Improving the prediction of arbovirus outbreaks: A comparison of climate-driven models for West Nile virus in an endemic region of the United States.

Models that forecast the timing and location of human arboviral disease have the potential to make mosquito control and disease prevention more effective. A common approach is to use statistical time-series models that predict disease cases as lagged functions of environmental variables. However, the simplifying assumptions required for standard modeling approaches may not capture important aspects of complex, non-linear transmission cycles. Here, we compared a set of alternative models of human West Nile virus (WNV) in 2004-2017 in South Dakota, USA. We used county-level logistic regressions to model historical human case data as functions of distributed lag summaries of air temperature and several moisture indices. We tested two variations of the standard model in which 1) the distributed lag functions were allowed to change over the transmission season, so that dependence on past meteorological conditions was time varying rather than static, and 2) an additional predictor was included that quantified the mosquito infection growth rate estimated from mosquito surveillance data. The best-fitting model included temperature and vapor pressure deficit as meteorological predictors, and also incorporated time-varying lags and the mosquito infection growth rate. The time-varying lags helped to predict the seasonal pattern of WNV cases, whereas the mosquito infection growth rate improved the prediction of year-to-year variability in WNV risk. These relatively simple and practical enhancements may be particularly helpful for developing data-driven time series models for use in arbovirus forecasting applications.

The Impact of Maternal Diabetes, Obesity and Race on Infant Birth Weights in South Dakota.

INTRODUCTION: Maternal obesity, high gestational weight gain and diabetes mellitus during pregnancy are known risk factors that correlate with high infant birth weight and the mother's race. Previous studies have focused on low birth weight, prematurity and infant mortality. This study examined the interaction between race, maternal risk factors and high infant birth weights at the population level in South Dakota to identify factors contributing to the high Native American infant birth weights. We hypothesized that high infant birth weights were associated with maternal diabetes, obesity and high gestational weight gain, and that Native American infants' higher birth weights were related to the prevalence of diabetes and obesity. MATERIALS AND METHODS: De-identified birth certificate data was provided by the South Dakota Department of Health. We used data for live infant births to South Dakota resident mothers from 2006 through 2011. The mothers were categorized as Native American or white by the mother's self-reported primary race. Infants were excluded from the study population for missing data, birth weight less than 350 g or gestational age less than 24 weeks or greater than 45 weeks. The study population included 11,416 Native American infants and 59,263 white infants for a total study population of 70,679 infants. Maternal variables (race, pre-pregnancy weight and body mass index [BMI], gestational weight gain, pre-pregnancy diabetes mellitus [DM], gestational diabetes [GDM] and delivery BMI) and infant variables (gestational age and birth weight) were analyzed using SPSS software. RESULTS: The mean birth weight (BW) of Native American (NA) infants (3377 g) was significantly greater than the mean BW of white (W) infants (3315 g) even though NA infants had a younger mean gestational age (p = 0.006). More NA infants were categorized as high birth weight (HBW) (11.8 percent) than W infants (8.5 percent). Both DM and GDM were significantly more common among NA mothers. Infants of NA mothers with GDM had a higher mean BW than infants of W mothers with GDM. There were more overweight and obese NA mothers (p = 0.006). In each maternal BMI category, NA infants had a higher mean BW. Mean BW was even higher for infants born to mothers with excessive gestational weight gain (GWG) for their BMI. The infants with the highest mean BW were born to obese NA mothers with GDM and excessive GWG (3680 g). Multivariable linear regression showed that race was the most significant variable affecting infant BW (R2 = 0.57, F = 692). Pre-pregnancy BMI, GWG and excessive GWG were also significant. The most significant interaction variables were race and GDM and race and BMI. CONCLUSIONS: Native American race, gestational diabetes mellitus, overweight and obese BMI, and excessive gestational weight gain for BMI were the most significant maternal factors associated with high infant birth weight. Mothers with any one risk factor gave birth to heavier infants. Mothers with all risk factors had infants with the highest mean birth weights in South Dakota. This large population-based study provides evidence that Native American mothers in South Dakota with GDM, overweight or obese BMI and excessive GWG are more likely to give birth to high birth weight infants. At-risk mothers should be educated regarding the risks and potential complications of high birth weight infants.

West Nile Review: 15 Years of Human Disease in South Dakota, 2002-2016.

