Risk for Asymptomatic Household Transmission of Clostridioides difficile Infection Associated with Recently Hospitalized Family Members.

We evaluated whether hospitalized patients without diagnosed Clostridioides difficile infection (CDI) increased the risk for CDI among their family members after discharge. We used 2001-2017 US insurance claims data to compare monthly CDI incidence between persons in households with and without a family member hospitalized in the previous 60 days. CDI incidence among insurance enrollees exposed to a recently hospitalized family member was 73% greater than enrollees not exposed, and incidence increased with length of hospitalization among family members. We identified a dose-response relationship between total days of within-household hospitalization and CDI incidence rate ratio. Compared with persons whose family members were hospitalized <1 day, the incidence rate ratio increased from 1.30 (95% CI 1.19-1.41) for 1-3 days of hospitalization to 2.45 (95% CI 1.66-3.60) for >30 days of hospitalization. Asymptomatic C. difficile carriers discharged from hospitals could be a major source of community-associated CDI cases.

COVID-19 modeling and non-pharmaceutical interventions in an outpatient dialysis unit.

This paper describes a data-driven simulation study that explores the relative impact of several low-cost and practical non-pharmaceutical interventions on the spread of COVID-19 in an outpatient hospital dialysis unit. The interventions considered include: (i) voluntary self-isolation of healthcare personnel (HCPs) with symptoms; (ii) a program of active syndromic surveillance and compulsory isolation of HCPs; (iii) the use of masks or respirators by patients and HCPs; (iv) improved social distancing among HCPs; (v) increased physical separation of dialysis stations; and (vi) patient isolation combined with preemptive isolation of exposed HCPs. Our simulations show that under conditions that existed prior to the COVID-19 outbreak, extremely high rates of COVID-19 infection can result in a dialysis unit. In simulations under worst-case modeling assumptions, a combination of relatively inexpensive interventions such as requiring surgical masks for everyone, encouraging social distancing between healthcare professionals (HCPs), slightly increasing the physical distance between dialysis stations, and-once the first symptomatic patient is detected-isolating that patient, replacing the HCP having had the most exposure to that patient, and relatively short-term use of N95 respirators by other HCPs can lead to a substantial reduction in both the attack rate and the likelihood of any spread beyond patient zero. For example, in a scenario with R0 = 3.0, 60% presymptomatic viral shedding, and a dialysis patient being the infection source, the attack rate falls from 87.8% at baseline to 34.6% with this intervention bundle. Furthermore, the likelihood of having no additional infections increases from 6.2% at baseline to 32.4% with this intervention bundle.

Risk for Clostridioides difficile Infection Among Hospitalized Patients Associated With Multiple Healthcare Exposures Prior to Admission.

BACKGROUND: Clostridioides difficile infection (CDI) is a common healthcare-associated infection and is often used as an indicator of hospital safety or quality. However, healthcare exposures occurring prior to hospitalization may increase risk for CDI. We conducted a case-control study comparing hospitalized patients with and without CDI to determine if healthcare exposures prior to hospitalization (ie, clinic visits, antibiotics, family members with CDI) were associated with increased risk for hospital-onset CDI, and how risk varied with time between exposure and hospitalization. METHODS: Records were collected from a large insurance-claims database from 2001 to 2017 for hospitalized adult patients. Prior healthcare exposures were identified using inpatient, outpatient, emergency department, and prescription drug claims; results were compared between various CDI case definitions. RESULTS: Hospitalized patients with CDI had significantly more frequent healthcare exposures prior to admission. Healthcare visits, antibiotic use, and family exposures were associated with greater likelihood of CDI during hospitalization. The degree of association diminished with time between exposure and hospitalization. Results were consistent across CDI case definitions. CONCLUSIONS: Many different prior healthcare exposures appear to increase risk for CDI presenting during hospitalization. Moreover, patients with CDI typically have multiple exposures prior to admission, confounding the ability to attribute cases to a particular stay.

Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) Framework for Describing and Reporting Multidrug-resistant Organism and Healthcare-Associated Infections Agent-based Modeling Methods.

