Visualizing statistical significance of disease clusters using cartograms.

BACKGROUND: Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological mapping. However, we do not have existing guidelines for visual assessment of statistical uncertainty. To address this shortcoming, we develop techniques for visual determination of statistical significance of clusters spanning one or more districts on a cartogram. We developed the techniques within a geovisual analytics framework that does not rely on automated significance testing, and can therefore facilitate visual analysis to detect clusters that automated techniques might miss. RESULTS: On a cartogram of the at-risk population, the statistical significance of a disease cluster is determinate from the rate, area and shape of the cluster under standard hypothesis testing scenarios. We develop formulae to determine, for a given rate, the area required for statistical significance of a priori and a posteriori designated regions under certain test assumptions. Uniquely, our approach enables dynamic inference of aggregate regions formed by combining individual districts. The method is implemented in interactive tools that provide choropleth mapping, automated legend construction and dynamic search tools to facilitate cluster detection and assessment of the validity of tested assumptions. A case study of leukemia incidence analysis in California demonstrates the ability to visually distinguish between statistically significant and insignificant regions. CONCLUSION: The proposed geovisual analytics approach enables intuitive visual assessment of statistical significance of arbitrarily defined regions on a cartogram. Our research prompts a broader discussion of the role of geovisual exploratory analyses in disease mapping and the appropriate framework for visually assessing the statistical significance of spatial clusters.

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century.

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.

Spatializing Area-Based Measures of Neighborhood Characteristics for Multilevel Regression Analyses: An Areal Median Filtering Approach.

Area-based measures of neighborhood characteristics simply derived from enumeration units (e.g., census tracts or block groups) ignore the potential of spatial spillover effects, and thus incorporating such measures into multilevel regression models may underestimate the neighborhood effects on health. To overcome this limitation, we describe the concept and method of areal median filtering to spatialize area-based measures of neighborhood characteristics for multilevel regression analyses. The areal median filtering approach provides a means to specify or formulate "neighborhoods" as meaningful geographic entities by removing enumeration unit boundaries as the absolute barriers and by pooling information from the neighboring enumeration units. This spatializing process takes into account for the potential of spatial spillover effects and also converts aspatial measures of neighborhood characteristics into spatial measures. From a conceptual and methodological standpoint, incorporating the derived spatial measures into multilevel regression analyses allows us to more accurately examine the relationships between neighborhood characteristics and health. To promote and set the stage for informative research in the future, we provide a few important conceptual and methodological remarks, and discuss possible applications, inherent limitations, and practical solutions for using the areal median filtering approach in the study of neighborhood effects on health.

Age-Dependent Differences in Frequent Mental Distress (FMD) of US Older Adults Living in Multigenerational Families versus Living Alone.

Frequent mental distress (FMD) is prevalent among older Americans, but less is known about disparities in FMD of older adults living in multigenerational families versus living alone. We pooled cross-sectional data (unweighted, n = 126,144) from the Behavioral Risk Factor Surveillance System (BRFSS) between 2016 and 2020 and compared FMD (≥14 poor mental health days in the past 30 days = 1; 0 otherwise) of older adults (≥65 years) living in multigenerational families versus living alone in 36 states. After controlling for covariates, findings indicate 23% lower odds of FMD among older adults living in multigenerational households compared to counterparts living alone (adjusted odds ratio (AOR): 0.77; 95% confidence interval (CI): 0.60, 0.99). Findings also show that the reduction in the odds of FMD with each 5 year increase in age was larger among older adults living in multigenerational families by 18% (AOR: 0.56; 95% CI: 0.46, 0.70) compared to older adults living alone (AOR: 0.74; 95% CI: 0.71, 0.77), and this difference was significant at the 5% significance level. Multigenerational living may have a protective association with FMD among older adults. Further research is needed to identify multigenerational family and non-kin factors that translate into mental health advantages for older adults.

Empirical evidence supporting the inclusion of multi-axes segregation in assessing US county health.

