[BLANK] >> Hello my name is Beverly Musick, I'm from Indiana University. And I'm here to talk today, about challenges of collecting data and resource constraints settings. So to start with, some of the physical challenges we experience are challenges with electricity. Quite often, there are power surges and resource constraint settings and the best way to counter these are with an uninterruptible power supply. Which can protect against voltage spikes. There are also frequent power outages and rolling blackouts. So a UPS or a battery system, can give a user 10 to 15 minutes to close all their applications and shut down computers. Normally when the unexpected outages occur. There are also solutions, with respect to solar powered batteries and gas powered generators. Other physical challenges include, internet access, which tend to be intermittent and in many places has a small band width. So, researchers would will definitely want to plan for additional time to transfer files from the sites. Geographical separation is also an issue. It requires costly face-to-face interactions and so, you want to build extra travel time into your budget. And most likely, you can rely on frequent conference calls to communicate directly with the sites. Even despite these physical challenges, we found that folks are very innovative in resource constrained settings. For example, in Mbarara, Uganda they built the clinic directly from the shipping crate that was used to send the supplies for the HIV Clinic. From the US to Uganda and here, you can see a picture of that crate were they took and cut out places for windows and doors. And were actually seeing patients inside the shipping crate, which became the facility for HIV care. In another instance in Kenya, they converted a living room from the IU guesthouse into the centralized data entry bank. There are often challenges in communication that stem from barriers in language. I recommend that you collect data using the participant's native language. And that you also have translate the instruments from English to the native language. And also have a back translation to ensure that the language you're using is accurate for that setting. You also want to try and use native speakers and on-site data entry clerks, as this allows for more immediate problem identification and resolution. So, the data entry clerks and the clinicians on the ground can interpret the notes made in the margin of a data collection form. Despite your efforts to overcome some of these communication challenges. There will be instances where you're not able to overcome them right away. And I have a few examples here, where things just sort of got lost in translation. So in a study of the elderly, we asked the interviewers to collect standing height. And in cases where the participant was too weak to stand, we asked them to collect the sitting height. As well as the height of the chair that the participant was sitting on. And, we found that there was one interviewer who's standing heights. The median of the standing heights were a little bit larger than other interviewers. And also, they had recorded chair height in all the cases where standing height was also recorded. And to our horror, we found out that the interviewer was asking this elderly participants to stand on the chair to measure their height. So, you always want to be looking at your data and make sure that your interviewers understand. Fully understand what you want them to collect. In another case, we ask for the site of cancer diagnosis and instead of putting the the site on the body of where the cancer was found. Interviewers or clinicians were writing in the location of the clinic, the geographical location of the clinic. And in other cases, we accidentally included zip codes in our data collection forms, in locations and countries that don't use zip codes. So there are also cultural differences, which require modifications to data collection forms. So for example, if you're testing for long-term memory, you need to use context-specific events. Such as, the death of Martin Luther King in the U.S. or the Kenyan independence from Britain in Kenya. Or something like the outbreak of the Nigerian civil war in West Africa, to measure long term memory in elderly population. In another case, we found that in Jamaica, it wasn't enough to ask about cigarette use or cigar use or chewing tobacco use. We also had to ask about marijuana use as one of the smoking habit categories. Another issue in cultural differences is, to be aware of collaborations and personal interactions with other countries. We often find people who want to please and may be inclined to tell you what you want to hear, and not necessarily what the reality is. In other cases, they are mild mannered or reserved and they may not initially speak up or express their needs. On one occasion, I was in Kenya for about a week and was asking my colleagues what they needed help with. What they needed training with, what they needed from me and they were reluctant to say anything until about an hour before my plane was leaving. And at which case they had quite a number of requests. So you have to be aware that there are differences in how folks are going to interact with you and how aggressive they may be. I mean in the Western world, we're much more strictly forward in our communications and we may even debate loudly over subjects. And you may not see too much of that in other cultures. So you just need to be aware that there may be differences in how folks interact in community. Another issue that we found in another cultural differences was, the lack of planning. Some cultures may be such that there does not require a lot of planning and so, you have to take extra care. In making sure that timelines are clearly communicated and in place. There are sometimes educational challenges with low literacy rates or even illiterate populations. And this posses a particular problem in identification of studied participants. In many cultures, people are known only by their nicknames, which are related to occupation or where they live. And their formal names, if known maybe given In different order or spelled differently on different occasions. Other common patient identifiers, such as dates of birth, may also not be usable in this population. As many many people do not know exactly when they were born. So what you might want to do is consider study identification cards, which include a photo graph. We also have employed methods for reviewing information collected from a previous encounters. To ensure that the participant has been correctly identified. Another educational challenge is research training. It's may be difficult to find qualified research partners in resource constraint settings. So, it's a good idea to plan time and resources for training and training maybe not just clinicians or research clinicians. But reasearch assistants, data managers, biostatisticians, etc. To fully assess data quality from resource constrained settings. We need to conduct in person data audits, where we select random sampled cases to be reviewed. And then we compare data recorded on the paper chart with the data that's entered into the EMR. And data audits can reveal data quality issues, which are undetectable by other methods. In one example, we realized that laboratory results were being entered on the return visit. Or in some cases, not at all, if there was no return visit, rather than on the date of the blood draw. So some of the samples, some of the results, look like they were one to three months older than they actually were. And it was skewing the results, because many of the sicker patients may not be returning to clinic. And so, their blood results never got into the database. Another case that we found was, quite often when medications or other information was omitted from a data collection form. The data entry clerk was returning to previous visits and entering information from previously populated encounter forms, regardless of the date. And this of course, can lead to problems with accuracy in data. So, just a few additional recommendations. I recommend you a pilot test all your data questions forms that you visit a study sites to obtain first-hand knowledge of how things work. And I think there's any substitute for seeing with your own eyes how things work and how the clinic flows and how people interact. It makes a world of difference, even when you return to your homeland and you're making decisions about the data that you see. It really helps to have an idea in mind about how the clinic operates. You definitely want to review your data early and often. And try to make each project a partnership with folks from the resource constrained setting. You just, it enhances, it makes the project better and it also gives back to the other countries. And you also want to respect and acknowledge local customs in any research that you do.
