethesda, MD);Ramya Ambikapathi, J Daniel Carreon,Vivek Charu, Viyada Doan, JhanelleGraham, Christel Hoest, and StaceyKnobler (Fogarty InternationalCenter, National Institutes of Health,Bethesda, MD); Dennis R Lang(Fogarty International Center,National Institutes of Health,Bethesda, MD; Foundation for theNational Institutes of Health,Bethesda, MD); Benjamin JJMcCormick, Monica McGrath, Mark A

Miller, Archana Mohale, GaurvikaNayyar, Stephanie Psaki, ZebaRasmussen, Stephanie A Richard,Jessica C Seidman, and Vivian Wang(Fogarty International Center,National Institutes of Health,Bethesda, MD); Rebecca Blank,Michael Gottlieb, and Karen HTountas (Foundation for the NationalInstitutes of Health, Bethesda, MD);Caroline Amour, Eliwaza Bayyo,Estomih R Mduma, Regisiana Mvungi,Rosemary Nshama, John Pascal,Buliga Mujaga Swema, and LadislausYarrot (Haydom Lutheran Hospital,Haydom, Tanzania); Tahmeed Ahmed,AM Shamsir Ahmed, Rashidul Haque,Iqbal Hossain, Munirul Islam, MustafaMahfuz, Dinesh Mondal, and FahmidaTofail (icddr,b, Dhaka, Bangladesh);Ram Krishna Chandyo, PrakashSunder Shrestha, Rita Shrestha, andManjeswori Ulak (Institute ofMedicine, Tribhuvan University,Kathmandu, Nepal); Aubrey Bauck,Robert E Black, and Laura E Caulfield(Johns Hopkins University, Baltimore,MD); William Checkley (JohnsHopkins University, Baltimore, MD;Fogarty International Center, NationalInstitutes of Health, Bethesda, MD);Margaret N Kosek, Gwenyth Lee,Kerry Schulze, and Pablo PeñataroYori (Johns Hopkins University,Baltimore, MD); Laura E. Murray-Kolb(The Pennsylvania State University,University Park, PA); A CatharineRoss (The Pennsylvania StateUniversity, University Park, PA);Barbara Schaefer (The PennsylvaniaState University, University Park, PA;Fogarty International Center, NationalInstitutes of Health, Bethesda, MD);Suzanne Simons (The PennsylvaniaState University, University Park, PA);Laura Pendergast (Temple University,Philadelphia, PA); Cláudia B Abreu,Hilda Costa, and Alessandra Di Moura(Universidade Federal do Ceara,Fortaleza, Brazil); José Quirino Filho(Universidade Federal do Ceara,Fortaleza, Brazil; FogartyInternational Center, NationalInstitutes of Health, Bethesda, MD);

Alexandre Havt, Álvaro M Leite, AldoAM Lima, Noélia L Lima, Ila F Lima,Bruna LL Maciel, Pedro HQSMedeiros, Milena Moraes, Francisco SMota, and Reinaldo B Oriá(Universidade Federal do Ceara,Fortaleza, Brazil); Josiane Quetz,Alberto M Soares, and Rosa MS Mota(Universidade Federal do Ceara,Fortaleza, Brazil); Crystal L Patil(University of Illinois, Chicago, IL);Pascal Bessong, Cloupas Mahopo,Angelina Maphula, Emanuel Nyathi,and Amidou Samie (University ofVenda, Thohoyandou, South Africa);Leah Barrett, Rebecca Dillingham,Jean Gratz, Richard L Guerrant, EricHoupt, William A Petri, Jr, JamesPlatts-Mills, and Rebecca Scharf(University of Virginia,Charlottesville, VA); Binob Shresthaand Sanjaya Kumar Shrestha (WalterReed, Armed Forces ResearchInstitute of Medical Sciences ResearchUnit, Kathmandu, Nepal); Tor Strand(Walter Reed, AFRIMS Research Unit,Kathmandu, Nepal; University ofBergen, Norway); and Erling Svensen(Haukeland University Hospital,Bergen, Norway; Haydom LutheranHospital, Haydom, Tanzania).

ABBREVIATIONS

ALRI: acute lower respiratoryinfection

BSID-III: Bayley Scales of Infantand Toddler Develop-ment, Third Edition

CAT: correlation-adjusted Studentt

HOME: Home Observation forMeasurement of theEnvironment

MAL-ED: The Etiology Risk Factorsand Interactions of En-teric Infections and Mal-nutrition and theConsequences for ChildHealth and Development

WPPSI-III: Wechsler Preschool andPrimary Scale of Intel-ligence, Third Edition


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DOI: https://doi.org/10.1542/peds.2019-3660

Accepted for publication Jun 3, 2020

Address correspondence to Laura E. Murray-Kolb, PhD, Department of Nutritional Sciences, The Pennsylvania State University, 110 Chandlee Laboratory, University

Park, PA 16802. E-mail: [email protected]

PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).

Copyright © 2020 by the American Academy of Pediatrics

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

FUNDING: The Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED)

Project was conducted as a collaborative project supported by the Bill and Melinda Gates Foundation (OPP47075), the Foundation for the National Institutes of

Health (AI130326), and the Fogarty International Center. Funded by the National Institutes of Health (NIH).

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

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Early Life Experiences and Trajectories of Cognitive