Determinants of residential greenness and its association with prostate cancer risk: A case-control study in Singapore

BACKGROUND

Exposure to greenness has been shown to be beneficial to health, but few studies have examined the association between residential greenness and prostate cancer (PCa) risk. Our main objectives were to identify the determinants of residential greenness, and to investigate if residential greenness was associated with PCa risk in Singapore.

METHODS

The hospital-based case-control study was conducted between April 2007 and May 2009. The Singapore Prostate Cancer Study (SPCS) comprised 240 prostate cancer cases and 268 controls, whose demographics and residential address were collected using questionnaires. Residential greenness was measured by normalized difference vegetation index (NDVI) around the participants' homes using a buffer size of 1 km. Determinants of NDVI were identified using a multivariable linear regression model. Logistic regression models were used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) of associations between NDVI and PCa risk, adjusting for potential confounders.

RESULTS

Having a BMI within the second quartile, as compared to the lowest quartile, was associated with higher levels of NDVI (β-coefficient = 0.263; 95% CI = 0.040-0.485) after adjusting for covariates. Additionally, being widowed or separated, as compared to being married, was associated with lower levels of NDVI (β-coefficient = -0.393; 95% CI = -0.723, -0.063). An interquartile range (IQR) increase in NDVI was positively associated with prostate cancer risk OR = 1.45; 95% CI = 1.02-2.07). Stratified analysis by tumour grade and stage showed that higher NDVI was associated with higher risk of low grade PCa.

CONCLUSION

Our findings suggested that residential greenness was associated with higher risk of PCa in Singapore. Future studies on the quality and type of green spaces, as well as other factors of residential greenness, in association with PCa risk should be conducted to better understand this relationship.

Federated Learning for Electronic Health Records

In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data to obtain sufficient statistical power for certain hypothesis testings as well as predictive and subgroup studies. The wide adoption of electronic health records (EHRs) has made multi-institutional collaboration much more feasible. However, concerns over infrastructures, regulations, privacy and data standardization present a challenge to data sharing across healthcare institutions. Federated Learning (FL), which allows multiple sites to collaboratively train a global model without directly sharing data, has become a promising paradigm to break the data isolation. In this study, we surveyed existing works on FL applications in EHRs and evaluated the performance of current state-of-the-art FL algorithms on two EHR machine learning tasks of significant clinical importance on a real world multi-center EHR dataset.

Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: A Feasibility Study Using SG_T2DM Dataset

Background  Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats.

Objective  The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept.

Methods  We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard.

Results  The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks.

Conclusion  Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.

Timing of tracheal intubation on mortality and duration of mechanical ventilation in critically ill children: A propensity score analysis

OBJECTIVE

We aimed to investigate whether early tracheal intubation (TI) is associated with a reduced risk of mortality and increased ventilator-free days (VFD).

METHODS

We performed a retrospective cohort study of children 0 to 18 years old in a pediatric intensive care unit (PICU), between 2008 and 2017. Patient demographics, vital signs, and laboratory findings were extracted. Using a time-dependent propensity score-matched algorithm, each patient was matched with another equally likely to be intubated within the same hour but was actually intubated with ≤2 hours, 2 to 4 hours, and 4 to 6 hours delays. Outcomes were mortality and VFD.

RESULTS

Among 333 patients, the median age was 1.72 years (interquartile range [IQR] 0.17-7.75). Thirty children died (9.0%) and the median PICU length of stay was 6.7 days (IQR 3.9-13.2). Early TI did not decrease mortality significantly when compared to a ≤2 hour delay (odds ratios [OR] 0.86; 95% CI, 0.40-1.85), a 2 to 4 hour delay (OR, 0.81; 95% CI, 0.39-1.69), or a 4 to 6 hour delay (OR, 0.87; 95% CI, 0.43-1.79). Similarly, early TI did not significantly increase VFD. Patients with early TI had 0.09 more VFD (95% CI -1.83 to 2.01) when compared to a delay within 2 hours, 0.23 more VFD (95% CI -1.66 to 2.13) when compared to a 2 to 4-hour delay and 0.56 more VFD (95% CI -1.49-2.61) when compared to a 4 to 6-hour delay.

CONCLUSIONS

We did not find a significant association between the timing of TI and mortality or VFD in critically ill children.