Introduction
Rationalization, as defined by George Ritzer has four key components: efficiency, calculability, predictability and control. Efficiency refers to how quickly one can achieve an outcome or a goal. Calculability emphasizes numbers and quantitative data that’s used to inform systems and responses. Predictability is meant to be the assurance that a product, system or response is identical every time a consumer receives it, and control focuses on the use of human actions and non-human technology to control the consumers and the people involved in the production of the good or process in an effort to reduce human error and inefficiency (Ritzer).
The field of epidemiology is no exception to the vast number of industries that have been rationalized since the industrial revolution. Efficiency has been increased by putting more of the work epidemiologists previously did onto members of the community to collect and upload data. They do this through the process of syndromic surveillance which allows people to self-report symptoms without going through the healthcare system and allows epidemiologists to have access to data that no longer lags weeks behind when symptoms actually started (Johnson). The increasing role of technology has been one of the largest components in improving efficiency and calculability because things like electronic thermometers, blood pressure machines and other health measurers have increased speed and accuracy while decreasing human error in data (Bates et al.). New technology has also increased the amount and types of data available, providing epidemiologists with data they previously may not have been able to measure, and allowing them to draw more accurate conclusions as they are able to consider more factors of influence in disease outbreaks (Johnson).
The development of organizations that create a standard for epidemiologists (both in their level and amount of education and in their work practices) that allow them to work together is a method both of control (by limiting those who can access the career to those with specific qualifications and skills), and efficiency. Having a highly skilled labor force who interact with each other easily has allowed for more data to be shared and collaborative work to be done with minimal effort (Hossain).
These are only a couple of examples of how the process of rationalization has impacted the field of epidemiology but it’s important not to discount the irrationalities that exist as a byproduct of rationalization. Things like standardizing infectious disease frameworks to the extent that they ignore key differences between communities, relying heavily on the community for data collection which risks losing certain segments of the population (e.g. the elderly) and having the public disagree with public health officials actually increase inefficiency, disenchantment, homogenization and dehumanization in the industry (BBC).
Organizations for Standardization: Education and Occupation
One of the earliest forms of rationalization in the occupation of epidemiology was the development of organizations for standardization. One of the earliest and most predominant organizations for standardization that was created in the field of epidemiology was the International Epidemiological Association, founded in 1847. Such organizations seek to standardize epidemiologists in two different ways: the first type of organization provides educational standards, while the second provides occupational standards.
The role of these organizations that standardize education in the field are a prime example of introducing control within the field. Epidemiology is an inherently unpredictable, uncontrollable field of work, but the control aspect of rationalization has been achieved through the development of widespread standards. Having educational standards limits those who can access the career to those with specific qualifications and skills, creating a highly skilled labor force who interact with each other easily and more efficiently. Requirement and evidence of these standards of education mean that there are certain essential skills and knowledge that are required for people to hold this profession today. This controls the entry of people into the field by ensuring that they are capable of the work demanded of them. This also increases efficiency in the profession because it ensures that every person entering the field has the same baseline knowledge and skills which makes them more effective at their job. They spend more time learning what they need to know for the job in a rigorous educational institution rather than learning on the job (Hossain).
The existence of these organizations also increases predictability. Standardization means that despite the global nature of the occupation, when working interactively with other epidemiologists in different places, it’s likely they’ve been through a very similar (if not identical) program of study. This allows people to make predictions about each other's capabilities in order to delegate tasks and make progress, making working together on a collaborative basis much easier (Hossain).
The second form of organizations of standardization ensures uniformity once people have finished their education and are active, working epidemiologists by providing occupational standards. Well-understood chains of command and frameworks for action increase control and predictability because people are limited in what they are able to do, but this also increases efficiency through specialization and division of labor.
CDC’s Framework for Preventing Infectious Disease
One well-known standardized framework in the field of epidemiology is the CDC’s Framework for Preventing Infectious Disease. The three key elements in this approach are as follows: “strengthen public health fundamentals, including infectious disease surveillance, laboratory detection, and epidemiologic investigation,” “identify and implement high‐impact public health interventions to reduce infectious diseases” and “develop and advance policies to prevent, detect, and control infectious disease.” This framework, while seemingly broad, indicates the order and series of steps that epidemiologists are expected to undertake when their goal is to prevent an infectious disease outbreak. The primary suggestion encourages the improvement of surveillance techniques, identification and implementation of high-impact tools (e.g. vaccination rollout, tools to reduce interaction between humans and animals) and the creation of effective policies and legislation to support public health endeavors. It also emphasizes the importance of the ‘one health approach’ that addresses infectious disease prevention with a multifaceted lens, encouraging epidemiologists to consider human, environmental and animal factors in infectious disease. The outlined priorities and suggestions in the Framework for Preventing Infectious Disease exist for the purpose of creating control but are also intentionally broad to acknowledge differences between places and cultures. It ensures that the most efficient and effective context-specific approaches can be adopted and applied to the situation (A CDC Framework).