Mathematical modeling of healthcare-associated infections and multidrug-resistant organisms improves our understanding of pathogen transmission dynamics and provides a framework for evaluating prevention strategies. One way of improving the communication among modelers is by providing a standardized way of describing and reporting models, thereby instilling confidence in the reproducibility and generalizability of such models. We updated the Overview, Design concepts, and Details protocol developed by Grimm et al [11] for describing agent-based models (ABMs) to better align with elements commonly included in healthcare-related ABMs. The Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) framework includes the following 9 key elements: (1) Purpose and scope; (2) Entities, state variables, and scales; (3) Initialization; (4) Process overview and scheduling; (5) Input data; (6) Agent interactions and organism transmission; (7) Stochasticity; (8) Submodels; and (9) Model verification, calibration, and validation. Our objective is that this framework will improve the quality of evidence generated utilizing these models.

Mapping autism risk loci using genetic linkage and chromosomal rearrangements.

Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.

Comparing multiple infection control measures in a nursing home setting: a simulation study.

OBJECTIVE: Compare the effectiveness of multiple mitigation measures designed to protect nursing home residents from infectious disease outbreaks. DESIGN: Agent-based simulation study. SETTING: Simulation environment of a small nursing home. METHODS: We collected temporally detailed and spatially fine-grained location information from nursing home healthcare workers (HCWs) using sensor motes. We used these data to power an agent-based simulation of a COVID-19 outbreak using realistic time-varying estimates of infectivity and diagnostic sensitivity. Under varying community prevalence and transmissibility, we compared the mitigating effects of (i) regular screening and isolation, (ii) inter-resident contact restrictions, (iii) reduced HCW presenteeism, and (iv) modified HCW scheduling. RESULTS: Across all configurations tested, screening every other day and isolating positive cases decreased the attack rate by an average of 27% to 0.501 on average, while contact restrictions decreased the attack rate by an average of 35%, resulting in an attack rate of only 0.240, approximately half that of screening/isolation. Combining both interventions impressively produced an attack rate of only 0.029. Halving the observed presenteeism rate led to an 18% decrease in the attack rate, but if combined with screening every 6 days, the effect of reducing presenteeism was negligible. Altering work schedules had negligible effects on the attack rate. CONCLUSIONS: Universal contact restrictions are highly effective for protecting vulnerable nursing home residents, yet adversely affect physical and mental health. In high transmission and/or high community prevalence situations, restricting inter-resident contact to groups of 4 was effective and made highly effective when paired with weekly testing.

Using a Fitbit-based Walking Game to Improve Physical Activity Among U.S. Veterans.

INTRODUCTION: Physical inactivity, hereafter inactivity, is a serious health problem among U.S. veterans, hereafter veterans. Inactive adults are at risk for adverse cardiac events and premature mortality. Specifically, among veterans, inactivity has been associated with a 23% increase in mortality. In order to increase physical activity among veterans, we developed Veterans Affairs (VA) MapTrek, a mobile-phone-based web app that allows users to take a virtual walk in interesting locations around the world while tracking their progress against that of others like themselves on an interactive map. Steps are counted by a commercially available Fitbit triaxial accelerometer, and users see their progress along a predefined scenic path overlaid on Google Maps. The objective of this study was to determine the effectiveness of VA MapTrek to increase physical activity in a population of veterans at risk for obesity-related morbidity. MATERIALS AND METHODS: We recruited overweight and obese veterans obtaining care at the Iowa City Veterans Affairs Health Center. Half of the veterans were assigned to participate in VA MapTrek. Each week, participants were assigned virtual walking races (Monday through Saturday), which followed a predetermined route that is displayed on Google Maps. The participant's position on the map is automatically updated each time their Fitbit syncs to their phone. In addition, challenges were issued periodically. Veterans in the control group were only given a Fitbit. We regressed daily step counts on the days of the week, the days since the start of the intervention period, whether the user was in the VA MapTrek or Control group, and an interaction between the study group and the days since the start of the intervention period. We included subject-specific random intercepts and subject-specific random slopes. This model was estimated using Bayesian Hamiltonian Monte Carlo using Stan's No-U-Turns sampler. We set vague, uniform priors on all the parameters. RESULTS: We enrolled 276 participants, but only 251 (102 in the control group and 149 in the VA MapTrek group) contributed data during the intervention period. Our analysis suggests an 86.8% likelihood that the VA MapTrek intervention led to a minimum increase of 1,000 daily steps over the 8-week period, compared to the control group. Throughout the 8-week intervention, we project that VA MapTrek participants would have taken an extra 96,627 steps, equivalent to 77.8 additional kilometers (km) (48.3 additional miles), assuming an average of 1,242 steps per km (2,000 steps per mile). CONCLUSIONS: Our study underscores the potential of VA MapTrek as an intervention for promoting walking among veterans who face elevated risks of obesity and cardiac issues. Rural veterans are a high-risk population, and new interventions like VA MapTrek are needed to improve veterans' health.

