Background: Environmental variables may have an impact on
many illnesses.
Objective: To correlate life expectancy (LE),
ecological, demographical/social, economic, life style variables (overall
defined as LEEDELs) with the most common illnesses in those 49 countries (49SC)
considered reliable by WHO in terms of Age Standardized Death Ratio (ASDR)
registry.
Material and Methods: ASDRs of 34 diseases (17 non
cancer and 17 cancers), retrieved from WHO records, were correlated with LEEDELs
in the years between 2000 and 2016.
Results: LE and population increase were respectively
4.5 years and 19.7 %. Most of the illnesses showed a significant decrease, a
part from pancreas cancer (+7%) and Alzheimer (+ 72 %), while HIV, digestive
diseases, prostate and brain cancers were not significantly modified.
In general, the modifications (positive or
negative) were more correlated with those LEEDELs indicative of welfare status
(GDP, cars, internet, cell phones), while social/demographical and ecological
variables showed a minimal impact. The pancreatic cancer was positively
correlated with cell phones.
Conclusions:
In the 49 SC, the welfare variables influence
positively most of the ASRDs. TBC, STD, diarrheal, peptic ulcer among
non-cancer illnesses while stomach, liver and cervix among cancers were bound
to a lower economic status. Pancreatic cancer was positively correlated with
cells.
Keywords: Ecology, Demography, Economy, Cancers, Diseases, Pancreatic cancer
INTRODUCTION
The relationship between LE, ecology,
demography/social, economy and life style (complexively reported as LEEDELs
variables) has been matter of so many reports and debates that it will be very
difficult to summarize all of them, a part from the common statement that
humans are spoiling the earth and compromising the life of the new generations
[1-3], in terms of loss of biodiversity [4,5], climate emergency [6], and CO2
emission [7].
How has the belief come about that our ancestors have done wrong in trying to provide a better life for us.
Is it true that in bringing a steak, vegetables and fruits on the family table we have compromised the life of our grandchildren? How is it a crime to provide comfort to our families by heating the houses in winter and providing some fresh air in hot summer?
Perhaps is necessary to focus our attention between progress and health.
The aim of the
present study was to analyze, in the period between 2000 and 2016, the
correlations between the Average Standard Death Ratio (ASDRs) and the most
common diseases with LEEDELs, in those countries considered reliable by WHO in
terms of data registration.
MATERIAL AND METHODS
Criteria of choice for the variables and time frame
The Age-Standardized Death Rate x 100,000
population (ASDRs) were considered for some diseases (non-cancer and cancers, Table 1) were compared to some of the
LEEDELs (Table 2).
ASDRs are free of the bias related to age
distribution unlike crude data or prevalence/incidence measures. The ASDRs data
listed as Global Health Estimates 2016 and published in 2018 were used [8].
The ASDRs do not consider the number of
inhabitants in the country with the consequence that values in small countries
(e.g., Bahamas- about 0.4 million inhabitants) have the same weight as for
larger countries (e.g. USA-about 322 million inhabitants), which can create a
bias in the average values of the 49 SC.
However, despite the increase of the
population, ASDRs remain significantly correlated (as reported in Table 1; r> 0.3772; p < 0.005). For this reason, ASDRs values were
considered sufficiently reliable.
For all the variables the values were
relative to both genders, a part from prostate cancer which was in relation to
males, and breast and cervix cancers concerning females only. Seventeen
ecological, environmental and demographic variables limited to 2016 were also
taken into consideration [8-10] for the correlation with the different
illnesses.
The list and the criteria of choice for the countries
In total,
the countries listed by WHO in terms of ASDRs are 191.The data used for
correlations were relative only to those 49 countries (selected countries or
SC) considered by WHO “with high completeness and quality of cause-of-death
assignment” that “may be compared and time series may be used for priority
setting and policy evaluation” [7].
List
of the countries
The
following 49 SC countries were considered:
Armenia,
Australia, Austria, Bahamas, Belgium, Brazil, Brunei, Canada, Chile, Croatia,
Cuba, Czechia, Denmark, Estonia, Finland, France, Germany, Grenada, Guatemala,
Hungary, Iceland, Ireland, Israel, Italy, Japan, Kyrgyzstan, Latvia, Lithuania,
Luxembourg, Malta, Macedonia, Mauritius, Mexico, Moldova, Netherlands, New
Zealand, Norway, Romania, Saint Vincent & Grenadinas, South Korea, Trinidad
& Tobago, United Kingdom, USA, Uzbekistan.
