piecewise-regression (aka segmented regression) in Python

Background: A growing but contrasting evidence relates air pollution to cognitive decline. The role of cerebrovascular diseases in amplifying this risk is unclear. Objectives: 1) Investigate the association between long-term exposure to air pollution and cognitive decline; 2) Test whether cerebrovascular diseases amplify this association. Methods: We examined 2,253 participants of the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). One major air pollutant (particulate matter ≤2.5μm, PM2.5) was assessed yearly from 1990, using dispersion models for outdoor levels at residential addresses. The speed of cognitive decline (Mini-Mental State Examination, MMSE) was estimated as the rate of MMSE decline (linear mixed models) and further dichotomized into the upper (25%fastest cognitive decline), versus the three lower quartiles. The cognitive scores were used to calculate the odds of fast cognitive decline per levels of PM2.5 using regression models and considering linear and restricted cubic splines of 10 years exposure before the baseline. The potential modifier effect of cerebrovascular diseases was tested by adding an interaction term in the model. Results: We observed an inverted U-shape relationship between PM2.5 and cognitive decline. The multi-adjusted piecewise regression model showed an increased OR of fast cognitive decline of 81%(95%CI = 1.2–3.2) per interquartile range difference up to mean PM2.5 level (8.6μg/m3) for individuals older than 80. Above such level we observed no further risk increase (OR = 0.89;95%CI = 0.74–1.06). The presence of cerebrovascular diseases further increased such risk by 6%. Conclusion: Low to mean PM2.5 levels were associated with higher risk of accelerated cognitive decline. Cerebrovascular diseases further amplified such risk.

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Piecewise regression equations for estimating stature: an anthropometric study in Spanish females

International Journal of Legal Medicine ◽

10.1007/s00414-020-02350-9 ◽

2020 ◽

Vol 134 (6) ◽

pp. 2289-2296

Author(s):

Gonzalo Saco-Ledo ◽

Jordi Porta ◽

Tesla A. Monson ◽

Marianne F. Brasil ◽

Derya Atamtürk ◽

...

Keyword(s):

Regression Equations ◽

Piecewise Regression ◽

Anthropometric Study

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Prediction of scour at abutments using piecewise regression

Proceedings of the Institution of Civil Engineers - Water Management ◽

10.1680/wama.11.00100 ◽

2014 ◽

Vol 167 (2) ◽

pp. 79-87 ◽

Cited By ~ 2

Author(s):

Amir Etemad-Shahidi ◽

Mohammad Sadegh Rohani

Keyword(s):

Piecewise Regression

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An analysis of Italian university students’ performance through segmented regression models: gender differences in STEM courses

Genus ◽

10.1186/s41118-021-00118-6 ◽

2021 ◽

Vol 77 (1) ◽

Author(s):

Andrea Priulla ◽

Nicoletta D’Angelo ◽

Massimo Attanasio

Keyword(s):

Gender Differences ◽

Regression Models ◽

First Year ◽

Strongly Correlated ◽

Segmented Regression ◽

Freshmen Students ◽

Female Performance ◽

And Mathematics ◽

The Relationship ◽

Better Than

AbstractThis paper investigates gender differences in university performances in Science, Technology, Engineering and Mathematics (STEM) courses in Italy, proposing a novel application through the segmented regression models. The analysis concerns freshmen students enrolled at a 3-year STEM degree in Italian universities in the last decade, with a focus on the relationship between the number of university credits earned during the first year (a good predictor of the regularity of the career) and the probability of getting the bachelor degree within 4 years. Data is provided by the Italian Ministry of University and Research (MIUR). Our analysis confirms that first-year performance is strongly correlated to obtaining a degree within 4 years. Furthermore, our findings show that gender differences vary among STEM courses, in accordance with the care-oriented and technical-oriented dichotomy. Males outperform females in mathematics, physics, chemistry and computer science, while females are slightly better than males in biology. In engineering, female performance seems to follow the male stream. Finally, accounting for other important covariates regarding students, we point out the importance of high school background and students’ demographic characteristics.

