New Options for Child Health Surveillance by State Health Departments

Until recently there were no child health surveillance instruments available to state health departments for children 1–14 years old. In recent years, several states have developed new surveillance instruments. This article includes information about examples of four types of child health surveys: (1) Behavioral Risk Factor Surveillance System (BRFSS) follow-back survey [phone-based in Colorado]; (2) Pregnancy Risk Assessment Monitoring System (PRAMS) re-interviews [PRAMS-based in Rhode Island]; (3) elementary school child health survey combined with dental screening and physical measurements of height and weight [school-based in Maine]; and (4) freestanding elementary school survey [school-based in Oregon]. The PRAMS-based survey was moderate in expense but addressed only issues related to 2 year olds. The phone-based survey was the most expensive but addressed issues of children 1–14 years old. The school-based surveys were moderate in expense, logistically complex, and were least likely to provide robust generalizable data.     

Impact of Caring for Children with Special Health Care Needs on the Family: Rhode Island’s Experience

Objectives: The impact of caring for children with special health care needs (CSHCN) on their families’ finances and employment was studied. Methods: Data from the 2001 National Survey of Children with Special Health Care Needs were used to measure financial and employment problems. The level of impact was examined by child’s age, gender, ethnicity, race, mother’s education, poverty status, and severity of the child’s condition. The association between core outcome measures and the level of family impact was determined after adjusting for potential confounding factors. Results: Financial or employment problems were more likely to occur among families with young children, with incomes below 200% poverty and with children whose condition usually/always affected their activities. Compared to families without these problems, impacted families were less likely to partner in decision making, be satisfied with services, receive comprehensive care in a medical home, have adequate health insurance, and feel service systems are organized for easy use. Conclusions: Many families of CSHCN face financial and employment problems and are less likely to have a medical home, adequate insurance, and access to health care services.     

Changes in Maternal Cigarette Smoking Among Pregnant WIC Participants in Rhode Island

Objectives To describe the relationship between the timing of entry into the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) among pregnant women in Rhode Island (RI) and changes in maternal cigarette smoking (MCS) during pregnancy. Methods MCS data gathered by WIC were analyzed for pregnant women who self-identified as smokers at the onset of pregnancy between the years 2001–2005. Bivariate and multivariate analyses were performed to examine the relationship between timing of WIC entry and both increased and decreased/quit MCS during pregnancy. Results Self-reports from smokers indicated that 9.5% quit smoking, 24.6% decreased MCS, 26.8% experienced no change, 33.5% increased MCS, and 5.6% attempted to quit MCS but failed during pregnancy. The adjusted odds ratio for smokers with 1st trimester WIC entry and increased MCS was 0.64 (95% CI 0.52, 0.79). Among smokers with 1st trimester PNC entry, the adjusted odds ratio for smokers with 1st trimester WIC entry and decreased/quit MCS was 1.51 (95% CI 1.17, 1.96). Conclusions Early WIC entry appears to be associated with improvements in MCS. Participants who entered WIC in the first trimester of pregnancy were less likely to increase smoking during pregnancy, and if they also had first trimester prenatal care, were more likely to decrease/quit smoking compared to those who entered WIC later. Programs that increase the rates of first trimester WIC entry may contribute to lower rates of MCS in the WIC population.     

Race disparities in childhood asthma: does where you live matter?

OBJECTIVE: This study investigates whether racial/ethnic disparities in childhood asthma prevalence can be explained by differences in family and neighborhood socioeconomic position (SEP). METHODS: Data were from the 2001 Rhode Island Health Interview Survey (RI HIS), a statewide representative sample of 2,600 Rhode Island households, and the 2000 U.S. Census. A series of weighted multivariate models were fitted using generalized estimating equations (GEE) for the logistic case to analyze the independent and joint effects of race/ethnicity and SEP on doctor-diagnosed asthma among 1,769 white, black and Hispanic children <18 years old. RESULTS: Compared with white children, black children were at increased odds for asthma and this effect persisted when measures of family and neighborhood SEP were included in multivariate models (AOR: 2.49; 95% Cl: 1.30-4.77). Black children living in poverty neighborhoods had substantially higher odds of asthma than Hispanic and white children in poverty areas and children in moderate- and high-income neighborhoods (AOR: 3.20: 95% Cl: 1.62-6.29). CONCLUSION: The high prevalence of asthma among black children in poor neighborhoods is consistent with previous research on higher-than-average prevalence of childhood asthma in poor urban minority communities. Changing neighborhood social structures that contribute to racial disparities in asthma prevalence remains a challenge.     

Perceived Weight Status Effect on Adolescent Health-Risk Behaviors: Findings from 2007 and 2009 Rhode Island Youth Risk Behavioral Survey

The goal of this study is to systematically examine the associations between perceived weight status and selected health-risk behaviors among Rhode Island adolescents. We utilized the biennial 2007 and 2009 Rhode Island representative Youth Risk Behavior Survey data. The combined statewide sample contained 5,423 randomly selected public high school students. Perceived weight status was classified into very underweight, slightly underweight, about the right weight, slightly overweight, and very overweight according to the question “How do you describe your weight?” Adolescent’s body mass index (BMI) was calculated from self-reported height and weight. BMI percentile was categorized as extremely underweight, underweight, normal, overweight, and obese. Multivariable logistic regression models were used to analyze perceived weight status associated with six categories of priority health-risk factors. Perceived very underweight and very overweight were statistically significantly associated with 17 out of 22 health-risk behaviors. The relationships can be expressed as a “U”-shaped curve in terms of odds ratios in adolescents. There were no such similar consistent patterns between BMI percentile categories and health-risk behaviors. Perceived weight status, rather than BMI percentile categories, has an important influence on health-risk behaviors. Our results may assist health programs to intervene with high-risk students by changing their cognitive behaviors.     

The Association Between Major Birth Defects and Preterm Birth

Objective To evaluate the association between preterm birth and major birth defects by maternal and infant characteristics and specific types of birth defects. Study Design We pooled data for 1995–2000 from 13 states with population-based birth defects surveillance systems, representing about 30% of all U.S. births. Analyses were limited to singleton, live births from 24–44 weeks gestational age. Results Overall, birth defects were more than twice as common among preterm births (24–36 weeks) compared with term births (37–41 weeks gestation) (prevalence ratio [PR] = 2.65, 95% confidence interval [CI] 2.62–2.68), and approximately 8% of preterm births had a birth defect. Birth defects were over five times more likely among very preterm births (24–31 weeks gestation) compared with term births (PR = 5.25, 95% CI 5.15–5.35), with about 16% of very preterm births having a birth defect. Defects most strongly associated with very preterm birth included central nervous system defects (PR = 16.23, 95% CI 15.49–17.00) and cardiovascular defects (PR = 9.29, 95% CI 9.03–9.56). Conclusions Birth defects contribute to the occurrence of preterm birth. Research to identify shared causal pathways and risk factors could suggest appropriate interventions to reduce both preterm birth and birth defects.