1 Health services considerations for atypical communities
Health services are most commonly geared toward pre-existing communities, with efforts at establishing services where they are absent or improving services in places where they already exist (Wright et al. 1998). Researchers might focus on catchment areas and their respective population size or demographic attributes, or on the capacity of health facilities to adequately address the needs of the catchment area (Bureau of Primary Health Care, n.d.; Grad 2002; Macharia et al. 2021). How many beds are available, how many physicians per unit of population are there, how far must a community member travel in order to receive care (Blanford et al. 2012; Macharia et al. 2021; McGrail 2012; Ouma et al. 2018)?
Occasionally there are special circumstances that require approaches that differ from this norm. For example, migrant communities may be more ephemeral. Seasonal migrants reside in one geographic location for a period of time and then move along to another geographic location depending on, for example, the agricultural calendar. Oftentimes such special, atypical communities are missed in national and regional healthcare systems, partially because of political contexts, but also because of nationality, language, and socio-cultural attributes or because of the ephemeral nature of the ‘community’ (Adhikary et al. 2020; Arcury and Quandt 2007; Hansen and Donohoe 2003; Rural Health Information Hub n.d.).
Another related situation is one in which a community has only recently been established. For example, during disasters and conflicts populations may be displaced, with groups of people moving to new locations and establishing settlements which last for varying amounts of time (Guha-Sapir 1991). Some of these settlements will last for short periods of time, with community members returning to the place of origin when it is safe to do so. Sometimes these settlements last for long periods of time as well. Refugee camps in Kenya, India, and Thailand have now existed for decades – over half a century in the case of Cooper’s Camp of West Bengal (Finch 2015).
There are organisations that are devoted to working with such displaced populations and increasingly migrant communities as well. International non- governmental organisations (NGO s) such as the International Organisation for Migration (IOM), Doctors Without Borders (MSF), the International Rescue Committee (IRC), as well as numerous local NGO s and community based organisations (CBO s) focus on the needs of these types of communities. These NGO s and CBO s often provide invaluable, life-sustaining services for migrant and displaced communities. However, there is also a chaotic nature to these settings that can hinder the sustained provision of quality services (Moss et al. 2006; Srikanok et al. 2017; World Health Organization (WHO), n.d.).
A related type of settlement, with unique characteristics, are those which have been formed by migrants or settlers primarily for the goal of making a living from extracting resources in that new geographic location. We will refer to this type of migrant as a ‘settler’ and the space and place which they occupy as a ‘settlement’. Extractive based settlements are common, and specifically in relation to artisanal and small scale mining (ASM), the World Bank (2020) has recently estimated that 44.75 million people are engaged in this sector, rising from 13 million in 2002, across 80 countries (Hentschel et al. 2002). Whilst much of this chapter focuses on settlements centred around mining as the extractive sector of focus, there are many other types of extractive industry around which settlements form (i.e. logging and agriculture). As such, the total number of people living in settlements that are based on small-scale and informal extractive industries is likely to be much higher.
Settlers living in extractive orientated settlements often have unique health needs and experiences. Such health needs can include above average exposure to mercury poisoning (Bose-O’Reilly et al. 2010; Calao-Ramos et al. 2021; Esdaile and Chalker 2018; Gibb and O’Leary 2014; Hentschel et al. 2002) and lead poisoning (Bartrem et al. 2014; Landrigan et al. 2022; Nriagu 1992) in ASM based settlements – even affecting people not directly engaged in mining itself. Communities living close to mines have also been shown to experience higher odds of respiratory infections (Saha et al. 2011). Sexually transmitted infections (STI s), including HIV, are also often highly prevalent in such extractive based settlements as well (Baltazar et al. 2015; Clift et al. 2003; Nigussie et al. 2021; Sagaon-Teyssier et al. 2017). Other communicable diseases can also be high, such as malaria or tuberculosis (Hentschel et al. 2002; Moyo et al. 2021; Smith et al. 2016).
Additionally, health interventions to address the health needs of these communities tend to be limited because of poor communal access to health services, in part because of the often geographically remote locations, or the legally ambiguous nature of the work and settlements (Hentschel et al. 2002; Schwartz et al. 2021). The health needs of such populations may be thus ‘hidden’ from government agencies or NGO s who could be in a position to implement health resources in these settlements.
The perceived temporality of extractive based settlements can also hinder official investment in health services for these communities, even when population size increases rapidly, and it can take extended periods of time (often decades) for the settlement to gain the prerequisite official recognition in order to qualify for health investment (Hentschel et al. 2002). Where health interventions are conducted, they often focus exclusively on mercury poisoning or mine-related occupational safety, potentially neglecting other health needs (Gottesfeld and Khoza 2022; Smith et al. 2016; Spiegel and Veiga 2005; Tsang et al. 2019) even when key stakeholders state other pressing health needs in their communities, such as infectious disease (Smith et al. 2016).
In this chapter we describe the processes that lead to the formation of such settlements and how the dynamics of such settlements, their surroundings and health needs evolve. We describe a conceptual framework (Figure 1) for understanding and describing a common process through which: new settlements are formed by settlers (Figure 1A); there are associated changes to the environment from the act of settlement (Figure 1B); the demography of that community changes over time (Figure 1C); and these changes in demography and environment impact the epidemiological dynamics of the community (Figure 1E and 1D). We will explain each of these components in the following sections, followed by a case study from an informal gold mining site in Western Ethiopia, and closing with recommendations for public health systems that incorporate a planetary health framework.
Conceptual diagram, indicating: (A) formation of a new settlement; (B) migration/settlement induced environmental changes; (C) demographic changes as the community evolves over time; (D) epidemiological changes that result from demographic changes; (E) epidemiological changes that result from environmental changes. The following chapter sections correspond to each element of this diagram
2 Establishment of a new settlement
Migrant destinations are not random. A broad array of complex socio-cultural, political, economic, and even epidemiological factors influence migration decisions (Fields 1976; Portes and Sensenbrenner 1993; Stark and Bloom 1985; Todaro 1980). Migrants often move to places that offer increased economic realisations and improved (or perceived improved) quality of life. Negative environments (from socio-cultural, political, economic, or epidemiological) may encourage individuals to out-migrate, whereas perceived positive environments might act as a ‘pull’ for migrants.
