Mortality and morbidity in populations in the vicinity of coal mining: A systematic review

Abstract

Background:
Evidence of the association of coal mining with health outcomes such as increased mortality and morbidity in the general population has been provided by epidemiological studies in the last 25 years. Given the diverse sources of data included to investigate different health outcomes in the exposed populations, the International Classification of Diseases (ICD) can be used as a single classification standard to compare the findings of studies conducted in different socioeconomic and geographic contexts. The ICD classifies diagnoses of diseases and other disorders as codes organized by categories and chapters.

Objectives:
Identify the ICD codes found in studies of morbidity and/or mortality in populations resident or in proximity of coal mining and assess the methods of these studies conducting a systematic review.

Methods:
A systematic database search of PubMed, EMBASE and Scopus following the PRISMA protocol was conducted to assess epidemiological studies from 1990 to 2016. The health outcomes were mapped to ICD codes and classified by studies of morbidity and/or mortality, and the categories and chapters of the ICD.

Results:
Twenty-eight epidemiological studies with ecological design from the USA, Europe and China were included. The exposed populations had increased risk of mortality and/or morbidity by 78 ICD diagnosis categories and 9 groups of ICD categories in 10 chapters of the ICD: Neoplasms, diseases of the circulatory, respiratory and genitourinary systems, metabolic diseases, diseases of the eye and the skin, perinatal conditions, congenital and chromosomal abnormalities, and external causes of morbidity. Exposed populations had non-increased risk of 9 ICD diagnosis categories of diseases of the genitourinary system, and prostate cancer.

Conclusions:
There is consistent evidence of the association of coal mining with a wide spectrum of diseases in populations resident or in proximity of the mining activities. The methods of the studies included in this review can be integrated with individual-level and longitudinal studies to provide further evidence of the exposure pathways linked to increased risk in the exposed populations.

Full text

R E S E A R C H A R T I C L E Open Access
Mortality and morbidity in populations in the
vicinity of coal mining: a systematic review
Javier Cortes-Ramirez
1*
, Suchithra Naish
2
, Peter D Sly
1
and Paul Jagals
1
Abstract
Background: Evidence of the association of coal mining with health outcomes such as increased mortality and
morbidity in the general population has been provided by epidemiological studies in the last 25 years. Given the
diverse sources of data included to investigate different health outcomes in the exposed populations, the
International Classification of Diseases (ICD) can be used as a single classification standard to compare the findings
of studies conducted in different socioeconomic and geographic contexts. The ICD classifies diagnoses of diseases
and other disorders as codes organized by categories and chapters.
Objectives: Identify the ICD codes found in studies of morbidity and/or mortality in populations resident or in
proximity of coal mining and assess the methods of these studies conducting a systematic review.
Methods: A systematic database search of PubMed, EMBASE and Scopus following the PRISMA protocol was
conducted to assess epidemiological studies from 1990 to 2016. The health outcomes were mapped to ICD codes
and classified by studies of morbidity and/or mortality, and the categories and chapters of the ICD.
Results: Twenty-eight epidemiological studies with ecological design from the USA, Europe and China were
included. The exposed populations had increased risk of mortality and/or morbidity by 78 ICD diagnosis categories
and 9 groups of ICD categories in 10 chapters of the ICD: Neoplasms, diseases of the circulatory, respiratory and
genitourinary systems, metabolic diseases, diseases of the eye and the skin, perinatal conditions, congenital and
chromosomal abnormalities, and external causes of morbidity. Exposed populations had non-increased risk of 9 ICD
diagnosis categories of diseases of the genitourinary system, and prostate cancer.
Conclusions: There is consistent evidence of the association of coal mining with a wide spectrum of diseases in
populations resident or in proximity of the mining activities. The methods of the studies included in this review can
be integrated with individual-level and longitudinal studies to provide further evidence of the exposure pathways
linked to increased risk in the exposed populations.
Keywords: Mortality, Morbidity, Coal mining, Systematic review, International classification of diseases,
Environmental health, Ecological studies, General population
Background
The impacts of coalmining on human health have been
of scientific concern since the sixteen century [1].
Epidemiological research has provided evidence of the
association of coal mining with diseases such as silicosis
[2,3] and chronic obstructive pulmonary disease [4]in
studies of workers since the 1930s [5]. Epidemiological
research has identified increased prevalence of chronic
respiratory diseases [6], cardiovascular disease [7] and
cancer [8], as well as physio-pathological mechanisms of
respiratory diseases in coal miners [9]. The cumulated
evidence of occupationally related studies is the main
body of evidence about the association of diseases with
exposure to coal mining.
In the last three decades epidemiological studies have
increasingly investigated the impacts of coal mining on
the general populations in proximity to coal mining [10].
These studies have found reduced health-related quality
of life [11], increased perceptions of detrimental health
conditions [12], and higher frequency of medical
* Correspondence: j.cortesramirez@uq.edu.au
1
Child Health Research Centre, Faculty of Medicine, The University of
Queensland, Brisbane, QLD, Australia
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Cortes-Ramirez et al. BMC Public Health (2018) 18:721
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consultations [13] in communities resident or in proxim-
ity of coal mining. Research about the state of health of
exposed populations has been conducted using data
from self-report health status and health surveys [14,15]
or health care costs [16]. Studies using data from hos-
pital records of these populations have found higher
rates of morbidity and mortality due to respiratory dis-
eases [10] and cancer [17], and measures of biomarkers
have evidenced greater exposures to environmental con-
taminants associated with the mining activities [18–20].
Other studies have identified increased rates of dental
disorders in coal mining regions of the USA [21] and
Europe [22], and studies in mining regions of developing
countries reported higher prevalence of parasitic diseases
in communities residing nearby coal mining [23,24].
The diverse health outcomes included in these studies
show that the health impacts of coal mining on general
population have been assessed using different ap-
proaches and sources of data.
This diversity of data is a challenge for more accur-
ately determining the health outcomes associated with
coal mining in populations in proximity to coal mining.
Whereas surveys are important tools to measure health
status, the indicators measured might not be comparable
between studies conducted in different geographic and
socioeconomic contexts [25]. On the other hand the bio-
logical effect of the exposures cannot always be estab-
lished; for instance increased levels of biomarkers can be
measured in people without having any manifestation of
disease [20]. However health outcomes that can be cate-
gorized according to a standard such as the International
Classification of Diseases (ICD) are reliable given the
underlying medical diagnosis process, consistent be-
tween different regional contexts. The ICD classifies
diagnoses in categories, blocks of categories, and chap-
ters according to the organ systems and clinical criteria
[26]. Records with medical diagnoses such as death cer-
tificates and hospital admissions register health out-
comes that can be categorized with the ICD.
This paper presents a systematic review of studies of
morbidity and/or mortality in populations resident or in
proximity of coal mining and the diagnoses identified ac-
cording to the ICD. The objectives of the study were to
conduct a systematic search of epidemiological studies;
identify the health outcomes found in the selected stud-
ies as classified by the ICD, and assess the methods of
these studies.
Methods
This review was done following the PRISMA protocol
[27]. The protocol can be accessed at http://www.crd.
york.ac.uk/PROSPERO/; (record: CRD42016052555).
PRISMA checklist shown in Additional file 1.
Systematic searches of studies reported in PubMed,
EMBASE and Scopus between January 1990 and October
2016 were conducted. Keyword combinations of “coal
mining”,“coal”,“prevalence”,“incidence”,“mortality”,
“morbidity”,“health impact”,“health outcome”,“inter-
national classification of diseases”,“hospitalization”,“hos-
pital discharge”,“hospital separation”,“disease”and
“death”were used. Search strategies shown in additional
file (see Additional file 2).
Eligibility criteria
For inclusion studies had to: 1) be designed to search for
association between coal mining and morbidity and/or
mortality in populations in proximity to coal mining; 2)
obtain data from hospital records, death certificates and/
or clinical assessments with medical diagnosis, 3) be
published in English. Studies whose subjects of study
were exclusively miners or workers were excluded.
