Integrating Time-Varying and Ecological Exposures into Multivariate Analyses of Hospital-Acquired Infection Risk Factors: A Review and Demonstration

2016 ◽  
Vol 37 (4) ◽  
pp. 411-419 ◽  
Author(s):  
Kevin A. Brown ◽  
Nick Daneman ◽  
Vanessa W. Stevens ◽  
Yue Zhang ◽  
Tom H. Greene ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Statistical Methods ◽  
Time Window ◽  
Ecological Factors ◽  
Time Varying ◽  
Multivariate Statistical ◽  
Individual Level ◽  
Infection Risk Factors ◽  
Hospital Acquired

OBJECTIVESHospital-acquired infections (HAIs) develop rapidly after brief and transient exposures, and ecological exposures are central to their etiology. However, many studies of HAIs risk do not correctly account for the timing of outcomes relative to exposures, and they ignore ecological factors. We aimed to describe statistical practice in the most cited HAI literature as it relates to these issues, and to demonstrate how to implement models that can be used to account for them.METHODSWe conducted a literature search to identify 8 frequently cited articles having primary outcomes that were incident HAIs, were based on individual-level data, and used multivariate statistical methods. Next, using an inpatient cohort of incident Clostridium difficile infection (CDI), we compared 3 valid strategies for assessing risk factors for incident infection: a cohort study with time-fixed exposures, a cohort study with time-varying exposures, and a case-control study with time-varying exposures.RESULTSOf the 8 studies identified in the literature scan, 3 did not adjust for time-at-risk, 6 did not assess the timing of exposures in a time-window prior to outcome ascertainment, 6 did not include ecological covariates, and 6 did not account for the clustering of outcomes in time and space. Our 3 modeling strategies yielded similar risk-factor estimates for CDI risk.CONCLUSIONSSeveral common statistical methods can be used to augment standard regression methods to improve the identification of HAI risk factors.Infect. Control Hosp. Epidemiol. 2016;37(4):411–419

scholarly journals Geographical variation and clustering are found in atrial fibrillation beyond socioeconomic differences: a Danish cohort study, 1987–2015

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Kirstine Wodschow ◽  
Kristine Bihrmann ◽  
Mogens Lytken Larsen ◽  
Gunnar Gislason ◽  
Annette Kjær Ersbøll
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Cohort Study ◽  
Incidence Rate ◽  
Socioeconomic Position ◽  
Geographical Variation ◽  
Incidence Rates ◽  
Socioeconomic Differences ◽  
Individual Level ◽  
Geographical Variations

Abstract Background The prevalence and incidence rate of atrial fibrillation (AF) increase worldwide and AF is a risk factor for more adverse cardiovascular diseases including stroke. Approximately 44% of AF cases cannot be explained by common individual risk factors and risk might therefore also be related to the environment. By studying geographical variation and clustering in risk of incident AF adjusted for socioeconomic position at an individual level, potential neighbourhood risk factors could be revealed. Methods Initially, yearly AF incidence rates 1987–2015 were estimated overall and stratified by income in a register-based cohort study. To examine geographical variation and clustering in AF, we used both spatial scan statistics and a hierarchical Bayesian Poisson regression analysis of AF incidence rates with random effect of municipalities (n = 98) in Denmark in 2011–2015. Results The 1987–2015 cohort included 5,453,639 individuals whereof 369,800 were diagnosed with an incident AF. AF incidence rate increased from 174 to 576 per 100,000 person-years from 1987 to 2015. Inequality in AF incidence rate ratio between highest and lowest income groups increased from 23% in 1987 to 38% in 2015. We found clustering and geographical variation in AF incidence rates, with incidence rates at municipality level being up to 34% higher than the country mean after adjusting for socioeconomic position. Conclusions Geographical variations and clustering in AF incidence rates exist. Compared to previous studies from Alberta, Canada and the United States, we show that geographical variations exist in a country with free access to healthcare and even when accounting for socioeconomic differences at an individual level. An increasing social inequality in AF was seen from 1987 to 2015. Therefore, when planning prevention strategies, attention to individuals with low income should be given. Further studies focusing on identification of neighbourhood risk factors for AF are needed.

scholarly journals Individual-Level Antibody Dynamics Reveal Potential Drivers of Influenza A Seasonality in Wild Pig Populations

2019 ◽  
Vol 59 (5) ◽  
pp. 1231-1242 ◽  
Author(s):  
Kim M Pepin ◽  
Kerri Pedersen ◽  
Xiu-Feng Wan ◽  
Fred L Cunningham ◽  
Colleen T Webb ◽  
...  
Keyword(s):  
Risk Factors ◽  
Influenza A ◽  
Disease Risk ◽  
Infection Risk ◽  
Time Varying ◽  
Long Distance ◽  
Individual Level ◽  
Wild Pigs ◽  
Host Sex ◽  
Domestic Swine