Following the establishment of a settlement by pioneer migrants, the population of the settlement often expands through fertility and continued migration into the settlement. Whilst these secondary migrants may have similar reasons for moving to the settlement (i.e. economic opportunity) other aspects of the decision-making process may differ. For example, once the settlement has been established, the ongoing flow of movement to the settlement may be influenced by social networks (Banerjee 1983; Boyd 1989; Ryan 2011).
Having personal ties to the new settlement can help reduce the social and economic costs of moving as there is more information known about the opportunities and conditions in the new settlement and aid with other assistance as well (Boyd 1989). When more people within a particular social network move to the new settlement that others in their network have already moved to, this creates favourable conditions to facilitate further migration of network members from origin communities to the new settlement. These flows can grow larger and can become self-sustaining, creating a chain within a social network to a new settlement (Boyd 1989).
Here we focus on a particular kind of migration: that of migrants who have moved to a new location with environmental characteristics that are sufficient (or perceived to be sufficient) for extractive economic enterprises. Examples could include informal logging, mining, farming, or ranching. Such examples span history.
We acknowledge that while economics often plays a major role in migration decisions, the choice to change geographies is also often complex, multifactorial, and multi-level (i.e. some decisions might be made at the household or community level rather than at the individual level (Boyd 1989; De Jong and Gardner 1981)). We do not focus on this aspect of migration here, but rather in a generalized process through which settlements emerge and exist, with an overarching focus on epidemiology and planetary health.
We also recognize settler colonialism as one variant of the type of migration we describe here, whereby settlers begin a new settlement, displace local and indigenous populations (often violently), and establish a new political system (Veracini 2016). While we do not focus explicitly on settler colonialism in this chapter, this type of settlement establishment also has similar ecological and epidemiological implications for a particular geographic space and for the people that both settle there and who originally inhabited the space. The environmental, demographic, and epidemiologic impacts of settler colonialism are well-documented, particularly with regards to frontier expansion in the USA, in the Amazon (e.g. Barros and Honório 2015; Singer and de Castro 2001) and elsewhere (see Regassa and Korf 2018 for an example from Ethiopia). We also recognize the assaults on human rights that accompany settler colonialism.
3 Settler-induced environmental change
Settlers alter the environments of their new settlements. The frontiers to which people migrate are often explicitly defined in terms of their potential for change induced by such movements, from initially undeveloped space to large land transformation (Baeza et al. 2017).
The introduction of extractive industry, even at a small artisan scale, in newly settled places, will have particularly stark implications for the local environment. Mining activities and other extractive enterprises are often associated with negative externalities, including: deforestation, water pollution (from sewage, waste or chemicals, and mining activities), soil erosion and threats of extinction to plants, insects and animals (Singer and de Castro 2001; Sonter et al. 2017). Gold mining specifically is estimated to release more mercury into the environment than any other sector – an estimate of 1,400 tonnes of mercury were released from ASM in 2011 (Intergovernmental Forum on Mining, Minerals, Metals and Sustainable Development (IGF), 2014) – and overall is estimated to contribute 37.7% of global mercury pollution (World Bank 2020). CO2 levels have also been shown to rapidly increase as a result of extractive industries, particularly those present in previously forested areas (Csillik and Asner 2020). For example, using remote sensing Csillik and Asner (2020) estimated that 1.12 Tg C emissions was created by gold mining activities in a 750,000 hectare area of the Peruvian Amazon between 2017–18.
These environmental changes from extractive industry, and wider global climate changes, can make settlers and the settlements they inhabit vulnerable. For example. extractive industries set up along rivers and coastlines (e.g. for extractive fishing enterprises) are prone to flooding from rising sea levels – a global climate change phenomena (Adelekan and Fregene 2014). Heat exposure risk from outdoor extractive work is also increasing because of climate change-induced temperature rises, in extractive orientated settlements (Nunfam et al. 2019). That such settlements are often geographically isolated, and lack sustained access to health services and infrastructure investment, can exacerbate the problem.
The impact of local environmental degradation can, at least in some cases, extend far beyond the community. Localised environmental degradation as a result of mining in the Amazon, for example, has also been suggested to be a major contributor to global climate change (Ellwanger et al. 2020; Kahhat et al. 2019). Nonetheless, the relationship between informal and small-scale mining with climate change has been under-studied, with most work focusing on large-scale mining communities instead.
Conversely, climate change may also be a driver of extractive settlement establishment and expansion, because of the precariousness of other economic activities and climate change-related disruptions to those endeavors (Bartrem et al. 2022; Fisher et al. 2019; Odell et al. 2018). For example, agricultural work is becoming less secure in some areas because of changing environments, which in turn fuels demand for non-farm work such as mining, especially among subsistence workers (Fisher et al. 2019). This can draw people to then establish or join settlements where other extractive-industry work is possible.
When people then begin to inhabit a new settlement, there is often a need to clear and alter the landscape for suitable living spaces and basic services. This may also include accompanying agricultural development for sustenance and additional economic opportunity and can result in a built-environment, previously unknown to the space the settlement now occupies. Potent environmental changes include establishment of building structures, drainage and alteration of natural water bodies, and establishment of irrigation and waste management facilities using varying degrees of technology.
The need for access to and within the settlement will also lead to the creation, widening or formalisation of tracks and roads across previously undeveloped landscapes. In terms of extractive industries such as mining, pollution of waterways may be an immediate byproduct of industry establishment, along with severe landscape changes such as soil erosion and deforestation. Similarly, land clearing for agricultural use, both at a small-scale and larger-scale, can lead to changes in soil quality and composition as well as disruption to animal, insect and plant dynamics and their habitats. All of these things involve altering the physical landscape of the space the settlement forms on, and can lead to resulting changes in the overlap between humans, pathogens, and vectors of pathogens (Gottdenker et al. 2014).
Over time, as the demographic composition of the community changes, because of increasing inward movement of migrants or changes to the fertility rate of the settled population, this too can stress space and resources, leading to expansion of the settlement (Sonter et al. 2017). For example, there may be a need to construct housing for the settler population, or to transform land for further productive potential, such as mine expansion or for the establishment of industries and supply chains to support the community and its constituents. In turn, this expansion further alters the landscape and characteristics of the environment.
4 Settlement demography: the life cycle of a new community
The demographic composition of a new community is driven by the demographic characteristics of its founders, as well as ongoing in- and out-migration (Castro and Rogers 1984). There are strong age patterns in migration, with most migrants being young adults (‘independent’ migrants), and occasionally their offspring (‘dependent’ migrants in the sense that they are linked to independent migrants) (Castro and Rogers 1983). A newly established settlement is therefore likely to reflect this pattern.