Paper selection and retrieval
Records were first identified in the search strategy, titles
and abstracts were screened, and full-text reports selected
and reviewed to identify eligible studies. A check of the
reference lists of the reports selected for full-text review
was done to identify potentially eligible studies (Fig. 1).
Data extraction and synthesis
A data form pre-piloted with 10 studies was used to ex-
tract data from eligible studies. The studies were orga-
nized in three classes: studies of mortality, morbidity
and, both mortality and morbidity, according to the
sources of data (i.e. death certificates, hospital records
and general practitioner consultations) (Data extraction
form shown in Additional file 3).
The results of each study were assessed to identify mea-
sures of the association of coal mining with mortality and/
or morbidity (i.e. risk measures: relative risk, odds ratio,
rate ratio and regression coefficient) in exposed versus
non-exposed populations. Exposed populations were de-
fined as residents of coal mining areas (e.g. counties with
coal mining within their boundaries) or populations in
proximity to coal mining (e.g. towns < 5 km to coal min-
ing). The health outcomes were matched with ICD codes
of diagnosis categories, block of categories, and/or chap-
ters according to the ICD-10 2016 revision Clinical Modi-
fication (ICD-10-CM) defined by the National Centre of
Health Statistics [28]. An ICD was listed to have “in-
creased risk”/“non-increased risk”if there were one or
more increased/no increased significant risk measures for
that ICD in the exposed populations. Significance was
establish as measures with p< 0.05 and confidence
intervals not crossing the null value. Significant covariates
were extracted and classified as sociodemographic, smok-
ing, obesity/overweight, environmental, and other
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comorbidities. ICDs reported with the ICD-9 revision and
health outcomes not reported as ICDs were classified by a
medical doctor.
Critical appraisal of the studies was done using a
modified scale of the checklist proposed by Dufault
and Klar [29]. The assessment was based on 10 items
with a maximum overall score of 12 points (Table 1).
For each item, if a study did not meet the criteria the
item was given a score of “0”, otherwise it received a
score of “1”,or“2”.Thegradeofthestudieswere
labelled: low (< 5 points); medium (5-8 points) and
high relevance (> 8 points).
Two independent researchers (JC and PJ) carried out
the literature search, articles selection process, data ex-
traction and synthesis between October 2016 and Febru-
ary 2017. Discrepancies were discussed with a third
researcher (SN) until consensus was reached.
Results
After exclusion of duplicates 3863 records were retrieved
from the databases search. Initial titles and abstracts
screening identified 121 potentially eligible studies. Inde-
pendent full-text review reduced this number to 23. Five
Fig. 1 Studies selection and retrieval flowchart
Table 1 Assessment scale. Adapted from Dufault and Klar [29]
Item Description
Study design and
focus (max = 4)
Sample size (max = 2) Number of ecologic units included in the analysis as a proportion of the total number
of units (3 levels: < 11% = 0 points; 11-79% = 1 point; > 79% = 2 points)
Level of inference (max = 1) The results of the analysis are not used to draw inferences for individuals
Pre-specification of
ecological units (max = 1)
Ecological units are selected to suit the hypothesis (as opposed to seemingly motivated by
convenience or necessity such as the use of districts, towns or counties)
Statistical
methodology
(max = 5)
Validity of statistical
inferences (max = 2)
Number of ecological units per covariate (3 levels: 0-10 = 0 points; 10-20 = 1 point; > 20 = 2
points)
Use of covariates (max = 1) Analysis adjusted for covariates (e.g. sociodemographic; environmental risk factors)
Proper adjustment for
covariates (max = 1)
Covariates are properly adjusted when regressed upon adjusted outcomes as recommended for
ecological studies [30]
Spatial effects (max = 1) Inclusion of spatial analysis
Quality of reporting
(max = 3)
Statement of study design
(max = 1)
Key elements of the study design are presented in the report
Justification of study design
(max = 1)
Justification of the ecological analysis, the rationale and the objectives are presented in the report
Discussion of cross-level bias
and limitations (max = 1)
Readers are cautioned about the limitations of the ecological design and/or the ecological fallacy
Cortes-Ramirez et al. BMC Public Health (2018) 18:721 Page 3 of 17
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more studies were added from the reference lists for a
final selection of 28 eligible studies (Fig. 1).
Characteristics of the eligible studies
The 28 selected studies were published between October
2000 and April 2016. Twenty two (79%) were conducted
in the USA in the region of Appalachia with the exception
of one study in Illinois [30]. Three studies (11%) from
China investigated populations in Shanxi province. Two
studies (7%) from the UK were conducted in North East
England. One study (4%) from Spain covered all Spanish
towns in proximity to mining activities (Table 2).
Fourteen studies (50%) retrieved data from death
certificates (studies of mortality), 10 (36%) from hospital
records or general practitioner consultations (studies of
morbidity), and 4 (14%) from both death certificates and
hospital records (studies of mortality and morbidity).
Health outcomes and ICD codes
Thirteen (46%) of the eligible studies presented health
outcomes already classified with the ICD-9 or ICD-10-
CM revisions. Twenty three (82%) of the studies found
significant risk measures for 88 ICD categories (single
diagnosis), 9 blocks of ICD (groups of categories within
the same chapter), 4 whole ICD chapters and 2 groups
of combined chapters, in 10 out of 21 chapters of the
ICD. These chapters include: neoplasms, diseases of the
circulatory system, diseases of the respiratory system,
diseases of the genitourinary system, endocrine and
metabolic diseases, diseases of the eye and adnexa, dis-
eases of the skin and subcutaneous tissue, conditions of
the perinatal period, congenital malformations and
chromosomal abnormalities, and external causes of mor-
bidity. Five (18%) of the studies found non-significant
risk measures (all increased) for ICD categories in the
chapters: diseases of the circulatory system, congenital
malformations and chromosomal abnormalities, and all
combined chapters. Of all ICD categories, 31 were
neoplasms, all malignant (i.e., cancer), affecting the fol-
lowing organ systems: integumentary, skeletal, endo-
crine, lymphatic and immune, respiratory, urinary,
digestive, reproductive, and central nervous systems.
Tables 3and 4show the list of ICD, organised by class
of study and ICD chapter.
Mortality
Significant risk measures of mortality were found in 12
studies of mortality [31–44] and one study of mortality
and morbidity [30]. Increased risk of mortality in exposed
populations was found for 7 grouped ICDs (blocks, chap-
ters and groups of combined chapters) and 64 ICD cat-
egories in the following chapters: neoplasms, diseases of
the circulatory system, diseases of the respiratory system,
diseases of the genitourinary system, and external causes
of morbidity. Non-increased risk of mortality in exposed
populations was found for 2 grouped ICDs and 28 ICD
categories in the chapters: neoplasms, diseases of the cir-
culatory system, diseases of the respiratory system, dis-
eases of the genitourinary system, and external causes of
morbidity (Table 3).
One of the studies [34] found non-significant in-
creased risk of mortality in exposed populations by all
causes of disease although the article was subjected to
erratum after the results were disputed [45]. Two of the
studies [32,42] found both increased and non-increased
risk of mortality in exposed populations for ICD categor-
ies in 4 chapters of the ICD (diseases of the circulatory
system, diseases of the respiratory system, diseases of
the genitourinary system, external causes of disease),
and one group of ICD chapters (all combined internal
and external causes). These disparities were found in
some but not all exposed population sub-groups in-
cluded in the studies. One of the studies [31] did not
calculate risk and rather identified increased mortality
for ICDs in three chapters (neoplasms, diseases of the
respiratory system, and external causes of morbidity)
from a graphical analysis of rates in exposed /non-ex-
posed populations.
Exposed populations were residents of USA counties
with occurrence of coal mining (coal mining counties) in
all mortality studies with exception of one study [33]
that included populations in Spanish towns in proximity
to coal mining.
Mortality and morbidity
Three studies of live and still births found increased and
non-increased risk measures with different significance
for association of residence and proximity to coal mining
with congenital malformations and chromosomal abnor-
malities. Liao et al. [46] found increased risk in mothers
resident in coal mining counties, and identified in-
creased rates in populations in proximity to coal mining
(measure not provided) [47]. Gu et al. [48] found higher
(although non-significant difference) mortality rates in
villages closer to coal mining plants compared to control
populations (Table 3).
