HTM Scale-up Not Having The Effect Expected

[tbhallett]

Whereas, the test script and the initial scenario run showed that the HTM scale-up was working as expected, it appears that the in practice they do not (see here).

The test script and the scenario both examine scale-up occurring instantly in year 2012, whereas the scale-up scenarios we want for the final analysis is for scale-up instantly in 2019.

Looking at this, I can see a few reasons why this might be occurring:

• We write to a parameter that does not exist:

  TLOmodel/src/tlo/methods/hiv.py

  Lines 1131 to 1132 in e926380

  	# prob ART start if dx, this is already 95% at 2020
  	p["prob_start_art_after_hiv_test"] = scaled_params["prob_start_art_after_hiv_test"]

• Updating of the rates of testing does not work as expected, as the values for years after 2020 are set to nan, and note that future years are never read due to this logic.
  

  TLOmodel/src/tlo/methods/hiv.py

  Lines 1124 to 1125 in e926380

  	p["hiv_testing_rates"]["annual_testing_rate_children"] = scaled_params["annual_testing_rate_children"]
  	p["hiv_testing_rates"]["annual_testing_rate_adults"] = scaled_params["annual_testing_rate_adults"]

(I don't think this section suffers from the same issue - but it looks a bit fragile, for similar reasons).

• We write new values for parameter values, but we do not rebuild the LinearModels that use them (the linear model stores the value of parameter when it is built, and does not update when those parameters are updated later)
  https://github.com/UCL/TLOmodel/blob/e9263803787825168955b0e24ed15c383a2ba0dd/src/tlo/methods/hiv.py#L1113C13-L1113C52
  

  TLOmodel/src/tlo/methods/hiv.py

  Line 1121 in e926380

  p["prob_circ_after_hiv_test"] = scaled_params["prob_circ_after_hiv_test"]

There may be other issues in the Tb and malaria codes of a similar nature.

I would propose doing the following:

• Update the scenario file so that it reflects a scale-up in 2019, running at scale, and confirming that this also shows there being no epidemiological impact. (If this doesn't confirm it, then I have had some mistakes in my Scenario file and in these observations)
• Searching for possible issues in hiv.py, tb.py and malaria.py and fixing
• Re-running the scenario
• Raising PR with fixes

[tdm32]

Whereas, the test script and the initial scenario run showed that the HTM scale-up was working as expected, it appears that the in practice they do not (see here).

The test script and the scenario both examine scale-up occurring instantly in year 2012, whereas the scale-up scenarios we want for the final analysis is for scale-up instantly in 2019.

Looking at this, I can see a few reasons why this might be occurring:

• We write to a parameter that does not exist:

  TLOmodel/src/tlo/methods/hiv.py

  Lines 1131 to 1132 in e926380

  	# prob ART start if dx, this is already 95% at 2020
  	p["prob_start_art_after_hiv_test"] = scaled_params["prob_start_art_after_hiv_test"]

• Updating of the rates of testing does not work as expected, as the values for years after 2020 are set to nan, and note that future years are never read due to this logic.
  

  TLOmodel/src/tlo/methods/hiv.py

  Lines 1124 to 1125 in e926380

  	p["hiv_testing_rates"]["annual_testing_rate_children"] = scaled_params["annual_testing_rate_children"]
  	p["hiv_testing_rates"]["annual_testing_rate_adults"] = scaled_params["annual_testing_rate_adults"]

(I don't think this section suffers from the same issue - but it looks a bit fragile, for similar reasons).

• We write new values for parameter values, but we do not rebuild the LinearModels that use them (the linear model stores the value of parameter when it is built, and does not update when those parameters are updated later)
  https://github.com/UCL/TLOmodel/blob/e9263803787825168955b0e24ed15c383a2ba0dd/src/tlo/methods/hiv.py#L1113C13-L1113C52
  

  TLOmodel/src/tlo/methods/hiv.py

  Line 1121 in e926380

  p["prob_circ_after_hiv_test"] = scaled_params["prob_circ_after_hiv_test"]

There may be other issues in the Tb and malaria codes of a similar nature.

I would propose doing the following:

• Update the scenario file so that it reflects a scale-up in 2019, running at scale, and confirming that this also shows there being no epidemiological impact. (If this doesn't confirm it, then I have had some mistakes in my Scenario file and in these observations)
• Searching for possible issues in hiv.py, tb.py and malaria.py and fixing
• Re-running the scenario
• Raising PR with fixes

Thanks for digging into this @tbhallett, I'll work on it this afternoon. Just confirming branch [hallett/analysis_for_vertical_and_horizontal_progs] is the most up-to-date one?