One metric that is used to quantify the age structure of a population is the dependency ratio (Hadley et al. 2011; Wachs et al. 2020): the ratio of persons in non-working age groups to those in working age groups (for example, a ratio of those in the 0–14 and 66 + age group to those in the 15–65 age group). A population with a high dependency ratio may experience difficulties in providing sustenance, as well as social, economic, and healthcare needs for the population. Conversely, a low dependency ratio might be considered desirable.
A variant of the dependency ratio (a ratio of consumers to producers, or the ‘C/P ratio’ (Chayanov 1966)) has often been used often in agricultural studies. C/P ratios differ from dependency ratios in that they weight consumers and producers by their relative contributions (production) and use (consumption), which likewise vary across the life-cycle of individuals (Han and Cheng 2017). For example, a 20-year-old adult might have a heavier production weight than a 2 year old or an 80 year old. A 2-year-old would consume food, but would contribute (produce) little for their respective population. Chayanov was interested in understanding and quantifying peasant farming household agricultural and economic outputs with regard to a society that was shifting toward socialism. More recently C/P ratios have been used in micro-level studies to measure general wellbeing and to predict out-migration from households, kin groups, and communities (Hammel 2005; Lee and Kramer 2002; Parker et al. 2014; Tomita et al. 2015).
The dependency, or C/P, ratio of a single population can shift over time, as the age and sex structure of the population evolves (Simon et al. 2012; Wachs et al. 2020). We refer to this shift in the demographic structure of the community as the ‘life cycle of the community’. Whereas high fertility populations have large proportions of young age groups, populations that have undergone a shift from high to low fertility will, over time, experience a shift toward a higher mean age of the population. Migrant communities (including the types of settlements that are the focus of this chapter) are often skewed toward young adults and their offspring. If there are no in-migrants to the community, and if the community members survive and don’t out-migrate, the mean age of the community will consistently increase over time. Conversely, in a system with in- and out-migration, the evolution of the age and sex structure of the population will be dependent both on the population dynamics of original inhabitants and of those who move in and out of the community.
A generalised process for a community without in- and out-migration could be one whereby: A.) the original founders were young adults, some with children and others soon to have children (a relatively low dependency ratio, depending on the proportion of children); B.) as those young adults and children age, the dependency ratio decreases (as children move out of dependency age or increase their productive abilities); C.) as the entire community reaches older ages, and if many of the community member survive, the dependency ratio could increase (as community members move into dependency ages and their productive abilities decrease). The fertility of individuals who have recently entered reproductive ages will influence the dependency ratio as well.
The age structure dynamics and progression within this process also depends on the duration of the settlement. Policymakers have for a long time viewed extractive settlements as temporary entities, with much of the literature now citing this perception of short temporality as often misguided and at the very least a hindrance to adequate healthcare and infrastructure support. These types of extractive industrial settlements have reoccurred across space and time and whilst some have been temporary – (Bryceson 2018) estimates that artisanal mining settlements during the California Gold rush in the 1800s lasted roughly six years – other settlements persist, even when the original extractive commodity is depleted (Bryceson 2018; Bryceson et al. 2020; Carson et al. 2020; Hentschel et al. 2002). The settlement’s persistence may in turn be influenced by the demography of the place at the time of commodity depletion. If people have established families and strong social networks in the community then it may be harder to move elsewhere, even if there is no longer as much economic opportunity in the present settlement (Bryceson 2018). More work is warranted in analysing and studying the lifespans of these types of settlements.
Furthermore, while we have focused on the age structure of communities, other demographic and socio-cultural characteristics may also shift. For example, the gender and ethnic composition of a community can evolve over periods of time. Given socio-cultural and economic implications of things like age, gender, and ethnicity, these factors too have importance for understanding the epidemiology (and epidemiological dynamics) of a community.
5 Epidemiological shifts following the life cycle of the settlement
The demographic characteristics of a community or settlement can have a profound impact on epidemiology and general healthcare needs. Since risk of morbidity or mortality for many diseases varies by age (and sometimes gender), the age and gender structure of a population can influence the overall burden of morbidity or mortality. This is the impetus behind age and gender standardization in calculating morbidity and mortality statistics at aggregate scales (Anderson and Rosenberg 1998).
For example, in recent years, we’ve experienced a pandemic of a novel coronavirus (SARS-CoV-2), which as of January 2022 has touched almost every population on the planet. In the early waves of the disease, it was clear that the heaviest burden of mortality fell on older age groups (e.g. in the Lombardi region of Italy (Boccia et al. 2020) or in New York City (Thompson et al. 2020)). Populations with larger compositions of older age groups experienced some of the greatest burdens of mortality (pre-existing morbidities and socio-economic factors were also important drivers of morbidity and mortality).
Shifts in demographic characteristics can likewise lead to shifts in epidemiological characteristics of a community or settlement. Communities with younger populations (i.e. low mean ages) will experience diseases that are most common among young age groups (i.e. diarrheal and respiratory diseases) and with reproductive health (i.e. neonatal diseases). An ageing community, whereby the mean age of the population has increased (often following a decrease in fertility and/or a decrease in overall mortality) might be expected to experience heavier burdens of non-communicable and chronic disease – which tend to be the leading causes of death for older age groups. Many populations have experienced shifts towards higher mean ages, with attributable shifts in the most important causes of morbidity and mortality. During such transitions, it is common to experience a combined burden of both infectious and non-communicable diseases, sometimes referred to as ‘the double burden of disease’ (Boutayeb 2006; Bygbjerg 2012; Ciccacci et al. 2020). In this scenario, a population may still be characterised by a somewhat moderate to high mortality and fertility rate and experiencing an accompanying burden of communicable and acute infections, but at the same time also experiencing a substantial disease burden from non-communicable and chronic diseases. These shifts can present a substantial burden for health care systems, which may be unable to easily shift focus and priorities as the demography and health needs of a population evolve (Colwill et al. 2008; Haddad et al. 2022).
For example, globally, there has been a demographic shift from high to low mortality and fertility over time and the average age of the global population is increasing. The number of people aged 65 years or older increased by 105% between 1990–2017 and is predicted to rise to 1.5 billion people by 2050 (Cheng et al. 2020). Meanwhile, the percentage of children in the global population has decreased gradually over the same time period. This change in global demographics has had consequential effects on the state of health (Colwill et al. 2008; Haddad et al. 2022). Until recently, there was a global increase in chronic and degenerative non-communicable diseases as major burdens of morbidity and mortality (World Health Organization (WHO), 2011). As fertility rates decline at a global level, and as populations age, morbidity and mortality from maternal and neonatal diseases have decreased (Cheng et al. 2020).