During the past 15 years, 2002-2016, West Nile virus (WNV) has emerged in South Dakota resulting in 509 neuroinvasive disease (NID) cases, 745 hospitalizations and 38 deaths. Culex tarsalis is the state's primary mosquito vector. South Dakota's average annual incidence of WNV-NID and death rate are the highest of any state in the U.S. WNV cases have been reported from all counties in the state. All age groups have been infected with cases peaking in the 40-44 year age group, but deaths peaking in cases 70 years and older. Although South Dakota's WNV season lasts six months, May-October, the first week of August has been the peak week of WNV disease onsets. West Nile is now enzootic in South Dakota. Every citizen, local mosquito control programs, medical and public health infrastructures must continue to prevent and respond to annual WNV outbreaks, and prepare for the next arboviral disease to emerge.

Integrating Environmental Monitoring and Mosquito Surveillance to Predict Vector-borne Disease: Prospective Forecasts of a West Nile Virus Outbreak.

INTRODUCTION: Predicting the timing and locations of future mosquito-borne disease outbreaks has the potential to improve the targeting of mosquito control and disease prevention efforts. Here, we present and evaluate prospective forecasts made prior to and during the 2016 West Nile virus (WNV) season in South Dakota, a hotspot for human WNV transmission in the United States. METHODS: We used a county-level logistic regression model to predict the weekly probability of human WNV case occurrence as a function of temperature, precipitation, and an index of mosquito infection status. The model was specified and fitted using historical data from 2004-2015 and was applied in 2016 to make short-term forecasts of human WNV cases in the upcoming week as well as whole-year forecasts of WNV cases throughout the entire transmission season. These predictions were evaluated at the end of the 2016 WNV season by comparing them with spatial and temporal patterns of the human cases that occurred. RESULTS: There was an outbreak of WNV in 2016, with a total of 167 human cases compared to only 40 in 2015. Model results were generally accurate, with an AUC of 0.856 for short-term predictions. Early-season temperature data were sufficient to predict an earlier-than-normal start to the WNV season and an above-average number of cases, but underestimated the overall case burden. Model predictions improved throughout the season as more mosquito infection data were obtained, and by the end of July the model provided a close estimate of the overall magnitude of the outbreak. CONCLUSIONS: An integrated model that included meteorological variables as well as a mosquito infection index as predictor variables accurately predicted the resurgence of WNV in South Dakota in 2016. Key areas for future research include refining the model to improve predictive skill and developing strategies to link forecasts with specific mosquito control and disease prevention activities.

Notes from the Field: Baseline Assessment of the Use of Ebola Rapid Diagnostic Tests--Forécariah, Guinea, October-November 2015.

The Ebola virus disease (Ebola) epidemic in West Africa began in Guinea in early 2014. The reemergence of Ebola and risk of ongoing, undetected transmission continues because of the potential for sexual transmission and other as yet unknown transmission pathways. On March 17, 2016, two new cases of Ebola in Guinea were confirmed by the World Health Organization. This reemergence of Ebola in Guinea is the first since the original outbreak in the country was declared over on December 29, 2015. The prefecture of Forécariah, in western Guinea, was considerably affected by Ebola in 2015, with an incidence rate of 159 cases per 100,000 persons. Guinea also has a high prevalence of malaria; in a nationwide 2012 survey, malaria prevalence was reported to be 44% among healthy children aged ≤5 years. Malaria is an important reason for seeking health care; during 2014, 34% of outpatient consultations were related to malaria.

Notes from the Field: Increase in Human Cases of Tularemia--Colorado, Nebraska, South Dakota, and Wyoming, January-September 2015.

Tularemia is a rare, often serious disease caused by a gram-negative coccobacillus, Francisella tularensis, which infects humans and animals in the Northern Hemisphere. Approximately 125 cases have been reported annually in the United States during the last two decades. As of September 30, a total of 100 tularemia cases were reported in 2015 among residents of Colorado (n = 43), Nebraska (n = 21), South Dakota (n = 20), and Wyoming (n = 16) (Figure). This represents a substantial increase in the annual mean number of four (975% increase), seven (200%), seven (186%) and two (70%) cases, respectively, reported in each state during 2004-2014.

Fourscore in South Dakota: lowering risk for longer life.

Life expectancy at birth in South Dakota is 79.5 years, ranking 18th longest in the U.S. South Dakota county-level life expectancies range from 73.2-82.1 years. County-level life expectancies correlate significantly with tobacco smoking, physical inactivity, obesity, diabetes, poverty and infant mortality rates, where counties with higher rates of smoking, inactivity, obesity, diabetes, poverty and infant mortality have shorter life expectancies. The critical disparity in life expectancy is the 12-year gap between American Indian and white South Dakotans.

Spatio-temporal epidemiology of human West Nile virus disease in South Dakota.

