Collecting data is about more than measuring global progress. What matters is not how big of a pile of data can be amassed—indeed, big data are not necessarily better. Quantity is not the same as quality, and to look at quality we must look beyond the outcome, and see “data collection” for what it is (or at least, what it should be)—a process. Much like the process of implementing development projects, the characteristics of the data collection process can make or break the meaning of data for the people whose lives, their struggles and resilience, are represented as numbers in a chart, shading on a map. I learned this during my time as a Peace Corps Response Volunteer in Ethiopia, as I witnessed the value of Ethiopia’s community-level actors, including local nurses, health extension workers, and the women’s health development army, for disease treatment and prevention. My story is about the tangible benefits of collecting locally disaggregated data and the process of collecting the data.
Trachoma is a bacterial infection of the eye that can spread through direct contact with infected eye or nasal discharge, or other by agents such as flies or washcloths. It is a leading cause of blindness and vision impairment in affected countries. The WHO has set a goal for the global elimination of blindness by 2020 (GET 2020) and developed a set of strategy recommendations known as SAFE: surgery, antibiotics, facial cleanliness, and environmental change. However, with only aggregate country-level data or WHO estimates available, countries cannot determine where trachoma is endemic, and the SAFE strategy most needed, at the district level. Simply put, we need disaggregated data to mobilize capabilities and resources and to monitor progress.
Enter the Global Trachoma Mapping Project, which aims to map district-level trachoma prevalence in regions throughout Ethiopia. You can read more about the project here, but I want to give you a glimpse of a day with the health care workers in Ethiopia who are carrying out the mapping, visiting the households that eventually become shaded areas on a prevalence map. While I was working at a clinic in Ethiopia, I was invited by an ophthalmologist to participate in the training, with a room full of nurses and staff from the local eye hospital. We started the day with a refresher training (most of the nurses had been trained before) and assessment, based on a standardized curriculum, for identifying different signs of trachoma and determining whether antibiotics or surgery are needed. Graders (those who would be examining people’s eyes) and Recorders (those who would be recording the information on smartphones) received separate trainings. Then, in teams each consisting of a grader and a recorder, we piled in a car and drove to a village to conduct mapping, the nurses singing songs about trachoma elimination most of the bumpy way.
At the village we met a woman from the health development army—a tremendous community-level resource in Ethiopia. Women from the health development army are assigned a number of households in their community for whom they will serve as a link for important education and health information, among other tasks. They know each household well, and are trusted by their neighbors, and importantly, are from the community they are serving. The woman accompanied us to each house, where we checked and documented each person’s eyes and their household’s access to water and a latrine. If anyone was absent from the house, a child or an adult, we made a note so we could return. If a child was found with signs of eye infection, we provided antibiotics for parents to administer.
Our tools were simple, yet powerful. Headlamps, a smartphone and antibiotics. But with these tools, each and every person in that village became a part of a crucial dataset for eliminating trachoma in the country. The GPS system on the phone also meant that each household, and this specific community, could be mapped, and we would be able to see correlations between trachoma prevalence and availability of water and latrines. We would be able to see places that called for urgent action as communities with endemic trachoma were filled in, and also see places where we could celebrate that, with continued prevention methods, trachoma did not have a tight grip. These data are standardized, and thus useable for national and global comparisons, and are loaded into the central data collection system almost immediately, and later, can be seen on maps accessible online.
When you are walking house to house, asking people for permission to enter their compound and check their eyes, you feel that there is a benefit to district level data collection, conducted by local people, that extends beyond the incredible data itself. I think allowing programs to be implemented based on global estimates, whether regarding disabilities, maternal mortality and morbidity, or neglected tropical diseases like trachoma, sends a message that their impact on the affected people’s lives isn’t worth the trouble of counting. When you start to count, when you are there, you know what the numbers represent. Loosing one’s eyesight in Ethiopia, especially when one lives in a rural village such as the one we visited, has a tremendous impact on one’s life. Also, checking the latrines and water access after checking people’s eyes for trachoma opens up a space for explaining to the household why we are connecting these two, perhaps opening up a space for subsequent SAFE strategy implementation. And in the cases where antibiotics were distributed, hopefully there is now one less household risking the transmission of trachoma between family members.
In closing, I must return to the singing in the car, because of what it symbolizes as it still plays in my head. These were not just songs about eradicating trachoma; they were the expression of the kind of energy that comes when people are working together to promote health for their brothers and sisters. These health workers who collected the data have seen firsthand what blindness means in Ethiopia, how much it impacts one’s ability to do crucial tasks like collecting water, herding animals, working, cooking, earning an income. They see the people who travel to their hospital hoping for a surgery to correct their blindness. Perhaps (hopefully) they, or other local health care workers will be involved in the SAFE strategy programs that can be developed based on these data. Of course, what remains to be seen is the action that will be taken with this data. The struggle for data is not over. But local actors going out to households, documenting people’s lived experiences of disease or lack of basic sanitation, in a way that can produce reliable, usable, meaningful and globally comparable data is a tremendous start. That is the power of local data.