High-risk Counties Research Articles

Spatiotemporal trends and ecological determinants in maternal mortality ratios in 2,205 Chinese counties, 2010-2013: A Bayesian modelling analysis.

BackgroundAs one of its Millennium Development Goals (MDGs), China has achieved a dramatic reduction in the maternal mortality ratio (MMR), although a distinct spatial heterogeneity still persists. Evidence of the quantitative effects of determinants on MMR in China is limited. A better understanding of the spatiotemporal heterogeneity and quantifying determinants of the MMR would support evidence-based policymaking to sustainably reduce the MMR in China and other developing areas worldwide.Methods and findingsWe used data on MMR collected by the National Maternal and Child Health Surveillance System (NMCHSS) at the county level in China from 2010 to 2013. We employed a Bayesian space–time model to investigate the spatiotemporal trends in the MMR from 2010 to 2013. We used Bayesian multivariable regression and GeoDetector models to address 3 main ecological determinants of the MMR, including per capita income (PCI), the proportion of pregnant women who delivered in hospitals (PPWDH), and the proportion of pregnant women who had at least 5 check-ups (PPWFC). Among the 2,205 counties, there were 925 (42.0%) hotspot counties, located mostly in China’s western and southwestern regions, with a higher MMR, and 764 (34.6%) coldspot counties with a lower MMR than the national level. China’s westernmost regions, including Tibet and western Xinjiang, experienced a weak downward trend over the study period. Nationwide, medical intervention was the major determinant of the change in MMR. The MMR decreased by 1.787 (95% confidence interval [CI]: 1.424–2.142, p < 0.001) per 100,000 live births when PPWDH increased by 1% and decreased by 0.623 (95% CI 0.436–0.798, p < 0.001) per 100,000 live births when PPWFC increased by 1%. The major determinants for the MMR in China’s western and southwestern regions were PCI and PPWFC, while that in China’s eastern and southern coastlands was PCI. The MMR in western and southwestern regions decreased nonsignificantly by 1.111 (95% CI −1.485–3.655, p = 0.20) per 100,000 live births when PCI in these regions increased by 1,000 Chinese Yuan and decreased by 1.686 (95% CI 1.275–2.090, p < 0.001) when PPWFC increased by 1%. Additionally, the western and southwestern regions showed the strongest interactive effects between different factors, in which the corresponding explanatory power of any 2 interacting factors reached up to greater than 80.0% (p < 0.001) for the MMR. Limitations of this study include a relatively short study period and lack of full coverage of eastern coastlands with especially low MMR.ConclusionsAlthough China has accomplished a 75% reduction in the MMR, spatial heterogeneity still exists. In this study, we have identified 925 (hotspot) high-risk counties, mostly located in western and southwestern regions, and among which 332 counties are experiencing a slower pace of decrease than the national downward trend. Nationally, medical intervention is the major determinant. The major determinants for the MMR in western and southwestern regions, which are developing areas, are PCI and PPWFC, while that in China’s developed areas is PCI. The interactive influence of any two of the three factors, PCI, PPWDH, and PPWFC, in western and southwestern regions was up to and in excess of 80% (p < 0.001).

Spatiotemporal and demographic characteristics of scrub typhus in Southwest China, 2006–2017: An analysis of population‐based surveillance data

Scrub typhus is a life-threatening vector-borne disease. During the past decade, the number of areas affected by this disease has expanded in many countries. In this study, we aim to identify the spatiotemporal and demographic characteristics of scrub typhus in Southwest China, an emerging endemic region for scrub typhus. Population-based surveillance data capturing scrub typhus cases in two provinces of Southwest China during 2006-2017 were retrieved. Descriptive temporal and spatial analyses were conducted and stratified by age group. The space-time scan statistic was used to identify spatiotemporal clusters of scrub typhus occurrence at the county level. During the study period, 30,001 scrub typhus cases were recorded in Southwest China, with a total of 61.0% (191/313) of counties being affected; most cases (94.3%) occurred in rural areas. The annual incidence rate increased substantially from 0.25/100,000 in 2006 to 5.38/100,000 in 2017 (>21-fold change). The 0- to 4-year-old and 45- to 64-year-old subgroups had the highest cumulative incidence rates (57.46 and 32.98/100,000, respectively). Furthermore, since 2006, the 0- to 4-year-old (slope=0.83, p<.01) and 45-64-year-old (slope=0.69, p<.01) age groups have had the highest increases in incidence of all age groups. The most likely spatial cluster of overall cases (relative risk=4.13, p<.01) occurred in the southern region of Southwest China and included 41 high-risk counties. In conclusion, scrub typhus appears to be widely distributed and rapidly increasing in Southwest China. Young children and middle-aged adults were the most severely affected groups, and the disease appeared to predominantly cluster in the southern part of Southwest China. Further in-depth surveys to determine the epidemiological characteristics and driving factors of this emerging disease and to facilitate effective control programmes among high-risk groups in the affected areas should be promoted.