To facilitate community action toward health equity, the County Health Rankings & Roadmaps program (CHR&R) assigns health rankings to US counties. The CHR&R conceptual model considers White-Black and White-non-White dissimilarity values to represent residential segregation as part of the family and social support subcomponent. As the US is greying and becoming more multi-racial-ethnic, the two-group White-centered segregation measures are inadequate to capture segregation among population subgroups in the US. Thus, we evaluate the relevancy of segregation measures that consider multiple racial, ethnic, and age groups in assessing US county health. Besides using the two-group dissimilarity index to measure White-centered racial segregation as conceptualized by CHR&R, the study also uses the multi-group generalized dissimilarity index to measure racial-ethnic-age segregation by counties, employing both aspatial and spatial versions of these measures. These indices are computed for counties using the 2015-2019 American Community Survey data at the census tract level. Descriptive statistics and regressions controlling for sociodemographic factors and healthcare access are used to assess the contributions of individual segregation measures to mortality (life expectancy, years of potential life lost and premature mortality) and morbidity (frequent mental distress, frequent physical distress, and low birth weight) indicators representing county health. Overall, correlations between these indicators and most segregation measures are significant but weak. Regression results show that many segregation measures are not significantly related to mortality indicators, but most are significantly associated with morbidity indicators, with the magnitudes of these associations higher for the multi-group racial-ethnic-age segregation index and its spatial version. Results provide evidence that racial-ethnic-age segregation is associated with county-level morbidity and that spatial measures capturing segregation of multiple population axes should be considered for ranking county health.

Changing Age Segregation in the US: 1990 to 2010.

Age segregation adversely impacts health and wellbeing. Prior studies, although limited, report increasing age segregation of the US. However, these studies are dated, do not comprehensively examine the spatiotemporal patterns and the correlates of intergenerational segregation, or suffer from methodological limitations. To address these gaps, we assess the spatiotemporal patterns of age segregation between 1990 and 2010 using census-tract data to compute the dissimilarity index (D) at the national, state, and county levels. Results contradict previous findings, providing robust evidence of decreasing age segregation for most parts of the country and across geographical levels. We also examine factors explaining adult-older adult segregation across counties between 1990 and 2010. Higher levels of rurality indicated lower levels of adult-older adult segregation, but this association diminished over time. Percent of older adults and percent of population in group quarters were inversely related to adult-older adult segregation, contrary to results from previous decades.

Issues in the Current Practices of Spatial Cluster Detection and Exploring Alternative Methods.

Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value (cold spot) spatial clusters for various purposes. However, these popular tools are based on the concept of spatial autocorrelation or association (SA), but do not explicitly consider if values are high or low enough to deserve attention. Resultant clusters may not include areas with extreme values that practitioners often want to identify when using these tools. Additionally, these tools are based on statistics that assume observed values or estimates are highly accurate with error levels that can be ignored or are spatially uniform. In this article, problems associated with these popular SA-based cluster detection tools were illustrated. Alternative hot spot-cold spot detection methods considering estimate error were explored. The class separability classification method was demonstrated to produce useful results. A heuristic hot spot-cold spot identification method was also proposed. Based on user-determined threshold values, areas with estimates exceeding the thresholds were treated as seeds. These seeds and neighboring areas with estimates that were not statistically different from those in the seeds at a given confidence level constituted the hot spots and cold spots. Results from the heuristic method were intuitively meaningful and practically valuable.

Spreading of COVID-19: Density matters.

Physical distancing has been argued as one of the effective means to combat the spread of COVID-19 before a vaccine or therapeutic drug becomes available. How far people can be spatially separated is partly behavioral but partly constrained by population density. Most models developed to predict the spread of COVID-19 in the U.S. do not include population density explicitly. This study shows that population density is an effective predictor of cumulative infection cases in the U.S. at the county level. Daily cumulative cases by counties are converted into 7-day moving averages. Treating the weekly averages as the dependent variable and the county population density levels as the explanatory variable, both in logarithmic scale, this study assesses how population density has shaped the distributions of infection cases across the U.S. from early March to late May, 2020. Additional variables reflecting the percentages of African Americans, Hispanic-Latina, and older adults in logarithmic scale are also included. Spatial regression models with a spatial error specification are also used to account for the spatial spillover effect. Population density alone accounts for 57% of the variation (R-squared) in the aspatial models and up to 76% in the spatial models. Adding the three population subgroup percentage variables raised the R-squared of the aspatial models to 72% and the spatial model to 84%. The influences of the three population subgroups were substantial, but changed over time, while the contributions of population density have been quite stable after the first several weeks, ascertaining the importance of population density in shaping the spread of infection in individual counties, and in their neighboring counties. Thus, population density and sizes of vulnerable population subgroups should be explicitly included in transmission models that predict the impacts of COVID-19, particularly at the sub-county level.

Measuring global spatial autocorrelation with data reliability information.

Assessing spatial autocorrelation (SA) of statistical estimates such as means is a common practice in spatial analysis and statistics. Popular spatial autocorrelation statistics implicitly assume that the reliability of the estimates is irrelevant. Users of these SA statistics also ignore the reliability of the estimates. Using empirical and simulated data, we demonstrate that current SA statistics tend to overestimate SA when errors of the estimates are not considered. We argue that when assessing SA of estimates with error, it is essentially comparing distributions in terms of their means and standard errors. Using the concept of the Bhattacharyya coefficient, we proposed the Spatial Bhattacharyya coefficient (SBC) and suggested that it should be used to evaluate the SA of estimates together with their errors. A permutation test is proposed to evaluate its significance. We concluded that the SBC more accurately and robustly reflects the magnitude of SA than traditional SA measures by incorporating errors of estimates in the evaluation.

