COVID-19 in Mexico: How the Systems Approach Can Improve Our Response

Abstract

Using Mexico as a model nation, the authors explore the systems approach for addressing COVID-19. The elements in any system are highly interconnected as they continuously influence each other. Public health challenges do not exist in a vacuum but are influenced by several factors including the social, economic, and political context. To understand any one factor, mapping the surrounding elements and analyzing the relationship between them and other elements in the rest of the system is necessary. Key elements of the impact and the response to the pandemic are examined by applying the systems thinking approach within the Mexican context. A systems analysis of COVID-19 is conducted using a systems map to illustrate the complexity of the system and to understand how to leverage the power of systems thinking to execute intersectoral, innovative, and highly impactful interventions for an improved response to the ongoing COVID-19 pandemic and pandemics in the future.

Introduction

The COVID-19 pandemic has been classified by leading public health agencies and experts as the most profound global health crisis since the Spanish Flu swept the globe one hundred years ago. At the time of writing,¹ there have been a staggering number of confirmed cases (217,848,001) of the novel coronavirus and 4,521,828 deaths in over 200 countries and territories [1]. In addition to the devastating loss of human life and wellbeing due to COVID-19, there has been a profound impact on all domains of life which has thrown many countries into dire economic recession. It has laid bare existing social problems such as weak healthcare infrastructure, unemployment, and widespread inequality [2]. The social and economic costs of the pandemic are nothing short of devastating, with tens of millions of people projected to fall into extreme poverty. The Brookings Institute estimates that by 2030, 588 million people could live in extreme poverty, an additional 50 million people compared with pre-COVID-19 estimates. The number of people worldwide who are undernourished is expected to increase by over 132 million [3]. As is the case in most humanitarian emergencies, poorer developing nations have taken the hardest hit. Disadvantaged groups, particularly people living in poverty who are marginalized have suffered the most. And Mexico is no exception. SARS-CoV-2 spreads rapidly regardless of geographical borders and politics. It has reached almost every country in the world. This has allowed the pandemic to become a transnational threat of a similar scale as climate change, security threat, mass displacement and migration, and other systemic crises [4]. Just as the virus itself has impacted countries around the globe, due to the increasingly connected world in which we live, the actions of individuals and governments have resulted in consequences that are deeply intertwined within the existing global socioeconomic and environmental systems [5].

Since the beginning of the pandemic, governments have been under immense pressure to effectively and quickly implement mitigation strategies and policies to slow the spread of the virus and protect their populations. While governments first focused on containment and mitigation efforts within their borders by issuing varying degrees of travel restrictions, stay-at-home mandates, and enforcing mask wearing and physical distancing, the situation quickly evolved and spread into other sectors–causing widespread disruption of the economic, social, and political systems [4]. As a result, the pandemic evolved into a global-scale intergovernmental crisis [4]. COVID-19 has been classified as a ‘wicked problem’ for policy-makers and other stakeholders around the world because it transcends health, environmental, social, and economic boundaries [2, 5]. It presents a highly complex, multifaceted challenge. It is, therefore, important that policy-makers and public health professionals approach it as such.

Above all, the pandemic has made it abundantly clear that linear thinking is not sufficient when trying to tackle a challenge with this level of complexity. Taking into account the human behavioral aspect of COVID-19 mitigation strategies, there is a need for political will and leadership. In light of this, decision-makers are now being forced to think more critically about the causation and consequences because not doing so will have damaging effects on health as well as socioeconomic conditions [6]. Systems thinking has been growing in popularity in the public health space. It now needs to be mainstreamed at every level from individuals to society and from the public to the private sector. Transnational crises such as COVID-19, climate change, and future pandemics require a systems approach.

In the following sections, we will define and explain the systems approach and advocate for its use in place of more traditional approaches that are widely used such as theories of change and logic models.

What is Systems Thinking?

Before diving into the details of systems thinking, it is important to first define what exactly is a system. One of the most prominent scholars in the field of systems thinking, Donella Meadows, defines a system as “a set of things—people, cells, molecules—interconnected in such a way that they produce their own pattern of behavior over time” [7]. In other words, a system is a group of interacting, interrelated, and interdependent components that form a complex and unified whole. When thinking about a system, it is important to keep in mind that it tends to be nested within other systems. For example, families are part of communities that are located in towns and cities, which are, in turn, located in countries.

