1 Yale University, School of Medicine, New Haven, CT.
2 University of Florida, College of Liberal Arts and Sciences, Gainesville, FL.
3 University of Florida, College of Medicine, Gainesville, FL.
4 University of South Florida, College of Public Health, Tampa, FL.
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ABSTRACT |
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child development; education, special; logistic models; morbidity; pregnancy in adolescence; socioeconomic factors
Abbreviations: EH, emotionally handicapped; EMH, educable mentally handicapped; IQ, intelligence quotient; LD, learning disabled; PI, physically impaired; PMH, profoundly mentally handicapped; TMH, trainable mentally handicapped
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INTRODUCTION |
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Many studies reported in the literature are inconclusive because of small sample sizes, potential biases in the study sample selection, and inadequate control for confounding factors. Some new designs, such as sibling studies and ingeniously chosen control groups, attempt to better control for confounding factors (3, 26
), but even in those studies, the separation of maternal age effects and the effects of other confounding factors is not complete. The current study population consisted of all Florida-born children who entered kindergarten in Florida public schools between 1992 and 1994. The large sample size (more than 300,000 records) allowed us to control several important confounders by using multivariable models and to study the effect of maternal teenage on rare outcomes.
Most previous studies that focus on the long-term outcomes of children use as their outcome measure scores on tests of academic achievement, neurologic functioning, and teacher and parent reports. We examine placement in special education classes and in remedial services programs as a result of demonstrated academic problems. Our study is population based and allows us to assess the effects of maternal age on functionally determined, school-based disabilities while controlling for a variety of sociodemographic confounders. It was motivated by the results from a previous study by our group (28), in which we compared the effects and the impact of a variety of perinatal and sociodemographic variables on classroom placement by fitting generalized logistic regression models and computing excess/deficit numbers based on these models. The basic study population and the outcome measure in both studies were the same, but in this study, we focus specifically on the effects of maternal age. In the previous analyses of this data set, we unexpectedly observed no detrimental effect of giving birth during the teen years when we controlled for all other risk factors considered, but a negative effect of older age was present. In this study, we examine the influence of confounders on the true effect of maternal age. We fit a variety of models to assess the confounding influences of risk factors for educational problems and investigate the effect of maternal age among subpopulations of teenage mothers.
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MATERIALS AND METHODS |
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Variables
Outcome variables. The outcome variable was educational placement in kindergarten into seven mutually exclusive special education categories designed to serve children with an educational disability, an academic problems category for children with milder educational problems, and a reference category consisting of children who attended regular classroom or gifted classes only. Assignment to special education was determined by the child's primary exceptionality, which identified the disability requiring the greatest allocation of personnel resources (in cases in which more than one disability was diagnosed). Only actual placement was considered. Special education categories included: 1) physically impaired (PI): severe skeletal or neuromuscular condition adversely affecting educational performance; 2) sensory impaired: deaf, blind, hard of hearing, or partially sighted; 3) profoundly mentally handicapped (PMH): intelligence quotient (IQ) less than 25; 4) trainable mentally handicapped (TMH): IQ between 25 and 54; 5) educable mentally handicapped (EMH): IQ between 55 and 69; 6) learning disabled (LD): psychological processing disorders marked by difficulties in the acquisition and use of language, reading, writing, or mathematics; and 7) emotionally handicapped (EH): condition resulting in persistent and maladaptive behaviors.
Procedures for eligibility determination and classification criteria of primary exceptionality are standardized throughout the state's 67 school districts. Placement criteria are dictated by Florida Board of Education rules in accordance with federal guidelines and are monitored by the Florida Bureau of Student Services and Exceptional Education (30, 31
) (upon request, the authors will provide the complete definitions of special education categories in Florida).