Despite a cold temperate climate and low human population density, the Northern Great Plains has become a persistent hot spot for human West Nile virus (WNV) disease in North America. Understanding the spatial and temporal patterns of WNV can provide insights into the epidemiological and ecological factors that influence disease emergence and persistence. We analyzed the 1,962 cases of human WNV disease that occurred in South Dakota from 2002-2012 to identify the geographic distribution, seasonal cycles, and interannual variability of disease risk. The geographic and seasonal patterns of WNV have changed since the invasion and initial epidemic in 2002-2003, with cases shifting toward the eastern portion of South Dakota and occurring earlier in the transmission season in more recent years. WNV cases were temporally autocorrelated at lags of up to six weeks and early season cumulative case numbers were correlated with seasonal totals, indicating the possibility of using these data for short-term early detection of outbreaks. Epidemiological data are likely to be most effective for early warning of WNV virus outbreaks if they are integrated with entomological surveillance and environmental monitoring to leverage the strengths and minimize the weaknesses of each information source.

School illness absenteeism during 2009 influenza A (H1N1) pandemic--South Dakota, 2009-2010.

Schools are important amplification settings of influenza virus transmission. We demonstrated correlation of school absenteeism (due to any illness) with other influenza A (H1N1) activity surveillance data during the 2009 pandemic. We collected nonspecific illness student absenteeism data from August 17, 2009 through April 3, 2010 from 187 voluntarily participating South Dakota schools using weekly online surveys. Relative risks (RR) were calculated as the ratio of the probability of absenteeism during elevated weeks versus the probability of absenteeism during the baseline weeks (RR = 1.89). We used Pearson correlation to associate absenteeism with laboratory-confirmed influenza cases, influenza cases diagnosed by rapid tests, influenza-associated hospitalizations and deaths reported in South Dakota during the 2009 H1N1 pandemic period. School-absenteeism data correlated strongly with data from these other influenza surveillance sources.

Premature mortality patterns among American Indians in South Dakota, 2000-2010.

BACKGROUND: American Indians in South Dakota have the highest mortality rates in the nation compared to other racial and ethnic groups and American Indians in other states. PURPOSE: Cause-related and age-specific mortality patterns among American Indians in South Dakota are identified to guide prevention planning and policy efforts designed to reduce mortality within this population, in both South Dakota and other parts of the U.S. METHODS: Death certificate data from South Dakota (2000-2010), on 5738 American Indians and 70,580 whites, were used to calculate age-specific mortality rates and rate ratios. These values were examined in order to identify patterns among the leading causes of death. Analyses were completed in 2011 and 2012. RESULTS: Within the South Dakota population, 70% of American Indians died before reaching age 70 years, compared to 25% of whites. Fatal injuries and chronic diseases were the leading causes of premature mortality. Nine leading causes of death showed consistent patterns of mortality disparity between American Indians and whites, with American Indians having significantly higher rates of mortality at lower ages. CONCLUSIONS: Premature mortality among American Indians in South Dakota is a serious public health problem. Unified efforts at the federal, tribal, state, and local levels are needed to reduce premature death within this population.

Vaccine-preventable diseases and vaccination rates in South Dakota.

Vaccine-preventable diseases have historically caused much illness and death in South Dakota. Sixty-seven diphtheria deaths were reported in 1892 and 1,017 polio cases were reported at the peak of the polio epidemic in 1952. As vaccines have been developed, licensed and put into wide use, the rates of diphtheria, polio, measles, smallpox and other diseases have successfully decreased leading to control, statewide elimination or eradication. Other diseases, such as pertussis, have been more difficult to control by vaccination alone. Although current vaccination coverage rates for South Dakota's kindergarten children surpass the Healthy People 2020 targets of 95 percent, the coverage rates for 2-year-old children and teenagers are below the target rates. Until vaccine-preventable diseases are eradicated globally, we must vigilantly maintain high vaccination coverage rates and aggressively apply control measures to limit transmission when diseases do occur in South Dakota.

State health department perceived utility of and satisfaction with ArboNET, the U.S. National Arboviral Surveillance System.

