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Biostatistical Studies and Application of Biostatistical Methods in Epidemiology
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Biostatistical Studies and Application of Biostatistical Methods in Epidemiology

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Health".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 11219

Special Issue Editor

Prof. Dr. How-Ran Guo

E-Mail Website
Guest Editor
1. Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan
2. Department of Occupational and Environmental Medicine, National Cheng Kung University Hospital, Tainan, Taiwan
Interests: public health; environmental health; occupational medicine; epidemiology; biostatistics; public health surveillance; arsenic; cancer; radiation; carbon monoxide

Special Issue Information

Dear colleagues,

There is a saying that epidemiology is about rates and proportions. While many epidemiologists might not totally agree with that, all epidemiologists would agree that biostatistics is essential to epidemiology. With the advancement of epidemiology, more and more statistical methods have been applied to epidemiologic studies, and more and more data analytic methods have been developed to solve epidemiological problems. For example, methods have been developed to handle missing data and deal with collinearity.

This Special Issue is intended to showcase the application of biostatistical methods to epidemiological studies. Empirical papers on epidemiological research using emerging or novel theories, methods, or technologies are welcome, and we will also highlight novel theoretical, methodological, or technological advances in biostatistical methods that can be applied to epidemiological studies, especially those on environmental or public health. In many cases, there are multiple methods that can be applied to analyze the same data set, but different methods sometimes lead to different results, or even different conclusions. Studies that compare results obtained using different statistical methods and discuss the choice of method are encouraged.

In addition to original research papers, review articles and case studies are also solicited. Re-analysis of data from a previous study using different methods is also relevant to this Special Issue.

Prof. Dr. How-Ran Guo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Biostatistics
  • Epidemiology
  • Applied statistics
  • Environmental health
  • Public health
  • Methodology

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Published Papers (3 papers)