Mutations in extracellular matrix genes NID1 and LAMC1 cause autosomal dominant Dandy-Walker malformation and occipital cephaloceles.

We performed whole-exome sequencing of a family with autosomal dominant Dandy-Walker malformation and occipital cephaloceles and detected a mutation in the extracellular matrix (ECM) protein-encoding gene NID1. In a second family, protein interaction network analysis identified a mutation in LAMC1, which encodes a NID1-binding partner. Structural modeling of the NID1-LAMC1 complex demonstrated that each mutation disrupts the interaction. These findings implicate the ECM in the pathogenesis of Dandy-Walker spectrum disorders.

Center competition and outcomes following liver transplantation.

In the United States, livers for transplantation are distributed within donation service areas (DSAs). In DSAs with multiple transplant centers, competition among centers for organs and recipients may affect recipient selection and outcomes in comparison with DSAs with only 1 center. The objective of this study was to determine whether competition within a DSA is associated with posttransplant outcomes and variations in patients wait-listed within the DSA. United Network for Organ Sharing data for 38,385 adult cadaveric liver transplant recipients undergoing transplantation between January 1, 2003 and December 31, 2009 were analyzed to assess differences in liver recipients and donors and in posttransplant survival by competition among centers. The main outcome measures that were studied were patient characteristics, actual and risk-adjusted graft and patient survival rates after transplantation, organ quality as quantified by the donor risk index (DRI), wait-listed patients per million population by DSA, and competition as quantified by the Hirschman-Herfindahl index (HHI). Centers were stratified by HHI levels as no competition or as low, medium (or mid), or high competition. In comparison with DSAs without competition, the low-, mid-, and high-competition DSAs (1) performed transplantation for patients with a higher risk of graft failure [hazard ratio (HR) = 1.24, HR = 1.26, and HR = 1.34 (P < 0.001 for each)] and a higher risk of death [HR = 1.21, HR = 1.23, and HR = 1.34 (P < 0.001 for each)] and for a higher proportion of sicker patients as quantified by the Model for End-Stage Liver Disease (MELD) score [10.0% versus 14.8%, 20.1%, and 28.2% with a match MELD score of 31-40 (P < 0.001 for each comparison)], (2) were more likely to use organs in the highest risk quartile as quantified by the DRI [18.3% versus 27.6%, 20.4%, and 31.7% (P ≤ 0.001 for each)], and (3) listed more patients per million population [18 (median) versus 34 (P = not significant), 37 (P = 0.005), and 45 (P = 0.0075)]. Significant variability in patient selection for transplantation is associated with market variables characterizing competition among centers. These findings suggest both positive and negative effects of competition among health care providers.

How many suffice? A computational framework for sizing sentinel surveillance networks.

BACKGROUND: Data from surveillance networks help epidemiologists and public health officials detect emerging diseases, conduct outbreak investigations, manage epidemics, and better understand the mechanics of a particular disease. Surveillance networks are used to determine outbreak intensity (i.e., disease burden) and outbreak timing (i.e., the start, peak, and end of the epidemic), as well as outbreak location. Networks can be tuned to preferentially perform these tasks. Given that resources are limited, careful site selection can save costs while minimizing performance loss. METHODS: We study three different site placement algorithms: two algorithms based on the maximal coverage model and one based on the K-median model. The maximal coverage model chooses sites that maximize the total number of people within a specified distance of a site. The K-median model minimizes the sum of the distances from each individual to the individual's nearest site. Using a ground truth dataset consisting of two million de-identified Medicaid billing records representing eight complete influenza seasons and an evaluation function based on the Huff spatial interaction model, we empirically compare networks against the existing Iowa Department of Public Health influenza-like illness network by simulating the spread of influenza across the state of Iowa. RESULTS: We show that it is possible to design a network that achieves outbreak intensity performance identical to the status quo network using two fewer sites. We also show that if outbreak timing detection is of primary interest, it is actually possible to create a network that matches the existing network's performance using 59% fewer sites. CONCLUSIONS: By simulating the spread of influenza across the state of Iowa, we show that our methods are capable of designing networks that perform better than the status quo in terms of both outbreak intensity and timing. Additionally, our results suggest that network size may only play a minimal role in outbreak timing detection. Finally, we show that it may be possible to reduce the size of a surveillance system without affecting the quality of surveillance information produced.

