Table 1. Key differences in study design, methods, and main findings between Nakamura et al. [4] and Caro et al. [5].

Methodological choice	Nakamura et al.	Caro et al.
Study design and analytical approach	Time series analysis using fixed effects regression analyses	Pre–post design using random effects regression analyses
Sensitivity analyses	Difference-in-difference regression method Sensitivity of models to different functional forms of time trends	Alternative model specifications (taking account of autocorrelation and two-step models)
Outcome measures	Changes in volume (ml) of household purchases of SSBs Changes in price (pesos/ml) of purchased SSBs by SKU	Changes in volume (ml) of household purchases of SSBs Changes in calories (kcal) of household purchases of SSBs Changes in price (pesos/ml) of purchased SSBs by SKU
Contextual (confounding) factors taken into account	Average monthly temperature Macroeconomic measure: unemployment rates	Seasonality (quarterly indicator variables, not specified) Macroeconomic measures: regional unemployment, population size, supermarket sales, economic index, and construction permits granted
Secondary analyses	Outcomes by SEG (low, middle, high) Outcomes in relation to the announcement of the SSB tax Analysis of the influence of the SSB tax on shopping behaviours—frequency of purchases, use of price promotions	Outcomes by SEG (low, high) Outcomes in relation to the announcement of the SSB tax (data not presented in paper, findings non-significant)
Data sources	Kantar Worldpanel Chile—household shopping panel Nutritional data on sugar content of products from several sources (covering 90% of top-selling SSBs represented in the Kantar dataset), including a large, nationally representative survey, manufacturers’ documents and webpages, and national health authorities’ surveillance systems; nutrition facts panel data from 90% of products purchased	Kantar Worldpanel Chile—household shopping panel Nutritional data on sugar content of products from nutrition facts panel data (79.8% of products), Mintel Latin America (19.9%), or Mintel North America (0.2%), or imputed using a systematic match based on sister products using package description, brand, and manufacturer (<0.1% of each beverage category)
Sample size	2,836 households	2,000 households with 2 months of data, 1,795 with 36 months of data
Time periods for analysis	46 months pre- and 14 months post-implementation of the SSB tax	22 months pre- and 14 months post-implementation of the SSB tax
Main findings	Overall −5.8% change in volume (ml) of SSBs purchased (−21.6% for high tax/sugar drinks, +3% for low tax/sugar drinks, −10% for no tax/sugar drinks) Overall −1.0% change in price (pesos) of SSBs purchased (−0.8% for high tax/sugar drinks, −1.7% for low tax/sugar drinks, +1.7% for no tax/sugar drinks) Differential effects by SEG: bigger effects on volume of high sugar drinks purchased in middle and high SEGs	Overall −1.9% change in volume (ml) of SSBs purchased (−3.4% for high tax/sugar drinks, +10.7% for low tax/sugar drinks, −3.1% for no tax/sugar drinks) Overall −2.3% change in kcal/capita/day purchased (−4.0% for high tax/sugar drinks, −5.3% for no tax/sugar drinks) No overall estimate of price (pesos) change (+3.9% for high tax/sugar ready-to-drink SSBs, +1.5% for low tax/sugar drinks, +1.8% for no tax/sugar drinks) Differential effects by SEG: bigger effects on volume of high sugar drinks purchased in high SEG

SEG, socio-economic group; SKU, stock keeping unit; SSB, sugar-sweetened beverage.