where O&O stands for overweight and obesity, f and g represent functions and food choice is a measure of how healthy the diet is. This will help to identify the proportion of overweight/obesity attributable to unhealthy diet, and also those NCD risks not mediated by overweight and obesity.

There are existing studies linking dietary risks to burden of NCDs in LMICs, reviewed in the previous section. Most of these studies either look at a single NCD, or a single dietary factor. The Global Burden of Disease project has however a comprehensive study, aiming to link 15 dietary factors with the NCD burden (GBD 2017 Diet Collaborators, 2019). Various concerns have been raised about these estimates. First, the relative ranking of different dietary factors changes somewhat markedly between the analysis using the 2017 data and that using the 2019 data (analyzed by Qiao et al. 2022): Stanton et al. (2022) comment on differences regarding red meat. Second, evidence on dietary intake from representative populations in LMIC are currently inadequate to produce robust and reliable estimates of intake across countries (Beal et al. 2021). A third concern conceptually is how the rest of the diet is adjusted in the model when one individual dietary factor is varied in order to understand its impact on NCD risk. GBD 2017 Diet Collaborators (2019) state that the underlying studies they use are unclear on this adjustment. Finally, GBD 2017 Diet Collaborators (2019) estimates of adverse effects of dietary factors (11m global deaths annually) far exceed the estimates from other studies cited above of adverse effects of overweight and obesity (4m globally); and GBD 2017 Diet Collaborators (2019) imply that the 11m do not include the 4m. One likely explanation for the last point is that of double counting. The diets that individuals consume do not necessarily have only one dietary risk factor, and hence these individuals are at higher risk for more than one NCD. Adding the risks across multiple trials for individual NCDs is therefore likely to overestimate dietary risks of NCDs in aggregate.

It may be possible to reduce the double-counting on the side of dietary factors by using a more parsimonious indicator of dietary quality, such as the GDRS-protect and GDRS-risk scores (for cross-country data), or the two GDQS sub-scores (for national data). We propose to try this approach.

This does not solve the problem of multiple NCDs, however. Although deaths from NCDs list a primary cause of death, individuals suffering from (and being treated for) NCDs may have multiple conditions, but the individual should only be counted once when calculating treatment costs and lost productivity costs. The pragmatic approach suggested here is to focus on two of the top two conditions in terms of cause of death globally, namely cardiovascular disease and diabetes. Cancer could be included in future, as ongoing prospective cohort studies provide more information on the dietary risks for a broader range of cancers. This may yield an overly conservative estimate of the impact of unhealthy diets on NCDs, but the underlying rule on such economic calculations is always to err on the side of conservatism in such estimates.

# 6. Preliminary Test of the Methodology Using Cross-Country Data

Some preliminary regressions were undertaken using cross-country data, while recognizing that analysis using data for individuals and households within one country would be preferable (discussed in the section immediately following this one). This section discusses results of using cross-country data, benefiting from the recent availability of data on several diet quality measures across over 50 LMIC. Data definitions are given in Table 5. Stata 17 ((https://stata.com) was used for the analysis, and the dataset created using data from public databases is available through the World Bank Microdata Library (https://dimewiki.worldbank.org/Microdata_Catalog). Cross-country data have several disadvantages, among them:

- Variables are not always available for the same year (see Table 5);

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