The Relationship between Parasitic Fleas and Small Mammals in Household of Western Yunnan Province, China

AbstractThe Yunnan Province has one of the most serious outbreaks of the plague epidemic in China. Small mammals and fleas are risk factors for the occurrence of plague in commensal plague foci. Understanding the relationship between parasitic fleas and small mammals will help control fleas and prevent the onset of the plague. Four hundred and twenty-one small mammals, belonging to 9 species, were captured. Of these, 170 small mammals (40.4%) were infested with fleas. A total of 992 parasitic fleas (including 5 species) was collected. The number of Leptopsylla Segnis and Xenopsylla Cheopis was 91.0%. The final multiple hurdle negative binomial regression model showed that when compared with Rattus Tanezumi, the probability of flea infestation on Mus musculus and other host species decreased from 58% to 99%, while the infestation with fleas from other host species increased 4.7 fold. The probability of flea prevalence in adult hosts increased by 74%, while the number of fleas decreased by 76%. The number of flea infestations in small male mammals increased by 62%. The number of fleas in small mammals weighing more than 59 grams has been multiplied by about 4. Rattus Tanezumi is the predominant species in households in West Yunnan Province, while Leptopsylla Segnis and Xenopsylla Cheopis are dominant parasitic fleas. There is a strong relationship between the abundance of parasitic fleas and the characteristics of small mammals (e.g. Species, age, sex, and body weight).

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The relationship between fleas and small mammals in households of the Western Yunnan Province, China

Scientific Reports ◽

10.1038/s41598-020-73690-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Jia-Xiang Yin ◽

Xiao-Ou Cheng ◽

Yun-Yan Luo ◽

Qiu-Fang Zhao ◽

Zhao-Fei Wei ◽

...

Keyword(s):

Small Mammals ◽

Host Species ◽

Yunnan Province ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Strong Relationship ◽

Negative Binomial Regression Model ◽

Small Male ◽

Western Yunnan ◽

The Relationship

Abstract The Yunnan province has one of the most serious outbreaks of the plague epidemic in China. Small mammals and fleas are risk factors for the occurrence of plague in commensal plague foci. Understanding the relationship between fleas and small mammals will help control fleas and prevent the onset of the plague. Four hundred and twenty-one small mammals, belonging to 9 species, were captured. Of these, 170 small mammals (40.4%) were found infested with fleas. A total of 992 parasitic fleas (including 5 species) were collected. The number of Leptopsylla segnis and Xenopsylla cheopis accounted for 91.03% (903/992). The final multiple hurdle negative binomial regression model showed that when compared with Rattus tanezumi, the probability of flea infestation with Mus musculus as well as other host species decreased by 58% and 99%, respectively, while the number of flea infestations of the other host species increased by 4.71 folds. The probability of flea prevalence in adult hosts increased by 74%, while the number of fleas decreased by 76%. The number of flea infestations in small male mammals increased by 62%. The number of fleas in small mammals weighing more than 59 g has been multiplied by about 4. R. tanezumi is the predominant species in households in the west Yunnan province, while L.segnis and X. cheopis were dominant parasitic fleas. There is a strong relationship between the abundance of fleas and the characteristics of small mammals (e.g. Species, age, sex, and body weight).

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The Impacts of Regional Regulatory Policies on the Prevention and Control of Chronic Diseases in China: A Mediation Analysis

Healthcare ◽

10.3390/healthcare9081058 ◽

2021 ◽

Vol 9 (8) ◽

pp. 1058

Author(s):

Huihui Huangfu ◽

Qinwen Yu ◽

Peiwu Shi ◽

Qunhong Shen ◽

Zhaoyang Zhang ◽

...

Keyword(s):

Chronic Diseases ◽

Community Service ◽

Mediation Analysis ◽

Prevention And Control ◽

Negative Binomial ◽

Mediating Effect ◽

Negative Binomial Regression Model ◽

Regulatory Policies ◽

And Control ◽

The Relationship

Regional regulatory policies (RPs) are a major factor in the prevention and control of chronic diseases (PCCDs) through the implementation of various measures. This study aimed to explore the impacts of RPs on PCCDs, with a focus on the mediating roles of community service. The soundness of the regulatory mechanism (SORM) was used to measure the soundness of RPs based on 1095 policy documents (updated as of 2015). Coverage provided by community service institutions (CSIs) and community health centres (CHCs) was used to represent community service coverage derived from the China Statistical Yearbook (2015), while the number of chronic diseases (NCDs) was used to measure the effects of PCCDs based on data taken from the 2015 China Health and Retirement Longitudinal Study survey. To assess the relationship between SORM, NCDs and community service, a negative binomial regression model and mediation analysis with bootstrapping were conducted. Results revealed that there was a negative correlation between SORM and NCDs. CSIs had a major effect on the relationship between RPs and PCCDs, while CHCs had a partial mediating effect. RPs can effectively prevent and control chronic diseases. Increased effort should also be aimed at strengthening the roles of CSIs and CHCs.

