Application of landscape epidemiology to assess potential public health risk due to poor sanitation

Defining Spatial Epidemiology: A Systematic Review and Re-orientation

BACKGROUND

Spatial epidemiology has emerged as an important subfield of epidemiology over the past quarter century. We trace the origins of spatial epidemiology and note that its emergence coincided with technological developments in spatial statistics and geography. We hypothesize that spatial epidemiology makes important contributions to descriptive epidemiology and analytic risk-factor studies but is not yet aligned with epidemiology's current focus on causal inference and intervention.

METHODS

We conducted a systematic review of studies indexed in PubMed that used the term "spatial epidemiolog*" in the title, abstract, or keywords. Excluded articles were not written in English, examined disease in animals, or reported biologic pathogen distribution only. We coded the included papers into five categories (review, demonstration of method, descriptive, analytic, and intervention) and recorded the unit of analysis (i.e., individual vs. ecological). We additionally examined articles coded as analytic ecologic studies using scales for lexical content.

RESULTS

A total of 482 articles met the inclusion criteria, including 76 reviews, 117 demonstrations of methods, 122 descriptive studies, 167 analytic studies, and 0 intervention studies. Demonstration studies were most common from 2006 to 2014, and analytic studies were most common after 2015. Among the analytic ecologic studies, those published in later years used more terms relevant to spatial statistics (incidence rate ratio =1.3; 95% confidence interval [CI] = 1.1, 1.5) and causal inference (incidence rate ratio =1.1; 95% CI = 1.1, 1.2).

CONCLUSIONS

Spatial epidemiology is an important and growing subfield of epidemiology. We suggest a re-orientation to help align its practice with the goals of contemporary epidemiology.

Using urban landscape pattern to understand and evaluate infectious disease risk

COVID-19 case numbers in 161 sub-districts of Wuhan were investigated based on landscape epidemiology, and their landscape metrics were calculated based on land use/land cover (LULC). Initially, a mediation model verified a partially mediated population role in the relationship between landscape pattern and infection number. Adjusted incidence rate (AIR) and community safety index (CSI), two indicators for infection risk in sub-districts, were 25.82∼63.56 ‱ and 3.00∼15.87 respectively, and central urban sub-districts were at higher infection risk. Geographically weighted regression (GWR) performed better than OLS regression with AICc differences of 7.951∼181.261. The adjusted R² in GWR models of class-level index and infection risk were 0.697 to 0.817, while for the landscape-level index they were 0.668 to 0.835. Secondly, 16 key landscape metrics were identified based on GWR, and then a prediction model for infection risk in sub-districts and communities was developed. Using principal component analysis (PCA), development intensity, landscape level, and urban blue-green space were considered to be principal components affecting disease infection risk, explaining 73.1 % of the total variance. Cropland (PLAND and LSI), urban land (NP, LPI, and LSI) and unused land (NP) represent development intensity, greatly affecting infection risk in urban areas. Landscape level CONTAG, DIVISION, SHDI, and SHEI represent mobility and connectivity, having a profound impact on infection risk in both urban and suburban areas. Water (PLAND, NP, LPI, and LSI) and woodland (NP, and LSI) represent urban blue-green spaces, and were particularly important for infection risk in suburban areas. Based on urban landscape pattern, we proposed a framework to understand and evaluate infection risk. These findings provide a basis for risk evaluation and policy-making of urban infectious disease, which is significant for community management and urban planning for infectious disease worldwide.

Understanding the Impact of Rainfall on Diarrhea: Testing the Concentration-Dilution Hypothesis Using a Systematic Review and Meta-Analysis

BACKGROUND

Projected increases in extreme weather may change relationships between rain-related climate exposures and diarrheal disease. Whether rainfall increases or decreases diarrhea rates is unclear based on prior literature. The concentration-dilution hypothesis suggests that these conflicting results are explained by the background level of rain: Rainfall following dry periods can flush pathogens into surface water, increasing diarrhea incidence, whereas rainfall following wet periods can dilute pathogen concentrations in surface water, thereby decreasing diarrhea incidence.

