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

Assessing the adequacy of self-reported alcohol abuse measurement across time and ethnicity: cross-cultural equivalence across Hispanics and Caucasians in 1992, non-equivalence in 2001–2002

<p>Abstract</p> <p>Background</p> <p>Do estimates of alcohol abuse reflect true levels across United States Hispanics and non-Hispanic Caucasians, or does culturally-based, systematic measurement error (i.e., measurement bias) affect estimates? Likewise, given that recent estimates suggest alcohol abuse has increased among US Hispanics, the field should also ask, "Does cross-ethnic change in alcohol abuse across time reflect true change or does measurement bias influence change estimates?"</p> <p>Methods</p> <p>To address these questions, I used confirmatory factor analyses for ordered-categorical measures to probe for measurement bias on two large, standardized, nationally representative, US surveys of alcohol abuse conducted in 1992 and 2001–2002. In 2001–2002, analyses investigated whether 10 items operationalizing DSM-IV alcohol abuse provided equivalent measurement across Hispanic (<it>n </it>= 4,893) and non-Hispanic Caucasians (<it>n </it>= 16,480). In 1992, analyses examined whether a reduced 6 item item-set provided equivalent measurement among 834 Hispanic and 14,8335 non-Hispanic Caucasians.</p> <p>Results</p> <p>In 1992, findings demonstrated statistically significant measurement bias for two items. However, sensitivity analyses showed that item-level bias did not appreciably bias item-set based alcohol abuse estimates among this cohort. For 2001–2002, results demonstrated statistically significant bias for seven items, suggesting caution regarding the cross-ethnic equivalence of alcohol abuse estimates among the current US Hispanic population. Sensitivity analyses indicated that item-level differences <it>did </it>erroneously impact alcohol abuse rates in 2001–2002, underestimating rates among Hispanics relative to Caucasians.</p> <p>Conclusion</p> <p>1992's item-level findings suggest that estimates of drinking related social or legal problems may underestimate these specific problems among Hispanics. However, impact analyses indicated no appreciable effect on alcohol abuse estimates resulting from the item-set. Efforts to monitor change in alcohol abuse diagnoses among the Hispanic community can use 1992 estimates as a valid baseline. In 2001–2002, item-level measurement bias on seven items did affect item-set based estimates. Bias underestimated Hispanics' self-reported alcohol abuse levels relative to non-Hispanic Caucasians. Given the cross-ethnic equivalence of 1992 estimates, bias in 2001–2002 speciously minimizes current increases in drinking behavior evidenced among Hispanics. Findings call for increased public health efforts among the Hispanic community and underscore the necessity for cultural sensitivity when generalizing measures developed in the majority to minorities.</p

The quality of cardiovascular disease care for adolescents with kidney disease: a Midwest Pediatric Nephrology Consortium study

Cardiovascular disease is the leading cause of increased mortality for adolescents with advanced kidney disease. The quality of preventive cardiovascular care may impact long-term outcomes for these patients

Advancing PROMIS’s methodology: results of the Third Patient-Reported Outcomes Measurement Information System (PROMIS ® ) Psychometric Summit

In 2002, the NIH launched the ‘Roadmap for Medical Research’. The Patient-Reported Outcomes Measurement Information System (PROMIS®) is one of the Roadmap’s key aspects. To create the next generation of patient-reported outcome measures, PROMIS utilizes item response theory (IRT) and computerized adaptive testing. In 2009, the NIH funded the second wave of PROMIS studies (PROMIS II). PROMIS II studies continue PROMIS’s agenda, but also include new features, including longitudinal analyses and more sociodemographically diverse samples. PROMIS II also includes increased emphasis on pediatric populations and evaluation of PROMIS item banks for clinical research and population science. These aspects bring new psychometric challenges. To address this, investigators associated with PROMIS gathered at the Third Psychometric Summit in September 2010 to identify, describe and discuss pressing psychometric issues and new developments in the field, as well as make analytic recommendations for PROMIS. The summit addressed five general themes: linking, differential item functioning, dimensionality, IRT models for longitudinal applications and new IRT software. In this article, we review the discussions and presentations that occurred at the Third PROMIS Psychometric Summit

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum

Fitting multilevel models in complex survey data with design weights: Recommendations

Abstract Background Multilevel models (MLM) offer complex survey data analysts a unique approach to understanding individual and contextual determinants of public health. However, little summarized guidance exists with regard to fitting MLM in complex survey data with design weights. Simulation work suggests that analysts should scale design weights using two methods and fit the MLM using unweighted and scaled-weighted data. This article examines the performance of scaled-weighted and unweighted analyses across a variety of MLM and software programs. Methods Using data from the 2005–2006 National Survey of Children with Special Health Care Needs (NS-CSHCN: n = 40,723) that collected data from children clustered within states, I examine the performance of scaling methods across outcome type (categorical vs. continuous), model type (level-1, level-2, or combined), and software (Mplus, MLwiN, and GLLAMM). Results Scaled weighted estimates and standard errors differed slightly from unweighted analyses, agreeing more with each other than with unweighted analyses. However, observed differences were minimal and did not lead to different inferential conclusions. Likewise, results demonstrated minimal differences across software programs, increasing confidence in results and inferential conclusions independent of software choice. Conclusion If including design weights in MLM, analysts should scale the weights and use software that properly includes the scaled weights in the estimation.</p