Evidence of the Benefits of Standardization
Evidence of the benefit of rationalization and standardization can be seen in the maternal and reproductive health area of public health in the US in particular. There have been historic disparities between white mothers and mothers of color in the US in this aspect of public health for a long time. Even when factors such as education, age and location are analyzed, mothers of color still typically suffer from significantly higher pregnancy and labor-related complications and deaths when compared to white mothers (Centers for Disease Control and Prevention). However, a recent study showed that standardized labor induction protocols in hospitals actually reduced these racial disparities. The protocol, or standardized framework, reduced neonatal morbidity in black women (down to 2.9% from 8.9%). It also reduced the percentage of black women who required a cesarean delivery (which is a much higher risk approach to labor than a natural birth, and evidence of a labor complication) from 34.2% to 25.7% (Hamm, Srinivas & Levine). This indicates that standardized frameworks and approaches to public health issues can not only increase predictability and efficiency, but also play an important role in reducing public health disparities between groups.
Non-Human Technology
The explosion of digital health technology and equipment was the second significant form of rationalization in the field of epidemiology. One of the earliest types of this equipment was a hand-pumped blood pressure machine, invented in the 1880s by Samuel Siegfried Karl Ritter von Basch (Frank). The development of machines with increased automation and precision then started being developed rapidly.
When dealing with infectious disease outbreaks, the most basic data to collect is of individual health factors. It is imperative that there are large quantities of this kind of data - enough to draw and extrapolate conclusions about entire populations on a solid and sound basis. Multiple technologies have been developed over time that enable doctors and epidemiologists alike to collect data more quickly and more efficiently from individual patients. Tools such as electronic thermometers and scales, blood pressure machines and other products of this nature (that allow highly accurate measurements of factors otherwise incalculable by the human eye) have greatly improved the efficiency, control and calculability of data and data collection that is essential to epidemiologists' roles (Courchay). Efficiency is improved because these technologies allow the same data epidemiologists were once hand collecting to be collected at an exponentially faster rate. This means that they can get to large swaths of populations at a much faster rate and get to the work of analysis and solution-implementation much quicker.
It also increases calculability because these technologies allow us to measure things that the human eye couldn’t have before. The quantitative nature of this data too, is an increase in calculability and allows mathematical models and computer modeling software’s to be created so conclusions are founded through the scientific process and strong evidence rather than arbitrary factors.
Control is also increased because human error in the collection process is removed. Epidemiologists no longer have to rely on a persons’ eyesight or worry about inaccuracy from large increments to provide us with data but maintain much larger amounts of control over the process and increase accuracy and efficiency by allowing a technology to do this data collection.
The role of the internet, which was developed in 1983 has also had incredibly significant impacts. The process of syndromic surveillance and event-based surveillance (processes developed in the mid 1990s following the combination of the development of digital health equipment and the internet) has become a large part of epidemiology since the development of the internet and plays a very important role in making epidemiologists jobs faster and more efficient. This type of surveillance gives this role of symptom collection and reporting to the public, asking them to provide personal, self-reported symptoms or other unusual activity that could signal a potential public health outbreak. Increased efficiency is clear to see in the case of syndromic surveillance, as epidemiologists no longer have to travel to collect data, but rather have it sent to their labs or computers directly. It also increases predictability by allowing the tracking of outbreaks in real time which allows epidemiologists to predict similar outbreaks in other places before they happen. Early tracking also allows predictions to be made about the rate of progression of the outbreak on a more accurate timescale and indicate possible ways to reduce transmission in the early stages of the outbreak (CDC and Prevention).
Shared Databases
Following the development of the above technologies and the internet, a third significant development in the field of epidemiology that helped rationalize and streamline the occupation was the increasing role of shared databases and computer modeling software’s that have a profound impact and role in shaping the work that epidemiologists do. Databases serve the function of creating a convenient location for epidemiologists to share data for a current, ongoing disease outbreak. This type of database seeks to share data across space rather than across time. One such example is the Open COVID-19 Data Working Group which was created by epidemiologists who accumulated and organized COVID-19 related data that existed in various different places across the internet, into one collective database (Johnson).
Databases provide evidence of increased calculability and improved efficiency and predictability. Increased predictability lies in the fact that such large amounts of data (evidence of increased calculability) are accessible to many epidemiologists, allowing them to draw conclusions from what they have access to, and extrapolate the trends they discover to their own situations. Databases are also highly efficient because it means that epidemiologists spend much less time exploring the internet and connecting disparate sets of data, and instead have everything in a single place. Because this is the case, it means that they can focus their efforts on higher order analysis and implementation.
The purpose of having access to other epidemiologists' information is to be able to apply it to different situations in the most efficient and productive way possible, in order to increase the health of people. Databases make this process much faster and more efficient.
