Aisling Halliden Professor Frazier Human Development/Data Science 21 March 2021
Global Diseases as Impediments to Human Development
Introduction
Throughout my research of disease burden estimation and precision epidemiology globally and more specifically Asia, many identifications have been made about how this is inhibiting human development in the regions of focus. Health care is one of the major determinants of freedom– the most crucial foundation of development. Limited social and economic arrangements such as facilities for health care is a major source of unfreedom. In the following studies of disease burden estimation, the need for more extensive analysis of the impact this has globally is strongly emphasized. Over the past century, there have been considerable hindrances to freedom as development because of increased mortality, disease, and cancer rates. The overarching concern and focal point that every article addressed was what burden the varying diseases would have on populations. As I narrowed the scope of my investigation, Asia became a focus of interest in the context of a comparison to global studies of burden estimation. Several studies suggest that many of the developing and developed countries face extreme burden. A closer analysis of these studies and models will show evidence of this crucial human development problem so prevalent around the world at present. Children are dying from these diseases, people are losing working hours when they get sick, presenting a huge problem to the economy if they cannot work jobs. Eight articles that will be discussed in this review cover and delineate estimations for a wide range of disease burdens; Arsenic exposure in Bangladesh, ‘acute gastroenteritis and foodborne illness,’ ‘water, sanitation, and hygiene’ associated disease globally, chronic obstructive pulmonary disease, atopic dermatitis in Asia, dengue in Asia, cancer attributable to infection in Asia, hypertension in China, lymphatic filariasis in mainland southeast Asia. Common across all of these studies is the discussion of political intervention and how recently, governments have been taking substantial steps to understand the burdens of these diseases and what steps need to be taken going forward in order to improve the well-being of their respective populations. Even with extensive research and data on these diseases, policy-makers are still in the dark about the consequences of these estimations and are severely lacking adequate measures to help decrease mortality rates and increase life expectancy. As I began my review, I aimed to compare global disease burden with Asia which brought me to a narrower focus on just one specific region, starting with mainland southeast Asia. In a systematic “random effects meta-analysis by country and year” (Dickson 1), lymphatic filariasis was estimated in 9 different countries throughout Southeast Asia. Lymphatic Filariasis has one of the greatest burdens out of any other disease globally. This is also true for the region of Southeast Asia. This study has important implications for budget and control programs in the future related to increased drug administration because it was found that despite some elimination of lymphatic filariasis, extensive burdens still exist. Without studies like these estimating burden, it is extremely difficult for policy-makers to induce change and advocate for improved programs. Implications The study that proposed the biggest implications for society was the estimation of the burden of disease from water, sanitation, and hygiene at a global level, because of the strong connection it has to the current public health crisis. In developing regions, this burden is significantly greater and can be argued that there are increased disadvantageous effects that “diseases related to water, sanitation, and hygiene have on poorer members of society” (Prüss 541). This is extremely significant because this presents one of the many ways in which underdeveloped populations live with great unfreedoms and therefore are restricted in development. Interesting about this article is the idea of elimination status in which some countries of focus such as Bangladesh and Malaysia are close to reaching. While some of these countries have reached elimination status which is the threshold for what is considered a limited number of lymphatic filariasis cases, there is still limited and unreliable data that make it hard to estimate the prevalence of LF infection and mortality due to LF. In this particular study, researchers were unable to make compelling estimations of disease burden in Lao PDR, Malaysia, and Vietnam. I think this is immensely meaningful in this kind of analysis because it is important to not just focus on the countries for which estimations can be made, but for countries in which there is insufficient to do so. It is imperative that we investigate the reasons for this as I believe it has a strong correlation to human development and there are certain unfreedoms experienced by populations in these regions that render them unable to participate in these studies. This seems to be a common trend among these studies. Current data is limited and, in some regions,, there is not a significant enough amount of data to make any concrete predictions or to implement elimination programs. Throughout all of the articles discussed, there are severe limitations that will prevent evaluation of disease burden estimations. Also significant to this review is discussion of hypertension and related cardiovascular disease seen in China. This article makes strong connections to “rapid economic development, urbanization” (Bundy 227) in China which are other aspects critical to developmental freedoms. While it is important to consider health and how it relates to human development, it is also advantageous to consider the interactions between economic and social arrangements. The accelerated that has been seen in China has made Cardiovascular Disease (CVD) the number one cause of death. Hypertension (HTN), which leads to CVD is the biggest disease burden seen in China and has come as a result of “demographic and epidemiologic transitions characterized by declines in fertility and child mortality and increases in life expectancy” (Bundy 227). What this article is doing is very important, because not only is it considering the estimations of this disease, it is also walking readers through the causes of this shift. Explanations of the causes and effects are helpful in analyzing other risk factors. In many of the studies, there was a combined approach in which a more conservative estimate was made and then interpreted. That was then coupled with the more practical and likely estimates. This was critical in interpreting these models because the more conservative estimates look at a fewer number of factors, whereas the practical estimates considered a wide variety of factors which is more realistic as the number of factors contributing to data results is hard to limit. In the case of estimating acute gastroenteritis globally, the importance of adequate surveillance became crucial as developing countries who do not have access to that have a limited understanding of foodborne disease and thus, fall behind in determining foodborne diseases. Amartya Sen outlines many of the unfreedoms that inhibit