The Influence of Tornado Activity, Impact, Memory, and Sentiment on Tornado Perception Accuracy among College Students

A survey consisting of open-ended and closed responses was administered at three universities in the eastern USA. The home counties of survey participants represented climatological tornado risks spanning from rarely impacted to frequently impacted. The first objective of this research was to classify climatological tornado risk for each county so that analyses of tornado perception accuracy could be evaluated. Perception accuracy was defined as the difference between what each participant perceived minus what actually happened. A manual classification scheme was created that uses the Storm Prediction Center’s Convective Outlook framework as county climatological risk categories. Participants from high-risk counties statistically significantly overestimated the numbers of violent tornadoes compared to participants from every risk category but moderate. Furthermore, participants from high-risk counties had significantly greater tornado impacts, thus validating the classification of high-risk. Participants from high, moderate, and slight-risk counties significantly overestimated the number of strong tornadoes compared to participants from enhanced-risk counties. There appeared to be no relationships between tornado memory and tornado sentiment with tornado perception accuracy. Possible explanations for the overestimation of the numbers of violent tornadoes in high-risk counties are discussed.

Spatial analysis of drug poisoning deaths in the American west: A comparison study using profile regression to adjust for collinearity and spatial correlation

BackgroundThe USA has seen dramatic increases in drug poisoning deaths (DPD) recently. State-level rates have responded to federal and state initiatives, yet the counties with the highest rates are stable. Spatial analysis enables investigators to identify the highest risk counties and most important risk factors, although results are often confounded by spatial autocorrelation and multicollinearity. MethodsProfile regression (PR) is an integrated method for cluster and regression analysis, which adjusts for spatial-autocorrelation and multi-collinearity. ResultsWith PR, three clusters were identified in the Western USA with most of NM, NV and UT and several counties in AZ, CO, ID and WY being high-risk. Cluster analysis in a previous study only identified high-risk counties in northern CA, NM and NV. Elevation, suicide and LDS population were positively, and population density was negatively linked with DPD for PR and standard regression (SR) showing differences between the mountain west and coastal areas. Complex relationships between DPD and several variables were identified by PR which was not possible with SR. ConclusionsStatistically principled methods like PR are needed for appropriate identification of the highest risk counties and important risk factors given the complex relationships with DPD. Funding for prevention, education and medical services should be targeted at rural, mountain communities in the west which have high %LDS and suicide rates. Counties with high %poverty and %Hispanic were also at high-risk. Individual-level studies are needed to confirm important risk factors in high-risk counties.

Spatial Clusters and Non-spatial Predictors of Tick-Borne Disease Diagnosis in Indiana

The purpose of this study was two-fold. First, we sought to identify spatial clusters of self-reported tick-borne disease (TBD) diagnosis in Indiana. Secondly, we determined the significant predictors of self-reported TBD diagnosis in a sample of Indiana residents. Study participants were selected from existing online panels maintained by Qualtrics and completed a cross-sectional survey (n = 3003). Our primary outcome of interest was self-reported TBD diagnosis (Yes/No). Cases and background population were aggregated to the county level. We used a purely spatial discrete Poisson model in SatScan® to determine significant clusters of high-risk TBD diagnosis counties. We also used X2 tests in bivariate analyses, to identify potential predictor variables for inclusion in an initial model, and backward elimination selection method to identify the final model. Two clusters of counties with significant high relative risk of self-reported TBD diagnosis in the southeast and southwest of Indiana were detected. Males in Indiana were more likely to self-report TBD diagnosis compared to females. Study participants who conducted a thorough tick check after being outdoors were significantly less likely to report TBD diagnosis compared to those who did not. Increased positive perceptions of TBD personal protective measures were associated with reduced self-reported TBD diagnosis. Older study participants were less likely to self-report TBD diagnosis compared to younger participants. The identification of two clusters of TBD diagnosis in southern Indiana is consistent with a northern spread of TBDs and suggests a need for continued surveillance of the counties in the vicinity of the observed clusters. Future studies should be designed to identify risk factors for TBD diagnosis in the affected counties of Indiana.