Morbidity
Significant risk measures of morbidity were found in 10
studies of morbidity [49–58] and one study of mortality
and morbidity [30]. Increased risk of morbidity in exposed
populations was found for 9 grouped ICDs (blocks and
chapters) and 21 ICD categories in the following chapters:
neoplasms, endocrine, nutritional and metabolic diseases,
diseases of the eye and adnexa, diseases of the circulatory
systems, diseases of the respiratory system, diseases of the
skin and subcutaneous tissue, conditions of the perinatal
period, and congenital malformations and chromosomal
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Table 2 Characteristics of the studies selected in the review
[citation]
authors (year)
Country Study class Health outcomes scoped Analytical method Critical appraisal
(relevance)
Funding
[47] Liao
et al. (2016)
China Mortality/
Morbidity
Neural tube defects Spatial
autocorrelation
Medium NNSFC-YTF-IGSNRS
a
[31] Woolley
et al. (2015)
USA Mortality Malignant and non-malignant respiratory
diseases, and external and all causes of death
Two sample t test Medium ARIES
b
[49] Talbott
et al. (2015)
USA Morbidity Diseases of the circulatory system Linear regression,
spatial regression
High ARIES
b
[30] Mueller
et al. (2015)
USA Mortality/
Morbidity
Cancer of lung, colon, breast, prostate,
and all combined cancer
Linear regression,
spatial autocorrelation
High CTMPHD-EFCR
c
[50] Lamm
et al. (2015)
USA Morbidity Congenital anomalies Poisson regression Medium ARIES
b
[32] Buchanich
et al. (2014)
USA Mortality Malignant neoplasms, and external
and all causes of death
Negative binomial
regression
Medium ARIES
b
[51] Brink
et al. (2014)
USA Morbidity Diseases of the respiratory system Linear regression Medium ARIES
b
[52] Liu
et al. (2013)
USA Morbidity Diabetes mellitus Multilevel linear
regression
Medium JHERC-NIOSH
d
[33] Fernandez-Navarro
et al. (2012)
Spain Mortality Malignant neoplasms Poisson regression,
spatial regression
High Spain Health
Research Fund
[34] Borak
et al. (2012)
USA Mortality All causes of death Linear regression Medium National Mining
Association
[35] Ahern and
Hendryx (2012)
USA Mortality Malignant neoplasms Linear regression Medium Not stated
[36] Esch and
Hendryx (2011)
USA Mortality Chronic diseases of
the circulatory system
Linear regression High Not stated
[53] Christian
et al. (2011)
USA Morbidity Lung cancer Spatial scan statistic Medium KLCRP
e
[37] Hendryx (2011) USA Mortality All causes of death Linear regression Medium Not stated
[54] Ahern
et al. (2011)
USA Morbidity Congenital anomalies Poisson regression,
spatial autocorrelation
High Not stated
[55] Ahern,
et al. (2011)
USA Morbidity Low birth weight Linear regression Medium Not stated
[46] Liao
et al. (2010)
China Mortality/
Morbidity
Neural tube defects Poisson regression,
spatial autocorrelation
High PNNSFC -HTRDPC –
NBRPP
f
[38] Hitt and
Hendryx (2010)
USA Mortality Cancer of the respiratory,
digestive and genitourinary
systems
Spatial
autocorrelation
Medium Not stated
[39] Hendryx
et al. (2010)
USA Mortality Diseases of the respiratory
and circulatory systems,
all Malignant neoplasms,
and all causes of death
Linear regression Medium Not stated
[40] Hendryx
et al. (2010)
USA Mortality Cancer of the respiratory, digestive,
genitourinary, hematopoietic, and
central nervous systems,
and melanoma
Linear regression,
spatial autocorrelation
High Not stated
[41] Hendryx
and Ahern (2009)
USA Mortality All causes of death Linear regression Medium Grant RRI-UWV
g
[42] Hendryx (2009) USA Mortality Diseases of the circulatory,
respiratory, and genitourinary
systems
Poisson regression Medium Grant RRI-UWV
[43] Hendryx
et al. (2008)
USA Mortality Lung cancer Linear regression Medium Grant RRI-UWV
[44] Hendryx (2008) USA Mortality All causes of death Linear regression Medium Not stated
[56] Hendryx
et al. (2007)
USA Morbidity Lung cancer, diseases of the
respiratory, circulatory,
and musculoskeletal systems,
diabetes, and mental disorders
Multilevel logistic
regression
Medium Not stated
Cortes-Ramirez et al. BMC Public Health (2018) 18:721 Page 5 of 17
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abnormalities. Non-increased risk of morbidity in exposed
populations was found for 18 ICD categories in the chap-
ters: diseases of the eye and adnexa, diseases of the re-
spiratory and genitourinary systems, and diseases of the
skin and subcutaneous tissue (Table 4).
One of the studies [49] found non-significant risk
(increased) of morbidity by diseases of the circulatory
system (whole chapter) in exposed populations. One
study [50] found non-significant risk (increased) of con-
genital malformations in new-borns to mothers resident
in coal mining counties after adjustment by groups and
type of hospitals. Two of the studies [57,58] found that
children in proximity to coal mining have both increased
and non-increased risk of ICD categories in three chap-
ters of the ICD (diseases of the eye and adnexa; diseases
of the skin and subcutaneous tissue, diseases of the cir-
culatory system). These disparities were found in one
out of five exposed sub-groups included in the two
studies.
Exposed populations were residents of USA coal mining
counties and USA minor civil divisions in all morbidity
studies, with exception of two studies [57,58]ofcommu-
nities in proximity to coal mining in northern England.
Covariates
All of the studies with the exception of two [31,50]in-
cluded covariates for adjustment in the statistical
analysis. Sociodemographic variables included socio-
economic indicators, race/ethnicity, education level,
and health-care access. Environmental variables in-
cluded soil or land cover type, proximity to rivers and
faults, elevation, exposure to pesticides, levels of heavy
metals in soil, and indoor and outdoor pollution.
Twenty five (89%) of the studies adjusted for sociode-
mographic variables, 13 (46%) for smoking, 6 (21%) for
obesity and/or overweight, 5 (19%) for other comorbidi-
ties or family history of comorbidity, and 4 (14%) for
environmental variables.
Studies design and methods
All studies followed an ecological design (i.e. one or
more variables included, at the group level). Regression
analyses were used in 23 (82%) of the studies. Six of the
studies (21%) included both regression and spatial ana-
lyses. Four (14%) of the studies conducted only spatial
analyses (Table 2).
The ecological units were assigned according to ad-
ministrative divisions (e.g., counties or towns) and geo-
graphical points. All studies from the USA used county
as the ecological unit with the exception of one study
[52] that used minor civil divisions (i.e. administrative
divisions of a county). Two studies in China [46,47]
used points in a grid designed for the study, the study in
Spain [33] used towns, and one study in China [48] used
buffer distance units.
Critical appraisal of the studies
The studies were assessed using a modified scale of a
checklist designed for quality assessment of ecological
studies [29]. None of the studies scored low relevant:
seven (25%) of the studies scored high and 21 (75%)
scored medium in the assessment scale. The scores for
each item are presented in Table 5.