development and this lack of technology seen in many developing countries serves as an example of that. Looking more closely at Asia and Bangladesh, respectively, it is evident that there is limited capacity in recording the data on cases. One article of significance was “The effect of arsenic mitigation interventions of disease burden in Bangladesh” (Lokuge), in which arsenic exposure fatalities are extremely high. The burden is exacerbated in Bangladesh because of health-care inaccessibility as well as unknown impact of intervention. This represents somewhat of a difference that was not seen in the other sources as it mainly assesses the effect of interventions themselves. There was a strong emphasis placed on what adverse and positive effects would come from interventions, rather than strict data points that showed what disease numbers would look like and not solutions. In the models of projections of COPD cases, an important question of policy was presented. Governments all over the globe were becoming increasingly concerned over the burden of this disease and major steps were taken to improve healthcare in order to be able to cope with the growing problem. Health as Emergent Property There is strong evidence that disease burden behaves as a complex adaptive system as it has the 5 most important features that make it up; It is essentially impossible to predict in perfect detail the outcomes, it is made up of emergent properties, it tends toward a greater complexity, does not tend towards an equilibrium, and each of the elements coevolve. There is this rich set of interactions between different factors involved in disease burden estimation. Because of the complexity of this system, there is no way to perfectly predict disease burden. An important example of this is the burden of food-borne diseases in which it is nearly impossible to definitively link every illness to food. There are too many complex factors at play to accurately give the number of cases. The number of acute gastroenteritis cases is estimated to be significantly lower than in indicative of the true burden of foodborne illnesses, leading to a sever underestimation in data. This is because people are undiagnosed as they may never seek medical care or get laboratory testing. Researchers do their best to make models that most closely fit what the consequences will be of certain diseases, but the future is never definitive. Another important feature that exemplifies disease burden as a complex adaptive system is the quick change from stability to unexpected shifts. The data may show consistency and then suddenly shift as the emergent properties begin to evolve. I believe that the health system is an emergent property that gives rise to certain conditions that create this inequality seen throughout the world. No one factor is responsible for the characteristics of this complex adaptive system, but the way in which they interact is what deems these diseases burdensome in different populations. It is a combination of technology, accessibility, and capital to a certain extent, that push the health system – which is under the umbrella of social and economic arrangements – to be an emergent property of this complex adaptive system. Gaps in Literature Despite such a large array of research and information, what many of these literatures lacked was an explanation of the implications it has for further into the future. For many of these studies, solely data and numbers were presented but no consideration of how this would impact countries on a broader scale, was presented. Another major gap seen in some of the literature was the use of past data to project future trends. Many of the sources seemed outdated as the data used had been from 20+ years prior. The most beneficial and relevant data comes from the present as it includes the longest span of time. Very few geospatial datasets were employed in each of this studies as most data was collected from worldwide databases dating back a couple of decades. It is the combination of all of these sources though that lead to the pursuance of greater development within each studied society, respectively. Looking Ahead The benefits of models extend far and wide as the applications for them can be used to solve the big gaps that we see in these studies. There is still a lot to be learned about why these vector and food-borne illnesses are prevalent in certain poorer populations throughout Asia as well as highly densely populated areas. We still need to answer the question of why people are getting sick. Across each of the studies, several different models were employed. Of greatest significance was the Markov-Type model seen in the estimation of water, sanitation, and hygiene disease burden and the TSIR model with a neural network in the study of seasonal dengue in southeast China. The TSIR model considers the time lags in transmission of vector pathogens. “I sub t is defined as the number of new local cases at time t and I’ sub t is the effective number of cases, both local and imported, that could have generated a local case at time t” (Oidtman). There is an equation then used to determine transmission coefficients. Models like this are extremely important as they also force us to consider how we can train out computers to learn from the data. In the study of the disease burden of COPD, they employed the use of the Markov-type model which “describe a disease number of linked health states and a cohort moves through these states from health to deteriorating health to (eventually) death” (McLean 247) Conclusion The study of disease burden estimation has led to a bigger question of health inequality in developing countries and health systems overall in regard to what they are able to offer. It is clear that there are major setbacks to development when determinants lead to these patterns of inequality in access to healthcare. To reiterate the most important focus in these studies discussed in the beginning of this review, there is an immense global problem impeding one of the most important aspects of human freedom and that is health. Overcoming these obstacles would change the course of a country’s development. It is crucial that these models were made as it has the potential to decrease mortality rates and increase life expectancy on a broad scale. More evaluative studies should be conducted to combat the growing dilemma of disease burden around the globe. Health and health systems is really the overarching concern and focal point of the review and each of the narrower studies I have discussed contribute to the bigger picture of disease burden. These models and estimations represent the need for a change in health and health systems all over the world and especially in the poorer regions of Asia which we discussed throughout the paper. It is not just about strictly looking at numbers and data collected from enormous databases, but it is about this process of precision epidemiology in determining ways to improve policy related to health. I think as we move beyond these models, it is important to focus more on how changing the present situation will help in the future. As of right now, there is a big focus on just the future, however those estimates are meaningless without looking at in the context of the current predicament we are in.
References