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RationaleEvidence for the association between fine particulate matter (PM2.5) and mortality among patients with tuberculosis (TB) is limited. Whether greenness protects air pollution-related mortality among patients with multidrug-resistant tuberculosis (MDR-TB) is completely unknown.Methods2305 patients reported in Zhejiang and Ningxia were followed up from MDR-TB diagnosis until death, loss to follow-up or end of the study (31 December 2019), with an average follow-up of 1724 days per patient. 16-day averages of contemporaneous Normalised Difference Vegetation Index (NDVI) in the 500 m buffer of patient’s residence, annual average PM2.5 and estimated oxidant capacity Ox were assigned to patients regarding their geocoded home addresses. Cox proportional hazards regression models were used to estimate HRs per 10 μg/m3 exposure to PM2.5 and all-cause mortality among the cohort and individuals across the three tertiles, adjusting for potential covariates.ResultsHRs of 1.702 (95% CI 1.680 to 1.725) and 1.169 (1.162 to 1.175) were observed for PM2.5 associated with mortality for the full cohort and individuals with the greatest tertile of NDVI. Exposures to PM2.5 were stronger in association with mortality for younger patients (HR 2.434 (2.432 to 2.435)), female (2.209 (1.874 to 2.845)), patients in rural (1.780 (1.731 to 1.829)) and from Ningxia (1.221 (1.078 to 1.385)). Cumulative exposures increased the HRs of PM2.5-related mortality, while greater greenness flattened the risk with HRs reduced in 0.188–0.194 on average.ConclusionsIndividuals with MDR-TB could benefit from greenness by having attenuated associations between PM2.5 and mortality. Improving greener space and air quality may contribute to lower the risk of mortality from TB/MDR-TB and other diseases.

While population biobanks have dramatically expanded opportunities for genome-wide association studies (GWAS), these large-scale analyses bring new statistical challenges. A key bottleneck is that phenotypes of interest are often partially missing. For example, phenotypes derived from specialized imaging modalities are often only measured for a subset of the cohort. Fortunately, biobanks contain surrogate phenotype information, in the form of routinely collected clinical data, that can often be leveraged to build machine learning (ML) models that accurately predict missing values of the target phenotype. However, simply imputing the missing values of the target phenotype can invalidate subsequent statistical inference. To address this significant barrier, we introduce SynSurr, an approach that jointly analyzes an incompletely observed target phenotype together with its predicted value from an ML model. As the ML model can combine or synthesize multiple sources of evidence to infer the missing phenotypic values, we refer to its prediction as a ``synthetic surrogate" for the target phenotype. SynSurr estimates the same effect size as a standard GWAS of the target phenotype, but does so with increased power when the synthetic surrogate is correlated with the target phenotype. Unlike classical imputation, SynSurr does not require that the synthetic surrogate is obtained from a correctly specified generative model, only that it is correlated with the target outcome. SynSurr is also computationally feasible at biobank scale and has been implemented in the open source R package SurrogateRegression. In a genome-wide ablation analysis of 2 well-studied traits from the UK Biobank (UKBB), SynSurr consistently recovered more of the associations present in the full sample than standard GWAS using the observed target phenotypes. When applied to 6 incompletely measured body composition phenotypes from the UKBB, SynSurr identified 15.6 times as many genome-wide significant associations than standard GWAS, on average, and did so at 2.9 times the level of significance. These associations were highly enriched for biologically relevant gene sets and overlapped substantially with known body composition associations from the GWAS catalog.