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Int. J. Environ. Res. Public Health 2021, 18, 3010. https://doi.org/10.3390/ijerph18063010 www.mdpi.com/journal/ijerph
Article
Applications of Systems Science to Understand and Manage
Multiple Influences within Children’s Environmental Health
in Least Developed Countries: A Causal Loop
Diagram Approach
Claire F. Brereton
1
and Paul Jagals
2,
*
1
Children’s Health and Environment Programme, Child Health Research Centre, University of Queensland,
Brisbane, QLD 4101, Australia; claire.brereton@uq.edu.au
2
Child Health Research Centre, University of Queensland, Brisbane, QLD 4101, Australia
* Correspondence: p.jagals@uq.edu.au
Abstract: Least developed countries (LDCs) are home to over a billion people throughout Africa,
Asia-Pacific, and the Caribbean. The people who live in LDCs represent just 13% of the global pop-
ulation but 40% of its growth rate. Characterised by low incomes and low education levels, high
proportions of the population practising subsistence living, inadequate infrastructure, and lack of
economic diversity and resilience, LDCs face serious health, environmental, social, and economic
challenges. Many communities in LDCs have very limited access to adequate sanitation, safe water,
and clean cooking fuel. LDCs are environmentally vulnerable; facing depletion of natural resources,
the effects of unsustainable urbanization, and the impacts of climate change, leaving them unable
to safeguard their children’s lifetime health and wellbeing. This paper reviews and describes the
complexity of the causal relationships between children’s health and its environmental, social, and
economic influences in LDCs using a causal loop diagram (CLD). The results identify some critical
feedbacks between poverty, family size, population growth, children’s and adults’ health, inade-
quate water, sanitation and hygiene (WASH), air pollution, and education levels in LDCs and sug-
gest leverage points for potential interventions. A CLD can also be a starting point for quantitative
systems science approaches in the field, which can predict and compare the effects of interventions.
Keywords: children’s environmental health; CEH; least developed countries; LDC; systems science;
systems thinking; causal loop diagram; CLD
1. Introduction
Children can be considered as least developed countries’ most valuable resources,
but in least developed countries (LDCs), their health is threatened by ecological degrada-
tion, pervasive inequalities, climate change, migration, and urbanisation [1–4]. There are
currently 46 LDCs, and these are home to over a billion of the world’s people [5]. LDCs
are diverse in geography, topography, and climates, and include mountainous countries
such as Nepal, tropical Pacific Island countries, and arid landlocked countries such as
Mali. However, they share common characteristics of low per capita income, an economy
dominated by subsistence activities, limited manufacturing, and an undiversified produc-
tion structure, low education levels, high fertility rates, and inadequate infrastructure [6].
In six LDCs, more than 70% of the population live below the international poverty line
[7].
LDCs account for 13% of the world’s population, but with birth rates averaging 4.2
children per woman, they will account for 45% of the global population growth by 2050.
Whilst two-thirds of people in LDCs still have rural subsistence lifestyles, urbanisation
Citation: Brereton, C.F.; Jagals, P.
Applications of Systems Science to
Understand and Manage Multiple
Influences within Children’s
Environmental Health in Least
Developed Countries: A Causal
Loop Diagram Approach. Int. J.
Environ. Res. Public Health 2021, 18,
3010. https://doi.org/
10.3390/ijerph18063010
Academic Editor: Paul Tchounwou
Received: 31 January 2021
Accepted: 8 March 2021
Published: 15 March 2021
Publisher’s Note: MDPI stays neu-
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (http://cre-
ativecommons.org/licenses/by/4.0/).
Int. J. Environ. Res. Public Health 2021, 18, 3010 2 of 26
rates are higher than the global average [8], with urban migration driven by rural poverty
and climate change [9]. Whilst global under-five mortality rates have decreased by 59%
since 1990 [10], morbidity related to early-life environmental exposures is increasing [11].
Direct and indirect effects of detrimental environmental exposures in childhood often per-
sist through adulthood [12–16], affecting people’s lifetime health and wellbeing and their
ability to contribute economically to their community and society. The future economic
potential of LDCs is thus directly linked to the health of their children.
Children’s environmental health (CEH) is the study of how environmental exposures
in early life influence health and development in childhood and the entire lifespan [17].
In two landmark reports, Preventing Disease through Healthy Environments and Healthy
Environments for Healthy People, the environment is defined as all the physical, chemi-
cal, and biological factors external to a person and all the related behaviours [18,19]. Such
definitions, whilst they recognise that social determinants are closely linked to vulnera-
bility to environment, may have over time contributed to a general perception of environ-
mental health (EH) as a discipline that focuses on modifiable physical, chemical, and bio-
logical environmental determinants of health within constructs such as water, sanitation
and hygiene (WASH), air pollution, chemical use, etc. In this paper, we use the terms “en-
vironment” or “environmental” to refer to the physical, chemical, and biological environ-
ment and refer to it as a domain whilst also considering social and economic domains and
their influences on children’s health outcomes in LDCs. A domain is used in the non-spe-
cialist sense to mean a sphere of activity or knowledge. For the purposes of this paper, EH
is therefore defined by the social, economic, and technological influences that link envi-
ronmental conditions to human health.
The science of systems thinking studies how component parts in a system connect,
react, and interact and helps us to see the forest as well as the trees. It increases our capa-
bility to recognise that cause and effect are non-linear, that the outcome of an event can
influence the cause, and that perceived problems can often be symptoms of other prob-
lems [20–22]. In systems thinking terminology, children’s health in LDCs and the environ-
mental, social, and economic factors that influence it are the CEH system, the product of
the interactions between a set of parts that influence and feed back into one another to
function as a whole. Whilst systems science has been used extensively in fields such as
environmental science and business, the application of its techniques has been limited in
EH [23–25]. Systems thinking can be contrasted with linear thinking, which assumes that
a cause leads to an effect with no feedbacks and that factors are independent. A major
shortcoming of linear thinking is that interventions can have unintended consequences;
for instance, the use of agri-supporting products like fertilisers and biocides leads to re-
sistant pests and weeds as well as excessive nutrient enrichment of receiving environ-
ments. This leads to a drastic decline of natural ecosystems, accumulations of toxins in
food chains, and pathogen resistance. This can be attributed to policies and practices not
considering the feedback loops in our system, which may change the outcomes of what
we try to achieve.
The CEH system in LDCs is complex and multifaceted. One common characteristic
of complex problems is that the root problem that is causing the symptoms is not always
apparent at first inspection, nor is the solution obvious once the problem has been defined
[26]. A systems thinking approach towards understanding the feedback loops may pro-
vide new insights and help to determine root causes.
A causal loop diagram (CLD) is a qualitative systems science tool that shows the re-
lationships between a set of variables (factors liable to change) operating in a system. It is
a powerful tool for identifying the non-linear feedback loops that operate in the system to
amplify or balance outcomes. It can help stakeholders to converge on a shared mental
model of a system, a set of beliefs, values, and assumptions that underly why things work
as they do [27]. This shared understanding about how something works and what is im-
portant can be used to enhance policy setting and decision-making. A CLD can also be the
Int. J. Environ. Res. Public Health 2021, 18, 3010 3 of 26
foundation for quantitative modelling techniques such as dynamic and agent-based mod-
elling [28].