Discussion
The ICD diagnosis codes presented in this systematic re-
view unify health outcomes found in diverse studies in a
single standard classification. We mapped ICDs to re-
ported health outcomes in studies of population in prox-
imity or resident of coal mining areas. These populations
Table 2 Characteristics of the studies selected in the review (Continued)
[citation]
authors (year)
Country Study class Health outcomes scoped Analytical method Critical appraisal
(relevance)
Funding
[48] Gu et al. (2007) China Mortality/
Morbidity
Neural tube defects Spatial
autocorrelation
Medium Grant
h
[57] Howel
et al. (2001)
UK Morbidity Diseases of the eyes and skin,
and diseases of the
respiratory system
Linear regression Medium Grant
i
[58] Pless-Mulloli
et al. (2000)
UK Morbidity Diseases of the eyes and skin,
and diseases of the
respiratory system
Linear regression Medium Grant
j
a
National Natural Science Foundation of China and the Yong Talent Fund of the Institute of Geographic Sciences and the Natural Resources Search
b
Industrial affiliate program at Virginia Tech; supported by members that include companies in the energy sector
c
The Caryll Towsley Moy PhD Endowed Fund for Collaborative Research
d
The Johns Hopkins Education and Research Centre, National Institute of Occupational Safety and Health
e
Kentucky Lung Cancer Research Program
f
The Project of the National Natural Science Foundation of China, the Hi-Tech Research and Development Program of China, the National Basic
Research Priorities Program of the Ministry of Science and Technology of the People’s Republic of China, and the Knowledge Innovation Program
g
Regional Research Institute, West Virginia University
h
National project on Population and Health, China
i
Medical Research Council (grant AIR/96/9) UK
j
Northern and Yorkshire Regional research and development grant UK
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Table 3 ICD-10-CM diagnosis categories, block of categories and chapters identified in studies of mortality and mortality/morbidity
ICD Increased risk y/n /
NS
a
/O
a
[citation]
Total exposed Values
Chapter: Neoplasms
C00-C97 (All malignant neoplasms) y [30,32,38–40] Population in 214 USA counties RR ranging 1.01-1.06 p< 0.05. Increased
regression coefficients for residence and
coal production
c
C15 (Cancer of oesophagus) y [35] Population in 82 USA counties r= 0.766(SE = 0.353) p< 0.05 SMR in
coal mining counties
C16 (Cancer of stomach) y [35] Population in 82 USA counties r= 0.935(SE = 0.482) p< 0.05 SMR in
coal mining counties
C18, C19, C20, C21
(Cancer of: colon, recto-sigmoid,
rectum, anus)
y[30,33,35] Population in 157 USA counties
and 48 Spanish towns
RR = 1.27(1.12-1.44) MR men-women and
RR = 1.31(1.13-1.52) MR men in towns <5Km
to coal mining. Increased regression
coefficients for residence, coal production
and type of coal mining county
c
C22 (Cancer of liver and intrahepatic
bile ducts)
y[33,35] Population in 82 USA counties
and 48 Spanish towns
r= 0.788(SE = 0.395) p< 0.05 SMR in coal
mining counties. RR = 1.69(1.09-2.63)
MR men in towns <5Km to coal mining
C32 (Cancer of larynx) y [40] Population in 29 USA counties r/adjusted R2 = 0.36/0.42 p< 0.002 SMR in
coal mining counties
C33 (Cancer of trachea) y [40] Population in 29 USA counties r/adjusted R2 = 0.36/0.42 p< 0.002 SMR in
coal mining counties
C34 (Cancer of bronchus and lung) y [30,33,35,40,43] Population in 82 USA counties
and 48 Spanish towns
RR = 1.22(1.01-1.49) MR men-women,
RR = 1.29(1.05-1.59) MR men in towns <5Km
to coal mining. Increased regression
coefficients for residence, coal production,
type of mining and type of coal mining
county
c
C30-C39 (Cancer of respiratory
and intrathoracic organs)
y[38]/O[31] Population in 29 USA counties Pearson coefficient = 0.47 p< 0.01 SMR in
counties with 1000Tons mined /Km2.
O: increased rates in a graphical analysis
and comparative t-test
C43 (Melanoma) y [40] Population in 29 USA counties r/adjusted R2 = 0.441/0.16 p< 0.002 SMR
coal mining counties, r= 0.324/0.1 p< 0.02
SMR by coal production
C53 (Cervical cancer) y [35] Population in 82 USA counties r= 0.699(SE = .325) p< 0.05 SMR in
coal mining counties
C61 (Prostate cancer) n [30] Population in 75 USA counties r=−0.32 p≤0.005 SMR by coal production
and type of coal mining county
C67 (Cancer of bladder) y [35] Population in 82 USA counties r= 1.33(SE = .438) p< 0.01 SMR in
coal mining counties
C70 (Cancer of meninges) y [40] Population in 29 USA counties r= 0.441/0.16 p< 0.002 SMR in coal mining
counties, r= 0.324/0.1 p< 0.02 SMR by coal
production
C71 (Brain cancer) y [33,40] Population in 29 USA counties
and 48 Spanish towns
RR = 1.75(1.19-2.57) MR men in towns <5Km
to coal mining. Increased regression
coefficients for residence and type of
mining
c
C72 (Cancer of spinal cord, cranial nerves
and other parts of central nervous system)
y[40] Population in 29 USA counties r/adjusted R2 = 0.441/0.16 p< 0.002 SMR in coal
mining counties, r= 0.324/0.1 p< 0.02 SMR by coal
production
C73 (Thyroid cancer) y [33] Population in 48 Spanish
towns
RR = 2.05(1.01-4.13) MR men, RR = 1.70
(1.02-2.84) MR women, in towns <5Km
to coal mining
C81 (Hodgkin lymphoma) y [40] Population in 29 USA counties r/adjusted R2 = 0.441/0.16 p< 0.002 SMR in coal
mining counties, r= 0.324/0.1 p< 0.02 SMR by
coal production
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Table 3 ICD-10-CM diagnosis categories, block of categories and chapters identified in studies of mortality and mortality/morbidity
(Continued)
ICD Increased risk y/n /
NS
a
/O
a
[citation]
Total exposed Values
C82, C83, C84, C85 (Follicular/non-
follicular, mature T/NK-cell, and other
specified/unspecified non-Hodgkin
lymphomas
y[40] Population in 29 USA counties r/adjusted R2 = 0.441/0.16 p< 0.002 SMR in
coal mining counties, r= 0.324/0.1 p< 0.02 SMR by
coal production
C88 (Malignant immuno-proliferative dis-
eases and other B-cell lymphomas)
y[40] Population in 29 USA counties r/adjusted R2 = 0.441/0.16 p< 0.002 SMR in
coal mining counties, r= 0.324/0.1 p< 0.02 SMR by
coal production
C90 (Multiple myeloma and
plasmacytoma)
y[40] Population in 29 USA counties r/adjusted R2 = 0.441/0.16 p< 0.002 SMR in
coal mining counties, r= 0.324/0.1 p< 0.02 SMR by
coal production
C91, C92, C93, C94, C95 (Lymphoid,
myeloid, monocytic, other specified
leukemia, unspecified leukemia)
y[35,40] Population in 82 USA counties r= 1.102(SE = .554) p< 0.05 SMR in coal mining
counties. r/adjustedR2 = 0.441/0.16 p< 0.002 SMR
in coal mining counties, r= 0.324/0.1 p< 0.02 SMR
by coal production
C96 (Unspecified cancer of lymphoid,
hematopoietic and related tissue)
y[40] Population in 29 USA counties r/adjusted R2 = 0.441/0.16 p< 0.002 SMR in
coal mining counties, r= 0.324/0.1 p< 0.02 SMR by
coal production
Chapter: Diseases of the circulatory system
I00-I78 (All diseases of the circulatory
system, except unclassified and
unspecific)
y[39] / NS [39] Population in 139 USA counties r= 14.32(6.61) p< 0.03 SMR in high production
coal mining counties. NS. 5.17 (5.97) p< 0.39 SMR
in low production coal mining counties
I10 (Essential Hypertension) y [36,42]/n[42]
b
Population in 129 USA counties RR ranging 0.96-1.28 in coal mining counties
c
.r=
16.9(7.5) p< 0.03 SMR in coal mining counties, r=
11.4(5.5) p< 0.05 SMR by coal production
I11 (Hypertensive heart disease) y [36,42]/n[42]
b
Population in 129 USA counties RR ranging 0.96-1.28 in coal mining counties
c
.r=
16.9(7.5) p< 0.03 SMR in coal mining counties, r=
11.4(5.5) p< 0.05 SMR by coal production
I12 (Hypertensive chronic kidney disease) y / n [42]
b
Population in 129 USA counties RR ranging 0.96-1.28 in coal mining counties
c
.
