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In response to public concerns and campaigns, some United Kingdom supermarkets have implemented policies to reduce less-healthy food at checkouts. We explored the effects of these policies on purchases of less-healthy foods commonly displayed at checkouts. We used a natural experimental design and two data sources providing complementary and unique information. We analysed data on purchases of small packages of common, less-healthy, checkout foods (sugary confectionary, chocolate, and potato crisps) from 2013 to 2017 from nine UK supermarkets (Aldi, Asda, Co-op, Lidl, M&S, Morrisons, Sainsbury's, Tesco, and Waitrose). Six supermarkets implemented a checkout food policy between 2013 and 2017 and were considered intervention stores; the remainder were comparators. Firstly, we studied the longitudinal association between implementation of checkout policies and purchases taken home. We used data from a large (n ≈ 30,000) household purchase panel of food brought home to conduct controlled interrupted time series analyses of purchases of less-healthy common checkout foods from 12 months before to 12 months after implementation. We conducted separate analyses for each intervention supermarket, using others as comparators. We synthesised results across supermarkets using random effects meta-analyses. Implementation of a checkout food policy was associated with an immediate reduction in four-weekly purchases of common checkout foods of 157,000 (72,700-242,800) packages per percentage market share-equivalent to a 17.3% reduction. This decrease was sustained at 1 year with 185,100 (121,700-248,500) fewer packages purchased per 4 weeks per percentage market share-equivalent to a 15.5% reduction. The immediate, but not sustained, effect was robust to sensitivity analysis. Secondly, we studied the cross-sectional association between checkout food policies and purchases eaten without being taken home. We used data from a smaller (n ≈ 7,500) individual purchase panel of food bought and eaten 'on the go'. We conducted cross-sectional analyses comparing purchases of common checkout foods in 2016-2017 from supermarkets with and without checkout food policies. There were 76.4% (95% confidence interval 48.6%-89.1%) fewer annual purchases of less-healthy common checkout foods from supermarkets with versus without checkout food policies. The main limitations of the study are that we do not know where in the store purchases were selected and cannot determine the effect of changes in purchases on consumption. Other interventions may also have been responsible for the results seen. There is a potential impact of checkout food polices on purchases. Voluntary supermarket-led activities may have public health benefits.

Abstract Introduction Online cigarette dealers have lower prices than brick-and-mortar retailers and advertise tax-free status.1–8 Previous studies show smokers search out these online alternatives at the time of a cigarette tax increase.9,10 However, these studies rely upon researchers’ decision to consider a specific date and preclude the possibility that researchers focus on the wrong date. The purpose of this study is to introduce an unbiased methodology to the field of observing search patterns and to use this methodology to determine whether smokers search Google for “cheap cigarettes” at cigarette tax increases and, if so, whether the increased level of searches persists. Methods Publicly available data from Google Trends is used to observe standardized search volumes for the term, “cheap cigarettes”. Seasonal Hybrid Extreme Studentized Deviate and E-Divisive with Means tests were performed to observe spikes and mean level shifts in search volume. Results Of the twelve cigarette tax increases studied, ten showed spikes in searches for “cheap cigarettes” within two weeks of the tax increase. However, the mean level shifts did not occur for any cigarette tax increase. Conclusion Searches for “cheap cigarettes” spike around the time of a cigarette tax increase, but the mean level of searches does not shift in response to a tax increase. The SHESD and EDM tests are unbiased methodologies that can be used to identify spikes and mean level shifts in time series data without an a priori date to be studied. SHESD and EDM affirm spikes in interest are related to tax increases. Implications • Applies improved statistical techniques (SHESD and EDM) to Google search data related to cigarettes, reducing bias and increasing power • Contributes to the body of evidence that state and federal tax increases are associated with spikes in searches for cheap cigarettes and may be good dates for increased online health messaging related to tobacco