Using sensor networks to study the effect of peripatetic healthcare workers on the spread of hospital-associated infections.

BACKGROUND: Super-spreading events, in which an individual with measurably high connectivity is responsible for infecting a large number of people, have been observed. Our goal is to determine the impact of hand hygiene noncompliance among peripatetic (eg, highly mobile or highly connected) healthcare workers compared with less-connected workers. METHODS: We used a mote-based sensor network to record contacts among healthcare workers and patients in a 20-bed intensive care unit. The data collected from this network form the basis for an agent-based simulation to model the spread of nosocomial pathogens with various transmission probabilities. We identified the most- and least-connected healthcare workers. We then compared the effects of hand hygiene noncompliance as a function of connectedness. RESULTS: The data confirm the presence of peripatetic healthcare workers. Also, agent-based simulations using our real contact network data confirm that the average number of infected patients was significantly higher when the most connected healthcare worker did not practice hand hygiene and significantly lower when the least connected healthcare workers were noncompliant. CONCLUSIONS: Heterogeneity in healthcare worker contact patterns dramatically affects disease diffusion. Our findings should inform future infection control interventions and encourage the application of social network analysis to study disease transmission in healthcare settings.

Comparison of Different Antibiotics and the Risk for Community-Associated Clostridioides difficile Infection: A Case-Control Study.

Background: Antibiotics are the greatest risk factor for Clostridioides difficile infection (CDI). Risk for CDI varies across antibiotic types and classes. Optimal prescribing and stewardship recommendations require comparisons of risk across antibiotics. However, many prior studies rely on aggregated antibiotic categories or are underpowered to detect significant differences across antibiotic types. Using a large database of real-world data, we evaluate community-associated CDI risk across individual antibiotic types. Methods: We conducted a matched case-control study using a large database of insurance claims capturing longitudinal health care encounters and medications. Case patients with community-associated CDI were matched to 5 control patients by age, sex, and enrollment period. Antibiotics prescribed within 30 days before the CDI diagnosis along with other risk factors, including comorbidities, health care exposures, and gastric acid suppression were considered. Conditional logistic regression and a Bayesian analysis were used to compare risk across individual antibiotics. A sensitivity analysis of antibiotic exposure windows between 30 and 180 days was conducted. Results: We identified 159 404 cases and 797 020 controls. Antibiotics with the greatest risk for CDI included clindamycin and later-generation cephalosporins, and those with the lowest risk included minocycline and doxycycline. We were able to differentiate and order individual antibiotics in terms of their relative level of associated risk for CDI. Risk estimates varied considerably with different exposure windows considered. Conclusions: We found wide variation in CDI risk within and between classes of antibiotics. These findings ordering the level of associated risk across antibiotics can help inform tradeoffs in antibiotic prescribing decisions and stewardship efforts.

Using Thermal Imaging to Track Cellulitis.

Background: Cellulitis is a common soft tissue infection and a major cause of morbidity. The diagnosis is based almost exclusively on clinical history and physical exam. To improve the diagnosis of cellulitis, we used a thermal camera to track how skin temperature of the affected area changed during a hospital stay for patients with cellulitis. Methods: We recruited 120 patients admitted with a diagnosis of cellulitis. Daily thermal images of the affected limb were taken. Temperature intensity and area were analyzed from the images. Highest daily body temperature and antibiotics administered were also collected.We estimated a longitudinal linear mixed-effects model with a random intercept for the affected body area. All observations on a given day were included, and we used an integer time indicator indexed to the initial day (ie, t = 1 for the first day the patient was observed, etc.). We then analyzed the effect of this time trend on both severity (ie, normalized temperature) and scale (ie, area of skin with elevated temperature). Results: We analyzed thermal images from the 41 patients with a confirmed case of cellulitis who had at least 3 days of photos. For each day that the patient was observed, the severity decreased by 1.63 (95% CI, -13.45 to 10.32) units on average, and the scale decreased by 0.63 (95% CI, -1.08 to -0.17) points on average. Also, patients' body temperatures decreased by 0.28°F each day (95% CI, -0.40 to -0.17). Conclusions: Thermal imaging could be used to help diagnose cellulitis and track clinical progress.