Data collection
The values up
to the fourth decimal place were taken from the WHO records. For LEEDELs only
the values of 2016 were considered because of very high correlation (r> 0.9) with previous years (2000 and
2010).
For LEEDELs the variables were taken from
the Atlante Geografico DE Agostini 2016 Ed De Agostini Novara Italy [9] and by
CIA World Factbook 2016 [10].
Life expectancy, ecological, demographic/social,
economic, life style variables (LEEDELs)
The following variables were chosen:
Life expectancy (LE): years
Population density: as number of
subjects/km2
Urban population: as % in comparison to the total population
GDP/inhabitant (Gross Domestic
Product/inhabitant) as total values/inhabitants (USD) of goods and final
services related to economic activities, capital investments
Unemployment: as % of people looking for a
job in relation to the labor force
GDP 1: GDP rate as % in relation to primary
industry bound to agriculture, forests, livestock, fishing
GDP 2: GDP rate as % in relation to
industry, mining and construction
GDP 3: GDP rate as % in relation to commerce,
transportation, communication, tourism, and insurance
GDP 2+3: sum of the rates as % related to
GDP 2 + GDP 3
Education: as % of the investments in
public and private instruction in relation to GDP
Hospital beds: number of hospital beds/1000
inhabitants
PM: particulate matter (PM2.5 and
PM10) in mcg/m3 measured in cities with > 100,000
inhabitants
Forests: rate as % of the country surface
covered by forests
Forests Km2: square kilometers
of forest/1000 inhabitants.
Cars: number of cars/1000 inhabitants
Cell: number of cell phones/1000
inhabitants
Internet: number of people with internet
connection/1000 inhabitants.
Statistical evaluation
For all the
variables the mean values and dispersion indexes were calculated.
The level of p
<0.05 was considered as the cut off. The Mann-Whitney U test was used to
calculate the difference in ASDRs among the periods (2000-2016), while for the
correlations ASDRs/LEEDELs the cut off was p <0.01.
In terms of
correlations among variables, following a linear Pairwise Correlation analysis,
the presence of some or more out outlier may compromise the r values. The
impact of the outlier was minimized using the Robust fit [11] further adjusted
following the method M of Huber [12].
The JMP14 Pro of SAS Institute was used for
the analysis.
RESULTS
The data concerning the population increase
for both genders were reported in Table 3.
The 49 SC represented between 19.4 % and 20.1
% of the world population. In the 191 SC between 2000 and 2016 the population
increased by 21.4 %, whereas in the 49 SC the increase was 11.8 % only.
The
modifications of the ASDRs for the selected diseases were reported in Table 1. The significant correlations
between 2000 and 2016 indicate that the values of the different diseases are
constant over time. This condition allows to consider the values of 2016 as a
reliable mirror for all the previous years. In the period 2000 Vs 2016 LE
increase of 5.7 % while the other LEEDLEs variables showed very different
trends (Table 2).
Most of the common
illnesses were significantly reduced, a part from Alzheimer which increased by
72%.
The average
reductions of HIV, diarrheal diseases, and chronic kidney diseases were not
statistically significant due to the large variance. In relation to cancers, a
statistically significant reduction was found for all cancers with the
exception of liver, and brain cancers showing almost identical ASDRs during the
entire period. The only cancer showing a significant increase was the
pancreatic cancer (+7 %).
Limited to year
2016, the LEEDLEs variable list comparing the 49 SC with the rest of the 142
countries was reported in Table 2.
The differences
between the two blocks of countries are consistent for most of the variables,
and not significant only for some of the ecological variables (forests) and
cell phones.
The correlations
between illnesses (non-cancer and cancers) and the LEEDLEs variables were
reported in Table 4.
The
negative correlation indicates that the increase of the disease corresponds to
a lower value of the variable, at the opposite a positive correlation corresponds
to the increase of the variable. As an
example: Alzheimer’s disease increases with the increase of LE, GP/inhab and
GP2+3 (high profit), internet connections and cars, while it decreases in case
of GP1 (low profit) and PM (particulate matter).
For a more readable evaluation, the
correlations between LEEDLEs Vs different illnesses, were reported in Table
5, according to the number of variables showing statistically significant r
correlation (0.338 as cut off for p<0.01), starting from diseases
showing the highest number of correlations (9) to the lowest (0) .