Characteristics of US Counties With High Opioid Overdose Mortality and Low Capacity to Deliver Medications for Opioid Use Disorder

Opioid overdose deaths in the United States continue to increase, reflecting a growing need to treat those with opioid use disorder (OUD). Little is known about counties with high rates of opioid overdose mortality but low availability of OUD treatment. To identify characteristics of US counties with persistently high rates of opioid overdose mortality and low capacity to deliver OUD medications. In this cross-sectional study of data from 3142 US counties from January 1, 2015, to December 31, 2017, rates of opioid overdose mortality were compared with availability in 2017 of OUD medication providers (24 851 buprenorphine-waivered clinicians [physicians, nurse practitioners, and physician assistants], 1517 opioid treatment programs [providing methadone], and 5222 health care professionals who could prescribe extended-release naltrexone). Statistical analysis was performed from April 20, 2018, to May 8, 2019. Demographic, workforce, lack of insurance, road density, urbanicity, opioid prescribing, and regional division county-level characteristics. The outcome variable, "opioid high-risk county," was a binary indicator of a high (above national) rate of opioid overdose mortality with a low (below national) rate of provider availability to deliver OUD medication. Spatial logistic regression models were used to determine associations with being an opioid high-risk county. Of 3142 counties, 751 (23.9%) had high rates of opioid overdose mortality. A total of 1457 counties (46.4%), and 946 of 1328 rural counties (71.2%), lacked a publicly available OUD medication provider in 2017. In adjusted models, compared with the West North Central division, counties in the East North Central, Mountain, and South Atlantic divisions had increased odds of being opioid high-risk counties (East North Central: odds ratio [OR], 2.21; 95% CI, 1.19-4.12; Mountain: OR, 4.15; 95% CI, 1.34-12.89; and South Atlantic: OR, 2.99; 95% CI, 1.26-7.11). A 1% increase in unemployment was associated with increased odds (OR, 1.09; 95% CI, 1.03-1.15) of a county being an opioid high-risk county. Counties with an additional 10 primary care clinicians per 100 000 population had a reduced risk of being opioid high-risk counties (OR, 0.89; 95% CI, 0.85-0.93), as did counties that were micropolitan (vs metropolitan) (OR, 0.67; 95% CI, 0.50-0.90) and those that had an additional 1% of the population younger than 25 years (OR, 0.95; 95% CI, 0.92-0.98). Counties with low availability of OUD medication providers and high rates of opioid overdose mortality were less likely to be micropolitan and have lower primary care clinician density, but were more likely to be in the East North Central, South Atlantic, or Mountain division and have higher rates of unemployment. Strategies to increase medication treatment must account for these factors.

Climate variability, satellite-derived physical environmental data and human leptospirosis: A retrospective ecological study in China

BackgroundIn the past three decades, the incidence rate of notified leptospirosis cases in China have steeply declined and are now circumscribed to discrete areas in the country. Previous research showed that climate and environmental variation may play an important role in leptospirosis transmission. However, quantitative associations between climate, environmental factors and leptospirosis in the high-risk areas in China, is still poorly understood. ObjectiveTo quantify the temporal effects of climate and remotely-sensed physical environmental factors on human leptospirosis in the high-risk counties in China. MethodsTime series seasonal decomposition was performed to explore the seasonality pattern of leptospirosis incidence in Mengla County, Yunnan and Yilong County, Sichuan for the period 2006–2016. Time series cross-correlation analysis was carried out to examine lagged effects of rainfall, relative humidity, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and land surface temperature (LST) on leptospirosis. The associations of climatic and physical environment factors with leptospirosis in each county were assessed by using a generalized linear regression model with negative binomial link, adjusted by seasonal components. ResultsLeptospirosis incidence in both counties showed strong and unique annual seasonality. Our results show that in Mengla County leptospirosis notifications exhibits a bi-modal temporal pattern while in Yilong County it follows a typical single epidemic curve. After adjusting for seasonality, the final best-fitting model for Mengla County indicated that leptospirosis notifications were significantly associated with present LST values (incidence rate ratio, IRR = 0.857, 95% confidence interval (CI):0.729-0.929) and rainfall at a lag of 6-months (IRR = 0.989; 95% CI: 0.985-0.993). The incidence of leptospirosis in Yilong was associated with rainfall at 1-month lag (IRR = 1.013, 95% CI: 1.003-1.023), LST (3-months lag) (IRR = 1.193, 95% CI: 1.095-1.301), and MNDWI (5-months lag) (IRR = 7.960, 95% CI: 1.241-47.66). ConclusionsOur study identified lagged effects between leptospirosis incidence and climate and remotely-sensed environmental factors in the two most endemic counties in China. Rainfall in combination with satellite derived physical environment factors provided better insight of the local epidemiology as well as good predictors for leptospirosis outbreak in both counties. This would also be an avenue for the development of leptospirosis early warning systems to support leptospirosis control in China.

