Trends in Resource Utilization by Children with Neurological Impairment in the United States Inpatient Health Care System: A Repeat Cross-Sectional Study

Background

Care advances in the United States (US) have led to improved survival of children with neurological impairment (NI). Children with NI may account for an increasing proportion of hospital resources. However, this assumption has not been tested at a national level.

Methods and Findings

We conducted a study of 25,747,016 US hospitalizations of children recorded in the Kids'' Inpatient Database (years 1997, 2000, 2003, and 2006). Children with NI were identified with International Classification of Diseases, 9th Revision, Clinical Modification diagnoses resulting in functional and/or intellectual impairment. We assessed trends in inpatient resource utilization for children with NI with a Mantel-Haenszel chi-square test using all 4 y of data combined. Across the 4 y combined, children with NI accounted for 5.2% (1,338,590) of all hospitalizations. Epilepsy (52.2% [n = 538,978]) and cerebral palsy (15.9% [n = 164,665]) were the most prevalent NI diagnoses. The proportion of hospitalizations attributable to children with NI did not change significantly (p = 0.32) over time. In 2006, children with NI accounted for 5.3% (n = 345,621) of all hospitalizations, 13.9% (n = 3.4 million) of bed days, and 21.6% (US$17.7 billion) of all hospital charges within all hospitals. Over time, the proportion of hospitalizations attributable to children with NI decreased within non-children''s hospitals (3.0% [n = 146,324] in 1997 to 2.5% [n = 113,097] in 2006, p<.001) and increased within children''s hospitals (11.7% [n = 179,324] in 1997 to 13.5% [n = 209,708] in 2006, p<0.001). In 2006, children with NI accounted for 24.7% (2.1 million) of bed days and 29.0% (US$12.0 billion) of hospital charges within children''s hospitals.

Conclusions

Children with NI account for a substantial proportion of inpatient resources utilized in the US. Their impact is growing within children''s hospitals. We must ensure that the current health care system is staffed, educated, and equipped to serve this growing segment of vulnerable children. Please see later in the article for the Editors'' Summary     

Population Health Impact and Cost-Effectiveness of Tuberculosis Diagnosis with Xpert MTB/RIF: A Dynamic Simulation and Economic Evaluation

Background

The Xpert MTB/RIF test enables rapid detection of tuberculosis (TB) and rifampicin resistance. The World Health Organization recommends Xpert for initial diagnosis in individuals suspected of having multidrug-resistant TB (MDR-TB) or HIV-associated TB, and many countries are moving quickly toward adopting Xpert. As roll-out proceeds, it is essential to understand the potential health impact and cost-effectiveness of diagnostic strategies based on Xpert.

Methods and Findings

We evaluated potential health and economic consequences of implementing Xpert in five southern African countries—Botswana, Lesotho, Namibia, South Africa, and Swaziland—where drug resistance and TB-HIV coinfection are prevalent. Using a calibrated, dynamic mathematical model, we compared the status quo diagnostic algorithm, emphasizing sputum smear, against an algorithm incorporating Xpert for initial diagnosis. Results were projected over 10- and 20-y time periods starting from 2012. Compared to status quo, implementation of Xpert would avert 132,000 (95% CI: 55,000–284,000) TB cases and 182,000 (97,000–302,000) TB deaths in southern Africa over the 10 y following introduction, and would reduce prevalence by 28% (14%–40%) by 2022, with more modest reductions in incidence. Health system costs are projected to increase substantially with Xpert, by US$460 million (294–699 million) over 10 y. Antiretroviral therapy for HIV represents a substantial fraction of these additional costs, because of improved survival in TB/HIV-infected populations through better TB case-finding and treatment. Costs for treating MDR-TB are also expected to rise significantly with Xpert scale-up. Relative to status quo, Xpert has an estimated cost-effectiveness of US$959 (633–1,485) per disability-adjusted life-year averted over 10 y. Across countries, cost-effectiveness ratios ranged from US$792 (482–1,785) in Swaziland to US$1,257 (767–2,276) in Botswana. Assessing outcomes over a 10-y period focuses on the near-term consequences of Xpert adoption, but the cost-effectiveness results are conservative, with cost-effectiveness ratios assessed over a 20-y time horizon approximately 20% lower than the 10-y values.

