CDC Kenya Tests Efficient Method for Uniquely Identifying HIV Cases

HIV CBS pilot data from 124 facilities in two high HIV-burden counties in Western Kenya
Following the launch of the UNAIDSExternal
Generally, data managers link records when the name or an identifying number for a patient is exactly the same in two (or more) records. This linkage is likely when a person is tested in one clinic and starts treatment in another location. This traditional approach is referred to as “deterministic” matching. However, this method is often cumbersome without the unique identifier and not feasible for large datasets, such as the thousands of HIV patients in Kenya. Another approach is to link records using partial matching of names, numbers, demographics or location, using computer-based analysis of the “best possible” match (referred to as “probabilistic” matching); and then evaluating the accuracy of the process to accept or reject the match. This better accounts for differences in spelling, nicknames, name order, etc. The “best possible” method, or probabilistic matching, which was tested in Kenya, may offer a solution for large datasets.
CDC Kenya epidemiologists, in collaboration with other partners (University of California San FranciscoExternal
This type of study is a necessary contribution to surveillance efforts in Kenya to monitor the country’s progress towards meeting the UNAIDS targets.