Abstract
The objective of this study is to develop new methods to better identify psychosocial risk such that children with the greatest risk of poor future outcomes receive more intensive preventive health services. Based on structured literature review and secondary data analysis, a 52-item psychosocial risk questionnaire was administered to 2,083 families of children (<36 months). To quantify the questionnaire’s construct validity, developmental concern was assessed with the Ages and Stages Questionnaire version II (ASQ) [n = 1,163]. An iterative model selection process was used to produce the most parsimonious predictive model. Model fit was examined using c-statistics, the Hosmer–Lemeshow test, and a heuristic measure of model overfit based on the fitted log-likelihood values and associated number of degrees of freedom. We found 13 items easily obtained from parental report produced a regression model with a c-statistic of 0.70. Using an integer scoring system derived from the regression model, we calculated stratum specific likelihood ratios to revise a given prior probability of ASQ failure. The posterior probability of ASQ failure was 44.9 % for a child in the highest risk group (score >25) on the questionnaire, more than double our observed average failure rate of 19.5 %, while it was less than 7 % for a child with the lowest possible score on the questionnaire. Thirteen parent-reported items can be compiled into a summary psychosocial risk questionnaire that predicts failure on developmental screening among preschool children. With further validation, this questionnaire could conceivably be used by clinicians to tailor pediatric preventive care to children at varying levels of risk.
Abbreviations
- ASQ:
-
Ages and Stages Questionnaire
- NLSY:
-
National Longitudinal Survey of Youth
- RCT:
-
Randomized controlled trial
- CSHCN:
-
Children with Special Health Care Needs Screener
- MSSI:
-
The Maternal Social Support Index
- PHQ-2:
-
The Patient Health Questionnaire-2
- S-TOFHLA:
-
Short version of the Test of Functional Health Literacy in Adults
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Acknowledgments
We gratefully thank the patients and practices belonging to the Pediatric Research Consortium (PeRC) at The Children’s Hospital of Philadelphia for their participation in this project. This work was supported by grants from the Commonwealth Fund (Drs. Pati, Localio, and Forrest, Mr. Bhatt, and Ms. Kavanagh) and from the Centers for Disease Control (Drs. Guevara, Localio, Gerdes).
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None to disclose.
Ethical standard
This study was reviewed and approved by the Institutional Review Board of The Children’s Hospital of Philadelphia and the State University of New York at Stony Brook.
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Ms. Kavanagh was with PolicyLab at the time of manuscript submission.
Appendices
Appendix 1
Imputation for Missing Data
As with typical pattern of missingness of responses in surveys, many subjects failed to complete or respond properly to one or more item. Of the 2,083 surveys successfully completed, annual household income was the only item with >10 % missing data. Of note, the items related to location and perception of childcare as well as the S-TOFHLA were not included in the instrument until February 5th, 2009 such that 1,831 and 1,527 subjects, respectively, completed these items corresponding to response rates of 87.9 and 73.3 % among the entire sample.
To avoid the potential bias and the reduction in statistical power from using only complete observations, we implemented multiple imputation (with 10 sets of imputed values) using the method of chained equations as programmed in Stata v 11.1 (Stata Corp; College Station Tx, 2009) [35–37]. This method requires that each variable with a missing value be specified in a regression equation with the proper form (e.g., nominal, binary, etc.) for the missing values. Continuous variables were imputed using a linear regression model, binary variables with missing values were imputed using a logistic regression, while those with nominal values used multinomial logit regression. Several variables were clearly ordered (child health, number of chronic conditions, education, income category) and were imputed using ordinal logistic regression. The chained equation methods imputes one value at a time, and then with the value filled in, proceeded to the next missing variable and its equation, which then assumed that the imputed value is known. At each imputation, the value chosen is taken from the posterior predictive distribution of from the regression, and unlike simple mean or regression-based imputation, this method adequately accounts for the additional variance arising when values must be filled in. The chain of equations is then repeated in 10 cycles to achieve better convergence. The entire process of 10 cycles is then repeated 10 times to obtain 10 sets of imputed values.
For each of the chained equations, we selected those other variables that in theory would predict the unobserved values. In addition, and as is proper for imputation, we included the following auxiliary variables: age, gender, the ACG morbidity index [68], number of chronic conditions, and whether the child failed ASQ, Modified Checklist for Autism in Toddlers (MCHAT), or a milestone at any time. The imputed values were then checked for out-of-range values and for similarity in the distributions of the complete values. This imputation permits the proper estimation of variance (confidence intervals) of any analysis by simply doing each analysis 10 times, each on a different completed (original data plus the imputed missing values) and then by combining two variance components: the average of the within imputation variances and the across imputation variance.
All reported results are from completed datasets using the imputation procedures previously described and analyzed in SAS®© v 9.1. Of the 2083 surveys successfully completed, annual household income was the only item with >10 % missing data. Of note, the S-TOFHLA and items related to location and perception of childcare were not included in the instrument until February 5th, 2009 such that 1,831 and 1,527 subjects, respectively, completed these items corresponding to response rates of 87.9 and 73.3 % among the entire sample.
Appendix 2
See Table 5.
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Pati, S., Guevara, J., Zhang, G. et al. A Family Psychosocial Risk Questionnaire for Use in Pediatric Practice. Matern Child Health J 17, 1990–2006 (2013). https://doi.org/10.1007/s10995-012-1208-3
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DOI: https://doi.org/10.1007/s10995-012-1208-3