Many studies have reported on children’s health and the environment, but it appears
that none have used a CLD (also known as an influence diagram) [28] to represent the big
picture, the underlying feedback mechanisms and potential key leverage points. The ob-
jectives of this paper are firstly to represent the major feedback loops that link children’s
health with the environmental, social, and economic domains in LDCs and secondly to
seek insights into potential leverage points and interventions.
In the Results section, the CEH system is represented by a CLD that contains four
interlinked sections; children’s health outcomes and the variables that influence them
grouped into environmental, social, and economic domains. These domains align with the
three pillars of sustainable development on which the UN Sustainable Development Goals
(SDGs) are based; environmental, social, and economic [29,30].
2. Methodology
We used the Institute for Health Metrics and Evaluation (IHME) Global Burden of
Disease results tool for 2019 [31] to identify the most significant estimated causes of child
mortality and morbidity for countries in the World Bank’s least developed countries cat-
egory and summarised the findings. We conducted a narrative review [32] of papers re-
trieved following a systematic search of current and past literature. The results were sum-
marised, in a table for children’s health outcomes and three tables for influencing factors
from environmental, social, and economic domains. These tables were used as the basis
for constructing the CLD. The most important loops, based on their relative contribution
to child morbidity and mortality, were then further investigated using the CLD and po-
tential leverage points for solutions identified in Results Section 3.5.
2.1. Literature Review
Information on CEH in LDCs is found in reports from studies focusing specifically
on LDCs, also in low- and middle-income countries (LMIC) and global health studies.
Reports and scientific papers published from January 2000 through December 2020 were
searched, screened, and reviewed according to their relevance, based on the primary and
secondary key terms.
Key search terms were developed to ensure that potentially relevant studies with
content relating to children’s health in LDCs were identified. First, searches were run us-
ing a composite primary search term “least developed country” OR “least developed
countries” OR “LDC” OR “LDCs” OR “low-income countries” OR “low- and middle-in-
come countries” OR “LMIC” with secondary search terms of “children’s health”, “envi-
ronmental health”, “children’s environmental health”, and “CEH”. The composite pri-
mary search term was next used with secondary search terms taken from the causes of
child mortality and morbidity identified in Table 1, e.g., “respiratory”. A further search
was run using the term “global children’s health”. Databases searched were PubMed,
Google Scholar, and the World Health Organization (WHO), United Nations International
Children’s Emergency Fund (UNICEF), United Nations Environment Programme
(UNEP), and World Bank publications databases. The same series of search terms were
then used in the Google search engine to identify additional grey literature. For all
sources, the first 100 results were checked, and a relevancy assessment approach [32] was
used. References from identified publications were also searched.
2.2. Causal Loop Diagram Principles
A CLD consists of variables and cause-effect links (also known as influencing links)
that connect to form causal loops, also known as feedback loops. Causal loops are either
reinforcing (vicious or virtuous circles) or balancing, where self-correction occurs within
Int. J. Environ. Res. Public Health 2021, 18, 3010 4 of 26
the system. Every causal loop tells a story that links cause and effect through feedback,
e.g.,
reinforcing—a dengue fever epidemic where the number of infected mosquitos
drives up the number of infected humans, which in turn increases the number of
infected mosquitos;
balancing—where sweating is initiated in response to heat to regulate human body
temperature.
The variables that represent the causal influences in the CLD are linked by directional
arrows, which represent causal associations. Associations are either:
reinforcing—denoted by a +, in which an increase in a variable causes an increase in
the variable it influences and vice versa, or e.g., internal air pollution increases res-
piratory disease;
opposing—denoted by a -, when an increase in a variable causes a decrease in the
variable it influences and vice versa, e.g., a clean water supply decreases WASH-re-
lated disease [28].
An even number of negative polarities in a loop denotes a reinforcing loop; an odd
number, a balancing loop. Hash marks on the connector arrows denote delays between
cause and effect. Variables in a CLD are either endogenous, both influencing and influ-
enced by other variables within the CLD, or exogenous, influencing but not being influ-
enced [22]. A further explanation of the notation of causal loop diagrams with examples
is given in Supplementary Material S1.
2.3. Table and Causal Loop Diagram Creation
Children’s health outcomes and their influencers as identified by the literature re-
view were all designated as variables for the CLD and grouped into four sections: chil-
dren’s health outcomes, environmental, social, and economic domains. A table was cre-
ated for each section, and each variable was mapped to:
variables that it directly influences;
variables that it is directly influenced by.
The mapping process identified two exogenous variables, remoteness and climate
change, which are discussed in Section 3.3.2.
These tables were used as the basis for constructing the CLD, which was created us-
ing Stella Architect software (iseesystems.com; Version 2.0.3). The CLD is a visual repre-
sentation of the mapping shown in the tables, with many loops, both reinforcing and bal-
ancing, identified. The CLD and tables were then reviewed and refined using an iterative
process. The most important loops in the CLD, based on their relative contribution to child
morbidity and mortality as identified in Table 1, were then further investigated and po-
tential leverage points for solutions were identified.
3. Results
3.1. Child Mortality and Morbidity in LDCs
Table 1 shows the disease groups that contribute the most to child mortality and life-
time morbidity as measured by deaths and years lived with disability (YLD) for diseases
originating in childhood. The health data are categorised by Level 2 ICD codes [31]. Child-
hood is defined as ages 0–14 (inclusive) in line with the definition used in the SDGs [33].
The most common cause of childhood death in LDCs, in common with global rank-
ings, is neonatal disorders, reflecting the high-risk 28-day post-natal period. Enteric dis-
ease followed by respiratory disease and neglected tropical diseases (NTDs) are the next
highest ranked. The largest contributor to lifetime morbidity is nutrition-related disease,
followed by skin and subcutaneous disease, which is prevalent in LDCs. The rankings are
similar for under-five mortality and morbidity, with the largest discrepancy between
Int. J. Environ. Res. Public Health 2021, 18, 3010 5 of 26
under-fives and under-fifteens in lifetime morbidity caused by mental disorders, which
are likely to be undiagnosed in under-fives.
Table 1. Child mortality and morbidity for all least developed countries (LDCs) [31].