Systems thinking, or a systems approach, is not a methodology but rather a way of thinking and focusing on relationships. It is a way of looking at a problem in which there are many different interconnected parts—all of which form a complex whole. Many real-world problems have multiple parts that come together, interact, and influence each other in various ways. A systems approach is used when you have these types of complex, non-linear problems because it can address the many areas that are involved with these cyclical issues.

In a systems approach, we try to capture the many variables that influence a problem. This is particularly helpful in public health as there are many factors that play a role in how we deal with disease, in outbreak scenarios, and on a day-to-day basis. These variables may be related to environmental, economic, social, political, and other factors all of which impact the public health problem. For example, while COVID-19 mitigation strategies such as mask wearing, hand washing, and stay-at-home orders might seem relatively straightforward, policy-makers and public health professionals need to take into account the availability of masks, access to clean water, rampant misinformation and mistrust, and the potential lack of political will to implement and support these strategies. A key element of the systems approach is the incorporation of potential unintended consequences of policy decisions, programs, and interventions. It is also important to note that a systems approach can be applied to any public health challenge, including, but not limited to, vaccination campaigns, infectious diseases, and non-communicable diseases.

Systems are inherently incredibly complex. And it can be difficult to understand all the ways in which the factors are connected to each other. It is, therefore, helpful to create a visual representation to see all the interconnected components that are related immediately and peripherally to the root problem. Systems thinking scholars present a set of options for how to visualize a system, such as the iceberg model, connected circles, and causal loop diagrams [8]. While each of these models is different, there are a few key elements that are similar in each of them: the public health challenge (e.g., the root problem), the many factors within the system that influence the root problem, the relationships between the factors, and how the system changes in a dynamic way [7]. For the purposes of this chapter, we will refer to the visual model of a system as a ‘systems map’ which borrows components from the models listed above.

Overall, a systems approach is a way of thinking when approaching problems and designing solutions. This approach to problem-solving embraces the nature of complex systems as dynamic, constantly changing, and governed by history and feedback. The influence and involvement of stakeholders and the context in which systems exist are critical [9].

Linear Models of Thinking

While systems thinking has been gaining traction in recent years, policy-makers and prominent public health organizations, such as the U.S. Centers for Disease Control (CDC) and United Nations Children's Fund (UNICEF), as well as ministries of health and local departments of health, still use traditional, linear frameworks when designing and evaluating programs and policies. Logic models, or ‘log-frames’ are tools used most often by the public health and development sectors to plan, describe, manage, communicate, and evaluate a particular program or intervention [10]. As can be seen in Fig. 1, they visually represent the relationships between a strategy’s activities and its intended effects and include the assumptions that drive the expectation that the program or intervention will be successful [10]. However, there are no clear relationships drawn between the elements and important factors such as misinformation, fear, and political will which are not taken into account. Logic models are based on linear relationships between program resources, activities, and outcomes (often broken down into short-, medium-, and long-term). Logic models are primarily used as tools for monitoring and evaluation through the development of measurable indicators that quantify the success of a particular program. While it is undoubtedly a valuable tool, there are two major shortcomings of the logic model. The depiction of the program in a linear way and the failure to embed the program within a specific context in which it is being implemented. By over-simplifying the program, logic models can create unrealistic expectations about what the program can change, especially when addressing a challenge as complex as COVID-19 [11]. That being said, the aim is not to criticize the logic model for something it was never intended to do, but to instead advocate that it is not sufficient for the current crisis.

