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Childhood Health: Original Article

Infant Growth During the First Year of Life and Subsequent Hospitalization to 8 Years of Age

Hui, L. L.a; Schooling, C. Marya; Wong, M. Y.b; Ho, L. M.a; Lam, T. H.a; Leung, Gabriel M.a

Author Information
doi: 10.1097/EDE.0b013e3181cd709e

Rapid growth during infancy may be associated with metabolic disorders in childhood1,2 and adulthood.3,4 It is a fundamental axiom of life-history evolution and ageing that humans, like other animals, work with a limited resource base and are, therefore, forced to tradeoff certain life-history parameters against each other.5 This suggests that there should be survival benefits of rapid infant growth, or a trade-off between a strategy that promotes both survival up to reproductive age and long-term health.6 Until recently, one of the key components of early survival was resistance to infections. Is it possible that rapid infant growth protects from infection? Such a theory would be consistent with the widely observed cultural preferences for “fat” babies, particularly in China. Given the cultural context and public policies promoting infant growth, especially in Chinese societies, identifying optimal growth trajectories for a range of outcomes has major public health implications. To our knowledge, only one previous study has investigated early growth and subsequent morbidity and mortality from infectious diseases; this study found growth to be protective. A few studies have examined postnatal growth and an intermediate marker (ie, lung function), with somewhat mixed results. Greater infant weight gain did not improve lung function in infants7,8; it was associated with better lung function in adulthood in one study9 but not in another.10

We used data from a contemporary, large, population-representative Hong Kong Chinese birth cohort, “Children of 1997.” In previous analysis of this cohort, faster growth during the first year of life, particularly in the first 3 months, was associated with higher childhood body mass index at 7 years.11 Detrimental effects of rapid growth may be compensated for by better immunity and protection from infectious disease during childhood, particularly for infants with poorer fetal growth. Here, we examined whether infant growth trajectory (from birth to 12 months) was associated with subsequent serious morbidity, proxied by all-cause and cause-specific hospitalization. We also examined whether there were any critical periods of growth (0–3 months or 3–12 months) and whether the effects of growth varied with sex or with an indicator of fetal development (ie, birth weight).


The Hong Kong “Children of 1997” Birth Cohort Study

We used data from a population-representative Hong Kong birth cohort (n = 8327), which covered 88% of all births during April and May of 1997.12–15 The “Children of 1997” cohort is almost entirely ethnic Chinese, recruited at the first postnatal visit at any of the 49 maternal and child health centers. In Hong Kong, families of newborns are encouraged to attend these centers for free postnatal care and to continue regular follow-up visits for free developmental checks, physical examinations, and vaccinations until the age of 6 years. Information on socioeconomic status (parental education, type of housing, and birth hospital), birth characteristics (birth weight, birth order, sex, gestational age, and method of delivery), infant feeding, and second-hand smoke exposure was collected at the initial and follow-up visits using self-administered questionnaires in Chinese.

From the medical records in the centers, we retrospectively collected weight (to 0.1 kg) measured at all routine follow-ups (ie, at 1, 3, 9, and 12 months). We collected data on inpatient admissions from the Hospital Authority. There is universal access in Hong Kong to essentially free hospitalization by the Hospital Authority (89% coverage of acute bed-days and 87% of admissions16). We matched our birth cohort members with patients born in April or May 1997 and admitted by 31 December 2005 mainly using birth certificate number (a unique identifier for all Hong Kong children). All matches were double-checked by hand.


We considered growth in 2 complementary ways. First, we considered growth trajectory from birth to 12 months. Weights were interpolated at exact ages (1, 3, 9, and 12 months). To group children with similar infant weight patterns into growth trajectories, we considered 2 person-centered techniques designed to classify individuals into distinct groups, ie, latent class analysis17,18 and group-based modeling.19 We did not consider growth mixture modeling20 because it is variable-focused and designed to identify how outcomes and exposures are related within an infant growth trajectory, rather then the prospective outcomes of a particular infant growth trajectory. However, latent class analysis produced better fitting models (with lower Akaike Information Criteria [AIC]) than group-based modeling (as shown in eAppendix 1, Moreover, the assumption of independence for repeated observations in weight over time periods in group-based modeling was violated. Hence, we used latent class analysis to construct sex-specific growth trajectories (as described in detail in eAppendix 2,, using means and variances for children with complete information on all weights as the starting point.

