A brave soul could do a "quick" meta-analysis! @petermacp.bsky.social

1) Sex differences in interactions even within households
2) Differential exposure outside household (restricting to low-incidence settings would help)
3) Men are more likely to have female partners! So female HHCs more likely exposed to male index patients: on average, more infectious for longer.

Thanks! Quite a lot of HHC studies out there, which we excluded here - but from those I read with sex-disaggregated data, I didn't get a sense of a clear trend for ⬆️ risk in either direction. It is difficult though to fully "control" on exposure, due to... [contd]

🚩🚩🚩🚩!

Lots of fun to work with a great team (not all on bluesky!) @petermacp.bsky.social @petedodd24.bsky.social @kchorton.bsky.social @esnightingale.bsky.social David Dowdy, Liz Corbett, Maria Krutikov & especially Mphatso Phiri & Helena Feasey who helped screen a LOT of papers 😵

Lots of caveats (see pre-print!) and it's not yet peer-reviewed.
Big thanks to all the authors who provided unpublished data (including some who dug out their floppy discs from 1998!), @britishlibrary.bsky.social for paper-hunting help, and original research participants. 🧵

So what?
-Disparities in exposure drive disparities in disease - need to tackle transmission in workplaces, public spaces, prisons...
-Improving men's access to rx essential to interrupt transmission
-Infection-focused interventions (vaccination, TPT) need to actively engage men for efficacy. 🧵

-Men experience consistently higher Mtb exposure than women, likely driving a substantial proportion of the M:F disparity in TB disease.
-Why? Social/behavioural differences mean adult men spend more time in high-transmission spaces, amplified by sex-assortative mixing & diagnostic delays in men 🧵

We found that from adolescence onwards, rates of Mtb conversion increased, and men were at higher risk of acquiring new Mtb infection than women. Mtb conversion was highest at age ~30, and was up to 1.5-times higher in men than in women. This was consistent across settings (regions, rural/urban). 🧵

We screened 25,000+ studies (😱 - grateful for the teamwork here!) and included data from 81 population-based #IGRA & #TST surveys performed in high-TB settings since 1993 ➡️500,000 participants from 38 countries. We used Bayesian meta-analysis to analyse Mtb immunoreactivity by age and sex. 🧵

👋 Hi! Mostly here for work 🤫! Came for the tuberculosis epidemiology, stayed for the ultrarunning content.