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Seasonal variation in mortality secondary to acute myocardial infarction in England and Wales: a secondary data analysis

BMJ Open ◽

10.1136/bmjopen-2017-019242 ◽

2018 ◽

Vol 8 (7) ◽

pp. e019242 ◽

Cited By ~ 2

Author(s):

Osakpolor Ogbebor ◽

Babatunde Odugbemi ◽

Ravi Maheswaran ◽

Kavya Patel

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Seasonal Variation ◽

Negative Binomial ◽

Secondary Data ◽

Contributory Factor ◽

Negative Binomial Regression Model ◽

Winter Mortality ◽

England And Wales ◽

The Relationship

BackgroundAcute myocardial infarction (AMI) is a leading cause of death globally. Increase in AMI mortality during winter has also been identified in existing literature. This has been associated with low outdoor and indoor temperatures and increasing age. The relationship between AMI and other factors such as gender and socioeconomic factors varies from study to study. Influenza epidemics have also been identified as a contributory factor.ObjectiveThis paper aims to illustrate the seasonal trend in mortality due to AMI in England and Wales with emphasis on excess winter mortality (EWM).MethodsMonthly mortality rates per 10 000 population were calculated from data provided by the UK Office for National Statistics (ONS) for 1997–2005. To quantify the seasonal variation in winter, the EWM estimates (EWM, EWM ratio, Excess Winter Mortality Index) for each year were calculated. Negative binomial regression model was used to estimate the relationship between increasing age and EWM.ResultsThe decline in mortality rate for AMI was 6.8% yearly between August 1997 and July 2005. Significant trend for reduction in AMI-associated mortality was observed over the period (p<0.001). This decline was not seen with EWM (p<0.001). 17% excess deaths were observed during winter. This amounted to about 20 000 deaths over the 8-year period. Increasing winter mortality was seen with increasing age for AMI.ConclusionEWM secondary to AMI does occur in England and Wales. Excess winter deaths due to AMI have remained high despite decline in overall mortality. More research is needed to identify the relationship of sex, temperature, acclimatisation, vitamin D and excess winter deaths due to AMI.

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Factors Associated with Seasonal Food Insecurity among Small-Scale Subsistence Farming Households in Rural Honduras

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17030706 ◽

2020 ◽

Vol 17 (3) ◽

pp. 706

Author(s):

Warren Dodd ◽

Marvin Gómez Cerna ◽

Paola Orellena ◽

Sally Humphries ◽

Margaux L. Sadoine ◽

...

Keyword(s):

Climate Change ◽

Food Insecurity ◽

Agricultural Production ◽

Negative Binomial ◽

Extreme Weather Events ◽

Small Scale ◽

Negative Binomial Regression Model ◽

Subsistence Agriculture ◽

Production Factors ◽

The Relationship

In the context of climate change, a nutritional transition, and increased pressures to migrate internally and internationally, this study examined the relationship between seasonal food insecurity and demographic, socioeconomic, and agricultural production factors among small-scale subsistence farmers in rural northern Honduras. Anchored by a partnership with the Fundación para la Investigación Participativa con Agricultores de Honduras (FIPAH) and the Yorito Municipal Health Centre, a cross-sectional household survey was administered in Yorito, Honduras, in July 2014. The study population included 1263 individuals from 248 households across 22 rural communities. A multivariate mixed effects negative binomial regression model was built to investigate the relationship between the self-reported number of months without food availability and access from subsistence agriculture in the previous year (August 2013–July 2014) and demographic, socioeconomic, and agricultural production variables. This study found a lengthier ‘lean season’ among surveyed household than previously documented in Honduras. Overall, 62.2% (95% confidence interval (CI): [59.52, 64.87]) of individuals experienced at least four months of insufficient food in the previous year. Individuals from poorer and larger households were more likely to experience insufficient food compared to individuals from wealthier and smaller households. Additionally, individuals from households that produced both maize and beans were less likely to have insufficient food compared to individuals from households that did not grow these staple crops (prevalence ratio (PR) = 0.83; 95% CI: [0.69, 0.99]). Receiving remittances from a migrant family member did not significantly reduce the prevalence of having insufficient food. As unpredictable crop yields linked to climate change and extreme weather events are projected to negatively influence the food security and nutrition outcomes of rural populations, it is important to understand how demographic, socioeconomic, and agricultural production factors may modify the ability of individuals and households engaged in small-scale subsistence agriculture to respond to adverse shocks.