[tdm32]

@tbhallett I'm wondering if there is an issue here with constraints on consumables and personnel. From my scale-up paper, there was only a small impact of HIV program scale-up for 2 reasons: 1. the program is already very close to hitting targets and the proposed scale-up is small, 2. the consumables availability limits the amount of scale-up that can occur.

I've tested this quickly on a small population and found that running the scenarios with consumables availability set to 'all' did lead to a reduction in AIDS deaths with HIV program scale-up (around 30% fall in a pop of 5,000).

So when we scale-up programs only (no HSS), the effects will be limited by cons availability. Both the baseline and the scale-up scenario will likely be affected by mode 2, I think in mode 2 even in the baseline scenarios many appts will not be able to run due to personnel shortages.

Could this be explaining why we see no difference between the baseline and scale-up scenarios? I'll set off a larger run with cons availability to 'all' and keep mode 1 throughout just to see. Hopefully will update you Thurs am.

[tbhallett]

@tbhallett I'm wondering if there is an issue here with constraints on consumables and personnel. From my scale-up paper, there was only a small impact of HIV program scale-up for 2 reasons: 1. the program is already very close to hitting targets and the proposed scale-up is small, 2. the consumables availability limits the amount of scale-up that can occur.

I've tested this quickly on a small population and found that running the scenarios with consumables availability set to 'all' did lead to a reduction in AIDS deaths with HIV program scale-up (around 30% fall in a pop of 5,000).

So when we scale-up programs only (no HSS), the effects will be limited by cons availability. Both the baseline and the scale-up scenario will likely be affected by mode 2, I think in mode 2 even in the baseline scenarios many appts will not be able to run due to personnel shortages.

Could this be explaining why we see no difference between the baseline and scale-up scenarios? I'll set off a larger run with cons availability to 'all' and keep mode 1 throughout just to see. Hopefully will update you Thurs am.

That might be a part of it, but not all (I think), because;

• There is no difference between scenario B (massive healthsystem strengthening) and J (massive health system stqrenghenin plus HTM scale-up)
• Some of the changes in the scale-up should be "free" of cost for the healthsystem, e.g. changes in beta.

[tbhallett]

We made the change whereby the linear models are rebuild following updating the parameters in #1413

The graphs produced were almost the same as the originals above.

[tbhallett]

Next job is to run a simplified version of the analysis, but...

• In Mode 1 (so we can see if it is the "mode 2 constraints" that is hampering the scale-up
• With the logs from HIV, TB and Malaria modules turned on, so that we can see if the service coverages are increasing as expected.

This is being done on within the branch PR'd at #1288 in file src/scripts/comparison_of_horizontal_and_vertical_programs/mini_analysis_for_testing/mini_version_scenario.py

job-id = htm_with_and_without_hss-2024-07-25T193651Z

UPDATE:

That job failed for mysterious reason. Second attempt is (from current tip of master c147e2b)

mini_htm_with_and_without_hss-2024-07-29T102041Z

[tbhallett]

@tdm has done experiments that confirm ---for a scale-up in 2015, to "maximal possible levels", in either Mode 1 or Mode 2, an effect on deaths is had for H, T, M scale-ups individually and collectively.

Therefore, the discrepancy between this success and the lack of an effect not the runs done so far using the Scenario files defined above could be as follows:

• The scale-up being in 2015 as opposed to 2019
• The scale-up being "max" in Tara's runs (vs. something slightly short of max) in the Scenario runs.
• Something to do with the differences in coding between the two runs (The Scenario file including *rescaling to Mode 2, scaling due to absences etc etc.)

Therefore, our action plan is as follows (in some new PRs)

• Tara to bring in the "max" variant of the scenarios so that we can switch to using these. [Let the Scale-up of HIV, TB, Malaria services go to "MAXIMAL" levels (as well as target levels) #1441]
• Re-run using max scenarios AND 2015 as the start date of the switching
• Remove rescaling due to staff absences (now that we've moved away from doing that). [Updates to the Horizontal and Vertical Programs Analysis #1439]

And also,

• Re-run the "mini" version of the analysis (HTM Scale-up Not Having The Effect Expected #1411 (comment)) to see if running just the HTM scale-up in Mode 1 has an effect.