Cost-Effectiveness of Baloxavir Marboxil Versus Oseltamivir or no Treatment for the Management of Influenza in the United States.

INTRODUCTION: This study sought to evaluate the cost-effectiveness of baloxavir marboxil compared with oseltamivir or no antiviral treatment from a US payer perspective using data from a real-world US administrative claims study. Given baloxavir's ability to rapidly stop viral shedding, the potential health economic implications of a baloxavir-induced population-level reduction in viral transmission was also explored. METHODS: A decision tree cost-effectiveness model was developed for seasonal influenza (2018-2020) using a lifetime time horizon with 3.0% discounting for costs and quality-adjusted life-years (QALYs). Patients aged ≥ 12 years could receive baloxavir, oseltamivir or no antiviral treatment. Patient characteristics, complications, and costs were derived from the Merative™ MarketScan® Research Databases including US commercial claims and Medicare and Medicaid Supplemental databases. A scenario analysis explored the impact of reduced viral transmission with baloxavir. RESULTS: In the base case analysis, baloxavir was cost-effective within a willingness-to-pay threshold of US$100,000/QALY compared with oseltamivir [incremental cost-effectiveness ratio (ICER), $6813/QALY gained] or no antiviral treatment (ICER, $669/QALY gained). The net monetary benefit (NMB) of baloxavir was $1180 and $6208 compared with oseltamivir and no treatment, respectively. The NMB of baloxavir increased linearly with reductions in viral transmission, where a 5% transmission reduction yielded an NMB of $2592 versus oseltamivir and $7621 versus no treatment. Baloxavir became dominant (more effective and less costly, with ICERs < 0) starting with a 12.0% reduction in viral transmission versus oseltamivir and 6.0% versus no antiviral treatment. CONCLUSION: Baloxavir was cost-effective compared with oseltamivir or no antiviral treatment. The potential of baloxavir to reduce viral transmission offers a substantial economic benefit from a US payer perspective.

Baloxavir is a prescription medicine that reduces the duration of flu symptoms and reduces the likelihood of complications from the flu, including serious complications that may require hospitalization. Baloxavir may reduce the spread of the flu to healthy people by reducing the amount and duration of virus shedding from infected people. We designed a model to estimate the cost benefits of using baloxavir versus another flu treatment, known as oseltamivir, or no flu treatment at all. Using baloxavir led to more cost savings than oseltamivir or no treatment for people in the US who have commercial health insurance. Baloxavir was even more cost-effective in the scenario where it reduced the number of flu cases (transmission benefit). This could ultimately have a meaningful benefit across a large health insurance population.

Real-World Evidence to Supplement Randomized Clinical Trials: Tocilizumab for Severe COVID-19 Pneumonia vs. a Cohort Receiving Standard of Care.

Randomized controlled trials (RCTs) remain the gold standard for evaluating treatment efficacy, but real-world evidence can supplement RCT results. Tocilizumab was not found to reduce 28-day mortality in a phase III, double-blind, placebo-controlled trial (COVACTA) among hospitalized patients with severe coronavirus disease 2019 (COVID-19) pneumonia. We created a real-world external comparator arm mirroring the COVACTA trial to confirm findings and assess the feasibility of using an external comparator arm to supplement an RCT. Eligible COVACTA participants in both the tocilizumab treatment and placebo arms were matched 1:1 using propensity score matching to persons without tocilizumab exposure in an external comparator arm. Adjusted Cox proportional hazard models estimated differences in 28-day mortality comparing COVACTA participants to matched external comparator arm participants. Patients in the COVACTA tocilizumab treatment arm had a similar risk of death compared with patients in the external comparator arm (hazard ratio (HR): 1.09, 95% confidence interval (CI): 0.64-1.84) with similar estimated 28-day mortality in the COVACTA tocilizumab treatment arm compared with the external comparator arm (18%, 95% CI: 13-24 vs. 19%, 95% CI: 13-24, P > 0.9). COVACTA placebo treatment arm participants had a similar risk of mortality (adjusted HR: 0.69, 95% CI: 0.32-1.46) compared with the external comparator arm. Using an external comparator arm has the potential to supplement RCT data and support results of primary RCT analyses.

Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System.

BACKGROUND: Natural language processing (NLP) of unstructured text from electronic medical records (EMR) can improve the characterization of COVID-19 signs and symptoms, but large-scale studies demonstrating the real-world application and validation of NLP for this purpose are limited. OBJECTIVE: The aim of this paper is to assess the contribution of NLP when identifying COVID-19 signs and symptoms from EMR. METHODS: This study was conducted in Kaiser Permanente Southern California, a large integrated health care system using data from all patients with positive SARS-CoV-2 laboratory tests from March 2020 to May 2021. An NLP algorithm was developed to extract free text from EMR on 12 established signs and symptoms of COVID-19, including fever, cough, headache, fatigue, dyspnea, chills, sore throat, myalgia, anosmia, diarrhea, vomiting or nausea, and abdominal pain. The proportion of patients reporting each symptom and the corresponding onset dates were described before and after supplementing structured EMR data with NLP-extracted signs and symptoms. A random sample of 100 chart-reviewed and adjudicated SARS-CoV-2-positive cases were used to validate the algorithm performance. RESULTS: A total of 359,938 patients (mean age 40.4 [SD 19.2] years; 191,630/359,938, 53% female) with confirmed SARS-CoV-2 infection were identified over the study period. The most common signs and symptoms identified through NLP-supplemented analyses were cough (220,631/359,938, 61%), fever (185,618/359,938, 52%), myalgia (153,042/359,938, 43%), and headache (144,705/359,938, 40%). The NLP algorithm identified an additional 55,568 (15%) symptomatic cases that were previously defined as asymptomatic using structured data alone. The proportion of additional cases with each selected symptom identified in NLP-supplemented analysis varied across the selected symptoms, from 29% (63,742/220,631) of all records for cough to 64% (38,884/60,865) of all records with nausea or vomiting. Of the 295,305 symptomatic patients, the median time from symptom onset to testing was 3 days using structured data alone, whereas the NLP algorithm identified signs or symptoms approximately 1 day earlier. When validated against chart-reviewed cases, the NLP algorithm successfully identified signs and symptoms with consistently high sensitivity (ranging from 87% to 100%) and specificity (94% to 100%). CONCLUSIONS: These findings demonstrate that NLP can identify and characterize a broad set of COVID-19 signs and symptoms from unstructured EMR data with enhanced detail and timeliness compared with structured data alone.

Retrospective cohort study to assess the association between treatment with tocilizumab and mortality among mechanically ventilated patients with COVID-19.

OBJECTIVES: Assess the association between tocilizumab administration and clinical outcomes among mechanically ventilated patients with COVID-19 pneumonia. DESIGN: Retrospective cohort study. SETTING: Large integrated health system with 9 million members in California, USA. PARTICIPANTS: 4185 Kaiser Permanente members hospitalised with COVID-19 pneumonia requiring invasive mechanical ventilation (IMV). INTERVENTIONS: Receipt of tocilizumab within 10 days of initiation of IMV. OUTCOME MEASURES: Using a retrospective cohort of consecutive patients hospitalised with COVID-19 pneumonia who required IMV in a large integrated health system in California, USA, we assessed the association between tocilizumab administration and 28-day mortality, time to extubation from IMV and time to hospital discharge. RESULTS: Among 4185 patients, 184 received tocilizumab and 4001 patients did not receive tocilizumab within 10 days of initiation of IMV. After inverse probability weighting, baseline characteristics were well balanced between groups. Patients treated with tocilizumab had a similar risk of death in the 28 days after intubation compared with patients not treated with tocilizumab (adjusted HR (aHR), 1.21, 95% CI 0.98 to 1.50), but did have a significantly longer time-to-extubation (aHR 0.71; 95% CI 0.57 to 0.88) and time-to-hospital-discharge (aHR 0.66; 95% CI 0.50 to 0.88). However, patients treated with tocilizumab ≤2 days after initiation of IMV had a similar risk of mortality (aHR 1.47; 95% CI 0.96 to 2.26), but significantly shorter time-to-extubation (aHR 0.37; 95% CI 0.23 to 0.58) and time-to-hospital-discharge (aHR 0.31; 95% CI CI 0.17 to 0.56) compared with patients treated with tocilizumab 3-10 days after initiation of IMV. CONCLUSIONS: Among mechanically ventilated patients with COVID-19, the risk of death in the 28-day follow-up period was similar, but time-to-extubation and time-to-hospital-discharge were longer in patients who received tocilizumab within 10 days of initiation of IMV compared with patients who did not receive tocilizumab.