Child
Mortality Disease
Group
U15
1
Deaths
Rank
U15
Deaths
%
U5
2
Deaths
Rank
U5
Deaths
%
Child Morbidity Dis-
ease Group
U15
YLD
3
Rank
U15
YLD %
U5
YLD
Rank
U5 YLD
%
Neonatal disorders
1
28.7
1
31.6
Nutritional
1
26.6
1
35.0
Enteric disease 2 15.1 3 14.5
Skin/subcutaneous
diseases 2 9.9 4 8.4
Respiratory disease
3
14.7
2
15.2
Other NCDs
3
9.8
2
12.0
NTDs
4
and malaria
4
10.2
4
10.1
Mental disorders
4
9.6
8
3.2
Other infectious diseases
5
9.0
5
8.7
NTDs and malaria
5
9.0
5
8.1
Other NCDs
6
8.3
6
8.6
Respiratory disease
6
6.7
7
6.5
HIV/AIDS
7
3.0
7
2.7
Neonatal disorders
7
6.3
3
9.3
Unintentional inju-
ries/transport injuries 8 4.2 9 2.9 Neurological disorders
8 5.8 9 2.9
Nutritional
9
2.4
8
2.5
Enteric disease
9
4.3
6
6.7
Neoplasms
10
1.1
10
0.7
Sense organ diseases
10
3.0
-
-
Other infectious dis-
eases - - 10 2.8
1
children aged under 15
2
children aged under 5
3
Years Lived with Disability
4
Neglected Tropical Diseases. Causes of
mortality/morbidity from Level 2 ICD codes [31]: Morbidity is measured in years lived with disability. Neonatal includes
maternal/neonatal disorders. Other infectious diseases include meningitis, measles. Other NCDs include congenital birth
defects and sudden infant death syndrome. Enteric diseases include diarrhoea and typhoid. NTDs include dengue fever,
yaws, trachoma, helminths including hookworm, ascariasis, and trichuriasis. Skin diseases include scabies and fungal skin
diseases. Respiratory includes upper and lower respiratory infections, tuberculosis, and chronic respiratory disease. Men-
tal disorders include intellectual disability. Italics: denote disease groups excluded from further analysis.
3.2. Exclusions from CLD Scope
Table 1 shows that disease groups for HIV/AIDS, sense organ diseases, and neo-
plasms contributed 3% or less to only one of mortality or morbidity. They were excluded
from the scope of the CLD because of their relatively small contribution to child health
outcomes relative to other disease groups. Disease groups for other NCDs and mental
disorders were excluded from further analysis because the literature review did not yield
sufficient evidence for a relationship between the disease and the environmental, social,
and economic domains to warrant their inclusion in the CLD.
3.3. Influencing Linkages
Tables 2–5 show the linkages of variables with their influencers and influences. The
tables show the cause and health effect pathways (e.g., internal air pollution leads to child-
hood respiratory disease) and also show links between variables such as economic devel-
opment, poverty, infrastructure, clean water access, morbidity, and poverty, which are
then represented visually in the CLD shown in Figure 1. Table notation and logic are as
follows:
Variables are described in the shortest form possible, e.g., vehicles means number of
vehicles, family size means number of people in the biological family, and clean wa-
ter means the availability of a clean water supply;
Polarities of links are shown, e.g., open defaecation increases WASH-related disease;
Each endogenous variable influences other variables in the tables and is in turn in-
fluenced by other variables. As an example, WASH-related disease is the variable in
the first row of Table 2. It appears as an influencer (reinforcing or positive) of malnu-
trition/stunting in the second row of Table 2 and is shown as being influenced by
open defaecation in Table 3;
Int. J. Environ. Res. Public Health 2021, 18, 3010 6 of 26
The relationship between the variable and the links appearing in the “influenced by”
column is summarised in the table text with supporting references. Relationships for
items in the influences column are summarised when they appear in the variable
column, usually in another table;
Only direct influencers are shown, e.g., the influence of improved sanitation on
WASH-related disease is not shown in Table 2, as improved sanitation is categorised
as an influencer of open defaecation and can be found in Table 3.
3.3.1. Health Outcomes
Children’s health outcomes in LDCs are grouped in Table 2 according to the main
environmental influences; thus, the WASH-related disease category includes enteric dis-
ease, skin disease, and parasitic diseases. Influences that generally improve child health
outcomes, e.g., access to health services, are linked to a consolidated child morbidity/mor-
tality outcome. Adult morbidity and premature mortality are also influenced by child
morbidity.
Int. J. Environ. Res. Public Health 2021, 18, 3010 7 of 26
Table 2. Children’s health outcome variables with influencing links.
Variable Influenced by +/− Influences +/−
Source
Water, sanitation and hygiene (WASH)-related disease—includes enteric diseases of which diarrhoea
is the most
prevalent, parasitic NTDs including hookworm and schistosomiasis, all of which have strong causal links with
polluted water, open defaecation, and inadequate hygiene. Helminth infections also increase the risk of diarrhoeal
disease. Skin diseases, e.g., scabies, yaws are strongly associated with inadequate hygiene measures and over-
crowding.
Clean water
Open defaecation
Hygiene
Overcrowding
−
+
−
+
Malnutrition/stunting
Child morbidity/mortality
+
+ [3,18,34–42]
Malnutrition/stunting—is the biggest contributor to YLD in LDCs. The effects of a lack of nutritious food are
compounded by acute and chronic diarrhoeal disease and helminth infections. A lack of micronutrients in child-
hood can also predispose to obesity in later life, increasing the risk of NCDs.
Adequate child nutrition
WASH-related disease
−
+
Child morbidity/mortality
Child education
+
−
+
[3,18,43–45]
Respiratory disease—is caused by both internal air pollution (IAP) and ambient air pollution (
AAP) exposure in
LDCs. Rural exposure is primarily through IAP whilst urban/peri-urban exposure is to both IAP
1
and AAP
2
.
Overcrowding increases transmission of infectious respiratory diseases such as TB.
Internal air pollution (IAP)
Ambient air pollution (AAP)
Overcrowding
+
+
+
Child morbidity/mortality + [3,18,34,46–
49]
Vector-borne disease—such as malaria,
which is the fourth largest contributor to child mortality. Dengue fever is
a growing threat in urban areas. Other vector-borne diseases include chikungunya fever, Zika virus, Chagas dis-
ease, and leishmaniasis. Infection cycles are perpetuated by mosquitos as well as flies, which use faeces in their
breeding cycles.
Vectors + Child morbidity/mortality + [3,4,18,50,51]
Childhood injury including traffic injuries—is a risk of unsafe surroundings. Whilst injury is perceived mainly
as a problem of an unsafe built environment, remoteness from health resources worsens the prognosis after in-
jury.
Overcrowding
Unsafe surroundings
+
+ Child morbidity/mortality + [52–54]
Child morbidity/mortality—is increased by inadequate access to health
services. In all LDCs, shortages of health
personnel, infrastructure, pharmaceutical supplies, and medical equipment are a limitation; in rural and remote
areas, these are exacerbated by the need to travel long distances with minimal transport infrastructure. Maternal
poor health leads to suboptimal birth outcomes such as fetal growth restriction, low birthweight,
and suboptimal
breastfeeding.
Disease group mortality/morbidity
Health services
Maternal health
+
−
−
Adult morbidity/premature mortality + [1,7]
Maternal health—Female childhood malnutrition, insufficient nutrition for pregnant women and mothers in
some LDCs, large families, and inability to plan and space families all contribute to poor maternal health.
Malnutrition/stunting
Family size
−
− Child morbidity/mortality + [45,55]
Adult morbidity/premature mortality—is a direct result of chronic adult illness with its roots in childhood, re-
ducing lifespan (average 63 in LDCs versus global lifespan of 73) and lifetime economic contribution in LDCs. Child morbidity + Poverty + [56–58]
Neonatal disorders—lead to neonatal deaths and, for survivors, increased mortality risk and morbidity such as
stunting, with subsequent adult mortality and morbidity implications.
