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      Parity and All-cause Mortality in Women and Men: A Dose-Response Meta-Analysis of Cohort Studies

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          Abstract

          To quantitatively assess the association between parity and all-cause mortality, we conducted a meta-analysis of cohort studies. Relevant reports were identified from PubMed and Embase databases. Cohort studies with relative risks (RRs) and 95% confidence intervals (CIs) of all-cause mortality in three or more categories of parity were eligible. Eighteen articles with 2,813,418 participants were included. Results showed that participants with no live birth had higher risk of all-cause mortality (RR= 1.19, 95% CI = 1.03–1.38; I 2  = 96.7%, P < 0.001) compared with participants with one or more live births. Nonlinear dose-response association was found between parity and all-cause mortality ( P for non-linearity < 0.0001). Our findings suggest that moderate-level parity is inversely associated with all-cause mortality.

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          Cost of mating in Drosophila melanogaster females is mediated by male accessory gland products.

          T. Chapman, L Liddle, J M Kalb … (1995)
          Female Drosophila melanogaster with environmentally or genetically elevated rates of mating die younger than controls. This cost of mating is not attributable to receipt of sperm. We demonstrate here that seminal fluid products from the main cells of the male accessory gland are responsible for the cost of mating in females, and that increasing exposure to these products increases female death rate. Main-cell products are also involved in elevating the rate of female egg-laying, in reducing female receptivity to further matings and in removing or destroying sperm of previous mates. The cost of mating to females may therefore represent a side-effect of evolutionary conflict between males.
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            Regression models in clinical studies: determining relationships between predictors and response.

            F Harrell, K. Lee, B Pollock (1988)
            Multiple regression models are increasingly being applied to clinical studies. Such models are powerful analytic tools that yield valid statistical inferences and make reliable predictions if various assumptions are satisfied. Two types of assumptions made by regression models concern the distribution of the response variable and the nature or shape of the relationship between the predictors and the response. This paper addresses the latter assumption by applying a direct and flexible approach, cubic spline functions, to two widely used models: the logistic regression model for binary responses and the Cox proportional hazards regression model for survival time data.
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              Reproductive factors and breast cancer.

              J Kelsey, M D Gammon, E M John (1993)
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 13 January 2016
                Publication date Collection: 2016
                Volume: 6
                Electronic Location Identifier: 19351
                Affiliations
                [1 ]Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, Hubei Province, China
                [2 ]Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, Hubei Province, China
                [3 ]Women and Children Medical Center of Jiang-an District , Wuhan, Hubei Province, China
                [4 ]Department of Medical Rehabilitation, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, Hubei Province, China
                Author notes
                Article
                Publisher Item ID: srep19351
                DOI: 10.1038/srep19351
                PMC ID: 4725925
                PubMed ID: 26758416
                SO-VID: 9e490573-c0d6-4661-aaa8-1119b8d5b431
                Copyright © Copyright © 2016, Macmillan Publishers Limited
                License:

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                Date received : 03 July 2015
                Date accepted : 08 December 2015
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                Subject: Article

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