We initially considered at least 4 clusters to take full advantage of our relatively large cohort, and to ensure we had sufficient granularity. We found that sex-specific models with 5 clusters and unequal within-cluster covariance matrices fit best, so we present results based on 5 sex-specific clusters from longitudinal latent class analysis. We did not impute any missing weights. Classification of a child with any missing weight is based on weights available. The allocation was repeated until the product of total posterior membership probabilities over all observations achieved its maximum. The figure shows the locally weighted regression lines for the 5 trajectories, as well as all the actual weights, for boys and for girls.

Growth trajectories and weight for girls and boys.

Second, we considered growth rate from birth to 3 (0–3) months and from 3 to 12 (3–12) months as the change in weight-for-age z-score, ie, standard deviation score. We included only term births (gestation of ≥37 weeks) because growth trajectories of premature infants are different,21 and initial analysis suggested that the association between growth rate and subsequent hospital admission differed by preterm status (ie, a model with an interaction term for growth rate and preterm status had a smaller AIC). Weight-for-age z-score was calculated relative to the 2006 World Health Organization (WHO) growth standards, because the weight of Hong Kong infants matches the new WHO standard closely.22 We used the akima package in R (version 2.3.1, R Development Core Team, Vienna, Austria) to interpolate the WHO standards onto a daily scale, so that z-scores were calculated at exact daily ages. The closest available weight measurements to 3 months (within 2 to 4 months) and 12 months (within 9 to 15 months) were used.

Outcome—Hospital Admissions

Our primary outcome was the number of subsequent hospital admissions for any infection, and for respiratory infections and gastritis/gastroenteritis separately. For completeness, we also considered admissions for noninfectious illnesses, any illness, and accidents. Accidents were also included as a counterfactual outcome to check for any systematic effects (perhaps due to uncontrolled confounding by socioeconomic position). Initial analysis showed that considering hospital use as ever-admission, number of admissions, or total bed-days produced broadly similar results, so we present here number of hospital admissions as the outcome. An initial plot of the percentage cumulative hospitalization by growth rate was consistent from infancy until 8 years of age, so we did not stratify by age.

We used in-patient admissions (defined as at least 1 overnight stay) between the age of 3 months (or 12 months) and 8.0 years, obtained from the primary discharge code. “Any infection” included respiratory infections (International Classification of Diseases 9, Clinical Modification, ICD9-CM, 033, 034.0, 381–382, 460–466, 477, 480–487, 493), gastritis/gastroenteritis (535.00, 535.50, 558.9, 538, 535.40), gastrointestinal tract infections (001–009, 787.91), urinary tract infections (599.0), fever (780.6), febrile convulsion (780.3), and other infectious and parasitic diseases. Full details are given in eAppendix 3 ( “Noninfectious illnesses” were all causes other than the above infections and accidents/injuries (ICD9-CM 800–999). Cohort members without any record of hospital admission were assumed to have had no admission, although we acknowledge (and have tried to compensate for) the obvious limitations of not having measured private hospital use (see Discussion). Some children might have emigrated; we identified 255 children who had had no recent contact either directly with the birth cohort study team or indirectly with government health service sectors; these were excluded in a sensitivity analysis.

