Use of Generalized Additive Models to identify risk factors of HIV/AIDS

2015 ◽  
Keyword(s):  
Risk Factors ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Hiv Aids

scholarly journals Current characteristics of animal rabies cases in Thailand and relevant risk factors identified by a spatial modeling approach

2021 ◽  
Vol 15 (12) ◽  
pp. e0009980
Author(s):  
Weerapong Thanapongtharm ◽  
Sarin Suwanpakdee ◽  
Arun Chumkaeo ◽  
Marius Gilbert ◽  
Anuwat Wiratsudakul
Keyword(s):  
Risk Factors ◽  
Generalized Additive Models ◽  
Spatial Models ◽  
Additive Models ◽  
Human Rabies ◽  
Spatial Risk ◽  
Risk Areas ◽  
Spatio Temporal ◽  
Current Characteristics ◽  
And Control

The situation of human rabies in Thailand has gradually declined over the past four decades. However, the number of animal rabies cases has slightly increased in the last ten years. This study thus aimed to describe the characteristics of animal rabies between 2017 and 2018 in Thailand in which the prevalence was fairly high and to quantify the association between monthly rabies occurrences and explainable variables using the generalized additive models (GAMs) to predict the spatial risk areas for rabies spread. Our results indicate that the majority of animals affected by rabies in Thailand are dogs. Most of the affected dogs were owned, free or semi-free roaming, and unvaccinated. Clusters of rabies were highly distributed in the northeast, followed by the central and the south of the country. Temporally, the number of cases gradually increased after June and reached a peak in January. Based on our spatial models, human and cattle population density as well as the spatio-temporal history of rabies occurrences, and the distances from the cases to the secondary roads and country borders are identified as the risk factors. Our predictive maps are applicable for strengthening the surveillance system in high-risk areas. Nevertheless, the identified risk factors should be rigorously considered and integrated into the strategic plans for the prevention and control of animal rabies in Thailand.

scholarly journals Who are the patients that default tuberculosis treatment? – space matters!

2017 ◽  
Vol 145 (6) ◽  
pp. 1130-1134 ◽  
Author(s):  
C. NUNES ◽  
R. DUARTE ◽  
A. M. VEIGA ◽  
B. TAYLOR
Keyword(s):  
Risk Factors ◽  
Urban Areas ◽  
Drug Users ◽  
Generalized Additive Models ◽  
Individual Characteristics ◽  
Additive Models ◽  
Individual Risk ◽  
Predictive Capacity ◽  
Logistic Regression Models ◽  
Treatment Regimens

SUMMARYThe goals of this article are: (i) to understand how individual characteristics affect the likelihood of patients defaulting their pulmonary tuberculosis (PTB) treatment regimens; (ii) to quantify the predictive capacity of these risk factors; and (iii) to quantify and map spatial variation in the risk of defaulting. We used logistic regression models and generalized additive models with a spatial component to determine the odds of default across continental Portugal. We focused on new PTB cases, diagnosed between 2000 and 2013, and included some individual information (sex, age, residence area, alcohol abuse, intravenous drug use, homelessness, HIV, imprisonment status). We found that the global default rate was 4·88%, higher in individuals with well-known risk profiles (males, immigrants, HIV positive, homeless, prisoners, alcohol and drug users). Of specific epidemiological interest was that our geographical analysis found that Portugal's main urban areas (the two biggest cities) and one tourist region have higher default rates compared to the rest of the country, after adjusting for the previously mentioneded risk factors. The challenge of treatment defaulting, either due to other individual non-measured characteristics, healthcare system failure or patient recalcitrance requires further analysis in the spatio-temporal domain. Our findings suggest the presence of significant within-country variation in the risk of defaulting that cannot be explained by these classical individual risk factors alone. The methods we advocate are simple to implement and could easily be applied to other diseases.

scholarly journals Risk factors for AIDS-defining illnesses among a population of poorly adherent people living with HIV/AIDS in Atlanta, Georgia

AIDS Care ◽  
2015 ◽  
Vol 27 (7) ◽  
pp. 844-848 ◽  
Author(s):  
Jeremy Y. Chow ◽  
Marcella Alsan ◽  
Wendy Armstrong ◽  
Carlos del Rio ◽  
Vincent C. Marconi
Keyword(s):  
Risk Factors ◽  
People Living With Hiv ◽  
Living With Hiv ◽  
Hiv Aids

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data

2021 ◽  
Author(s):  
Mark David Walker ◽  
Mihály Sulyok
Keyword(s):  
Generalized Additive Models ◽  
Information Criterion ◽  
Additive Models ◽  
Online Data ◽  
German Government ◽  
Explanatory Variables ◽  
Mobility Data ◽  
Community Mobility ◽  
The Relationship ◽  
Potential Use

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals. Methods Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865–0.868) and van Walraven’s weights (0.863, 95% CI, 0.862–0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI, 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights. Conclusions All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

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