Translational Health Disparities Research in a Data-Rich World.

Background: Despite decades of research and interventions, significant health disparities persist. Seventeen years is the estimated time to translate scientific discoveries into public health action. This Narrative Review argues that the translation process could be accelerated if representative data were gathered and used in more innovative and efficient ways. Methods: The National Institute on Minority Health and Health Disparities led a multiyear visioning process to identify research opportunities designed to frame the next decade of research and actions to improve minority health and reduce health disparities. "Big data" was identified as a research opportunity and experts collaborated on a systematic vision of how to use big data both to improve the granularity of information for place-based study and to efficiently translate health disparities research into improved population health. This Narrative Review is the result of that collaboration. Results: Big data could enhance the process of translating scientific findings into reduced health disparities by contributing information at fine spatial and temporal scales suited to interventions. In addition, big data could fill pressing needs for health care system, genomic, and social determinant data to understand mechanisms. Finally, big data could lead to appropriately personalized health care for demographic groups. Rich new resources, including social media, electronic health records, sensor information from digital devices, and crowd-sourced and citizen-collected data, have the potential to complement more traditional data from health surveys, administrative data, and investigator-initiated registries or cohorts. This Narrative Review argues for a renewed focus on translational research cycles to accomplish this continual assessment. Conclusion: The promise of big data extends from etiology research to the evaluation of large-scale interventions and offers the opportunity to accelerate translation of health disparities studies. This data-rich world for health disparities research, however, will require continual assessment for efficacy, ethical rigor, and potential algorithmic or system bias.

Over-expression of a zeatin O-glucosylation gene in maize leads to growth retardation and tasselseed formation.

To study the effects of cytokinin O-glucosylation in monocots, maize (Zea mays L.) transformants harbouring the ZOG1 gene (encoding a zeatin O-glucosyltransferase from Phaseolus lunatus L.) under the control of the constitutive ubiquitin (Ubi) promoter were generated. The roots and leaves of the transformants had greatly increased levels of zeatin-O-glucoside. The vegetative characteristics of hemizygous and homozygous Ubi:ZOG1 plants resembled those of cytokinin-deficient plants, including shorter stature, thinner stems, narrower leaves, smaller meristems, and increased root mass and branching. Transformant leaves had a higher chlorophyll content and increased levels of active cytokinins compared with those of non-transformed sibs. The Ubi:ZOG1 plants exhibited delayed senescence when grown in the spring/summer. While hemizygous transformants had reduced tassels with fewer spikelets and normal viable pollen, homozygotes had very small tassels and feminized tassel florets, resembling tasselseed phenotypes. Such modifications of the reproductive phase were unexpected and demonstrate a link between cytokinins and sex-specific floral development in monocots.

Combating against methicillin-resistant Staphylococcus aureus - two fatty acids from Purslane (Portulaca oleracea†L.) exhibit synergistic effects with erythromycin.

OBJECTIVES: The aims of this study were to identify the active ingredients from Portulaca oleracea L. (PO) that could provide synergism with antibiotics against methicillin-resistant Staphylococcus aureus (MRSA) and their possible mechanisms of resistance inhibition. METHODS: High-speed counter-current chromatography (HSCCC) coupled with gas chromatography-mass spectrometry and a panel of laboratory MRSA strains were used for checkerboard and efflux inhibitory assays. KEY FINDINGS: Linoleic and oleic acids were identified from HSCCC fraction 18 of PO with synergistic antibacterial activity when combined with erythromycin against RN4220/pUL5054. Ethidium bromide efflux inhibitory studies revealed that linoleic and oleic acids may interfere the activity of MsrA pump. By comparing among a panel of linoleic and oleic acids analogues, unsaturated fatty acids in salt form with cis configuration and an increase in number of double bonds were found to further increase the antibacterial activity when used alone or in combination with antibiotics. CONCLUSION: This study reported for the first time that two active ingredients, namely linoleic and oleic acids, were identified from PO with synergistic antibacterial activity when combined with erythromycin against MRSA RN4220/pUL5054 and possibly act by inhibiting the efflux pumps of the bacteria cells.

Confirmation of linkage of hereditary partial lipodystrophy to chromosome 1q21-22.