Statistical Analysis

We assessed the association of growth with the number of subsequent hospital admissions to 8.0 years of age using multivariable negative binomial regression, from which we report the incidence rate ratio (IRR) with 95% confidence interval (CI). We specifically explored effect-measure modification by sex or birth weight (or, where appropriate, weight at 3 months) in the association of growth with serious morbidity, comparing the AIC between models with and without the relevant interaction terms. Potential confounders considered were gestational age (37, 38, 39, 40, or ≥41 weeks), birth order (first, second, third, or more), feeding type (never breast-fed, exclusively breast-fed less than 3 months or partially breast-fed, or exclusively breast-fed 3 months or more), highest education of either parent (≤9th grade, 10th–11th grade, or ≥12th grade), and type of housing (private or public). After preliminary analysis, birth order, feeding type, and housing type were not included as they did not change the growth-rate effect sizes for all admissions by more than 5%.23

We considered 2 other potentially relevant confounders, first markers of reverse causality and second use of other facilities from which we could not obtain usage information. Inevitably, in a population-representative birth cohort there will be children with an underlying pathology that causes growth failure, susceptibility to infections, and hospital use. We identified these congenital conditions, including cancers and genetic abnormalities (full details in eAppendix 3,, and any children admitted to hospital at any time for such conditions (n = 92). Second, although public hospitals in Hong Kong provide 89% of the bed-days,16 some infants and children, especially those born in private hospitals, use private facilities. We included type of birth hospital (private or public) as a potential confounder to allow for the probable greater use of public hospitals by the less advantaged.

We present 3 models. Model 1 shows unadjusted associations. Model 2 adjusts for sex, gestational age, highest parental education, birth hospital, and congenital conditions, so as to show whether, independent of the social causes of growth, a particular growth pattern is related to hospital admissions. Model 3 additionally adjusted for birth weight z-score (or where appropriate z-score for weight at 3 months) to identify the effect of growth independent of initial postnatal size.

Statistical analyses were performed using R version 2.6.2 (R Development Core Team, Vienna, Austria) and Stata version 9.0 statistical software (Stata Corp, College Station, TX). A program in SAS (version 9.1) codes was written to generate the growth trajectories by latent class analysis.

The study was reviewed by and received approval from the University of Hong Kong-Hospital Authority Hong Kong West Cluster Joint Institutional Review Board and the Ethics Committee of the Department of Health, Government of the Hong Kong SAR, People's Republic of China.


There were 7834 term and 433 preterm births in the cohort, and 60 with missing gestational age. One term birth was missing sex, 7833 had growth trajectories, 7185 (92%) had growth rates at 0–3 months, and 6905 (88%) had growth rates at 3–12 months. Of the 7833 included children, 3193 had at least 1 admission to a public hospital between the ages of 3 months to 8 years, with 57% of them having only 1 admission. In total, there were 6442 admissions of which 2633 were due to respiratory infections (1746 children, 22%), 389 due to gastritis/gastroenteritis (362 children, 5%), 1399 due to other infections (1095 children, 14%), 498 due to accidents and injuries (462 children, 6%), and 1483 due to noninfectious illnesses (883 children, 11%) (eAppendix 3, Overall, admissions were higher in boys (incidence rate ratio = 1.44 [95% confidence interval = 1.33–1.55]); in infants weighing less than 2.5 kilograms at birth (1.67 [1.33–2.09]), in infants born in public hospitals (2.04 [1.84–2.26]), and in children with less educated (≤9th grade) compared with well educated (≥12th grade) parents (1.42 [1.27–1.58]). Admissions due to accidents or injuries were higher in children with less educated parents (1.62 [1.22–2.15]) and in children living in public housing (1.9 [1.45–2.49]).

Infants in trajectories I and II had below-average weight at birth (Table 1). Infants in trajectory I fell further behind in the first year, while trajectory II infants stayed on the track predicted by their birth weight. Trajectory III and IV infants both had birth weights closer to the WHO average, with those in trajectory III accelerating in their growth, while trajectory IV infants stayed on track. Finally, trajectory V infants were born heavier and accelerated in their growth. Infants in trajectories IV and V were more likely to have longer gestation and higher birth order. Trajectory V infants were more likely to have highly educated parents, while those in trajectory I were most likely to have congenital abnormalities. Growth rate at 0–3 months was higher in infants with lower birth weight, shorter gestational age, and more highly educated parents.

Baseline Characteristics by Trajectory and Growth Rate for 7833 Children in the Hong Kong “Children of 1997” Birth Cohort

Table 2 shows the associations of growth trajectory with hospital admission between 12 months and 8 years. Growth trajectory was unrelated to admission for respiratory infections in all models. Infants with low birth weight who grew slowly (trajectory I) had more admissions for any infection (model 2), although this was attenuated by adjustment for birth weight (model 3). Infants in trajectory I also had more admissions for noninfectious illnesses, with the other 4 trajectories fairly similar to each other. There was no clear association between growth trajectory and admission for accidents. There was no evidence that the associations varied with sex or birth weight (eAppendix 4, Results were similar after excluding children without recent contact or with congenital diseases (eAppendix 5,

Associations of Growth Trajectory at 0–12 Months With Number of Hospital Admissions Between 1 and 8 Years of Age

Table 3 shows the associations of growth rate measured simply as birth weight z-score or change in weight-for-age z-score. Growth is considered change in weight-for-age z-score from birth to 3 months and from 3 to 12 months. Results are shown without (model 2) and with (model 3) adjustment for baseline weight for age z-score. Lower birth weight was associated with noninfectious illnesses, but not with infectious illnesses. Growth rate in the first 3 months was more weakly associated with noninfectious illness. These associations varied with sex and baseline weight (eAppendix 4,

Incident Rate Ratios for Hospital Admission Until 8 Years per Unit-increase in Birth Weight z-score and Change in Weight-for-age z-score at 0–3 Months and 3–12 Months


In this prospective, population-representative birth cohort, none of the growth parameters (growth trajectories from birth to 12 months and change of weight-for-age z-scores from birth to 3 months or from 3 months to 12 months) was associated with hospital admission for respiratory infections, regardless of birth weight. Rapid growth was not associated with fewer admissions for any infection-related disease, although slow growth in infants with lower birth weights was associated with infection-related admissions. These infants were also more often admitted for noninfectious illnesses. As such, our observations do not provide support for the hypothesis that better immunity is a developmental trade-off for the later metabolic risks associated with rapid infant growth.

We used 2 approaches to characterize growth, ie, as weight trajectories and as changes in weight-for-age z-score. Changes in weight-for-age z-score, although often used, compare differences only between 2 points, rather than assessing overall growth patterns; such changes also have the disadvantage of regression to the mean. Moreover, in our study infants were not all measured at the same age, and so the changes in weight-for-age z-scores related to slightly different time periods. In contrast, growth trajectories, although data-driven, take into account the differing timing of the measurements and encapsulate growth over the entire period. However, growth trajectories preclude the identification of critical periods, have an essentially arbitrary number of classes, and are inevitably not completely distinct; thus they should be interpreted as qualitative approximations to reality.

Despite this attempt to provide a comprehensive approach to modeling, there are some caveats. First, we did not have data on admissions to private hospitals. Growth was associated with birth in a private hospital and potentially with subsequent private hospital use. Thus, it is possible that hospital admission for some children was under-ascertained, and that the resulting biases in the associations of growth with admissions were not systematic. Possible confounding was reduced by adjustment for socioeconomic position and type of birth hospital. Moreover, it is unlikely that the absence of an association of rapid growth with infectious diseases admissions would be produced by such bias.

Second, we assumed that children without admission records had never been admitted. It is possible that absence of admission records was due to leaving Hong Kong; however, sensitivity analyses demonstrated that exclusion of the 4% with no recent contact either directly with the birth cohort study team or indirectly with government health service sectors did not change the results.