The academic problems category comprised milder educational intervention or remediation programs and practices used in Florida that could not be analyzed separately without bias because of local districts' variations in assigning students to these programs. Speech and language impairment was a type of assignment for remedial services for disorders of language, articulation, fluency, or voice. The Federal Chapter 1/Title 1 Basic Program provided educational services to low-achieving students, and nonpromotion to first grade was defined as a child who was assigned to kindergarten again the following year.
Children with both a special education primary exceptionality and an academic problem were placed in the special education category.
Predictor variables. Maternal age was a four-category variable with a young teenage group (ages 1117 years), a late teenage group (ages 1819 years), older mothers (age 36 years), and mid-age mothers (ages 2035 years). Mother's age was determined from the child's birth certificate. Several sociodemographic risk factors were considered: mother's education, mother's marital status, race, sex, and poverty. All of these predictors except poverty were obtained from the birth records. The definition of poverty was based on whether the child was eligible for free or reduced lunch in kindergarten. Mother's education was defined as less than high school, high school, and greater than high school education. Race/ethnicity had three categories: Black, White, and other, with the category other being predominantly Hispanic (93 percent). Marital status was defined as single or married. All sociodemographic predictors except child's sex are potential confounders for the relation between giving birth during the teen years and the response because they are significantly related both to the main predictor of interest and to the response. A few perinatal variables were also considered: birth weight, a seven-category variable (450749, 750999, 1,0001,499, 1,5002,499, 2,5002,999, 3,0004,749, and 4,7506,049 g), congenital anomaly, complications of labor, and prenatal care were yes or no variables, and previous pregnancy experience was defined as previous failed pregnancies, no previous pregnancy, or one or more previous, successful pregnancies with no failures.
Statistical methods
The CATMOD procedure in SAS (32) was used to fit generalized logistic regression models for multinomial responses to assess the unadjusted and the adjusted effects of maternal age on the outcome. The emphasis of the analyses was on the effects of giving birth during the teen years, but the effects of older maternal age were simultaneously assessed. Generalized odds ratios, with regular classroom placement as the reference category, were used to measure the effect on each educational disability of levels of maternal age in relation to the reference category 2035 years. Ninety-five percent confidence intervals were constructed for all estimated odds ratios. Odds ratios significantly greater than one indicate detrimental effects of younger (or older) than normal age, while odds ratios significantly less than one indicate protective effects of younger (or older) age.
We first fitted two main effects models: 1) an univariable model in which maternal age was the only predictor and 2) a multivariable model with main effects for all predictors considered. Standardized percentages were computed based on the two fitted models, as outlined in the appendix. The standardized percentages from the univariable model are not adjusted for other factors and, hence, are exactly the same as the raw percentages. Large differences between the two sets of percentages suggest that confounding is present.
A stepwise model fitting was used to identify the strongest sociodemographic confounders of the relation between maternal age and the outcome. Perinatal variables did not appear to be confounders, since the maternal age effect estimated from the complete multivariable model was essentially the same as that from the model with only sociodemographic predictors. Therefore, we started with a generalized logistic regression model with main effects for maternal age, maternal education, marital status, poverty, race, and sex and then dropped potential confounders one at a time on the basis of the change in the estimated generalized odds ratios in the maternal age groups. At each step, the predictor that led to the greatest change in odds ratios was considered to be the strongest confounder and was dropped. The odds ratios for older age in the complete multivariable model were not significantly different from those in the univariable model, so the decisions were based only on the estimates for the maternal teenage categories. The patterns were consistent for all outcomes in both teenage groups (odds ratios changed in the same direction). Therefore, the "greatest confounder" was the one that changed the significance of the largest number of odds ratios or, in the case of a tie, the one that led to the larger differences in absolute values of the odds ratios. A limitation of the current strategy is that the relative importance of a predictor depends on the other predictors present in the model.
