What is the Prevalence of Children with Special Health Care Needs? Toward an Understanding of Variations in Findings and Methods Across Three National Surveys
ABSTRACT To compare and consider sources of variation in the prevalence and characteristics of children with special health care needs (CSHCN) identified using the CSHCN Screener across the 2001 National Survey of Children with Special Health Care Needs (NS-CSHCN), the 2003 National Survey of Children's Health (NSCH) and the 2001-2004 Medical Expenditures Panel Surveys (MEPS).
For each survey, national prevalence rates and the demographic, health and health need characteristics of CSHCN age 0-17 years were estimated and compared. The stability of CSHCN state prevalence rankings between the NS-CSHCN and NSCH was assessed. Logistic regression analysis produced adjusted odds of identification for subgroups of children. CSHCN Screener sampling and administration were profiled across eight methodology parameters for each survey.
Compared to the NS-CSHCN (12.8%), CSHCN prevalence for children age 0-17 years was 4.8 points higher for the NSCH (17.6%) and 6.0-6.5 points higher across the four 2001-2004 MEPS datasets (18.8-19.3%). The adjusted probability of identification by child's demographic characteristics was stable across all datasets as were state prevalence rankings between the NS-CSHCN and the NSCH. CSHCN identified through the NS-CSHCN were slightly more likely to meet more than one CSHCN Screener criteria, to meet the "above routine need or use of services" criterion and to miss two or more weeks of school due to illness compared to the NSCH, suggesting that CSHCN with less complex and/or serious health consequences may not have been as likely to be identified through the NS-CSHCN. CSHCN prevalence did not change significantly between 2001 and 2004 MEPS, although some off-setting increases or decreases in the proportion of CSHCN meeting specific CSHCN Screener criteria occurred and reflects trends toward increased prescription medication use in children.
When CSHCN Screener administration methods remained similar across years (2001-2004 MEPS), CSHCN prevalence rates were stable. When methods varied between surveys, CSHCN prevalence rates differed. These differences suggest that prevalence is best expressed as a range, rather than as a point estimate. However, once identified, characteristics and health needs of CSHCN were stable across surveys evaluated, each of which has unique strengths for purposes of policy and research.
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ABSTRACT: Since 2000, the Children with Special Health Care Needs (CSHCN) Screener (CS) has been widely used nationally, by states, and locally as a standardized and brief survey-based method to identify populations of children who experience chronic physical, mental, behavioral, or other conditions and who also require types and amounts of health and related services beyond those routinely used by children. Common questions about the CS include those related to its development and uses; its conceptual framework and potential for under- or overidentification; its ability to stratify CSHCN by complexity of service needs and daily life impacts; and its potential application in clinical settings and comparisons with other identification approaches. This review recaps the development, design, and findings from the use of the CS and synthesizes findings from studies conducted over the past 13 years as well as updated findings on the CS to briefly address the 12 most common questions asked about this tool through technical assistance provided regarding the CS since 2001. Across a range of analyses, the CS consistently identifies a subset of children with chronic conditions who need or use more than a routine type or amount of medical- and health-related services and who share common needs for health care, including care coordination, access to specialized and community-based services, and enhanced family engagement. Scoring algorithms exist to stratify CSHCN by complexity of needs and higher costs of care. Combining CS data with clinical diagnostic code algorithms may enhance capacity to further identify meaningful subgroups. Clinical application is most suited for identifying and characterizing populations of patients and assessing quality and system improvement impacts for children with a broad range of chronic conditions. Other clinical applications require further implementation research. Use of the CS in clinical settings is limited because integration of standardized patient-reported health information is not yet common practice in most settings or in electronic health records. The CS continues to demonstrate validity as a non-condition-specific, population-based tool that addresses many of the limits of condition or diagnosis checklists, including the relatively low prevalence of many individual conditions and substantial within-diagnosis variations and across-diagnoses similarities in health service needs, functioning, and quality of care. Copyright © 2014 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.Academic Pediatrics 12/2014; 15(2). DOI:10.1016/j.acap.2014.10.003 · 2.23 Impact Factor
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ABSTRACT: Maltreated youth have a high prevalence of acute and chronic mental and physical health problems, but it is not clear whether these problems are related to maltreatment or to a disadvantaged environment. To compare health status and health care use of maltreated youth who had an open case with child protective services to comparison youth living in the same community, we conducted a secondary analysis of caregiver reports for 207 maltreated adolescents (mean age 11.9 years) and 142 comparison adolescents (mean age 12.3 years) living in urban Los Angeles, using questionnaire data from a larger longitudinal study framed in a socio-ecological model. Caregivers included biological parents, relatives, and unrelated caregivers. Analyses included t-test, MANOVA, chi-square, and multivariable logistic regression. Caregivers reported similar rates of physical health problems but more mental health problems and psychotropic medicine use in maltreated youth than in the comparison youth, suggesting that maltreated youths' higher rates of mental health problems could not be attributed to the disadvantaged environment. Although there were no differences in health insurance coverage, maltreated youth received preventive medical care more often than comparison youth. For all youth, having Medicaid improved their odds of receiving preventive health and dental care. Attention to mental health issues in maltreated adolescents remains important. Acceptance of Medicaid by neighborhood-based and/or school-based services in low-income communities may reduce barriers to preventive care. © 2014 Wiley Periodicals, Inc.Research in Nursing & Health 12/2014; 38(1). DOI:10.1002/nur.21634 · 1.16 Impact Factor
Article: Contraception for Adolescents[Show abstract] [Hide abstract]
ABSTRACT: A working knowledge of contraception will assist the pediatrician in both sexual health promotion as well as treatment of common adolescent gynecologic problems. Best practices in adolescent anticipatory guidance and screening include a sexual health history, screening for pregnancy and sexually transmitted infections, counseling, and if indicated, providing access to contraceptives. Pediatricians' long-term relationships with adolescents and families allow them to help promote healthy sexual decision-making, including abstinence and contraceptive use. Additionally, medical indications for contraception, such as acne, dysmenorrhea, and heavy menstrual bleeding, are frequently uncovered during adolescent visits. This technical report provides an evidence base for the accompanying policy statement and addresses key aspects of adolescent contraceptive use, including the following: (1) sexual history taking, confidentiality, and counseling; (2) adolescent data on the use and side effects of newer contraceptive methods; (3) new data on older contraceptive methods; and (4) evidence supporting the use of contraceptives in adolescent patients with complex medical conditions.Pediatrics 09/2014; 134(4). DOI:10.1542/peds.2014-2300 · 5.30 Impact Factor