Fig. 1

Simplified logic model for COVID-19 mitigation strategies. Source Created by the authors

Another popular model that is widely used in the public health space is the Theory of Change (ToC). There are several different definitions of the ToC. For example, Breur et al. define it as “an approach which describes how a program brings about specific long-term outcomes through a logical sequence of intermediate outcomes”, while Isabel Vogel takes it a step further and defines it as “an outcomes-based approach which applies critical thinking to the design, implementation, and evaluation of initiatives and programs intended to support change in their context” [10, 13]. While the terms ‘logic models’ and ‘Theories of Change’ are often used interchangeably and their visual depictions tend to be quite similar, there are notable differences. Existing literature notes that ToCs were developed as an extension of tools such as Logic Models or ‘log-frames’ in an effort to allow for a more detailed explanation of the context, underlying assumptions, and pathways of change that the linearity and rigidity of log-frames do not permit [14]. Additionally, the ToC tends to map out larger goals rather than granular programs, and because of this, it may incorporate simultaneous, complementary strategies to ultimately reach the overarching goal. As Fig. 2 shows, ToC often looks more or less like a logic model but it builds on the traditional log-frame to begin to explore, not only what is expected to happen, but also why it will happen by more explicitly highlighting the pathways between the elements. In this way, a ToC attempts to establish the underlying causes of change and works to understand the context in which a program operates. While sometimes this is represented visually, it is often included in a narrative that accompanies the model. The narrative explains in more detail the factors that exist outside the program such as social, political, and environmental conditions that may impact the result of a program or intervention, and taken together, the Theory of Change and the supplementary narrative are perceived as ‘living documents’ that can be changed when there is new information and as unintended consequences arise [12].

Fig. 2

Simplified theory of change for a health communication strategy to slow the spread of COVID-19. Source Created by the authors

While theories of change certainly take a more meaningful step in the desired direction compared to logic models, showing the relationships between activities and outcomes and taking into account the context and unintended consequences, they still do not capture the complexity of particular public health challenges and how they are embedded within the larger systems at play. There is a robust literature on how to develop a ToC with respect to interventions on a theoretical basis. However, there is little evidence in terms of its application to complex public health challenges [15].

Why Systems Thinking?

While using a logic model or theory of change can certainly be useful in certain scenarios, there are several public health challenges in this case COVID-19 that demand a more holistic approach that considers its complexity. The World Health Organization (WHO) recently published a report strongly advocating for the use of a systems thinking approach when developing plans for tackling complex social and health issues, which means moving beyond traditional linear approaches. As previously noted, the impact of COVID-19 goes well beyond physical health and extends to mental health, economics, education, food security, environment, and politics. Pandemics are non-linear phenomena wherein one small perturbation in the system may trigger disproportionate, exponential systemic reactions [16]. Taking all of these factors into consideration helps policy-makers and public health experts select the ‘right’ intervention as well as any additional interventions needed to reduce negative consequences [2]. For example, issuing stay-at-home orders has been a very common intervention. But it has massive economic implications for businesses and individuals. In order to mitigate this, other strategies such as a stimulus package and providing meals to those who may not have access to food are necessary. That being said, our mental expectations naturally follow a linear pattern, and longer-term changes or other changes to the system are often disregarded. Systems thinking is by its nature uncomfortable. It requires a rewiring of the brain to begin to see challenges that exist within complex systems. However, it is imperative that we adjust our approach before threats to public health such as future pandemics, climate change, and mass migration among others before they become more frequent. Traditional ways of thinking are simply insufficient. Rather than a linear model, creating a visual representation of a system using causal loop diagrams better captures a multi-dimensional, layered program model, while also providing a more complete understanding of the relationships among program elements, which, in turn, enables evaluators to examine influences and dependencies between and within program components [17].

In the subsequent sections, we will use Mexico as a case study and analyze how the country has addressed the pandemic and how the response may have been different or could have been improved by utilizing a systems approach.

Mexico’s General Perspective

The United Mexican States consists of 32 states with approximately 124 million inhabitants, making it the 11th most populous country in the world. It is a young country with nearly 27% of its population under the age of 15. Most recent data from the OECD (Organization for Economic Co-operation and Development) shows Mexico’s GDP (gross domestic product) per capita in the lowest section for this group of countries—19,127 USD. It has the highest level of income inequality among 36 countries that form the OECD. Also, within this organization, in 2017, Mexico was at the lower level regarding life expectancy—around 75 years—compared to other countries, perhaps due to it being in the lowest group on health spending. In 2016, approximately 58% of the rural residents were facing poverty. Two years later in 2018, almost 18 million people did not have any insurance coverage. Its unemployment rate is about 4.4%. Mexico scored 0.78 in the Human Development Index in 2019 compared to the US (0.928) and India (0.645), placing it in the high development section. Its expenditure on education was 4.52% in 2017 with an increased tendency. The statistics from 2018 showed that the adult literacy rate was 95.38%. Fifty-six percent of the households in Mexico have access to the internet with an annual increase of 9.58% and mobile cell phone subscriptions are 95.7% per 100 inhabitants [18].