Because of a lack of sufficient variability in education within the maternal teenage categories, it was difficult to separate the effect of age from that of education in these groups. Thus, we further examined the effect of age within subgroups of teenage mothers. The rarity of special education placement also prevented us from considering two-way interactions in these analyses. To check whether there was an age effect in the youngest teenage group, we performed an additional analysis in which we considered only children of mothers aged 1117 years with less than a high school education who were unmarried and poor. This group is typical of the socio-demographic conditions for young teenagers. We then fitted generalized logistic regression models with maternal age as a continuous predictor and computed estimated generalized odds ratios for the effect of a 1-year increase in maternal age. This analysis addresses the question of whether there is a detrimental effect of younger age for the most typical teenagers.
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RESULTS |
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In a supplemental analysis, we also checked for confounders among the perinatal predictors by adding perinatal predictors to the first model in table 3 one at a time, but no significant changes in the odds ratios were observed. The estimated odds for the effect of older age also did not change significantly when sociodemographic and perinatal variables were dropped and were added to the model, respectively. On the other hand, when parity was also controlled for, almost all protective effects of giving birth during the teen years disappeared. This may explain why children of younger mothers appear to be less likely to demonstrate academic problems, learning disabilities, and trainable mental handicaps than do children of older mothers. Children of teenage mothers are less likely to have older siblings and may get more attention at home than do children of older mothers (19).
Because of the complete confounding present in the youngest teenage group between maternal education and maternal teen age (no mothers with more than a high school education were available in this age group), it is impossible to separate the effects of maternal age and maternal education for children of mothers aged 1117 years. To check whether there is an effect (possibly biological) of age among younger mothers, we considered the population of children of mothers aged 1117 years with less than a high school education who were unmarried and poor. This subsample contained the vast majority of mothers in the age group 1117 years. Some significant detrimental effects of younger age were observed in this subpopulation (table 4). Among young teenagers aged 1117 years, being younger by 1 year led to a significant increase of about 44 percent in the odds for placement in the EH group and of about 24 percent in the odds for placement in EMH group. For Blacks, in particular, being younger by 1 year was associated with a significant increase in the odds of placement in EH, LD, EMH, and TMH, while among Whites, the odds increased significantly only for academic problems. In all other cross-classifications of sociodemographic factors, the age effect was either not estimable or not significant.
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DISCUSSION |
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It should be pointed out that although teen age birth does not appear to have a detrimental effect per se on educational outcome, it may contribute to low maternal education, unmarried status, and/or poverty, factors with known, large, negative effects on educational disabilities. Hence, although maternal age does not directly influence the outcome, it may have an indirect effect through the intermediate sociodemographic factors. Fortunately, sociodemographic factors such as maternal education are remedial, and intervention programs targeted at teenage mothers have been shown to ameliorate some of the negative consequences of teenage parenting (33). These findings underscore the importance and value of high school graduation programs for teenage mothers.
Unlike teen age, older maternal age was found to be a risk factor for certain types of educational disabilities regardless of whether other risk factors were controlled. Hence, children of older mothers are more likely to have PI, TMH, or academic problems in kindergarten, possibly as a direct result of the older age of the mothers. While the effect on PI and TMH may be due to structural damage and hence reflects biological disadvantage, the category academic problems encompasses milder educational problems, and hence, the effect of older age may be attributable to unmeasured environmental factors. With increasing numbers of women giving birth at older ages, if a causal relation between increased maternal age and adverse outcomes is confirmed, the impact of this factor is likely to increase.
This study considered a variety of educational disabilities in kindergarten and revealed a number of important associations. To understand those associations better, it is imperative to consider possible mechanisms through which maternal age affects specific outcomes. For example, a study by Williams and Decoufle (34) focuses on mental retardation (corresponding to PMH, TMH, and EMH combined) and attributes the increased incidence of codevelopmental retardation among children of older White mothers to Down's syndrome. A number of studies (13
, 14
, 16
, 18
, 22
) attribute lower cognitive scores (corresponding to the categories LD and academic problems) among children of teenagers to decreased vocalization and poor parenting practices.
