A Platform for Monitoring Regional Antimicrobial Resistance, Using Online Data Sources: ResistanceOpen

Current and emerging strategies to curb antibiotic-resistant urinary tract infections

Rising rates of antibiotic resistance in uropathogenic bacteria compromise patient outcomes and prolong hospital stays. Consequently, new strategies are needed to prevent and control the spread of antibiotic resistance in uropathogenic bacteria. Over the past two decades, sizeable clinical efforts and research advances have changed urinary tract infection (UTI) treatment and prevention strategies to conserve antibiotic use. The emergence of antimicrobial stewardship, policies from national societies, and the development of new antimicrobials have shaped modern UTI practices. Future UTI management practices could be driven by the evolution of antimicrobial stewardship, improved and readily available diagnostics, and an improved understanding of how the microbiome affects UTI. Forthcoming UTI treatment and prevention strategies could employ novel bactericidal compounds, combinations of new and classic antimicrobials that enhance bacterial killing, medications that prevent bacterial attachment to uroepithelial cells, repurposing drugs, and vaccines to curtail the rising rates of antibiotic resistance in uropathogenic bacteria and improve outcomes in people with UTI.

Natural Antimicrobial Mixtures Disrupt Attachment and Survival of E. coli and C. jejuni to Non-Organic and Organic Surfaces

The contact and adherence of bacteria to various surfaces has significant consequences on biofilm formation through changes in bacterial surface structures or gene expression with potential ramifications on plant and animal health. Therefore, this study aimed to investigate the effect of organic acid-based mixtures (Ac) on the ability Campylobacter jejuni and Escherichia coli to attach and form biofilm on various surfaces, including plastic, chicken carcass skins, straw bedding, and eggshells. Moreover, we aimed to explore the effect of Ac on the expression of E. coli (luxS, fimC, csgD) and C. jejuni (luxS, flaA, flaB) bacterial genes involved in the attachment and biofilm formation via changes in bacterial surface polysaccharidic structures. Our results show that Ac had a significant effect on the expression of these genes in bacteria either attached to these surfaces or in planktonic cells. Moreover, the significant decrease in bacterial adhesion was coupled with structural changes in bacterial surface polysaccharide profiles, impacting their adhesion and biofilm-forming ability. Essentially, our findings accentuate the potential of natural antimicrobials, such as Ac, in reducing bacterial attachment and biofilm formation across various environments, suggesting promising potential applications in sectors like poultry production and healthcare.

Standardization and evaluation of indicators for quantifying antimicrobial use on U.S. dairy farms

Antimicrobial resistance (AMR) is a global One Health threat. A portion of AMR development can be attributed to antimicrobial use (AMU) in animals, including dairy cattle. Quantifying AMU on U.S. dairy farms is necessary to inform antimicrobial stewardship strategies and help evaluate the relationship between AMU and AMR. Many AMU indicators have been proposed for quantifying AMU in dairy cattle. However, these indicators are difficult to interpret and compare because they differ in the type of data used, the calculation approach, and the definitions of variables and parameters used in the calculation. Therefore, we selected 16 indicators (count-based, mass-based, and dose-based) applicable for quantifying AMU on U.S. dairy farms. We systematized the indicators by standardizing their variables and parameters to improve their interchangeability, interpretation, and comparability. We scored indicators against six data-driven criteria (assessing their accuracy, data and effort needs, and level of privacy concern) and five stewardship-driven criteria (assessing their ability to capture trends and inform antimicrobial stewardship). The derived standardized indicators will aid farmers and veterinarians in selecting suitable indicators based on data availability and stewardship needs on a farm. The comparison of indicators revealed a trade-off requiring farmers to balance the granularity of data necessary for an accurate indicator and effort to collect the data, and a trade-off relevant to farmers interested in data sharing to inform stewardship because more accurate indicators are typically based on more sensitive information. Indicators with better accuracy tended to score better in stewardship criteria. Overall, two dose-based indicators, estimating the number of treatments and administered doses, scored best in accuracy and stewardship. Conversely, two count-based indicators, estimating the length of AMU, and a mass-based indicator, estimating the mass of administered antimicrobials, performed best in the effort and privacy criteria. These findings are expected to benefit One Health by aiding the uptake of farm-level AMU indicators by U.S. dairy farms.