A block of 8 illnesses showed an almost
identical tendency with negative correlations for LE, % of urban population,
GDP/inhabitants, % GDP 3, % GDP 2+3, internet connections/inhabitant and
positive correlation for % GP1. The only diseases with a completely different
pattern was Alzheimer, characterized by specular/opposite correlations for all
the variables
compared to the rest of the block, with the addition of PM which showed to be
negative. Lymphoma also showed a pattern similar to Alzheimer a part from LE
which was not significant.
PM showed
a positive impact for ischemic stroke and stomach cancer only. All the other
diseases with a number of significant correlations between 7 to 1 each seemed
to a different pattern for some of the variables (see discussion).
A block
of 6 illnesses had no correlation with any of the LEEDLEs, meaning they seemed
to develop independently from the environmental variables.
For a
more comprehensive overview, the different LEEDLEs was also summarized
separately and reported in Tables 5-9.
Life expectancy
In term of LE dependence the results are
reported in Table 6.
All the correlations for the listed illness
with LE were negative, with the exception of Alzheimer’s disease.
Ecological variables
The classical ecological variables, such as
population density, forests % in the country, and forests km2/1000 inhabitants,
did not reach the cut off correlation of p < 0.01 with the listed illnesses,
and were not reported in the table. The only correlated variable was PM as
reported in Table 7.
It seemed that PM increases the ASDRs in
the case of ischemic stroke and stomach cancer, while an opposite effect was
found for Alzheimer’s disease, ovarian cancer and lymphoma.
Demographic/social variables
The % of urban
population was the only demographic variable showing some significant
correlation, while all the others, hospital beds, and education investments
were found inconsistent.
Data were reported in Table 8.
Economic variables
GDP/inhabitant
and % of GP1, GDP 3 were the variables showing some significant negative
correlation.
GDP 2 was not significantly correlated and
was summed up with GDP 3. This last variable is complementary to % GP1. Data
were summarized in Table 9.
A block
of 8 illnesses (TBC, STD, ischemic stroke, peptic ulcer, digestive diseases,
liver, stomach, and cervix cancers were showing a similar correlation pattern:
negative for GDP/inhabitants and % GPD 3, and positive for the other GDPs (1 %
and 2+3 %). Lymphoma and Alzheimer’s disease were the only diseases with ASDRs
specular to the other. Colorectal cancer seemed influenced by GDPs only
(positive for 1% GDP and negative for % GDP 2+3).
Life style variables
These
variables seemed to be the most discriminant for non-cancer and cancer diseases
accounting for 21 different illnesses as reported in Table 10.
The ASDRs of most of the illnesses (18/21) were found to be negatively correlated with cars and internet connections, while Alzheimer’s disease, pancreas cancer, and lymphoma showed specular positive correlations.
Pancreatic
cancer and chlamydia were the only illnesses showing a positive correlation
with cellular phones.
DISCUSSION
In a previous study, a more sophisticated
analysis (stochastic and non-stochastic) was done in the 191 countries (the complete
list according to WHO) without considering the ASDRs [13]. In terms of LEEDLEs,
similar correlations were found for GDPs, PM, and for those variables
characteristic of developed countries (cars, mobile phones, internet
connections), while the classical ecological variables were in consistent in
terms of LE.
The results of the present
investigation have the limitations due to the differences between the 49 SC and
the rest of the 142
countries in terms of LEEDLEs (see Table
2).
This
means that the results cannot be taken as a worldwide picture, and have to be
considered within the limit of the 49 SC which represent about 20 % of the
total population.
The
choice of ASDRs as main variable can be a further limitation, because each
disease could be concomitant with other illnesses which may precipitate the
death. Furthermore, for chlamydia it was not possible to differentiate between Chlamydia trachomatis or Chlamydia pneumoniae since no data were
available.
Despite
these limitations, some interesting observations can be drawn from the
analysis. Between 2000 and 2016 in the 49 SC the ASDRs of almost all the
diseases were significantly reduced a part of liver, kidney, brain cancer,
which were not significantly modified, while pancreatic cancer was increasing
by about 7 %. The overall improvement of the diseases can be to the therapies
prolonging the survival, and also some positive modifications of the environment
cannot be excluded.
