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Background Most studies rely on clustered analyses to study how the characteristics of health facilities influence individual outcomes. Our aim was to perform a probabilistic linkage between individual and health facility data to enable individual-level analyses. Methods We linked data from the most recent female questionnaire from 11 countries monitored by the Performance Monitoring for Action 2020 to a master health facility dataset (appending all rounds of surveys). Only women that reported which type of facility they visited were considered in the analysis. A probabilistic linkage was performed using 13 blocking variables (e.g., facility type and cluster of residence/location of the woman/facility) and 11 matching variables (e.g., types of contraceptive methods used/offered by the women/facility). Each concordant matching variable received a + 1 score, or a 0 score otherwise. We assessed linkage quality by pooled odds ratio of non-matches according to wealth tertiles (richest vs. poorest) and area of residence (urban vs. rural) using a meta-analytical approach. Results A total of 21,102 women and 7,056 facilities were considered in the linkage process. The average match rate was 57.9%, ranging from 42.5% in Indonesia to 69.1% in Burkina Faso. The pooled odds of non-match were 74% higher for the richest women compared to the poorest, and 67% higher for women living in urban areas compared to rural areas. Conclusions High match rates were achieved in countries with sufficient information on public and private facilities. The lack of information about private facilities contributed to the higher odds of non-match among the better off. Key messages We performed a probabilistic linkage approach to link individual and health facility data, making it possible to understand how the characteristics of health facilities can influence individual-level outcomes. Our findings also bring light to the importance of sampling both public and private facilities, aiming to maximise match rates and reduce differences on match rates according to socio demographic characteristics of the sample.

1369Probabilistic data linkage of health facility and individual data using PMA2020 surveys