Interrupted time series (ITS) is a powerful and increasingly popular design for evaluating public health and health service interventions. The design involves analyzing trends in the outcome of interest and estimating the change in trend following an intervention relative to the counterfactual (the expected ongoing trend if the intervention had not occurred). There are two key components to modeling this effect: first, defining the counterfactual; second, defining the type of effect that the intervention is expected to have on the outcome, known as the impact model. The counterfactual is defined by extrapolating the underlying trends observed before the intervention to the postintervention period. In doing this, authors must consider the preintervention period that will be included, any time-varying confounders, whether trends may vary within different subgroups of the population and whether trends are linear or nonlinear. Defining the impact model involves specifying the parameters that model the intervention, including for instance whether to allow for an abrupt level change or a gradual slope change, whether to allow for a lag before any effect on the outcome, whether to allow a transition period during which the intervention is being implemented, and whether a ceiling or floor effect might be expected. Inappropriate model specification can bias the results of an ITS analysis and using a model that is not closely tailored to the intervention or testing multiple models increases the risk of false positives being detected. It is important that authors use substantive knowledge to customize their ITS model a priori to the intervention and outcome under study. Where there is uncertainty in model specification, authors should consider using separate data sources to define the intervention, running limited sensitivity analyses or undertaking initial exploratory studies.

Since May 1, 2018, every alcoholic drink sold in Scotland has had minimum unit pricing (MUP) of £0·50 per unit. Previous studies have indicated that the introduction of this policy reduced alcohol sales by 3%. We aimed to assess whether this has led to reductions in alcohol-attributable deaths and hospitalisations. Study outcomes, wholly attributable to alcohol consumption, were defined using routinely collected data on deaths and hospitalisations. Controlled interrupted time series regression was used to assess the legislation's impact in Scotland, and any effect modification across demographic and socioeconomic deprivation groups. The pre-intervention time series ran from Jan 1, 2012, to April 30, 2018, and for 32 months after the policy was implemented (until Dec 31, 2020). Data from England, a part of the UK where the intervention was not implemented, were used to form a control group. MUP in Scotland was associated with a significant 13·4% reduction (95% CI –18·4 to –8·3; p=0·0004) in deaths wholly attributable to alcohol consumption. Hospitalisations wholly attributable to alcohol consumption decreased by 4·1% (–8·3 to 0·3; p=0·064). Effects were driven by significant improvements in chronic outcomes, particularly alcoholic liver disease. Furthermore, MUP legislation was associated with a reduction in deaths and hospitalisations wholly attributable to alcohol consumption in the four most socioeconomically deprived deciles in Scotland. The implementation of MUP legislation was associated with significant reductions in deaths, and reductions in hospitalisations, wholly attributable to alcohol consumption. The greatest improvements were in the four most socioeconomically deprived deciles, indicating that the policy is positively tackling deprivation-based inequalities in alcohol-attributable health harm. Scottish Government.

To describe the use and reporting of interrupted time series methods in drug utilization research. We completed a systematic search of MEDLINE, Web of Science, and reference lists to identify English language articles through to December 2013 that used interrupted time series methods in drug utilization research. We tabulated the number of studies by publication year and summarized methodological detail. We identified 220 eligible empirical applications since 1984. Only 17 (8%) were published before 2000, and 90 (41%) were published since 2010. Segmented regression was the most commonly applied interrupted time series method (67%). Most studies assessed drug policy changes (51%, n = 112); 22% (n = 48) examined the impact of new evidence, 18% (n = 39) examined safety advisories, and 16% (n = 35) examined quality improvement interventions. Autocorrelation was considered in 66% of studies, 31% reported adjusting for seasonality, and 15% accounted for nonstationarity. Use of interrupted time series methods in drug utilization research has increased, particularly in recent years. Despite methodological recommendations, there is large variation in reporting of analytic methods. Developing methodological and reporting standards for interrupted time series analysis is important to improve its application in drug utilization research, and we provide recommendations for consideration.