The main causes of death in the country are diabetes and ischemic heart disease both with a common factor—obesity; ranking worldwide as the second-highest and the highest country for overweight and obesity in the overall population and in children, respectively [18]. Its share of the GDP was 1.172 billion USD in 2015. The total healthcare expenditure was 5.7%. The Mexican health system is formed by three different schemes; employment-based or social security, public institutions for the uninsured, and the private sector. This structure of the health system provides the users with a wide range of services but pushes them to be enrolled in diverse healthcare institutions. The main providers of health services are the ministries of health at different levels (state and federal). Private insurance just covers 8% of the general population. There are a total of 4,341 hospitals in the country, of which 30% are public and concentrated in urban areas, while just 3.3% are located in rural settings. Mexico has a rate of 1.9 physicians per 1,000 inhabitants and 2.8 nurses per 1,000; a lower number of physicians compared to the average of the OECD countries which is 3.3 per 1,000 [18].

COVID-19 in Mexico

The first case of COVID-19 in Mexico was diagnosed by the National Institute of Respiratory Diseases in late February 2020. This was announced at the morning conference of President López Obrador on the 28th of that month. According to the data reported to WHO, from January 3, 2020 to August 31, 2021, there were 2587.16 confirmed cases of COVID-19 per 100,000 population and 200.23 deaths per 100,000 population (Fig. 3).

Fig. 3

Cases and deaths per 100,000 population as of August 31, 2021, in some OECD countries. Source Data from WHO, 2021

Initially, the Federal Government was confident that the pandemic would not advance much. Within the following month, an aggressive information campaign and a call for ‘social distancing’ was initiated. The initial approach by the Federal Government was based on the sentinel model recommended by the Pan American Health Organization (PAHO). A daily press conference was held by the Vice Minister for Prevention and Health Promotion and a social distance communication campaign was implemented.

As previously mentioned, the testing system was hampered by two factors. First, testing was not widespread due to deficient infrastructure, thus only the most seriously ill could be tested. Second, this problem was compounded by very low political will with the Vice Minister describing massive testing as a ‘waste of time, effort, and money’ [19]. The number of tests carried out to confirm COVID-19 infection was among the lowest in the world—69.29 tests per 1,000 as of August 21, 2021 (Table 1) [20]. This testing strategy contributed to Mexico being officially reported as one of the deadliest countries for COVID-19. However, hospital capacity was reinforced by strengthening the infrastructure and expanding access to health services. Public hospitals added temporary beds, adapted hospitals to ‘only COVID-19 hospitals’, and hired additional personnel. Around 6,500 physicians and 12,600 nurses were hired on a temporary basis. The ramping up of hospital capacity resulted in almost a quadruplication of intensive care beds from 2,446 to 11,634 [21].

Table 1 Total tests performed relative to the size of population as of August 30, 2021

An underfunded health system and the relatively high level of informal labor—which affected about 60% of the population—made it particularly vulnerable to the spread of the virus due to the inability to stay home and lack of financial resources to stop working among other issues [22]. Furthermore, the pandemic also occurred at a time when there were shortages of supplies and human resources in the health system mainly due to budget cuts.

An analysis of the vaccination strategy shows that the approach was correct. It focused on mortality distribution by age group, comorbidities, and geographical distribution of the burden of mortality—the number of deaths in a municipality over the total number of deaths. Mexico’s, 2,457 municipalities are divided into tertiles in which the accumulation of deaths was the changing factor. This strategy was designed by a Technical Advising Group (TAG) for COVID-19 which recommended vaccinating health personnel first followed by the population according to the risk of death due to comorbidities and in those who live in poor regions with high population density. This was consistent with the official recommendation of the WHO. There is evidence to suggest that the strategy that first protected the most susceptible and then reactivated the economy. It is most successful in the medium term [23]. Data shows that with eight vaccines approved for emergency use by the Federal Health Commission for Protection (COFEPRIS), as of August 30, 2021, around 44% of Mexico’s population had received at least one dose, and 25% were fully vaccinated (Table 2) [24].

