The use of Logic regression in epidemiologic studies to investigate multiple binary exposures: an example of occupation history and amyotrophic lateral sclerosis

Clostridium perfringens in central Colombia: frequency, toxin genes, and risk factors

Clostridium perfringens is an opportunistic bacterium that causes intestinal diseases in both humans and animals. This study aimed to assess the frequency of C. perfringens and the presence of toxin-encoding genes in fecal samples from individuals with or without gastrointestinal symptoms in the Department of Boyacá, Colombia. Additionally, risk factors associated with carriage and disease development were analyzed. A total of 114 stool samples were analyzed using a molecular test based on specific polymerase chain reaction (PCR) targeting 16S-rRNA and alpha toxin (cpa) genes. For individuals with a positive result for the PCR test, stool samples were cultured on Tryptose Sulfite Cycloserine (TSC) agar. Two to five colonies forming units were selected based on phenotypic characteristics, resulting in 56 bacterial isolates. These isolates were then analyzed for toxin-coding genes associated with gastrointestinal diseases. In addition, sociodemographic and clinical data from 77 individuals were also analyzed. The overall frequency of C. perfringens was 19.3% (n = 22/114). The detection frequency in 77 individuals with clinical data was 16.6% (n = 5/30) among symptomatic individuals and 21.2% (n = 10/47) among asymptomatic individuals. All 56 isolates obtained carried the cpa gene, while cpb2 was present in 10.7% (n = 6/56); cpe and cpb genes were not detected. Notably, diabetes and autoimmune diseases are significantly associated with an increased risk of C. perfringens detection (adjusted OR 8.41: 95% CI 1.32-35.89). This study highlights an elevated frequency of C. perfringens and the presence of the cpb2 gene in asymptomatic individuals compared with their symptomatic counterparts. These findings offer insights into the distribution and virulence factors of C. perfringens at a micro-geographical level. This information supports the need for developing tailored prevention strategies based on local characteristics to promote active surveillance programs based on molecular epidemiology.

Identifying interactions among factors related to death occurred at the scene of traffic accidents: Application of "logic regression" method

Aim

Traffic accidents are caused by several interacting risk factors. This study aimed to investigate the interactions among risk factors associated with death at the accident scene (DATAS) as an indicator of the crash severity, for pedestrians, passengers, and drivers by adopting "Logic Regression" as a novel approach in the traffic field.

Method

A case-control study was designed based on the police data from the Road Traffic Injury Registry in northwest of Iran during 2014-2016. For each of the pedestrians, passengers, and drivers' datasets, logic regression with "logit" link function was fitted and interactions were identified using Annealing algorithm. Model selection was performed using the cross-validation and the null model randomization procedure.

Results

regarding pedestrians, "The occurrence of the accident outside a city in a situation where there was insufficient light" (OR = 6.87, P-value<0.001) and "the age over 65 years" (OR = 2.97, P-value<0.001) increased the chance of DATAS. "Accidents happening in residential inner-city areas with a light vehicle, and presence of the pedestrians in the safe zone or on the non-separate two-way road" combination lowered the chance of DATAS (OR = 0.14, P-value<0.001). For passengers, "Accidents happening in outside the city or overturn of the vehicle" combination (OR = 8.55, P-value<0.001), and "accidents happening on defective roads" (OR = 2.18, P-value<0.001) increased the odds of DATAS; When "driver was not injured or the vehicle was two-wheeled", chance of DATAS decreased for passengers (OR = 0.25, p-value<0.001). The odds of DATAS were higher for "drivers who had a head-on accident, or drove a two-wheeler vehicle, or overturned the vehicle" (OR = 4.03, P-value<0.001). "Accident on the roads other than runway or the absence of a multi-car accident or an accident in a non-residential area" (OR = 6.04, P-value<0.001), as well "the accident which occurred outside the city or on defective roads, and the drivers were male" had a higher risk of DATAS for drivers (OR = 5.40, P-value<0.001).