Leptospirosis, Climate and Satellite-based Environmental Factors: A Temporal Modeling

ObjectiveTo quantify the effects of climate variability, selected remotely-sensed environmental factors on human leptospirosis in the high-risk counties in China.IntroductionLeptospirosis is a zoonotic disease caused by the pathogenic Leptospira bacteria and is ubiquitously distributed in tropical and subtropical regions. Leptospirosis transmission driven by complex factors include climatic, environmental and local social conditions 1. Each year, there are about 1 million cases of human leptospirosis reported globally and it causes approximately 60,000 people lost their lives due to infection 2. Yunnan Province and Sichuan Province are two of highly endemic areas in the southwest China that had contributed for 47% of the total national reported cases during 2005-2015 3. Factors underlying local leptospirosis transmission in these two areas is far from clear and thus hinder the efficacy of control strategies. Hence, it is essential to assess and identify local key drivers associated with persistent leptospirosis transmission in that areas to lay foundation for the development of early-warning systems. Currently, remote sensing technology provides broad range of physical environment data at various spatial and temporal scales 4, which can be used to understand the leptospirosis epidemiology. Utilizing satellite-based environmental data combined with locally-acquired weather data may potentially enhance existing surveillance programs in China so that the burden of leptospirosis could be reduced.MethodsThis study was carried out in two counties situated in different climatic zone in the southwestern China, Mengla and Yilong County (Fig 1). Total of 543 confirmed leptospirosis cases reported during 2006-2016 from both counties were used in this analysis. Time series decomposition was used to explore the long-term seasonality of leptospirosis incidence in two counties during the period studied. Monthly remotely-sensed environmental data such as normalized difference vegetation index (NDVI), modified normalized water difference index (MNDWI) and land surface temperature (LST) were collected from satellite databases. Climate data include monthly precipitation and relative humidity (RH) data were obtained from local weather stations. Lagged effects of rainfall, humidity, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and land surface temperature (LST) on leptospirosis was examined. Generalized linear model with negative binomial link was used to assess the relationships of climatic and physical environment factors with leptospirosis. Best-fitted model was determined based on the lowest information criterion and deviance.ResultsLeptospirosis incidence in both counties showed strong and unique annual seasonality. Bi-modal temporal pattern was exhibited in Mengla County while single epidemic curve was persistently demonstrated in Yilong County (Fig 2). Total of 10 and 20 models were generated for Mengla and Yilong County, respectively. After adjusting for seasonality, final best-fitted models indicated that rainfall at lag of 6-month (incidence rate ratio (IRR)= 0.989; 95% confidence interval (CI) 0.985-0.993, p&lt;0.001) and current LST (IRR=0.857, 95%CI:0.729-0.929, p&lt;0.001) significantly associated with leptospirosis in Mengla County (Table 1). While in Yilong, rainfall at 1-month lag, MNDWI (5-months lag) and LST (3-months lag) were associated with an increased incidence of leptospirosis with a risk ratio of 1.013 (95%CI: 1.003-1.023), 7.960 (95%CI: 1.241-47.66) and 1.193 (95%CI:1.095-1.301), respectively.ConclusionsOur study identified lagged effect and relationships of weather and remotely-sensed environmental factors with leptospirosis in two endemic counties in China. Rainfall in combination with satellite derived physical environment factors such as flood/water indicator (MNDWI) and temperature (LST) could help explain the local epidemiology as well as good predictors for leptospirosis outbreak in both counties. This would also be an avenue for the development of leptospirosis early warning system in to support leptospirosis control in China.

