Pm2.5 And Ambient Air Pollution: Effects On Medicaid Spending And Hospitalizations

Rationale: Fine particulate matter (\u3c2.5 um), or PM2.5, and Ozone has been linked to a number of respiratory and cardiovascular conditions and is a known trigger for acute events. Though previous studies have addressed these correlations, few have examined PM2.5’s acute effects on inpatient admissions and health care costs, by utilizing narrower time intervals than other research projects. Objectives: To identify whether trends in hospitalization spending in short time-intervals is associated with PM2.5 measures, and to create a predictive model for spending based on two major categories of outcomes: selected CV conditions and respiratory conditions known to be associated with PM2.5 exposure that we will identify by Medicaid charge codes. We also attempt to model inpatient admissions altogether as an alternative outcome. Methods: We link Medicaid charge information for all procedures in Texas to daily air-quality data sourced from 63 EPA sites in the state (providing more comprehensive geographical and temporal coverage) and fit a longitudinal mixed model to extrapolate costs and risks of additional inpatient stays due to respiratory conditions from particulate matter AQI readings. Outcomes are identified by APR-DRG codes listed in the Blue Ribbon Medicaid set and exposure measurements are sourced from the EPA’s monitoring stations’ data mart. Our study covered September 2010 to August 2011. We also adjust for other potential covariates and exposures like Ozone in latter models to help explain some of the variation in outcomes. Measurements and Main Results: We find positive association between environmental PM2.5 and healthcare spending. Our simpler multilevel linear model gave us these cost estimates: rates of Respiratory and CV charges to Medicaid increase $3.95 million dollars to each increase of ug/m^3 in PM2.5, under our simplest per capita model. After controlling for other variables in a secondary-model we find that ozone is the more significant predictor of costs/charges, and one of our models produced an annual$600,000 increase per additional 0.01 ppm of exposure over the year. We also find that ozone is a better predictor of count data and is more statistically significant when fixed time covariates are also entered into our predictive model. Conclusion: This suggests additional savings from instituting more restrictive clear air regulations in terms of ozone and pm2.5. In addition, this creates additional burden for local medical centers. These measurements can be utilized to predict morbidities based off of air quality, as well as estimate the impact to Medicaid in terms of its financial strain. This information can be utilized in resource allocation in terms of hospital staffing and local medical needs, as well as on larger scale, aiding policy decisions. As well in a subset of our models that include other airborne contaminants and fixed time covariates, we find that ozone is more significant of a predictor

Genome-wide association study of primary tooth eruption identifies pleiotropic loci associated with height and craniofacial distances

Twin and family studies indicate that the timing of primary tooth eruption is highly heritable, with estimates typically exceeding 80%. To identify variants involved in primary tooth eruption we performed a population based genome-wide association study of ‘age at first tooth’ and ‘number of teeth’ using 5998 and 6609 individuals respectively from the Avon Longitudinal Study of Parents and Children (ALSPAC) and 5403 individuals from the 1966 Northern Finland Birth Cohort (NFBC1966). We tested 2,446,724 SNPs imputed in both studies. Analyses were controlled for the effect of gestational age, sex and age of measurement. Results from the two studies were combined using fixed effects inverse variance meta-analysis. We identified a total of fifteen independent loci, with ten loci reaching genome-wide significance (p<5x10−8) for ‘age at first tooth’ and eleven loci for ‘number of teeth’. Together these associations explain 6.06% of the variation in ‘age of first tooth’ and 4.76% of the variation in ‘number of teeth’. The identified loci included eight previously unidentified loci, some containing genes known to play a role in tooth and other developmental pathways, including a SNP in the protein-coding region of BMP4 (rs17563, P= 9.080x10−17). Three of these loci, containing the genes HMGA2, AJUBA and ADK, also showed evidence of association with craniofacial distances, particularly those indexing facial width. Our results suggest that the genome-wide association approach is a powerful strategy for detecting variants involved in tooth eruption, and potentially craniofacial growth and more generally organ development

DO IT Trial: vitamin D Outcomes and Interventions in Toddlers - a TARGet Kids! randomized controlled trial.

