Case Study: Socioeconomic Impacts of Viral Public Health Shocks on the Caribbean Region
About a year ago, I started a research project to identify the socioeconomic impacts of viral public health shocks on the Caribbean region. I examined data from 6 outbreaks in different time periods to attempt to identify patterns over the years that could potentially lead to a knowledge bank of lessons learned from different regions and innovative approaches to addressing public health issues. I do not work in the public health industry, so this project was done purely out of curiosity. My interest was piqued after experiencing the COVID-19 pandemic. It was unlike anything I had ever seen in my lifetime, so I became interested in identifying trends and proposing policy solutions that can benefit the public and enable governments to be better prepared for future health shocks. While I haven’t published any official research paper, this blog is a summary of my preliminary analysis on the data that I’ve collected with the help of the Eastern Caribbean Central Bank.
For this research, I focused specifically on the Eastern Caribbean Currency Union (ECCU) members, Caribbean Community (CARICOM) members, and the Regional Trading Partners who don’t belong to these groups. I approached this study with 3 theories in mind:
There are similarities between how different regions were affected by the outbreaks.
There are differences between how different regions were affected by the outbreaks.
Males and females were not equally impacted by the outbreaks.
These are theories, not facts. So the subsequent analysis must continue using statistical tests and modeling to prove or disprove the theories. This will be completed at a later date. For now, I am sharing what I’ve discovered so far. All theories require further investigation but I will discuss an overview of the exploratory analysis.
As reflected in the image above, the 6 viral public health shocks in question are the Spanish Flu, SARS, Swine Flu, Chikungunya, Zika and COVID-19. In order to assess the impact of these viral shocks on the Caribbean region, data was also collected for the region’s Global Trading Partners and other Small Island Developing States around the world. It’s one thing to assess the Caribbean as an isolated region, but it’s very enlightening to compare the region to other parts of the world to see if there are any applicable lessons to learn from others.
Theories 1 and 2 suggested that there are similarities and differences between the impact of the outbreaks on different regions. Analyzing the data revealed some similarities.
First, real GDP declined in some of the regions, indicating that the shocks may have affected the economy. However, are these changes statistically significant? Second, there were more female cases overall but more male deaths in comparison. This was an interesting insight which begs the question of whether the difference in male versus female deaths is statistically significant. The third similarity was that similar age groups of people were affected by these viral outbreaks.
Some of the differences are as follows. First, the Global Trading Partners showed higher male unemployment while other groups showed higher female unemployment after the outbreaks began. The regions may vary in the gender composition of the workforce, especially in particular industries, so this insight makes sense. Second, the outbreaks resulted in different impacts on the unemployment rates, which indicates that there was no consistent trend among the regions.
Third, the respiratory outbreaks resulted in a decline in tourists, but the trend differed for mosquito-borne viruses. This is an indicator that the way in which a virus spreads may have an impact on tourism. The Caribbean is heavily dependent on the tourism sector, so it’s imperative that leaders and health officials understand how certain shocks would impact tourism and take innovative measures to ensure that the industry remains afloat.
My third theory is that males and females were unequally impacted by the outbreaks. While the sex-disaggregated data was limited among countries, the overall global trend is that there are historically always more female cases than males, but in the case of COVID-19 for example, we can see that even if there were more female cases, there were more male deaths which tells a very interesting story about how the sexes are impacted by infection, recovery and death. Why is it that more males are dying from viral outbreaks? Some researchers have suggested that females are more likely to go to the doctor, get tested and get treated, while males are more likely to avoid trips to the doctor altogether. I personally don’t have the data to support this theory but it is an interesting idea to explore. Since the Spanish Flu was so long ago (1918), it was difficult to find a lot of data, but the chart below illustrates deaths in the US before, during and after the outbreak.
Although there is a consistent pattern of more male deaths than female deaths in that era, it is also clear that during the outbreak, there was no deviation from that norm. Based on this data, should public health officials treat males and females differently when trying to curb the impact of a shock?
This analysis simply scratches the surface of all that there is to uncover with this topic, but my primary recommendation at this time is that leaders should focus on improving data/digital infrastructure as well as social infrastructure. More data disaggregation is needed in order to determine how different age groups, sexes and other groups of people are affected, and who needs to be targeted for recovery and prevention efforts. Data availability is also paramount. There can be no proper analysis without sufficient data. Lastly, training and education in data collection and collation for health professionals is needed to ensure that decisions can be made based on substantial facts. Much more work needs to be done to make more substantial policy recommendations, but this is just one example of how people can use data to ask the right questions and make important decisions.