Predicting use of a gait-stabilizing device using a Wii Balance Board.

Gait-stabilizing devices (GSDs) are effective at preventing falls, but people are often reluctant to use them until after experiencing a fall. Inexpensive, convenient, and effective methods for predicting which patients need GSDs could help improve adoption. The purpose of this study was to determine if a Wii Balance Board (WBB) can be used to determine whether or not patients use a GSD. We prospectively recruited participants ages 70-100, some who used GSDs and some who did not. Participants first answered questions from the Modified Vulnerable Elders Survey, and then completed a grip-strength test using a handgrip dynamometer. Finally, they were asked to complete a series of four 30-second balance tests on a WBB in random order: (1) eyes open, feet apart; (2) eyes open, feet together; (3) eyes closed, feet apart; and (4) eyes closed, feet together. The four-test series was repeated a second time in the same random order. The resulting data, represented as 25 features extracted from the questionnaires and the grip test, and data from the eight balance tests, were used to predict a subject's GSD use using generalized functional linear models based on the Bernoulli distribution. 268 participants were consented; 62 were missing data elements and were removed from analysis; 109 were not GSD users and 97 were GSD users. The use of velocity and acceleration information from the WBB improved upon predictions based solely on grip strength, demographic, and survey variables. The WBB is a convenient, inexpensive, and easy-to-use device that can be used to recommend whether or not patients should be using a GSD.

Primary stroke centers should be located using maximal coverage models for optimal access.

BACKGROUND AND PURPOSE: The current self-initiated approach by which hospitals acquire Primary Stroke Center (PSC) certification provides insufficient coverage for large areas of the United States. An alternative, directed, algorithmic approach to determine near optimal locations of PSCs would be justified if it significantly improves coverage. METHODS: Using geographic location-allocation modeling techniques, we developed a universal web-based calculator for selecting near optimal PSC locations designed to maximize the population coverage in any state. We analyzed the current PSC network population coverage in Iowa and compared it with the coverage that would exist if a maximal coverage model had instead been used to place those centers. We then estimated the expected gains in population coverage if additional PSCs follow the current self-initiated model and compared it against the more efficient coverage expected by use of a maximal coverage model to select additional locations. RESULTS: The existing 12 self-initiated PSCs in Iowa cover 37% of the population, assuming a time-distance radius of 30 minutes. The current population coverage would have been 47.5% if those 12 PSCs had been located using a maximal coverage model. With the current self-initiated approach, 54 additional PSCs on average will be needed to improve coverage to 75% of the population. Conversely, only 31 additional PSCs would be needed to achieve the same degree of population coverage if a maximal coverage model is used. CONCLUSIONS: Given the substantial gain in population access to adequate acute stroke care, it appears justified to direct the location of additional PSCs or recombinant tissue-type plasminogen activator-capable hospitals through a maximal coverage model algorithmic approach.

KELVIN: a software package for rigorous measurement of statistical evidence in human genetics.

This paper describes the software package KELVIN, which supports the PPL (posterior probability of linkage) framework for the measurement of statistical evidence in human (or more generally, diploid) genetic studies. In terms of scope, KELVIN supports two-point (trait-marker or marker-marker) and multipoint linkage analysis, based on either sex-averaged or sex-specific genetic maps, with an option to allow for imprinting; trait-marker linkage disequilibrium (LD), or association analysis, in case-control data, trio data, and/or multiplex family data, with options for joint linkage and trait-marker LD or conditional LD given linkage; dichotomous trait, quantitative trait and quantitative trait threshold models; and certain types of gene-gene interactions and covariate effects. Features and data (pedigree) structures can be freely mixed and matched within analyses. The statistical framework is specifically tailored to accumulate evidence in a mathematically rigorous way across multiple data sets or data subsets while allowing for multiple sources of heterogeneity, and KELVIN itself utilizes sophisticated software engineering to provide a powerful and robust platform for studying the genetics of complex disorders.