Because of the lack of information on the sociodemographic status of the mothers on the kindergarten records, almost all predictor variables considered were measured at birth. Therefore, the true effect of factors such as maternal education and marital status may be underestimated. The risk for educational problems for a child whose teenage mother completed high school after the child's birth is likely to be smaller than that for a child whose teenage mother did not advance her education after the birth; yet both cases are treated the same way in our sample. The effect of this type of mismeasurement on our findings will be to diminish the apparent significance of maternal education because the high-risk group of mothers with a low level of education at birth also includes those who increase their level of education after the birth of their children. It is advisable that in future studies sociodemographic statuses both at birth and later in life be taken into account when long-term outcome is of interest.
It was somewhat surprising that none of the perinatal variables acted as confounders for the relation between giving birth in the teen years and educational problems. This finding may at least be partially explained by the long-term outcome under consideration being less affected by adverse biological conditions at birth than some short-term outcomes such as low birth weight and infant mortality. As a result of the analysis, it appears that there is no indirect effect of maternal age on the outcome through the considered perinatal variables once the sociodemographic factors are controlled for.
Among the sociodemographic predictors studied, maternal education appeared to be the strongest confounder, but marital status, poverty, and race were also very important. In our choice of possible confounders, we were limited by the information available on the birth certificate and in the kindergarten records. There are additional risk factors such as injury after birth, near drownings, lead poisoning, and other toxic exposures that should be included in future studies. In addition, predictors such as poverty could be measured more precisely when information from several years is considered.
Because of the rarity of the outcome and of the complete confounding between youngest teen age and maternal education, we were unable to study interactions between the risk factors. The restriction of the study sample to only young teenagers with less than a high school education who were unmarried and poor and the treatment of maternal age as a continuous variable allowed us to assess the independent effect of age on the outcome within the subpopulation of most typical teenage mothers. It is not clear, however, whether the observed detrimental effect of a 1-year decrease in age for certain mild educational disabilities (EH and EMH) is attributed to purely biological causes, to sociodemographic causes, or to a combination of both. The fact that no age effect was observed for the most severe disabilities in younger mothers, if not explained by small sample sizes in the restricted population, does give some credence that there may not be a biological disadvantage of younger age with regard to disabilities in kindergarten. A more plausible explanation is that children of younger teenagers are at a disadvantage because of environmental factors.
In conclusion, children of teenage mothers are at a higher risk for disabilities in kindergarten, but this increased risk appears to be due not to a biological effect of the young age of the mother per se but to the confounding influences of associated sociodemographic and/or environmental factors. Prevention of teenage pregnancies should continue to be an important public policy goal, and programs should target teenage mothers to ameliorate the effects of more important predictors such as low maternal education, single marital status, poverty, and minority race that are likely to continue to place the children of teenage mothers at risk for adverse outcomes after birth.
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APPENDIX |
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where ncr is the number of children born to mothers aged 2035 years (the standard population) for a fixed combination c of levels of the other factors, and Pjcl are the model-based estimates of the probabilities that the jth outcome occurs given the lth level of the risk factor and the cth combination of levels of the other factors. When Pjcl is based on the univariable model (with maternal age as the only predictor), the standardized percentages are exactly equal to the raw percentages, whereas when Pjcl is based on the multivariable model, the standardized percentages are adjusted for all remaining factors in the model.
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ACKNOWLEDGMENTS |
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The authors thank Lavan Dukes, Dr. Tom Fisher, Julia Smith, Kathy Peck, and Shan Goff of the Florida Department of Education; Meade Grigg and Dan Thompson of the Florida Department of Health; and the staff of the Perinatal Data/Research Center.
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NOTES |
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The work was completed while Ralitza Gueorguieva was a Visiting Assistant Professor in the Perinatal/Data Research Center at the University of Florida, Gainesville, FL.
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REFERENCES |
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