Environmental and socioeconomic drivers in infectious disease
Authors: Maria Cristina Schneider, Gustavo Machado
Journal: The Lancet Planetary Health (2018)

To respond to the current challenges that the world is facing, the integrated vision that human beings, animals, and the environment are linked has never been so important. Understanding these links is necessary to better recommend strategies to predict, prevent, respond to, and mitigate the challenges, taking into account an environmental and socioeconomic background that might not change in a short period of time, but which could worsen if we do not take action. Several published studies1Hamrick PN Aldighieri S Machado G et al.Geographic patterns and environmental factors associated with human yellow fever presence in the Americas.PLOS Neglected Tropical Diseases. 2017; 11: e0005897Crossref PubMed Scopus (48) Google Scholar, 2Muñoz-Zanzi C Mason MR Encina C Astroza A Romero A Leptospira contamination in household and environmental water in rural communities in southern Chile.Int J Environ Res Public Health. 2014; 11: 6666-6680Crossref PubMed Scopus (62) Google Scholar, 3Semenza JC Sudre B Oni T Suk JE Giesecke J Linking environmental drivers to infectious diseases: the European Environment and Epidemiology Network.PLOS Neglected Tropical Diseases. 2013; 7: e2323Crossref PubMed Scopus (30) Google Scholar have analysed the association of infectious diseases with possible environmental or geographical factors as drivers, such as precipitation, temperature, ecoregions, soil types, and altitude; as well as socioeconomic or demographic factors such as gross domestic product, poverty rate, percentage rural population, cattle raising methods, and agricultural processes. Spatial epidemiology is one method of measuring these drivers that was applied in these studies. Among authors reviewing the concept of space in epidemiology, Pavlovsky was one of the first researchers to study the interrelated components of microclimate, flora, and fauna in disease occurrence.4Pavlovsky EN Natural nidality of transmissible diseases.Am J Public Health Nations Health. 1967; 57: 913Crossref Google Scholar, 5Czeresnia D Ribeiro AM The concept of space in epidemiology: a historical and epistemological interpretation.Cadernos de Saúde Pública. 2000; 16: 595-605Crossref PubMed Google Scholar Considering environmental factors as the basic setting, when human beings penetrate and interfere in this scenario, more components could be added in the form of social determinants. Spatial analyses in epidemiology are a vast field and a viable option to study the geographical distribution of diseases. Different methods could be used, including disease mapping, geographical correlation studies, risk assessment in relation to a point-source or line-source, and disease clustering.6Elliott P Wakefield J Best N Briggs D Spatial epidemiology: methods and applications. Oxford Scholarship Online, 2001; published online Sep 2009.http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780198515326.001.0001/acprof-9780198515326-chapter-1Date accessed: April 11, 2018Google Scholar A wide range of methods and software have been developed and applied to understand disease distribution, and used to explore underlying risk factors considered to be spatially, temporally, or spatiotemporally structured.7Walter SD Elliott P Wakefield JC Best NG Briggs D Disease mapping: a historical perspective. Spatial epidemiology: methods and applications. Oxford University Press, Oxford2000Google Scholar The selection of which analytical tool to use can be a challenge. This was the topic of a study by Helen J Mayfield and colleagues published in The Lancet Planetary Health.8Mayfield H Lowry JH Watson CH Kama M Nilles EJ Lau CL Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study.Lancet Planet Health. 2018; 2: e223-e232Summary Full Text Full Text PDF PubMed Scopus (30) Google Scholar They compared two types of regression (geographically weighted logistic regression [GWLR] and a standard logistic regression) to quantify the spatial variation in the relative importance of five environmental and sociodemographic covariates: maximum rainfall, cattle density, distance to rivers, poverty rate, and residential setting [urban or rural]) on leptospirosis transmission in Fiji. Mayfield and colleagues showed that the Aikaike information criterion value of the GWLR model was 1935·2 compared with 1254·2 for the standard logistic regression model, indicating that the GWLR model was more efficient. Both models produced similar odds ratios for the covariates, but GWLR also detected spatial variation in the effect of each covariate. Maximum rainfall had the least variation across space (median OR 1·30, IQR 1·27–1·35), and distance to river varied the most (1·45, 1·35–2·05). Mayfield and colleagues used the spatial regression method applied to point-reference data that were further integrated into area data (ie municipality) to describe risk factors associated with human cases of leptospirosis. Among the widely used techniques to model such multilevel disease distribution data (individual, clustered, or grouped)9Kreft I de Leeuw J Introducing multilevel modeling. SAGE Publications, London1998Crossref Google Scholar are the maximum likelihood and the Bayesian methods. Both approaches tend to achieve the same objective of estimating spatially the structure of the disease system under investigation. These types of studies often use computer-based spatial information and remote sensing data10US National Aeronautics and Space AdministrationRemote sensing: all images 2018; published online April 11.https://earthobservatory.nasa.gov/Images/category.php?cat_id=96Date accessed: April 11, 2018Google Scholar, 11Melesse AM Weng Q Thenkabail PS Senay GB Remote sensing sensors and applications in environmental resources mapping and modelling.Sensors (Basel). 