Familial lipodystrophy is a genetically heterogeneous set of disorders characterized by a total or partial absence of subcutaneous fat, diabetes mellitus or impaired glucose tolerance, hyperlipidemia, and hypermetabolism [Senior and Gellis, 1964]. One subtype, familial partial lipodystrophy Dunnigan (FPLD), is a rare autosomal dominant trait that results in an gradual loss of subcutaneous fat in the lower trunk and limbs, Type V hyperlipoproteinemia, hypertriglyceridemia, and insulin-resistant diabetes. Previous reports of this condition have been limited to case reports or very small families. Recently, Peters et al. reported on linkage of five families of Western European descent to a 5.3 cM region on chromosome 1q21-22 between the flanking markers D1S305 and D1S1600 [Peters et al., 1998: Nat Genet 18:292-295]. We performed linkage and haplotype analysis using highly polymorphic, microsatellite markers on a large, multigeneration Caucasian kindred of German ancestry. The maximum two-point lod score achieved was 4.96 at theta(max) = 0 for marker D1S2721. Multipoint analysis gave an overall maximum lod score of 6.27 near marker D1S2721. The results of the haplotype analysis support the minimal candidate region as reported by Peters et al.

Polysomnographic studies in amyotrophic lateral sclerosis.

We retrospectively reviewed 17 polysomnograms (PSG) in symptomatic amyotrophic lateral sclerosis (ALS) patients to assess the type and frequency of sleep disordered events and correlated these findings with pulmonary function tests (PFTs), presenting complaints, presence of bulbar dysfunction, and response to bi-level positive airway pressure (PAP) treatment. PSG revealed abnormalities in 16 patients. Complaints of orthopnea, daytime sleepiness (but not morning headaches) and a low negative inspiratory force (NIF) correlated with sleep disruption. However, neither the forced vital capacity (FVC) nor the NIF reliably predicted any specific PSG finding. Twelve of 13 patients treated with bi-level PAP responded favorably. Since the response to bi-level PAP is frequently gratifying, PSG should strongly be considered in ALS patients with suspected sleep disturbances.

Myoglobinuria.

Myoglobinuria refers to an abnormal pathologic state in which an excessive amount of myoglobin is found in the urine, imparting a cola-like hue, usually in association with myonecrosis and a clinical picture of weakness, myalgias, and edema. Myoglobinuria is produced by multiple causes: any condition that accelerates the use or interferes with the availability of oxygen or energy substrates to muscle cells can result in myoglobinuria, as can events that produce direct muscle injury, either mechanical or chemical. Acute renal failure is the most serious complication, which can be prevented by prompt, aggressive treatment. In patients surviving acute attacks, recovery of muscle and renal function is usually complete.

Spasmodic dysphonia symptoms as initial presentation of amyotrophic lateral sclerosis.

A patient initially diagnosed with adductor-type spasmodic dysphonia was referred for botulinum toxin (Botox) injections, but found on subsequent evaluation to have amyotrophic lateral sclerosis, and therefore Botox was not administered. This unique case underscores the need to delay botulinum toxin treatments in any patient with recent onset symptoms, and to obtain thorough motor speech and voice, otolaryngologic, and neurologic evaluation in all patients prior to consideration for injection.

Segmental near nerve sensory conduction studies of the medical and lateral plantar nerve.

Tarsal Tunnel Syndrome (TTS) can be difficult to diagnose: electrophysiologic corroboration is important and has therapeutic implications. Conventional electrodiagnostic techniques are insensitive: motor latency abnormalities exist in only 52%; sensory responses are frequently absent (a nonlocalizing finding). Additionally, previously described near nerve techniques do not isolate conduction velocity (CV) measurement to the short segment across the flexor retinaculum (FR), which would theoretically improve sensitivity. We describe a technique which allows for the determination of segmental sensory CVs of the medial (MP) and lateral (LP) plantar nerves, both below (BFR) and across (AFR) the FR. Seventeen normal patients (age 22-45) were studied. Near nerve recording electrodes were positioned close to the specified nerve below and above the FR. Ring electrode stimulation (RES) of digits I (MP) or V (LP) and direct near nerve stimulation (NNS) BFR were performed. With RES digit I (n = 17), mean CV (toe to BFR) was 39.0 +/- 7.1 m/s; CV (AFR) 47.9 +/- 6.2 m/s. CV (AFR) following NNS (MP) (n = 16) was 49.4 +/- 5.1 m/s. With RES digit V (n = 10), mean CV (toe to BFR) was 36.4 +/- 3.4 m/s; CV (AFR) 57.5 +/- 6.9 m/s. CV (AFR) with NNS (LP) (n = 14) was 59.8 +/- 6.2 m/s. In conclusion, segmental MP and LP sensory CVs can be reliably obtained with near nerve technique. This approach may improve the diagnostic sensitivity of EMG in TTS.