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1.
J Dev Behav Pediatr ; 39(4): 325-334, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-29557857

RESUMO

BACKGROUND: It is unknown how insurance status affects elements of evaluation at developmental behavioral (DB) pediatric sites. OBJECTIVE: To compare DB referrals, evaluation, and treatment for children with Medicaid and private insurance. DESIGN/METHODS: Fifty-six developmental behavioral pediatricians at 12 sites recorded anonymous data on structured forms for ≤15 consecutive referrals. Children with Medicaid (n = 309) and private insurance (n = 393) were compared on sociodemographic factors, referral concerns, evaluation elements, and resulting diagnoses. All significant bivariate findings were verified in multivariable models controlling for site and sociodemographic characteristics. RESULTS: Those with Medicaid were significantly less likely to be white (30% vs 63%) and to have parents who went beyond high school (50% vs 92%) and who spoke English (89% vs 97%) (all p < 0.001). Referral sources were similar, except that fewer children with Medicaid were self-referred (12% vs 22%; p < 0.01). Both groups presented with multiple concerns, ∼3/child, especially speech and language delays, autism spectrum disorder, and attention-deficit hyperactivity disorder. Children with Medicaid tended to present more often with concerns about other behavior problems (14% vs 7%; p = 0.05). Wait times to appointments were similar (∼20 weeks), and visits were over 2 hours in length. Only 1 of 92 services and recommendations differed significantly. CONCLUSION: There were few differences in care, but most DB patients presented with multiple concerns and had complex evaluations. Regardless of the insurance type, they experienced long wait times that may be detrimental to therapeutic outcomes.


Assuntos
Comportamento Infantil , Serviços de Saúde da Criança/estatística & dados numéricos , Seguro Saúde/estatística & dados numéricos , Medicaid/estatística & dados numéricos , Pediatras/estatística & dados numéricos , Comportamento Problema , Encaminhamento e Consulta/estatística & dados numéricos , Criança , Pré-Escolar , Feminino , Humanos , Masculino , Estados Unidos
2.
J Dev Behav Pediatr ; 38(9): 683-689, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28914731

RESUMO

OBJECTIVE: To obtain and review workforce data, given the critical demand for developmental pediatricians (DPs). METHODS: Survey of demographics and professional activities of DP physician faculty at Developmental-Behavioral Pediatric (DBP) Research Network fellowship training sites. RESULTS: Of the eligible providers at 12 centers, 76% (n = 50) completed surveys. They were on average 50 years old and mostly female (86%), white (82%), and working full time (74%). Full timers reported a mean 50.2-hour week made up of clinical work (23.2 hours), supervision (5.9 hours), research (8.8 hours), administration (5.2 hours), teaching (1.5 hours), advocacy (1.1 hours), and other (4.3 hours). Compared with those >10 years out of training, the 20 physicians (40%) ≤10 years out of fellowship were more likely to be nonwhite (p = .003). Overall faculty interest/expertise (I/E) was highest in autism (90%) and attention deficit hyperactivity disorder (ADHD; 86%). Those ≤10 years out of fellowship had more I/E in autism (p = .05) and less in chronic illness (p = .06) and parenting (p = .06). DPs practiced most frequently in a General DBP Clinic (74%), followed by clinics specific for: Autism (36%), Toddlers (22%), ADHD (20%), Infants (18%) and Preschoolers (16%). Common clinics were Autism (9), syndrome specific (9), ADHD (6), and School-Aged (5). CONCLUSION: Developmental pediatrician faculty in DBP training sites feed the pipeline of much needed DP physicians. This survey provides baseline information on the professional activities of DP faculty and found changing demographics and I/E as well as a wide variety of clinic types.


Assuntos
Centros Médicos Acadêmicos/estatística & dados numéricos , Pesquisa Biomédica/estatística & dados numéricos , Docentes/estatística & dados numéricos , Pediatras/estatística & dados numéricos , Carga de Trabalho/estatística & dados numéricos , Adulto , Feminino , Humanos , Masculino , Pessoa de Meia-Idade
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