Social and geographical disparities in Twitter use during Hurricane Harvey

Socialmedia such as Twitter is increasingly beingused as an effective platform to observe human behaviors in disastrous events.However,uneven social media use among different groups of population in different regions could lead to biased consequences and affect disaster resilience.This paper studies the Twitter use during 2017 Hurricane Harvey in 76 counties in Texas and Louisiana.We seek to answer a fundamental question:did socialgeographical disparities of Twitter use exist during the three phases of emergency management(preparedness,response,recovery)?We employed a Twitter data mining framework to process the data and calculate two indexes:Ratio and Sentiment.Regression analyses between the Ratio indexes and the social-geographical characteristics of the counties at the three phrases reveal significant social and geographical disparities in Twitter use during Hurricane Harvey.Communities with higher disasterrelated Twitter use in Harvey generally were communities having better social and geographical conditions.These results of Twitter use patterns can be used to compare with future similar studies to see whether the Twitter use disparities have increased or decreased.Future research is also needed to examine the effects of Twitter use disparities on disaster resilience and to test whether Twitter use can predict community resilience.

Revealing the linguistic and geographical disparities of public awareness to Covid-19 outbreak through social media

The Covid-19 has presented an unprecedented challenge to public health worldwide.However,residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts.This study analyzed the global Twitter data from January 1st to June 30th,2020,to answer two research questions.What are the linguistic and geographical disparities of public awareness in the Covid-19 outbreak period reflected on social media?Does significant association exist between the changing Covid-19 awareness and the pandemic outbreak?We established a Twitter data mining framework calculating the Ratio index to quantify and track awareness.The lag correlations between awareness and health impacts were examined at global and country levels.Results show that users presenting the highest Covid-19 awareness were mainly those tweeting in the official languages of India and Bangladesh.Asian countries showed more disparities in awareness than European countries,and awareness in Eastern Europe was higher than in central Europe.Finally,the Ratio index had high correlations with global mortality rate,global case fatality ratio,and country-level mortality rate,with 21-31,35-42,and 13–18 leading days,respectively.This study yields timely insights into social media use in understanding human behaviors for public health research.

Comparative analysis of breast and lung cancer survival rates and clinical trial enrollments among rural and urban patients in Georgia

Objectives:Rural patients have poor cancer outcomes and clinical trial(CT)enrollment compared to urban patients due to attitudinal,awareness,and healthcare access differential.Knowledge of cancer survival disparities and CT enrollment is important for designing interventions and innovative approaches to address the stated barriers.The study explores the potential disparities in cancer survival rates and clinical trial enrollments in rural and urban breast and lung cancer patients.Our hypotheses are that for both cancer types,urban cancer patients will have longer 5-year survival rates and higher enrollment rates in clinical trials than those in rural counties.Methods:We compared breast and lung cancer patients’survival rates and enrollment ratios in clinical trials between rural(RUCC 4-9)and urban counties in Georgia at a Comprehensive Cancer Center(CCC).To assess these differences,we carried out a series of independent samples t-tests and Chi-Square tests.Results:The outcomes indicate comparable 5-year survival rates across rural and urban counties for breast and lung cancer patients,failing to substantiate our hypothesis.While clinical trial enrollment rates demonstrated a significant difference between breast and lung cancer patients at CCC,no significant variation was observed based on rural or urban classification.Conclusion:These findings underscore the need for further research into the representation of rural patients with diverse cancer types at CCC and other cancer centers.Further,the findings have considerable implications for the initiation of positive social change to improve CT participation and reduce cancer survival disparities.

Population health and sociodemographic variables as predictors of access to cardiac medicine and surgery in Haiti