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Research

10 pages, 879 KiB  
Article
Use of Generalized Additive Model to Detect the Threshold of δ-Aminolevulinic Acid Dehydratase Activity Reduced by Lead Exposure
by Chan-Ching Huang, Chen-Cheng Yang, Te-Yu Liu, Chia-Yen Dai, Chao-Ling Wang and Hung-Yi Chuang
Int. J. Environ. Res. Public Health 2020, 17(16), 5712; https://doi.org/10.3390/ijerph17165712 - 7 Aug 2020
Cited by 6 | Viewed by 3009
Abstract
Background: Lead inhibits the enzymes in heme biosynthesis, mainly reducing δ-aminolevulinic acid dehydratase (ALAD) activity, which could be an available biomarker. The aim of this study was to detect the threshold of δ-aminolevulinic acid dehydratase activity reduced by lead exposure. Methods: We collected [...] Read more.
Background: Lead inhibits the enzymes in heme biosynthesis, mainly reducing δ-aminolevulinic acid dehydratase (ALAD) activity, which could be an available biomarker. The aim of this study was to detect the threshold of δ-aminolevulinic acid dehydratase activity reduced by lead exposure. Methods: We collected data on 121 lead workers and 117 non-exposed workers when annual health examinations were performed. ALAD activity was determined by the standardized method of the European Community. ALAD G177C (rs1800435) genotyping was conducted using the polymerase chain reaction and restricted fragment length polymorphism (PCR-RFLP) method. In order to find a threshold effect, we used generalized additive models (GAMs) and scatter plots with smoothing curves, in addition to multiple regression methods. Results: There were 229 ALAD1-1 homozygotes and 9 ALAD1-2 heterozygotes identified, and no ALAD2-2 homozygotes. Lead workers had significantly lower ALAD activity than non-exposed workers (41.6 ± 22.1 vs. 63.3 ± 14.0 U/L, p < 0.001). The results of multiple regressions showed that the blood lead level (BLL) was an important factor inversely associated with ALAD activity. The possible threshold of BLL affecting ALAD activity was around 5 μg/dL. Conclusions: ALAD activity was inhibited by blood lead at a possible threshold of 5 μg/dL, which suggests that ALAD activity could be used as an indicator for lead exposure regulation. Full article
(This article belongs to the Special Issue Biostatistical Studies and Application of Biostatistical Methods in Epidemiology)
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13 pages, 752 KiB  
Article
Cadmium Is Associated with Type 2 Diabetes in a Superfund Site Lead Smelter Community in Dallas, Texas
by Bert B. Little, Robert Reilly, Brad Walsh and Giang T. Vu
Int. J. Environ. Res. Public Health 2020, 17(12), 4558; https://doi.org/10.3390/ijerph17124558 - 24 Jun 2020
Cited by 23 | Viewed by 2860
Abstract
Objective: To test the hypothesis that cadmium (Cd) exposure is associated with type 2 diabetes mellitus (T2DM). Materials and Methods: A two-phase health screening (physical examination and laboratory tests) was conducted in a lead smelter community following a Superfund Cleanup. Participants were African [...] Read more.
Objective: To test the hypothesis that cadmium (Cd) exposure is associated with type 2 diabetes mellitus (T2DM). Materials and Methods: A two-phase health screening (physical examination and laboratory tests) was conducted in a lead smelter community following a Superfund Cleanup. Participants were African Americans aged >19 years to <89 years. Multiple logistic regression was used to analyze T2DM regressed on blood Cd level and covariates: body mass index (BMI), heavy metals (Ar, Cd, Hg, Pb), duration of residence, age, smoking status, and sex. Results: Of 875 subjects environmentally exposed to Cd, 55 were occupationally exposed to by-products of lead smelting and 820 were community residents. In addition, 109 T2DM individuals lived in the community for an average of 21.0 years, and 766 non-T2DM individuals for 19.0 years. T2DM individuals (70.3%) were >50 years old. Blood Cd levels were higher among T2DM subjects (p < 0.006) compared to non-T2DM individuals. Logistic regression of T2DM status identified significant predictors: Cd level (OR = 1.85; 95% CI: 1.14–2.99, p < 0.01), age >50 years (OR = 3.10; 95% CI: 1.91–5.02, p < 0.0001), and BMI (OR = 1.07; CI: 1.04–1.09, 0.0001). In meta-analysis of 12 prior studies and this one, T2DM risk was OR = 1.09 (95% CI: 1.03–1.15, p < 0.004) fixed effects and 1.22 (95% CI: 1.04–1.44, p < 0.02) random effects. Discussion: Chronic environmental Cd exposure was associated with T2DM in a smelter community, controlling for covariates. T2DM onset <50 years was significantly associated with Cd exposure, but >50 years was not. Meta-analysis suggests that Cd exposure is associated with a small, but significant increased risk for T2DM. Available data suggest Cd exposure is associated with an increased propensity to increased insulin resistance. Full article
(This article belongs to the Special Issue Biostatistical Studies and Application of Biostatistical Methods in Epidemiology)
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13 pages, 1326 KiB  
Article
Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran
by Naeimehossadat Asmarian, Seyyed Mohammad Taghi Ayatollahi, Zahra Sharafi and Najaf Zare
Int. J. Environ. Res. Public Health 2019, 16(22), 4460; https://doi.org/10.3390/ijerph16224460 - 13 Nov 2019
Cited by 22 | Viewed by 4733
Abstract
Hierarchical Bayesian log-linear models for Poisson-distributed response data, especially Besag, York and Mollié (BYM) model, are widely used for disease mapping. In some cases, due to the high proportion of zero, Bayesian zero-inflated Poisson models are applied for disease mapping. This study proposes [...] Read more.
Hierarchical Bayesian log-linear models for Poisson-distributed response data, especially Besag, York and Mollié (BYM) model, are widely used for disease mapping. In some cases, due to the high proportion of zero, Bayesian zero-inflated Poisson models are applied for disease mapping. This study proposes a Bayesian spatial joint model of Bernoulli distribution and Poisson distribution to map disease count data with excessive zeros. Here, the spatial random effect is simultaneously considered into both logistic and log-linear models in a Bayesian hierarchical framework. In addition, we focus on the BYM2 model, a re-parameterization of the common BYM model, with penalized complexity priors for the latent level modeling in the joint model and zero-inflated Poisson models with different type of zeros. To avoid model fitting and convergence issues, Bayesian inferences are implemented using the integrated nested Laplace approximation (INLA) method. The models are compared according to the deviance information criterion and the logarithmic scoring. A simulation study with different proportions of zero exhibits INLA ability in running the models and also shows slight differences between the popular BYM and BYM2 models in terms of model choice criteria. In an application, we apply the fitting models on male breast cancer data in Iran at county level in 2014. Full article
(This article belongs to the Special Issue Biostatistical Studies and Application of Biostatistical Methods in Epidemiology)
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