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      The Epidemiological Influence of Climatic Factors on Shigellosis Incidence Rates in Korea

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          Abstract

          Research has shown the effects of climatic factors on shigellosis; however, no previous study has evaluated climatic effects in regions with a winter seasonality of shigellosis incidence. We examined the effects of temperature and precipitation on shigellosis incidence in Korea from 2002–2010. The incidence of shigellosis was calculated based on data from the Korean Center for Disease Control and Prevention (KCDC, Cheongju, Korea), and a generalized additive model (GAM) was used to analyze the associations between the incidence and climatic factors. The annual incidence rate of shigellosis was 7.9 cases/million persons from 2002–2010. During 2007–2010, high incidence rates and winter seasonality were observed among those aged ≥65 years, but not among lower age groups. Based on the GAM model, the incidence of shigellosis is expected to increase by 13.6% and 2.9% with a temperature increase of 1 °C and a lag of two weeks and with a mean precipitation increase of 1 mm and a lag of five weeks after adjustment for seasonality, respectively. This study suggests that the incidence of shigellosis will increase with global climate change despite the winter seasonality of shigellosis in Korea. Public health action is needed to prevent the increase of shigellosis incidence associated with climate variations.

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          Global burden of Shigella infections: implications for vaccine development and implementation of control strategies.

          Few studies provide data on the global morbidity and mortality caused by infection with Shigella spp.; such estimates are needed, however, to plan strategies of prevention and treatment. Here we report the results of a review of the literature published between 1966 and 1997 on Shigella infection. The data obtained permit calculation of the number of cases of Shigella infection and the associated mortality occurring worldwide each year, by age, and (as a proxy for disease severity) by clinical category, i.e. mild cases remaining at home, moderate cases requiring outpatient care, and severe cases demanding hospitalization. A sensitivity analysis was performed to estimate the high and low range of morbid and fatal cases in each category. Finally, the frequency distribution of Shigella infection, by serogroup and serotype and by region of the world, was determined. The annual number of Shigella episodes throughout the world was estimated to be 164.7 million, of which 163.2 million were in developing countries (with 1.1 million deaths) and 1.5 million in industrialized countries. A total of 69% of all episodes and 61% of all deaths attributable to shigellosis involved children under 5 years of age. The median percentages of isolates of S. flexneri, S. sonnei, S. boydii, and S. dysenteriae were, respectively, 60%, 15%, 6%, and 6% (30% of S. dysenteriae cases were type 1) in developing countries; and 16%, 77%, 2%, and 1% in industrialized countries. In developing countries, the predominant serotype of S. flexneri is 2a, followed by 1b, 3a, 4a, and 6. In industrialized countries, most isolates are S. flexneri 2a or other unspecified type 2 strains. Shigellosis, which continues to have an important global impact, cannot be adequately controlled with the existing prevention and treatment measures. Innovative strategies, including development of vaccines against the most common serotypes, could provide substantial benefits.
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            Evaluation of reporting timeliness of public health surveillance systems for infectious diseases

            Background Timeliness is a key performance measure of public health surveillance systems. Timeliness can vary by disease, intended use of the data, and public health system level. Studies were reviewed to describe methods used to evaluate timeliness and the reporting timeliness of National Notifiable Diseases Surveillance System (NNDSS) data was evaluated to determine if this system could support timely notification and state response to multistate outbreaks. Methods Published papers that quantitatively measured timeliness of infectious disease surveillance systems operating in the U.S. were reviewed. Median reporting timeliness lags were computed for selected nationally notifiable infectious diseases based on a state-assigned week number and various date types. The percentage of cases reported within the estimated incubation periods for each disease was also computed. Results Few studies have published quantitative measures of reporting timeliness; these studies do not evaluate timeliness in a standard manner. When timeliness of NNDSS data was evaluated, the median national reporting delay, based on date of disease onset, ranged from 12 days for meningococcal disease to 40 days for pertussis. Diseases with the longer incubation periods tended to have a higher percentage of cases reported within its incubation period. For acute hepatitis A virus infection, which had the longest incubation period of the diseases studied, more than 60% of cases were reported within one incubation period for each date type reported. For cryptosporidiosis, Escherichia coli O157:H7 infection, meningococcal disease, salmonellosis, and shigellosis, less than 40% of cases were reported within one incubation period for each reported date type. Conclusion Published evaluations of infectious disease surveillance reporting timeliness are few in number and are not comparable. A more standardized approach for evaluating and describing surveillance system timeliness should be considered; a recommended methodology is presented. Our analysis of NNDSS reporting timeliness indicated that among the conditions evaluated (except for acute hepatitis A infection), the long reporting lag and the variability across states limits the usefulness of NNDSS data and aberration detection analysis of those data for identification of and timely response to multistate outbreaks. Further evaluation of the factors that contribute to NNDSS reporting timeliness is warranted.
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              Effects of Extreme Precipitation to the Distribution of Infectious Diseases in Taiwan, 1994–2008

              The incidence of extreme precipitation has increased with the exacerbation of worldwide climate disruption. We hypothesize an association between precipitation and the distribution patterns that would affect the endemic burden of 8 infectious diseases in Taiwan, including water- and vector-borne infectious diseases. A database integrating daily precipitation and temperature, along with the infectious disease case registry for all 352 townships in the main island of Taiwan was analysed for the period from 1994 to 2008. Four precipitation levels, 350 mm, were categorized to represent quantitative differences, and their associations with each specific disease was investigated using the Generalized Additive Mixed Model and afterwards mapped on to the Geographical Information System. Daily precipitation levels were significantly correlated with all 8 mandatory-notified infectious diseases in Taiwan. For water-borne infections, extreme torrential precipitation (>350 mm/day) was found to result in the highest relative risk for bacillary dysentery and enterovirus infections when compared to ordinary rain (<130 mm/day). Yet, for vector-borne diseases, the relative risk of dengue fever and Japanese encephalitis increased with greater precipitation only up to 350 mm. Differential lag effects following precipitation were statistically associated with increased risk for contracting individual infectious diseases. This study’s findings can help health resource sector management better allocate medical resources and be better prepared to deal with infectious disease outbreaks following future extreme precipitation events.
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                10 October 2018
                October 2018
                : 15
                : 10
                : 2209
                Affiliations
                [1 ]Department of Preventive Medicine College of Medicine, Eulji University, Daejeon 34824, Korea; syjace@ 123456nate.com (Y.-J.S.); mira@ 123456eulji.ac.kr (M.P.)
                [2 ]Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, Suwon 16419, Korea; hkcheong@ 123456skku.edu
                [3 ]Department of Preventive Medicine, College of Medicine, Korea University, Seoul 02841, Korea; myungki@ 123456korea.ac.kr
                [4 ]Department of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Korea; jyshin@ 123456knu.ac.kr
                [5 ]Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea; cyberdoc@ 123456snu.ac.kr
                [6 ]Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang 10408, Korea; moranki@ 123456ncc.re.kr
                Author notes
                [* ]Correspondence: jslim@ 123456eulji.ac.kr ; Tel.: +82-42-259-1683
                Author information
                https://orcid.org/0000-0002-5543-8574
                https://orcid.org/0000-0003-2758-9399
                https://orcid.org/0000-0002-8761-3744
                Article
                ijerph-15-02209
                10.3390/ijerph15102209
                6210993
                30309010
                3871b330-a7aa-4f95-b352-93fa67ba69f1
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 22 August 2018
                : 06 October 2018
                Categories
                Article

                Public health
                meteorological factors,infectious diarrheal disease,shigellosis,seasonal variation

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