= Solution
Under the more plausible <Quasi-Poisson regression>, the year <Wald statistic> is $-1.686$, with reported <p-value> $0.0922$. An approximate 95% <confidence interval> is
$$
\beta_1\in-0.09816\pm1.96(0.05821)=(-0.21225,0.01593),
\qquad e^{\beta_1}\in(0.8088,1.0161).
$$
Thus \b[the estimated fall is about 9.35%, but the data do not establish a reduction at the 5% level after allowing for <overdispersion>]. The interval allows both a sizeable reduction and a small increase. The before–after comparison also lacks a contemporaneous randomized control, so <causal inference> about the campaign would require addressing other changes between years. The small <Poisson regression> <p-value> is not sufficient evidence of a campaign effect when its <variance> assumption is unsuitable.
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