Maternal health
IAP
−
+ Child morbidity/premature mortality + [18,45,55]
1
Internal Air Pollution
2
Ambient Air Pollution.
Int. J. Environ. Res. Public Health 2021, 18, 3010 8 of 26
3.3.2. Environmental Domain
The environment in LDCs is heterogeneous, with wide variation in geography, cli-
mate, and population density. LDCs contain both urban population concentrations and
remote rural settlements. Endogenous variables in this domain that influence children’s
health in LDCs are shown in Table 3. Two exogenous variables were identified in the en-
vironment: climate change and remoteness. They can be seen in the “influenced by” col-
umn of Table 3. In practical terms, exogenous variables cannot be influenced by other
variables in the model. This means that in the context of this model, our focus for inter-
ventions should lie elsewhere.
LDCs contribute only 0.5% of the annual carbon dioxide emissions that are driving
climate change, producing 0.17 million kt of a global annual total of 34 m kt [57].
Climate change impacts are specific to individual countries and regions, but all LDCs
are vulnerable to the effects of climate change, manifested in rising temperatures,
changing landscapes, and increased magnitude and frequency of natural disasters
[52]. Climate change has thus been treated as an exogenous variable, influencing but
not being influenced by the other variables in the CLD [22].
Remoteness is part of the economic vulnerability index for LDCs, calculated as an
indicator of distance from world markets [59], and is a structural obstacle to the de-
velopment of adequate infrastructure. Whilst an LDC can improve its infrastructure
and services, it cannot change its geographical remoteness, which is thus an exoge-
nous variable.
Int. J. Environ. Res. Public Health 2021, 18, 3010 9 of 26
Table 3. Variables in environmental domain with influencing links.
Variable
Influenced by
+/−
Influences
+/−
Source
Improved water supply—delivered through water services infrastructure. Poverty prevents community-based improvement of
water supplies when not provided by the state. Disasters affect water supply by damaging infrastructure and by flooding,
which
leads to contamination of both natural and improved water supplies.
Infrastructure
Poverty
Disasters
+
−
−
Clean water
Hygiene
+
+ [3,18,52,54,60]
Clean water—In urban/peri-urban areas, polluted watercourses are the only water source for many. Clean water is depleted
through open defaecation and pollution of natural resources through activities such as logging or mining. In rural areas, clean
water may be available from natural sources such as springs but may be too far from dwellings to be easily accessible.
Improved water supply
Open defaecation
Natural resource depletion/pollution
Disasters
+
−
−
−
WASH-related disease − [3,18,52,54,60]
Improved sanitation—is generally dependent on an improved water supply and adequate investment in infrastructure to build
and maintain it. Poverty prevents community-based sanitation improvements. Overcrowding in urban/peri-urban settings acts
against sufficient improved sanitation.
Improved water supply
Infrastructure
Poverty
Overcrowding
+
+
−
−
Open defaecation
− [3,18,41,54,61,62]
Open defaecation—is the norm for up to 70% of people in some LDCs, both in rural and peri-urban settings. In addition to
providing access to adequate sanitation, behaviour change, influenced by education, is required to effect optimal use of sani
tation
and to overcome cultural norms and/or taboos.
Improved sanitation
Cultural norms
−
+
Clean water
Vectors
WASH-related disease
−
+
+
[3,18,41,54,61,62]
Vectors—open waste dumps, pooled surface waters, and open faeces in LDCs attract insects and rodents, especially in over-
crowded settings. Climate change-related temperature rise increases vector breeding sites.
Open defaecation
Household waste management
Climate change *
+
−
+
Vector-borne disease + [51,63,64]
Overcrowding—mostly in urban/peri-urban areas. Unsafe surroundings are often associated with a poor quality built environ-
ment and slum dwellings with insecure structures. Culture in some LDCs requires extended family to house migrating relatives,
which increases overcrowding.
Poverty
Urban migration
Cultural norms
+
+
+
Injury
Respiratory disease
Skin diseases
+
+
+
[18,39,49]
Internal air pollution (IAP)—caused by biomass cooking and lighting fuel, including wood and kerosene. Second-
hand smoking
and AAP that enters the home also contribute to IAP.
Biomass cooking
Second-hand smoke
AAP
+
+
+
Respiratory disease
Ambient air pollution
+
+ [3,18,65]
Second-hand smoke—smoking is an established part of the culture in many LDCs.
Cultural norms
+
IAP
+
[3,18,65]
Ambient air pollution (AAP) and vehicle pollution—in urban environments with concentrated populations, open burning of
waste as well as vehicle traffic contribute to AAP. IAP becomes AAP. LDCs rarely have effective pollution controls and are likely
to import ageing vehicles that no longer comply with richer countries’ stricter emission standards. Poorly surfaced roads create
dust pollution. Rising temperatures due to climate change increase AAP.
IAP
Outdoor waste burning
Vehicle pollution
Climate change *
+
+
+
+
Respiratory disease + [18,34,65,66]
Household waste management—is often inadequate in LDCs, particularly in urban areas, due to lack of infrastructure and fund-
ing, leading to household and other waste burning. Infrastructure + Vectors
Outdoor waste burning
−
− [67]
Natural resource depletion and pollution—is caused by increased population pressure, overexploiting resources beyond their
sustainable limits (e.g., harvesting wood for fuel), and poor waste management. Pollution compromises natural water supplies.
Population
Cooking with biomass fuel
+
−
Deforestation/desertification
Adequate child nutrition
Clean water
+
−
−
[68,69]
Deforestation/desertification—is caused by natural resource depletion and accelerated by climate change.
Climate change *
+
[68,69]
Natural disasters—are increasing in frequency and magnitude. Many LDCs are in disaster-
prone areas and do not have resources
to mitigate the effects of natural disasters due to both inadequate infrastructure and inadequate disaster response resources Climate change * +
Injury
Vectors
Clean water
Urban migration
+
+
−
+
[70,71]
* Exogenous variable.
Int. J. Environ. Res. Public Health 2021, 18, 3010 10 of 26
3.3.3. Social Domain
The social domain encompasses the social relationships and cultural constructs
within which people function and interact. Components of the social domain include cul-
tural and religious beliefs and practices, family structures, social and power relations, and
inequalities. Social domain components function at multiple scales: households, extended
kin networks, communities, and cities. Social domains are dynamic and change over time
[72]. Variables in the social domain that influence children’s health in LDCs are family
size, education levels, and culture, as shown in Table 4.
The term “cultural norms” has been used in the tables to denote the set of beliefs and
practices that influence many aspects of life in LDCs. Some examples are food preparation
and cooking practices, acceptability of smoking, and views on the optimum number of
children for women. Cultural norms may support or be detrimental to children’s health.
They will change over time, driven by influences such as education. In Table 3, cultural
norms are represented as a force that resists and slows down positive change. In general,
higher levels of adult education reduce the strength of detrimental habits and taboos and
improve cultivating of health-supporting behaviours, for instance, by optimising sanita-
tion and hygiene practices if water infrastructure and services are available. Without ed-
ucation, low adoption or declines in usage occur as communities revert to their traditions
of open defaecation [23]. Cultural norms can also be seen influencing variables in both the
environmental and economic domains shown in Tables 3 and 5.