Third, only 8%–12% of the eligible birth cohort members were excluded from the analyses and 35% of their families indicated at the first interview that they intended to raise their infants in mainland China. Thus, missing infant growth data were due partly to cohort members temporarily leaving Hong Kong, rather than attributes of the infants or their families that related to health and risks of diseases. Although infants who had missing growth data were more likely to have less educated parents, to live in public housing, and to have been born in public hospitals, they were no different in their birth weight and body mass index at 7 years from other cohort members, and they did not have more hospital admissions than others in the study.

It has previously been shown that infants with lower birth weights are more susceptible to infections in infancy and childhood,24,25 as we also found. Potentially, infant growth could compensate for the detrimental effect of small birth size on respiratory health,26 although there is some evidence that rapid postnatal growth is associated with decreased lung function.7,8 Unlike a previous study in Brazil,27 we did not find fast infant growth associated with fewer hospital admissions due to respiratory infections, regardless of birth weight. However, our study differed from that study in several respects. We considered growth at an earlier age, ie, 0–12 months rather than at 0–20 months; we considered all subsequent hospital use until the age of 8 years rather than hospital use at 2.5 years to 3.5 years; we adjusted for several markers of socioeconomic position; and we found consistent results when considering growth both as a trajectory and as a change in weight-for-age z-score.

Infant growth is a common indicator of healthy development. Our study does not suggest that faster growth during the first year of life in an affluent setting protects children from infectious diseases. Under-nutrition is a leading cause of infant and child mortality.28 The dominant paradigm in research concerning infant nutrition and infection has been from the perspective of the risks of under-nutrition and specific nutrient deficiencies.29 Infants born small who grew slowly had more admissions for infectious illnesses, and particularly for noninfectious illnesses. This may be a result of under-nutrition and specific deficiencies. Alternatively, it is possible that growth is a marker of the child's underlying health state, rather than a protective factor—ie, reverse causality. The association between slower growth and more hospital use for noninfectious illnesses reported here may not be causal but could instead be a parallel marker of the child's underlying health state and ability to grow—perhaps with 0–3 months being the critical period for the infant to demonstrate its fitness, and hence warrant continued parental investment.

In conclusion, we found no evidence that fast infant growth was associated with a compensatory lower risk of serious infectious morbidity. Infants born with low birth weight who grew slowly had more admissions for both any infection and for noninfectious illnesses. Clearly, infants at lower birth weights are a more vulnerable group, where the ability to grow fast may reflect good health status. In other infants, there do not appear to be any immunologic benefits of rapid growth, although we cannot rule out the possibility of such growth having other benefits, such as promoting cognitive development. Maximum growth rates may not be ideal, and potential adverse effects of over-nutrition in infants should be considered.


We thank the Hospital Authority for providing inpatient admission data. We thank the Family Health Service, Department of Health, Government of the Hong Kong SAR for collaborating on the study and facilitating the recruitment and follow-up of subjects; and Keith Tin and Eileen Yeung for providing assistance in data extraction.


