measure both untreated hypertension (using blood pressure in the survey sample) but also to include treated hypertension (presumably by asking the respondent about medications at the time blood pressure is measured). It is possible that surveys are less good at capturing treated hypertension. The NCD-Protect score has the expect effect on reducing hypertension (significant) and is negative but not significant in the equation for diabetes. There are significant regional differences for diabetes. The NCD variables are likely to be affected by the quality of the health system. NCDs are more likely to be detected and treated in higher income countries. Thus, although one might expect incidence of NCDs to rise with income, premature mortality from NCDs (prior to age 70) is higher in LMICs. The results, given the data constraints noted, provide some encouragement for finding the expected effects of diet quality variables within individual countries.

# 7. Potential Testing of the Methodology Using National Datasets

National health and nutrition survey data provide potential for further testing of the methodology. National data have several advantages. Within countries there is less variation in the ability of health services to screen and treat diseases including NCDs, although there remain problems of socio-economic gradients and rural/urban differences affecting decisions to seek care and obtain access. In a large dataset, it is possible to conduct analysis separately for different age cohorts. Given the progression of the nutritional transition, different age cohorts will face different conditions throughout their lifespan, and cross section data can permit modeling of this. Six datasets were noted above as being potentially suitable, namely health and nutrition surveys for Brazil, Mexico, China, Philippines, Andhra Pradesh (India), and South Africa. These include three lower-middle income countries/states and three upper-middle income countries.

A disadvantage is that, to our knowledge, there are no low-income countries with such datasets. A suggested methodology for how to apply this to low-income countries in the same region is as follows. One could use the health and nutrition dataset from a given region to estimate the probability of cardiovascular risk and diabetes risk (via hypertension and blood glucose) among adults over 25 by BMI, by age cohorts and by socioeconomic group. These rates could then be applied to similar socioeconomic groups in low-income countries who, it is predicted, will follow similar paths.

This is possible for cardiovascular disease, the number one cause of death globally, as well as for diabetes. Although it would be desirable to extend this to the number two risk, all cancers combined, making estimates is more difficult. Unlike cardiovascular disease, there are no readily measurable biomarkers indicating longer-term risk, and the term cancer covers a large number of heterogeneous conditions affecting different sites such that prospective studies of cancer require very large populations. There are fewer large scale prospective cohort studies of cancer than there are involving CVD. Our understanding of the dietary risks for cancer is still evolving.

# 8. Potential for Testing Using Prospective Cohort Studies

Prospective cohort studies have become more common as researchers note their usefulness for NCD conditions whose antecedents begin even prior to adulthood, but which mainly manifest in disease outcomes three or more decades later. Ongoing prospective cohort studies which include dietary recall and health measures include PURE (in 27 high, middle and low income countries: <u>https://www.phri.ca/research/pure/),</u> ELSA-Brazil (began 2008-2010: Aquino et al. 2012), the US Nurses Health Study (https://nurseshealthstudy.org), the EPIC study <u>(https://iarc.epic.fr) in a number of European countries,</u> 13