Int. J. Environ. Res. Public Health 2021, 18, 3010 11 of 26
Table 4. Variables in social domain with influencing links.
Variable Influenced by +/−
Influences +/− Source
Child education—is reduced by poor health and malnutrition and exposure to biomass fuel,
which affect cognitive ability. It is
impaired by poverty and by the expectations and priorities of the child’s carers, who may prioritise work or care of younger
children over a child’s education, particularly for females. In rural areas, a lack of electricity and low access to schools reduce
study opportunities.
Poverty
Adult education
Child morbidity/malnutrition
Electricity
−
+
−
+
Adult educational attainment + [45,73–
75]
Adult educational attainment—adult education levels are improved as better educated children grow into adults. Child education +
Malnutrition/stunting
Family size
Hygiene
−
−
+
[59,76]
Cultural norms—cultural norms and behaviours that affect many influencers of children’s environmental health
(
CEH) are
influenced by education levels. Examples are the inverse relationship between adult female education level and family size and
cooking with biomass fuel,
an established tradition in most LDCs. Similarly, open defaecation
in rural settlements is associated
with privacy and comfort in many LDCs, and a move to improved sanitation can only be made with
both infrastructure in place
and a change of cultural norms.
Adult educational attainment −
Family size
Adequate child nutrition
Open defaecation
Cooking with biomass fuel
Second-hand smoke
Overcrowding/unsafe surroundings
−
−
+
+
+
+
[76,77]
Family planning availability—LDCs have both low contraceptive use (39%) and a high unmet need for family planning (22%).
Poverty is associated with a lack of access to modern family planning services, whereas l
iving in urban areas is associated with
better access.
Poverty
Urban migration
+
− Family size − [78]
Family size—cultural norms and expectations, reinforced by the requirement for children to support their parents, keep birth
rates high, particularly in rural areas where the cost of raising a child is low and access to modern family planning services is
limited or non-existent.
Family planning availability
Cultural norms
−
+
Population
Adequate child nutrition
+
− [78,79]
Hygiene—handwashing and hygienic food preparation require an improved water supply close to the home. The definition of
a basic water supply is up to 30 min round trip, which is not conducive to hygienic habits. Adult education, both general and
WASH specific, is a prerequisite for the establishment of hygiene in families/communities.
Improved water supply
Soap
Adult educational attainment
+
+
+
WASH-related disease − [60]
Int. J. Environ. Res. Public Health 2021, 18, 3010 12 of 26
3.3.4. Economic Domain
Children’s health is directly affected by their economic status, with clear evidence of
influencing links between economic status and EH assets such as clean water, sanitation,
clean fuel, and electricity [80,81]. Table 5 shows the variables in the economic domain that
influence children’s health in LDCs, including the availability of health services and urban
migration driven by rural poverty.
Int. J. Environ. Res. Public Health 2021, 18, 3010 13 of 26
Table 5. Variables in economic domain with influencing links.
Variable Influenced by +/−
Influences +/−
Source
Poverty—
low economic development leads to limited individual economic opportunity in LDCs. This
is reinforced by low levels of education and large family sizes,
which drain resources and increase the
family income needed to live above the poverty line. Illness and premature death, with their origins
in childhood morbidity, deprive families of their breadwinners or require discretionary but often non-
existent health expenditure. Rural poverty drives urban migration, which also reinfo
rces poverty due
to higher food and living costs and inadequate infrastructure, particularly in informal settlements.
Economic development
Adult morbidity/premature mortality
Adult educational attainment
Urban migration
Family size
−
+
−
+
−
Urban migration/overcrowding
Adequate child nutrition
Lack of access to clean fuel
Improved sanitation/soap
+
−
+
−
[54,82,83]
Economic development—influenced by the remoteness that characterises LDCs and by adult health
outcomes. There are many other influences outside the scope of this study that could form an eco-
nomics-focused causal loop diagram (CLD).
Remoteness *
Adult morbidity/premature mortality
−
−
Poverty
Infrastructure/electricity
−
+ [56,58,59]
Adequate child nutrition—has different influencers in urban and rural settings. Living below the
poverty line does not in itself deny children access to sufficient
nutrition as long as natural resources
can support food production or hunting/fishing. Large family sizes make consistent availability of
food more difficult, particularly in urban areas where a subsistence lifestyle cannot be practised. In
some LDCs, cultural practices mean that food priority is not given to children.
Poverty
Family size
Natural resource depletion/pollution
Adult education
Cultural norms
−
−
−
+
−
Malnutrition/stunting − [44,45,77,83,84]
Soap—food takes priority over soap when there are limited financial resources. Poverty − Hygiene + [18]
Urban migration—LDCs are less urbanised than their more developed counterparts, with an average
of 70% of their populations living in rural settings, but their urbanisation
rates are higher than global
averages as rural poverty and climate change drives families to cities, often to live in urban or peri-
urban informal settlements with poor infrastructure.
Poverty
Climate change *
+
+
Overcrowding
Poverty
Family planning availability
Family size
+
+
+
−
[9,79,85]
Infrastructure/electricity—which includes transport, WASH, and health facilities, is caused by low
funding and compounded by the remoteness of many LDCs. Infrastructure is easily damaged by dis-
asters, which are increasing as the impacts of climate change worsen. Population growth does not af-
fect the absolute level of infrastructure and electricity investment but does influence its per capita
availability. Small-scale solar electricity is still economically out of reach of many people in LDCs.
Economic development
Remoteness *
Population growth
+
−
−
Health services/clean fuel/improved water
Household waste management
+
+ [70,86]
Health services—many rural children in LDCs can only reach
health facilities on a planned trip, if at
all, leaving them vulnerable in emergency situations. Rural health facilities are short of qualified per-
sonnel, essential supplies, and medicines and may have no electricity, compromising the cold chain.
Urban resources are stretched by growing populations. Items such as mosquito nets are in short sup-
ply.
Infrastructure
Remoteness *
+
− Child morbidity/mortality − [7,81]
Clean fuel—rural poor cannot afford clean alternatives to biomass fuels such as wood, which they
can collect for free but at great personal costs in time and distance travelled. Alternatives are unavail-
able if there is no electricity or distribution infrastructure.
Poverty
Infrastructure/electricity
−
+ Cooking with biomass fuel − [6,7]
Cooking with biomass fuel—cultural as well as financial barriers must also be overcome to change
behaviours to move towards clean cooking methods.
Clean fuel
Cultural norms
−
+ IAP + [18,87]
Vehicles
—as poverty reduces, vehicle numbers, particularly in urban areas, grow. Poverty − Vehicle emissions + [4,7,88]
* Exogenous variable.
Int. J. Environ. Res. Public Health 2021, 18, 3010 14 of 26
3.4. Overall Causal Loop Diagram
The CLD shown in Figure 1 represents the non-linear causal relationships in the chil-
dren’s health system in LDCs based on the relationships identified in the literature review.
