Ministries of health are not passive recipients of aid in our model, their buy-in and willingness to commit resources is fundamental for success and in our perspective the pathway to both scale and sustainability. It is indeed true however, that if I would come with funding they would gladly accept it. Increasingly we see that they are requesting funders to support in purchasing IMPALA One of the key reasons we would like to the Trial is that it will make it easier for them (and funders like UNICEF, UNITAID etc) to justify this in policy and budgeting. So at this stage we see significant buy-in and commitment from governments in the scale-up, but freeing up own resources at sufficient degree to buy entire systems is not yet feasible. We think we can also get there without the trial, but the trial would help a lot. We are also confident that we can easily deliver this trial together with our partners, as we already drafted the protocols and plans to that.
As with regards to your standpoint on RPās assessment --> would you be able to explain why you are so steadfast about it not being correct? Right now you make a claim that they are overstating the effect, but there is a 72% discount already in their estimated effect, which has been largely subjective based on study design. I wonder what arguments you would have to say their assessment is not correct and that the effect should be closer to 0 beyond the weaker study designs.
In my view the fact that the patients before and after where matched for severity in the pediatric design and that we have a controlled interrupted time series for neonatal care is also a quite strong design. (https://āāresearchonline.lshtm.ac.uk/āāid/āāeprint/āā4648480/āā) And last but not least that all evidence points in the same direction makes chance alone very unlikely.
One thing that is also worth considering You mention the confidence interval is very large which is because the occurrence of the event (death) is quite rare. However if you look at the primary outcome of Critical Illness Events which is an intermediary outcome to mortality and is a lot more frequent, the confidence interval is a lot smaller so the claims about those are quite robust. Moreover the supportive evidence relating to impact on workload, stress levels and confidence of health workers is also important. In general our intervention achieves its effects not just through better monitoring, but also by freeing up time of health workers. This has also been quantified in multiple studies.
As with regards to other studies, I am afraid I must agree that there is hardly any good studies out there within our population. There are several studies on early warning scores, but few on monitoring and especially not in children. Why I believe it works well in this population and why this is different from many other monitoring interventions
a) it is as much about unburdening health workers and freeing up their time as it is about getting better data on what is happening with the child. Our data show health workers consistently report reduced workload and stress levels after implementation. Also direct observation shows it saves a lot of time (from 3.3 hours per shift to 1.8 hours per shift)
b) these are very high risk children, the standard of care is very little if any monitoring, it is easy to improve from there and get better outcomes
c) interventions are relatively simple and readily available (IV fluids, oxygen, antibiotics and glucose, for neonates it can be as simple as rocking the body to prevent severe apnea)
d) as mentioned it is not just monitoring, it is embedded in an entire program of training and continued support with a quality improvement component. Therefore we address a much broader range of potential barriers to timely, quality care then just the lack of vital signs monitoring
In general I feel that there is a huge lack of attention on monitoring in these settings. It is often an afterthought and even in very large programs oxygen and other treatments are prioritized over other things. So for me this is one of the main reasons our effect sizes can be so great. To me monitoring is the foundation of hospitalized patients, especially the most critical/āvulnerable patients. The lack of attention on this and lack of awareness about the gaps are huget bottlenecks to improving quality of care.
Some quick thoughts:
- Iād be wary of field building that is too optimisey/ātoo pressuring. I put more emphasis on cool social events that bring people together in contexts where they have the opportunity for high quality conversations.
- This can be particularly important because the scene is otherwise relatively fractured. Yes, some employees of some labs hang out with each other, but on the whole thereās not so much mixing between Ants and non Ants, openAI employees and not etc. This is partly because some people in AI safety are outright hostile to lab employees. My guess is that outright hostility is both ~unethical and doesnāt further AI safety goals, compared to the alternative of having more conversations.
- I donāt think there are quick magic fixes to the above. probably doing great community building involves paying someone ~fulltime to being a great community person, who gets to know people and learns over time how to do the events that interest people and grows the network bit by bit. Retention is prob one of the most important metrics to look for. A great community builder cares for the community intrinsically, for its members intrinsically, not mostly as means, but incidentally does serve global good purposes by allowing better communication and understanding.
- Some reasons to do field building is because there will people new to the scenes who might have potential to do great things and could be accelerated if connected to appropriate people. Field building and community building arenāt exactly the same.