Explore the vast range of titles available.

The shape of the relationship between a continuous exposure variable and a binary disease variable is often central to epidemiologic investigations. This article investigates a number of issues surrounding inference and the shape of the relationship. Presuming that the relationship can be expressed in terms of regression coefficients and a shape parameter, we investigate how well the shape can be inferred in settings which might typify epidemiologic investigations and risk assessment. We also consider a suitable definition of the median effect of exposure, and investigate how precisely this can be inferred. This is done both in the case of using a model acknowledging uncertainty about the shape parameter and in the case of ignoring this uncertainty and using a two-step method, where in step one we transform the predictor and in step two we fit a simple logistic model with transformed predictor. All these investigations require a family of exposure-disease relationships indexed by a shape parameter. For this purpose, we employ a family based on the Box–Cox transformation. La forme de la relation entre une variable continue d’exposition et une variable binaire de maladie est souvent centrale dans les enquêtes épidémiologiques. Les auteurs explorent certains problèmes liés à l’inférence et à la forme de cette relation. En supposant que la relation peut s’exprimer par des coefficients de régression, ils étudient à quel point les formes typiquement attendues dans les enquêtes épidémiologiques et l’évaluation des risques peuvent être inférées. Ils considèrent également une définition appropriée pour l’effet médian d’exposition, et investiguent à quel point elle peut être inférée. Ils considèrent deux cas, l’un en reconnaissant l’incertitude par rapport au paramètre de forme, et l’autre en ignorant cette incertitude dans le cadre d’une méthode en deux étapes consistant en une transformation des prédicteurs, suivie de l’ajustement d’une régression logistique sur ces variables transformées. Ces investigations requièrent une famille de relations entre l’exposition et la maladie indexée par un paramètre de forme. Les auteur utilisent une famille basée sur la transformée de Box-Cox.

We examined racial/ethnic disparities for COVID-19 seroconversion and hospitalization within a prospective cohort (n = 6,740) in the United States enrolled in March 2020 and followed-up through October 2021. Potential SARS-CoV-2 exposure, susceptibility to COVID-19 complications, and access to healthcare varied by race/ethnicity. Hispanic and Black non-Hispanic participants had more exposure risk and difficulty with healthcare access than white participants. Participants with more exposure had greater odds of seroconversion. Participants with more susceptibility and more barriers to healthcare had greater odds of hospitalization. Race/ethnicity positively modified the association between susceptibility and hospitalization. Findings might help to explain the disproportionate burden of SARS-CoV-2 infections and complications among Hispanic/Latino/a and Black non-Hispanic persons. Primary and secondary prevention efforts should address disparities in exposure, vaccination, and treatment for COVID-19.

At least 246 cases of coronavirus disease (COVID-19) have been linked to nightclubs in Seoul, South Korea. During the April 30-May 5 holiday, young adults from across the country who visited nightclubs in Seoul contracted COVID-19 and spread it nationally. Nightclubs were temporarily closed to limit COVID-19 spread.