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Seasonal Trends for Environmental Illness Incidence in the U.S. Army

Military Medicine ◽

10.1093/milmed/usab072 ◽

2021 ◽

Author(s):

David W DeGroot ◽

Catherine A Rappole ◽

Paige McHenry ◽

Robyn M Englert

Keyword(s):

Risk Mitigation ◽

Cold Season ◽

Incidence Rates ◽

Cold Weather ◽

Medical Surveillance ◽

Piecewise Regression ◽

Exertional Heat Illness ◽

Policies And Procedures ◽

Icd Codes ◽

The U.S

ABSTRACT Introduction The incidence of and risk factors for exertional heat illness (EHI) and cold weather injury (CWI) in the U.S. Army have been well documented. The “heat season”, when the risk of EHI is highest and application of risk mitigation procedures is mandatory, has been arbitrarily defined as May 1 through September 30, while the “cold season” is understood to occur from October 1 to April 30 each year. The proportions of EHI and CWI that occur outside of the traditional heat and cold seasons are unknown. Additionally, it is unknown if either of the seasonal definitions are appropriate. The primary purpose of this study was to determine the proportion of EHI and of CWI that occur within the commonly accepted seasonal definitions. We also report the location-specific variability, seasonal definitions, and the demographic characteristics of the populations. Methods The U.S. Army installations with the highest frequency of EHI and of CWI from 2008 to 2013 were identified and used for analysis. In total there were 15 installations included in the study, with five installations used for analysis in both the EHI and CWI projects. In- and out-patient EHI and CWI data (ICD-9-CM codes 992.0 to 992.9 and ICD codes 991.0 to 991.9, respectively) were obtained from the Defense Medical Surveillance System. Installation-specific denominator data were obtained from the Defense Manpower Data Center, and incidence rates were calculated by week, for each installation. Segmental (piecewise) regression analysis was used to determine the start and end of the heat and cold seasons. Results Our analysis indicates that the heat season starts around April 22 and ends around September 9. The cold season starts on October 3 and ends on March 24. The majority (n = 6,445, 82.3%) of EHIs were diagnosed during the “heat season” of May 1 to September 30, while 10.3% occurred before the heat season started (January1 to April 30) and 7.3% occurred after the heat season ended (October 1 to December 31). Similar to EHI, 90.5% of all CWIs occurred within the traditionally defined cold season, while 5.7% occurred before and 3.8% occurred after the cold season. The locations with the greatest EHI frequency were Ft Bragg (n = 2,129), Ft Benning (n = 1,560), and Ft Jackson (n = 1,538). The bases with the largest proportion of CWI in this sample were Ft Bragg (17.8%), Ft Wainwright (17.2%), and Ft Jackson (12.7%). There were considerable inter-installation differences for the start and end dates of the respective seasons. Conclusions The present study indicates that the traditional heat season definition should be revised to begin ∼3 weeks earlier than the current date of May 1; our data indicate that the current cold season definition is appropriate. Inter-installation variability in the start of the cold season was much larger than that for the heat season. Exertional heat illnesses are a year-round problem, with ∼17% of all cases occurring during non-summer months, when environmental heat strain and vigilance are lower. This suggests that EHI mitigation policies and procedures require greater year-round emphasis, particularly at certain locations.

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Discovery of polynomial equations for regression

Advances in Methodology and Statistics ◽

10.51936/uogl8142 ◽

2004 ◽

Vol 1 (1) ◽

pp. 131-142

Author(s):

Ljupčo Todorovski ◽

Sašo Džeroski ◽

Peter Ljubič

Keyword(s):

Efficient Method ◽

Regression Models ◽

Predictive Accuracy ◽

State Of The Art ◽

Numerical Data ◽

Predictive Performance ◽

Polynomial Equations ◽

Regression Methods ◽

Piecewise Regression ◽

Standard Regression

Both equation discovery and regression methods aim at inducing models of numerical data. While the equation discovery methods are usually evaluated in terms of comprehensibility of the induced model, the emphasis of the regression methods evaluation is on their predictive accuracy. In this paper, we present Ciper, an efficient method for discovery of polynomial equations and empirically evaluate its predictive performance on standard regression tasks. The evaluation shows that polynomials compare favorably to linear and piecewise regression models, induced by the existing state-of-the-art regression methods, in terms of degree of fit and complexity.