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Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Forests ◽

10.3390/f10050377 ◽

2019 ◽

Vol 10 (5) ◽

pp. 377 ◽

Cited By ~ 4

Author(s):

Zhangwen Su ◽

Haiqing Hu ◽

Mulualem Tigabu ◽

Guangyu Wang ◽

Aicong Zeng ◽

...

Keyword(s):

Negative Binomial ◽

Negative Binomial Regression ◽

Negative Binomial Regression Model ◽

Stable Model ◽

Wildfire Occurrence ◽

Great Xing’An Mountains ◽

Binomial Regression ◽

The Impact ◽

The Relationship ◽

Better Than

Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial regression (GWNBR) models to determine the relationship between wildfire occurrence and its drivers factors in the boreal forests of the Great Xing’an Mountains, northeast China. Using geo-weighted techniques to consider the geospatial information of meteorological, topographic, vegetation type and human factors, we aimed to verify whether the performance of the NB model can be improved. Our results confirmed that the model fitting and predictions of GWNBR model were better than the global NB model, produced more precise and stable model parameter estimation, yielded a more realistic spatial distribution of model predictions, and provided the detection of the impact hotpots of these predictor variables. We found slope, vegetation cover, average precipitation, average temperature, and average relative humidity as important predictors of wildfire occurrence in the Great Xing’an Mountains. Thus, spatially differing relations improves the explanatory power of the global NB model, which does not explain sufficiently the relationship between wildfire occurrence and its drivers. Thus, the GWNBR model can complement the global NB model in overcoming the issue of nonstationary variables, thereby enabling a better prediction of the occurrence of wildfires in large geographical areas and improving management practices of wildfire.

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Race/Ethnicity Moderation on the Relationship Between Neighborhood Minority Composition and Depressive Symptoms

Innovation in Aging ◽

10.1093/geroni/igab046.2356 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 621-621

Author(s):

Seungjong Cho

Keyword(s):

Depressive Symptoms ◽

Negative Binomial ◽

Ethnic Composition ◽

Negative Binomial Regression Model ◽

Poverty Level ◽

Combined Model ◽

Individual Level ◽

Race Ethnicity ◽

The Relationship ◽

Racial Ethnic

Abstract Research showed neighborhood of residence is an important determinant of depressive symptoms. However, the complex effects of neighborhood racial/ethnic composition and individuals’ race/ethnicity on depressive symptoms were not fully explored in previous studies. This study tested whether individuals’ own race/ethnicity moderates the relationship between neighborhood racial/ethnic composition and depressive symptoms. Applying social disorganization theory, this study investigates the relationships between neighborhood racial/ethnic composition (proportion of racial and ethnic minorities), individual race/ethnicity, and depressive symptoms. This study used a merged data from Health and Retirement Study 2016 and the American Community Survey 2014-2018 (N=5,241; all age 50 or older). This study applied a mixed-effects negative binomial regression model. It has four statistical models by race/ethnicity: (a) non-Hispanic Blacks only, (b) Hispanics only, (c) non-Hispanic Whites only, and (d) combined model. Covariates were included two individual-level variables (age and gender) and three census tract-level variables (the proportions of (a) population of income below the poverty level; (b) population of unemployed; (c) population of aged 65 and over). The results showed none of the neighborhood racial/ethnic minority composition was associated with depressive symptoms for the final combined model. The moderation effects of individuals’ race/ethnicity were not significant. Not as a moderator but as the main effect, both non-Hispanic Blacks and Hispanics had higher depressive symptoms, compared to non-Hispanic Whites. Living in neighborhoods with higher poverty rates and females were also related to higher depressive symptoms. This study contributes to explore the subtle nature of depressive symptomatology and race both at individual-level and neighborhood-level.

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Network Position and Crowdsourcing Innovation Contribution Behavior: The Moderating Role of Knowledge Absorption Capacity

Complexity ◽

10.1155/2021/9969509 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Qingliang Meng ◽

Yi Hang ◽

Xiaojun Chen

Keyword(s):