Understanding how landscape, host, and pathogen traits contribute to disease exposure requires systematic evaluations of pathogens within and among host species and geographic regions. The relative importance of these attributes is critical for management of wildlife and mitigating domestic animal and human disease, particularly given rapid ecological changes, such as urbanization. We screened >1000 samples from sympatric populations of puma (Puma concolor), bobcat (Lynx rufus), and domestic cat (Felis catus) across urban gradients in six sites, representing three regions, in North America for exposure to a representative suite of bacterial, protozoal, and viral pathogens (Bartonella sp., Toxoplasma gondii, feline herpesvirus‐1, feline panleukopenea virus, feline calicivirus, and feline immunodeficiency virus). We evaluated prevalence within each species, and examined host trait and land cover determinants of exposure; providing an unprecedented analysis of factors relating to potential for infections in domesticated and wild felids. Prevalence differed among host species (highest for puma and lowest for domestic cat) and was greater for indirectly transmitted pathogens. Sex was inconsistently predictive of exposure to directly transmitted pathogens only, and age infrequently predictive of both direct and indirectly transmitted pathogens. Determinants of pathogen exposure were widely divergent between the wild felid species. For puma, suburban land use predicted increased exposure to Bartonella sp. in southern California, and FHV‐1 exposure increased near urban edges in Florida. This may suggest interspecific transmission with domestic cats via flea vectors (California) and direct contact (Florida) around urban boundaries. Bobcats captured near urban areas had increased exposure to T. gondii in Florida, suggesting an urban source of prey. Bobcats captured near urban areas in Colorado and Florida had higher FIV exposure, possibly suggesting increased intraspecific interactions through pile‐up of home ranges. Beyond these regional and pathogen specific relationships, proximity to the wildland–urban interface did not generally increase the probability of disease exposure in wild or domestic felids, emphasizing the importance of local ecological determinants. Indeed, pathogen exposure was often negatively associated with the wildland–urban interface for all felids. Our analyses suggest cross‐species pathogen transmission events around this interface may be infrequent, but followed by self‐sustaining propagation within the new host species.

Sickness behavior is broadly represented in vertebrates, usually in association with the fever response in response to acute infections. The reactions to sickness behavior in a group member or potential group member in humans is quite variable, depending upon circumstances. In animals, the reactions to sickness behavior in a group member or potential group member evoke a specific response that reflects the species-specific lifestyle. Groups of animals can employ varied strategies to reduce or address exposure to sickness. Most of these have scarcely been studied in nature from a disease perspective: (1) adjusting exposure to sick conspecifics or contaminated areas; (2) caring for a sick group member; (3) peripheralization and agonistic behaviors to strange non-group conspecifics; and (4) using special strategies at parturition when newborn are healthy but vulnerable. Unexplored in this regard is infanticide, where newborn that are born with very little immunity until they receive antibody-rich colostrum, could be a target of maternal infanticide if they manifest signs of sickness and could be infectious to littermates. The strategies used by different species are highly specific and dependent upon the particular circumstances. What is needed is a more general awareness and consideration of the possibilities that avoiding or adapting to sickness behavior may be driving some social behaviors of animals in nature.

Newcastle disease is a devastating poultry disease that often causes significant economic losses in poultry in the developing countries of Africa, Asia, as well as South and Central America. Velogenic Newcastle disease virus (NDV) outbreaks are associated with high mortalities, which can threaten household livelihoods, especially in the rural areas, and lead to loss of high-quality proteins in the form of meat and eggs, as well as household purchasing power. In this study, we exposed unvaccinated Ghanaian and Tanzanian chickens of six local ecotypes to velogenic NDV strains, measured NDV response traits, sequenced their DNA on a genotyping-by-sequencing platform, and performed variance component analyses. The collected phenotypes included: growth rates (pre- and post-exposure); lesion scores (gross lesion severity) in the trachea, proventriculus, intestine, and cecal tonsils; natural antibody levels; anti-NDV antibody levels at 7 days post exposure (dpe); tear and cloacal viral load at 2, 4, and 6 dpe; and survival time. Heritability estimates were low to moderate, ranging from 0.11 for average lesion scores to 0.36 for pre-exposure growth rate. Heritability estimates for survival time were 0.23 and 0.27 for the Tanzanian and Ghanaian ecotypes, respectively. Similar heritability estimates were observed when data were analyzed either separately or combined for the two countries. Survival time was genetically negatively correlated with lesion scores and with viral load. Results suggested that response to mesogenic or velogenic NDV of these local chicken ecotypes could be improved by selective breeding. Chickens that are more resilient to velogenic NDV can improve household livelihoods in developing countries.

Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Bill & Melinda Gates Foundation.