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The Impact of a National Antimicrobial Stewardship Program on Antibiotic Prescribing in Primary Care: An Interrupted Time Series Analysis

Clinical Infectious Diseases ◽

10.1093/cid/ciy902 ◽

2018 ◽

Vol 69 (2) ◽

pp. 227-232 ◽

Cited By ~ 14

Author(s):

Violeta Balinskaite ◽

Alan P Johnson ◽

Alison Holmes ◽

Paul Aylin

Keyword(s):

Primary Care ◽

Time Series ◽

Broad Spectrum ◽

Interrupted Time Series ◽

Series Data ◽

Antibiotic Prescribing ◽

Segmented Regression ◽

Broad Spectrum Antibiotic ◽

Stewardship Program ◽

The Impact

Abstract Background The Quality Premium was introduced in 2015 to financially reward local commissioners of healthcare in England for targeted reductions in antibiotic prescribing in primary care. Methods We used a national antibiotic prescribing dataset from April 2013 until February 2017 to examine the number of antibiotic items prescribed, the total number of antibiotic items prescribed per STAR-PU (specific therapeutic group age/sex-related prescribing units), the number of broad-spectrum antibiotic items prescribed, and broad-spectrum antibiotic items prescribed, expressed as a percentage of the total number of antibiotic items. To evaluate the impact of the Quality Premium on antibiotic prescribing, we used a segmented regression analysis of interrupted time series data. Results During the study period, over 140 million antibiotic items were prescribed in primary care. Following the introduction of the Quality Premium, antibiotic items prescribed decreased by 8.2%, representing 5933563 fewer antibiotic items prescribed during the 23 post-intervention months, as compared with the expected numbers based on the trend in the pre-intervention period. After adjusting for the age and sex distribution in the population, the segmented regression model also showed a significant relative decrease in antibiotic items prescribed per STAR-PU. A similar effect was found for broad-spectrum antibiotics (comprising 10.1% of total antibiotic prescribing), with an 18.9% reduction in prescribing. Conclusions This study shows that the introduction of financial incentives for local commissioners of healthcare to improve the quality of prescribing was associated with a significant reduction in both total and broad-spectrum antibiotic prescribing in primary care in England.

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Developers, Quality Control and Download Volume in Open Source Software (OSS) Projects

Journal of Organizational and End User Computing ◽

10.4018/joeuc.2017040103 ◽

2017 ◽

Vol 29 (2) ◽

pp. 43-66

Author(s):

Geoffrey Hill ◽

Pratim Datta ◽

Candice Vander Weerdt

Keyword(s):

Quality Control ◽

Public Good ◽

Open Source ◽

Open Source Software ◽

Inflection Point ◽

Returns To Scale ◽

Tragedy Of The Commons ◽

Curvilinear Relationship ◽

Segmented Regression ◽

The Commons

The open-source software (OSS) movement is often analogized as a commons, where products are developed by and consumed in an open community. However, does a larger commons automatically beget success or does the phenomenon fall prey to the tragedy of the commons? This research forwards and empirically investigates the curvilinear relationship between developers and OSS project quality and a project's download volume. Using segmented regression on over 12,000 SourceForge OSS projects, findings suggest an inflection point in the number of contributing developers on download volume – suggesting increasing and diminishing returns to scale from adding developers to OSS projects. Findings support the economic principle of the tragedy of the commons, a concept where an over-allocated (large number) of developers, even in an open-source environment, can lead to resource mismanagement and reduce the benefit of a public good, i.e. the OSS project.

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