3142 U.S. Counties with High Opioid-Overdose Mortality and Low Capacity to Deliver Medications for Opioid Use Disorder: an Observational Study

OBJECTIVES/SPECIFIC AIMS: To identify characteristics of counties with persistently high opioid-overdose rates and low capacity to deliver medications for OUD (MOUD). METHODS/STUDY POPULATION: Setting: County-level opioid-overdose death data, 2013-2016, and 2017 publicly-available treatment provider data for MOUD: buprenorphine-waivered providers, opioid treatment programs (OTPs), and extended-release naltrexone providers. Participants: Populations in 3,142 U.S. counties. 24,851 buprenorphine-waivered providers; 1,517 OTPs; and 5,222 extended-release naltrexone providers. Measurements: The outcome variable, “opioid high-risk county”, was a binary indicator of high (above average) opioid-overdose rates with low (below median) MOUD availability rates. We used spatial logistic regression models to determine correlates of being a high-risk county. RESULTS/ANTICIPATED RESULTS: 46.4% of all counties, and 71.2% of rural counties, lacked a publicly-available MOUD provider in 2017. In adjusted models, rural counties had 53% greater odds of being high-risk than urban counties. Counties in the East South Central, West South Central, and South Atlantic divisions had over twice the odds of being high-risk than counties in the West North Central division. Primary care provider density, greater traversability, and a higher proportion of the population under age 25 were all protective against a county being opioid high-risk. DISCUSSION/SIGNIFICANCE OF IMPACT: Counties with both low MOUD provider availability and high opioid-overdose death rates are significantly more likely to be rural, have less primary care providers per capita, and in the southern regions. Strategies to increase MOUD must account for these factors.

County-level socioeconomic and crime risk factors for substantiated child abuse and neglect.

Rates of substantiated child abuse and neglect vary significantly across counties. Despite strong cross-sectional support for links between social-contextual characteristics and abuse and neglect, few longitudinal studies have tested relations between these risk factors and substantiated rates of abuse/neglect. The goal of this study was to identify county-level socioeconomic and crime factors associated with substantiated abuse/neglect rates over 13 years (2004-2016). Annual county-level data for Tennessee, obtained from the KIDS COUNT Data Center, included rates of substantiated child abuse and neglect, children's race and ethnicity, births to unmarried women, teen birth rate, children in families receiving Supplemental Nutrition Assistance Program (SNAP) benefits, and children in families receiving Temporary Assistance for Needy Families. Annual county-level crime report data, obtained from the Tennessee Incident Based Reporting System, included sexual offenses, non-sexual assaults, stalking incidents, thefts, property damage, and drug-related offenses. Bayesian spatio-temporal models indicated that substantiated child abuse and neglect rates were independently and positively associated with teen birth rates, percentages of births to unmarried mothers, drug-related offenses, and percentages of children receiving SNAP benefits. In contrast, substantiated child abuse and neglect rates were negatively associated with percentages of African-American youth. The findings highlighted distinct demographic, socioeconomic, and crime factors associated with substantiated child abuse and neglect rates and have the potential to enhance identification of high-risk counties that could benefit from targeted abuse and neglect prevention efforts.

Space-time scan for identification of risk areas for hospitalization of children due to asthma in Mato Grosso, Brazil.

Asthma is the result of a complex interaction between genetic, environmental and socioeconomic factors. It represents a serious global public health problem. The goal of this study was to identify geographic areas for priority actions in order to control of asthma in children. Ecological study that space-time statistic Scan was used. Non-elective, short-stay (type 1) paid authorizations of hospitalizations were selected according to hospitalizations year and children place of residence. In the two periods of the study, the high risk primary cluster was located in the region of Barra do Bugres (relative risk = 8.17, in the first period, and 10.37, in the second). The number of high-risk clusters increased from 8, in the period 2001-2004, to 9, in 2005-2012; while low-risk clusters decreased from 6, in the initial period, to 4, in the latest. The priority geographic areas for attention and intervention for children with asthma are the region around Barra do Bugres, which remained in the two periods with high risk primary clusters and the southwest border of the State that presented increase of the risk. Furthermore, there was an increase of 87% in the number of high risk counties and a reduction of 28% of the counties of protection. In conclusion, the surroundings areas of Barra do Bugres and Porto Estrela and the east and northeast border of the state are priority for health care, once there was an increased risk of hospitalization of children due to asthma.

Spatiotemporal patterns and risk factors concerning hepatitis B virus infections in the Beijing-Tianjin-Hebei area of China.

