[Show abstract][Hide abstract] ABSTRACT: This literature review of 46 articles uses the ecological model as a framework for organizing concepts and themes related to health care transition among youth with disabilities or special health care needs (SHCN). Transition involves interactions in immediate and distal environmental systems. Important interactions in immediate environments include those with family members, health care providers, and peers. Activities in distal systems include policies at the governmental and health system levels. The ecological model can help researchers and practitioners to design experimental interventions in multiple settings that ensure smooth transitions and support the well-being of youth with disabilities or SHCN.
Journal of pediatric nursing 12/2010; 25(6):505-50. · 0.92 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To examine insurance regain among youth with no, nonsevere, and severe disabilities.
The data source for this study was the Survey of Income and Program Participation 2001. We examined insurance regain among youth with no, nonsevere, and severe disabilities between the ages of 15 and 25 using a longitudinal design. Kaplan-Meier survival functions provided estimates of uninsurance spell durations measured in waves, or 4-month intervals. We conducted a discrete time survival analysis adjusting for personal characteristics.
This study includes 1,310 youth who entered the SIPP with insurance and became uninsured. 985 youth (75%) regained insurance. Based on SIPP waves, median duration of uninsurance was two waves (between 5 and 8 months) for youth with severe disabilities and three waves (between 9 and 12 months) for youth with nonsevere disability. Youth with nonsevere disabilities had decreased odds of regaining health insurance compared to youth without disabilities (odds ratio .73; 95% confidence interval: .57, .92; p=.01).
Youth with severe disabilities and youth without disabilities had similar odds of and durations to insurance regain. In contrast, youth with nonsevere disabilities had lower odds of regaining insurance and experienced longer durations of uninsurance compared to peers without disabilities. We recommend additional research into the implications of Medicaid eligibility pathways and employment barriers for youth with nonsevere disabilities.
Journal of Adolescent Health 12/2009; 45(6):556-63. · 2.97 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To compare insured youth (age 15-25 years) with and without disabilities on risk of insurance loss. We conducted a cross-sectional study using data from the Survey of Income and Program Participation 2001. Descriptive statistics characterized insured youth who maintained and lost insurance for at least 3 months over a 3-year time frame. We conducted logistic regression to calculate the association between disability and insurance loss. Adjustment variables were gender, race, ethnicity, age, work or school status, poverty status, type of insurance at study onset, state generosity, and an interaction between disability and insurance type. This study includes 2,123 insured youth without disabilities, 320 insured youth with non-severe disabilities, and 295 insured youth with severe disabilities. Thirty-six percent of insured youth without disabilities lost insurance compared to 43% of insured youth with non-severe disabilities and 41% of insured youth with severe disabilities (P = .07). Youth with non-severe disabilities on public insurance have an estimated 61% lower odds of losing insurance (OR: 0.39; 95% CI: 0.16, 0.93; P = .03) compared to youth without disabilities on public insurance. Further, youth with severe disabilities on public insurance have an estimated 81% lower odds of losing insurance (OR: 0.19; 95% CI: 0.09, 0.40; P < .001) compared to youth without disabilities. When examining youth with private insurance, we find that youth with severe disabilities have 1.63 times higher odds (OR: 1.63; 95% CI: 1.03, 2.57; P = .04) of losing health insurance compared to youth without disabilities. Insurance type interacts with disability severity to affect odds of insurance loss among insured youth.
Maternal and Child Health Journal 06/2009; 14(1):67-74. · 2.24 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Objective: To compare uninsured youths with and without disabilities aging into adulthood on insurance regain over three years. Design: This study included 1430 youths between the ages of 15 and 25 who lost insurance between 2001 and 2004. Data were from the Survey of Income and Program Participation 2001, a panel survey administered by the US Census Bureau. We categorized respondents as without disabilities, with nonsevere disabilities, or with severe disabilities. Persons with nonsevere disabilities experienced difficulty with functional activities and activities of daily living. Persons with severe disabilities were unable to perform or required assistance to perform functional activities and activities of daily living. To examine the association between regaining insurance and disability, we conducted a parametric event history analysis adjusting for gender, race, ethnicity, age, work/school status, and poverty. Findings: Seventy percent of youths without disabilities, 59% of youths with nonsevere disabilities, and 75% of youths with severe disabilities regained insurance during the study period. Estimated median duration of uninsurance was 4.5 months for persons without disabilities, 10.5 months for persons with nonsevere disabilities, and 4 months for persons with severe disabilities. The hazard of regaining insurance was significantly associated with disability status. Persons with nonsevere disabilities were significantly less likely to regain insurance compared to peers without disabilities after adjusting for gender, race, ethnicity, age, work and school status, and poverty status. (HR: .71, 95% CI: 53, .94) Conclusions: Disability severity was associated with regaining insurance among the 15 to 25 year old youths in this study.
136st APHA Annual Meeting and Exposition 2008; 10/2008
[Show abstract][Hide abstract] ABSTRACT: To analyze genetic clinic utilization in Washington State and to explore factors associated with utilization.
Our analysis included data from the 9 of 15 genetic clinics that consistently reported to the Washington State Minimum Data Set between 1995 and 2004. Prenatal genetics services were excluded. We described utilization with yearly counts of patients and analyze patient volume according to age, sex, and residence.
The total number of patients at nine genetic clinics in Washington increased from 1804 patients in 1995 to 3536 patients in 2004 with growth increasing at an average 8% each year. Although adults aged 35 years and over comprised 14% of all patients in 1995, they comprised almost 28% in 2004. The number of females aged 35 years and older increased markedly during this time frame.
Nine genetic clinics in Washington experienced growth in utilization and changes in the mix of patients served between 1995 and 2004. We suggest further study of how test availability, public awareness of genetics, supply of genetics providers, and changing regulations and insurance policies influence utilization of genetics clinics.
Genetics in medicine: official journal of the American College of Medical Genetics 11/2007; 9(10):713-8. · 3.92 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: States include genetics services among their public health programs, but budget shortfalls raise the question, is genetics an essential part of public health? We used the Essential Services of Public Health consensus statement and data from state genetics plans to analyze states' public health genetics programs. Public health genetics programs fulfill public health obligations: birth defects surveillance and prevention programs protect against environmental hazards, newborn screening programs prevent injuries, and clinical genetics programs ensure the quality and accessibility of health services. These programs fulfill obligations by providing 4 essential public health services, and they could direct future efforts toward privacy policies, research on communications, and rigorous evaluations.
American Journal of Public Health 05/2007; 97(4):620-5. · 3.93 Impact Factor