Background In Haiti,cardiovascular disease is a leading cause of morbidity and mortality,with congenital and rheumatic heart disease comprising a large portion of disease burden.However,domestic disparities in cardiac care access and their impact on clinical outcomes remain poorly understood.We analyzed population-level sociodemographic variables to predict cardiac care outcomes across the 10 Haitian administrative departments.Methods This cross-sectional study combined data from a 2016-17 Haitian national survey with aggregate outcomes from the Haiti Cardiac Alliance(HCA)database(n=1817 patients).Using univariate and multivariable regression analyses,the proportion of HCA patients belonging to each of three clinical categories(active treatment,lost to follow-up,deceased preoperatively)was modeled in relation to six population-level variables selected from national survey data at the level of the administrative department.Results In univariate analysis,higher department rates of childhood growth retardation were associated with a lower proportion of patients in active care(OR=0.979[0.969,0.989],p=0.002)and a higher proportion of patients lost to follow-up(OR=1.016[1.006,1.026],p=0.009).In multivariable analysis,the proportion of department patients in active care was inversely associated with qualified prenatal care(OR=0.980[0.971,0.989],p=0.005),and child growth retardation(OR=0.977[0.972,0.983]),p=0.00019).Similar multivariable results were obtained for department rates of loss to follow-up(child growth retardation:OR=1.018[1.011,1.025],p=0.002;time to nearest healthcare facility in an emergency:OR=1.004[1.000,1.008,p=0.065)and for preoperative mortality(prenatal care:OR=0.989[0.981,0.997],p=0.037;economic index:OR=0.996[0.995,0.998],p=0.007;time to nearest healthcare facility in an emergency:OR=0.992[0.988,0.996],p=0.0046).Conclusions Population-level survey data on multiple variables predicted domestic disparities in HCA clinical outcomes by region.These findings may help to identify underserved areas in Haiti,where increased cardiac care resources are required to improve health equity.This approach to analyzing clinical outcomes through the lens of population-level survey data may inform future health policies and interventions designed to increase cardiac care access in Haiti and other low-income countries.

Improving social media use for disaster resilience: challenges and strategies

This paper develops a social media-disaster resilience analysis framework by categorizing types of social media use and their challenges to better understand and assess its role in disaster resilience research and management.The framework is derived primarily from several case studies of Twitter use in three hurricane events in the United States-Hurricanes Isaac,Sandy,and Harvey.The paper first outlines four major contributions of social media data for disaster resilience research and management,which include serving as an effective communication platform,providing ground truth information for emergency response and rescue operations,providing information on people's sentiments,and allowing predictive modeling.However,there are four_key challenges to its uses,which include,easy spreading of false information,social and geographical disparities of Twitter use,technical issues on processing and analyzing big and noisy data,especially on improving the locational accuracy of the tweets,and algorithm bias in Al and other types of modeling.Then,the paper proposes twenty strategies that the four sectors of the social media community-organizations,individuals,social media companies,and researchers-could take to improve social media use to increase disaster resilience.

Landscape of DILI-related adverse drug reaction in China Mainland

Drug-induced liver injury(DILI)is a type of bizarre adverse drug reaction(ADR)damaging liver(L-ADR)which may lead to substantial hospitalizations and mortality.Due to the general low incidence,detection of L-ADR remains an unsolved public health challenge.Therefore,we used the data of 6.673 million of ADR reports from January 1st,2012 to December 31st,2016 in China National ADR Monitoring System to establish a new database of L-ADR reports for future investigation.Results showed that totally 114,357 ADR reports were retrieved by keywords searching of liver-related injuries from the original heterogeneous system.By cleaning and standardizing the data fields by the dictionary of synonyms and English translation,we resulted 94,593 ADR records reported to liver injury and then created a new database ready for computer mining.The reporting status of L-ADR showed a persistent 1.62-fold change over the past five years.The national population-adjusted reporting numbers of L-ADR manifested an upward trend with age increasing and more evident in men.The annual reporting rate of L-ADR in age group over 80 years old strikingly exceeded the annual DILI incidence rate in general population,despite known underreporting situation in spontaneous ADR reporting system.The percentage of herbal and traditional medicines(H/TM)L-ADR reports in the whole number was 4.5%,while 80.60%of the H/TM reports were new findings.There was great geographical disparity of reported agents,i.e.more cardiovascular and antineoplastic agents were reported in higher socio-demographic index(SDI)regions and more antimicrobials,especially antitubercular agents,were reported in lower SDI regions.In conclusion,this study presented a large-scale,unbiased,unified,and computer-minable L-ADR database for further investigation.Age-,sex-and SDI-related risks of L-ADR incidence warrant to emphasize the precise pharmacovigilance policies within China or other regions in the world.