Conclusions

Introduction of Xpert could substantially change TB morbidity and mortality through improved case-finding and treatment, with more limited impact on long-term transmission dynamics. Despite extant uncertainty about TB natural history and intervention impact in southern Africa, adoption of Xpert evidently offers reasonable value for its cost, based on conventional benchmarks for cost-effectiveness. However, the additional financial burden would be substantial, including significant increases in costs for treating HIV and MDR-TB. Given the fundamental influence of HIV on TB dynamics and intervention costs, care should be taken when interpreting the results of this analysis outside of settings with high HIV prevalence. Please see later in the article for the Editors'' Summary     

Efficiency and power as a function of sequence coverage, SNP array density, and imputation

High coverage whole genome sequencing provides near complete information about genetic variation. However, other technologies can be more efficient in some settings by (a) reducing redundant coverage within samples and (b) exploiting patterns of genetic variation across samples. To characterize as many samples as possible, many genetic studies therefore employ lower coverage sequencing or SNP array genotyping coupled to statistical imputation. To compare these approaches individually and in conjunction, we developed a statistical framework to estimate genotypes jointly from sequence reads, array intensities, and imputation. In European samples, we find similar sensitivity (89%) and specificity (99.6%) from imputation with either 1× sequencing or 1 M SNP arrays. Sensitivity is increased, particularly for low-frequency polymorphisms (MAF < 5%), when low coverage sequence reads are added to dense genome-wide SNP arrays--the converse, however, is not true. At sites where sequence reads and array intensities produce different sample genotypes, joint analysis reduces genotype errors and identifies novel error modes. Our joint framework informs the use of next-generation sequencing in genome wide association studies and supports development of improved methods for genotype calling.     

Chapter 13: Mining Electronic Health Records in the Genomics Era

Abstract: The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information. However, EHRs are designed primarily for clinical care, not research, so reuse of clinical EHR data for research purposes can be challenging. Difficulties in use of EHR data include: data availability, missing data, incorrect data, and vast quantities of unstructured narrative text data. Structured information includes billing codes, most laboratory reports, and other variables such as physiologic measurements and demographic information. Significant information, however, remains locked within EHR narrative text documents, including clinical notes and certain categories of test results, such as pathology and radiology reports. For relatively rare observations, combinations of simple free-text searches and billing codes may prove adequate when followed by manual chart review. However, to extract the large cohorts necessary for genome-wide association studies, natural language processing methods to process narrative text data may be needed. Combinations of structured and unstructured textual data can be mined to generate high-validity collections of cases and controls for a given condition. Once high-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and validation. Since EHR data includes a broad sampling of clinically-relevant phenotypic information, it may enable multiple genomic investigations upon a single set of genotyped individuals. This chapter reviews several examples of phenotype extraction and their application to genetic research, demonstrating a viable future for genomic discovery using EHR-linked data.

What to Learn in This Chapter

• Describe the types of information available in Electronic Health Records (EHRs), and the relative sensitivity and positive predictive value of each
• Describe the difference between unstructured and structured information in the EHR
• Describe methods for developing accurate phenotype algorithms that integrate structured and unstructured EHR information, and the roles played by billing codes, laboratory values, medication data, and natural language processing
• Describe recent uses of EHR-derived phenotypes to study genome-phenome relationships
• Describe the cost advantages unique to EHR-linked biobanks, and the ability to reuse genetic data for many studies
• Understand the role of EHRs to enable phenome-wide association studies of genetic variants

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

SCF ubiquitin ligases in the maintenance of genome stability

In response to genotoxic stress, eukaryotic cells activate the DNA damage response (DDR), a series of pathways that coordinate cell cycle arrest and DNA repair to prevent deleterious mutations. In addition, cells possess checkpoint mechanisms that prevent aneuploidy by regulating the number of centrosomes and spindle assembly. Among these mechanisms, ubiquitin-mediated degradation of key proteins has an important role in the regulation of the DDR, centrosome duplication and chromosome segregation. This review discusses the functions of a group of ubiquitin ligases, the SCF (SKP1-CUL1-F-box protein) family, in the maintenance of genome stability. Given that general proteasome inhibitors are currently used as anticancer agents, a better understanding of the ubiquitylation of specific targets by specific ubiquitin ligases may result in improved cancer therapeutics.