Similar patterns are seen at national scales (Figure 2). Japan is an example of an extremely low fertility nation, and the top five diseases contributing to disability-adjusted life years (DALY s) in Japan include four non-communicable diseases (cancers as the top contributor) and the fifth related to injury (Global Burden of Disease (GBD), 2019). In high fertility nations such as Nigeria, infectious diseases (especially maternal and neonatal diseases, and enteric diseases) remain high contributors to the overall disease burden (GBD 2019). The United Arab Emirates (UAE) has a particularly stark and skewed population pyramid, towards young men of working age due in part to its status as a hub for expatriate workers (Blair and Sharif 2012). The fertility rate of the country is also low, with a small proportion of young children. The country has likewise experienced a shift towards non-communicable diseases being the dominated cause of disability adjusted life years per 100,000 people in the country. Four of the top five diseases that contribute to the disease burden are non-communicable as of 2019, with transport injuries being the third cause of DALY s in the country (GBD 2019).
Epidemiological shifts in the burden of disease as the demographic makeup of a population changes are not restricted to aggregate (national or global level) populations. Similar patterns have been described at smaller scales (camps, communities, and even at a household level) (Holck and Cates 1982; Parker et al. 2018; Srikanok et al. 2017; Tomita et al. 2015). For example, at a community level, in a study of pioneer settlements at Machadinho in the Brazilian Amazon, Singer and de Castro, (2001) found the mean age of the settlement population to increase over time and the percentage of the population aged 0–14 to decrease by 7.2% in the first ten years. Sellers et al. (2017) found similar results in new pioneer settlements in the Ecuadorian Amazon between 1990–2014, with the mean age of the settlement increasing over time whilst simultaneously, the fertility rate and dependency ratio of the population decreased. They also found in their case study that the sex ratio of the settlement became more balanced over time, a change from the initial imbalance of mostly young men. Also in the Amazon, Feged-Rivadeneira et al. (2018) studied the changing demographic and epidemiological profiles of community populations in Colombia and argued that the demographic changes are influential in shaping the epidemiology of the population. For example, they found incidence of malaria infections to be higher in settlements with higher proportions of children. Meanwhile, at a household level, a study by Geard et al. (2015) modelled demographic changes for the spread and control of diseases and found that a decline in household fertility was associated with a decrease in disease incidence and increase in mean age of infection at the household level.
Population pyramids for three different population types: (A) Low fertility, older mean age (Japan); (B) Low fertility and skewed toward working age males (UAE); and (C) high fertility and therefore skewed toward younger ages (Nigeria)
6 Patterns and pathways to epidemiological changes resulting from environmental transformation
Anthropogenic environmental change is a significant factor in infectious disease epidemiology (Gottdenker et al. 2014; Patz et al. 2004). Human encroachment into new environments can expose individuals to pathogens that they have not previously encountered. Likewise, anthropogenic environmental changes can shift the distributions of flora and fauna of landscapes. Such alterations can lead to changes to epidemiology through shifts in contact patterns between humans, non-human hosts or environmental sources of pathogens, and vectors of disease (e.g. ticks, mites, sandflies, mosquitoes). This can result in increased disease incidence of emerging and re-emerging infections, occasionally of pandemic proportions (Baeza et al. 2017; Ellwanger et al. 2020; Plowright et al. 2021; Quick and Fry 2018; Rogalski et al. 2017; Snowden 2019).
Focusing on settlements and Plasmodium infections in the Amazon, Castro and colleagues (Castro et al. 2019; de Castro et al. 2006; Singer and de Castro 2001) described a general epidemiologic pattern following the establishment of new settlements. Initially there is an epidemic stage, where malaria incidence rapidly increases following the onset of a new settlement. Over time (approximately 5 years) Castro and colleagues describe a shift toward stable malaria incidence.
Castro and colleagues attribute the initial epidemic stage to novel environmental exposures among the settlers. If the settlers are immunologically naïve (having come from non-malarious landscapes) they may likewise have higher proclivity for development of symptomatic infections (which will be more obvious in the data than asymptomatic infections which are rarely detected).
The first housing structures of a new Amazonian settlement may be in or near forested areas, requiring some clearance of the forest for construction (both for space and for materials) (Barros and Honório 2015; Macdonald and Mordecai 2019). Barros and Honório (2015) found that new settlers to the Amazon tended to be concentrated closest to forested areas. Deforested areas near these settlements exhibited clustering of Anopheles darlingi (an important vector of malaria) larva, and settlers who lived closest to those larval clusters had greater risk of malaria infection than those who lived farther (> 400 m) away. Roadways and other land-clearing exercises lead to further fragmentation of the landscape, potentially increasing habitats that are suitable for some arthropod vectors and/or increasing human-vector contact (Conn et al. 2002; Cuenca et al. 2021; Norris 2004; Silbergeld et al. 2002; Singer and de Castro 2001; Walsh et al. 1993). In particular, dengue fever cases have been associated with population expansion and land-clearing, including roadway expansion, in Acre, Brazil 2000–2015 (Lana et al. 2017). Additionally, one of the most important vectors of malaria in the Americas (An. darlingi) is thought to preferentially inhabit deforested areas (Vittor et al. 2006).
Land clearance may also lead to less-permeable grounds, resulting in pools of water that can act as habitats for arthropod vectors. In settlements centred around extractive industries such as mining, the work itself can directly cultivate new breeding grounds for vector habitats and provide a prime disease transmission environment between humans and the vector (Conn et al. 2002; Silbergeld et al. 2002; Singer and de Castro 2001). In particular, run-off pits used in mining can collect rain and run-off water.
Some recent studies have found an association between stagnant water collected in mining work and incidence of Buruli ulcer in Ghana. This association is likely the result of the metal-laced water found in mining acting as a fertile breeding ground for Mycobacterium ulcerans – the bacteria causing Buruli ulcer (Hagarty et al. 2015; Wu et al. 2015). Meanwhile other occupational disease risks from land use change include Rift Valley fever, which has associated with livestock and forestry work in Africa, fuelled by exposure to Aedes mosquito vectors and infected livestock (LaBeaud et al. 2015; Olaleye et al. 1996).