I13 (Hypertensive heart and chronic
kidney disease)
y[36] Population in 90 USA counties r= 16.9(7.5) p< 0.03 SMR in coal mining counties, r
= 11.4(5.5) p< 0.05 SMR by coal production
I21 (ST elevation and non-ST myocardial
infarction)
y[36,42]/n[42]
b
Population in 129 USA counties RR ranging 0.96-1.28 in coal mining counties
c
.r=
16.9(7.5) p< 0.03 SMR in coal mining counties, r=
11.4(5.5) p< 0.05 SMR by coal production
I24 (Other acute ischemic heart diseases) y / n [42]
b
Population in 129 USA counties RR ranging 0.96-1.2) in coal mining counties
c
I25 (Chronic ischemic heart disease) y [36,42]/n[42]
b
Population in 129 USA counties RR ranging 0.96-1.2) in coal mining counties
c
.r=
16.9(7.5) p< 0.03 SMR in coal mining counties, r=
11.4(5.5) p< 0.05 SMR by coal production
I31, I33, (Endocarditis, other diseases
of pericardium)
y/n[42]
b
Population in 129 USA counties RR ranging 0.89-1.10 in coal mining counties
c
I40 (Acute myocarditis) y / n [42]
b
Population in 129 USA counties RR ranging 0.89-1.10 in coal mining counties
c
I50 (Heart failure) y [36,42]/n[42]
b
Population in 129 USA counties RR ranging 0.89-1.10 in coal mining counties
c
.r=
16.9(7.5) p< 0.03 SMR in coal mining counties, r=
11.4(5.5) p< 0.05 SMR by coal production
I70 (Atherosclerosis) y [36] Population in 90 USA counties r= 16.9(7.5) p< 0.03 SMR in coal mining counties, r
= 11.4(5.5) p< 0.05 SMR by coal production
Chapter: Diseases of the respiratory
system (J00-J99)
y[39]/O[31] Population in 139 USA counties r= 6.29(SE = 1.79) p< 0.001 SMR in
coal mining counties, r= 9.81(SE = 2.32)
p< 0.0001 SMR by type of coal mining
county. O: increased rates in a graphical
analysis and comparative t-test
J12, J13, J14, J15, J16, J17, J18
(Viral pneumonia, and pneumonia due to:
streptococcus, Haemophilus influenzae,
other bacteria, non-classified organism,
unspecified organism)
y/n[42]
b
Population in 129 USA counties RR ranging 0.89-1.13 in coal mining counties
c
Cortes-Ramirez et al. BMC Public Health (2018) 18:721 Page 8 of 17
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have increased risk of a wide spectrum of diseases encom-
passing 78 ICD categories and 9 groups of ICDs, classified
in 10 out of 21 chapters of the ICD-10-CM. The ICDs
were reported by studies of mortality and/or morbidity de-
signed to assess ecological exposures, namely ecological
studies, in the USA, UK, Spain and China.
Scope of health outcomes and ICDs identified in the
selected studies
The majority of the studies found increased risk of one
or more ICD diagnosis categories in exposed versus
non-exposed, especially for ICDs in the chapters
neoplasms, diseases of the circulatory and respiratory
systems, and congenital anomalies; that were reported in
both mortality and morbidity studies. Two thirds of the
studies found increased mortality by cancer in exposed
populations and nearly 40% of all ICDs identified were
ICD categories of cancer. Most of the mortality studies
found higher risk of cancer of lung and colon, and all
combined cancer, and two of the morbidity studies
found increased risk of cancer of lung and colon. These
results show a consistent association of coal mining with
higher mortality and morbidity by cancer in populations
near coal mining.
Table 3 ICD-10-CM diagnosis categories, block of categories and chapters identified in studies of mortality and mortality/morbidity
(Continued)
ICD Increased risk y/n /
NS
a
/O
a
[citation]
Total exposed Values
J20, J21 (Acute bronchitis, bronchiolitis) y / n [42]
b
Population in 129 USA counties RR ranging 0.89-1.13 in coal mining counties
c
J22 (Unspecified acute lower
respiratory infection)
y/n[42]
b
Population in 129 USA counties RR ranging 0.89-1.13 in coal mining counties
c
J40, J41, J42 (Mucopurulent, simple,
and not specified Bronchitis)
y/n[42]
b
Population in 129 USA counties RR = 1.07(1.04-1.10) males, RR = 1.11
(1.07-1.15) females; in coal mining counties
J43, J44 (Emphysema, other chronic
obstructive pulmonary disease)
y/n[42]
b
Population in 129 USA counties RR = 0.94(0.90-0.98) females in coal mining
counties
J45 (Asthma) y / n [42]
b
Population in 129 USA counties RR = 1.04(1.02-1.06) males in coal mining
counties
Chapter: Diseases of the genitourinary system
N03, N04, N05 (Chronic/unspecified
nephritic syndrome, nephrotic syndrome)
y[42] Population in 129 USA counties RR ranging 1.08-1.19 in coal mining
counties
c
N17, N18, N19 (Acute and unspecific
kidney failure, chronic kidney disease)
y[42] Population in 129 USA counties RR ranging 1.08-1.19 in coal mining counties
c
Chapter: External causes of morbidity (V00-
V99)
y/n[32]
b
/
O[31]
Population in 31 USA counties RR ranging 0.91-1.12 in coal mining
counties
c
. O: increased rates in a graphical
analysis and comparative t-test
ICD categories in more than one ICD chapter
A00-Y89 (All combined internal and
external causes)
y[32,39,41]/n
[32]
b
/NS[34]/
O[31]
Population in 139 USA counties RR ranging 0.97-1.03 in coal mining
counties
c
. Increased regression coefficients
for residence and type of mining
c
. NS:
r= 4.68(8.96) p< 0.6003 SMR in coal mining
counties
d
. O: increased rates in a graphical
analysis and comparative t-test
A00-R99 (All combined internal causes) y [37,44] Population in 139 USA counties Adjusted rranging 21.5-63.0 SMR by
residence and type of mining
c
ICD categories in studies of mortality and morbidity
Chapter: Congenital malformations, deformations and chromosomal abnormalities
Q00, Q01, Q05, Q04.2
(Anencephaly, encephalocele,
spina bifida, holoprosencephaly)
y[46]/O[47]/
O-NS [48]
204 geographical grid points in
Heshun province (China). 46
villages in Zhongyang and
Jiaokou Counties (China)
RR = 1.338(1.004-1.783) p< 0.05 cases to
mothers resident in coal mining areas.
O: increased rate cases in populations < 8 km
to coal transport roads (p< 0.007), not
measure provided. O-NS: higher mortality
rate (281.2/10000) in villages closer to
coal mining plants/control population:
chi-square test (p< 0.364)
Increased risk y/n: one or more risk measure increased/non-increased in exposed versus non-exposed populations. rregression coefficient, SE standard error, MR
mortality rate, RR relative risk, SMR standardised mortality rate
a
NS/O: Not significant/Other measure
b
Disparities found in different exposed sub-groups
c
All risk measures provided in additional material [see Additional file 4].