The
aspect of environmental modifications opens the door to many hypotheses. All
the ecological variables, with exception of PM (see later), seemed not
important. LE increase together with the decrease of almost all the illnesses
witness that the present environment does not seem so negative. One may
speculate about the quality of life which was not considered in the present
study. However, the human needs are primarily to stay alive and to improve the
welfare of immediate and future generations. The second need is food, and the
choice to substitute forests with crops is consequential on that. If it is true
that pollution has been the consequence of this, it is also true that
starvation has been strongly reduced in the last 20 years, and the aim of FAO
Sustainable goals WHO was to allow all humans by 2030 to have access to
sufficient food for surviving (Zero Hunger Challenge) [14].
The
challenge will be to reach this goal reducing the pollution, accomplishing this
task with the contribution of every generation.
Among
the environmental variables, those related to welfare (GDP 2+3, cars, internet,
cells) were increasing LE. The inconsistency of investment in education and the
hospital beds in terms of LE, can be considered in the light of the profit:
once resources are sufficient, it is much easy to take advantage of the
available institutions (school, hospitals) despite some limitation.
From the
analysis it seems evident that LE was reduced either by non-cancer illnesses
(TBC, strokes, peptic ulcer, digestive diseases, CVD, diabetes type 2, HIV,
STD) and also by some cancers (stomach, cervix, and prostate), but for all the
other illnesses no correlation was found. Some of the illnesses (16/34) were
shown to be reduced by the improvement of life style (see Table 10), Alzheimer’s disease and lymphomas being the only two
exceptions since they came out with specular correlations, probably a natural
consequence of living longer. Some illnesses (10/34) were more typical for poor
living conditions (high % GP1) such as TBC, STD, strokes, peptic ulcer, CVD,
and digestive diseases, among non-cancer diseases, and stomach, liver, and
cervix among cancers (see Table 9).
Other diseases (6/34) did not belong to any
of the LEEDELs (see Table 5: variable
= 0) all were cancers (breast, bladder, brain, thyroid and leukemia) a part of
glomerulonephritis. A couple of issues should be mentioned concerning cell
phones and PM.
The cell phones
The cell phones have been addressed as a
cause of cancer by a consistent number of authors.
Based on research reports done before 2014,
the electromagnetic field (EMF) produced by mobile phones was classified by the
international Agency for Research on Cancer (IARC) as possibly carcinogenic to
humans [15]. More recently, the European EMF guidelines were established [16], addressing
all the possible sources of pollution (e.g. cell, tablets, TV broadcast
antennas), concluding that certain diseases such as Alzheimer and male
infertility may be the consequences of this event.
The American Cancer Society (ASC) in 2018
stated that the reports of the US National Toxicology Program (NTP) “were still
inconclusive, and that, so far, a higher cancer risk in people has not been
seen, but that people who concerned should wear an earpiece when using the cell
phone” [8].
In the present research, the only significant
correlations were shown for pancreatic cancer and chlamydia infection in
females. No other diseases were emerging, and brain cancer seemed to be
unaffected.
However, the activity of EMF was documented
to reduce insulin secretion from in vitro insulinoma [17] and in rats the
exposure to EMF impacts insulin secretion by influencing the size of pancreatic
islets [18].
On the basis of experimental studies,
suggestions were made to enhance intracellular insulin concentration in
insulin-secreting cells in that they could be useful for cell transplantation
in diabetes mellitus [19].
More recently the High-Frequency EMF was
found to modify both insulin secretion and blood glucose levels in rats [20].
In humans, for the moment it is not
documented whether EMF increases or decreases the insulin secretion in diabetes
type 2 (2DM). In a research done using the same ASDRs as in the present study [35], 2DM was shown to reduce the risk
of pancreatic cancer, which may mean that blood glucose increases and hyper
insulinemia are not culprits of the cancer development. More attention should
be paid to the reduction of pancreas cells apoptosis driving them toward the
malignancy. In other terms, under EMF stimulation, no matter whether the
influence is positive or negative, it seems evident the Langherans islets can
be affected.
There is no clear explanation about the
relationship between cell phones and pancreatic cancer in humans. However, when
cell phones are not used but still operating, usually they are kept in the
pockets or in the bags: the relative EMF can impact easily the anatomic
position of pancreas which is very close.
Chlamydia also was positively correlated with
cell phones, and despite no specific data are available in the literature, this
infection could be affected by EMF. The sensitivity of bacteria to EMF has been
documented with conflicting results. Some authors described for both
Gram-positive and Gram-negative bacteria a reduction of growth together with a
morphology modification [21], while more recently other authors showed a growth
increase of some E. coli strains using
irradiation frequencies between 60 and 40 Hz [22]. The differences between
results could be determined by the experimental conditions, particularly by the
frequencies which were used, or even by the anatomical parts where bacteria
were isolated [23]. One aspect should be analyzed in that angiogenesis seems to
be stimulated by EMF [24,25]. Although all these investigations should be
confirmed, the hypothesis seems consistent that the angiogenesis stimulation
may allow chlamydia to spread locally, and also far from the common anatomical
part were usually it resides.