Dehumanization and Disenchantment
One irrationality is the risk of increased dehumanization in the occupation. Because of the rapidly increasing use and reliance on technology for data collection rather than actual people, a sentiment of separation is created between epidemiologists and the people they’re trying to help. This shift in methods of data collection means that there is no longer as much human-on-human interaction as there was when the career first developed (Swanson). People in communities become data points and numbers on a screen instead of individuals with stories and lives.
This dehumanization, or separation of data points from the actual person they came from, can also result in disenchantment, that is, a disappointment or disillusion from the career that epidemiologists previously respected or enjoyed doing. Considering the fact that most people enter this field to help people and make a tangible difference in people’s lives, the influence and use of technology and associated sentiments of dehumanization mean that people don’t feel as though they’re doing the work they set out to initially.
The Risks and Benefits of Homogenization
The conversation about the risk of homogenization is arguably one of the most interesting when exploring rationalization and the development of irrationalities in the occupation of epidemiology. The idea of homogenization in this case refers to standardized frameworks and systemic responses to certain public health and infectious disease outbreak situations. The danger of homogenization in epidemiology occurs under a certain set of conditions, in which the frameworks that are designed and laid out actually decrease efficiency. This occurs when the frameworks are too specific and lack consideration of important factors that differentiate communities and outbreaks from each other.
One example where frameworks were implemented well were in different countries COVID-19 responses. In China, where the political system consists of a communist government and people are used to being told what to do, harsher restrictions were able to be implemented with less pushback or dissent (Wang). Alternatively, in the US, where individualization and freedoms and liberties are so strongly emphasized, the government knew they had to go about implementing public health measures in a subtler way (Lewis). This brief example shows the importance of taking into account environmental, political, cultural and economic factors when epidemiologists make public health recommendations. If they don’t consider these important factors and general measures are applied across the board, disregarding key differences between communities, this can ultimately lead to greater inefficiency.
Most infectious disease outbreaks aren’t as wide-scale as the COVID pandemic has been, and a significant proportion of them are concentrated in less developed nations. Often governments and NGOs work together to fight these outbreaks, but this ‘outsider’ support often attempts to implement blanket policies and measures that don’t take into account important differentiating factors as well as they should.
However, as people are beginning to recognize the negative impacts of homogenization, they’re starting to alter their approach. One positive example of homogenization is the Global Malaria Action Plan. The 275-page document outlines the importance of eradicating malaria globally and explores global policy as well as providing a cost-benefit analysis of investment in malaria control. The first sentence in the section titled ‘Introduction to Global Strategy’ acknowledges that “individual countries are often best positioned to know which actions are most appropriate depending on the populations at risk, the level of transmission, the degree to which interventions are in place, and the capacity of countries’ health systems to take these efforts further.” It then delves into the three key components of this framework: controlling malaria, eliminating malaria and funding research into new tools and approaches. The breadth of these factors is essential in allowing each country its own context-specific approach. The document also outlines regional strategies, differentiating between the prevalence of malaria, the vectors that transmit it and the species that cause it in different areas and recognizing these key differences in different global regions (including Africa, the Americas, Asia-Pacific and the Middle East and Eurasia). This framework, or approach to treating and eradicating malaria is a very good example of how homogenization can be useful, but context-specific research and strategies are also essential if the desired outcome is efficiency and success, rather than further inefficiency resulting from incorrect or unuseful approaches (Roll Back Malaria Partnership).
Conclusion
The future of epidemiology, or of any other occupation, is difficult to determine at this time in the world when technological development and globalization are increasing so rapidly, but in my research, I discovered that the combination of rationalization with the occupation of epidemiology shows an optimistic future. While some people predicted that the increasing role of technology in both data collection and analysis in the occupation would have caused a decline in the number of epidemiologists, data actually shows the opposite effect. The U.S. Bureau of Labor Statistics indicates that the employment change in epidemiologists between 2020 and 2030 is predicted to be a 30% increase. For comparison purposes, the BLS suggests that the average growth rate for all occupations in this same time period is 8% (US BLS). The reason that this is the case is likely the fact that because rudimentary tasks of data collection are now occurring with limited human involvement thanks to rationalization, more time is freed up for epidemiologists to spend on complex, higher-order thinking and meta-analysis. These types of tasks, non-routine and cognitive in nature, can’t be replaced by code or robots, so epidemiologists' main, and most important jobs of analyzing data in order to find trends, drawing conclusions and resolving public health crises are all but guaranteed to be safe (Markoff, 2016).
Ritzer’s principles of rationalization in the field of epidemiology epitomize the benefits of the process of rationalization and shows how useful it can be to humans, with limited detrimental effects. Aside from the irrationalities that are more difficult to overcome such as dehumanization and disenchantment, epidemiologists have learned to harness the potential irrationality of homogenization in order to reduce health inequities, disparities and inefficiencies from the implementation of new technologies. These irrationalities are often time-limited and overcome with additional training. And even though dehumanization and disenchantment do exist, they don’t prevent epidemiologists from doing their job.
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