d
These results were disputed, and the article subjected to erratum
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Table 4 ICD-10-CM diagnosis categories, block of categories and chapters identified in studies of morbidity
ICD Increased risk y/n /
NS
a
[citation]
Total exposed Values
Chapter: Neoplasms
C18-C21 (Colorectal cancer) y [30] Population in 75
USA counties
r= 0.37 p< 0.009 incidence rate in recently
mined coal mining counties
C34 (Cancer of bronchus and lung) y [30,53] Population in 75
USA counties
Adjusted r= 0.36 p< 0.001 incidence rate in
coal mining counties. RR = 1.21 p< 0.01 and RR = 1.17
p< 0.01 in 2 clusters with coal mining counties
Chapter: Endocrine, nutritional and metabolic diseases
E10, E11, E13 (Diabetes mellitus type 1 and 2,
other specified diabetes mellitus)
y[52] Population in 225
USA minor civil
divisions
r= 0.116(0.059) p< 0.05 blood sugar levels by coal
mining area density, r= 0.124(0.056) p< 0.01 blood
sugar levels by proximity to abandoned coalmines
Chapter: Diseases of the eye and adnexa
H57.9 (Unspecific disorder of eyes) y / n [57,58]
b
5 northern England
communities
(2000-20,000
persons)
Adjusted OR = 0.23(0.10-0.49) GP-consultations in 1
of 5 communities (1.4 km to coal mining). Adjusted
OR = 1.43(1.20-1.70) GP-consultations in all other
communities (0.8-1.3 Km to coal mining)
Chapter: Diseases of the circulatory system (I00-I99) NS [49] Population in
28 USA counties
Adjusted hospitalisation rates in coal mining counties
by type and coal production = r ranging 0.01-0.05
(p> 0.05)
I10, I11, I12, I13, I15 (Primary HTA, hypertensive
heart disease and chronic kidney disease,
secondary hypertension)
y[56] Population in
73 USA counties
OR = 1.003(1.001-1.005) hospitalisation rates in
coal mining counties by coal production
Chapter: Diseases of the respiratory system (J00-J99) y [51] Population in
28 USA counties
r= 0.064(0.025) men-women, r= 0.064(0.022)
p< 0.006 men, r= 0.063(0.029) p< 0.032 women;
hospitalisation rates in coal mining counties,
by coal production
J40, J41, J42, J43, J44, J47 (Specified/unspecified
bronchitis, emphysema, chronic obstructive
pulmonary disease, bronchiectasis)
y[56] Population in
73 USA counties
OR = 1.003(1.001-1.006) hospitalisation rates in
coal mining counties
J98.9 Unspecific respiratory disorders y / n [57,58]
b
5 northern England
communities
(2000-20,000
persons)
Adjusted OR = 0.23(0.10-0.49) GP-consultations in 1
of 5 communities (1.4 km to coal mining). Adjusted
OR = 1.43(1.20-1.70) GP-consultations in all other
communities (0.8-1.3 Km to coal mining)
Chapter: Diseases of the skin and subcutaneous tissue
L98.9 (Unspecific disorders of skin) y / n [57,58]
b
5 northern England
communities
(2000-20,000
persons)
Adjusted OR = 0.23(0.10-0.49) GP-consultations in 1
of 5 communities (1.4 km to coal mining). Adjusted
OR = 1.43(1.20-1.70) GP-consultations in all other
communities (0.8-1.3 Km to coal mining)
Chapter: Diseases of the genitourinary system
N00.3, N00.8, N00.9, N01.3, N02.2, N03, N04, N05,
N08 (Acute nephritic syndromes, hematuria with
diffuse membranous glomerulonephritis, chronic
nephritic syndrome, nephrotic syndrome,
unspecified nephritic syndrome and glomerular
disorders classified elsewhere)
n[56] Population in
73 USA counties
OR = 0.997(0.994-0.999) hospitalisation rates
in coal mining counties
N17, N18, N19 (Acute and unspecific kidney failure,
chronic kidney disease)
n[56] Population in
73 USA counties
OR = 0.997(0.994-0.999) hospitalisation rates in
coal mining counties
N25, N26.9, N27 (Unspecific contracted and small
kidney, renal sclerosis)
n[56] Population in
73 USA counties
OR = 0.997(0.994-0.999) hospitalisation rates in
coal mining counties
Chapter: Certain conditions originating in the perinatal period
P07.0, P07.1 (Extremely and other low birth
weight new-born)
y[55] Mothers in
29 USA counties
OR = 1.16(1.08-1.25) p< 0.0002, OR = 1.14(1.04-1.25)
p< 0.0033; cases in coal mining counties
Cortes-Ramirez et al. BMC Public Health (2018) 18:721 Page 10 of 17
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The association of coal mining with cancer has been
documented in epidemiological studies of workers since
the 1930s [5], and increased risk of cancer of lung and
stomach has been evidenced in epidemiological studies of
occupational exposed populations [5,59,60]. It should be
noted that populations in studies of this review could in-
clude coalminers that were part of the chosen exposed
communities. Given that most coalminers are men, almost
all of the studies conducted statistical analyses adjusting
by gender to segregate populations with and without oc-
cupational exposure. The results evidence an association
of coal mining with cancer of lung and colon, adjusted by
gender, in the exposed populations. Furthermore, the
study of Fernandez-Navarro et al. found increased risk of
colorectal and thyroid cancer in only women of popula-
tions in proximity to coal mining [33]. They also found
that only men in these populations had increased risk of
cancer of liver, brain and thyroid, a group of diagnoses
for which there is scarce research from studies of
workers. These findings suggest some association of
coal mining with cancer of organs scarcely investigated
in studies of both occupational and non-occupational
exposed populations.
The other ICDs of cancer in this review include cat-
egories such as cancer of stomach, oesophagus, kidney,
bladder, brain, leukaemia and cervical cancer. There is
little research about these diseases in the general popula-
tion, regarding their proximity to coal mining. Studies of
coal miners have found increased risk of cancer of stom-
ach, kidney and bladder [17]. Other studies in coal
miners found increased risk of leukaemia [61] and can-
cer of brain [62] although the results were inconclusive
and exposures could be related to electrical and mag-
netic fields rather than contact with sub products of the
mining activities. For some ICD categories of can-
cer found in this review such as cancer of oesophagus,
brain and meninges, thyroid, leukaemia and cervical
cancer, we did not find evidence of their association with
occupational exposures. The increased risk of these di-
verse types of cancer in the general population studied
can indicate that cancer development follows different
exposure pathways in communities resident, or in prox-
imity of coal mining. Whereas in ecological studies other
risk factors such as socioeconomic variables can account
for effects at the group level of the analysis, all studies in
this review conducted analyses adjusting for sociodemo-
graphic and other variables (with the exception of one
study that adjusted only for type of hospitals [50]). The
multiple ICDs of cancer identified in studies of this re-
view reflect that cancer more than a disease in isolation
is in fact many diseases -mostly chronic- of different
organ systems. Since coal mining implies industrial
activities prolonged for many years, eventually differ-
ent exposure pathways can relate coal mining with
biological insult on diverse organ systems that result
in disease.
Table 4 ICD-10-CM diagnosis categories, block of categories and chapters identified in studies of morbidity (Continued)
ICD Increased risk y/n /
NS
a
[citation]
Total exposed Values
Chapter: Congenital malformations, deformations
and chromosomal abnormalities (Q00-Q99)
y[50,54]/NS [50] New-borns in
86 USA counties
Adjusted PRR ranging 1.10-1.63 new-born
hospitalisations in coal mining counties (adjusted for
socioeconomic variables). Crude PRR ranging
1.43-2.39 p< 0.001 new-born hospitalisations in
coal mining counties. NS: Adjusted PRR ranging
1.01-1.08 p> 0.05 new-born hospitalisations in
coal mining counties (adjusted by type and group
of hospital)
Q00-07 (Congenital malformations of the
nervous system)
y[54] New-borns in
86 USA counties
Adjusted PRR = 1.36(1.11-1.67) new-born
hospitalisations in coal mining counties
Q20-34 (Congenital malformations of the
circulatory and respiratory systems)
y[54] New-borns in
86 USA counties
Adjusted PRR = 1.93(1.73-2.15) new-born
hospitalisations in coal mining counties
Q35-45 (Congenital malformations of the
digestive system)
y[54] New-borns in
86 USA counties
Adjusted PRR = 1.41(1.17-1.71) new-born
hospitalisations in coal mining counties
Q50-64 (Congenital malformations of
genitals and urinary system)
y[54] New-borns in
86 USA counties
Adjusted PRR = 1.35(1.19-1.54) new-born
hospitalisations in coal mining counties
Q65-79 (Congenital malformations and
deformations of the musculoskeletal system)
y[54] New-borns in
86 USA counties
Adjusted PRR = 1.30(1.20-1.41) new-born
hospitalisations in coal mining counties
Q80-89 (Other congenital malformations) y [54] New-borns in
86 USA counties
Adjusted PRR = 1.13(1.04-1.23) new-born
hospitalisations in coal mining counties
Increased risk y/n: one or more risk measure increased/non-increased in exposed versus non-exposed populations. rregression coefficient, OR odds ratio, RR rela-
tive risk, PRR prevalence rate ratio
a
NS: Not significant
b
Disparities found in different exposed sub-groups
All risk measures provided in additional material [see Additional file 4]
Cortes-Ramirez et al. BMC Public Health (2018) 18:721 Page 11 of 17
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Table 5 Critical appraisal of selected studies. Modified scale from Dufault and Klar [29]
Author (year) Study design and focus Statistical methodology Quality of reporting Score
Sample size Level of
inference
Pre-specification
of ecological
units
Validity of
statistical
inferences
Use of
covariates
Spatial
effects
Proper
adjustment
for covariates
Statement
of study
design
Justification
of study
design
Discussion of
cross-level bias
and limitations
Points
Liao et al.