AbstractSwine are important in the ecology of influenza A virus (IAV) globally. Understanding the ecological role of wild pigs in IAV ecology has been limited because surveillance in wild pigs is often for antibodies (serosurveillance) rather than IAVs, as in humans and domestic swine. As IAV antibodies can persist long after an infection, serosurveillance data are not necessarily indicative of current infection risk. However, antibody responses to IAV infections cause a predictable antibody response, thus time of infection can be inferred from antibody levels in serological samples, enabling identification of risk factors of infection at estimated times of infection. Recent work demonstrates that these quantitative antibody methods (QAMs) can accurately recover infection dates, even when individual-level variation in antibody curves is moderately high. Also, the methodology can be implemented in a survival analysis (SA) framework to reduce bias from opportunistic sampling. Here we integrated QAMs and SA and applied this novel QAM–SA framework to understand the dynamics of IAV infection risk in wild pigs seasonally and spatially, and identify risk factors. We used national-scale IAV serosurveillance data from 15 US states. We found that infection risk was highest during January–March (54% of 61 estimated peaks), with 24% of estimated peaks occurring from May to July, and some low-level of infection risk occurring year-round. Time-varying IAV infection risk in wild pigs was positively correlated with humidity and IAV infection trends in domestic swine and humans, and did not show wave-like spatial spread of infection among states, nor more similar levels of infection risk among states with more similar meteorological conditions. Effects of host sex on IAV infection risk in wild pigs were generally not significant. Because most of the variation in infection risk was explained by state-level factors or infection risk at long-distances, our results suggested that predicting IAV infection risk in wild pigs is complicated by local ecological factors and potentially long-distance translocation of infection. In addition to revealing factors of IAV infection risk in wild pigs, our framework is broadly applicable for quantifying risk factors of disease transmission using opportunistic serosurveillance sampling, a common methodology in wildlife disease surveillance. Future research on the factors that determine individual-level antibody kinetics will facilitate the design of serosurveillance systems that can extract more accurate estimates of time-varying disease risk from quantitative antibody data.

scholarly journals COVID-19 infection and attributable mortality in UK care homes: cohort study using active surveillance and electronic records (March–June 2020)

2021 ◽  
Author(s):  
Peter F Dutey-Magni ◽  
Haydn Williams ◽  
Arnoupe Jhass ◽  
Greta Rait ◽  
Fabiana Lorencatto ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Care Home ◽  
Case Fatality ◽  
Epidemiological Data ◽  
Care Homes ◽  
Attributable Mortality ◽  
Term Care ◽  
Individual Level ◽  
Staffing Ratios

Abstract Background epidemiological data on COVID-19 infection in care homes are scarce. We analysed data from a large provider of long-term care for older people to investigate infection and mortality during the first wave of the pandemic. Methods cohort study of 179 UK care homes with 9,339 residents and 11,604 staff. We used manager-reported daily tallies to estimate the incidence of suspected and confirmed infection and mortality in staff and residents. Individual-level electronic health records from 8,713 residents were used to model risk factors for confirmed infection, mortality and estimate attributable mortality. Results 2,075/9,339 residents developed COVID-19 symptoms (22.2% [95% confidence interval: 21.4%; 23.1%]), while 951 residents (10.2% [9.6%; 10.8%]) and 585 staff (5.0% [4.7%; 5.5%]) had laboratory-confirmed infections. The incidence of confirmed infection was 152.6 [143.1; 162.6] and 62.3 [57.3; 67.5] per 100,000 person-days in residents and staff, respectively. Sixty-eight percent (121/179) of care homes had at least one COVID-19 infection or COVID-19-related death. Lower staffing ratios and higher occupancy rates were independent risk factors for infection. Out of 607 residents with confirmed infection, 217 died (case fatality rate: 35.7% [31.9%; 39.7%]). Mortality in residents with no direct evidence of infection was twofold higher in care homes with outbreaks versus those without (adjusted hazard ratio: 2.2 [1.8; 2.6]). Conclusions findings suggest many deaths occurred in people who were infected with COVID-19, but not tested. Higher occupancy and lower staffing levels were independently associated with risks of infection. Protecting staff and residents from infection requires regular testing for COVID-19 and fundamental changes to staffing and care home occupancy.

Incidence and impact of hospital-acquired complications in an internal medicine unit of a reference hospital in Cameroon: a prospective cohort study

2020 ◽  
Author(s):  
Colette Sih ◽  
Bertrand Hugo Mbatchou-Ngahane ◽  
Yannick Mboue-Djieka ◽  
Marie C Ngueng-Eke ◽  
Nicole T Mbarga ◽  
...  
Keyword(s):  
Internal Medicine ◽  
Risk Factors ◽  
Cohort Study ◽  
Incidence Rate ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Hospital Costs ◽  
Medical Costs ◽  
Direct Medical Costs ◽  
Hospital Acquired

