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      Slums, Space, and State of Health—A Link between Settlement Morphology and Health Data

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          Abstract

          Approximately 1 billion slum dwellers worldwide are exposed to increased health risks due to their spatial environment. Recent studies have therefore called for the spatial environment to be introduced as a separate dimension in medical studies. Hence, this study investigates how and on which spatial scale relationships between the settlement morphology and the health status of the inhabitants can be identified. To this end, we summarize the current literature on the identification of slums from a geographical perspective and review the current literature on slums and health of the last five years (376 studies) focusing on the considered scales in the studies. We show that the majority of medical studies are restricted to certain geographical regions. It is desirable that the number of studies be adapted to the number of the respective population. On the basis of these studies, we develop a framework to investigate the relationship between space and health. Finally, we apply our methodology to investigate the relationship between the prevalence of slums and different health metrics using data of the global burden of diseases for different prefectures in Brazil on a subnational level.

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          Most cited references 121

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          Burden of disease in Brazil, 1990–2016: a systematic subnational analysis for the Global Burden of Disease Study 2016

          Summary Background Political, economic, and epidemiological changes in Brazil have affected health and the health system. We used the Global Burden of Disease Study 2016 (GBD 2016) results to understand changing health patterns and inform policy responses. Methods We analysed GBD 2016 estimates for life expectancy at birth (LE), healthy life expectancy (HALE), all-cause and cause-specific mortality, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs), and risk factors for Brazil, its 26 states, and the Federal District from 1990 to 2016, and compared these with national estimates for ten comparator countries. Findings Nationally, LE increased from 68·4 years (95% uncertainty interval [UI] 68·0–68·9) in 1990 to 75·2 years (74·7–75·7) in 2016, and HALE increased from 59·8 years (57·1–62·1) to 65·5 years (62·5–68·0). All-cause age-standardised mortality rates decreased by 34·0% (33·4–34·5), while all-cause age-standardised DALY rates decreased by 30·2% (27·7–32·8); the magnitude of declines varied among states. In 2016, ischaemic heart disease was the leading cause of age-standardised YLLs, followed by interpersonal violence. Low back and neck pain, sense organ diseases, and skin diseases were the main causes of YLDs in 1990 and 2016. Leading risk factors contributing to DALYs in 2016 were alcohol and drug use, high blood pressure, and high body-mass index. Interpretation Health improved from 1990 to 2016, but improvements and disease burden varied between states. An epidemiological transition towards non-communicable diseases and related risks occurred nationally, but later in some states, while interpersonal violence grew as a health concern. Policy makers can use these results to address health disparities. Funding Bill & Melinda Gates Foundation and the Brazilian Ministry of Health.
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            Health & Demographic Surveillance System Profile: The Nairobi Urban Health and Demographic Surveillance System (NUHDSS).

            The Nairobi Urban Health and Demographic Surveillance System (NUHDSS) was the first urban-based longitudinal health and demographic surveillance platform in sub-Saharan Africa (SSA). The NUHDSS was established in 2002 to provide a platform to investigate the long-term social, economic and health consequences of urban residence, and to serve as a primary research tool for intervention and impact evaluation studies focusing on the needs of the urban poor in SSA. Since its inception, the NUHDSS has successfully followed every year a population of about 65,000 individuals in 24,000 households in two slum communities--Korogocho and Viwandani--in Nairobi, Kenya. Data collected include key demographic and health information (births, deaths including verbal autopsy, in- and out-migration, immunization) and other information that characterizes living conditions in the slums (livelihood opportunities, household amenities and possessions, type of housing etc.). In addition to the routine data, it has provided a robust platform for nesting several studies examining the challenges of rapid urbanization in SSA and associated health and poverty dynamics. NUHDSS data are shared through internal and external collaborations, in accordance with the Centre's guidelines for publications, data sharing.
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              Human Leptospirosis Infection in Fiji: An Eco-epidemiological Approach to Identifying Risk Factors and Environmental Drivers for Transmission

              Leptospirosis is an important zoonotic disease in the Pacific Islands. In Fiji, two successive cyclones and severe flooding in 2012 resulted in outbreaks with 576 reported cases and 7% case-fatality. We conducted a cross-sectional seroprevalence study and used an eco-epidemiological approach to characterize risk factors and drivers for human leptospirosis infection in Fiji, and aimed to provide an evidence base for improving the effectiveness of public health mitigation and intervention strategies. Antibodies indicative of previous or recent infection were found in 19.4% of 2152 participants (81 communities on the 3 main islands). Questionnaires and geographic information systems data were used to assess variables related to demographics, individual behaviour, contact with animals, socioeconomics, living conditions, land use, and the natural environment. On multivariable logistic regression analysis, variables associated with the presence of Leptospira antibodies included male gender (OR 1.55), iTaukei ethnicity (OR 3.51), living in villages (OR 1.64), lack of treated water at home (OR 1.52), working outdoors (1.64), living in rural areas (OR 1.43), high poverty rate (OR 1.74), living <100m from a major river (OR 1.41), pigs in the community (OR 1.54), high cattle density in the district (OR 1.04 per head/sqkm), and high maximum rainfall in the wettest month (OR 1.003 per mm). Risk factors and drivers for human leptospirosis infection in Fiji are complex and multifactorial, with environmental factors playing crucial roles. With global climate change, severe weather events and flooding are expected to intensify in the South Pacific. Population growth could also lead to more intensive livestock farming; and urbanization in developing countries is often associated with urban and peri-urban slums where diseases of poverty proliferate. Climate change, flooding, population growth, urbanization, poverty and agricultural intensification are important drivers of zoonotic disease transmission; these factors may independently, or potentially synergistically, lead to enhanced leptospirosis transmission in Fiji and other similar settings.
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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
                19 March 2020
                March 2020
                : 17
                : 6
                Affiliations
                [1 ]Chair of Fluid Systems, Technical University of Darmstadt, Otto-Berndt-Str. 2, 64287 Darmstadt, Germany
                [2 ]Klinikum Darmstadt, Grafenstraße 9, 64283 Darmstadt, Germany
                Author notes
                [* ]Correspondence: john.friesen@ 123456fst.tu-darmstadt.de ; Tel.: +49-6151-16-27100
                Article
                ijerph-17-02022
                10.3390/ijerph17062022
                7143924
                32204347
                © 2020 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/).

                Categories
                Article

                Public health

                health data, global burden, remote sensing, informal settlements, slums

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