Beijing-Tianjin-Hebei is the largest urban agglomeration in northern China, but the spatiotemporal patterns and risk factors concerning hepatitis B virus (HBV) incidence in this area have been unclear. The present study aimed to reveal the spatiotemporal epidemiological features of HBV infection and quantify the association between HBV infection and socio-economic risk factors. The data on HBV cases in Beijing-Tianjin-Hebei from 2007 to 2012 was collected for each county. The Bayesian space-time hierarchy model and the GeoDetector method were used to reveal spatiotemporal patterns and detect risk factors. High-risk regions were mainly distributed in the underdeveloped rural areas in the north and mid-south of the study region, while low-risk regions were mainly distributed in the urban and western areas. The HBV annual incidence rate decreased substantially over the 6-year period, dropping from 7.34/105 to 5.51/105. Compared with this overall trend, 38.5% of high-risk counties showed a faster decrease, and 35.9% of high-risk counties exhibited a slower decrease. Meanwhile, 29.7% of low-risk counties had a faster decrease, and 44.6% of low-risk counties exhibited a slower decrease. Socio-economic factors were strongly associated with the spatiotemporal patterns and variation. The population density and gross domestic product per capita were negatively associated with HBV transmission, with determinant powers of 0.17 and 0.12, respectively. The proportion of primary industry and the number of healthcare workers were positively associated with the disease incidence, with determinant powers of 0.11 and 0.8, respectively. The interactive effect between population density and the other factors exerted a greater influence on HBV transmission than that of these factors measured independently.

Geographical and temporal distribution of the residual clusters of human leptospirosis in China, 2005\u20132016

Human leptospirosis outbreaks still persistently occur in part of China, indicating that leptospirosis remains an important zoonotic disease in the country. Spatiotemporal pattern of the high-risk leptospirosis cluster and the key characteristics of high-risk areas for leptospirosis across the country are still poorly understood. Using spatial analytical approaches, we analyzed 8,158 human leptospirosis cases notified during 2005–2016 across China to explore the geographical distribution of leptospirosis hotspots and to characterize demographical, ecological and socioeconomic conditions of high-risk counties for leptospirosis in China. During the period studied, leptospirosis incidence was geographically clustered with the highest rate observed in the south of the Province of Yunnan. The degree of spatial clustering decreased over time suggesting changes in local risk factors. However, we detected residual high-risk counties for leptospirosis including counties in the southwest, central, and southeast China. High-risk counties differed from low-risk counties in terms of its demographical, ecological and socioeconomic characteristics. In high-risk clusters, leptospirosis was predominantly observed on younger population, more males and farmers. Additionally, high-risk counties are characterized by larger rural and less developed areas, had less livestock density and crops production, and located at higher elevation with higher level of precipitation compare to low-risk counties. In conclusion, leptospirosis distribution in China appears to be highly clustered to a discrete number of counties highlighting opportunities for elimination; hence, public health interventions should be effectively targeted to high-risk counties identified in this study.

Environmental Determinants of Hemorrhagic Fever with Renal Syndrome in High-Risk Counties in China: A Time Series Analysis (2002-2012).

The transmission pattern of hemorrhagic fever with renal syndrome (HFRS) is associated with environmental conditions, including meteorological factors and land-cover. In the present study, the association between HFRS and environmental factors (including maximum temperature, relative humidity, rainfall, and normalized difference vegetation index) were explored in two typical counties in Northeast and two counties in Northwest China with severe HFRS outbreaks by using seasonal autoregressive integrated moving average model with exogenous variables (SARIMAX). The results showed that rainfall with 3- to 4-month lag was closely associated with HFRS in the two counties in Northeast China, whereas relative humidity with 1- or 5-month lag significantly impacts HFRS transmission in the two counties in Northwest China. Moreover, the SARIMAX models exhibit accurate forecasting ability of HFRS cases. Our findings provide scientific support for local HFRS monitoring and control, and the development of a HFRS early warning system.