Anthropogenic driven environmental change such as deforestation, from increasing urbanisation, have also resulted in disturbances in the forest fringe and increasing vector-human interactions in other contexts, such as Malaysia (Brock et al. 2019; Byrne et al. 2021; Cuenca et al. 2021; Davidson et al. 2019; Fornace et al. 2016; William et al. 2013). Such environmental transformation of land has been associated with a rise in P. knowlesi malaria cases in Malaysia, particularly through the landscape fragmentation/change and the resulting increase in the suitability of larval habitats in close proximity to human settlements (Byrne et al. 2021; Fornace et al. 2016).
Importantly, associations between landscapes, human populations, and infectious diseases can exhibit a large amount of spatial and temporal heterogeneity. While studies have documented associations between deforestation and increases of a given disease in one location, the pattern may differ widely in other locations or over time (Gottdenker et al. 2014). Likewise, environmental changes can simultaneously lead to decreases in the burden of one disease and increases in another. For example, in much of Southeast Asia malaria is considered a disease of rural, forested areas (Prothero 1999; Rosenberg and Maheswary 1982). As deforestation and urbanisation have drastically changed the landscape over the last half century or more, there has been a general decrease in malaria and an increase in Aedes borne diseases with important Aedes mosquito vectors thriving in urban environments (Kolimenakis et al. 2021; Li et al. 2014)).
Environmental changes are not the only drivers of epidemiological shifts. Initial migrant populations to pioneer settlements are often characterised by limited socio-economic means, with settlers often having limited education and knowledge of prevalent diseases in the settlement environment (Douine et al. 2020; Singer and de Castro 2001). Migrants to pioneer settlements frequently live in housing that offers little protection from arthropod vectors, and with poor overall sanitary conditions (Argaw et al. 2021; Guyant et al. 2015; Tadesse et al. 2021). They may need to bathe and collect water in river ways that are host to extensive mosquito populations.
On top of increased opportunity for human-vector interactions from environmental change in new settlements, apparent disease incidence may be further facilitated by the immunological naivety of the migrants that populate the settlement. In much of the world, malaria is now overwhelmingly concentrated in rural or remote areas (in Southeast Asia and in the Americas this is often in forested areas). Many settlers originate from high population centres located far from malarious regions and have little or no pre-existing immunity (Alemu et al. 2014; Castro et al. 2019; Deressa et al. 2006; Malede et al. 2018; Martens and Hall 2000; Nega and Meskal 1991; Singer and de Castro 2001; Tadesse et al. 2021). Alongside the previously mentioned socio-economic problems (living in poor housing conditions) and little or no biological defence (being immunologically naïve), individuals who are encountering new disease systems may not have socio-cultural or behavioural defences against the new disease. Bednet use, skin protections (clothing or topical chemicals), and other behavioural factors can influence risk of infection but often must be learned either through experience or education (Pooseesod et al. 2021).
Finally, long-term changes in the environment continue to impact disease ecology. As a population grows or expands, there will likely be continued alterations to the environment. In some cases, this can lead to improvements of overall health of the settlement – especially if changes lead to improved access to diagnosis and treatment, public health programmes, and improvements in living conditions and hygiene (Singer and de Castro 2001). Conversely, some settlements in rural and remote areas exist on the margins of society and never receive such structural improvements. Even for those that do eventually achieve such improvements, the timing in between original settlement and acquiring needed structural improvements can be fraught with heavy burdens of disease.
7 Case study: Malaria concerns and perceptions in a newly-established informal gold mining settlement in Gambella Region of Western Ethiopia
7.1 Malaria and migration in Ethiopia
In 2019 there were approximately 900,000 confirmed cases, and an estimated total of 5.5 million cases, of malaria in Ethiopia (Ministry of Health Ethiopia 2020). The impact of malaria on Ethiopia is stark, with the economic cost of malaria recently estimated at 102.8 million US Dollars per annum and an estimation that malaria accounts for 30% of all DALY s in the country (Ministry of Health Ethiopia 2020). However, there have been significant decreases in reported malaria incidence and malaria-related deaths in the country, with a 47% reduction in malaria cases and a 58% reduction in deaths, between 2016 and 2019 (Ministry of Health Ethiopia 2010, 2020). Nonetheless, recent estimates are that 52% of the total population in Ethiopia is at risk of malaria as of 2020 (Ministry of Health Ethiopia 2020). The Ethiopian government has made malaria control a key public health policy, and has expressed determination for malaria elimination in some of its regions, along with near-zero malaria cases in other areas of the country (Ministry of Health Ethiopia 2010, 2020).
Nonetheless, there are many barriers to actualising this goal. While Plasmodium falciparum is the most common malaria species in most of Africa, Ethiopia is endemic to both P. falciparum and P. vivax (Ministry of Health Ethiopia 2020; Taffese et al. 2018). Ethiopia also exhibits extreme heterogeneity in malaria endemicity across the nation (Ministry of Health Ethiopia 2020; Nega and Meskal 1991; Taffese et al. 2018; Tulu 1993). Large population centres are primarily clustered in high elevation areas and have little-to-no malaria transmission, in comparison to lower elevation regions with high malaria endemicity (Ministry of Health Ethiopia 2010, 2020; Nega and Meskal 1991; Taffese et al. 2018; Tulu 1993). Malaria cases remain high along the low-elevation western border regions (Taffese et al. 2018). Gambella Region, along the international border with South Sudan, has the highest annual incidence of malaria in Ethiopia – 6% of all total malaria cases in the country and 21% of malaria cases among under 5s in 2015 (Tadesse et al. 2021).
7.2 Highland-to-lowland migration in Ethiopia
Whilst historically Ethiopia’s population has mostly been concentrated in the central highlands of the country, there have been increasing movements of people from high-elevation areas to low-elevation areas since the 1950s (Deressa et al. 2006; Hailemariam and Kloos 1993; Meskal and Kloos 1989; Nega and Meskal 1991). An array of factors have led to the increasing stream of people to relocate, temporarily or permanently, from highland areas to malaria endemic lowlands. These include: effective malaria control strategies making low-land areas more hospitable, increasing population density and resulting pressures on environmental resources and economic opportunities in highland areas (Deressa et al. 2006; Nega and Meskal 1991; Tulu 1993). These factors have led to some directed government policies to invest in developmental projects, with related schemes to resettle parts of the highland-located population in lowland areas (Deressa et al. 2006; Hailemariam and Kloos 1993; McCann 2014; Meskal and Kloos 1989; Tadesse et al. 2021; Tulu 1993). Aside from these formal streams of population movement, individuals and groups of migrants likewise move to lowland areas for economic purposes on their own, outside of official government or corporate sponsored projects.