Electronic information systems for One Health surveillance of antimicrobial resistance: a systematic scoping review

INTRODUCTION

Electronic information systems (EIS) that implement a 'One Health' approach by integrating antimicrobial resistance (AMR) data across the human, animal and environmental health sectors, have been identified as a global priority. However, evidence on the availability, technical capacities and effectiveness of such EIS is scarce.

METHODS

Through a qualitative synthesis of evidence, this systematic scoping review aims to: identify EIS for AMR surveillance that operate across human, animal and environmental health sectors; describe their technical characteristics and capabilities; and assess whether there is evidence for the effectiveness of the various EIS for AMR surveillance. Studies and reports between 1 January 2000 and 21 July 2021 from peer-reviewed and grey literature in the English language were included.

RESULTS

26 studies and reports were included in the final review, of which 27 EIS were described. None of the EIS integrated AMR data in a One Health approach across all three sectors. While there was a lack of evidence of thorough evaluations of the effectiveness of the identified EIS, several surveillance system effectiveness indicators were reported for most EIS. Standardised reporting of the effectiveness of EIS is recommended for future publications. The capabilities of the EIS varied in their technical design features, in terms of usability, data display tools and desired outputs. EIS that included interactive features, and geospatial maps are increasingly relevant for future trends in AMR data analytics.

CONCLUSION

No EIS for AMR surveillance was identified that was designed to integrate a broad range of AMR data from humans, animals and the environment, representing a major gap in global efforts to implement One Health approaches to address AMR.

A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria

Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.

Antimicrobial Resistance Surveillance to Support Decision-Making in a High-Prevalence Region: An Evaluation

Despite a high use of antibiotics and a significant burden of infectious disease, ongoing monitoring and reporting of antimicrobial resistant pathogens in rural and regional Australia is insufficient. Many geographically isolated regions of Australia have limited infrastructure, resources and fall outside of surveillance reach, limiting health services’ ability to provide an early warning signal and appropriate response. To monitor trends in the development of antimicrobial resistance (AMR), identify high-risk populations and to evaluate effectiveness of control and prevention in rural and regional Australia, a subnational surveillance system termed HOTspots was developed. To promote the best use of public health resources through the development of effective and efficient surveillance systems, we evaluated HOTspots and its prototype surveillance platform for data quality, acceptability, representativeness, and timeliness. We used the Centers for Disease Prevention and Control (CDC) guidelines for evaluating public health surveillance systems and assessed the four attributes using a descriptive analysis of quantitative data and a thematic analysis of qualitative data. We report that the HOTspots surveillance system and its prototype platform effectively captures and represents AMR data across Northern Australia. The descriptive analysis of HOTspots data demonstrated some variation in data completeness but that data validity and representativeness were high. Thematic analysis of interview transcripts found that the system was acceptable, with almost all study participants identifying timeliness, online accessibility, and community representativeness as drivers for adoption of the system, and that the system provided timely data. The evaluation also identified areas for improvement and made recommendations to the HOTspots surveillance system and its associated prototype platform.

Confidence interval methods for antimicrobial resistance surveillance data

Background

Antimicrobial resistance (AMR) is one of the greatest global health challenges today, but burden assessment is hindered by uncertainty of AMR prevalence estimates. Geographical representation of AMR estimates typically pools data collected from several laboratories; however, these aggregations may introduce bias by not accounting for the heterogeneity of the population that each laboratory represents.

Methods

We used AMR data from up to 381 laboratories in the United States from The Surveillance Network to evaluate methods for estimating uncertainty of AMR prevalence estimates. We constructed confidence intervals for the proportion of resistant isolates using (1) methods that account for the clustered structure of the data, and (2) standard methods that assume data independence. Using samples of the full dataset with increasing facility coverage levels, we examined how likely the estimated confidence intervals were to include the population mean.