Giant cell arteritis treatment patterns and rates of serious infections.

OBJECTIVES: Giant cell arteritis (GCA) afflicts older adults who may have age- and comorbidity-related risks for infection and is treated with immunosuppressants that increase risk of infection. We examined GCA treatment patterns and rates of serious infections in two real-world cohorts in the U.S. METHODS: We identified two GCA cohorts using two U.S. health insurance databases, Medicare (public, 2007-2017) and MarketScan (commercial, 2015-2019), by applying a validated claims-based algorithm with positive predictive value 79.0% for GCA. We required age ≥50 years and assessed baseline comorbidities, dispensing of immunosuppressants and prophylactic antibiotics, and vaccine administration. We calculated incidence rates (IR) of serious infections, defined as bacterial or viral infections requiring hospitalisation based on primary inpatient diagnosis code. Multivariable Cox proportional hazards models estimated hazard ratios for risk of serious infection for prespecified covariates. RESULTS: The Medicare cohort included 734 patients, 28% male, mean age 77.1; the MarketScan cohort included 1022 patients, 30% male, mean age 68.4. More than 85% used prednisone ≥60mg daily at index date and <10% used tocilizumab. Serious infections developed in 27.9% of Medicare and 7.2% of MarketScan patients: IR per 100 person-years = 10.7 (95% CI 9.3, 12.2) in Medicare and 6.3 (95% CI 5.0, 7.9) in MarketScan. Older age and higher frailty score were significantly associated with increased risk for serious infection. CONCLUSIONS: In these two U.S. GCA cohorts, high-dose glucocorticoids were the most common initial treatment, and over 25% of Medicare and 7% of MarketScan patients developed serious infection during follow-up. Older age and higher frailty score were associated with higher risk of serious infections, though maximum daily prednisone dose was not. Pneumocystis jiroveci pneumonia was rare in two GCA cohorts despite infrequent use of prophylactic antibiotics.

Severity of COVID-19 and adverse long-term outcomes: a retrospective cohort study based on a US electronic health record database.

OBJECTIVE: To identify potential risk factors for adverse long-term outcomes (LTOs) associated with COVID-19, using a large electronic health record (EHR) database. DESIGN: Retrospective cohort study. Patients with COVID-19 were assigned into subcohorts according to most intensive treatment setting experienced. Newly diagnosed conditions were classified as respiratory, cardiovascular or mental health LTOs at >30-≤90 or >90-≤180 days after COVID-19 diagnosis or hospital discharge. Multivariate regression analysis was performed to identify any association of treatment setting (as a proxy for disease severity) with LTO incidence. SETTING: Optum deidentified COVID-19 EHR dataset drawn from hospitals and clinics across the USA. PARTICIPANTS: Individuals diagnosed with COVID-19 (N=57 748) from 20 February to 4 July 2020. MAIN OUTCOMES: Incidence of new clinical conditions after COVID-19 diagnosis or hospital discharge and the association of treatment setting (as a proxy for disease severity) with their risk of occurrence. RESULTS: Patients were assigned into one of six subcohorts: outpatient (n=22 788), emergency room (ER) with same-day COVID-19 diagnosis (n=11 633), ER with COVID-19 diagnosis≤21 days before ER visit (n=2877), hospitalisation without intensive care unit (ICU; n=16 653), ICU without ventilation (n=1837) and ICU with ventilation (n=1960). Respiratory LTOs were more common than cardiovascular or mental health LTOs across subcohorts and LTO incidence was higher in hospitalised versus non-hospitalised subcohorts. Patients with the most severe disease were at increased risk of respiratory (risk ratio (RR) 1.86, 95% CI 1.56 to 2.21), cardiovascular (RR 2.65, 95% CI 1.49 to 4.43) and mental health outcomes (RR 1.52, 95% CI 1.20 to 1.91) up to 6 months after hospital discharge compared with outpatients. CONCLUSIONS: Patients with severe COVID-19 had increased risk of new clinical conditions up to 6 months after hospital discharge. The extent that treatment setting (eg, ICU) contributed to these conditions is unknown, but strategies to prevent COVID-19 progression may nonetheless minimise their occurrence.