The interaction between internal migration from highland areas of Ethiopia to lowland areas, and its corresponding effects on malaria incidence has often been highlighted as a potent public health concern, dating back to at least the 1980s e.g. Meskal and Kloos (1989). There are two overarching themes in the literature concerning this interaction between internal migration and malaria in Ethiopia: Migrants having heightened risk for malaria, and migrants’ origin communities also having increased risk for increasing malaria incidence (from importation when migrants return home).
7.3 Migrants’ increased risk of malaria
Several studies have shown that migrants have increased risk of malaria in Ethiopia (Argaw et al. 2021; Aschale et al. 2018; Demissie et al. 2021; Haile et al. 2017; Malede et al. 2018; Tadesse et al. 2021; Tesfahunegn et al. 2019). Many of these studies have taken place in highland settings, with migrants to lowland areas having larger malaria burdens as a result of their exposure to malarious environments. Fewer studies have explored the risk of migrants and ‘locals’ within malarious environments (but do see Degefa et al. 2015).
There are several dominant explanations for the heightened risk of malaria among migrants (Argaw et al. 2021; Deressa et al. 2006; Nega and Meskal 1991; Tadesse et al. 2021). Migrants from highland areas of the country are immunologically naive to malaria and are therefore at increased risk of experiencing symptomatic infections when infected (Alemu et al. 2014; Deressa et al. 2006; Malede et al. 2018; Martens and Hall 2000; Tadesse et al. 2021). Supporting this explanation are similar findings regarding malaria acquisition in migrant settings elsewhere, in Colombia (Castellanos et al. 2016) and Brazil (Souza et al. 2019).
Migrants are also at increased risk for malaria acquisition because of the risky environments in which they work (Argaw et al. 2021; Tadesse et al. 2021) and because of their often low socio-economic status (Argaw et al. 2021; Haile et al. 2017; Tadesse et al. 2021). In particular, poor housing options for migrants exacerbate exposure to malaria vectors and increases risk of malaria infection (Haile et al. 2017; Tadesse et al. 2021). This problem may be particularly acute for independent migrants, who will not have companies that provide bed nets, sleeping quarters, or access to diagnosis and treatment (which occurs in some large scale agricultural projects). Migrants or settlers in a new location might also not have the same level of malaria knowledge as those who have spent long periods of time living in malarious environments, either through lived experiences or through public health education campaigns that understandably target malarious areas. Importantly, knowledge about malaria and malaria prevention does not always lead to malaria preventive behaviours (Demissie et al. 2021).
7.4 Study objectives
Here we report results from a pilot study at a remote gold mining settlement in Gambella Region, Ethiopia that has been recently settled. The recent establishment of the settlement means that the ecological impact of migration to the area and the resulting anthropogenic change is only just beginning to be seen and we know of no prior research on either the environmental change or the health situation of this settlement. We focused specifically on understanding the places of origin of settlers in this location, their current occupations in the settlement, and on the general healthcare and malaria situation in the settlement during our pilot study.
7.5 Study location
Lunga is a settlement in Gambella Region of Western Ethiopia. The settlement is centred around an informal gold mining site and is the product of recent pioneer migration, only being established in 2017–2018 when discovery of gold in the area prompted word-of-mouth, informal migration to the site. Mining in this location is of the artisan and small-scale mining variety (ASM). Due to the small-scale nature of this type of mining, the industry’s contribution to Ethiopia’s GDP has historically been low (Ethiopian Extractive Industries Transparency Initiative (EEITI), 2016) and such settlements have not been a national priority (for public health or otherwise). Nonetheless, mining is viewed as a viable occupation for many Ethiopians and mining prospects have started to attract more and more people from across Ethiopia, particularly among those who are traditionally disadvantaged in the labour market. In the wider Gambella Region, mining has attracted migrants from across the country due the potential economic opportunities it can provide and as a result the mining industry in Gambella dominated by migrants from other regions in Ethiopia – over 95% of all mining operations estimated to be conducted by migrants (EEITI 2016).
Malaria is endemic to the region and Gambella regularly has one of the highest burdens of the disease in Ethiopia (Haileselassie et al. 2022). Plasmodium falciparum is the most common causative agent of malaria in the area, though P. vivax is also prevalent. Malaria cases in the area peak at coinciding times with land-use changes, such as developmental agricultural project investments including rice intensification and irrigation projects in 2012 and 2013 (Haileselassie et al. 2022). Furthermore, whilst data on land use change is not officially collected at a local level, Haileselassie et al. (2022) note that Abobo district health officials have indicated that there is ongoing and extensive land use change in the local area.
8 Data and Methods
We conducted a pilot household survey (n = 51) in March of 2020 in Lunga settlement in Gambella Region of Ethiopia using a household registry and by selecting households at random.
We interviewed heads of households using a questionnaire that was developed to assess the knowledge, attitudes, and practices (KAP) of community members with regard to malaria; access to health services; and places of origin. The survey also included questions about housing materials, the duration of time in which a settler had lived in Lunga, occupations, and educational attainment. The settler places of origin were geocoded using a registry of Woredas and regions (Ethiopian administrative units). We then calculated the Euclidean distance and mapped the migration trajectories between a settler’s Woreda of origin and the study location in Gambella. We then generated scores (proportion of correct answers) across four main question types related to malaria KAP. We used simple descriptive statistical analyses to assess the general KAP of the setting with regards to malaria.
We also downloaded a raster layer for forest loss to visualize potential changes in forest coverage in and around the settlement. The raster came from the Global Forest Change dataset which was generated from Landsat images from 2000 through 2020 (Hansen et al. 2013). The raster layer indicates areas (pixels) which shift from being forested to non-forested, by year, from 2001–2020. Maps were created using QGIS version 3.4.9 and R statistical software version 4.0.3.
9 Results
In the several years since the discovery of gold in Lunga, migration to the site has rapidly expanded the population and led to the creation of the settlement. The estimated population size of the settlement in 2021 was estimated at around 12,000 people (though the population remains in flux). There are official plans to make the settlement into a Kebele administrative area (an administrative title for areas populated with around 3,000–5,000 people) in the near future (source: word of mouth information from public health officials in Gambella).