(2016)
211 0010 110 7
Woolley
et al.
(2015)
210 0000 101 5
Talbott
et al.
(2015)
210 2110 101 9
Mueller
et al.
(2015)
210 2110 111 10
Lamm
et al.
(2015)
210 2000 000 5
Buchanich
et al.
(2014)
110 2101 001 7
Brink et al.
(2014)
210 2100 100 7
Liu et al.
(2013)
210 2101 010 8
Fernandez-
Navarro
et al. (2012)
210 2110 101 9
Ahern and
Hendryx (2012)
210 2100 110 8
Borak et al.
(2012)
210 2100 000 6
Ahern et al.
(2011)
210 2111 111 11
Esch and Hendryx
(2011)
210 2100 111 9
Christian et al.
(2011)
210 2110 010 8
Hendryx (2011) 2 1 0 2 1 0 0 0 0 1 7
Ahern, MacKay
and Hamilton
(2011)
210 2101 000 7
Liao et al. (2010) 2 1 1 2 1 1 0 1 1 0 10
Hendryx et al.
(2010a)
210 2110 101 9
Hendryx, Fedorko
and Halverson
(2010)
210 2100 100 7
Hitt and
Hendryx (2010)
210 0010 001 5
Hendryx (2009) 2 1 0 2 1 0 0 0 0 1 7
Hendryx and
Ahern (2009)
210 2100 000 6
Hendryx (2008) 2 1 0 2 1 0 0 1 0 0 7
Hendryx
et al. (2008)
210 2100 000 6
Hendryx
et al. (2007)
210 2101 101 9
Cortes-Ramirez et al. BMC Public Health (2018) 18:721 Page 12 of 17
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Exposure pathways need to be considered for the in-
creased risk of congenital anomalies found in some of
the studies. The five studies that included live or still
births to mothers resident or in proximity of coal mining
found increased risk of congenital anomalies, yet there
was variation in the significance of the results. Two
studies [46,47] found increased risk of neural tube de-
fects (NTD), i.e. congenital anomalies of the nervous
system, and a third study found increased but non-
significant, mortality by NTD in live and still births in
populations close to coal mining [48]. These findings
were consistent with a fourth study by Ahern et al. that
found increased risk of hospitalisation by NTD and con-
genital anomalies in other four organ systems, as well as
all combined congenital anomalies [54]. The fifth study
(Lamm et al.) used the same sample of Ahern et al.
(USA counties) and found non-significant (increased)
risk of hospitalisation by all combined congenital anom-
alies, after adjustment by type and groups of hospitals
[50]. The study of Lamm et al. however, did not adjust
by socioeconomic variables. The combined evidence of
these studies suggests that pregnant women resident or
in proximity of coal mining have a higher risk of carry-
ing pregnancies affected by congenital malformations or
chromosomal abnormalities.
The possible exposure pathways related to congenital
anomalies in populations in proximity to coal mining
are not established although it is accepted that most
congenital anomalies result from interaction between
both genetic and environmental factors [63]. There is in-
creasing evidence of the association of congenital anom-
alies with exposure to environmental risk factors.
Increased risk of congenital anomalies was found in
mothers with higher blood levels of environmental pol-
lutants such as arsenic and cadmium in coal mining
areas of China [64]. These results follow another study
that reported higher blood concentrations of arsenic and
cadmium from coal mining in pregnant women from
the same region [65]. Increased rates of congenital
malformations and adverse pregnancy outcomes have
been associated with air pollution [66,67] and there are
established links between congenital anomalies and en-
vironmental tobacco smoke [68,69]. Moreover,
environmental exposures have been associated with low
birth weight, a perinatal condition with physio-
pathological mechanisms related to congenital anomalies
[70,71]. One of the morbidity studies in this review
(Ahern et al.) found increased risk of low and extremely
low birth weight in new-borns to mothers resident in
coal mining counties [55]. The women included in the
Ahern et al. study [55] were part of the same popula-
tions in the studies that found increased risk of congeni-
tal anomalies in the USA. This supports the plausibility
of exposure pathways between coal mining and patho-
genic effects on the foetus development and low birth
weight.
Almost 40% of the mortality studies searched the asso-
ciation of coal mining with all combined causes of dis-
ease. Five of these studies found increased risk of
mortality by all causes in people resident of USA open-
cut coal mining counties and one more study identified
increased rates of mortality in a comparative analysis
[31]. Only one of the mortality studies found non-
significant (increased) mortality by all combined causes
in a re-analysis of data used in three other mortality
studies in this review [34]. However the original authors
disputed the methods and results, and the article was
subjected to erratum [45]. All of these studies adjusted
their analyses for socioeconomic variables and other co-
variates such as smoking and other co-morbidities. These
findings consistently show that communities resident in
USA coal mining counties bear a higher risk of general
mortality compared to non-coal mining counties.
A smaller number of the studies found significant risk
of diseases of the circulatory and respiratory systems in
residents of coal mining areas. Esch and Hendryx found
higher mortality by five ICDs of chronic cardiovascular
diseases in exposed populations [36]. These findings
were consistent with a study of Hendryx who found in-
creased risk for the same ICD categories and other 16
ICD categories of diseases of the respiratory system [44].
This study however, also found non-increased risk of all
of the ICD categories in different exposed subgroups of
population. Whereas three studies of morbidity found
increased risk of hospitalisations and medical consulta-
tions by respiratory diseases [56–58] the studies of
Table 5 Critical appraisal of selected studies. Modified scale from Dufault and Klar [29](Continued)
Author (year) Study design and focus Statistical methodology Quality of reporting Score
Sample size Level of
inference
Pre-specification
of ecological
units
Validity of
statistical
inferences
Use of
covariates
Spatial
effects
Proper
adjustment
for covariates
Statement
of study
design
Justification
of study
design
Discussion of
cross-level bias
and limitations
Points
Gu et al. (2007) 2 1 0 0 1 1 0 0 1 0 6
Howel
et al. (2001)
210 2101 000 7
Pless-Mulloli
et al. (2000)
210 2101 000 7
Cortes-Ramirez et al. BMC Public Health (2018) 18:721 Page 13 of 17
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Pless-Mulloli et al. [58] and Howel et al. [57] found non-
increased rates of consultations in one out of five sub-
groups of exposed communities. From the results of
these studies in the general population there is a signifi-
cant association between coal mining and diseases of the
circulatory and respiratory systems although there is not
consistency in the studies’findings to characterise the
trend by these diseases in affected communities.
One of the studies [30] found non-increased risk of
prostate cancer in exposed populations. Whereas no
other study of this review included prostate cancer, these
findings concur with results of studies in workers
reviewed by Jenkins et al. [17]. We did not find pub-
lished evidence to support biological plausibility of lower
risk of prostate cancer in coal mining exposed popula-
tions. Nevertheless, a hypothetical protective effect of
coal mining on prostate cancer has been explored previ-
ously in coalminers although the authors did not infer
specific exposure pathways or hazards related to the
lower risk [72]. On the other hand, Hendryx et al. found
lower ORs of hospitalizations rates in residents of USA
coal mining counties by 15 ICD categories of diseases of
the genitourinary system [56]. In another study Hendryx
found only increased mortality rates for some of these
ICDs in similar populations [42], however these two
studies differ in the sources of data (death certificates
versus hospital records). These findings suggest a lower
risk of diagnoses involving organs of the genitourinary
system in residents of USA coal mining counties related
with underlying mechanisms not yet investigated.