Ecological risk assessment of the Gannan Plateau, northeastern Tibetan Plateau

Desertification, soil salinization and grassland degradation are the major environmental hazards faced by the Gannan Plateau, northeastern Tibetan Plateau. Ecological risk assessment plays an important role in formulating environmental management strategies yet little attention to this region. In this study, we established an ecological risk assessment index system based on 30 evaluation indices in the categories of hydrometeorology, ecological environment, ground surface disturbance, and society and economy for the Gannan Plateau. An entropy method was used to calculate an index weight, and subsequently the matter-element method was used together with extension theory to establish a matter-element extension model of ecological risk. We assessed the ecological risk in this region by calculating the degree of association between index layer, system layer and target layer, and the cumulative ecological risk index. The degrees of ecological risk for the counties of the region were determined by using ArcGIS which would represent a spatial heterogeneity of the risk grade in production. Our results showed that the areas of high ecological risk were in Zhouqu County and Zhuoni County, and others were of low risk (Hezuo City, Diebu County, Xiahe County and Lintan County) or intermediate risk (Maqu County). The results of the assessment were in accord with the actual observed situation. Thus, our ecological risk assessment index system is appropriate for this region and suggests that high risk counties need a priori ecological protection. Such research could provide a technological support which would potentially prevent or reduce disasters by establishing an ecological barrier to promote the sustainable development of Gannan Plateau.

Enhanced surveillance for Rift Valley Fever in livestock during El Niño rains and threat of RVF outbreak, Kenya, 2015-2016

BackgroundIn mid-2015, the United States’ Pandemic Prediction and Forecasting Science and Technical Working Group of the National Science and Technology Council, Food and Agriculture Organization Emergency Prevention Systems, and Kenya Meteorological Department issued an alert predicting a high possibility of El-Niño rainfall and Rift Valley Fever (RVF) epidemic in Eastern Africa.Methodology/Principal findingsIn response to the alert, the Kenya Directorate of Veterinary Services (KDVS) carried out an enhanced syndromic surveillance system between November 2015 and February 2016, targeting 22 RVF high-risk counties in the country as identified previously through risk mapping. The surveillance collected data on RVF-associated syndromes in cattle, sheep, goats, and camels from >1100 farmers through 66 surveillance officers. During the 14-week surveillance period, the KDVS received 10,958 reports from participating farmers and surveillance officers, of which 362 (3.3%) had at least one syndrome. The reported syndromes included 196 (54.1%) deaths in young livestock, 133 (36.7%) abortions, and 33 (9.1%) hemorrhagic diseases, with most occurring in November and December, the period of heaviest rainfall. Of the 69 herds that met the suspect RVF herd definition (abortion in flooded area), 24 (34.8%) were defined as probable (abortions, mortalities in the young ones, and/or hemorrhagic signs) but none were confirmed.Conclusion/SignificanceThis surveillance activity served as an early warning system that could detect RVF disease in animals before spillover to humans. It was also an excellent pilot for designing and implementing syndromic surveillance in animals in the country, which is now being rolled out using a mobile phone-based data reporting technology as part of the global health security system.

Using Public Health Data for Soil Pb Hazard Management in Ohio

To evaluate how frequently elevated soil lead (Pb) hazards (≥400 ppm Pb) were identified in existing blood Pb site investigations in Ohio. This study evaluated 3050 site investigations from the Ohio Department of Health for children with blood Pb levels at or above 10 μg dL that contained bare soil Pb data. Data were collected from existing databases maintained by the Ohio Department of Health. All data were de-identified prior to analysis. The data used included blood Pb level test results for children (18 years or younger) in Ohio, with most data coming from children younger than 6 years. The main outcome measures were blood Pb levels and identified Pb concentrations in environmental media, including soil, paint, and dust. Data were organized and summarized according to county. Summary statistics were generated on the basis of type of environmental media and county. Soil samples were collected in approximately 5% of all blood Pb cases in Ohio between 1999 and 2015. Median bare soil Pb was 1030 mg Pb kg (range, 0-345 021 mg Pb kg soil). Fifty-six of Ohio's 88 counties had at least 1 soil sample above 10 000 ppm (mg Pb kg). Multiple Pb hazards were identified, including bare soil (74% frequency), deteriorated exterior Pb paint (74%), deteriorated interior Pb paint (72%), and settled Pb dust (72%). Bare soil collected from identified dripline areas contained 2638 ppm Pb above soils collected from bare soil play areas (P = .02). Ninety assessments (3%) contained a bare soil hazard, with no other identified hazards. No trend was found comparing county mean or median Pb with county population. Previously identified high-risk counties for elevated blood Pb levels did not have an elevated prevalence of bare soil Pb hazards compared with other counties (P = .64). Site investigators should anticipate finding and managing elevated bare soil Pb in locations throughout Ohio. When communicating with the public about bare soil Pb hazards, practitioners and policy makers should emphasize the importance of addressing all potential Pb exposure sources. Findings demonstrate the importance of the individual home environment for exposure, as previously identified high-risk counties for elevated blood Pb levels were not different from other counties.

