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Background: Algorithms for predicting retinopathy of prematurity (ROP) requiring treatment need to be validated in Indian settings to determine if the burden of screening can be reduced without compromising the sensitivity of existing gestation and weight-based cut offs., Objective: To evaluate the performance of the available algorithms namely, WINROP (Weight, Insulin-like growth factor I, Neonatal ROP), CHOP-ROP (Children's Hospital of Philadelphia ROP) and ROPScore in predicting type 1 ROP and time from alarm to treatment by each algorithm., Study Design: Ambispective observational., Setting: Tertiary care neonatal intensive care unit in India., Participants: Neonates less than 32 weeks or less than 1500 g born between July, 2013 to June, 2019 (N=578), who underwent ROP screening., Primary Outcome: Sensitivity, specificity and time from alarm to treatment by each algorithm., Results: The sensitivity and specificity of WINROP was 85% and 36%, for CHOP-ROP it was 54% and 71%, and for ROPScore it was 73% and 67%, respectively in detecting type 1 ROP. A total of 50/51 (98%) of neonates with type 1 ROP underwent treatment at median gestation of 9 weeks and median time from alarm to treatment by WINROP, CHOP-ROP and ROPScore was 7, 7 and 3 weeks, respectively., Conclusion: WINROP, CHOP-ROP and ROPScore were not sensitive enough to replace the gestational age, weight and risk factor-based screening criteria for type 1 ROP.

Background: Different hypotheses have been proposed in life course epidemiology on how a time-varying exposure can affect health or disease later in life. Researchers are often interested in investigating the probability of these hypotheses based on observed life course data. However, current techniques based on model/variable selection do not provide a direct estimate of this probability. We propose an alternative technique for a continuous exposure, using a Bayesian approach that has specific advantages, to investigate which life course hypotheses are supported by the observed data., Methods: We demonstrate the technique, the relevant life course exposure model (RLM), using simulations. We also analyse data from a case-control study on risk factors of oral cancer, with repeated measurements of betel quid chewing across life. We investigate the relative importance of chewing one quid of betel per day, at three life periods: ≤20 years, 21-40 years and above 40 years of age, on the risk of developing oral cancer., Results: RLM was able to correctly identify the life course hypothesis under which the data were simulated. Results from the case-control study showed that there was 74.3% probability that betel quid exposure earlier in life, compared with later, results in higher odds of developing oral cancer later in life., Conclusions: RLM is a useful option to identify the life course hypothesis supported by the observed data prior to the estimation of a causal effect.