More than 7.15 million cases of domestically acquired infectious waterborne illnesses occurred in the United States in 2014, causing 120,000 hospitalizations and 6,600 deaths. We estimated disease incidence for 17 pathogens according to recreational, drinking, and nonrecreational nondrinking (NRND) water exposure routes by using previously published estimates. In 2014, a total of 5.61 million (95% credible interval [CrI] 2.97-9.00 million) illnesses were linked to recreational water, 1.13 million (95% CrI 255,000-3.54 million) to drinking water, and 407,000 (95% CrI 72,800-1.29 million) to NRND water. Recreational water exposure was responsible for 36%, drinking water for 40%, and NRND water for 24% of hospitalizations from waterborne illnesses. Most direct costs were associated with pathogens found in biofilms. Estimating disease burden by water exposure route helps direct prevention activities. For each exposure route, water management programs are needed to control biofilm-associated pathogen growth; public health programs are needed to prevent biofilm-associated diseases.

Consider assessing the evidence for an exposure variable and a disease variable being associated, when the true exposure variable is more costly to obtain than an error-prone but nondifferential surrogate exposure variable. From a study design perspective, there are choices regarding the best use of limited resources. Should one acquire the true exposure status for fewer subjects or the surrogate exposure status for more subjects? The issue of validation is also central, i.e., should we simultaneously measure the true and surrogate exposure variables on a subset of study subjects? Using large-sample theory, we provide a framework for quantifying the power of testing for an exposure–disease association as a function of study cost. This enables us to present comparisons of different study designs under different suppositions about both the relative cost and the performance (sensitivity and specificity) of the surrogate variable. We present simulations to show the applicability of our theoretical framework, and we provide a case-study comparing results from an actual study to what could have been seen had true exposure status been ascertained for a different proportion of study subjects. We also describe an extension of our ideas to a more complex situation involving covariates. Devant le problème visant à déterminer l’association entre une variable d’exposition et une maladie, est-il préférable de choisir la vraie mesure d’exposition qui est coûteuse, ou une variable substitut non différentielle mais sujette aux erreurs? D’une point de vue de planification d’expérience, il importe d’utiliser le plus efficacement possible des ressources limitées. Est-il préférable d’obtenir la vraie exposition d’un nombre plus restreint d’individus, ou la variable substitut d’un plus grand nombre? Le défi de la validation est également central: faudrait-il mesurer simultanément l’exposition et sa variable substitut pour un certain nombre de sujets? En faisant appel à la théorie asymptotique, les auteurs offrent un cadre pour quantifier la puissance d’un test d’association entre l’exposition et une maladie en fonction du coût de l’étude. Ils comparent ainsi plusieurs plans d’expérience selon plusieurs hypothèses sur le coût relatif et la performance (sensibilité et spécificité) de la variable substitut. Ils présentent des simulations qui montrent que leur cadre théorique est applicable, et décrivent une étude de cas comparant les résultats d’une véritable étude à ce qu’ils auraient pu être si l’exposition réelle avait été mesurée pour une proportion différente des sujets. Les auteurs décrivent également une généralisation de leurs idées au cas plus complexe comportant des covariables.

In the United States, tropical cyclones cause destructive flooding that can lead to adverse health outcomes. Storm-driven flooding contaminates environmental, recreational, and drinking water sources, but few studies have examined effects on specific infections over time. We used 23 years of exposure and case data to assess the effects of tropical cyclones on 6 waterborne diseases in a conditional quasi-Poisson model. We separately defined storm exposure for windspeed, rainfall, and proximity to the storm track. Exposure to storm-related rainfall was associated with a 48% (95% CI 27%-69%) increase in Shiga toxin-producing Escherichia coli infections 1 week after storms and a 42% (95% CI 22%-62%) in increase Legionnaires' disease 2 weeks after storms. Cryptosporidiosis cases increased 52% (95% CI 42%-62%) during storm weeks but declined over ensuing weeks. Cyclones are a risk to public health that will likely become more serious with climate change and aging water infrastructure systems.