Predicting Response to Tocilizumab Monotherapy in Rheumatoid Arthritis: A Real-world Data Analysis Using Machine Learning.

OBJECTIVE: Tocilizumab (TCZ) has shown similar efficacy when used as monotherapy as in combination with other treatments for rheumatoid arthritis (RA) in randomized controlled trials (RCTs). We derived a remission prediction score for TCZ monotherapy (TCZm) using RCT data and performed an external validation of the prediction score using real-world data (RWD). METHODS: We identified patients in the Corrona RA registry who used TCZm (n = 452), and matched the design and patients from 4 RCTs used in previous work (n = 853). Patients were followed to determine remission status at 24 weeks. We compared the performance of remission prediction models in RWD, first based on variables determined in our prior work in RCTs, and then using an extended variable set, comparing logistic regression and random forest models. We included patients on other biologic disease-modifying antirheumatic drug monotherapies (bDMARDm) to improve prediction. RESULTS: The fraction of patients observed reaching remission on TCZm by their follow-up visit was 12% (n = 53) in RWD vs 15% (n = 127) in RCTs. Discrimination was good in RWD for the risk score developed in RCTs, with area under the receiver-operating characteristic curve (AUROC) of 0.69 (95% CI 0.62-0.75). Fitting the same logistic regression model to all bDMARDm patients in the RWD improved the AUROC on held-out TCZm patients to 0.72 (95% CI 0.63-0.81). Extending the variable set and adding regularization further increased it to 0.76 (95% CI 0.67-0.84). CONCLUSION: The remission prediction scores, derived in RCTs, discriminated patients in RWD about as well as in RCTs. Discrimination was further improved by retraining models on RWD.

Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA.

OBJECTIVES: To develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19. DESIGN: Retrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores and calibration plots in the test set. SETTING: Optum de-identified COVID-19 Electronic Health Record dataset including over 700 hospitals and 7000 clinics in the USA. PARTICIPANTS: 17 086 patients hospitalised with COVID-19 between 20 February 2020 and 5 June 2020. MAIN OUTCOME MEASURE: All-cause mortality while hospitalised. RESULTS: The full model that included information on demographics, comorbidities, laboratory results, and vital signs had good discrimination (C-index=0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index=0.79) was only slightly better than a model that only included age (C-index=0.76). Across the study period, predicted mortality was 1.3% for patients aged 18 years old, 8.9% for 55 years old and 28.7% for 85 years old. Predicted mortality across all ages declined over the study period from 22.4% by March to 14.0% by May. CONCLUSION: Age was the most important predictor of all-cause mortality, although vital signs and laboratory results added considerable prognostic information, with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase and white cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The full model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.

Identification of Acute Giant Cell Arteritis in Real-World Data Using Administrative Claims-Based Algorithms.

OBJECTIVE: The objective of this study was to validate claims-based algorithms for identifying acute giant cell arteritis (GCA) that will help generate real-world evidence on comparative effectiveness research and epidemiologic studies. Among patients identified by the GCA algorithm, we further investigated whether GCA flares could be detected by using claims data. METHODS: We developed five claims-based algorithms based on a combination of International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes, specialist visits, and dispensed medications using Medicare Parts A, B, and D linked to electronic medical records (2006-2014). Acute cases of GCA were determined by chart review using the treating physician's diagnosis of GCA as the gold standard. Among the patients identified with acute GCA, we assessed if a GCA flare occurred during the year after initial diagnosis. RESULTS: The number of patients identified by each algorithm ranged from 220 to 896. Positive predictive values (PPVs) of the algorithms ranged from 60.7% to 84.8%. Requirement for disease-specific workups, multiple diagnosis codes, or specialist visits improved the PPVs. The highest PPV (84.8%) was noted in an algorithm that required two or more diagnosis codes of GCA from inpatient, emergency department, or outpatient rheumatology visits plus a prednisone-equivalent dose greater than or equal to 40 mg/day occurring 14 days before or after the second ICD-9 diagnosis date, with the cumulative days' supply greater than or equal to 14 days. Among patients identified as having GCA, 18.2% of patients had definite evidence of a flare and 25% had a potential flare. CONCLUSION: A claims-based algorithm requiring two or more ICD-9 diagnosis codes from inpatient, emergency department, or outpatient rheumatology visits and high-dose glucocorticoid dispensing can be a useful tool to identify acute GCA cases in large administrative claims databases.

The sequence of disease-modifying anti-rheumatic drugs: pathways to and predictors of tocilizumab monotherapy.

BACKGROUND: There are numerous non-biologic and biologic disease-modifying anti-rheumatic drugs (bDMARDs) for rheumatoid arthritis (RA). Typical sequences of bDMARDs are not clear. Future treatment policies and trials should be informed by quantitative estimates of current treatment practice. METHODS: We used data from Corrona, a large real-world RA registry, to develop a method for quantifying sequential patterns in treatment with bDMARDs. As a proof of concept, we study patients who eventually use tocilizumab monotherapy (TCZm), an IL-6 antagonist with similar benefits used as monotherapy or in combination. Patients starting a bDMARD were included and were followed using a discrete-state Markov model, observing changes in treatments every 6 months and determining whether they used TCZm. A supervised machine learning algorithm was then employed to determine longitudinal patient factors associated with TCZm use. RESULTS: 7300 patients starting a bDMARD were followed for up to 5 years. Their median age was 58 years, 78% were female, median disease duration was 5 years, and 57% were seropositive. During follow-up, 287 (3.9%) reported use of TCZm with median time until use of 25.6 (11.5, 56.0) months. Eighty-two percent of TCZm use began within 3 years of starting any bDMARD. Ninety-three percent of TCZm users switched from TCZ combination, a TNF inhibitor, or another bDMARD. Very few patients are given TCZm as their first DMARD (0.6%). Variables associated with the use of TCZm included prior use of TCZ combination therapy, older age, longer disease duration, seronegative, higher disease activity, and no prior use of a TNF inhibitor. CONCLUSIONS: Improved understanding of treatment sequences in RA may help personalize care. These methods may help optimize treatment decisions using large-scale real-world data.

Prenatal Maternal Serum Concentrations of Per- and Polyfluoroalkyl Substances in Association with Autism Spectrum Disorder and Intellectual Disability.

BACKGROUND: Emerging work has examined neurodevelopmental outcomes following prenatal exposure to per- and polyfluoroalkyl substances (PFAS), but few studies have assessed associations with autism spectrum disorder (ASD). OBJECTIVES: Our objective was to estimate associations of maternal prenatal PFAS concentrations with ASD and intellectual disability (ID) in children. METHODS: Participants were from a population-based nested case-control study of children born from 2000 to 2003 in southern California, including children diagnosed with ASD (n=553), ID without autism (n=189), and general population (GP) controls (n=433). Concentrations of eight PFAS from stored maternal sera collected at 15-19 wk gestational age were quantified and compared among study groups. We used logistic regression to obtain adjusted odds ratios for the association between prenatal PFAS concentrations (parameterized continuously and as quartiles) and ASD versus GP controls, and separately for ID versus GP controls. RESULTS: Geometric mean concentrations of most PFAS were lower in ASD and ID groups relative to GP controls. ASD was not significantly associated with prenatal concentrations of most PFAS, though significant inverse associations were found for perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS) [adjusted ORs for the highest vs. lowest quartiles 0.62 (95% CI: 0.41, 0.93) and 0.64 (95% CI: 0.43, 0.97), respectively]. Results for ID were similar. CONCLUSIONS: Results from this large case-control study with prospectively collected prenatal measurements do not support the hypothesis that prenatal exposure to PFAS is positively associated with ASD or ID. https://doi.org/10.1289/EHP1830.

A Survey of Parents with Children on the Autism Spectrum: Experience with Services and Treatments.

INTRODUCTION: Autism spectrum disorders (ASD) are lifelong neurodevelopmental disorders, and little is known about how parents address the health and psychosocial consequences of ASD. Few studies have examined use of various treatments and services in a large, diverse sample of children with ASD and their families. OBJECTIVE: This paper presents methods to create an autism research resource across multiple large health delivery systems and describes services and treatments used by children with ASD and their families. METHODS: Four study sites conducted a Web survey of parents of children and adolescents with ASD who were members of Kaiser Permanente. We tabulated data distributions of survey responses and calculated χ2 statistics for differences between responders and nonresponders. RESULTS: The children of the 1155 respondents were racially and ethnically diverse (55% white, 6% black, 5% Asian, 9% multiracial, 24% Hispanic) and representative of the total population invited to participate with respect to child sex (83% male), child age (57% < 10 years), and ASD diagnosis (64% autistic disorder). The most frequently used services and treatments were Individualized Education Programs (85%), family physician visits (78%), and occupational and speech therapy (55% and 60%, respectively). Home-based programs frequently included implementation of social skills training (44%) and behavior management (42%). Prescription medication use was high (48%). Caregivers reported disruption of personal and family routines because of problem behaviors. CONCLUSION: These survey data help to elucidate parents' experiences with health services for their children with ASD and serve as a potential resource for future research.

Medical Conditions in the First Years of Life Associated with Future Diagnosis of ASD in Children.

This study examines medical conditions diagnosed prior to the diagnosis of autism spectrum disorder (ASD). Using a matched case control design with 3911 ASD cases and 38,609 controls, we found that 38 out of 79 medical conditions were associated with increased ASD risk. Developmental delay, mental health, and neurology conditions had the strongest associations (ORs 2.0-23.3). Moderately strong associations were observed for nutrition, genetic, ear nose and throat, and sleep conditions (ORs 2.1-3.2). Using machine learning methods, we clustered children based on their medical conditions prior to ASD diagnosis and demonstrated ASD risk stratification. Our findings provide new evidence indicating that children with ASD have a disproportionate burden of certain medical conditions preceding ASD diagnosis.

Psychotropic Medication Use among Insured Children with Autism Spectrum Disorder.

This study examined psychotropic medication use among 7901 children aged 1-17 with autism spectrum disorder (ASD) in five health systems, comparing to matched cohorts with no ASD. Nearly half (48.5 %) of children with ASD received psychotropics in the year observed; the most common classes were stimulants, alpha-agonists, or atomoxetine (30.2 %), antipsychotics (20.5 %), and antidepressants (17.8 %). Psychotropic treatment was far more prevalent among children with ASD, as compared to children with no ASD (7.7 % overall), even within strata defined by the presence or absence of other psychiatric diagnoses. The widespread use of psychotropics we observed, particularly given weak evidence supporting the effectiveness of these medications for most children with ASD, highlights challenges in ASD treatment and the need for greater investment in its evaluation.

Health Services Utilization Among Children With and Without Autism Spectrum Disorders.

Using data from multiple health systems (2009-2010) and the largest sample to date, this study compares health services use among youth with and without an autism spectrum disorder (ASD)-including preventive services not previously studied. To examine these differences, we estimated logistic and count data models, controlling for demographic characteristics, comorbid physical health, and mental health conditions. Results indicated that youth with an ASD had greater health care use in many categories, but were less likely to receive important preventive services including flu shots and other vaccinations. An improved understanding of the overall patterns of health care use among this population could enable health systems to facilitate the receipt of appropriate and effective health care.

Prevalence and Predictors of Complementary and Alternative Medicine Use in a Large Insured Sample of Children with Autism Spectrum Disorders.

