We finished the Microfinance and Impact on Public Health class on Friday. During the course of the class, we (the students and faculty) built a model of the interaction between these two disciplines. Jean and I are now meeting with various stakeholders to get their input on the model. Jose Miguel, COO here, is the first to see it this afternoon. We then will meet with officials at FONDESA, the microfinance institution we worked with. Then the last two or three days of the week, we will try to meet with each of the credit analysts and families that the students interviewed. We’ll show them the model and ask the question, “Does this show how microcredit has impacted your family?” In other words, how well does it fit with what they have experienced? So we have a busy week ahead. We are also in the process of submitting paper proposals and writing rough drafts for articles.
Jeanie: In addition we are summarizing our class work and evaluations. Today I will report on the health status comparisons between Santiago and Omaha.
You may remember the class from 21 January from the blog of that day. Here is a brief summary: We began class by reviewing the concepts of the social determinants of health and the epidemiologic transition. Then we accessed WHO (World Health Organization) tables Dennis had located for me back in November. We compared Haiti (one of the least developed countries in the world), the DR (a middle developed country which shares the island with Haiti), and the US. We began with economic and demographic data. I let the students pick from the WHO tables what statistics they were interested in and then we created a table on the white board. There were no surprises but it was still quite interesting. For example, the average age in Haiti is 21 while in the US it is 36! The epidemiologic transition suggests that infant mortality decreases before fertility does—that is, families continue having many babies, knowing that many of them will die. But when they don’t die, it creates an age bubble (kind of like the boomer bubble that happened in the US for different reasons). One of the interesting stats we found is that the adolescent fertility rate in the DR is a LOT higher than either Haiti or the US. We suggested that at some point the students might want to look into that further.
After exploring WHO data on education, mortality, infectious v chronic disease and other health measures we divided the class into groups of 2 to 3 students. Each group got a list of about 5 health measures. Ideally, we wanted students to compare the cities of Santiago and Omaha on these measures—realistically, we encouraged them to at least compare the DR with the state of Nebraska rather than the whole US. For most of the measures I had already found sources of data (actually, Dennis found many of them!!) which I made available to the students, although they still had to sort through the tables and/or process some of the data. For example, we had total number of adult deaths in Omaha in a particular year and the total adult population but not the adult mortality per 1,000 people. We also had some of the data in a report in Spanish. For one measure in each group we had only the Santiago/DR or Omaha/Nebraska but not both.
The teams had several days to find the data for their assigned measures and discuss it. They then created a one-pager with the data and their speculations and questions about the statistics they found. We distributed this document to all the students a few hours before class. In reviewing it, I pulled up several points to discuss with the students before beginning to discuss the actual data. We spent time considering correlation and causation, description and explanation. We also discussed single data points in contrast to trends. Finally, we talked about how to present discussions about the meaning of statistics, making it clear when statements are based on other information and when they are speculation.
As a class we then discussed the findings by looking at them in groups. We began with economic indicators (ie: unemployment) and other social indicators (ie: high school graduation). We found that even these basic statistics were hard to compare. Students noted that the data sources they had found did not explain how they had defined “unemployment”. In the DR, many, many people are employed in the informal sector and we do not know whether or how they were counted. From earlier class content and discussion, we listed the possible health impacts of the economic and social indicators. We then compared health services (ie: number of doctors per 10,000 people). Students were shocked that the US measles immunization percentage was lower than in the DR. This was also one of the statistics where the students could clearly see the challenges of finding comparable data. One group found the percentage of 1-year-olds immunized for measles in the DR but could only find the percent of children in Nebraska who were “fully immunized” by the age of two. Finally, we looked at mortality figures and at other health indicators such as tobacco use. Again, surprises for the students: more than 25% of adults over 15 in Douglas County (Omaha) smoke while just over 15% of adults in the DR smoke. In discussion the students recognized how few tobacco ads they had seen along with connecting lower smoking with extreme poverty.
The final step of this assignment was a one-page reflection. We gave them 4 questions as guidance but asked students to focus only on 1 or 2: What most caught your attention about the health comparisons between Santiago and Omaha? What would you like to know more about to better compare the two? What do you think is the most important health issue to address in each population? What do you think ILAC and/or Creighton could do to improve the health of each population?
From observation and self-report, we know the students found this exercise to be interesting and stimulating. They looked hard for data; they thought about what the data meant and they asked questions. One group wrote in their one-page summary “we are confused by these results. Why would the United States have a lower [measles] immunization rate compared to that of the Dominican Republic?” Class discussion included both reasons the US rate could be lower and reasons the DR percentage could be inaccurate. Overall, the students in class stayed engaged with the data and discussion for over two hours.
Students reported surprise at how difficult it was to find comparable statistics. They identified problems with definitions, missing data, and questions about bias and the accuracy of reported data. For example, from what they have seen in the few weeks we have been here and from what they have learned in other classes, they find it very unlikely that 99% of pregnant women here receive at least one prenatal visit. Some women live 2 or more hours from the nearest medical facility but it is possible that at least some of these receive a prenatal visit from a community health worker such as the Cooperadores de Salud trained here at the Center. Equally, they questioned the statistic of a 99% adult literacy rate in the US. One student wrote, “I had never really questioned statistics before.” He found the uncertainty a little “disconcerting.”
The activity also helped students understand on a deeper level how interconnected many factors that contribute to health status are. As noted above, they discussed in some depth possible explanations for the lower smoking rates in the DR. With more time we could have explored the expanding export of US cigarettes and social justice issues of tobacco promotion in minority communities. Students also recognized that one cannot say the Omaha is more healthy because there are more doctors and longer life spans. From their perspective, Dominicans are much healthier emotionally and spiritually but need basic health education, improved nutrition, and sanitation, while people in the US need to increase their physical activity, lose weight, and quite smoking.
This activity reinforced and expanded another of the objectives we had for the class: understanding the differences between quantitative and qualitative research and the value and strengths of each. Students commented throughout the course that they had not realized that the kind of interviewing we did was “research” and some commented on how much they enjoyed it. Several wrote in their reflections on this exercise about the connections between the two. One wrote, “since our arrival we have…witnessed the poor education system here in the D.R. but seeing the actual [population with a high school diploma] statistics really made me aware of this tragic reality and disparity.” From the other side, one wrote that statistics seem a “poor indicator of reality” and another commented, “I would like to collect more qualitative data…it takes into account factors such as happiness and family relationships that quantitative data cannot count.”
Overall, I think the activity worked very well in exposing students to the importance and limitations of data as well as the complexity of “measuring” health. Because of the short time frame, they may have also developed a few inaccurate impressions of data. For example, in general the definitions used for data are reported but may not be easy to find and the students had neither the time nor the expertise to find them—so they assumed the definitions were not available. Equally, sometimes they assumed the data itself did not exist. That is probably true in some cases; in many, however, we just do not know where to look. I am sure we will continue to find more sources of data so that will be less of a problem when we do this class again.