PM
value
This variable has some peculiar aspects that
need to be clarified. The first is that PM values were relative to cities with
>100,000 inhabitants which represent only a part of the total population. Furthermore,
PM is a complex mixture of chemical components, to be considered together with
many gases such as methane (CH4), ozone (O3), carbon monoxide (CO), sulfate
(SO3), nitrogen dioxide (NO2) aerosols, and all the possible widespread air
pollutants present wherever people live. These particles are able to penetrate
deeply into the respiratory tract, and therefore constitute a risk for health. The
WHO estimated in 2000 that the exposure
to PM caused 800,000 deaths and 6.4 million years lived with disability (YLDs)
in the developing countries accounting for two thirds of this burden [26].
In general, WHO stated that there is no
evidence of a safe level of exposure to PM or a threshold below which no
adverse health effects occur. In the recent study, >30 % of the population
was found to live in areas exceeding the WHO level target of 35 mcg/m3.
This safe limit was reached only in some of 49 SC countries, and the levels were
found to be even worse in the remaining countries (see Table 2).
Furthermore, the data recorded in this study
represent an average of the cities where the monitoring stations were
available. In order to present air quality largely representative for human
exposure, measurements of residential areas, commercial and mixed areas were
used.
Stations characterized as particular “hot
spots” or exclusively industrial areas were not included, and in some of the country’s
particles < PM10 was largely based on estimates [27].
There are several studies conducted in
different parts of the world showing the negative effect of the PM on health
[26-30], but still there is need for further research to define the long term
toxicity [31] and whether some components and sources of PM may be more toxic
than others [32]. The indoor air pollution is also something that should be
considered since it is causing apparently 3.7 million deaths [33].
In the WHO Update report of 2016 [34] a
comparison was done to determine the trend of PM in the world between 2008 and
2013, ending up with an estimation of 5% increase, despite some fluctuation
within the macro-regions that were analyzed. In the same period the LE was also
increased in practically every of the 191 countries considered in this study,
no matter whether the PM was increasing or not. This indicates that more
precise measures should be taken for PM, because in the present scenario it
seems that they have some positive effect on LE. The present findings of a
negative correlation with Alzheimer’s disease (a decrease of Alzheimer
corresponding to an increase of PM) and positive correlation with ischemic
stroke (an increase of ischemic stroke corresponding to an increase of PM) have
no clear explanation, unless some hypotheses about microvascular thrombosis,
and brain inflammation is made.
CONCLUSION
The most interesting issues emerging from the
present study were the non-interference of the classical ecological variables
on LE, a part of PM showing positive and negative interference respectively for
Alzheimer’s disease and ischemic stroke. In relation to the illnesses in
general, it seemed evident they have different patterns, most of the time
showing that welfare variables (GDP, cars, internet connections) have a
positive effect in reducing the burden of ASDRs.
Despite being limited to the 49 SC, the world
tendency is to live longer and keep the diseases under control through the
improvement of life style and financial resources. For some diseases this is
not sufficient since, at the opposite, they may appear late in the age such as
Alzheimer’s disease and lymphoma.
An interesting finding concerns the
connection between pancreatic cancer and EMF pollution, witnessing that the
increase of cell phones use may be relevant to the increase of this cancer
which was the only disease growing significantly (+7%) in the period from 2000
and 2016.
It is time that ecologists, climatologists
and clinicians started a crosstalk…. it’s never too late, provided they are
minded the solutions reside in the evolution and never in the revolution.
ACKNOWLEDGEMENT
We are thankful to the WHO that allowed the
public availability of the data base: the use of these data can be extremely
helpful for authors who need details on the epidemiology of different
diseases.
AUTHOR CONTRIBUTIONS
Cornelli U conceived the trial, retrieved
some of the WHO data and wrote the article; Belcaro G retrieved part of the WHO
data; Martino Recchia was responsible for the statistical analysis.
All the authors
read and approved the final manuscript.
FUNDING
No funding was requested
or received by the authors.
CONFLICT OF INTEREST
No conflict of
interest.
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