Negative Binomial ◽

Positive Impact ◽

Knowledge Exchange ◽

Negative Binomial Regression ◽

Absorption Capacity ◽

Negative Binomial Regression Model ◽

Network Position ◽

The Individual ◽

The Relationship ◽

Knowledge Absorption

Purpose. The purpose of this study is to examine the relationship between network position and crowdsourcing innovation contribution behavior and the moderating effects of knowledge absorption capacity on the aforementioned relationship. Design/Methodology/Approach. Focusing on the Chinese context, the study conducts empirical research with the user’s knowledge-sharing network of the MIUI community to test the research model. The negative binomial regression model which is suitable for processing discrete data is used to examine the main effects of the network position, knowledge absorption capacity, and crowdsourcing innovation contribution behavior. Findings. The findings reveal that the closer the user gets to the center of the network, the more likely they will contribute. The users’ knowledge absorption capacity can help stimulate the users’ crowdsourcing innovation contribution behavior, and the users with stronger knowledge absorption capacity are more likely to transform their network position advantages into innovative contribution behaviors. Practical Implications. The study provides evidence that network position has a positive impact on their crowdsourcing innovation contribution behavior, and knowledge absorption capacity promotes the crowdsourcing innovation behavior of users. Managers should encourage users to occupy a favorable network position and increase knowledge exchange with other users, while at the same time continuously improving their own knowledge absorption capacity. Originality/Value. This study combines social network theory and the individual mindset to introduce knowledge absorptive capacity into the relationship model of the user’s network position and crowdsourcing innovation contribution behavior, thereby constructing a complete path of “knowledge supply-knowledge acquisition-knowledge application-knowledge output.” The study contributes to provide a theoretical basis for an in-depth understanding of the influence relationship between network position and crowdsourcing innovation contribution behavior. Also, it provides a reference for enterprises to carry out practical crowdsourcing innovation community governance and improve innovation performance.

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Negative Binomial regression model for analysis of the relationship between hospitalization and air pollution

Atmospheric Pollution Research ◽

10.1016/j.apr.2017.10.010 ◽

2018 ◽

Vol 9 (2) ◽

pp. 333-341 ◽

Cited By ~ 6

Author(s):

Leda G. Ardiles ◽

Yara S. Tadano ◽

Silvano Costa ◽

Viviana Urbina ◽

Maurício N. Capucim ◽

...

Keyword(s):

Air Pollution ◽

Regression Model ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Negative Binomial Regression Model ◽

Binomial Regression ◽

The Relationship

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Freeway Incident Frequency Analysis Based on CART Method

PROMET - Traffic&Transportation ◽

10.7307/ptt.v26i3.1308 ◽

2014 ◽

Vol 26 (3) ◽

pp. 191-199 ◽

Cited By ~ 4

Author(s):

Xuecai Xu ◽

Željko Šarić ◽

Ahmad Kouhpanejade

Keyword(s):

Regression Model ◽

Influencing Factors ◽

Negative Binomial ◽

Classification And Regression Tree ◽

Future Research ◽

Negative Binomial Regression Model ◽

Classification And Regression ◽

The Impact ◽

The Relationship ◽

Incident Frequency

Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.

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Host–parasite interactions of rodent hosts and ectoparasite communities from different habitats in Germany

Parasites & Vectors ◽

10.1186/s13071-021-04615-7 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Anna Obiegala ◽

Leonie Arnold ◽

Martin Pfeffer ◽

Matthias Kiefer ◽

Daniel Kiefer ◽

...

Keyword(s):

Small Mammals ◽

Host Species ◽

Negative Binomial ◽

Influencing Factor ◽

Predatory Mites ◽

Mite Species ◽

Flea Species ◽

Natural Habitats ◽

Mean Intensity ◽

Trapping Location

Abstract Background Small mammals are important maintenance hosts of ectoparasites as well as reservoir hosts for many arthropod-borne pathogens. In Germany, only a few studies have investigated ectoparasite communities on small mammals in their natural habitats. The aim of this study was to assess the species diversity and parameters influencing the mean intensity and prevalence of macroscopically visible ectoparasites, such as fleas, predatory mites and ticks. Methods A total of 779 small mammals and 3383 ticks were available from earlier investigations for the data analysis of the current study from three differently structured study sites. In addition, fleas and predatory mites were collected from the captured rodents and taxonomically identified. Regression analyses were conducted on the group (ticks/mites/fleas) and species levels using hurdle models for the abundance of ectoparasite groups and a negative binomial model for the abundance of species. Results Nearly 90% of the small mammals analyzed were infested with ectoparasites, with an average of 7.3 specimens per host. Hosts were infested with up to six species of ectoparasites simultaneously. In total, 12 flea, 11 mite and three tick species were detected. Ticks were more prevalent than fleas or mites, with > 80% of the hosts in urban and forest areas hosting ticks and around 60% of hosts presenting fleas, and only 20–40% of hosts presenting mites. Polyparasitism had a statistically significant influence on the prevalence of the investigated tick, mite and flea species, with odds ratios of > 1.0. Trapping location, season and host characteristics had significant influences on some—but not all—of the investigated species. Conclusions The diversity of flea species was unexpectedly high and higher than that reported in comparable studies, which can be explained by the differently structured habitats and regions examined in this study. Polyparasitism was a key influencing factor and had a positive effect on the prevalence and/or abundance of the predominant tick, flea and mite species occurring on small mammals. Season, trapping location, host species and sex of the host species also had an influence on the prevalence and mean intensity of certain, but not all, ectoparasite species.

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