Multilevel mHealth Intervention Increases Physical Activity of Older Adults Living in Retirement Community.

BACKGROUND: Regular physical activity is crucial for healthy aging, but older adults are the least active age group. This study explored the feasibility, acceptability, and efficacy of a multilevel mHealth intervention for increasing physical activity of older adults living in a retirement community. METHODS: Participants included 54 older adults (mean age = 81.2 y, 77.8% female, 98.1% white) living in a retirement community. Participants received a Fitbit Zip and access to a multilevel mHealth physical activity intervention (MapTrek Residential) for 8 weeks. Physical activity (in steps per day) and intervention compliance (days worn) were measured objectively with the Fitbit for 12 weeks (8-wk intervention plus 4-wk follow-up). Psychosocial outcomes (social support, self-efficacy, and outcome expectations) were assessed at baseline and 8 weeks. Acceptability outcomes were assessed with an open-ended process evaluation survey and focus groups. Descriptive statistics and linear mixed models were used to examine intervention effects. RESULTS: Participants increased daily steps from 5438 steps per day at baseline (95% CI, 4620 to 6256) to 6201 steps per day (95% CI, 5359 to 7042) at week 8 (P < .0001) but this was not maintained at 12 weeks (P = .92). CONCLUSIONS: Our multilevel mHealth physical activity intervention was effective for increasing physical activity older adults over 8 weeks. Additional research focused on maintaining physical activity gains with this approach is warranted.

Gamifying accelerometer use increases physical activity levels of individuals pre-disposed to type II diabetes.

Physical activity is important for preventing obesity and diabetes, but most obese and pre-diabetic patients are not physically active. We developed a Fitbit-based game called MapTrek that promotes walking. We recruited obese and pre-diabetic patients. Half were randomly assigned to the control group and given a Fitbit alone. The others were given a Fitbit plus MapTrek. The MapTrek group participated in 6 months of weekly virtual races. Each week, participants were placed in a race with 9 others who achieved a similar number of steps in the previous week's race. Participants moved along the virtual route by the steps recorded on their Fitbit and received daily walking challenges via text message. Text messages also had links to the race map and leaderboard. We used a Bayesian mixed effects model to analyze the number of steps taken during the intervention. A total of 192 (89%) participants in the control group and 196 (91%) in the MapTrek group were included in the analyses. MapTrek significantly increased step counts when it began: MapTrek participants walked almost 1,700 steps more than the control group on the first day of the intervention. We estimate that there is a 97% probability that the effect of MapTrek is at least 1,000 additional steps per day throughout the course of the 6-month intervention and that MapTrek participants would have walked an additional 81 miles, on average, before the effect ended. Our MapTrek intervention led to significant extra walking by the MapTrek participants.

Spatiotemporal clustering of in-hospital Clostridioides difficile infection.

OBJECTIVE: To determine whether Clostridioides difficile infection (CDI) exhibits spatiotemporal interaction and clustering. DESIGN: Retrospective observational study. SETTING: The University of Iowa Hospitals and Clinics. PATIENTS: This study included 1,963 CDI cases, January 2005 through December 2011. METHODS: We extracted location and time information for each case and ran the Knox, Mantel, and mean and maximum component size tests for time thresholds (T = 7, 14, and 21 days) and distance thresholds (D = 2, 3, 4, and 5 units; 1 unit = 5-6 m). All tests were implemented using Monte Carlo simulations, and random CDI cases were constructed by randomly permuting times of CDI cases 20,000 times. As a counterfactual, we repeated all tests on 790 aspiration pneumonia cases because aspiration pneumonia is a complication without environmental factors. RESULTS: Results from the Knox test and mean component size test rejected the null hypothesis of no spatiotemporal interaction (P < .0001), for all values of T and D. Results from the Mantel test also rejected the hypothesis of no spatiotemporal interaction (P < .0003). The same tests showed no such effects for aspiration pneumonia. Our results from the maximum component size tests showed similar trends, but they were not consistently significant, possibly because CDI outbreaks attributable to the environment were relatively small. CONCLUSION: Our results clearly show spatiotemporal interaction and clustering among CDI cases and none whatsoever for aspiration pneumonia cases. These results strongly suggest that environmental factors play a role in the onset of some CDI cases. However, our results are not inconsistent with the possibility that many genetically unrelated CDI cases occurred during the study period.