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      The Association between Anthropometric Failure and Toilet Types: A Cross-Sectional Study from India

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          ABSTRACT.

          Sustainable Development Goal 6.2 aims to end open defecation by 2030 by ensuring universal access to private household toilets. However, private toilets might not be feasible for poor households. As a result, policy makers and academics have suggested well-managed shared sanitation facilities as an alternative solution. Less is known about the associations between shared sanitation use and health. Using data from the fifth round of the National Family Health Survey from 2019 to 2021, we estimated the association between usual defecation location and child anthropometry outcomes among children under 2 years in India. The primary exposure was usual defecation location at the household level. We compared both shared improved toilet use and open defecation to private, improved toilet use. We used linear regression to estimate the associations between the exposures and linear outcomes: height-for-age Z-score, weight-for-height Z-score, and weight-for-age Z-score. We used Poisson regression with a log link to estimate the prevalence ratios of stunting, wasting, and underweight. After controlling for environmental, maternal, socioeconomic, and child confounders, we found no differences in six child anthropometry outcomes when comparing shared toilet use or open defecation to private toilet use. This finding was consistent across both urban and rural households. Our findings confirm the null associations between private toilet use and child growth found in previous studies, and that this association does not vary if the toilet is being shared. Future research should examine these differences between private and shared toilets in the context of other health outcomes.

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          A modified poisson regression approach to prospective studies with binary data.

          G Zou (2004)
          Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.
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            Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio

            Background Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. Methods We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. Results Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. Conclusions Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.
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              Demographic and health surveys: a profile.

              Demographic and Health Surveys (DHS) are comparable nationally representative household surveys that have been conducted in more than 85 countries worldwide since 1984. The DHS were initially designed to expand on demographic, fertility and family planning data collected in the World Fertility Surveys and Contraceptive Prevalence Surveys, and continue to provide an important resource for the monitoring of vital statistics and population health indicators in low- and middle-income countries. The DHS collect a wide range of objective and self-reported data with a strong focus on indicators of fertility, reproductive health, maternal and child health, mortality, nutrition and self-reported health behaviours among adults. Key advantages of the DHS include high response rates, national coverage, high quality interviewer training, standardized data collection procedures across countries and consistent content over time, allowing comparability across populations cross-sectionally and over time. Data from DHS facilitate epidemiological research focused on monitoring of prevalence, trends and inequalities. A variety of robust observational data analysis methods have been used, including cross-sectional designs, repeated cross-sectional designs, spatial and multilevel analyses, intra-household designs and cross-comparative analyses. In this profile, we present an overview of the DHS along with an introduction to the potential scope for these data in contributing to the field of micro- and macro-epidemiology. DHS datasets are available for researchers through MEASURE DHS at www.measuredhs.com.
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                Author and article information

                Journal
                Am J Trop Med Hyg
                Am J Trop Med Hyg
                tpmd
                tropmed
                The American Journal of Tropical Medicine and Hygiene
                The American Society of Tropical Medicine and Hygiene
                0002-9637
                1476-1645
                13 February 2023
                April 2023
                13 February 2023
                : 108
                : 4
                : 811-819
                Affiliations
                [ 1 ]Department of Global Health & Social Medicine, Harvard Medical School, Boston, Massachusetts;
                [ 2 ]Division of Epidemiology, School of Public Health, University of California, Berkeley, California;
                [ 3 ]Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan;
                [ 4 ]Division of Health Policy & Management, College of Health Sciences, Korea University, Seoul, South Korea;
                [ 5 ]Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts;
                [ 6 ]Harvard Center for Population and Development Studies, Cambridge, Massachusetts
                Author notes
                [* ]Address correspondence to Anoop Jain, Department of Global Health & Social Medicine, Harvard Medical School, 250 Grand Ave., Oakland, CA 94610. E-mail: anoop_jain@ 123456hms.harvard.edu

                The data that support the findings of this study are openly available in India: Standard DHS, 2015-2016 Dataset at https://dhsprogram.com/data/dataset/India_Standard-DHS_2015.cfm?flag=0.

                Authors’ addresses: Anoop Jain, Department of Global Health & Social Medicine, Harvard Medical School, Cambridge, MA, E-mail: anoop_jain@ 123456hms.harvard.edu . Helen O. Pitchik, Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, E-mail: hpitchik@ 123456berkeley.edu . Caleb Harrison, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, E-mail: harrisct@ 123456umich.edu . Rockli Kim, Division of Health Policy & Management, College of Health Sciences, Korea University, Seoul, South Korea, E-mail: rok495@ 123456mail.harvard.edu . S.V. Subramanian, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA; Harvard Center for Population and Development Studies, Cambridge, MA, E-mail: svsubram@ 123456hsph.harvard.edu .

                Article
                tpmd220138
                10.4269/ajtmh.22-0138
                10077020
                36780894
                87d58fb5-3e73-40c7-813e-5e78bfad7d68
                © The author(s)

                This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 February 2022
                : 22 November 2022
                Page count
                Pages: 9
                Categories
                Research Article

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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