A low-cost 3D-printable differential scanning fluorometer for protein and RNA melting experiments.

Differential scanning fluorimetry (DSF) is a widely used biophysical technique with applications to drug discovery and protein biochemistry. DSF experiments are commonly performed in commercial real-time polymerase chain reaction (qPCR) thermal cyclers or nanoDSF instruments. Here, we report the construction, validation, and example applications of an open-source DSF system for 176 €, which, in addition to protein-DSF experiments, also proved to be a versatile biophysical instrument for less conventional RNA-DSF experiments. Using 3D-printed parts made of polyoxymethylene, we were able to fabricate a thermostable machine chassis for protein-melting experiments. The combination of blue high-power LEDs as the light source and stage light foil as filter components was proven to be a reliable and affordable alternative to conventional optics equipment for the detection of SYPRO Orange or Sybr Gold fluorescence. The ESP32 microcontroller is the core piece of this openDSF instrument, while the in-built I2S interface was found to be a powerful analog-to-digital converter for fast acquisition of fluorescence and temperature data. Airflow heating and inline temperature control by thermistors enabled high-accuracy temperature management in PCR tubes (±0.1 °C) allowing us to perform high-resolution thermal shift assays (TSA) from exemplary biological applications.

Association between early-childhood antibiotic exposure and subsequent asthma in the US Medicaid population.

BACKGROUND: Although socioeconomically disadvantaged children have an increased risk of asthma, the association between early-childhood antibiotics and the incidence of asthma among such children has had limited study. OBJECTIVE: To examine the association between antibiotic fills in the first 2 years of life and risk of developing asthma among children enrolled in Medicaid plans. METHODS: This retrospective cohort study of children with continuous medical and pharmacy coverage from birth to 2.5 years of age was performed from July 1, 2012, to November 31, 2018. We excluded children with a diagnosis of asthma before 2.5 years of age. Hazard ratios (HRs) and 95% CIs were estimated from Cox proportional hazards regression models. Covariates included sex, preterm birth, cesarean delivery, and mother's asthma status. RESULTS: There were 79,582 children in the study cohort of whom 29,931 (37.6%) had 0 antibiotic prescriptions filled, 27,403 (34.4%) had 1 or 2 prescriptions filled, and 22,248 (28.0%) had 3 or more prescriptions filled. A total of 2381 new cases of asthma were observed in 89,545 person-years of follow-up. After adjustment, receipt of 1 or 2 antibiotics was associated with an increased risk of developing asthma, relative to 0 antibiotics (HR, 1.34; 95% CI, 1.21-1.49), and receipt of 3 or more antibiotics was associated with greater increased risk relative to 0 antibiotics (HR, 1.71; 95% CI, 1.54-1.90). After adjustment, the absolute risk of developing asthma by age 4.0 years increased from 2.7% (0 antibiotics) to 3.6% (1-2 antibiotics) and 4.5% (≥3 antibiotics). CONCLUSION: Antibiotic prescriptions filled in the first 2 years of life were associated with an increased risk of asthma diagnosis from 2.5 to 5 years of age in a Medicaid population.

Principles of Child Health Care Financing.

