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Graphic: 1 | Audio: 1 | 3D: 1Â . Mar 19, 2020 @ 03:43PM by Ruthann White, Prisma V184.108.40.2068Premium Apk Crack.[@R44]\]. In our sample, women of lower SES scores had higher odds of exposure to the highest levels of the alcohol policy \[[@R28]\] than higher-SES participants. The higher odds of exposure among lower-SES women may contribute to the observed gender differences in the protective effects of alcohol policy on alcohol-related adverse health effects \[[@R45]\]. Our results indicated no significant association between poverty (a better proxy of income and wealth) and adult exposure to the highest level of the alcohol policy (compared with the lowest) but a significant positive association with perceived availability of lower-cost alcohol (compared with no alcohol) during the past 30 days. Poverty may not affect exposure to alcohol policy at the community level, but it may affect exposure in the workplace and transportation. However, income and wealth levels have been associated with unintentional injuries in non-urban regions \[[@R46]\]. These associations may be explained by the higher rate of injury among men \[[@R16]\] and the higher frequency of alcohol use in men than women \[[@R15]\]. An individual’s perception of an increased availability of alcohol in their social environment has been associated with increased alcohol consumption and binge drinking \[[@R47]\]. Similarly, in our sample, higher poverty scores were associated with higher alcohol consumption rates. Though drinking patterns and alcohol use are more common among men than women in China \[[@R48]\], alcohol use is associated with health problems, including injuries \[[@R46],[@R49]\]. Study limitations should be noted in interpreting our findings. First, our main outcome, exposure to the highest level of the alcohol policy, was a binary variable indicating exposure to an *ideal* level of the policy but did not distinguish between the actual implementation of policy initiatives that may differ in quality. These analyses were restricted to cross-sectional data, which prevents inference about causality, and there was a relatively small sample size, which limits the precision of our estimates. Second, alcohol outlet measures could not be used to demonstrate the coverage of policies in our sample because they were unavailable. Third, we were unable to link data on poverty to our health data because