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Answer by brb2436
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You can check out the methodology of calculating the most recent dataset (2019). It seems quite legitimate: internationally shared data, Bayesian modeling, compliance with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER), etc.

I wonder if any methods/assumptions/biases were carried over from the earlier study that you share. The main bias can be of omission, since health can be a relatively insignificant influence of one's wellbeing. For example, I found (Categorized tab, q4) that only 1/30 slum residents wanted Health to change the most but 8/28 wanted to live 0 additional years (q16). So, people can be healthy (have high QALY) but suffer (low WALY). The dataset can be accurate.

This focus bias can be due to the priority perceptions of the researchers in 1996 (who may have valued health, perhaps since subjective wellbeing improvements were not as readily possible?) in combination with the experimenter bias of the context experts (e. g. due to authority dynamics in these contexts).

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Aaron Gertler 🔸
Thanks for the link! I was aware of the most recent study, but you prompted me to dig deep and see what they said about their survey methodology.  The most relevant bits I found were sections 4.8 and 4.8.1 in this PDF, which describe multiple surveys done across a bunch of countries.  I'm still not sure where to find actual response counts by country or demographic data on respondents — it's easy to find tons of data on how different health issues are ranked and how common they are, but not to find a full "factory tour" of how the estimates were put together. I'd still be interested in more data on those points (I have to imagine it's buried somewhere in those 1800 pages).
brb2432
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Yes, for the YLL estimates they combined different datasets to find accurate causes of death disaggregated by age, sex, location, and year. There should be little bias since data is objective and 'cleaned' using relevant expert knowledge. The authors

  • Used vital registration (VR)[1] data and combined them with other sources if these were incomplete (2.2.1, p. 22 the PDF)[2]
  • Disaggregated the data by "age, sex, location, year GBD cause" (p. 32 the PDF) and made various adjustments for mis-diagnoses and mis-classifications, noise, non-representative data,
... (read more)
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lukeprog
+1 to the question, I tried to figure this out a couple years ago and all the footnotes and citations kept bottoming out without much information having been provided.
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by Aaron Gertler 🔸
1 min read2 answers 2

45

Global health & developmentAdjusted life yearBurden of disease
Frontpage

I recently tried to figure out where DALYs come from.

After a bit of searching, the best I could find was this report on the origin of the metric (the first Global Burden of Disease assessment). The report includes this explanation:

And:

But I'm left with many other questions:

  • How many health workers were consulted?
  • Were people other than health workers consulted, especially people who have themselves experienced the relevant health issues?
  • Were DALY values updated in successive instances of the GBD?
  • Are transcripts of any of these "formal exercises" available somewhere?

 

Ideally, I'd love to find a document/video that covers DALYs in the style of a factory tour video; I want to know what goes into them, who is involved, and what the creation process looks like.

Does anyone know of such a resource, and/or the answers to any of my questions?

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Aaron Gertler 🔸9
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This is excellent, thanks!

These two papers, in particular, were what I was looking for. The corresponding information on QALYs was also great.

(For future readers of my post, the relevant info is under the "descriptive system" and "valuation methods" subheadings in Derek's post.)

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Amber Dawn2
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I just had the exact same question, so thanks Aaron for asking this, and Derek for giving this answer :)

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brb243

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You can check out the methodology of calculating the most recent dataset (2019). It seems quite legitimate: internationally shared data, Bayesian modeling, compliance with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER), etc.

I wonder if any methods/assumptions/biases were carried over from the earlier study that you share. The main bias can be of omission, since health can be a relatively insignificant influence of one's wellbeing. For example, I found (Categorized tab, q4) that only 1/30 slum residents wanted Health to change the most but 8/28 wanted to live 0 additional years (q16). So, people can be healthy (have high QALY) but suffer (low WALY). The dataset can be accurate.

This focus bias can be due to the priority perceptions of the researchers in 1996 (who may have valued health, perhaps since subjective wellbeing improvements were not as readily possible?) in combination with the experimenter bias of the context experts (e. g. due to authority dynamics in these contexts).

Aaron Gertler 🔸5
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Thanks for the link! I was aware of the most recent study, but you prompted me to dig deep and see what they said about their survey methodology. 

The most relevant bits I found were sections 4.8 and 4.8.1 in this PDF, which describe multiple surveys done across a bunch of countries. 

I'm still not sure where to find actual response counts by country or demographic data on respondents — it's easy to find tons of data on how different health issues are ranked and how common they are, but not to find a full "factory tour" of how the estimates were pu... (read more)

Reply
5
lukeprog
+1 to the question, I tried to figure this out a couple years ago and all the footnotes and citations kept bottoming out without much information having been provided.
2
brb243
Yes, for the YLL estimates they combined different datasets to find accurate causes of death disaggregated by age, sex, location, and year. There should be little bias since data is objective and 'cleaned' using relevant expert knowledge. The authors * Used vital registration (VR)[1] data and combined them with other sources if these were incomplete (2.2.1, p. 22 the PDF)[2] * Disaggregated the data by "age, sex, location, year GBD cause" (p. 32 the PDF) and made various adjustments for mis-diagnoses and mis-classifications, noise, non-representative data,  shocks, and distributed the cause of death data where it made most sense to them, using different complex modeling methods (Section 2 the PDF) * Calculated YLL by summing the products of "estimated deaths by the standard life expectancy at age of death"[3] For YLD estimates, where subjectivity can have larger influence on the results, the authors also compiled and cleaned data, then estimated incidence[4] and prevalence, [5] they severity, using disability weights (DWs) (Section 4 intro, p. 435 the PDF) * Used hospital visit data (disaggregated by "location, age group, year, and sex" (p. 438) to get incidence and prevalence of diseases/disabilities. Comorbidity correction used a US dataset. * 140 non-fatal causes were modeled (of which 11 (79–89) relate to mental health diagnoses) (pp. 478–482) * For each of the causes for a few different severity levels, sequelae were specified.[6] * Disability weights were taken from a database (GBD 2019) and matched with the sequelae. * [Section 4.8.1]"For GBD 2010[7]  [disability weights] focused on measuring health loss rather than welfare loss" (p. 472). Data was collected in 5 countries (the study samples are claimed to be representative[8]), through in-person computer-assisted interviews and an online survey  (advertised in the researchers' networks) (p. 472). * The in-person survey participants were asked a series of questions about which of two person
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Jeff Kaufman 🔸4
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Here are some notes from when I looked into this a few years ago: https://www.jefftk.com/p/disability-weights

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Aaron Gertler 🔸2
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Thanks! The correlation graphs were helpful to see, though I'm sad about the muddled results from the graph in the updated section.

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