PURPOSE: The purpose of the present study was to examine the prevalence and predictors of complementary and alternative medicine (CAM) use as well as parental perceptions of CAM efficacy in a large, geographically diverse sample of children with Autism Spectrum Disorders (ASD). METHODOLOGY: Data were obtained from a web-based survey administered to parents of children with ASD at four sites participating in the Mental Health Research Network (MHRN). The web survey obtained information about services and treatments received by children with ASD as well as the caregivers' experiences with having a child with ASD. RESULTS: Approximately 88% of the sample had either used CAM in the past or had recently used some type of CAM. The following characteristics were associated with CAM use: greater parental education, younger child age, a mix of regular and special classroom settings and prescription drug use in the past three months. CONCLUSIONS: The use of CAM was very prevalent in this large, geographically diverse sample of children with ASD. It is critical that providers be prepared to discuss the advantages and potential side effects with families to help them make well-informed health care decisions and prevent possible CAM-drug interactions.

Validation of Autism Spectrum Disorder Diagnoses in Large Healthcare Systems with Electronic Medical Records.

To identify factors associated with valid Autism Spectrum Disorder (ASD) diagnoses from electronic sources in large healthcare systems. We examined 1,272 charts from ASD diagnosed youth <18 years old. Expert reviewers classified diagnoses as confirmed, probable, possible, ruled out, or not enough information. A total of 845 were classified with 81% as a confirmed, probable, or possible ASD diagnosis. The predictors of valid ASD diagnoses were >2 diagnoses in the medical record (OR 2.94; 95% CI 2.03-4.25; p < 0.001) and being male (OR 1.51; 95% CI 1.05-2.17; p = 0.03). In large integrated healthcare settings, at least two diagnoses can be used to identify ASD patients for population-based research.

Prenatal and neonatal thyroid stimulating hormone levels and autism spectrum disorders.

Thyroid hormones are critical for normal brain development. This study examined autism spectrum disorders (ASD) and thyroid stimulating hormone (TSH) levels measured in mid-pregnancy maternal serum and infant blood after birth. Three groups of children born in Orange County, CA in 2000-2001 were identified: ASD (n = 78), developmental delay (n = 45), and general population controls (GP) (n = 149). Samples were retrieved from prenatal and newborn screening specimen archives. Adjusted logistic regression models showed inverse associations between ASD and log transformed TSH levels in maternal serum samples (ASD vs. GP: OR [95 % CI] 0.33 [0.12-0.91], Early Onset ASD vs. GP: 0.31 [0.10-0.98]). Results for thyroid levels in newborn blood samples were similar though not significant (ASD vs. GP: 0.61 [0.18-2.04]).

Prenatal and neonatal peripheral blood mercury levels and autism spectrum disorders.

BACKGROUND: Prenatal and early-life exposures to mercury have been hypothesized to be associated with increased risk of autism spectrum disorders (ASDs). OBJECTIVES: This study investigated the association between ASDs and levels of total mercury measured in maternal serum from mid-pregnancy and infant blood shortly after birth. METHODS: The study sample was drawn from the Early Markers for Autism (EMA) Study. Three groups of children who were born in Orange County, CA in 2000-2001 were identified: children with ASD (n=84), children with intellectual disability or developmental delay (DD) (n=49), and general population controls (GP) (n=159). Maternal serum specimens and newborn bloodspots were retrieved from the California Department of Public Health prenatal and newborn screening specimen archives. Blood mercury levels were measured in maternal serum samples using mass spectrometer and in infant bloodspots with a 213 nm laser. RESULTS: Maternal serum and infant blood mercury levels were significantly correlated among all study groups (all correlations >0.38, p<0.01). Adjusted logistic regression models showed no significant associations between ASD and log transformed mercury levels in maternal serum samples (ASD vs. GP: OR [95% CI]=0.96 [0.49-1.90]; ASD vs. DD: OR [95% CI]=2.56 [0.89-7.39]). Results for mercury levels in newborn blood samples were similar (ASD vs. GP: OR [95% CI]=1.18 [0.71-1.95]; ASD vs. DD: OR [95% CI]=1.96 [0.75-5.14]). CONCLUSIONS: Results indicate that levels of total mercury in serum collected from mothers during mid-pregnancy and from newborn bloodspots were not significantly associated with risk of ASD, though additional studies with greater sample size and covariate measurement are needed.