The Mental and Physical Health of Mothers of Children with Special Health Care Needs in the United States

OBJECTIVE: To determine the prevalence of poor mental and physical health among mothers of children with special health care needs (CSHCN) and to determine the association between maternal health and the child\u27s number of special health care needs (SHCN) and severity of ability limitation. METHODS: We used the combined 2016-2018 National Survey of Children\u27s Health Dataset of 102,341 children ages 0-17 including 23,280 CSHCN. We used regression models to examine the associations of a child\u27s number of SHCN and ability limitations with maternal health. RESULTS: Twice as many mothers of CSHCN had poor mental and physical health compared to non-CSHCN (mental 10.3% vs. 4.0%, p \u3c .001; physical 11.9% vs 5.0%, p \u3c .001). In regression models, increased number of SHCN and severity of activity limitations were associated with significantly increased odds of poor maternal health. CONCLUSIONS FOR PRACTICE: Mothers of CSHCN have worse health compared to mothers of non-CSHCN, especially those who experience social disadvantage and those with children with complex SHCN or severe ability limitations. Interventions to improve the health of these particularly vulnerable caregivers of CSHCN are warranted

A roadmap to reduce information inequities in disability with digital health and natural language processing

People with disabilities disproportionately experience negative health outcomes. Purposeful analysis of information on all aspects of the experience of disability across individuals and populations can guide interventions to reduce health inequities in care and outcomes. Such an analysis requires more holistic information on individual function, precursors and predictors, and environmental and personal factors than is systematically collected in current practice. We identify three key information barriers to more equitable information: (1) a lack of information on contextual factors that affect a person’s experience of function; (2) under-emphasis of the patient’s voice, perspective, and goals in the electronic health record; and (3) a lack of standardized locations in the electronic health record to record observations of function and context. Through analysis of rehabilitation data, we have identified ways to mitigate these barriers through the development of digital health technologies to better capture and analyze information about the experience of function. We propose three directions for future research on using digital health technologies, particularly natural language processing (NLP), to facilitate capturing a more holistic picture of a patient’s unique experience: (1) analyzing existing information on function in free text documentation, (2) developing new NLP-driven methods to collect information on contextual factors, and (3) collecting and analyzing patient-reported descriptions of personal perceptions and goals. Multidisciplinary collaboration between rehabilitation experts and data scientists to advance these research directions will yield practical technologies to help reduce inequities and improve care for all populations

A roadmap to reduce information inequities in disability with digital health and natural language processing.

People with disabilities disproportionately experience negative health outcomes. Purposeful analysis of information on all aspects of the experience of disability across individuals and populations can guide interventions to reduce health inequities in care and outcomes. Such an analysis requires more holistic information on individual function, precursors and predictors, and environmental and personal factors than is systematically collected in current practice. We identify 3 key information barriers to more equitable information: (1) a lack of information on contextual factors that affect a person's experience of function; (2) underemphasis of the patient's voice, perspective, and goals in the electronic health record; and (3) a lack of standardized locations in the electronic health record to record observations of function and context. Through analysis of rehabilitation data, we have identified ways to mitigate these barriers through the development of digital health technologies to better capture and analyze information about the experience of function. We propose 3 directions for future research on using digital health technologies, particularly natural language processing (NLP), to facilitate capturing a more holistic picture of a patient's unique experience: (1) analyzing existing information on function in free text documentation; (2) developing new NLP-driven methods to collect information on contextual factors; and (3) collecting and analyzing patient-reported descriptions of personal perceptions and goals. Multidisciplinary collaboration between rehabilitation experts and data scientists to advance these research directions will yield practical technologies to help reduce inequities and improve care for all populations

The Mental and Physical Health of Mothers of Children with Special Health Care Needs in the United States

OBJECTIVE: To determine the prevalence of poor mental and physical health among mothers of children with special health care needs (CSHCN) and to determine the association between maternal health and the child\u27s number of special health care needs (SHCN) and severity of ability limitation. METHODS: We used the combined 2016-2018 National Survey of Children\u27s Health Dataset of 102,341 children ages 0-17 including 23,280 CSHCN. We used regression models to examine the associations of a child\u27s number of SHCN and ability limitations with maternal health. RESULTS: Twice as many mothers of CSHCN had poor mental and physical health compared to non-CSHCN (mental 10.3% vs. 4.0%, p \u3c .001; physical 11.9% vs 5.0%, p \u3c .001). In regression models, increased number of SHCN and severity of activity limitations were associated with significantly increased odds of poor maternal health. CONCLUSIONS FOR PRACTICE: Mothers of CSHCN have worse health compared to mothers of non-CSHCN, especially those who experience social disadvantage and those with children with complex SHCN or severe ability limitations. Interventions to improve the health of these particularly vulnerable caregivers of CSHCN are warranted

Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding

We describe the genome sequencing of an anonymous individual of African origin using a novel ligation-based sequencing assay that enables a unique form of error correction that improves the raw accuracy of the aligned reads to >99.9%, allowing us to accurately call SNPs with as few as two reads per allele. We collected several billion mate-paired reads yielding ∼18× haploid coverage of aligned sequence and close to 300× clone coverage. Over 98% of the reference genome is covered with at least one uniquely placed read, and 99.65% is spanned by at least one uniquely placed mate-paired clone. We identify over 3.8 million SNPs, 19% of which are novel. Mate-paired data are used to physically resolve haplotype phases of nearly two-thirds of the genotypes obtained and produce phased segments of up to 215 kb. We detect 226,529 intra-read indels, 5590 indels between mate-paired reads, 91 inversions, and four gene fusions. We use a novel approach for detecting indels between mate-paired reads that are smaller than the standard deviation of the insert size of the library and discover deletions in common with those detected with our intra-read approach. Dozens of mutations previously described in OMIM and hundreds of nonsynonymous single-nucleotide and structural variants in genes previously implicated in disease are identified in this individual. There is more genetic variation in the human genome still to be uncovered, and we provide guidance for future surveys in populations and cancer biopsies

A small-cell lung cancer genome with complex signatures of tobacco exposure.

Cancer is driven by mutation. Worldwide, tobacco smoking is the principal lifestyle exposure that causes cancer, exerting carcinogenicity through >60 chemicals that bind and mutate DNA. Using massively parallel sequencing technology, we sequenced a small-cell lung cancer cell line, NCI-H209, to explore the mutational burden associated with tobacco smoking. A total of 22,910 somatic substitutions were identified, including 134 in coding exons. Multiple mutation signatures testify to the cocktail of carcinogens in tobacco smoke and their proclivities for particular bases and surrounding sequence context. Effects of transcription-coupled repair and a second, more general, expression-linked repair pathway were evident. We identified a tandem duplication that duplicates exons 3-8 of CHD7 in frame, and another two lines carrying PVT1-CHD7 fusion genes, indicating that CHD7 may be recurrently rearranged in this disease. These findings illustrate the potential for next-generation sequencing to provide unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with cancer

A map of human genome variation from population-scale sequencing

The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10−8 per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic researc