Using the multivariate spatio-temporal Bayesian model to analyze traffic crashes by severity

Unobserved heterogeneity across space, time, and crash type is often non-negligible in crash frequency modeling. When multiple crash types with spatial and temporal features are analyzed, multivariate spatio-temporal models should be considered. For this study, we analyzed the yearly county-level fatal, major injury, and minor injury crashes in Iowa from 2006 to 2015 using a multivariate spatio-temporal Bayesian model. The model adopted a multivariate spatial structure, a multivariate temporal structure, and a multivariate spatio-temporal interaction structure to account for possible correlations across injury severities over space, time, and spatio-temporal interaction, respectively. Income and weather indicators were found to have no significant effects on crash frequencies in the presence of vehicle miles traveled and unemployment rate. Both spatial and temporal effects were found to be important, and they played nearly the same roles for all three crash types in the studied dataset. Counties located in north and southwest Iowa were found to tend to have fewer crashes than the remaining counties. All three crash types generally showed descending trends from 2006 to 2015. They also had significantly positive correlations between each other in space but not in time. The crude crash rates and predicted crash rates were generally consistent for major injury and minor injury crashes but not for low-count fatal crashes. High-risk counties were identified using the posterior expected rank by the predicted crash cost rate, which was more able to truly represent the underlying traffic safety status than the rank by the crude crash cost rate.

Tennessee's In-state Vulnerability Assessment for a "Rapid Dissemination of Human Immunodeficiency Virus or Hepatitis C Virus Infection" Event Utilizing Data About the Opioid Epidemic.

Knowing which factors contribute to county-level vulnerability to a human immunodeficiency virus (HIV)/hepatitis C virus (HCV) outbreak, and which counties are most vulnerable, guides public health and clinical interventions. We therefore examined the impact of locally available indicators related to the opioid epidemic on prior national models of HIV/HCV outbreak vulnerability. Tennessee's 95 counties were the study sample. Predictors from 2012 and 2013 were used, mirroring prior methodology from the US Centers for Disease Control and Prevention (CDC). Acute HCV incidence was the proxy measure of county-level vulnerability. Seventy-eight predictors were identified as potentially predictive for HIV/HCV vulnerability. We used multiple dimension reduction techniques to determine predictors for inclusion and Poisson regression to generate a composite index score ranking county-level vulnerability for HIV/HCV. There was overlap of high-risk counties with the national analysis (25 of 41 counties). The distribution of vulnerability reinforces earlier research indicating that eastern Tennessee is at particularly high risk but also demonstrates that the entire state has high vulnerability. Prior research placed Tennessee among the top states for opioid prescribing, acute HCV infection, and greatest risk for an HIV/HCV outbreak. Given this confluence of risk, the Tennessee Department of Health expanded upon prior work to include more granular, local data, including on opioid prescribing. We also explored nonfatal and fatal overdoses. The more complete statewide view of risk generated, not only in eastern counties but also in the western corridor, will enable local officials to monitor vulnerability and better target resources.

Sociodemographic Determinants of Waitlist and Posttransplant Survival Among End-Stage Liver Disease Patients.

While regional organ availability dominates discussions of distribution policy, community-level disparities remain poorly understood. We studied micro-geographic determinants of survival risk and their distribution across Donor Service Areas (DSAs). Scientific Registry of Transplant Recipients records for all adults waitlisted for liver transplantation 2002-2014 were reviewed. The primary exposure variables were county-level sociodemographic risk, as measured by the Community Health Score (CHS), a previously-validated composite index local health conditions, and distance to listing transplant center. Among 114 347 patients, the median CHS was 19.4 (range: 0-40). Compared the lowest risk counties (CHS 1-10), highest-risk counties (CHS 31-40) had more black (14.6% vs. 5.4%), publicly insured (44.9% vs. 33.0), and remote candidates (34.0% vs. 15.1% living >100 miles away). Higher-CHS candidates had greater waitlist mortality in Cox multivariable (HR 1.16 for CHS 31-40, 95% CI 1.11-1.21) and competing risks analysis (sHR 1.07, 95% CI 0.99-1.14). Post-transplant survival was similar across CHS quartiles. Living >25 miles from the transplant center conferred excess mortality risk (sHR 1.08, 95% CI 1.03-1.12). Proposed distribution changes would disproportionately impact DSAs with more high-CHS or distant candidates. Low-income, rural and minority patients experience excess mortality while awaiting transplant, and risk disproportionately worse outcomes with reduced organ availability under current proposals.