Results

Methods constructing 95% confidence intervals while accounting for possible within-cluster correlations (Survey and standard methods adjusted to employ cluster-robust errors), were more likely to include the sample mean than standard methods (Logit, Wilson score and Jeffreys interval) operating under the assumption of independence. While increased geographical coverage improved the probability of encompassing the mean for all methods, large samples still did not compensate for the bias introduced from the violation of the data independence assumption.

Conclusion

General methods for estimating the confidence intervals of AMR rates that assume data are independent, are likely to produce biased results. When feasible, the clustered structure of the data and any possible intra-cluster variation should be accounted for when calculating confidence intervals around AMR estimates, in order to better capture the uncertainty of prevalence estimates.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13756-021-00960-5.

Filling the gaps in the global prevalence map of clinical antimicrobial resistance

Surveillance is critical in containing globally increasing antimicrobial resistance (AMR). Affordable methodologies to prioritize AMR surveillance efforts are urgently needed, especially in low- and middle-income countries (LMICs), where resources are limited. While socioeconomic characteristics correlate with clinical AMR prevalence, this correlation has not yet been used to estimate AMR prevalence in countries lacking surveillance. We captured the statistical relationship between AMR prevalence and socioeconomic characteristics in a suite of beta-binomial principal component regression models for nine pathogens resistant to 19 (classes of) antibiotics. Prevalence data from ResistanceMap were combined with socioeconomic profiles constructed from 5,595 World Bank indicators. Cross-validated models were used to estimate clinical AMR prevalence and temporal trends for countries lacking data. Our approach provides robust estimates of clinical AMR prevalence in LMICs for most priority pathogens (cross-validated q ² > 0.78 for six out of nine pathogens). By supplementing surveillance data, 87% of all countries worldwide, which represent 99% of the global population, are now informed. Depending on priority pathogen, our estimates benefit 2.1 to 4.9 billion people living in countries with currently insufficient diagnostic capacity. By estimating AMR prevalence worldwide, our approach allows for a data-driven prioritization of surveillance efforts. For carbapenem-resistant Acinetobacter baumannii and third-generation cephalosporin-resistant Escherichia coli, specific countries of interest are located in the Middle East, based on the magnitude of estimates; sub-Saharan Africa, based on the relative prevalence increase over 1998 to 2017; and the Pacific Islands, based on improving overall model coverage and performance.

Quantification of antimicrobial usage in adult cows and preweaned calves on 40 large Wisconsin dairy farms using dose-based and mass-based metrics

Use of antimicrobials in animal agriculture is under increasing scrutiny, but the quantity of antimicrobials used on large US dairy farms has not been evaluated using data from large farms and different metrics. This study investigated total antimicrobial usage (AMU) in adult dairy cows and preweaned calves (PWC) and contrasted 2 metrics used for measurement of AMU. Wisconsin dairy farms were eligible if they had >250 lactating cows, maintained computerized animal health records, and were willing to allow researchers access to treatment records. Animal health data for a 1-yr period was retrospectively collected from computerized records, and a farm visit was performed to verify case definitions and recording accuracy. Both dose-based (animal daily doses; ADD) and mass-based (total mg of antimicrobials per kg of body weight; BW) metrics were calculated at the herd, cow, and PWC levels. Descriptive statistics for AMU were examined for both age groups. Mean AMU was compared among active ingredients and route of usage using ANOVA models that included farm as a random variable. At enrollment, farms (n = 40) contained approximately 52,639 cows (mean: 1,316 ± 169; 95% CI: 975, 1657) and 6,281 PWC (mean: 180 ± 33; 95% CI: 112, 247). When estimated using ADD, total herd AMU was 17.2 ADD per 1,000 animal-days (95% CI: 14.9, 19.5), with 83% of total herd-level AMU in adult cows. When estimated using the mass-based metric, total herd AMU was 13.6 mg of antimicrobial per kilogram of animal BW (95% CI: 10.3, 17.0), with 86% of total AMU used in adult cows. For cows, 78% of total ADD (15.8 ADD per 1,000 cow-d) was administered as intramammary (IMM) preparations. In contrast, when AMU was estimated using a mass-based metric, IMM preparations represented only 24% of total AMU (12.1 mg of antimicrobial/kg of cow BW). For cows, ceftiofur was the primary antimicrobial used and accounted for 53% of total ADD, with 80% attributed to IMM and 20% attributed to injectable treatments. When estimated using a mass-based metric, ampicillin was the predominant antimicrobial used in cows and accounted for 33% of total antimicrobial mass per kilogram of BW. When AMU was estimated for PWC using ADD, injectable antimicrobials represented 79% of total usage (28.3 ADD per 1,000 PWC-d). In contrast, when AMU was estimated for PWC using a mass-based metric, injectable products represented 42% of total AMU, even though more farms administered antimicrobials using this route. When AMU in PWC was summarized using ADD, penicillin represented 32% of AMU, and there were no significant differences in ADD among ampicillin, oxytetracycline or enrofloxacin. When a mass-based metric was used to estimate AMU in PWC, oral products (sulfadimethoxine and trimethoprim-sulfa) represented more than half of the total AMU given to this group. Overall, these results showed that choice of metric and inclusion of different age groups can substantially influence interpretation of AMU on dairy farms.

Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens

(1) Background: The high use of antibiotics has made the issue of antimicrobial resistance (AMR) increasingly serious, which poses a substantial threat to the health of animals and humans. However, there remains a certain gap in the AMR system and risk assessment models between China and the advanced world level. Therefore, this paper aims to provide advanced means for the monitoring of antibiotic use and AMR data, and take piglets as an example to evaluate the risk and highlight the seriousness of AMR in China. (2) Methods: Based on the principal component analysis method, a drug resistance index model of anti-E. coli drugs was established to evaluate the antibiotic risk status in China. Additionally, based on the second-order Monte Carlo methods, a disease risk assessment model for piglets was established to predict the probability of E. coli disease within 30 days of taking florfenicol. Finally, a browser/server architecture-based visualization database system for animal-derived pathogens was developed. (3) Results: The risk of E. coli in the main area was assessed and Hohhot was the highest risk area in China. Compared with the true disease risk probability of 4.1%, the result of the disease risk assessment model is 7.174%, and the absolute error was 3.074%. Conclusions: Taking E. coli as an example, this paper provides an innovative method for rapid and accurate risk assessment of drug resistance. Additionally, the established system and assessment models have potential value for the monitoring and evaluating AMR, highlight the seriousness of antimicrobial resistance, advocate the prudent use of antibiotics, and ensure the safety of animal-derived foods and human health.

The role of "spillover" in antibiotic resistance

Antibiotic use is a key driver of antibiotic resistance. Understanding the quantitative association between antibiotic use and resulting resistance is important for predicting future rates of antibiotic resistance and for designing antibiotic stewardship policy. However, the use-resistance association is complicated by "spillover," in which one population's level of antibiotic use affects another population's level of resistance via the transmission of bacteria between those populations. Spillover is known to have effects at the level of families and hospitals, but it is unclear if spillover is relevant at larger scales. We used mathematical modeling and analysis of observational data to address this question. First, we used dynamical models of antibiotic resistance to predict the effects of spillover. Whereas populations completely isolated from one another do not experience any spillover, we found that if even 1% of interactions are between populations, then spillover may have large consequences: The effect of a change in antibiotic use in one population on antibiotic resistance in that population could be reduced by as much as 50%. Then, we quantified spillover in observational antibiotic use and resistance data from US states and European countries for three pathogen-antibiotic combinations, finding that increased interactions between populations were associated with smaller differences in antibiotic resistance between those populations. Thus, spillover may have an important impact at the level of states and countries, which has ramifications for predicting the future of antibiotic resistance, designing antibiotic resistance stewardship policy, and interpreting stewardship interventions.

Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations

BACKGROUND

In the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease's rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19.

OBJECTIVE

The aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations.

