Accommodating the ecological fallacy in disease mapping in the absence of individual exposures

Greenness, air pollution, and temperature exposure effects in predicting premature mortality and morbidity: A small-area study using spatial random forest model

BACKGROUND

Although studies have provided negative impacts of air pollution, heat or cold exposure on mortality and morbidity, and positive effects of increased greenness on reducing them, a few studies have focused on exploring combined and synergetic effects of these exposures in predicting these health outcomes, and most had ignored the spatial autocorrelation in analyzing their health effects. This study aims to investigate the health effects of air pollution, greenness, and temperature exposure on premature mortality and morbidity within a spatial machine-learning modeling framework.

METHODS

Years of potential life lost reflecting premature mortality and comparative illness and disability ratio reflecting chronic morbidity from 1673 small areas covering Greater Manchester for the year 2008-2013 obtained. Average annual levels of NO2 concentration, normalized difference vegetation index (NDVI) representing greenness, and annual average air temperature were utilized to assess exposure in each area. These exposures were linked to health outcomes using non-spatial and spatial random forest (RF) models while accounting for spatial autocorrelation.

RESULTS

Spatial-RF models provided the best predictive accuracy when accounted for spatial autocorrelation. Among the exposures considered, air pollution emerged as the most influential in predicting mortality and morbidity, followed by NDVI and temperature exposure. Nonlinear exposure-response relations were observed, and interactions between exposures illustrated specific ranges or sweet and sour spots of exposure thresholds where combined effects either exacerbate or moderate health conditions.

CONCLUSION

Air pollution exposure had a greater negative impact on health compared to greenness and temperature exposure. Combined exposure effects may indicate the highest influence of premature mortality and morbidity burden.

Excess all-cause mortality during COVID-19 outbreak: potential role of untreated cardiovascular disease

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has rapidly spread globally. Due to different testing strategies, under-detection of positive subjects and COVID-19-related-deaths remains common. Aim of this analysis was to assess the real impact of COVID-19 through the analysis of 2020 Italian all-cause mortality data compared to historical series.

METHODS

We performed a retrospective analysis of 2020 and 2015-2019 all-cause mortality data released by the Italian National Institute for Statistics (ISTAT) for the time period January 1^st-March 21^st. This preliminary sample included 1084 Italian municipalities showing at least 10 deaths during the above-mentioned timeframe and an increase in mortality of more than 20% as compared to the previous five years (2015-2019), with a resulting coverage of 21% of Italian population. The difference between 2020 observed and expected deaths (mean of weekly deaths in 2015-2019) was computed, together with mortality rate ratio (MRR) for each of the four weeks following detection of the first autochthonous COVID-19 case in Italy (February 23^rd, 2020 - March 21^st, 2020), as well as for this entire timeframe. Subgroup analysis by age groups was also performed.

RESULTS

Overall MRR was 1.79 [1.75-1.84], with an observed excess mortality of 8750 individuals in the investigated sample, which in itself outweighs Italian Civil Protection report of only 4,825 COVID-19-related deaths across Italy, as of March 21. Subgroup analysis did not show any difference in mortality rate in '0-14 years' age group, while MRRs were significantly increased in older age groups, in particular in patients >75 years (MRR 1.84 [1.79-1.89]). In addition, week-by-week analysis showed a progressive increase in MRR during this period, peaking in the last week (March 15^th, 2020 - March 21^st, 2020) with an estimated value of 2.65 [2.53-2.78].

CONCLUSIONS

The analysis of all-cause mortality data in Italy indicates that reported COVID-19-related deaths are an underestimate of the actual death toll. All-cause death should be seen as the epidemiological indicator of choice to assess the real mortality impact exerted by SARS-CoV-2, given that it also best reflects the toll on frail patient subsets (e.g. the elderly or those with cardiovascular disease).

Spatio-temporal analysis of differences in campylobacteriosis incidence between urban and rural areas in the Southern District Health Board, New Zealand

The objective of this paper is to investigate differences in campylobacteriosis incidence between urban and rural areas in the Southern District Health Board of New Zealand between 2000 and 2015. The data were analysed using a Bayesian change-point model to evaluate how campylobacteriosis incidence changed over time and to see whether the dynamics differed between rural and urban areas. A conditional auto regressive error term was introduced to account for any spatial effects. The results of our analysis showed that campylobacteriosis incidence increased between 2000 and 2005, decreased between 2006 and 2008 then stabilised from 2009 onward. In addition we found that the changes in incidence were greater in urban areas than in rural ones.

Bias correction of bounded location error in binary data

Binary regression models for spatial data are commonly used in disciplines such as epidemiology and ecology. Many spatially referenced binary data sets suffer from location error, which occurs when the recorded location of an observation differs from its true location. When location error occurs, values of the covariates associated with the true spatial locations of the observations cannot be obtained. We show how a change of support (COS) can be applied to regression models for binary data to provide coefficient estimates when the true values of the covariates are unavailable, but the unknown location of the observations are contained within nonoverlapping arbitrarily shaped polygons. The COS accommodates spatial and nonspatial covariates and preserves the convenient interpretation of methods such as logistic and probit regression. Using a simulation experiment, we compare binary regression models with a COS to naive approaches that ignore location error. We illustrate the flexibility of the COS by modeling individual-level disease risk in a population using a binary data set where the locations of the observations are unknown but contained within administrative units. Our simulation experiment and data illustration corroborate that conventional regression models for binary data that ignore location error are unreliable, but that the COS can be used to eliminate bias while preserving model choice.

Modeling Schistosoma japonicum Infection under Pure Specification Bias: Impact of Environmental Drivers of Infection

Uncertainties in spatial modeling studies of schistosomiasis (SCH) are relevant for the reliable identification of at-risk populations. Ecological fallacy occurs when ecological or group-level analyses, such as spatial aggregations at a specific administrative level, are carried out for an individual-level inference. This could lead to the unreliable identification of at-risk populations, and consequently to fallacies in the drugs’ allocation strategies and their cost-effectiveness. A specific form of ecological fallacy is pure specification bias. The present research aims to quantify its effect on the parameter estimates of various environmental covariates used as drivers for SCH infection. This is done by (i) using a spatial convolution model that removes pure specification bias, (ii) estimating group and individual-level covariate regression parameters, and (iii) quantifying the difference between the parameter estimates and the predicted disease outcomes from the convolution and ecological models. We modeled the prevalence of Schistosoma japonicum using group-level health outcome data, and city-level environmental data as a proxy for individual-level exposure. We included environmental data such as water and vegetation indexes, distance to water bodies, day and night land surface temperature, and elevation. We estimated and compared the convolution and ecological model parameter estimates using Bayesian statistics. Covariate parameter estimates from the convolution and ecological models differed between 0.03 for the nearest distance to water bodies (NDWB), and 0.28 for the normalized difference water index (NDWI). The convolution model presented lower uncertainties in most of the parameter estimates, except for NDWB. High differences in uncertainty were found in night land surface temperature (0.23) and elevation (0.13). No significant differences were found between the predicted values and their uncertainties from both models. The proposed convolution model is able to correct for a pure specification bias by presenting less uncertain parameter estimates. It shows a good predictive performance for the mean prevalence values and for a positive number of infected people. Further research is needed to better understand the spatial extent and support of analysis to reliably explore the role of environmental variables.