Restricting the advertisement of products with high fat, salt, and sugar (HFSS) content has been recommended as a policy tool to improve diet and tackle obesity, but the impact on HFSS purchasing is unknown. This study aimed to evaluate the impact of HFSS advertising restrictions, implemented across the London (UK) transport network in February 2019, on HFSS purchases. Over 5 million take-home food and drink purchases were recorded by 1,970 households (London [intervention], n = 977; North of England [control], n = 993) randomly selected from the Kantar Fast Moving Consumer Goods panel. The intervention and control samples were similar in household characteristics but had small differences in main food shopper sex, socioeconomic position, and body mass index. Using a controlled interrupted time series design, we estimated average weekly household purchases of energy and nutrients from HFSS products in the post-intervention period (44 weeks) compared to a counterfactual constructed from the control and pre-intervention (36 weeks) series. Energy purchased from HFSS products was 6.7% (1,001.0 kcal, 95% CI 456.0 to 1,546.0) lower among intervention households compared to the counterfactual. Relative reductions in purchases of fat (57.9 g, 95% CI 22.1 to 93.7), saturated fat (26.4 g, 95% CI 12.4 to 40.4), and sugar (80.7 g, 95% CI 41.4 to 120.1) from HFSS products were also observed. Energy from chocolate and confectionery purchases was 19.4% (317.9 kcal, 95% CI 200.0 to 435.8) lower among intervention households than for the counterfactual, with corresponding relative reductions in fat (13.1 g, 95% CI 7.5 to 18.8), saturated fat (8.7 g, 95% CI 5.7 to 11.7), sugar (41.4 g, 95% CI 27.4 to 55.4), and salt (0.2 g, 95% CI 0.1 to 0.2) purchased from chocolate and confectionery. Relative reductions are in the context of secular increases in HFSS purchases in both the intervention and control areas, so the policy was associated with attenuated growth of HFSS purchases rather than absolute reduction in HFSS purchases. Study limitations include the lack of out-of-home purchases in our analyses and not being able to assess the sustainability of observed changes beyond 44 weeks. This study finds an association between the implementation of restrictions on outdoor HFSS advertising and relative reductions in energy, sugar, and fat purchased from HFSS products. These findings provide support for policies that restrict HFSS advertising as a tool to reduce purchases of HFSS products.

Little evidence is available on the use of telehealth for antenatal care. In response to the COVID-19 pandemic, we developed and implemented a new antenatal care schedule integrating telehealth across all models of pregnancy care. To inform this clinical initiative, we aimed to assess the effectiveness and safety of telehealth in antenatal care. We analysed routinely collected health data on all women giving birth at Monash Health, a large health service in Victoria (Australia), using an interrupted time-series design. We assessed the impact of telehealth integration into antenatal care from March 23, 2020, across low-risk and high-risk care models. Allowing a 1-month implementation period from March 23, 2020, we compared the first 3 months of telehealth integrated care delivered between April 20 and July 26, 2020, with conventional care delivered between Jan 1, 2018, and March 22, 2020. The primary outcomes were detection and outcomes of fetal growth restriction, pre-eclampsia, and gestational diabetes. Secondary outcomes were stillbirth, neonatal intensive care unit admission, and preterm birth (birth before 37 weeks' gestation). Between Jan 1, 2018, and March 22, 2020, 20 031 women gave birth at Monash Health during the conventional care period and 2292 women gave birth during the telehealth integrated care period. Of 20 154 antenatal consultations provided in the integrated care period, 10 731 (53%) were delivered via telehealth. Overall, compared with the conventional care period, no significant differences were identified in the integrated care period with regard to the number of babies with fetal growth restriction (birthweight below the 3rd percentile; 2% in the integrated care period vs 2% in the conventional care period, p=0·72, for low-risk care models; 5% in the integrated care period vs 5% in the conventional care period, p=0·50 for high-risk care models), number of stillbirths (1% vs 1%, p=0·79; 2% vs 2%, p=0·70), or pregnancies complicated by pre-eclampsia (3% vs 3%, p=0·70; 9% vs 7%, p=0·15), or gestational diabetes (22% vs 22%, p=0·89; 30% vs 26%, p=0·06). Interrupted time-series analysis showed a significant reduction in preterm birth among women in high-risk models (–0·68% change in incidence per week [95% CI −1·37 to −0·002]; p=0·049), but no significant differences were identified in other outcome measures for low-risk or high-risk care models after telehealth integration compared with conventional care. Telehealth integrated antenatal care enabled the reduction of in-person consultations by 50% without compromising pregnancy outcomes. This care model can help to minimise in-person interactions during the COVID-19 pandemic, but should also be considered in post-pandemic health-care models. None.

