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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 9567

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Prof. Dr. Barry Borman

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Environmental Health Intelligence NZ, Massey University, PO Box 756, Wellington 6140, New Zealand
Interests: environmental health indicators; birth defects; epidemiology; environmental health; surveillance
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

In these times of the COVID-19 pandemic, epidemiology and public health surveillance have become of greater prominence and significance. Both remain fundamental to all aspects of public health and have contributed to major gains in improving and protecting the health of populations. Of major importance is the translation of findings from these research areas into public health action, and in their roles as evidence for policy development and decision-making.

Prof. Dr. Barry Borman
Guest Editor

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Keywords

epidemiology
public health surveillance
translational research

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Published Papers (4 papers)

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Research

10 pages, 2105 KiB

Open AccessArticle

Early Detection of SARS-CoV-2 Epidemic Waves: Lessons from the Syndromic Surveillance in Lombardy, Italy

by Giorgio Bagarella, Mauro Maistrello, Maddalena Minoja, Olivia Leoni, Francesco Bortolan, Danilo Cereda and Giovanni Corrao

Int. J. Environ. Res. Public Health 2022, 19(19), 12375; https://doi.org/10.3390/ijerph191912375 - 28 Sep 2022

Cited by 3 | Viewed by 1635

Abstract

We evaluated the performance of the exponentially weighted moving average (EWMA) model for comparing two families of predictors (i.e., structured and unstructured data from visits to the emergency department (ED)) for the early detection of SARS-CoV-2 epidemic waves. The study included data from 1,282,100 ED visits between 1 January 2011 and 9 December 2021 to a local health unit in Lombardy, Italy. A regression model with an autoregressive integrated moving average (ARIMA) error term was fitted. EWMA residual charts were then plotted to detect outliers in the frequency of the daily ED visits made due to the presence of a respiratory syndrome (based on coded diagnoses) or respiratory symptoms (based on free text data). Alarm signals were compared with the number of confirmed SARS-CoV-2 infections. Overall, 150,300 ED visits were encoded as relating to respiratory syndromes and 87,696 to respiratory symptoms. Four strong alarm signals were detected in March and November 2020 and 2021, coinciding with the onset of the pandemic waves. Alarm signals generated for the respiratory symptoms preceded the occurrence of the first and last pandemic waves. We concluded that the EWMA model is a promising tool for predicting pandemic wave onset. Full article

(This article belongs to the Special Issue Epidemiology and Health Surveillance Research)

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9 pages, 579 KiB

Open AccessArticle

The Association between Altitude and Waist–Height Ratio in Peruvian Adults: A Cross-Sectional Data Analysis of a Population-Based Survey

by Akram Hernández-Vásquez and Diego Azañedo

Int. J. Environ. Res. Public Health 2022, 19(18), 11494; https://doi.org/10.3390/ijerph191811494 - 13 Sep 2022

Cited by 1 | Viewed by 1880

Abstract

To evaluate the association between altitude and cardiometabolic risk calculated with the weight–height ratio (WHtR) in the Peruvian adult population via the cross-sectional data analysis of the Peruvian Demographic and Health Survey 2021. A total of 26,117 adults from 18 to 64 years of age were included in the analysis. The dependent variable was cardiometabolic risk, defined as “Yes” if the WHtR was ≥0.5 and “No” if the WHtR was <0.5. Exposure was altitude of residence categorized as: <1500 meters above sea level (masl); 1500 to 2499 masl; 2500 to 3499 masl; and ≥3500 masl. Crude and adjusted Poisson regression models were used to calculate prevalence ratios (PR) with 95% confidence intervals (CI). The mean WHtR in the population was 0.59 (standard deviation: 0.08), and 87.6% (95% CI: 86.9–88.2) were classified as at risk. After adjusting for sex, age, education level, well-being index, and area of residence, living at altitudes between 2500 and 3499 masl (aPR: 0.98; 95% CI: 0.96–1.00) and ≥3500 masl (aPR: 0.95; 95% CI: 0.93–0.97) were associated with lower cardiometabolic risk in comparison with living at <1500 masl. An inverse association was identified between living at a higher altitude and the proportion of cardiometabolic risk in the Peruvian adult population. However, at least 8 out of 10 people were identified as at risk in all categories of altitude. Full article

(This article belongs to the Special Issue Epidemiology and Health Surveillance Research)

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11 pages, 742 KiB

Open AccessArticle

Association between Altitude and the Framingham Risk Score: A Cross-Sectional Study in the Peruvian Adult Population

by Akram Hernández-Vásquez, Rodrigo Vargas-Fernández and Manuel Chacón-Diaz

Int. J. Environ. Res. Public Health 2022, 19(7), 3838; https://doi.org/10.3390/ijerph19073838 - 24 Mar 2022

Cited by 6 | Viewed by 2952

Abstract

To determine the association between altitude and the Framingham risk score in the Peruvian population, we performed a cross-sectional analytical study of data collected by the 2017–2018 Food and Nutrition Surveillance by Life Stages survey. The outcome of this study was the Framingham 10-year cardiovascular disease event risk prediction, which is composed of six modifiable and non-modifiable coronary risk factors. A generalized linear model (GLM) of the gamma family and log link function was used to report the crude and adjusted β coefficients. Several sensitivity analyses were performed to assess the association of interest. Data from a total of 833 surveyed participants were included. After adjusting for educational level, poverty level, alcohol consumption, physical activity level, the presence of any limitation, obesity, and area of residence, it was observed that altitude ≥ 2500 m above sea level (β = −0.42 [95% CI: −0.69 to −0.16]) was negatively and significantly associated with a decrease in the Framingham 10-year risk score. High altitude was significantly and negatively associated with Framingham 10-year risk scores. Our results will allow prevention strategies considering modifiable risk factors to avoid the development of cardiovascular diseases, especially in people living at low altitudes. Full article

(This article belongs to the Special Issue Epidemiology and Health Surveillance Research)

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9 pages, 852 KiB

Open AccessArticle

Real-Time Detection of Flu Season Onset: A Novel Approach to Flu Surveillance

by Jialiang Liu and Sumihiro Suzuki

Int. J. Environ. Res. Public Health 2022, 19(6), 3681; https://doi.org/10.3390/ijerph19063681 - 19 Mar 2022

Cited by 2 | Viewed by 1980

Abstract

The current gold standard for detection of flu season onset in the USA is done retrospectively, where flu season is detected after it has already started. We aimed to create a new surveillance strategy capable of detecting flu season onset prior to its starting. We used an established data generation method that combines Google search volume and historical flu activity data to simulate real-time estimates of flu activity. We then applied a method known as change-point detection to the generated data to determine the point in time that identifies the initial uptick in flu activity which indicates the imminent onset of flu season. Our strategy exhibits a high level of accuracy in predicting the onset of flu season at 86%. Additionally, on average, we detected the onset three weeks prior to the official start of flu season. The results provide evidence to support both the feasibility and efficacy of our strategy to improve the current standard of flu surveillance. The improvement may provide valuable support and lead time for public health officials to take appropriate actions to prevent and control the spread of the flu. Full article

(This article belongs to the Special Issue Epidemiology and Health Surveillance Research)

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