Abstract Background Hospital-acquired complications (HACs) contribute to increased morbidity, mortality and hospital costs. However, their burden is often overlooked in resource-limited settings. We sought to determine the incidence, risk factors and effects of HACs on direct medical costs. Methods This was a prospective cohort study conducted in the Internal Medicine inpatient ward of Douala General Hospital over 3 mo. Patients were examined daily from admission to discharge, transfer or death. Incidence of HACs was calculated and risk factors of HACs were determined using univariate and multivariate regression models. Results The cumulative incidence rate of HACs in 230 participants was 29.2/1000 patient-days. The incidence rate of infectious and non-infectious complications was 8.4/1000 and 20.9/1000 patient-days, respectively. The most common HAC was constipation (8.3/1000 patient-days). The most common infection was urinary tract infection (3.7/1000 patient-days). HIV infection and length of stay >8 d were significantly associated with the occurrence of HACs. Deep vein thrombosis was associated with the highest direct medical cost. Conclusion The incidence of HACs is high in our setting and leads to increased length of hospital stays as well as greater direct medical costs. Thus, there is a need for effective preventive strategies.

scholarly journals Incidence, Risk Factors and Impact on Clinical Outcomes of Bloodstream Infections in Patients Hospitalised with COVID-19: A Prospective Cohort Study

Antibiotics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1031
Author(s):  
Andrea Cona ◽  
Alessandro Tavelli ◽  
Andrea Renzelli ◽  
Benedetta Varisco ◽  
Francesca Bai ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Bloodstream Infections ◽  
Corticosteroid Treatment ◽  
Risk Of Mortality ◽  
Increased Risk ◽  
Incidence Risk ◽  
Observational Cohort ◽  
Healthcare Associated ◽  
Hospital Acquired

With the aim of describing the burden and epidemiology of community-acquired/healthcare-associated and hospital-acquired bloodstream infections (CA/HCA-BSIs and HA-BSIs) in patients hospitalised with COVID-19, and evaluating the risk factors for BSIs and their relative impact on mortality, an observational cohort study was performed on patients hospitalised with COVID-19 at San Paolo Hospital in Milan, Italy from 24 February to 30 November 2020. Among 1351 consecutive patients hospitalised with COVID-19, 18 (1.3%) had CA/HCA-BSI and 51 (3.8%) HA-BSI for a total of 82 episodes of BSI. The overall incidence of HA-BSI was 3.3/1000 patient-days (95% CI 2.4–4.2). Patients with HA-BSI had a longer hospital stay compared to CA/HCA-BSI and no-BSI groups (27 (IQR 21–35) vs. 12 (7–29) vs. 9 (5–17) median-days, p < 0.001) but a similar in-hospital mortality (31% vs. 33% vs. 25%, p = 0.421). BSI was not associated with an increased risk of mortality (CA/HCA-BSI vs. non-BSI aOR 1.27 95% CI 0.41–3.90, p = 0.681; HA-BSI vs. non-BSI aOR 1.29 95% CI 0.65–2.54, p = 0.463). Upon multivariate analysis, NIMV/CPAP (aOR 2.09, 95% CI 1.06–4.12, p = 0.034), IMV (aOR 5.13, 95% CI 2.08–12.65, p < 0.001) and corticosteroid treatment (aOR 2.11, 95% CI 1.06–4.19, p = 0.032) were confirmed as independent factors associated with HA-BSI. Development of HA-BSI did not significantly affect mortality. Patients treated with corticosteroid therapy had double the risk of developing BSI.

scholarly journals Neighborhood Poverty Increases Risk for Cigarette Smoking From Age 30 to 39

2018 ◽  
Vol 53 (9) ◽  
pp. 858-864 ◽  
Author(s):  
Christopher Cambron ◽  
Rick Kosterman ◽  
J David Hawkins
Keyword(s):  
Risk Factors ◽  
Cigarette Smoking ◽  
Community Sample ◽  
Smoking Behavior ◽  
Development Project ◽  
Time Varying ◽  
Individual Level ◽  
Neighborhood Poverty ◽  
Curve Modeling ◽  
Over Time

Abstract Background Lower socioeconomic status (SES) has been associated with higher rates of smoking. Few longitudinal studies have examined indicators of SES at both the neighborhood- and individual-level over time in conjunction with proximal risk factors of cigarette smoking. Purpose To examine associations of time-varying measures of SES, demographic factors, and proximal risk factors for smoking net of average trajectories of smoking behavior from ages 30 to 39 in a community sample. Methods Data from the Seattle Social Development Project (N = 752), a theory-driven longitudinal study originating in Seattle, WA, were used to estimate trajectories of smoking from age 30 to 39. Time-varying measures of neighborhood poverty, coworker smoking, partner smoking, depression, anxiety, education, income, marital status, and parenthood were associated with smoking over time using latent growth curve modeling. Results Results indicated that living in higher poverty neighborhoods was uniquely associated with a greater likelihood of smoking net of average trajectories of smoking from age 30 to 39, gender and race/ethnicity, time-varying measures of SES and demographics, and time-varying measures of proximal risk factors for smoking. Conclusions Living in higher poverty neighborhoods presents a unique risk for smoking among adults aged 30 to 39 above and beyond multiple aspects of SES and other potential mechanisms relating SES to smoking.

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