It has been structured into four sections: health (grey), environment (green), social domain
(pink), and economic domain (blue). Where variables can be categorised in more than one
section, e.g., overcrowding, which could be viewed as both an environment and an eco-
nomic variable, the colours overlap. Many causal loops can be identified, reflecting the
complexity of the system. All of the loops that include children’s health outcomes include
variables in at least two other sections, showing their interconnectedness. The majority of
the many loops in this diagram show reinforcing cycles, causing accelerated growth or
decline. Interventions discussed later can be used to change the direction of these causal
loops.
The balancing and reinforcing loops considered to be most important are shown on
the CLD, but its complexity makes it hard to trace the connections, so they are split out
and discussed in Section 3.4. Connections that are discussed are shown in colour or in
black, e.g., blue from clean water to WASH-related disease. All others are shown in dark
grey.
The term cultural norms as described in Section 3.3.3 describes a very broad range of
human behaviours and customs. The table entries and the CLD show links from cultural
norms to variables not only in the social domain but also to variables in the environmental
and the economic domain. The CLD represents the links from cultural norms to variables
in the environmental, social, and economic domains with dotted connectors to recognise
that they are generalised and may not apply in all LDCs.
children’s health outcomes
environment
social domain
economic domain
positive effect
-> +
negative effect
-> -
influenced by culture
- - - - >
delayed effect
||
copy of variable
family size
exogenous variable
remoteness
-
+
+
+
+
+
-
+
+
+
+
+
+
-
-
+
+
-
+
-
-
-
+
+
+
+
++
+
+
-
+
+
-
+
+
+
-
+
+
+
+
-
+
+
-
-
+
-
+
+
-
-
-
+
-
+
-
+
+
+
+
+
-
+
-
-
+
+
-
-
+
+
+
-
+
+
-
+
-
-
+
+
-
-
+
-
+
+
+-
-
-
+
-+
-
-
+
+
-
+
-
+
+
+
-
-
+
adult morbidity/premature
mortality
malnutrition/
stunting
vector borne
disease
WASH related
disease
injuries respiratory
disease
improved
water supply
clean
water
Poverty
vectors
clean fuel
hygiene
household
waste management
adequate
child
nutrition
cooking with
biomass fuel
internal air pollution
overcrowding/unsafe
surroundings
child mortality
family size
second hand
smoke
ambient air pollution
deforestation/
desertification
population
climate
change
health services
natural resource
depletion/pollution
health services
open
defaecation
vehicles
improved
sanitation
urban
migration
cultural
norms
maternal health
adult
educational
attainment
child
educational
attainment
disasters
family planning
availability
vehicle
pollution
outdoor
waste burning
child
morbidity
soap
economic
development
neonatal
disorders
infrastructure /
electricity
remoteness
disasters
internal air pollution
family size
climate
change
climate
change
infrastructure /
electricity
infrastructure /
electricity
R1
R2
R3
R5
R6
R7
R8 R9
B1
R10
R11
R12
R13
R14
R15
B2
R4
Figure 1. Causal loop diagram for children’s environmental health system.
3.5. Analysis of Causal Loops
Areas of the CLD for more detailed analysis were chosen by referencing Table 1 and
selecting the loops that include the disease groups, which cause the largest percentages of
child mortality and lifelong morbidity. A loop focusing on the effects of population
growth on children’s health was also added after linkages were noted in Sections 3.5.1 and
Int. J. Environ. Res. Public Health 2021, 18, 3010 15 of 26
3.5.5. Reinforcing and balancing feedback loops are highlighted and discussed. The loops
can all be traced in Figure 1, but some positions have been rearranged for ease of reading.
3.5.1. Nutritional Deficiency Loops
The most significant cause of lifetime morbidity from diseases contracted in child-
hood is nutritional deficiencies, primarily including protein/energy malnutrition, with
26.6% of all morbidity caused by this disease group [31]. Loop R1, shown in red in Figure
2, shows the reinforcing cycle of poverty, which reduces a family’s ability to provide ad-
equate child nutrition. A reduction in adequate nutrition leads, with a cumulative and
delayed effect, to malnutrition and/or stunting, which in its turn reinforces child morbid-
ity [2,18,43,44]. As malnourished children develop into adults, the disease burden estab-
lished in childhood remains with them, leading to adult morbidity and decreased life ex-
pectancy. This decreases the adult’s capacity to contribute economically to the family, re-
inforcing poverty and completing the loop. Note that this loop contains two negative and
three positive polarities and is a reinforcing loop because the negatives counteract each
other. Adequate child nutrition is also diminished by depletion of natural resources, par-
ticularly in rural settings where foraging or hunting provides food sources.
Figure 2. Loops influencing nutritional disease.
Another important loop exhibiting reinforcing behaviour is R2 (purple/red), which
shows the effect of family size on malnutrition [45]. The new loop connectors are shown
in purple; poverty reduces access to modern family planning, increasing family size,
which decreases the likelihood of adequate child nutrition; the loop is then completed by
tracing the red arrows around the common linkage through malnutrition/stunting > child
morbidity > adult morbidity > poverty.
Loop R3 (dark green/red) shows how malnutrition/stunting reduces children’s edu-
cation through impaired cognitive ability and school absences [73] which, as children
grow into adults, has a detrimental effect on levels of adult education. Increasing adult
education supports adequate child nutrition and vice versa. The reinforcing loop is com-
pleted by the link back to malnutrition/stunting shown by the red arrow. Loop R4
(green/dark green) directly connects adult educational attainment to children’s educa-
tional attainment. Better educated adults are more likely to value and prioritise the edu-
cation of their children. If child education levels increase, a virtuous circle of education
level improvement is created; if they decrease, the reverse happens. Loop R5 (starting with
orange dotted connector) shows how cultural norms, which are generally challenged by
Int. J. Environ. Res. Public Health 2021, 18, 3010 16 of 26
increasing levels of education, reinforce both the expectation of and desire for larger fam-
ilies [7]. The loop continues with a connection to adequate child nutrition (purple) and
can be traced through malnutrition/stunting to child education and adult education. In
some LDCs, a cultural norm is a lack of food priority for children [77]; this too is chal-
lenged by education. Potential opportunities to reverse negative reinforcing loops are
child nutrition and education interventions.
3.5.2. WASH-Related Disease Loops
Enteric disease, including diarrhoeal disease and typhoid, is estimated to be 95% at-
tributable to inadequate WASH in LDCs [31,89]. Skin diseases, responsible for almost no
child mortality but an estimated 9.9% of lifetime morbidity [31], are influenced by inade-
quate WASH and overcrowding. Loop R6 (blue/red) in Figure 3 shows the reinforcing
loop connecting water access, clean water, and WASH-related disease, which includes en-
teric disease and skin diseases [3,18,34–42]. If an LDC’s government or external organisa-
tions do not provide a service, people and communities living in poverty, whether in in-
formal settlements or a rural setting, do not have the resources to improve their own water
supplies. Sufficient accessible clean water reduces all WASH-related disease. Improved
water access is also a prerequisite for most improved sanitation services [60–62]. Loop R7
(brown joining red) shows the dependency of improved sanitation on improved water
supplies. Improved sanitation is a prerequisite for the reduction or elimination of open
defaecation, but poverty reduces the ability of communities to maintain sanitation facili-
ties, and in some LDCs, there are powerful cultural traditions that impede a transition
away from open defaecation. Education is required to support understanding of the ben-
efits, as illustrated by the dotted lines linking adult education with cultural norms and
open defaecation. Exposed faeces support the spread of pathogens, which increase
WASH-related disease.