Table 2 Share of the population that has been partly or fully vaccinated against COVID-19 as of August 30, 2021

Applying the Systems Approach to the Mexican Context

The complexity of the COVID-19 pandemic can be overwhelming, especially when attempting to implement interventions to control the impact of the virus in the community. In Mexico, the number of competing factors to consider places the policy-maker and the program implementer in a difficult position. By utilizing the systems thinking approach, the individual can better visualize this complexity and the factors in and around a potential intervention, revealing additional impacts and unintended consequences that may result from the implementation of the intervention in the real world.

To illustrate this process, a systems map using knowledge from the previous section has been created (Fig. 4). The process by which the map was created and the way to most effectively utilize it for public health purposes will be discussed in the following section.

Fig. 4

A basic systems map analyzing the system surrounding the prevalence of COVID-19 in Mexico. Note Key themes including political, educational, health, and economic are color coded. Interactions between factors are also colo coded; with red meaning a negative interaction and blue meaning a positive interaction

Building the Systems Map

As previously stated, the systems approach serves the purpose of capturing and visualizing the factors surrounding an issue by mapping their potential influence. There is no standard process for creating a systems map. Whatever is easiest for the creator to understand and provides the clearest interpretation by others should be used. In the case of the map in Fig. 4, four areas surrounding the prevalence of COVID-19 in Mexico were explored: political, educational, health, and economic. These four dimensions motivated the inclusion of the factors shown in the map that influence the central factor, COVID-19 prevalence in Mexico.

This map was created by simply adding any known factors that would affect the central bubble and placing them around it in rough quadrants corresponding with their closest related dimension. It would be an immeasurable task to add an exhaustive list of factors to any systems map. The intention is not to provide a perfect tool to begin with but to provide a more accurate estimate of real-world scenarios than provided by the traditional theory of change or logic model. For this reason, it is unnecessary to fixate on adding every possible factor. It is better to add the known factors that quickly come to mind from situational knowledge and readily available research. The map in this chapter was created using the experience of the authors of the Mexican COVID-19 response as well as additional research needed to set the context.

After the major factors were arranged around the center, more linear causal chains were added. For example, ‘number of tests done’ was one of the original factors in the map. However, to complete the chain, the outcomes of testing and positive cases and negative cases were added as well as the preceding factors like the availability of tests and distance to clinics. Using this logic, the remaining factors were added to the map until the relevant linear causal chains seemed complete.

The final step in the creation of this map was to code all of the relationships between factors as either ‘positive’ or ‘negative’ interactions and deliberately seek any influences between the non-linear factors. The concept of ‘positive’ and ‘negative’ reactions can be a bit confusing but they are critical to systems thinking and the use of the map as a tool for the design of programs and policies. The systems map is dynamic. At any point, all the interactions are happening at the same time. The ‘positive’ or ‘negative’ nature of one can cause a cascade of effects throughout the system. In essence, a positive reaction would be one in which both factors have the same reaction to a change in the system. As one increases the other increases. Or the opposite, as one decreases so does the other. A ‘negative’ interaction is often described as inverse or opposite. As one factor increases, the other decreases or as one decreases, the other increases. Every factor in the map has one of these interactions with multiple other factors. Mapping them and labeling them allow the map to better represent the dynamic nature of real-world scenarios.

As interactions are mapped, it is likely that other factors or interactions that were not previously considered come to mind. This is also common when one is deliberately looking for interactions with unrelated factors. These factors should be added to the map and their interactions fully explored as well. When the addition of factors and interactions on the map begins to feel repetitive, the map is deemed to be complete.

Important Elements of the Systems Map

Feedback Loops

The non-linearity of systems thinking and systems maps means that there are multiple-feedback loops throughout any map. Feedback loops are circular chains of events where the chain of interactions returns to the original point of disturbance in the system. In the context of Mexico, there is one example that is quite straightforward and clear. In the map (Fig. 4), the center bubble ‘prevalence of COVID-19 in Mexico’ has a positive reaction to ‘fear of COVID’. As the prevalence increases so does general fear of the virus. The increase in fear leads to an increase in protective behaviors, in this case, ‘mask wearing’. More ‘mask wearing’ leads to less ‘community spread’ in turn leads to less ‘positive cases’ and finally leads to less ‘prevalence of COVID-19 in Mexico’, the original factor that started the interactions in the loop (Fig. 5).

Fig. 5

A closer look at the feedback loop being referenced from Fig. 4. Note Factors in the loop have bold borders and interactions within the look are shown via bold arrows

Feedback loops are all over the map. Consider the following loop that takes a slightly different path through the map with the same start and end point of ‘prevalence of COVID-19 in Mexico’. Increased prevalence increases fear just like in the last loop. However, increased fear also leads to more ‘misinformation’. The increase in ‘misinformation’ leads to a decrease in ‘mask wearing’ and a subsequent increase in ‘community spread’. Increased ‘community spread’ drives up ‘positive cases’ which in turn drives up the ‘prevalence of COVID-19 in Mexico’. These are two feedback loops with the same start and end point but with opposite effects on our outcome of interest (Fig. 6).

Fig. 6

A closer look at the feedback loop being referenced from Fig. 4. Note Factors in the loop have bold borders and interactions are shown via bold arrows

Feedback loops exist in all systems. Understanding where they exist and how they overlap gives the public health practitioner and policy-maker a much better sense of how their intervention will change, not just the linear interactions between factors, but also the circular feedback of the system and the factors surrounding that intervention.

Leverage Points

For any looking to employ systems thinking, recognizing leverage points is a critical skill. A leverage point is a location within the system, often a single factor, that has a large impact on multiple other factors. Leverage points are recognized through the number of interactions they have in the systems map. The more interactions, the more influential a change in that factor will be on the entire system.

Using the Mexico systems map, one of the recognizable leverage points is ‘misinformation’. This factor influences six separate factors on the map across multiple sectors. If one were to investigate a way to influence the system, the ‘misinformation’ factor would be the place that would have a big impact with a small change. This is the critical importance of identifying the leverage point. It is the place where the largest impact can be made in the system with the smallest input. There can be multiple leverage points in any system and affecting any of them can make large changes, but recognizing them and the downstream effects they will have on the system is critical for any systems analysis.

Unintended Consequences

One of the distinguishing factors of systems analysis as opposed to logic models and theories of change is the ability to show potential unintended consequences of an action. While the factors from other models can be identified in a systems map, it is the additional interactions between these factors and other elements in the system that offer greater insights into the overall impact of a program on the entire system. In essence, examining these interactions can provide more comprehensive information about the potential effects of the program. These effects outside of the smaller model types, whether negative or positive, are called unintended consequences. They can be identified simply by outlining the primary pathway a program is targeting in the system and then examining all of the chains of effects each individual factor in the primary path has on the surrounding system. Often the resulting effects can be surprising and even counter to the ultimate goal of the primary chain. For example, in the map created for the Mexican context (Fig. 1), a primary goal targeting vaccination to reduce COVID-19 prevalence that is successful will cause effects beyond its own pathway. This includes a reduction in overall fear of the virus that could reduce people's motivation to get vaccinated, and thereby reduce the vaccination rate. This would be running within the system at the same time as the successful campaign to improve vaccination. For this reason, it is critical to examine the unintended consequences within a system. Programmers and policy-makers can either build in layers to address these offshoots, find the potential consequences to be negligible, or even find the consequences to be too great to overcome and scrap the proposal altogether. Without systems thinking and mapping these pathways, unintended consequences would go unnoticed (Fig. 8).