BackgroundVitamin D levels are alarmingly low (&lt;75 nmol/L) in 65-70% of North American children older than 1 year. An increased risk of viral upper respiratory tract infections (URTI), asthma-related hospitalizations and use of anti-inflammatory medication have all been linked with low vitamin D. No study has determined whether wintertime vitamin D supplementation can reduce the risk of URTI and asthma exacerbations, two of the most common and costly illnesses of early childhood. The objectives of this study are: 1) to compare the effect of 'high dose' (2000 IU/day) vs. 'standard dose' (400 IU/day) vitamin D supplementation in achieving reductions in laboratory confirmed URTI and asthma exacerbations during the winter in preschool-aged Canadian children; and 2) to assess the effect of 'high dose' vitamin D supplementation on vitamin D serum levels and specific viruses that cause URTI.Methods/designThis study is a pragmatic randomized controlled trial. Over 4 successive winters we will recruit 750 healthy children 1-5 years of age. Participating physicians are part of a primary healthcare research network called TARGet Kids!. Children will be randomized to the 'standard dose' or 'high dose' oral supplemental vitamin D for a minimum of 4 months (200 children per group). Parents will obtain a nasal swab from their child with each URTI, report the number of asthma exacerbations and complete symptom checklists. Unscheduled physician visits for URTIs and asthma exacerbations will be recorded. By May, a blood sample will be drawn to determine vitamin D serum levels. The primary analysis will be a comparison of URTI rate between study groups using a Poisson regression model. Secondary analyses will compare vitamin D serum levels, asthma exacerbations and the frequency of specific viral agents between groups.DiscussionIdentifying whether vitamin D supplementation of preschoolers can reduce wintertime viral URTIs and asthma exacerbations and what dose is optimal may reduce population wide morbidity and associated health care and societal costs. This information will assist in determining practice and health policy recommendations related to vitamin D supplementation in healthy Canadian preschoolers

Complications And Length Of Stay Following Spine Surgery: Analyzing Local And National Cohorts

Complications following spine surgery are widely reported but poorly characterized. The effect of preoperative comorbidities and postoperative complications on length of stay (LOS) has not been evaluated. It would be ideal to have a clearer understanding of the variables affecting LOS to facilitate setting expectations and control costs. Using complications and LOS as outcomes, we can also characterize the risks inherent with surgical practices, such as the use of iliac crest bone graph (ICBG) in spinal fusion. The study consisted of three aspects. First, the effect of pre and perioperative variables on LOS for 103 patients undergoing posterior lumbar fusion at Yale was examined. Next, the National Surgical Quality Improvement Program (NSQIP) database was used to determine the variables associated with extended LOS and complications following 2,164 anterior cervical discectomy and fusion (ACDF) procedures. Finally, 13,927 spinal fusion cases from the NSQIP database were analyzed to determine the effect of harvesting ICBG on operative time, complications, LOS, and readmission. Multivariate analysis was used throughout the study to control for confounding while evaluating statistical significance. For lumbar fusion, average LOS was 3.6 ± 1.8 days. 79% had a stay of four days or less. Preoperative variables associated with increased LOS were age and ASA score. Heart disease was significantly associated with decreased LOS. Postoperative complications occurred in 32% of patients and led to a LOS of 5.1 ± 2.3 days vs. 2.9 ± 0.9 days for patients with no complication. For ACDF, average LOS was 2.0 ± 4.0. Age ≥ 65, functional status, transfer from facility, preoperative anemia, and diabetes were the preoperative factors predictive of extended LOS. Major complications, minor complications, and extended surgery time were the perioperative factors associated with increased LOS. 71 (3.3%) had a total of 92 major complications. ASA score ≥ 3, preoperative anemia, age ≥ 65, extended surgery time and male gender were predictive of major complications. Meanwhile, postoperative blood transfusion (OR 1.5), extended operative time (+ 22.0 min) and LOS (+0.2 days) were significantly associated with ICBG use. After lumbar fusion, patients that are older and have widespread systemic disease tend have longer LOS, but no single comorbidity was predictive of LOS. After ACDF, 1 in 33 patients develops a major post-operative complication, which are associated with an increased LOS of 5 days. Current ICBG usage in spinal fusion is low, with rates between 3.4% and 12.4% depending on approach. Use of ICBG is associated with extended operative time, extended LOS, and postoperative blood transfusion

Social and economic impact of diabetics in Bangladesh: protocol for a case-control study

Background: Diabetes affects both individuals and their families and has an impact on economic and social development of a country. Information on the availability, cost, and quality of medical care for diabetes is mostly not available for many low-and middle-income countries including Bangladesh. Complications from diabetes, which can be devastating, could largely be prevented by wider use of several inexpensive generic medicines, simple tests and monitoring and can be a cost saving intervention. This study will provide an in-depth and comprehensive picture of social and economic impacts of diabetes in Bangladesh and propose clear recommendations for improving prevention and management of diabetes. The objectives of the study are: 1) To study the association between diabetes and other health problems and its social impacts 2) To estimate the economic impact of diabetes including total direct and indirect costs 3) To measure the impact of diabetes on quality of life among diabetes patients in Bangladesh 4) To study the impact of diabetes on the health care system Methods: This is a case-control study comparing cases with type 2 diabetes to controls without diabetes matched on age, sex and place of residence. 564 cases and 564 controls will be selected from the outpatient department of a tertiary hospital in Dhaka, Bangladesh. Data on socioeconomic status, health utility index, direct and indirect costs for diabetes, medication adherence, quality of life, treatment satisfaction, diet, physical activity, mental state examination, weight, height, hip and waist circumference, blood pressure, pulse, medication history, laboratory data and physical examination will be conducted. Outcome measures: The primary outcome measures will be association between diabetes and other health problems, cost of diabetes, impact of diabetes on quality of life and secondary outcome measures are impact of diabetes on healthcare systems in Bangladesh. Discussion: This study will provide an in-depth and comprehensive picture of social and economic impacts of diabetics in Bangladesh and propose clear recommendations for improving prevention and management of diabetics. It will help to develop programs and policies for better management of Diabetics and cost effective strategies in Bangladesh context