OBJECTIVES: We assessed the perceived utility of data collected through ArboNET, the national arboviral surveillance system, and evaluated state health department user satisfaction with system function. METHODS: We used an online assessment tool to collect information about types of arboviral surveillance conducted, user satisfaction with ArboNET's performance, and use of data collected by the system. Representatives of all 53 reporting jurisdictions were asked to complete the assessment during spring 2009. RESULTS: Representatives of 48 (91%) jurisdictions completed the assessment. Two-thirds of respondents were satisfied with ArboNET's overall performance. Most concerns were related to data transmission, particularly the lack of compatibility with the National Electronic Disease Surveillance System (NEDSS). Users found mosquito (85%), human disease (80%), viremic blood donor (79%), and veterinary disease (75%) surveillance data to be useful. While there was disagreement about the usefulness of avian mortality and sentinel animal surveillance, only 15% of users supported eliminating these categories. Respondents found weekly maps and tables posted on the U.S. Geological Survey (92%) and CDC (88%) websites to be the most useful reports generated from ArboNET data. Although many jurisdictions were willing to report additional clinical or laboratory data, time and resource constraints were considerations. Most respondents (71%) supported review and possible revision of the national case definition for human arboviral disease. CONCLUSIONS: As a result of this assessment, CDC and partner organizations have made ArboNET NEDSS-compatible and revised national case definitions for arboviral disease. Alternative data-sharing and reporting options are also being considered. Continued evaluation of ArboNET will help ensure that it continues to be a useful tool for national arboviral disease surveillance.

Landscape-level spatial patterns of West Nile virus risk in the northern Great Plains.

Understanding the landscape-level determinants of West Nile virus (WNV) can aid in mapping high-risk areas and enhance disease control and prevention efforts. This study analyzed the spatial patterns of human WNV cases in three areas in South Dakota during 2003-2007 and investigated the influences of land cover, hydrology, soils, irrigation, and elevation by using case-control models. Land cover, hydrology, soils, and elevation all influenced WNV risk, although the main drivers were different in each study area. Risk for WNV was generally higher in areas with rural land cover than in developed areas, and higher close to wetlands or soils with a high ponding frequency. In western South Dakota, WNV risk also decreased with increasing elevation and was higher in forested areas. Our results showed that the spatial patterns of human WNV risk were associated with landscape-level features that likely reflect variability in mosquito ecology, avian host communities, and human activity.

A pre-pandemic outbreak of triple-reassortant swine influenza virus infection among university students, South Dakota, 2008.

BACKGROUND: After identifying a student with triple-reassortant swine influenza virus (SIV) infection and pig exposure at a livestock event, we investigated whether others were infected and if human-to-human transmission occurred. METHODS: We conducted a cohort study and serosurvey among persons exposed to (1) event pigs, (2) other pigs, (3) the index case, and (4) persons without pig or index case exposure. Confirmed cases had respiratory specimens positive for SIV within 2 weeks of the index case's illness. Probable and suspected cases had illness and (1) exposure to any pig or (2) contact with a confirmed case preceding illness. Probable cases were seropositive. Suspected cases did not give serum samples. RESULTS: Of 99 event pig-exposed students, 72 (73%) participated in the investigation, and 42 (42%) provided serum samples, of whom 17 (40%) were seropositive and 5 (12%) met case criteria. Of 9 students exposed to other pigs, 2 (22%) were seropositive. Of 8 index case-exposed persons and 10 without exposures, none were seropositive. Pig-exposed persons were more likely to be seropositive than persons without pig exposure (37% vs 0%, P < .01). CONCLUSIONS: We identified an outbreak of human SIV infection likely associated with a livestock event; there was no evidence of human-to-human transmission.

Geographic variability in geocoding success for West Nile virus cases in South Dakota.

Geocoding, the process of assigning each case a set of coordinates that closely approximates its true location, is an important component of spatial epidemiological studies. The failure to accurately geocode cases adversely affects the validity and strength of conclusions drawn from the analysis. We investigated whether there were differences among geographic locations and demographic classes in the ability to successfully geocode West Nile virus (WNV) cases in South Dakota. We successfully geocoded 1354 cases (80.8%) to their street address locations and assigned all 1676 cases to ZIP code tabulation areas (ZCTAs). Using spatial scan statistics, significant clusters of non-geocoded cases were identified in central and western South Dakota. Geocoding success rates were lower in areas of low population density and on Indian reservations than in other portions of the state. Geocoding success rates were lower for Native Americans than for other races. Spatial epidemiological studies should consider the potential biases that may result from excluding non-geocoded cases, particularly in rural portions of the Great Plains that contain large Native American populations.

Tobacco deaths in South Dakota.

Seventeen percent of South Dakota deaths during 2004-2007 were associated with tobacco use. Cancer, heart disease and chronic lower respiratory disease deaths were most commonly linked to tobacco use. Men were more likely to die of a tobacco-associated disease than women. Tobacco use during pregnancy increased the infant mortality rate. Reducing tobacco use would reduce early and preventable deaths.