OBJECTIVES

In this analysis, we explored the extent to which the concentration-dilution hypothesis is supported by published literature.

METHODS

To this end, we conducted a systematic search for articles assessing the relationship between rain, extreme rain, flood, drought, and season (rainy vs. dry) and diarrheal illness.

RESULTS

A total of 111 articles met our inclusion criteria. Overall, the literature largely supports the concentration-dilution hypothesis. In particular, extreme rain was associated with increased diarrhea when it followed a dry period [incidence rate ratio ( IRR ) = 1.26 ; 95% confidence interval (CI): 1.05, 1.51], with a tendency toward an inverse association for extreme rain following wet periods, albeit nonsignificant, with one of four relevant studies showing a significant inverse association (IRR = 0.911 ; 95% CI: 0.771, 1.08). Incidences of bacterial and parasitic diarrhea were more common during rainy seasons, providing pathogen-specific support for a concentration mechanism, but rotavirus diarrhea showed the opposite association. Information on timing of cases within the rainy season (e.g., early vs. late) was lacking, limiting further analysis. We did not find a linear association between nonextreme rain exposures and diarrheal disease, but several studies found a nonlinear association with low and high rain both being associated with diarrhea.

DISCUSSION

Our meta-analysis suggests that the effect of rainfall depends on the antecedent conditions. Future studies should use standard, clearly defined exposure variables to strengthen understanding of the relationship between rainfall and diarrheal illness. https://doi.org/10.1289/EHP6181.

Influences of Land-Use Dynamics and Surface Water Systems Interactions on Water-Related Infectious Diseases—A Systematic Review

Human interactions with surface water systems, through land-use dynamics, can influence the transmission of infectious water-related diseases. As a result, the aim of our study was to explore and examine the state of scientific evidence on the influences of these interactions on water-related infectious disease outcomes from a global perspective. A systematic review was conducted, using 54 peer-reviewed research articles published between 1995 and August 2019. The study revealed that there has been an increase in the number of publications since 2009; however, few of these publications (n = 6) made explicit linkages to the topic. It was found that urban and agricultural land-use changes had relatively high adverse impacts on water quality, due to high concentrations of fecal matter, heavy metals, and nutrients in surface water systems. Water systems were found as the common “vehicle” for infectious disease transmission, which in turn had linkages to sanitation and hygiene conditions. The study found explicit linkages between human–surface water interaction patterns and the transmission of water-based disease. However, weak and complex linkages were found between land-use change and the transmission of water-borne disease, due to multiple pathways and the dynamics of the other determinants of the disease. Therefore, further research studies, using interdisciplinary and transdisciplinary approaches to investigate and enhance a deeper understanding of these complexities and linkages among land use, surface water quality, and water-related infectious diseases, is crucial in developing integrated measures for sustainable water quality monitoring and diseases prevention.

Purity, Pollution, and Space: Barriers to Latrine Adoption in Post-disaster India

This study examines the adoption of latrines provided as part of reconstruction efforts after the 2004 tsunami in India. Primary data from 274 households encompassing 1154 individuals were collected from 14 villages. GLM and GLMM tests indicate that sex (more females adopted than males) is a statistically significant factor in latrine adoption (p = 0.046 and p = 0.005, respectively), while income, education, and male age cohorts were significant only in the GLM model. Regression analyses show that six social and demographic variables are somewhat predictive of latrine usage (R² = 0.123). Thus, while quantitative methods provided a contextual summation, qualitative methods ultimately explained why individuals chose to adopt or abandon the latrines. Interviews (n = 76) and focus group discussions (n = 14) revealed that latrine adoption is influenced by cultural conceptualizations of purity, pollution, and space. For example, conceptualizations of purity and pollution led some households to deem latrines as profane and thus a barrier to the entry of gods, while spatial constraints forced others to convert latrine space to other beneficial uses (e.g., puja room and storage area). Finally, the cost of pumping septic tanks and shared infrastructure arose as barriers to latrine adoption. These barriers underscore the importance of economics as well as community demand, capacity, and cohesion in latrine adoption.