Just over half of the interviewed participants at the Gambella gold mining settlement (total of 51) were female (54.9%, n = 28). The median age of participants was 27 years (range of 19–45 years) and women had a lower median age (25 vs 28). Protestantism was the most common religion reported by the participants (47.06%, n = 24), followed by Ethiopian Orthodox Christianity (37.25%, n = 19) and then Islam (15.69%, n = 8). There were eight different self-reported ethnicities: the most common was Agnauk (31.37%, n = 16), followed by Amhara (27.45%, n = 14), and Oromo (27.45%, n = 14).
One third (n = 17) of the participants could read and write, but had no formal education. Approximately 13.73% (n = 7) could not read and write, while only 9.8% (n = 5) had 10 cumulative years of education. Most interviewed participants (n = 43) were married and living together whereas 5.88% (n = 3) were divorced, 1.96% (n = 1) were married but not living together, 1.96% (n = 1) were not married but were living together, 1.96% (n = 1) were widowed and 3.92% (n = 2) were single. The mean household size in the sample was 3.67 people and 18 participants (35%) lived in households with at least one child under five years.
Alongside gold mining, farming, merchants, and running a private business were other professions that participants reported as their primary occupation. The median age was lowest for farmers, whilst joint-highest for merchants and private business. The most common profession among men was gold mining (43.48%, n = 22) and the least common profession was farming (8.7%, n = 4). Comparatively, the joint-most common profession among women was merchant and private business (35.71%, n = 18 each).
Among the study participants, men tended to have lived at the settlement for longer periods of time than women. The mean length of time lived in the gold mining settlement for men was 26.8 months, compared with 16.6 months for women. Male farmers tended to have lived in the settlement for longer than men or women employed in other occupations.
Household construction was generally poor. The most common wall type was plastic (n = 31) then wood (n = 20). The most common roof type for houses in the sample was plastic (n = 38), followed by grass (n = 11), then iron sheet (n = 2). One household in the sample had a toilet. No households in the sample had electricity, a TV or a radio and 45 households had a mobile phone.
While most people surveyed at the study site had moved there from nearby Woredas with low-elevation topography, there was a proportion of migrants who had moved to the study site from high elevation areas much further away (Figure 3). Three quarters (75%, n = 38) of the participants reported moving to the new settlement from elsewhere in the region, whilst 25% (n = 13) came from outside of Gambella Region. Approximately half (49%, n = 25) of all participants reported moving to the new settlement from the same woreda in Gambella Region, as the new settlement.
Locations of origin woredas for migrants at Lunga settlement, Gambella Region, Ethiopia, by number of people per origin woreda
With regards to the KAP survey, the median proportion of correct scores in the population sample regarding malaria symptoms was 0.25 and the median proportion of correct answers regarding knowledge about protection against malaria was 0.40. Meanwhile the median proportion of correct answers regarding knowledge of the causes of malaria was 1.00. The median proportion of correct answers among the population sample for beliefs about who was at risk for serious cases of malaria was 0.60. However, only 17.65% (n = 9) of respondents correctly believed that pregnant women were at risk of a serious case of malaria and 88.24% (n = 45) of migrants interviewed stated they did not use mosquito nets in their household. The dominant reported explanation for not using mosquito nets was because they did not own them. Among those who did report owning mosquito nets, 83.33% (n = 37) reported using one the night prior to the interview.
Of the 13.71% (n = 7) of households that reported a household member being sick with fever in the past two weeks, 100% of them reported seeking treatment at a formal health facility, mostly at a health centre but also at private clinics. The nearest health facility was approximately a 2 hour walk from the settlement.
Figure 4 displays changes in forest coverage before and after the settlement at Lunga. There was minimal forest loss prior to 2016, and a rapid increase in forest loss post settlement (from 2017 to 2020). This aligns with the discovery of gold in the area and the resulting increased movement of people to the area to establish a settlement at Lunga, in around 2017–2018. The coloured pixels showing forest loss indicate a diagonal pattern from north-west to south-west of the settlement; this represents the clearing of a section of the forest for the creation of a road that runs by the settlement in 2017/18, around the same time that the settlement started to be established. This highlights the start of anthropogenic environmental change in the area of, and surrounding, the new settlement, Lunga (Figure 1B). Further research will be needed to monitor the environmental changes to the space over time and to evaluate potential impacts of this on the epidemiological situation of the area (Figure 1E).
Forest loss by year (2001–2020) around Lunga settlement (Hansen et al. 2013). Forest loss is defined as the change from forest to non-forest state (when all trees in a stand are eliminated). Forest loss is represented by a colour pixel that indicates the year in which the loss took place in that pixel (2001–2020)
10 Discussion
The composition of the settlement was diverse, with community members coming from both proximal and distal geographic regions, from different ethnic groups and religions (Figure 1A and 1C). While gold mining is the major draw from most to this settlement, other occupations exist and have built up around the gold mining. As expected, most study participants were young adults (Figure 1C). The demographic characteristics of the community, in age and gender makeup but also in ethnicity, religion, and other characteristics, may shift over time.
While a majority of the study participants correctly identified the causes of malaria, most could not correctly identify the signs of, nor methods of protection against, malaria (Figure 1D). Few participants were also aware that pregnant women were at significant risk from malaria. Additionally, most participants did not have access to mosquito nets, but showed willingness to use them if they were made available. Similarly, individuals that reported experiencing a recent fever in their households all reported seeking treatment at formal health facilities, even though the nearest health facility is roughly 2 hours walk away from the settlement. These findings indicate that public health outreach and material investment is needed in this area and that such outreach could be effective among the local population if made available.
Presumably, the continued population expansion at this settlement will also alter the epidemiology of the place (Figure 1D and 1E). Due to their oft remote geographic location, along with their informal and small-scale nature, mining communities in Ethiopia experience limited health coverage and access to health facilities (EEITI 2016). Limited access to healthcare facilities and resources is expected to contribute to worse health outcomes for the population (Carrara et al. 2006; Desai et al. 2014; Landier et al. 2018; Zurovac et al. 2014) and an increase in disease prevalence (Figure 1D).
Part of this limited access to healthcare is a result of the informal nature of this migration flow (Figure 1A). Whilst informal migration across the country is common, in other circumstances where official economic migration flows are planned there are often concomitant government or business led investments. For example, formalisation can give visibility of the population to local and national decision makers, which may lead to a dispersion of funds for health services and interventions. While formal migration may not always lead to investment in health services for migrants, it is arguably more likely to occur in a situation of planned migrations than in informal, hidden migration streams (Tadesse et al. 2021).
