Study protocol: assessing the association between corporate financial influence and implementation of policies to tackle commercial determinants of non-communicable diseases: a cross-sectional analysis of 172 countries

INTRODUCTION: There are many case studies of corporations that have worked to undermine health policy implementation. It is unclear whether countries that are more exposed to corporate financial influence are systematically less likely to implement robust health policies that target firms’ financial interests. We aim to assess the association between corporate financial influence and implementation of WHO-recommended policies to constrain sales, marketing and consumption of tobacco, alcohol and unhealthy foods. METHODS AND ANALYSIS: We will perform a cross-sectional analysis of 172 WHO Member States using national datasets from 2015, 2017 and 2020. We will use random effects generalised least squares regression to test the association between implementation status of 12 WHO-recommended tobacco, alcohol and diet policies, and corporate financial influence, a metric that combines disclosure of campaign donations, public campaign finance, corporate campaign donations, legislature corrupt activities, disclosure by politicians and executive oversight. We will control for GDP per capita, population aged >65 years (%), urbanisation (%), level of democracy, continent, ethno-linguistic fractionalisation, legal origin, UN-defined ‘Small Island Developing States’ and Muslim population (%) (to capture alcohol policy differences). We will include year dummies to address the possibility of a spurious relationship between the outcome variable and the independent variables of interests. For example, there may be an upward global trend in policy implementation that coincides with an upward global trend in the regulation of lobbying and campaign finance. ETHICS AND DISSEMINATION: As this study uses publicly available data, ethics approval is not required. The authors have no conflicts of interest to declare. Findings will be submitted to a peer-reviewed journal for publication in the academic literature. All data, code and syntax will be made publicly available on GitHub.