Girschik et al. [72] conducted a systematic review of
prostate cancer in studies of coal miners, measuring a
combined effect size of 0.74 (95% CI 0.67 to 0.81), and
suggested some occupational conditions as possible ex-
planations. Nevertheless the proposed mechanisms do
not extrapolate to the results in studies of this review,
and their link with possible exposure pathways need to
be investigated in further research.
Methodological design of the studies and control of bias
The selection criteria in this systematic review did not
include a specific study design yet all of the studies
selected followed an ecological design (one or more vari-
ables grouped as rates or percentages). This circum-
stance is telling of some of the complexities faced by
authors when addressing research of health outcomes
associated with environmental exposures (e.g. exposures
related with coal mining), that are eminently ecological.
In many cases, the choice of this kind of design is the
only way to conduct the research given the limitations
to access protected data. A third of the authors of stud-
ies in this review noted that they follow an ecological de-
sign given restrictions to access individual data. The use
of an ecological design in all selected studies, besides
being an indicator of consistency seems to be adequate
to approach important characteristics of the exposure,
for example; the large regionalisation of coal mining
areas, the indiscriminate impacts on surrounding popu-
lations and the validity of spatial and spatiotemporal
analyses. Notwithstanding ecological studies have several
limitations, especially the risk of ecological bias (i.e. con-
founding effect introduced by grouping variables). The
PRISMA statement requires an assessment of risk of
bias for studies included in systematic reviews, prefera-
bly with the use of standardised scales [27]. We used a
modified assessment scale that has been applied in other
reviews of ecological studies [73], instead of adapting
standard scales more generally used (e.g. Newcastle-
Ottawa scale). We consider this as the best approach to
assess ecological studies, which include methodological
characteristics such as samples based on ecological units
and spatial analyses along with other statistical tests that
cannot be evaluated with other assessment scales.
In the critical appraisal, all of the studies scored
medium or highly relevant though there was important
variability between scores of the scale’s criteria. The
most pertinent issue was related to the use and adjust-
ment for covariates in regression analyses to control risk
of ecological bias [74]. Only seven of the studies con-
ducted a proper adjustment for covariates as suggested
for ecological studies [75]. All other studies did not in-
clude covariates in the regression analyses or included
covariates measured as percentages instead of adjusting
by age (as done for dependent variables). Given that cal-
culation of age-adjusted rates for covariates is not always
possible because of lack, or restrictions of data sources,
this is a common limitation of ecological studies. How-
ever there were consistent results between studies that
did proper adjustment for covariates and 17 studies that
did not, while including similar exposed/non-exposed
populations (e.g. the study of Buchanic et al. [32] and
the studies of Henryx et al. [39], Hit and Hendryx [38],
and Woolley et al. [31]). Likewise these results con-
curred with two of the studies conducted in regions
other than the Appalachia [30,33] indicating minor dis-
tortion (if any) of the results. Another aspect of the crit-
ical appraisal was related to the “quality of reporting”
criterion. Only half of the studies did include an explicit
statement about the study design, and discussed the risk
of ecological bias. This presupposes a greater responsi-
bility for the reader to be aware of the studies’design
and draw conclusions from their results. We analysed
results of each study regarding the populations sampled
and considering consistency between results of different
studies. Since ecological studies are most useful to pro-
vide exploratory analysis and generate hypothesis, ra-
ther than establishing causal links [76] the quality
assessment of the studies did not affect majorly our
Cortes-Ramirez et al. BMC Public Health (2018) 18:721 Page 14 of 17
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synthesis and interpretation of results. None of the
studies scored low relevant and there were consistent
results in two or more studies, for risk characterisation
of ICD categories in most of the ICD chapters, as dis-
cussed in the previous section.
The results of studies selected in this review can con-
tribute to hypothesis testing in further research. A good
example is in the results of the study of Liu et al. who
found significant increased levels of blood sugar in dia-
betic residents of USA coal mining counties after adjust-
ment for covariates [52]. Peer review literature about
diseases of the endocrine system in populations in prox-
imity to coal mining is scarce and these findings suggest
an association needed to be studied in other coal mining
regions. In another study, Hendryx found increased risk of
mortality for, amid other diseases, acute bronchitis, bron-
chiolitis and emphysema in residents of coal mining coun-
ties [42]. To the best of our knowledge, this is the first
epidemiological study to measure a significant association
of coal mining with these ICD categories in exposed pop-
ulations. Whereas these findings can be corroborated with
longitudinal studies, they are also complementary for re-
search in the environmental health field: Inclusion of large
groups of diagnosis categories can allow identification of
underlying associations that cannot be measured in stud-
ies of more specific groups of diseases. Most of the studies
in this review were designed to measure risk of diseases
already studied for their association with coal mining, in
part because of supporting evidence provided by studies
in coalminers. The inclusion in the analysis of groups of
disease beyond diagnosis categories restricted to results of
previous research can evidence increased risk of unex-
pected health outcomes and lead to further research about
different exposure pathways.
Limitations
Our search criteria excluded studies for which ICD
codes could not be assigned because the sources of
health data were not validated on medical diagnoses.
This restricted the inclusion of health conditions or dis-
orders associated with coal mining detected in surveys
and other non-medical assessments. We considered
more relevant to focus on the validity of health out-
comes and the use of a single classification standard.
Studies of only coalminers were excluded. Since workers
could be part of the population in proximity to coal
mining, their exclusion could impact the ICDs to be
identified. However studies of workers are usually de-
signed to assess occupational exposures (e.g. vibration,
use/not use of masks), and coalminers are mostly men,
two characteristics that are not representative of the
general population. We balanced the exclusion of studies
of occupational exposed populations against the chance
of selection bias of ICDs in populations in the vicinity of
coal mining. All selected studies in the review followed
an ecological design. The issues related to ecological
studies treated in the discussion imply that the interpret-
ation of results for individual studies must be contextua-
lised with the populations sampled and the statistical
analyses of each study. Given that the selected articles
were published in English, we did not assess studies
from other coal mining regions reported in other lan-
guages. Whereas there seems to be a significant regional
concentration of studies, we could not identify ICDs as-
sociated with coal mining in regions other than the
USA, Europe and China.
Conclusions
There is consistent evidence of the association of coal
mining with a wide spectrum of diseases, especially cancer
and congenital anomalies, in populations resident or in
proximity of the mining activities. The studies that have
investigated these associations were designed to measure
exposures at the group level thus other research methods
such as individual-level and longitudinal studies can be in-
tegrated to provide further evidence of the exposure path-
ways. Although coal mining is undertaken worldwide, the
majority of the studies have been conducted in a few
countries. More epidemiological studies of populations in
coal mining areas are needed to expand the results of this
review to most geographical regions.
Additional files
Additional file 1: Checklist of items of the PRISMA protocol addressed.
(DOCX 26 kb)
Additional file 2: Search strategies carried on Pubmed, Embase and
Scopus. (DOCX 17 kb)
Additional file 3: Data extraction form, Items of data collected from the
eligible studies. (DOCX 18 kb)
Additional file 4: All measures of risk and covariates reported in the
eligible studies. (DOCX 167 kb)
Abbreviations
ICD: International classification of diseases; ICD-10-CM: International
classification of diseases 10th edition, clinical modification
Acknowledgements
The authors want to thank Scott Macintyre for his valuable advice on the
systematic search strategy and Dr. Ruby Michael for spirited discussions and
relevant comments on the manuscript.
Availability of data and materials
All data generated or analysed during this study are included in this
manuscript and its additional files.
Authors’contributions
JC and PJ carried out the literature search, articles selection, data extraction
and synthesis. JC classified the health outcomes according to the ICD-10-CM
and was the main contributor to writing the manuscript. SN collaborated in
the synthesis and was the third researcher when JC and PJ did not reach
consensus. PJ and PS were major contributors in writing the manuscript. All
authors read and approved the final manuscript.
Cortes-Ramirez et al. BMC Public Health (2018) 18:721 Page 15 of 17
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Ethics approval and consent to participate
Not applicable
Competing interests
The authors declare that they have no competing interests
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Child Health Research Centre, Faculty of Medicine, The University of
Queensland, Brisbane, QLD, Australia.
2
School of Public Health and Social
Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD,
Australia.
Received: 1 November 2017 Accepted: 25 April 2018
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