1. Stettler N, Bovet P, Shamlaye H, Zemel BS, Stallings VA, Paccaud F. Prevalence and risk factors for overweight and obesity in children from Seychelles, a country in rapid transition: the importance of early growth. Int J Obes Relat Metab Disord. 2002;26:214–219.
2. Singhal A, Cole TJ, Fewtrell M, et al. Promotion of faster weight gain in infants born small for gestational age: is there an adverse effect on later blood pressure? Circulation. 2007;115:213–220.
3. Ekelund U, Ong KK, Linne Y, et al. Association of weight gain in infancy and early childhood with metabolic risk in young adults. J Clin Endocrinol Metab. 2007;92:98–103.
4. Osmond C, Barker DJ, Winter PD, Fall CH, Simmonds SJ. Early growth and death from cardiovascular disease in women. BMJ. 1993;307:1519–1524.
5. Johnston SL, Grune T, Bell LM, et al. Having it all: historical energy intakes do not generate the anticipated trade-offs in fecundity. Proc Biol Sci. 2006;273:1369–1374.
6. van Bodegom D, May L, Meij HJ, Westendorp RG. Regulation of human life histories: the role of the inflammatory host response. Ann N Y Acad Sci. 2007;1100:84–97.
7. Lucas JS, Inskip HM, Godfrey KM, et al. Small size at birth and greater postnatal weight gain: relationships to diminished infant lung function. Am J Respir Crit Care Med. 2004;170:534–540.
8. Turner SW, Zhang G, Young S, et al. Associations between post natal weight gain, change in post natal pulmonary function, formula feeding and asthma. Thorax. 2007.
9. Canoy D, Pekkanen J, Elliott P, et al. Early growth and adult respiratory function in men and women followed from the fetal period to adulthood. Thorax. 2007;62:396–402.
10. Strachan DP, Orfei L, Rudnicka A, Wadsworth M. Early influences on adult lung function in two national British birth cohorts. Arch Dis Child. 2007;93:570–574.
11. Hui LL, Schooling CM, Leung SS, et al. Birth weight, infant growth, and childhood body mass index: Hong Kong's children of 1997 birth cohort. Arch Pediatr Adolesc Med. 2008;162:212–218.
12. Lam TH, Leung GM, Ho LM. The effects of environmental tobacco smoke on health services utilization in the first eighteen months of life. Pediatrics. 2001;107:E91.
13. Leung GM, Ho LM, Lam TH. Maternal, paternal and environmental tobacco smoking and breast feeding. Paediatr Perinat Epidemiol. 2002;16:236–245.
14. Leung GM, Ho LM, Lam TH. Second hand smoke exposure, smoking hygiene, and hospitalization in the first 18 months of life. Arch Pediatr Adolesc Med. 2004;158:687–693.
15. Leung GM, Ho LM, Lam TH. The economic burden of environmental tobacco smoke in the first year of life. Arch Dis Child. 2003;88:767–771.
16. Census and Statistics Department. Thematic Household Survey Report No. 12 (2002 data). Wan Chai, Hong Kong: Government of the Hong Kong SAR, Census and Statistics Department; 2003.
17. Vermunt JK. Latent class and finite mixture models for multilevel data sets. Stat Methods Med Res. 2008;17:33–51.
18. Goodman LA. Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika. 1974;61:215–231.
19. Nagin DS. Group-Based Modeling of Development. Cambridge, MA: Harvard University Press; 2005.
20. Muthen B. Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In: Kaplan D, ed. Handbook of Quantitative Methodology for the Social Sciences. Newbury Park, CA: Sage Publications; 2004:345–368.
21. Casey PH, Kraemer HC, Bernbaum J, et al. Growth patterns of low birth weight preterm infants: a longitudinal analysis of a large, varied sample. J Pediatr. 1990;117(2 pt 1):298–307.
22. WHO. WHO child growth standards based on length/height, weight and age. Acta Paediatr Suppl. 2006;450:76–85.
23. Rothman KJ, Greenland S. Modern Epidemiology. 2nd ed. Philadelphia: Lippincott-Raven; 1998.
24. Hviid A, Melbye M. The impact of birth weight on infectious disease hospitalization in childhood. Am J Epidemiol. 2007;165:756–761.
25. Yuan W, Basso O, Sorensen HT, Olsen J. Indicators of fetal growth and infectious disease in childhood—a birth cohort with hospitalization as outcome. Eur J Epidemiol. 2001;17:829–834.
26. Rona RJ, Gulliford MC, Chinn S. Effects of prematurity and intrauterine growth on respiratory health and lung function in childhood. BMJ. 1993;306:817–820.
27. Victora CG, Barros FC, Horta BL, Martorell R. Short-term benefits of catch-up growth for small-for-gestational-age infants. Int J Epidemiol. 2001;30:1325–1330.
28. Schaible UE, Kaufmann SH. Malnutrition and infection: complex mechanisms and global impacts. PLoS Med. 2007;4:e115.
29. Solomons NW. Malnutrition and infection: an update. Br J Nutr. 2007;98(suppl 1):S5–S10.

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