A Proposed Strategy Based on the Map

Despite a large low-income population in Mexico, educational levels appear to be high. Educational levels directly influence the health literacy of the population, which increases vaccination acceptance. A study published by Lazarus et al. projected Mexico with a vaccine acceptance rate of 76.3% due to a decrease in misinformation and related perceptions like optimism bias and ultimately improved community engagement [25]. By following this route and identifying the factors previously mentioned as leverage points, a behavioral communication strategy, focused on decreasing the optimism bias and misinformation, could be developed (Fig. 7).

Fig. 7

A closer look at the visualization of the described ‘misinformation’ leverage point. Note Factors affected by interactions with misinformation and the interaction arrows are shown in bold

Fig. 8

A closer look at the unintended consequence described. Note All factors and interactions are shown in bold. The unintended consequence factor ‘fear of COVID’ is shown in red text

An example based on evidence from two protocols developed at the Universidad Autonoma de Queretaro in Mexico shows that ‘task shifting’ or ‘train the trainers’ strategies work due to the confidence the population of Queretaro has in its peers and trained professionals [25, 26]. Both of these strategies were developed using identified leverage points from a systems analysis including educational levels, the age of the surveyed participants and patients, the lack of trust in the official institutions, and high peer support levels. The adaptation of these strategies in a national context could be assessed through Fig. 4, a broader analysis of the situation at that point in time.

Applications Beyond Mexico

Applying systems thinking to public health issues is becoming increasingly important in our interconnected world. As seen through the above example of systems analysis using Mexico as a model, many complexities in public health work are diverse and highly interconnected. By using systems thinking, public health professionals can better visualize and plan for this complexity. Beyond the example of Mexico, this process can be applied to any number of issues in the public health space. One example of the use of systems thinking is in a novel course developed by New York University and UNICEF, titled “Behavioral Communication Strategies for Global Epidemics” [28]. Participants in the course start by using a systems approach to understand outbreaks and humanitarian crises in their full complexity. Through this process, participants develop strategies that have the largest impact on the system and allow them to think critically about and plan for unintended consequences. The result is a set of strategies to mitigate the spread of disease that are robust, feasible, effective, and context-specific. There is no set methodology for systems mapping and analysis other than providing wide flexibility to adjust to any challenge. Simply observe the issue of interest and choose a central factor to map around to start. From there the research and body of knowledge at the time will guide the creation of the map. An understanding of the basic components of the system as described above, will help to make the map a more useful tool. Regardless of the issue, location, and context of a systems analysis, the accompanying systems map places public health professionals in a stronger position to confidently move forward with programs and policies.

Conclusions

Systems thinking provides a new and innovative perspective to complex challenges. Any health issue in any country that increases the burden of disease needs a broad analysis of the different spheres and areas involved. Through a systems map, the loops, leverage points, and unintended consequences can be found to target new interventions or to strengthen existing ones. It is important to remember, however, that system maps have their limitations. The complexity of real-world systems is too great to be fully visualized. There are a number of immeasurable factors that are ever-changing both in number and levels of influence. A static systems map does not change the way a true dynamic system can and will. There are ways in which maps can be created for public health use. Involving as many local stakeholders as possible, keeping the scope of the map within a narrow space and time, and making adjustments to the map as time goes, on allows the best possible representativeness of the actual real-world scenario to be depicted.

The COVID-19 pandemic provides a perfect example of why systems thinking is so important. The modern world has never before seen a virus affect its entire population with such ease. There is not a single nation on this planet that has not felt the effects of the COVID-19 pandemic. In our ever more interconnected world, persons in every continent, region, nation, state, and city live within their own systems that are separate yet all influencing each other to some degree. Strategies and policies which worked in one location or community may not work at all in another, and the public health field cannot pretend that solutions are one size fits all. The intricacies of each of these levels of systems can be visualized to aid in making participatory, innovative, and highly effective interventions and policies that can make a big impact on their target communities while minimizing the damage from unintended consequences. Systems thinking is a tool for public health professionals to take advantage of a field where undoubtedly, everything affects everything. During emergencies such as COVID-19, non-communicable diseases (NCDs), neglected tropical diseases, and issues related to water, sanitation, and hygiene, it is crucial for public health professionals to comprehend the broader system's impact. This understanding is essential to achieving more significant impact and better results for the populations they serve.