After passage of the Patient Protection and Affordable Care Act, more children and young adults have become insured and have benefited from health care coverage than at any time since the creation of the Medicaid program in 1965. From 2009 to 2015, the uninsurance rate for children younger than 19 years fell from 9.7% to 5.3%, whereas the uninsurance rate for young adults 19 to 25 years of age declined from 31.7% to 14.5%. Nonetheless, much work remains to be done. The American Academy of Pediatrics (AAP) believes that the United States can and should ensure that all children, adolescents, and young adults from birth through the age of 26 years who reside within its borders have affordable access to high-quality and comprehensive health care, regardless of their or their families' incomes. Public and private health insurance should safeguard existing benefits for children and take further steps to cover the full array of essential health care services recommended by the AAP. Each family should be able to afford the premiums, deductibles, and other cost-sharing provisions of the plan. Health plans providing these benefits should ensure, insofar as possible, that families have a choice of professionals and facilities with expertise in the care of children within a reasonable distance of their residence. Traditional and innovative payment methodologies by public and private payers should be structured to guarantee the economic viability of the pediatric medical home and of other pediatric specialty and subspecialty practices to address developing shortages in the pediatric specialty and subspecialty workforce, to promote the use of health information technology, to improve population health and the experience of care, and to encourage the delivery of evidence-based and quality health care in the medical home, as well as in other outpatient, inpatient, and home settings. All current and future health care insurance plans should incorporate the principles for child health financing outlined in this statement. Espousing the core principle to do no harm, the AAP believes that the United States must not sacrifice any of the hard-won gains for our children. Medicaid, as the largest single payer of health care for children and young adults, should remain true to its origins as an entitlement program; in other words, future fiscal or regulatory reforms of Medicaid should not reduce the eligibility and scope of benefits for children and young adults below current levels nor jeopardize children's access to care. Proposed Medicaid funding "reforms" (eg, institution of block grant, capped allotment, or per-capita capitation payments to states) will achieve their goal of securing cost savings but will inevitably compel states to reduce enrollee eligibility, trim existing benefits (such as Early and Periodic Screening, Diagnostic, and Treatment), and/or compromise children's access to necessary and timely care through cuts in payments to providers and delivery systems. In fact, the AAP advocates for increased Medicaid funding to improve access to essential care for existing enrollees, fund care for eligible but uninsured children once they enroll, and accommodate enrollment growth that will occur in states that choose to expand Medicaid eligibility. The AAP also calls for Congress to extend funding for the Children's Health Insurance Program, a plan vital to the 8.9 million children it covered in fiscal year 2016, for a minimum of 5 years.

Value-Based Insurance Design Pharmacy Benefits for Children and Youth With Special Health Care Needs: Principles and Opportunities.

Value-based insurance design (VBID) represents an innovative approach to health insurance coverage. In the context of pharmacy benefits, the goal of VBID is to minimize access barriers to the most effective and appropriate treatments for specific medical conditions. Both private and public insurance programs have explored VBID pharmacy projects primarily for medical conditions affecting adults. To date, evidence for VBID pharmacy programs for children and youth with special health care needs (CYSHCN) appears lacking. There appears to be potential for VBID concepts to be applied to pharmacy coverage benefiting CYSHCN. An overview of VBID pharmacy principles and guiding principles are presented. Opportunities for the creation of pharmacy programs with a value-based orientation and challenges to the redesign of pharmacy benefits are identified. VBID pharmacy coverage principles may be helpful to improve medication use and important clinical outcomes while lowering barriers to medication use for the population of CYSHCN. Pilot projects of VBID pharmacy benefits for children and youth should be explored. However, many questions remain.

Children's Health Insurance Program (CHIP): accomplishments, challenges, and policy recommendations.

Sixteen years ago, the 105th Congress, responding to the needs of 10 million children in the United States who lacked health insurance, created the State Children's Health Insurance Program (SCHIP) as part of the Balanced Budget Act of 1997. Enacted as Title XXI of the Social Security Act, the Children's Health Insurance Program (CHIP; or SCHIP as it has been known at some points) provided states with federal assistance to create programs specifically designed for children from families with incomes that exceeded Medicaid thresholds but that were insufficient to enable them to afford private health insurance. Congress provided $40 billion in block grants over 10 years for states to expand their existing Medicaid programs to cover the intended populations, to erect new stand-alone SCHIP programs for these children, or to effect some combination of both options. Congress reauthorized CHIP once in 2009 under the Children's Health Insurance Program Reauthorization Act and extended its life further within provisions of the Patient Protection and Affordable Care Act of 2010. The purpose of this statement is to review the features of CHIP as it has evolved over the 16 years of its existence; to summarize what is known about the effects that the program has had on coverage, access, health status, and disparities among participants; to identify challenges that remain with respect to insuring this group of vulnerable children, including the impact that provisions of the new Affordable Care Act will have on the issue of health insurance coverage for near-poor children after 2015; and to offer recommendations on how to expand and strengthen the national commitment to provide health insurance to all children regardless of means.

Evidence-based use of second-generation antipsychotics in a state Medicaid pediatric population, 2001-2005.

OBJECTIVE: The purpose of this study was to identify children in a state Medicaid population who were newly treated with second-generation antipsychotics from 2001 through 2005, to classify each use of these agents as evidence based or not depending on the child's diagnoses, and to identify factors associated with the likelihood of evidence-based use of the medication. METHODS: A Medicaid claims database was used to retrospectively identify enrollees receiving initial outpatient treatment with a second-generation antipsychotic between 2001 and 2005. To capture all relevant treatments and diagnoses, claims were examined from January 2000 through December 2006. The final sample included 11,700 children under age 18. The primary measure of interest was the proportion for whom use of the antipsychotic was based on evidence. Evidence-based use (categorized as strong, plausible, or weak evidence) was defined as any use of the agent for a diagnosis supported by a clinical trial published before the end of 2005. Trend analysis and logistic regression were used. RESULTS: The number of children newly treated with second-generation antipsychotics increased from 1,482 in 2001 to 3,110 in 2005. Of the new users of these agents during the study period, 41.3% had no diagnosis for which such treatment was supported by a published study. The medication with the highest level of non-evidence-based use was aripiprazole (77.1%), and risperidone had the lowest (30.6%). CONCLUSIONS: The number of children receiving second-generation antipsychotics doubled in this Medicaid population between 2001 and 2005, and a large proportion of the treatments were not supported by evidence from clinical studies.

FRET imaging of cells transfected with siRNA/liposome complexes.

By monitoring the efficiency of fluorescence resonance energy transfer of dyes attached to the different strands of siRNA, the structural integrity of the latter can be traced inside cells. Here, the experimental details of dye-labeled siRNA construction, tissue culture, and transfection with liposomally formulated siRNAs are given, as well as the conditions for confocal microscopy and an algorithm allowing the visualization of intact siRNA after image data treatment. The method allows rapid screening of different liposomal siRNA formulations, obtained by small scale dual asymmetric centrifugation with high entrapping efficiency.

Good and poor adherence: optimal cut-point for adherence measures using administrative claims data.

OBJECTIVE: To identify the adherence value cut-off point that optimally stratifies good versus poor compliers using administratively derived adherence measures, the medication possession ratio (MPR) and the proportion of days covered (PDC) using hospitalization episode as the primary outcome among Medicaid eligible persons diagnosed with schizophrenia, diabetes, hypertension, congestive heart failure (CHF), or hyperlipidemia. RESEARCH DESIGN AND METHODS: This was a retrospective analysis of Arkansas Medicaid administrative claims data. Patients > or =18 years old had to have at least one ICD-9-CM code for the study diseases during the recruitment period July 2000 through April 2004 and be continuously eligible for 6 months prior and 24 months after their first prescription for the target condition. Adherence rates to disease-specific drug therapy were assessed during 1 year using MPR and PDC. MAIN OUTCOME MEASURE AND ANALYSIS SCHEME: The primary outcome measure was any-cause and disease-related hospitalization. Univariate logistic regression models were used to predict hospitalizations. The optimum adherence value was based on the adherence value that corresponded to the upper most left point of the ROC curve corresponding to the maximum specificity and sensitivity. RESULTS: The optimal cut-off adherence value for the MPR and PDC in predicting any-cause hospitalization varied between 0.63 and 0.89 across the five cohorts. In predicting disease-specific hospitalization across the five cohorts, the optimal cut-off adherence values ranged from 0.58 to 0.85. CONCLUSIONS: This study provided an initial empirical basis for selecting 0.80 as a reasonable cut-off point that stratifies adherent and non-adherent patients based on predicting subsequent hospitalization across several highly prevalent chronic diseases. This cut-off point has been widely used in previous research and our findings suggest that it may be valid in these conditions; it is based on a single outcome measure, and additional research using these methods to identify adherence thresholds using other outcome metrics such as laboratory or physiologic measures, which may be more strongly related to adherence, is warranted.