METHODS

We discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the prevention and control of COVID-19. Next, we discuss China's new data collection methods and new information assembled after the outbreak of COVID-19. Based on the data and information collected in China, we analyzed the application of big data technology from the perspectives of data sources, data application logic, data application level, and application results. In addition, we analyzed the issues, challenges, and responses encountered by China in the application of big data technology from four perspectives: data access, data use, data sharing, and data protection. Suggestions for improvements are made for data collection, data circulation, data innovation, and data security to help understand China's response to the epidemic and to provide lessons for other countries' prevention and control of COVID-19.

RESULTS

In the process of the prevention and control of COVID-19 in China, big data technology has played an important role in personal tracking, surveillance and early warning, tracking of the virus's sources, drug screening, medical treatment, resource allocation, and production recovery. The data used included location and travel data, medical and health data, news media data, government data, online consumption data, data collected by intelligent equipment, and epidemic prevention data. We identified a number of big data problems including low efficiency of data collection, difficulty in guaranteeing data quality, low efficiency of data use, lack of timely data sharing, and data privacy protection issues. To address these problems, we suggest unified data collection standards, innovative use of data, accelerated exchange and circulation of data, and a detailed and rigorous data protection system.

CONCLUSIONS

China has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.

Geospatial epidemiology of Staphylococcus aureus in a tropical setting: an enabling digital surveillance platform

Delivery of information to clinicians on evolving antimicrobial susceptibility needs to be accurate for the local needs, up-to-date and readily available at point of care. In northern Australia, bacterial infection rates are high but resistance to first- and second-line antibiotics is poorly described and currently-available datasets exclude primary healthcare data. We aimed to develop an online geospatial and interactive platform for aggregating, analysing and disseminating data on regional bacterial pathogen susceptibility. We report the epidemiology of Staphylococcus aureus as an example of the power of digital platforms to tackle the growing spread of antimicrobial resistance in a high-burden, geographically-sparse region and beyond. We developed an online geospatial platform called HOTspots that visualises antimicrobial susceptibility patterns and temporal trends. Data on clinically-important bacteria and their antibiotic susceptibility profiles were sought from retrospectively identified clinical specimens submitted to three participating pathology providers (96 unique tertiary and primary healthcare centres, n = 1,006,238 tests) between January 2008 and December 2017. Here we present data on S. aureus only. Data were available on specimen type, date and location of collection. Regions from the Australian Bureau of Statistics were used to provide spatial localisation. The online platform provides an engaging visual representation of spatial heterogeneity, demonstrating striking geographical variation in S. aureus susceptibility across northern Australia. Methicillin resistance rates vary from 46% in the west to 26% in the east. Plots generated by the platform show temporal trends in proportions of S. aureus resistant to methicillin and other antimicrobials across the three jurisdictions of northern Australia. A quarter of all, and up to 35% of methicillin-resistant S. aureus (MRSA) blood isolates in parts of the northern Australia were resistant to inducible-clindamycin. Clindamycin resistance rates in MRSA are worryingly high in regions of northern Australia and are a local impediment to empirical use of this agent for community MRSA. Visualising routinely collected laboratory data with digital platforms, allows clinicians, public health physicians and guideline developers to monitor and respond to antimicrobial resistance in a timely manner. Deployment of this platform into clinical practice supports national and global efforts to innovate traditional disease surveillance systems with the use of digital technology and to provide practical solutions to reducing the threat of antimicrobial resistance.

Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections

Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.

Fluoroquinolone use for uncomplicated urinary tract infections in women: a retrospective cohort study

OBJECTIVES

The United States Food & Drug Administration released an advisory in 2016 that fluoroquinolones be relegated to second-line agents for uncomplicated urinary tract infections (UTIs) given reports of rare but serious side effects; similar warnings have followed from Health Canada and the European Medicines Agency. The objective was to determine whether alternative non-fluoroquinolone agents are as effective as fluoroquinolones in the treatment of UTIs.

METHODS

We conducted a retrospective population-based cohort study using administrative health data from six Canadian provinces. We identified women (n = 1 585 997) receiving antibiotic treatment for episodes of uncomplicated UTIs (n = 2 857 243) between January 1 2005 and December 31 2015. Clinical outcomes within 30 days from the initial antibiotic dispensation were compared among patients treated with a fluoroquinolone versus non-fluoroquinolone agents. High-dimensional propensity score adjustments were used to ensure comparable treatment groups and to minimize residual confounding.