There has been some recognition of the current unmet needs and gaps in health service provision for migrant communities by the Ethiopian government (Ministry of Health Ethiopia 2020; Nega and Meskal 1991) and there have been attempts to rectify this gap in service provision by increasing health facility provision across the country (Taffese et al. 2018) and establishing some health infrastructure in migrant communities. However, this is often limited to migrant populations involved in major developmental projects with formalised migration support and promoted by government actors and policy. Overall, this study illustrates some of the planetary health-related problems associated with new settlements, especially given the rural and remote setting, in an already strained healthcare system.
11 Conclusions and recommendations
In this chapter we’ve presented a conceptual framework for understanding and describing co-incident shifts in demographic, environmental, and epidemiological patterns following settlement of a new community for extractive purposes (Figure 1). While there is a wealth of literature describing similar situations from other settings, we argue that there are commonalities that exist across most of these settings that are worth describing and worth considering specifically with regard to health services and public health interventions, within a holistic Planetary Health framework. This type of community is widespread, as is the neglect of public health resources for such communities.
Importantly, such settlements often exist outside of the realm of normal healthcare delivery, for a wide variety of reasons. Migrants are often missed in official government health delivery, and the type of settlement we describe here would have the same problems (Riza et al. 2020; World Health Organization (WHO), 2019). Furthermore, the unofficial (sometimes illegal) nature of this type of settlement community often leads the population to be under the radar or hidden from normal healthcare delivery. The precarious legal nature of such settlements is often exacerbated by tensions with other established or indigenous communities in surrounding areas, who may resent and view the new settlement as imposing on their own livelihoods and resources. This can make implementing new, or expanding existing local health services, difficult. Special, locally tailored considerations are often needed to reach such populations (Bonevski et al. 2014; Crawley et al. 2013; Elgabry and Camilleri 2021; Krabbe et al. 2021). Finally, many such settlements are in remote, rural areas or in regions that are deemed unsafe (conflict zones, contested regions) – further complicating access for normal services (Chatham House 2019; Druce et al. 2019; Footer and Rubenstein 2013; Nickerson 2015; Sousa and Hagopian 2011). Regardless, it is crucial that health services are provided for such communities, both from a humanitarian perspective and because they are often linked to other communities (to which they could transfer disease, etc.).
We have three main recommendations for such settlements. If an aggregation of humans exists, it needs access to basic healthcare services. First, if the settlement has only recently been established, healthcare provision may be possible through travelling community workers (Lee et al. 2006; McGowan et al. 2020; Omam et al. 2021). However, this is only a short-term fix as ready access to diagnosis and treatment of illnesses needs to be steady, so that access is not delayed (Prentice and Pizer 2007), and this is not achieved through periodic visits by outside healthcare workers.
Second, once it is clear that the settlement will be long-term, efforts should be made toward establishing a community health system within the settlement (Lee and Kim 2018; Mullany et al. 2010; National Heart Lungs Blood Institute (NHLBI), 2022; Riza et al. 2020). This will necessitate either having a healthcare worker relocate to the community, or training a member of the community, to do the healthcare work. Again, it is imperative that the healthcare worker be a regular resident of the community. Frequent travel out of the community will mean that health services are irregular and could lead to disruptions in early diagnosis and treatment, which can lead to onward transmission of infectious diseases or delayed treatment of diseases which often lead to worse health outcomes. Ensuring such constant availability may necessitate a salary and budget for the community health worker.
There are ongoing efforts to fund and expand community health worker programs already, in low resource and remote settings. Examples include Last Mile Health in Liberia and a recent philanthropic project in Africa, aided by The Global Fund (The Global Fund 2022). However, there is a need to ensure that people living within atypical settlements – such as extractive based settlements – receive benefits from these programs and are not ignored because of their often remote or politically precarious situation. Indeed, creating community health programs within these extractive communities may also alleviate tensions between different communities within the same locality, that may otherwise resent sharing resources with ‘newcomers’ or ‘outsiders’.
Third, health considerations should go beyond a human-centred approach, and should include the built environment (healthy housing and access to clean water) and other inhabitants of the environment (other species that coexist in the location). Inherently, there are questions about environmental sustainability with regard to this type of settlement, and examples of environmental degradation are common (Barraclough and Ghimere 1995). Conversely, forests (and other environs) are a home to a significant number of people worldwide, who depend on forests for their homes and livelihoods (Cheng et al. 2017; Pokharel et al. 2007; World Bank 2013). Frontier areas and their environments can potentially be pathways to economic gains. For example, forests can provide a safety net for its users and inhibitors, to help provide supplementary income, and opportunities for foraging and nourishment, as well as long term opportunities for subsistence activities and economic gain. Rather than bar individuals, often with low socio-economic standing, from access to natural resources, sustainable use of such resources should be encouraged. Oftentimes for indigenous peoples, sustainability is already well-understood and advocacy of their traditional practices may be needed (Johnson et al. 2016; McGregor et al. 2020; Sherpa 2015). In other circumstances, as when the settlers are not local or indigenous, educational efforts may be warranted. To ensure such methods are incorporated within the extractive industry of a particular settlement, a community-focused approach and community participation should be encouraged to overcome many of the barriers identified to sustainable extractive work. Community and cooperative groups may in turn be more successful at promoting sustainable methods, when using the available knowledge and technology resources within their own community (Hilson et al. 2007; Massaro and de Theije 2018).
In conclusion, we have described a general settlement process that is sufficiently common as to warrant a conceptual framework, and planetary health approaches to addressing the health of settler communities. Arguably, such an approach could lead to development of improved health for the communities and their environments. Oftentimes such settlements are viewed with disdain and the community members are hidden from official government provisions. The legal and political circumstances under which such settlements and communities exist complicates provision of health services. Regardless, basic public health services should be provided to all despite legal status, nationality, or indigeneity. We do recognize the need to balance environmental concerns against population health, but the current status quo is one whereby these communities are ignored and neglected, leading to both widespread environmental degradation and poor population health – both of which then impact the wider landscape. While environmental degradation is certainly a concern, we believe that a formalized process for dealing with this very human process would be optimal.
Acknowledgements
DMP was partially supported by a grant from the Bill & Melinda Gates Foundation (INV-028123).
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