Prospective validation of eight different adherence measures for use with administrative claims data among patients with schizophrenia.

OBJECTIVE: The aim of this study was to compare the predictive validity of eight different adherence measures by studying the variability explained between each measure and hospitalization episodes among Medicaid-eligible persons diagnosed with schizophrenia on antipsychotic monotherapy. METHODS: This study was a retrospective analysis of the Arkansas Medicaid administrative claims data. Continuously eligible adult schizophrenia (ICD-9-CM = 295.**) patients on antipsychotic monotherapy were identified in the recruitment period from July 2000 through April 2004. Adherence rates to antipsychotic therapy in year 1 were calculated using eight different measures identified from the literature. Univariate and multivariable logistic regression models were used to prospectively predict all-cause and mental health-related hospitalizations in the follow-up year. RESULTS: Adherence rates were computed for 3395 schizophrenic patients with a mean age of 42.9 years, of which 52.5% (n = 1782) were females, and 52.8% (n = 1793) were white. The proportion of days covered (PDC) and continuous measure of medication gaps measures of adherence had equal C-statistics of 0.571 in predicting both all-cause and mental health-related hospitalizations. The medication possession ratio (MPR) continuous multiple interval measure of oversupply were the second best measures with equal C-statistics of 0.568 and 0.567 for any-cause and mental health-related hospitalizations. The multivariate adjusted models had higher C-statistics but provided the same rank order results. CONCLUSIONS: MPR and PDC were among the best predictors of any-cause and mental health-related hospitalization, and are recommended as the preferred adherence measures when a single measure is sought for use with administrative claims data for patients not on polypharmacy.

An empirical basis for standardizing adherence measures derived from administrative claims data among diabetic patients.

OBJECTIVE: To compare the predictive validity of 8 different adherence measures by studying the variability explained between each measure and 2 outcome measures: hospitalization episodes and total nonpharmacy cost among Medicaid eligible persons diagnosed with diabetes. RESEARCH DESIGN: This study was a retrospective analysis of the Arkansas Medicaid administrative claims data from January 2000 to December 2006. SUBJECTS: Diabetic (ICD-9-CM = 250.0 x - 250.9 x, where x = 0 or 2) patients were identified in the recruitment period July 2000 through April 2004. Patients had to be >or=18 years old and have at least 2 prescription fills in the index period for an oral antidiabetic drug. MEASURES: : Adherence rates to oral antidiabetic therapy were contrasted using the following 8 measures; including the medication possession ratio (MPR), proportion of days covered (PDC), refill compliance rate (RCR), compliance ratio (CR), medication possession ratio, modified (MPRm), continuous measure of medication gaps (CMG), and continuous multiple interval measure of oversupply (CMOS and continuous, single interval measure of medication acquisition (CSA). Multivariate and univariate linear and logistic regression models were used to prospectively predict nonpharmacy costs and hospitalizations in the follow-up year. RESULTS: A total of 4943 diabetic patients were studied. In predicting any cause hospitalization, univariate models with PDC and CMG had the highest predictive validity (C-statistic: 0.544). Multivariate models with MPR, PDC, CMG or continuous multiple interval measure of oversupply (CMOS) as adherence measures had the highest C-statistics of 0.701 in predicting diabetes specific hospitalizations. None of the adherence measures were significantly associated with nonpharmacy cost. CONCLUSIONS: MPR and PDC had the highest predictive validity for hospitalization episodes. These 2 measures should be considered first when selecting among adherence measures when using administrative prescription claims data.