RESULTS

Fluoroquinolone use for UTI declined over the study period in five of six Canadian provinces and accounted for 22.3-48.5% of treatments overall. The pooled effect across the provinces indicated that fluoroquinolones were associated with fewer return outpatient visits (OR 0.89, 95%CI 0.87-0.92), emergency department visits (OR 0.74, 95%CI 0.61-0.89), hospitalizations (OR 0.83, 95%CI 0.77-0.88), and repeat antibiotic dispensations (OR 0.77, 95%CI 0.75-0.80) within 30 days.

CONCLUSIONS

Fluoroquinolones are associated with improved clinical outcomes among women with uncomplicated UTIs. This benefit must be weighed against the risk of fluoroquinolone resistance and rare but serious fluoroquinolone side effects when selecting first-line treatment for these patients.

The distribution of antibiotic use and its association with antibiotic resistance

Antibiotic use is a primary driver of antibiotic resistance. However, antibiotic use can be distributed in different ways in a population, and the association between the distribution of use and antibiotic resistance has not been explored. Here, we tested the hypothesis that repeated use of antibiotics has a stronger association with population-wide antibiotic resistance than broadly-distributed, low-intensity use. First, we characterized the distribution of outpatient antibiotic use across US states, finding that antibiotic use is uneven and that repeated use of antibiotics makes up a minority of antibiotic use. Second, we compared antibiotic use with resistance for 72 pathogen-antibiotic combinations across states. Finally, having partitioned total use into extensive and intensive margins, we found that intense use had a weaker association with resistance than extensive use. If the use-resistance relationship is causal, these results suggest that reducing total use and selection intensity will require reducing broadly distributed, low-intensity use.

Big Data's Role in Precision Public Health

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

Evaluating the Relationship Between Hospital Antibiotic Use and Antibiotic Resistance in Common Nosocomial Pathogens

OBJECTIVE The relationship between hospital antibiotic use and antibiotic resistance is poorly understood. We evaluated the association between antibiotic utilization and resistance in academic and community hospitals in Ontario, Canada. METHODS We conducted a multicenter observational ecological study of 37 hospitals in 2014. Hospital antibiotic purchasing data were used as an indicator of antibiotic use, whereas antibiotic resistance data were extracted from hospital indexes of resistance. Multivariate regression was performed, with antibiotic susceptibility as the primary outcome, antibiotic consumption as the main predictor, and additional covariates of interest (ie, hospital type, laboratory standards, and patient days). RESULTS With resistance data representing more than 90,000 isolates, we found the increased antibiotic consumption in defined daily doses per 1,000 patient days (DDDs/1,000 PD) was associated with decreased antibiotic susceptibility for Pseudomonas aeruginosa (-0.162% per DDD/1,000 PD; P=.119). However, increased antibiotic consumption predicted increased antibiotic susceptibility significantly for Escherichia coli (0.173% per DDD/1,000 PD; P=.005), Klebsiella spp (0.124% per DDD/1,000 PD; P=.004), Enterobacter spp (0.194% per DDD/1,000 PD; P=.003), and Enterococcus spp (0.309% per DDD/1,000 PD; P=.001), and nonsignificantly for Staphylococcus aureus (0.012% per DDD/1,000 PD; P=.878). Hospital type (P=.797) and laboratory standard (P=.394) did not significantly predict antibiotic susceptibility, while increased hospital patient days generally predicted increased organism susceptibility (0.728% per 10,000 PD; P<.001). CONCLUSIONS We found that hospital-specific antibiotic usage was generally associated with increased, rather than decreased hospital antibiotic susceptibility. These findings may be explained by community origins for many hospital-diagnosed infections and practitioners choosing agents based on local antibiotic resistance patterns. Infect Control Hosp Epidemiol 2017;38:1457-1463.

Big Data for Infectious Disease Surveillance and Modeling

We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts.