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Proximity to mining industry and respiratory diseases in children in a community in Northern Chile: A cross-sectional study

Environmental Health volume 15, Article number: 66 (2016) Cite this article

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Abstract

Background

In a community in northern Chile, explosive procedures are used by two local industrial mines (gold, copper). We hypothesized that the prevalence of asthma and rhinoconjunctivitis in the community may be associated with air pollution emissions generated by the mines.

Methods

A cross-sectional study of 288 children (aged 6–15 years) was conducted in a community in northern Chile using a validated questionnaire in 2009. The proximity between each child’s place of residence and the mines was assessed as indicator of exposure to mining related air pollutants. Logistic regression, semiparametric models and spatial Bayesian models with a parametric form for distance were used to calculate odds ratios and 95 % confidence intervals.

Results

The prevalence of asthma and rhinoconjunctivitis was 24 and 34 %, respectively. For rhinoconjunctivitis, the odds ratio for average distance between both mines and child’s residence was 1.72 (95 % confidence interval 1.00, 3.04). The spatial Bayesian models suggested a considerable increase in the risk for respiratory diseases closer to the mines, and only beyond a minimum distance of more than 1800 m the health impact was considered to be negligible.

Conclusion

The findings indicate that air pollution emissions related to industrial gold or copper mines mainly occurring in rural Chilean communities might increase the risk of respiratory diseases in children.

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Background

The mining industry has been one of the most important contributors to the Chilean economy with a share of 13 % of the gross domestic product [1]. Gold and copper are extracted in open-pit mining facilities through excavation—a process that uses explosives and heavy machinery. Most of the air pollutants emerging form open pit mining are total suspended particulate (TSP) matter and particles, including aerodynamic diameter smaller than 10 μm (PM10) [2, 3]. The gold and copper mining activities generating these particles are mainly drilling, blasting, sediments loading and unloading, road transport over unpaved roads and losses from exposed over-burden dumps, residuals handling plants and exposed pit faces [4]. Dust after the explosion and transportation of materials and have been identified as the main source of TSP and PM10 pollution [5–7] in open-pit mining regions. High concentrations of particulate material (TSP and PM10) have been associated with asthma and allergies among others diseases [8–11]. Asthma is a common disease that affects children and adults of all ages. According to findings of the International Study of Asthma and Allergies in Childhood (ISAAC), the prevalence of asthma in childhood in Latin America is on average around 13 % and ranging between 13 and 16 % in Chile [12, 13].

Using measurements of air pollutants, associations between elevated levels of ambient air pollution and frequency of asthma attacks (emergency room visits/hospital admissions) have been shown [14–17]. Using proximity to the air pollution source as a proxy of exposure, studies suggested associations between living in areas close to industrial zones and the risk of asthma in children based on spatial analyses [18, 19]. In these studies, mainly logistic regression models and generalized additive models (GAM) [20] have been used. One limitation of the first approach is the use of arbitrarily chosen cut-off points for quartiles of exposure. For the latter, the interpretation of the results might be challenging, particularly when the association between the exposure and the outcome is non linear.

Countries in South America are experiencing high prevalence of asthma with rates ranging from 18 to 27 % [13, 21]. In Chile, higher prevalence of asthma and allergies was found in urban than in rural areas [22]. To the best of our knowledge associations between point sources of air pollution and respiratory health in children have not been studied in Chile to date. Exposure to open-pit mining is of particular interest as the exposure is widespread in Chile and other Latin American countries. In some communities, residents live less than one kilometer away from these open-pit mines.

Therefore, we examined associations between average distance of child’s place of residence to the two open-pit mines in a rural mining community and the prevalence of respiratory disease in children. The community is a mining dependent town located in Region IV in Chile with less than 10.000 inhabitants located at 1050 m above the sea level. Using data from a cross-sectional study, we assessed associations between the distance to the mines and respiratory diseases applying Bayesian semiparametric and parametric models including a spatial effect.

Methods

Study population

Data were used from a cross-sectional questionnaire based study in school children in a small town in Chile, which has one open-pit gold and one open-pit copper mine. Children and their parents or legal guardians provided written informed consent. The study included school children from 1st to 6th grade attending the two major local elementary schools. These two schools were the only ones in the community offering complete elementary education, and approximately 84 % of children in grades 1st to 6th were registered in these schools. Parental questionnaires with instructions were mailed to children’s homes. Further details about the study design are reported in Ohlander et al. [23].

Data

The original study included 288 children (out of 418 children invited to participate, response rate: 69 %). Data on outcome variables and potential confounders were ascertained using a validated questionnaire in Spanish [24]. We groped the variables into three categories:

Child health outcomes

Asthma: a child was considered to have asthma, if the child was reported to ever have had asthma confirmed by a medical doctor or if the child had taken asthma medication during the last 12 months prior to the survey. Asthma medication was considered, because patients in Chile are often not aware of their asthma diagnosis (personal communication).

Rhinoconjunctivitis: positive report of one or more of the following nasal symptoms during the past 12 months prior to the survey: sneezing, itching, nasal congestion or rhinitis in conjunction with itchy, red, and watery eyes.

Characteristics of the participants

Sex (female vs. male), age (6–7 years vs. 8–9 years, 10–11 years and 12 years or more), parental history of atopic disease defined as reported history of asthma, rhinitis or eczema in the child’s biological mother or father (no vs. yes). Indicators of socioeconomic status in this setting were whether the mother (no vs. yes) or father (no vs. yes) of the participant were working, and whether the child was living with both parents (no vs. yes).

Other sources of air pollution

The following variables were considered as additional sources of exposure: current cigarette exposure at home (no vs. yes), type of heating used at home (other vs. coal and gas), pavement of the nearest street (no vs. yes) (“no” indicates the nearest street was mainly a dirt road), the child’s preferred play area (inside vs outside), and time spent at home (less than 3 h/day vs. 3–6 h/day, more than 6 h/day).

Exposure assessment

In the absence of ambient measurements of emission or of personal exposures, we used the proximity to the open pit mines as proxy of exposure [25, 26]. We used the residential proximity to the gold and the copper mines, and the average distance to both. During the fieldwork, the geographical coordinates of each place of residence was obtained using global positioning system (GPS). The locations of the main extraction places were geocoded using official information given by the companies. The Harvesine formula for distance was used to account for the curvature of the earth [27]. The lower quartile of distance to each mine separately and of the average distance was selected a-priori as cut-off point of the exposure categories.

Statistical analysis

To assess associations between mining exposure and health outcomes, we conducted a three-step analysis 1) Logistic regressions were fitted for the two outcomes using the dichotomized proximity variable; 2) Bayesian structured additive regressions were used to estimate a possible non-linear effect on the respiratory diseases related to the proximity to the mines; results of this part were used to calibrate the subsequent modeling step; 3) Bayesian models with a parametric distance function were used to estimate associations between distance to the mines and the outcomes, and to identify a minimum distance to the point sources beyond which the health impact is considered non statistically significant. To adjust for possible regional effects, which are not captured by distance to the mine or the individual’s characteristics, we included an additional spatial effect. All analyses where done using the statistical software R [28].

Logistic regression

We estimated associations between distance from the mines (i.e., gold mine, copper mine and average distance to both mines) and the respiratory diseases using logistic regression models adjusting for potential confounders. Distance was dichotomized at the first quartile, i.e., the quartile closest to the point sources was categorized as 1 for children living in the closest quartile, 0 otherwise. A variable was considered a potential confounder if it was associated with the outcome in the crude models. All subsequent analyses were adjusted for these variables. To assess the potential influence of item non-response, we performed sensitivity analyses employing multiple imputation (MI) under the assumption of missing at random. Seven data sets were generated with the MI procedure and combined to create pooled estimates using Rubin’s rules [29]. We then compared the pooled with the complete case estimates. The MI process and model estimation were done using the R libraries Amelia [30] and Zelig [31], respectively.

Bayesian structured additive regression

We investigated non-linear associations between air pollution exposure proxies (i.e., the distance variable) and respiratory diseases using Bayesian generalized structured additive regression (STAR) [32] from a Bayesian perspective. The model has the equation: logit(π i ) = β 0 + f(d ik ) + Xβ, where f(d ik ) is an unknown smooth function of the distance d ik of child i to mine k. X comprises additional confounders with effects β. We used Bayesian P-splines f(d ik ). We assumed independent diffuse priors for the estimated effects of the potential confounders β, the intercept β 0 and the P-splines [33].

The Markov chain Monte Carlo methods was implemented through the software BayesX [34] using 70,000 iterations including a burn-in phase of 15,000 iterations. Two chains were generated with different starting values. Finally, convergence was evaluated with trace plots and the convergence statistics provided by the software BayesX.

We used the results of the Bayesian Structured Additive Regression to calibrate the prior distributions for the Bayesian models with parametric distance functions. This approach is preferable when little or nothing is known about the shape of the relationship between the proximity to the point sources of pollution and the disease [35]. Then, STAR models could be seen as a calibration step for models with a parametric form for the distance.

Bayesian models with a parametric distance function

We fitted Bayesian models with a parametric distance function proposed by [25, 36, 37] and used the Bayesian perspective suggested by [26], and extended these models for multiple diseases with one point sources and a spatial effect. These models have the function [38],

$$ logit\left({\pi}_i(s)\right)={\beta}_0+ f\left({d}_{i k}\left|{\alpha}_k,{\phi}_k,{\delta}_k\right.\right)+\mathrm{X}\upbeta +\mathrm{S}\left(\mathrm{s}\right) $$
(1)

where f(.) is the parametric function of the distance proposed in [39] as

$$ f\left({d}_{ik}\left|{\alpha}_k,{\phi}_k,{\delta}_k\right.\right)=\left\{\begin{array}{rr}\hfill 1+{\alpha}_k,& \hfill if\ {d}_{ik}\le\ {\delta}_k\\ {}\hfill 1+{\alpha}_k \exp \left\{{\left(\frac{d_{ik}-{\delta}_k}{\phi_k}\right)}^2\right\},& \hfill if\ {d}_{ik}>{\delta}_k\end{array}\right. $$
(2)

Here 1 + α k reflecting the excess of odds ratio at the mine, δ k represents the radius of the plateau around the mine, ϕ k represents the rate at which the risk decreases with each additional kilometer of distance to the edge of the plateau. An additional spatial effect S(s) corrects for any regional differences in the risk of the disease, which could not be explained by the potential confounders or by the proximity to the mines. Model construction was performed in a hierarchical procedure:

  • Model 0: logit(π i ) = β 0 + Xβ, including only potential confounders

  • Model 1: logit(π i (s)) = β 0 + Xβ + S(s), including a spatial variation.

  • Model 2: logit(π i (s)) = β 0 + f(d ik | α k , ϕ k , δ k ) + Xβ, including the parametric proximity function.

  • Model 3: logit(π i (s)) = β 0 + f(d ik |α k , ϕ k , δ k ) + Xβ + S(s), including an additional spatial effect to explore if there is a remaining spatial variation which could not be explained by the proximity or the confounders.

The models were compared using the deviance information criterion (DIC) [40] where lower values indicate an improvement of the fit. These models were estimated using the software JAGS [41] called from R.

Results

The final sample included 275 children; 3 children with missing residence location, and 10 children who lived in isolated zones outside of the municipality (thus were outliers) were excluded. Children’s mean (± sd) age was 9.05 years (aged 6 to 15 years), and 46 % were girls. Most children lived with both parents (67 %) and 25 % of mothers worked. More than half of the children reported to be at home 6 h or more per day (54 %). The main place for play was outside (Table 1).

Table 1 Sociodemographic characteristics of the study participants for the total population and by different outcomes
Full size table

The prevalence of asthma was 24 %, and the prevalence of rhinoconjunctivitis was 34 %. The mean (± sd) distance to the gold mine was 2.1 (± 0.26) km (median: 2.1 km; range 1.3–2.8 km) and 1.9 (± 0.37) km (median: 2.0 km; range 0.9–3.1 km) for the copper mine. For the average distance to both mines the mean was 2.1 km (± 0.26) km (median: 2.0; range 1.3–2.7). The prevalence of the diseases among the 25 % of children living closest to the mines, was between 30 and 45 %, and was slightly higher than for the remaining sample (Table 1). A geographical map of children’s place of residence is presented in the Additional file 1: Figure SA.1.

The adjusted odds ratios for the respiratory outcomes were higher with closer proximity to the mines. Strongest associations were estimated for the combined outcome for children living on average closer than 1.8 km to both mines. Asthma was associated with parental history of atopic diseases, living with both parents, and having a working mother (Additional file 1: Figure SB.1). Including these covariates in a multiple regression, did not change the estimated effects of proximity to the mines and respiratory diseases appreciably. Including imputed data did also not change our findings appreciably (Table 2), therefore we used complete case data for the further analyses. The associations estimated using the multiple imputation dataset are presented in Additional file 1: Figure SC.1.

Table 2 Association between distance to the mines and respiratory diseases. Sensitivity analysis for missing data. Complete cases, multiple imputation and adjustment for potential confounders
Full size table

The adjusted STAR models confirmed the results of the logistic regressions for all outcomes (Figs. 1 and 2) with those living closer to the mines having higher risks for respiratory diseases. A slight “U” shape in the estimations of the associations was seen for the copper mine, and for average distance in both outcomes (Figs. 1 and 2). For the association between distances to the copper mine and asthma no decrease towards zero was estimated. The findings for the two outcomes were consistent, i.e., with closer distance to the gold mine the risk of all respiratory diseases increased about exp(1) with confidence interval reaching from exp(0) to exp(2). Within a distance of 2 km to the gold mine we estimated a positive effect suggesting an increased risk for the respiratory outcomes. Around the copper mine, the risk was increased by exp(0.5) with BCIs between exp(1) and exp(2). The odds ratios were positive up to a distance of around 1.7 km. Combining the proximity to both mines by using the average distance resulted in similar estimates as the analyses of the distance to the gold mine only. The unadjusted models did not differ appreciably from the adjusted models (Additional file 1: Figures SD.1 to SD.2).

Fig. 1
figure1

Association between proximity to the mines and asthma adjusted for parental congenital atopic diseases, if the child lived with both parents and if the child’s mother worked. Dotted vertical line indicates the first quartile of the distance. Plots are based on participants’ data living within minimum and maximum of the distances from the mines. The shaded area is the 95 % Bayesian confidence intervals

Full size image
Fig. 2
figure2

Adjusted associations between proximity to mines and rhinoconjunctivitis for parental congenital atopic diseases. Dotted vertical line indicates the first quartile of the distance. Only for subject within distance range from the mine. The shaded area is 95 % Bayesian confidence interval

Full size image

Using the STAR models (Figs. 1 to 2), we found a maximum OR of exp(1) at the minimum distance to the mines, with the lower and upper limits of the confidence intervals around exp(1) and exp(2), respectively. The construction of the prior distributions is presented in the Additional file 1: (Section F.1).

The parametric Bayesian models 0 to 3 indicated that model 3, including the proximity-risk function together with the spatial terms resulted in a better fit for all outcomes and exposures (Additional file 1: Table SD.1); the DIC was lower than for other models.

Table 3 shows the parameter estimates and the 95 % Bayesian confidence intervals for each mine. Generally, the point estimates of \( \widehat{\alpha_k} \) lay within 1.6–2.4, for \( \widehat{\phi_k} \) had values of 1.05–1.13 and \( \widehat{\delta_k} \) between 0.72 and 0.95. Figure 3 shows the posterior \( \widehat{f_k} \) estimates for distance functions. These graphs present similar values at the source of around 2, and it maintains the risk constant until around 1 km of distance from the mines; at further distance, the risk declines to 1 at around 2.5 km of distance to the mine. The 95 % confidence intervals for \( \widehat{f_k} \) are above 1 up to a distance of 1.8 km. Plots of the random terms Ŝ k (s) using T = 40 spatial nodes are presented in the Additional file 1: Figures SE.1 to SE.2.

Table 3 Posterior estimates and 95 % Bayesian confidence intervals for parameters of the proximity-odds function, α, ϕ and δ in the model 3 for the three respiratory diseases
Full size table
Fig. 3
figure3

Association between proximity to mines and each respiratory disease using the Model 3. Gold mine, Copper mine and Average distance. The shaded area is 95 % Bayesian confidence interval

Full size image

Discussion

The findings of our community based study in Chile suggest that proximity of children’s residence to open-pit mines, one of the main income sources in many low and middle income countries, is associated with allergic rhinoconjunctivitis and asthma with markedly increased risks in children living closest to the mines. We used different measures of proximity as proxy for ambient air pollution exposure which resulted in comparable results.

The results of our study corroborate previous study from Latin America suggesting increased risks for asthma within a distance of two kilometers to different air pollution sources including power plants, quarries or petroleum refineries [18]. Using categorical distance measures, investigators also found an increased prevalence of respiratory diseases and allergies in children who lived near wood industries in Alaska [19]. Indeed, the use of cut-off points is arbitrary, making the use of STAR models an alternative allowing a broader perspective on the exposure-outcome association in this kind of analysis.

We also estimated the spatial effect to capture the residual variation not accounted for by the population at risk, and to estimate risks across the study area. Models including an effect of distance from the mine on the respiratory diseases resulted in a better fit; however, the improvements were small.

Variables included as potential confounders for asthma were parental history of atopic disease, child’s mother working and living with both parents. For rhinitis only parental history of atopic disease played a role and was included in the adjusted models. Exposure to indoor sources of pollutants such as type of heating and smoking in presence of the child was not relevant for the the respiratory diseases in our data. Parental occupation was not included, because the work environment did not involve exposure to contaminants that could be associated with asthma attacks or any other respiratory disease in the children. We considered residential area rather than school location, as children reported to spend most leisure time at home. We assumed children spent only the remaining time at school. Since our study population only consisted of children from two different schools, the variation of school-based exposure will be small.

Our study has several limitations. Asthma and rhinoconjunctivitis diagnosis was reported by parents or children themselves even though the questionnaire was intended to be answered by the children’s parents; this could have led to an inaccurate report of the prevalence of the diseases, because most parents or children may not have been aware of the diagnosis. However, the inclusion of asthma medication data should have improved the accuracy of the outcome assessment. Likewise, the symptoms of rhinoconjunctivitis were self-reported. However, both questions were previously validated within the ISAAC study [24].

There were no emission measurements available of the mining related exposures. Unfortunately, we do not have any information on the particle concentration or composition in the mining community. In general, the particles include minerals, chemicals, heavy metals, aluminum, mercury, and arsenic; several of these components are known to be associated with impaired respiratory health [42]. Moreover, it is also known that particulate material may act as allergenic compound and thus might not only contribute to non allergic asthma and rhinitis, but also to allergic and atopic asthma [43]. Additionally, we did not incorporate information on wind direction or other geographical information (i.e. the angle of the mines to each residence) [44]. Since these data were not available in our study region, we used spatial models to estimate risks. However, misclassification of exposure is likely non-differential which may bias our findings likely towards the null. However, this approach can be used for risk estimation in other communities in Latin America with similar mining procedures where measurement data are often not available.

We found wide Bayesian confidence intervals which may be explained by the “flat” likelihood of these kinds of models, and the high standard errors in the estimates that usually result in posterior estimates close to the mean of the prior distribution [38, 45], especially for the α k . Therefore, we used different types of prior distribution to evaluate this choice of the α parameter (see Section F.2 and Additional file 1: Figures SF.1 to SF.3) as a sensitivity analysis. Our findings of two previous studies [45, 46] that suggested prior distributions for parametric form in the distance function should be carefully chosen.

This study has several strengths. We assessed the models specifications by permitting distance-risk relationships to be nonlinear and free of a pre-specified shape. We compared traditional statistical methods with most recent approaches proposed in the literature as easy to interpret. Semiparametric methods suffer from a number of limitations for incorporating information about a realistic shape of the proximity-risk [26, 35]. Therefore, we combined Bayesian semiparametric models to specify the prior distributions for parametric form, since no or little epidemiological data on the association was available.

We implemented complex models in standard statistical software. These implementations could be extended to consider a model for multiple diseases and multiple sources, avoiding the specification of a single model for each disease, potentially taking into account scenarios in which the outcomes are correlated.

Conclusions

Our findings suggest that proximity to mines using open-pit procedures increases the risk for asthma and rhinoconjunctivitis in children. In communities with high concentrations of environmental pollution from mining facilities, the adverse effect on children’s health highlights the importance of stricter emissions regulating policies. Most important in terms of public health would be emission control and reduction at the emission source. In addition, education campaigns regarding the identification and control of respiratory disease in order to improve the diagnosis and treatment of respiratory diseases in children are suggested. Finally, the proposed threshold distance might be used as an initial indicator to define zones which should be restricted for residential use. Such a policy might implicate the relocation of parts of the population. Adequate prediction models would allow estimations of possible health impacts of such a policy in terms children’s health in the affected areas. Impacts of such a policy should also be examined from different societal perspectives.

Abbreviations

BCI, Bayesian confidence interval; CI, confidence interval; GAM, Generalized additive models; GPS, Global positioning system; ISAAC, International study of asthma and allergies in childhood; Rhinoc., Rhinoconjunctivitis

References

  1. 1.

    Ministerio de Minería de Chile. Minería en Chile: impacto en regiones y desafíos para su desarrollo [monograph on the Internet]. Santiago de Chile: Ministerio de Minería de Chile and Comisión Chilena del Cobre; 2012 [cited 2014 Jan 10]. Available from: http://www.cochilco.cl/descargas/estadisticas/libro/Libro_Mineria_en_Chile_Impacto_en_Regiones_y_Desafios_para_su_Desarrollo.pdf [ENG]:Mining in Chile: impact on regions and challenges for development.

  2. 2.

    Huertas JI, Huertas ME, Izquierdo S, González ED. Air quality impact assessment of multiple open pit coal mines in northern Colombia. J Environ Manage. 2012;93(1):121–9.

  3. 3.

    Huertas JI, Huertas ME, Solís DA. Characterization of airborne particles in an open pit mining region. Sci Total Environ. 2012;423:39–46.

  4. 4.

    Chakraborty M, Ahmad M, Singh R, Pal D, Bandopadhyay C, Chaulya S. Determination of the emission rate from various opencast mining operations. Environ Model Software. 2002;17(5):467–80.

  5. 5.

    Huertas JI, Camacho DA, Huertas ME. Standardized emissions inventory methodology for open-pit mining areas. Environ Sci Pollut Res. 2012;19(7):2784–94.

  6. 6.

    Trivedi R, Chakraborty M, Tewary B. Dust dispersion modeling using fugitive dust model at an opencast coal project of Western Coalfields Limited, India. J Sci Ind Res. 2009;68(1):71.

  7. 7.

    Eisler R. Health risk of gold miners: a synoptic review. Environ Geochem Health. 2003;25:325–45.

  8. 8.

    Guarnieri M, Balmes JR. Outdoor air pollution and asthma. Lancet. 2014;383(9928):1581–92.

  9. 9.

    Olmo N, Saldiva P, Braga A, Lin C, Santos U, Pereira L. A review of low-level air pollution and adverse effects on human health: implications for epidemiological studies and public policy. Clinics. 2011;66(4):681–90.

  10. 10.

    Wong G. Air Pollution and Health. The Lancet Respiratory Medicine. 2014;2(1):8-9.

  11. 11.

    O’Neill M, Bell M, Ranjit N, Cifuentes L, Loomis D, Gouvela N, et al. Air pollution and mortality in Latin America the role of education. Epidemiology. 2008;19(6):810–9.

  12. 12.

    Mallol J, Solé D, Baeza-Bacab M, Aguirre V, Soto M, Baena-Cagnani C, et al. Regional variation in asthma symptom prevalence in Latin American children. J Asthma. 2010;47:644–50.

  13. 13.

    Camargos AM, Borges WG, Silva A, Sarinho E, Rosário N, de Freitas Souza L, et al. Prevalence of symptoms of eczema in Latin America: results of the International Study of Asthma and Allergies in Childhood (ISAAC) Phase 3. J Investig Allergol Clin Immunol. 2010;20(4):311–23.

  14. 14.

    Smargiassi A, Kosatsky T, Hicks J, Plantane C, Armstrong B, Villeneuve PJ, et al. Risk of asthmatic episodes in children exposed to sulfur dioxide stack emissions from a refinery point source in Montreal, Canada. Environ Health Perspect. 2009;117(4):653–9.

  15. 15.

    Oyana TJ, Rivers PA. Geographic variations of childhood asthma hospitalization and outpatient visits and proximity to ambient pollution sources at a US-Canada border crossing. Int J Health Geogr. 2005;4(1):1.

  16. 16.

    Arbex M, Martins L, de Oliveira R, Pereira L, Arbex F, Cançado J, et al. Air pollution from biomass burning and asthma hospital admissions in a sugar cane plantation area in Brazil. J Epidemiol Community Health. 2007;61:395–400.

  17. 17.

    Linares B, Guizar JM, Amador N, Garcia A, Miranda V, Perez JR, et al. Impact of air pollution on pulmonary function and respiratory symptoms in children. Longitudinal repeated-measures study. BMC Pulm Med. 2010;10(1):1.

  18. 18.

    Loyo-Berríos N, Irizarry R, Hennessey J, Tao X, Matanoski G. Air pollution sources and childhood asthma attacks in Cataño, Puerto Rico. Am J Epidemiol. 2007;165:927–35.

  19. 19.

    Rava M, Crainicianu C, Marcon A, Cazzoletti L, Pironi V, Silocchi C, et al. Proximity to wood industries and respiratory symptoms in children: A sensitivity analysis. Environ Int. 2012;38:37–44.

  20. 20.

    Wood S. Generalized Additive Models: An Introduction with R. Boca Raton, Florida: Chapman & Hall/CRC; 2006. ISBN 1-58488-474-6.

  21. 21.

    Pitrez P, Stein R. Asthma in Latin America: the Dawn of a New Epidemic. Curr Opin Allergy Clin Immunol. 2008;8(5):378–83.

  22. 22.

    Kausel L, Boneberger A, Calvo M, Radon K. Childhood asthma and allergies in Urban, Semiurban, and Rural Residential Sectors in Chile. Scientific World Journal. 2013;2013:937935.

  23. 23.

    Ohlander J, Huber SM, Schomaker M, Heumann C, Schierl R, Michalke B, et al. Risk factors for mercury exposure of children in a Rural Mining Town in Northern Chile. PLoS One. 2013;8(11):e79756.

  24. 24.

    Fernandez CM, Fernandez-Benitez M, Miranda MP, Grima FG. Validation of the Spanish version of the Phase III ISAAC questionnaire on asthma. J Investig Allergol Clin Immunol. 2005;15(3):201.

  25. 25.

    Diggle P, Morris SE, Wakefield JC. Point-source modelling using matched case-control data. Biostatistics. 2000;1(1):89–105.

  26. 26.

    Wakefield JC, Morris SE. The Bayesian modelling of disease risk in relation to a point source. J Am Stat Assoc. 2001;96(453):77–91.

  27. 27.

    Bomford G. Geodesy. Oxford: Clarendon; 1980.

  28. 28.

    R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria; 2013. ISBN 3-900051-07-0. Available from: http://www.R-project.org/.

  29. 29.

    Roderick R, Rubin D. Statistical Analysis With Missing Data. New York: John Wiley & Sons, 1987.

  30. 30.

    Honaker J, King G, Blackwell M. Amelia II: a program for missing data. J Stat Softw. 2011;45(7):1–47. https://www.jstatsoft.org/article/view/v045i07.

  31. 31.

    Owen M, Imai K, King G, Lau O. Zelig: Everyone’s Statistical Software; 2013. R package version 4.2-1. Available from: http://CRAN.R-project.org/package=Zelig.

  32. 32.

    Brezger A, Lang S. Generalized structured additive regression based on Bayesian P-Splines. Comput Stat Data Anal. 2006;50:947–91.

  33. 33.

    Lang S, Brezger A. Bayesian P-splines. J Comput Graphical Stat. 2004;13(1):183–212.

  34. 34.

    Umlauf N, Kneib T, Lang S, Zeileis A. R2BayesX: Estimate Structured Additive Regression Models with BayesX; 2013. R package version 0.3-1. Available from: http://CRAN.R-project.org/package=R2BayesX.

  35. 35.

    Rodrigues A, Diggle P, Assuncao R. Semiparametric approach to point source modelling in epidemiology and criminology. J R Stat Soc. 2010;59(3):533–42.

  36. 36.

    Diggle P. A point process modelling approach to raised incidence of a rare phenomenon in the vicinity of a prespecified point. Journal of the Royal Statistical Society Series A (Statistics in Society). 1990;153(3): 349–62.

  37. 37.

    Diggle P, Rowlingson BS. A conditional approach to point process modelling of elevated risk. J R Stat Soc A Stat Soc. 1994;157(2):433–40.

  38. 38.

    Calculli C, Pollice A, Serinelli M. Spatial analysis of the risk of multiple cancers in relation to a petrochemical plant. Environmetrics. 2011;23(2):175–82.

  39. 39.

    Diggle P, Morris SE, Elliot P, Shaddick G. Regression modelling of disease risk in relation to a point sources. J R Stat Soc A Stat Soc. 1997;160(3):491–505.

  40. 40.

    Spiegelhalter D, Best N, Carlin B, van der Linde A. Bayesian measures of model complexity and fit (with discussion). J R Stat Soc Series B. 2002;64:583–639.

  41. 41.

    Plummer M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling; 2003. Available from: http://mcmc-jags.sourceforge.net/.

  42. 42.

    Monjezi M, Shahriar K, Dehghani H, Samimi Namin F. Environmental impact assessment of open-pit mining in Iran. Environ Geol. 2008;58(1):205–16.

  43. 43.

    Schwarze PE, Øvrevik J, Låg M, Refsnes M, Nafstad P, Hetland RB, et al. Particulate matter properties and health effects: consistency of epidemiological and toxicological studies. Hum Exp Toxicol. 2006;25(10):559–79.

  44. 44.

    Lawson A. On the analysis of mortality events associated with a prespecified fixed point. J R Stat Soc A Stat Soc. 1993;156(3):363–77.

  45. 45.

    Dreassi E, Lagazio C, Maule M, Magnani C, Biggeri A. Sensitivity analysis of the relationship between disease occurrence and distance from a putative source of pollution. Geospat Health. 2008;2(2):263–71.

  46. 46.

    Li S, Mukherjee B, Batterman S. Point source modeling of matched case-control data with multiple disease subtypes. Stat Med. 2012;31:3617–37.

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Acknowledgments

We thank all children and parents involved in the study. To the reviewers for their comments, to Francis Rodríguez and Dr. Jessica Gerlich for helpful comments.

Funding

The research reported in this manuscript has been funded for Center of International Health (CIH) University of Munich (Project-No. 399-08) in the design stage and data collection and partially in the data analysis. Ronald Herrera received the ColCiencias scholarship (Ref. 648) for his doctoral studies, for data interpretation, part of the data analysis and write the final manuscript.

Availability of data and material

Data will not be shared for regulations from the universities involved in the research.

Authors’ contributions

RH performed the analysis and wrote the manuscript. KR, SC, OvE, and DM conceived the original idea and design. KR, UB, OvE contributed to the analysis and their statistical interpretation. RH, KR, OvE, and UB edited the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the University Hospital Munich (LMU) (Project-No. 399-08) and by the Ethics Committee of the Universidad Católica del Norte in Coquimbo, Chile (Project-No. 04/08).

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Affiliations

  1. Occupational and Environmental Epidemiology and NetTeaching Unit, Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital Munich (LMU), Munich, Germany

    • Ronald Herrera
    • , Katja Radon
    •  & Stella Cifuentes

  2. Department of Community Health Sciences Fielding School of Public Health University of California Los Angeles, Los Angeles, CA, USA

    • Ondine S. von Ehrenstein

  3. Facultad de Medicina, Universidad Diego Portales, Santiago, Chile

    • Daniel Moraga Muñoz

  4. Facultad de Medicina, Latin American Center of the Center for International Health Ludwig-Maximilians-University, Universidad Católica del Norte, Coquimbo, Chile

    • Daniel Moraga Muñoz

  5. Institute for Medical Informatics, Biometry and Epidemiology-IBE University of Munich (LMU), Munich, Germany

    • Ursula Berger

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Corresponding author

Correspondence to Ronald Herrera.

Additional file

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Proximity to mining industries and respiratory diseases in children of a Northern Chilean Community: A Cross-sectional study Supplemental Material. (PDF 3236 kb)

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Herrera, R., Radon, K., von Ehrenstein, O.S. et al. Proximity to mining industry and respiratory diseases in children in a community in Northern Chile: A cross-sectional study. Environ Health 15, 66 (2016) doi:10.1186/s12940-016-0149-5

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Keywords

  • Asthma
  • Children
  • Mining
  • Bayesian analysis
  • Air pollution

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