Figure 3. Loops influencing WASH-related disease.
Loop R8 (pink joining red) shows how poverty reduces access to soap through a lack
of resources to purchase it [18]. Adequate hygiene, including hand hygiene after defaeca-
tion and before food preparation, requires soap but also improved water access as in most
cases, a natural clean water source is not close enough to the household to provide ade-
quate water for hygiene purposes. In common with the loops shown in Figure 2, enteric
disease increases child morbidity as repeated acute illness leads to chronic disease and
later, either directly or through increasing malnutrition and stunting, to adult morbidity.
Leverage points to reverse negative reinforcing loops are water and sanitation interven-
tions combined with education, both general and specific.
Int. J. Environ. Res. Public Health 2021, 18, 3010 17 of 26
3.5.3. Air Pollution-Related Disease Loops
Figure 4 shows loop R9 (dark red joining red), a reinforcing loop linking the use of
biomass fuel and respiratory disease, responsible for 14.7% of childhood mortality and
6.7% of morbidity in LDCs It illustrates how poverty reduces a household’s ability to ac-
quire clean fuel and equipment with which to use it [7], forcing households to create in-
ternal air pollution through the use of biomass fuel, which is often available with no fi-
nancial outlay to rural families (although it depletes natural resources, which in itself has
consequences for the health of the environment and for the ability of the environment to
provide for children’s nutrition). The respiratory disease burden of both mortality and
morbidity with its roots in childhood is possibly one of the most difficult to address; long
lead times mean that policymakers do not necessarily relate adult health consequences to
lung damage sustained in childhood. A move away from biomass fuel requires not only
clean fuel availability and affordability but also a willingness to embrace new ways of
cooking, supported by an understanding of the health implications [87]. As with WASH-
related disease loops, adult morbidity and premature mortality reinforce poverty.
Figure 4. Loops influencing air pollution-related disease.
Loop B1 (dark blue joining dark red then red) describes a balancing loop between
poverty and vehicle pollution. If poverty increases, the number of vehicles reduces. The
converse of this is that reducing poverty increases vehicle pollution and increases child
and adult respiratory disease [88]. In this case, the positive health effects of poverty re-
duction are offset by a negative health effect of unsustainable development. The assump-
tion here is that vehicles produce pollution; in LDC, vehicles are likely to be old and heav-
ily polluting and are often exported from higher-income countries whose stricter regula-
tions they no longer meet. Leverage points are air pollution reduction and clean cooking
interventions combined with education.
3.5.4. Vector-Related Disease and Skin Disease Loops
Vector-related disease accounts for an estimated 10% of child mortality and 9% of
lifetime morbidity in LDCs [31]. Dengue fever is a growing urban problem whilst malaria
still claims the greatest number of children’s lives in both rural and urban settings, partic-
ularly in African LDCs [18,51]. Loop R10 in Figure 5 (dark purple joining red) shows links
between poverty, urban migration, overcrowding, numbers of vectors due to inadequate
household waste management services, and vector-related disease. Overcrowding, par-
ticularly in informal settlements, is influenced in many LDCs by cultural obligations to
house extended family and also contributes to skin disease transmission, WASH-related
Int. J. Environ. Res. Public Health 2021, 18, 3010 18 of 26
disease, and the spread of infectious diseases [54]. Loop R11 (brown joining dark purple
and red) shows how poverty negatively influences improved sanitation, linking to open
defaecation and increased numbers of vectors. Leverage points are in improving the built
environment, waste management services and reduction of vector habitat as well as the
WASH-related leverage points discussed earlier.
Figure 5. Loops influencing vector-related disease.
3.5.5. Neonatal Disease Loops
Loop R12 in Figure 6 is depicted in purple, linking poverty to family planning avail-
ability, family size, maternal health, neonatal disorders, and child morbidity. The loop
continues in red through to adult morbidity and poverty. Neonatal disorders cause 28.7%
of child mortality [31], with an estimated 20% attributed to the environment [89], but this
percentage does not include the influence of maternal health and nutrition and access to
health services, so one could argue that the total environmental attribution should be
higher. CEH discussions do not generally include the influence of family size and mater-
nal health on neonatal disorders or children’s health in general [45]; we believe that the
CLD makes a case for doing so. A shorter reinforcing loop R13 also associates poverty
with lower access to family planning availability, thus supporting increased family sizes.
Int. J. Environ. Res. Public Health 2021, 18, 3010 19 of 26
Figure 6. Loops influencing neonatal disease.
Balancing loop B2 (dark purple/purple) depicts poverty driving migration from rural
to urban or peri-urban areas. Access to modern family planning services is improved by
moving to an urban area, with large variations between LDCs. This access acts to reduce
family sizes, which helps to lift families out of poverty. However, urban migration in
LDCs is increasingly forced by climate change and population growth, which leaves fam-
ilies unable to survive in rural areas, and another small reinforcing loop, R14, linking ur-
ban migration to increasing poverty, shows poverty and child malnutrition increasing in
urban/peri-urban areas [83], counteracting the balancing effects of smaller families.
3.5.6. Population Growth Loops
Figure 7 shows that loop R15, starting in purple and moving through black and red,
links household poverty with lower family planning availability and population growth.
A larger population puts more demands on infrastructure, reducing the resources availa-
ble per capita, whether for roads, improved water supplies, or health services. Improved
health services reduce child morbidity and vice versa. Child morbidity leads to adult mor-
bidity, which reinforces poverty, completing the loop. This vicious circle, which connects
the situation of individual families to broad population growth and its socioeconomic im-
pacts, and the situation in LDCs to the broader economic issues, is not, as far as we are
aware, discussed in the CEH context even though lower per capita resources clearly have
the potential to negatively affect children’s health outcomes.
Int. J. Environ. Res. Public Health 2021, 18, 3010 20 of 26
Figure 7. Loop influencing population growth.
3.6. Leverage Points
A key environmental determinant of health is sufficient and available clean water
supplied close to the household to deliver not only safe potable water but also water for
hygiene and sanitation purposes. This is well known, but Figure 2 reinforces the need for
this leverage point to be supported by education, both general and specific, to support the
uptake of water and sanitation interventions, to support cultural change where needed to
overcome traditions and taboos that work against uptake, and to support the maintenance
of WASH infrastructure. Reductions in both WASH-related and vector-borne diseases are
the potential results. An estimated 60% of WASH benefits come from the elimination of
open defaecation in communities [60], but this can be hard to achieve because of the many
prerequisites.
Air pollution, both internal air pollution (IAP) and ambient air pollution (AAP), is an
environmental determinant of health in LDCs, and interventions that support the uptake
of clean cooking are leverage points for children’s lifetime health but need to be supported
by education and community engagement. The relative cost of fuel is important; in rural
areas, as long as biomass fuel is free, interventions that include education and a supply of
clean cookstoves are unlikely to deliver improvements to households living in poverty
without ongoing financial support. Leverage points for the reduction of AAP, particularly
in urban areas, are vehicle emission reduction and regulation and household waste man-
agement interventions to reduce burning.