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data

Background: Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed. Methods: We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool. Results: In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/ signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years). Conclusions: The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data

Background: Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed. Methods: We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool. Results: In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/ signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years). Conclusions: The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics

Reducing Thirty-Day Hospital Readmissions in Drug and Medication Poisoning: An Observational Study

A common and costly occurrence in the United States is thirty-day hospital readmissions. Awareness of 30-day hospital readmissions is currently a national priority. To reduce avoidable readmissions, the Patient Protection and Affordable Care Act of 2010 established a “Hospital Readmission Reduction Program” implemented to provide possible solutions for preventable thirty-day readmissions. Part of this policy states that hospitals with higher than expected adjusted re-hospitalization rates have lower reimbursement rates. One specific area known to be a cause of thirty-day hospital readmissions is drug and medication poisoning. An observational study of data from the Nationwide Readmissions Database is being used to help identify contributing factors and provide suggestions for preventable thirty-day readmissions relative to drug and medication poisoning. Factors that include: gender; demographics; cost index; socio-economic, and hospital factors are identified to aid in the understanding of thirty-day hospital readmission of drug and medication poisoning. Finally, suggestions based on quantitative analyses contribute to the understanding of risk factors of thirty-day readmissions in drug and medication poisoning occurrences. Outcomes include statistical significance in gender and significance in the cost index of the individual patient; such as the ability to pay or not to pay for services rendered. Certain socio-economic factors whereas contributed, however, overall socioeconomic status was not significant along with hospital specific factors being insignificant. The study resulted in the identification of factors to aid in drug/medication episodic occurrences in a patient population experiencing thirty-day readmissions. Prevention strategy from both a clinical and practical application may be used to initiate cost saving applications. Future studies suggest expanding on drug and medication poisoning in certain sub-specific populations, further identifying illegal vs. legal drug/medication differentiation, and conducting international comparisons based on current findings

The Economic Analysis of Kidney-Exchange Networks

This dissertation investigates the effects of the largest national kidney-exchange network, National Kidney Registry (NKR), on the speed of finding a transplant and the quality of donors in participant hospitals. It also examines the effect of surgeons in the network-adoption decision of hospitals. The prohibition of monetary transactions for human organs under U.S. law generates a shortage of kidneys available for transplant. In 2017, about 100,000 patients were waiting on the long wait-lists to receive a kidney for transplant. Creation of distinct kidney-exchange networks that find compatible matches between patients who each have willing but incompatible living kidney donors reduced this shortage. This dissertation uses the data from the scientific registry of transplant recipients and the National Kidney Registry data on the list of participant hospitals by year. The first chapter estimates the change in the probability of receiving a transplant conditional on wait-time for patients after a hospital adopts the NKR network. Using survival analysis accounting for the competing risks, I find that the probability of finding a transplant from a living donor increases by 0.25 percentage points in hospitals participating in the network. This positive effect is mainly driven from the additional indirect-living transplants that these networks can accommodate through exchange transplants. The second chapter investigates how the quality of living donors changes as the use of the NKR network expands. I use the variation in the period before and after the adoption of NKR by hospitals and run a difference-in-differences method. Further, I use a Coarsend Exact Matching to correct for the imbalance between treatment and control groups. My finding suggests that the quality of living donors as measured by age, body mass index, and blood type decreases in participant hospitals. Specifically, I show that living donors in NKR affiliated hospitals are on average 8 months older, have 0.19 points higher body mass indexes, and are 3.8 percent less likely to have an O blood-type. The final chapter analyzes the fragmentation in the participation of hospitals in these networks. We (with Bobby W. Chung) investigate the influence of surgeons in expanding the use of the NKR by hospitals. We find that hospitals that are connected through mutual surgeons are more likely to adopt the NKR network. Specifically, we find that one more adoption by connected hospitals increases the probability of the focal hospital to adopt by about 4 percentage points. This trend shows a diminishing magnitude as the number of connected hospitals increases. This effect is stronger for surgeons that have performed a larger number of transplant surgeries and for hospitals that have more than one mutual surgeon

A Microsoft-Excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.BACKGROUND: The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves. METHODS: We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL's interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation. RESULTS: We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software. CONCLUSIONS: Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows for more efficient and transparent critical appraisal of network meta-analyses, enhanced standardization of reporting, and integration with health economic evaluations which are frequently Excel-based.CC is a recipient of a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (funding reference number—CGV 121171) and is a trainee on the Canadian Institutes of Health Research Drug Safety and Effectiveness Network team grant (funding reference number—116573). BH is funded by a New Investigator award from the Canadian Institutes of Health Research and the Drug Safety and Effectiveness Network. This research was partly supported by funding from CADTH as part of a project to develop Excel-based tools to support the conduct of health technology assessments. This research was also supported by Cornerstone Research Group