Send to

Choose Destination
Plast Reconstr Surg Glob Open. 2018 Sep 14;6(9):e1910. doi: 10.1097/GOX.0000000000001910. eCollection 2018 Sep.

Geospatial Analysis of Risk Factors Contributing to Loss to Follow-up in Cleft Lip/Palate Care.

Author information

Department of Surgery, Division of Plastic, Maxillofacial and Oral Surgery, Duke University Medical Center, Durham, N.C.
Thrombosis Research Institute, London, United Kingdom.
Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, N.C.
Duke Global Health Institute, Duke University, Durham, N.C.
Division of Emergency Medicine, Department of Surgery, Duke University Medical Center, Durham, N.C.
Duke Global Neurosurgery and Neuroscience, Department of Neurosurgery, Duke University Medical Center, Durham, N.C.



Multidisciplinary cleft care depends on follow-up at specified time points to monitor and address functional or aesthetic concerns that may arise during a child's development. However, loss to follow-up (LTFU) is common and can lead to missed opportunities for therapeutic and surgical intervention. This study explores clinical, demographic, and geographic determinants of LTFU in cleft care.


Medical records were retrospectively evaluated for 558 pediatric patients of a single mid-volume cleft team. The primary outcome was LTFU. Spatial dependency was evaluated using variograms. The probability of LTFU was assessed using a generalized linear geostatistical model within a Bayesian framework. Risk maps were plotted to identify vulnerable communities within our state at higher risk of LTFU.


Younger age at last encounter was a strong predictor of LTFU (P < 0.0001), even when ignoring spatial dependency among observations. When accounting for spatial dependency, lower socioeconomic status [OR = 0.98; 95% CI = (0.97-0.99)] and cleft phenotype [OR = 0.55; 95% CI = (0.36, 0.81)] were significant predictors of LTFU. Distance from the cleft team and rural/urban designation were not statistically significant predictors. Cartographic representation of predicted probability of LTFU revealed vulnerable communities across our state, including in the immediate vicinity of our cleft center.


Geostatistical methods are able to identify risk factors missed by traditional statistical analysis. Knowledge of vulnerable populations allow a cleft team to allocate more resources toward high-risk areas to rectify or prevent deficiencies in care.

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center