2007; 7: 3209-3241Crossref PubMed Scopus (175) Google Scholar for the environmental variables that, together with public health data, can support decision making. A study on leptospirosis in Nicaragua was undertaken in 2012 with the participation of the country's decision makers.12Schneider MC Nájera P Aldighieri S et al.Leptospirosis outbreaks in Nicaragua: identifying critical areas and exploring drivers for evidence-based planning.Int J Environ Res Public Health. 2012; 9: 3883-3910Crossref PubMed Scopus (39) Google Scholar Nicaragua had experienced floods and outbreaks of leptospirosis for many years, and spatial and statistical analysis were used to describe the critical areas and drivers. The variable presenting the most evident pattern of association was soil type (volcanic origin had the highest association). Precipitation and percentage of rural population were also associated with areas of high transmission. This information could be used in planning prevention and response strategies to leptospirosis outbreaks in Nicaragua, taking into consideration the critical areas, and periods of the year with higher precipitation and an increase in cases. This transdisciplinary approach to create tools for decision making in public health could be more widely used. Several open-access platforms with environmental and socioeconomic data, disaggregated at subnational administrative levels, are available to facilitate this kind of analysis. Collation of all different types of information related to ecosystems, animals, and human beings in a multidisciplinary view is part of the concept of the American Veterinary Medical Association's One Health approach.13King LJ Anderson LR Blackmore CG et al.Executive summary of the AVMA One Health Initiative Task Force report.J Am Vet Med Assoc. 2008; 233: 259-261Crossref PubMed Scopus (96) Google Scholar Leptospirosis is an excellent example of this approach, because the disease affects a many animal species, both wild and domestic, which serve as sources of infection for human beings, most of the time from exposure through water and soil.14WHOHuman leptospirosis: guidance for diagnosis, surveillance and control. World Health Organization, Geneva2003http://apps.who.int/iris/handle/10665/42667Date accessed: April 11, 2018Google Scholar Some studies mention the importance of environmental and socioeconomic drivers in leptospirosis.15Lau CL Dobson AJ Smythe LD et al.Leptospirosis in American Samoa 2010: epidemiology, environmental drivers, and the management of emergence.Am J Trop Med Hyg. 2012; 86: 309-319Crossref PubMed Scopus (47) Google Scholar, 16Schneider MC Najera P Pereira M et al.Leptospirosis in in Rio Grande do Sul, Brazil: an ecosystem approach in the animal-human interface.PLOS Neglected Tropical Diseases. 2015; 9: e0004095Crossref PubMed Scopus (38) Google Scholar Additionally, leptospirosis outbreaks with thousands of cases have increasingly been reported associated with natural disasters and flooding, such as those in Malaysia (2000), Jakarta (2002), Mumbai (2000 and 2005), and the Philippines (2009).16Schneider MC Najera P Pereira M et al.Leptospirosis in in Rio Grande do Sul, Brazil: an ecosystem approach in the animal-human interface.PLOS Neglected Tropical Diseases. 2015; 9: e0004095Crossref PubMed Scopus (38) Google Scholar With climate change, these occurrences could become more frequent. There is no doubt that leptospirosis is an important public health issue globally, with an estimated one million human cases per year. Oceania represents one of the highest estimates of disease morbidity and mortality in the world, just behind south and southeast Asia.17Costa F Hagan JE Calcagno J et al.Global morbidity and mortality of leptospirosis: a systematic review.PLOS Neglected Tropical Diseases. 2015; 9: e0003898Crossref PubMed Scopus (876) Google Scholar More information is urgently needed to advance leptospirosis so that it no longer remains a neglected tropical disease. We are responsible for the views expressed in this publication, and they do not necessarily represent the decisions or policies of the Pan American Health Organization. We declare no competing interests. Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling studyGWLR provided a valuable method for modelling spatial heterogeneity of covariates for leptospirosis infection and their relative importance over space. Results of GWLR could be used to inform more place-specific interventions, particularly for diseases with strong environmental or sociodemographic drivers of transmission. Full-Text PDF Open Access