The growth of urban and peri-urban informal settlements, reinforced by poverty-re-
lated and climate change-driven migration, threatens children’s health through over-
crowding and lack of infrastructure. Interventions that improve services, including waste
management to informal settlements, are leverage points for children’s health.
All the major causal loops that include child morbidity connect through to adult mor-
bidity and reinforcement of poverty. It follows that poverty reduction in LDCs will im-
prove health outcomes unless it is countered by negative health effects such as increases
in air pollution. However, continuing population growth, which effectively reduces infra-
structure and health services available at an individual level and puts pressure on natural
resources, reinforces poverty. Leverage points to address population growth are interven-
tions in education and family planning. One successful intervention in LDCs has been
immunisation programmes; these have delivered significant reductions in child mortality
globally but have only increased population growth in LDCs.
Family planning availability has not been considered in the CEH field as one of the
tools for improving children’s health in LDCs, but the impacts on maternal health,
Int. J. Environ. Res. Public Health 2021, 18, 3010 21 of 26
neonatal outcomes, children’s nutritional health, and access to health services are insight
from this CLD and support a case for its inclusion as a leverage point.
4. Discussion
4.1. Application of the CLD
The CLD demonstrated in this paper is a system with many reinforcing causal loops
that explain current behaviours of the CEH system through the interaction of a selected
suite of endogenous variables [28]. Building a CLD based primarily on literature-derived
variables is unusual; a more common approach is to collaboratively build or modify a
CLD from participatory discussions with stakeholders [90]. This can either be done in
community settings, with groups of policymakers/influencers or both, and can be a pow-
erful tool for effective engagement in LDCs, lowering the risk of policy failure due to lack
of cultural understanding [91].
Nevertheless, our work creates a better understanding of the often unsighted influ-
encing loops that connect the environmental, social, and economic domains, highlighting
and reinforcing known leverage points such as the need for education, cultural awareness,
and community engagement if interventions are to yield positive results. It also shows
how compounding delays reduce awareness of health outcomes; for example, the time
delay between children’s respiratory clinic visits and adult respiratory-related mortality
does not result in air quality policy interventions in LDCs because more immediate con-
cerns dictate policy priorities.
These insights, particularly the links between population growth and children’s
health, give us a big picture understanding of the issues facing LDCs and emphasise the
need for support from more developed countries. There are many agencies active in LDCs
delivering individual aid-based interventions, but the CLD highlights the need for collab-
oration across sectors to avoid suboptimal outcomes or unintended consequences. It also
shows how LDCs, with external support, need to address poverty as a structural determi-
nant of health, possibly in the context of the SDGs.
The selection of causal loops for further discussion in Section 3 does not mean that
other disease groups, e.g., injuries, are unimportant and should not be analysed. One lim-
itation of a CLD is that it is qualitative only; the choice of loops to discuss was based on
quantitative information from Table 1. Similarly, disease groups such as mental health
without sufficient literature-based evidence for links to the environmental, social, and eco-
nomic domains in LDCs should not be ignored and point to a research gap.
The discussion of leverage points in Section 3.5 shows some examples of how inter-
ventions could change health outcomes. From a CLD perspective, interventions can
change the polarity of reinforcing loops from detrimental to beneficial outcomes. This
CLD is necessarily high-level; using the tool to focus on a specific EH problem, environ-
ment, or socioeconomic setting enables greater depth and more specific insights, which
can guide decision-making about policy setting and practice.
4.2. Potential Application for Systems Science in CEH
The complex problem of poor CEH in LDCs cannot be solved solely through linear
cause and effect approaches to problem-solving, such as the provision of water infrastruc-
ture to reduce WASH-related disease. A systems science approach has real potential to
support decision-making in research as well as policy setting and practice [26]. In a quan-
titative systems science approach, dynamic simulation models are developed from a CLD
and explicitly quantify relationships between influencing variables and describe their
rates of change over time. Such models can then be used to simulate and compare the
impacts of different policies and practices over time and to identify potential feedbacks,
limitations, and inhibitors. Specifically, interventions can be simulated, and the levels of
investment required to change the polarity of reinforcing loops and the time delays can
be estimated in combination with a range of different assumptions.
Int. J. Environ. Res. Public Health 2021, 18, 3010 22 of 26
Using these methods, we could, for example, estimate how child mortality and mor-
bidity over the last 30 years will change into the future considering multiple and hetero-
genous influence variables and not, for instance, just the size of the investment or the po-
tential uptake by the community. Similarly, we could extend the modelling to link adverse
child health outcomes in LDCs to subsequent adult health outcomes [92], gaining a greater
understanding of how morbidity with its origins in childhood is influencing adult health
expectations. Many more influences can be defined and modelled. For example, the extent
to which reduced child mortality contributes to population growth, which then reinforces
poverty. Aid-funded interventions in infrastructure, for example, health service facilities
or improved water supplies, will have a positive impact on children’s health outcomes,
but we can now model how population growth and urban densification stretch these re-
sources to the limit when considering the influencers from all domains.
To be useful to decision-makers and give meaningful results, a model would need to
be built for a specific country or area and stratified into urban and rural segments. Lack
of data in LDCs may make this task more difficult, but systems science can overcome such
limitations to a degree by incorporating expert opinion and data from similar domains
(e.g., other LDCs and proxies) into models.
5. Conclusions
Understanding is merely a starting point; delivering scientific recommendations that
lead to action and sustained progress is surely the most important goal of CEH research.
Systems science, currently underutilised in this field, can make an important contribution
in all CEH settings, including LDCs. A CLD is a powerful tool in its own right for explor-
ing and recognising the many interconnections between the environmental, social, and
economic domains and their influence on children’s health outcomes and creating a
shared understanding, and is also a first step in a quantitative dynamic modelling process.
Our CLD shows the need to include a policy on population growth, family size, and fam-
ily planning availability as an influencer of children’s health.
CEH most often focuses on environmental influences on health, and our CLD ap-
proach demonstrates how these should be augmented with social and economic influ-
ences and shows the impacts of poverty, low levels of education, and inadequate infra-
structure on children’s health in LDCs.
Supplementary Materials: The following are available online at www.mdpi.com/1660-
4601/18/6/3010/s1, Supplementary S1: CLD creation and terminology.
Author Contributions: Conceptualization, C.F.B.; methodology, C.F.B. and P.J.; writing—original
draft preparation, C.F.B.; writing—review and editing, C.F.B. and P.J. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Ethical review and approval were waived for this study due
to research only using existing collections of data that contain only non-identifiable data about hu-
man beings.
Informed Consent Statement: Not applicable.
Data Availability Statement: Publicly available datasets were analyzed in this study. The data can
be found at http://www.healthdata.org/gbd/2019 (accessed on 25 January 2021).
Acknowledgments: This research was supported by the University of Queensland.
Conflicts of Interest: The authors declare no conflict of interest.
Int. J. Environ. Res. Public Health 2021, 18, 3010 23 of 26
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