Conclusion

By focusing on identifying interaction effects among risk factors associated with DATAS through logic regression, this study contributes to the understanding of the complex nature of traffic accidents and the potential for reducing their occurrence rate or severity. According to the results, the simultaneous presence of some risk factors such as the quality of roads, skill of drivers, physical ability of pedestrians, and compliance with traffic rules play an important role in the severity of the accident. The revealed interactions have practical significance and can play a significant role in the problem-solving process and facilitate breaking the chain of combinations among the risk factors. Therefore, practical suggestions of this study are to control at least one of the risk factors present in each of the identified combinations in order to break the combination to reduce the severity of accidents. This may have, in turn, help the policy-makers, road users, and healthcare professionals to promote road safety through prioritizing interventions focusing on effect size of simultaneous coexistence of crash severity determinants and not just the main effects of single risk factors or their simple two-way interactions.

Determination of Significant Parameters on the Basis of Methods of Mathematical Statistics, and Boolean and Fuzzy Logic

Among the set of parameters for which data are collected for decision-making based on artificial intelligence methods, often only some of the parameters are significant. This article compares methods for determining the significant parameters based on the theory of mathematical statistics, and fuzzy and boolean logic. The testing was conducted on several test data sets with a different number of parameters and different variability of parameter values. It was shown that for data sets with a small number of parameters (<5), the most accurate result was given for a method based on the theory of mathematical statistics and boolean logic. For a data set with a large number of parameters—the most suitable is the method of fuzzy logic.

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Background

Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH).

Methods

Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report.

Results

Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness).

Conclusions

While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta

Background

Short-term associations between extreme heat events and adverse health outcomes are well-established in epidemiologic studies. However, the use of different exposure definitions across studies has limited our understanding of extreme heat characteristics that are most important for specific health outcomes or subpopulations.

Methods

Logic regression is a statistical learning method for constructing decision trees based on Boolean combinations of binary predictors. We describe how logic regression can be utilized as a data-driven approach to identify extreme heat exposure definitions using health outcome data. We evaluated the performance of the proposed algorithm in a simulation study, as well as in a 20-year time-series analysis of extreme heat and emergency department visits for 12 outcomes in the Atlanta metropolitan area.

Results

For the Atlanta case study, our novel application of logic regression identified extreme heat exposure definitions that were associated with several heat-sensitive disease outcomes (e.g., fluid and electrolyte imbalance, renal diseases, ischemic stroke, and hypertension). Exposures were often characterized by extreme apparent minimum temperature or maximum temperature over multiple days. The simulation study also demonstrated that logic regression can successfully identify exposures of different lags and duration structures when statistical power is sufficient.

Conclusion

Logic regression is a useful tool for identifying important characteristics of extreme heat exposures for adverse health outcomes, which may help improve future heat warning systems and response plans.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01278-x.

Joint and interactive effects between health comorbidities and environmental exposures in predicting amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a rare yet devastating neurodegenerative condition. The mechanisms leading to ALS are most certainly complex and likely involve a joint contribution of several factors with possible synergistic or antagonistic interactions. To provide a better understanding of the association between non-genetic factors and ALS, we evaluated the joint exposure to multiple health and environmental factors linked with ALS in our previous studies, also screening for high-dimensional interactions.

METHODS

We used data from a nested case-control study within the Danish population, with 1086 ALS cases from 1982 to 2009, jointly investigating 4 hospital-based diagnoses - diabetes, obesity, physical/stress trauma, cardiovascular disease (CVD) during 1977-2009; and 4 environmental exposures - lead, formaldehyde, diesel exhaust, and solvents, assessed from individual occupational history. All covariates were evaluated as ever/never exposed, and we used targeted machine learning techniques to screen for important joint predictors and interactions. These were then evaluated in a final logistic regression model adjusting for potential confounders (age, SES, geography). All analyses were stratified by sex.

RESULTS

Among men, trauma and solvents were associated with higher odds of ALS (OR = 1.55, 95% CI: 1.08-2.23; OR = 1.49, 95% CI: 1.17-1.89, respectively), and presented a negative interaction (OR = 0.49, 95% CI: 0.30-0.80). A positive diesel/CVD interaction was observed (OR = 1.56, 95% CI: 0.94-2.60). Among women, solvents, trauma, lead, and CVD were associated with higher odds of ALS, and a negative lead/solvents interaction was documented (OR = 0.52, 95% CI: 0.42-0.63).

CONCLUSIONS

This study is one of the first attempts to evaluate joint and interactive effects of multiple risk factors on ALS, identifying potential synergistic and antagonistic mechanisms.