Background The Swedish Primary Health Care (PHC) system has, like in other European countries, undergone a gradual transition towards marketization and privatization, most distinctly through a 2010 choice reform. The reform led to an overall but regionally heterogenous expansion of private PHC providers in Sweden, and with evidence also pointing to possible inequities in various aspects of PHC provision. Evidence on the reform’s impact on population-level primary health care performance and equity in performance remains scarce. The present study therefore aimed to examine whether the increase in private provision after the reform impacted on population-average rates of avoidable hospitalizations, as well as on corresponding socioeconomic inequities. Methods This register-based study used a multiple-group interrupted time-series design for the study period 2001–2017, with the study population ( N  = 51 million observations) randomly drawn from the total Swedish population aged 18–85 years. High, medium, and low implementing comparison groups were classified by tertiles of increase in private PHC providers after the reform. PHC performance was measured by avoidable hospitalizations, and socioeconomic position by education and income. Interrupted time series analysis based on individual-level data was used to estimate the reform impact on avoidable hospitalization risk, and on inequities through the Relative Index of Inequality (RII). Results All three comparisons groups displayed decreasing risk of avoidable hospitalizations but increasing socioeconomic inequities across the study period. Compared to regions with little change in provision after the reform, regions with large increase in private provision saw a steeper decrease in avoidable hospitalizations after the reform (relative risk (95%): 1.6% (1.1; 2.1)), but at the same time steeper increase in inequities (by education: 2.0% (0.1%; 4.0); by income: 2.2% (-0.1; 4.3)). Conclusions The study suggests that the increase in private health care centers, enabled by the choice reform, contributed to a small improvement when it comes to overall PHC performance, but simultaneously to increased socioeconomic inequities in PHC performance. This duality in the impact of the Swedish reform also reflects the arguments in the European health policy debate on patient choice PHC models, with hopes of improved performance but fears of increased inequities.

Background To prevent tobacco use in Korea, the national quitline number was added to tobacco packages in December 2012, tobacco prices were raised by 80% in January 2015, and graphic health warning labels were placed on tobacco packages in December 2016. This study evaluated the association of these tobacco packaging and pricing policies with suicide mortality in Korea. Methods Monthly mortality from suicide was obtained from Cause-of-Death Statistics in Korea from December 2007 to December 2019. Interrupted time-series analysis was performed using segmented Poisson regression models. Relative risks (RRs) and 95% confidence intervals (CIs) were calculated adjusted for suicide prevention strategies. Results Suicide mortality was 20 per 1,000,000 in December 2007 and showed a downward trend over the study period. After the implementation of tobacco packaging and pricing policies, suicide mortality immediately declined by − 0.09 percent points (95% CI =  − 0.19 to 0.01; P  > 0.05) for the national quitline number, − 0.22 percent points (95% CI =  − 0.35 to − 0.09; P  < 0.01) for tobacco prices, and − 0.30 percent points (95% CI =  − 0.49 to − 0.11; P  < 0.01) for graphic health warning labels. The corresponding RRs for these post-implementation changes compared with the pre-implementation level were 0.91 (95% CI = 0.83 to 1.00), 0.80 (95% CI = 0.70 to 0.91), and 0.74 (95% CI = 0.61 to 0.90), respectively. Significant associations between tobacco control policies and suicide mortality were observed even when stratified by sex and region. Conclusions The findings of this study provide new evidence for an association between tobacco control policies and deaths by suicide. An array of effective tobacco control policies should be considered for prevention programs targeting suicide.

Recent empirical work in several economic fields, particularly environmental and energy economics, has adapted the regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular threshold in time. In this guide for practitioners, we discuss several features of this regression discontinuity in time framework that differ from the more standard cross-sectional RD framework. First, many applications (particularly in environmental economics) lack cross-sectional variation and are estimated using observations far from the temporal threshold. This common empirical practice is hard to square with the assumptions of a cross-sectional RD, which is conceptualized for an estimation bandwidth shrinking even as the sample size increases. Second, estimates may be biased if the time-series properties of the data are ignored (for instance, in the presence of an autoregressive process), or more generally if short-run and long-run effects differ. Finally, tests for sorting or bunching near the threshold are often irrelevant, making the framework closer to an event study than a regression discontinuity design. Based on these features and motivated by hypothetical examples using air quality data, we offer suggestions for the empirical researcher wishing to use the RD in time framework.