Huan Jiang

Bio: Huan Jiang is an academic researcher from Centre for Addiction and Mental Health. The author has contributed to research in topics: Life expectancy & Consumption (economics). The author has an hindex of 2, co-authored 4 publications receiving 16 citations.

Classifying Alcohol Control Policies with Respect to Expected Changes in Consumption and Alcohol-Attributable Harm: The Example of Lithuania, 2000-2019.

Abstract: Due to the high levels of alcohol use, alcohol-attributable mortality and burden of disease, and detrimental drinking patterns, Lithuania implemented a series of alcohol control policies within a relatively short period of time, between 2008 and 2019. Based on their expected impact on alcohol consumption and alcohol-attributable harm, as well as their target population, these policies have been classified using a set of objective criteria and expert opinion. The classification criteria included: positive vs. negative outcomes, mainly immediate vs. delayed outcomes, and general population vs. specific group outcomes. The judgement of the alcohol policy experts converged on the objective criteria, and, as a result, two tiers of intervention were identified: Tier 1—highly effective general population interventions with an anticipated immediate impact; Tier 2—other interventions aimed at the general population. In addition, interventions directed at specific populations were identified. This adaptable methodological approach to alcohol control policy classification is intended to provide guidance and support for the evaluation of alcohol policies elsewhere, to lay the foundation for the critical assessment of the policies to improve health and increase life expectancy, and to reduce crime and violence.

Classifying Alcohol Control Policies with Respect to Expected Changes in Consumption and Alcohol-Attributable Harm: The Example of Lithuania

Abstract: Given the high levels of overall volume of alcohol use, detrimental drinking patterns, and high levels of alcohol-attributable mortality and burden of disease, Lithuania implemented a series of alcohol control policies within a relatively short period of time (2008 to 2019). Based on their expected impact on alcohol consumption and alcohol-attributable harm, as well as their target population, the respective policies were classified using a set of objective criteria and expert opinion. The classification criteria included: positive vs. negative outcomes, mainly immediate versus delayed outcomes, and general population versus specific group outcomes. The judgement of the alcohol policy experts converged on the objective criteria, and, as a result, two tiers of intervention were identified: Tier 1 – general population interventions with an anticipated immediate impact; Tier 2 – other interventions aimed at the general population. In addition, interventions for specific populations were identified. This adaptable methodological approach to alcohol control policy classification is intended to provide guidance and support the evaluation of alcohol policies elsewhere, lay the foundation for the critical assessment of the respective policies to improve health and increase life expectancy, and to reduce crime and violence. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 18 January 2021 doi:10.20944/preprints202101.0354.v1 © 2021 by the author(s). Distributed under a Creative Commons CC BY license.

Evaluating the Impact of Alcohol Policy on Suicide Mortality: A Sex-Specific Time-Series Analysis for Lithuania.

Abstract: It is reasonable to believe that the alcohol policy environment can impact the suicide mortality rates in a given country, considering the well-known link between alcohol use and death by suicide. ...

Determining the sex-specific distributions of average daily alcohol consumption using cluster analysis: is there a separate distribution for people with alcohol dependence?

Abstract: It remains unclear whether alcohol use disorders (AUDs) can be characterized by specific levels of average daily alcohol consumption. The aim of the current study was to model the distributions of average daily alcohol consumption among those who consume alcohol and those with alcohol dependence, the most severe AUD, using various clustering techniques. Data from Wave 1 and Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions were used in the current analyses. Clustering algorithms were applied in order to group a set of data points that represent the average daily amount of alcohol consumed. Gaussian Mixture Models (GMMs) were then used to estimate the likelihood of a data point belonging to one of the mixture distributions. Individuals were assigned to the clusters which had the highest posterior probabilities from the GMMs, and their treatment utilization rate was examined for each of the clusters. Modeling alcohol consumption via clustering techniques was feasible. The clusters identified did not point to alcohol dependence as a separate cluster characterized by a higher level of alcohol consumption. Among both females and males with alcohol dependence, daily alcohol consumption was relatively low. Overall, we found little evidence for clusters of people with the same drinking distribution, which could be characterized as clinically relevant for people with alcohol use disorders as currently defined.

Alcohol control policy measures and all-cause mortality in Lithuania: an interrupted time–series analysis

Abstract: Background and aims Alcohol use has been identified as a major risk factor for burden of mortality and disease, particularly for countries in eastern Europe. During the past two decades, several countries in this region have implemented effective alcohol policy measures to combat this burden. The aim of the current study was to measure the association between Lithuania's alcohol control policies and adult all-cause mortality. Design Interrupted time-series methodology by means of general additive models. Setting Lithuania. Participants Adult population of Lithuania, aged 20 years and older. Measurements Alcohol control policies were ascertained via a document review of relevant legislation materials. Policy effects were evaluated as follows: (1) slope changes in periods of legislative (non-)activity with regard to alcohol control policy (analysis 1); (2) level changes of three interventions following recommendations of the World Health Organization (analysis 2); and (3) level changes of seven interventions judged a priori by an international panel of experts (analysis 3). Mortality was measured by sex-stratified and total monthly age-standardized rates of all-cause mortality for the adult population. Findings During the period 2001-18, effective alcohol control policy measures were implemented on several occasions, and in those years the all-cause mortality rate declined by approximately 3.2% more than in years without such policies. In particular, the implementation of increased taxation in 2017 was associated with reduced mortality over and above the general trend for men and in total for all analyses, which amounted to 1452 deaths avoided (95% confidence interval = -166 to -2739) in the year following the implementation of the policy. Conclusions Alcohol control policies in Lithuania appear to have reduced the overall adult all-cause mortality over and above secular trends.

Dose-Response Relationships between Levels of Alcohol Use and Risks of Mortality or Disease, for All People, by Age, Sex, and Specific Risk Factors.

Abstract: Alcohol use has been causally linked to more than 200 disease and injury conditions, as defined by three-digit ICD-10 codes. The understanding of how alcohol use is related to these conditions is essential to public health and policy research. Accordingly, this study presents a narrative review of different dose–response relationships for alcohol use. Relative-risk (RR) functions were obtained from various comparative risk assessments. Two main dimensions of alcohol consumption are used to assess disease and injury risk: (1) volume of consumption, and (2) patterns of drinking, operationalized via frequency of heavy drinking occasions. Lifetime abstention was used as the reference group. Most dose–response relationships between alcohol and outcomes are monotonic, but for diabetes type 2 and ischemic diseases, there are indications of a curvilinear relationship, where light to moderate drinking is associated with lower risk compared with not drinking (i.e., RR < 1). In general, women experience a greater increase in RR per gram of alcohol consumed than men. The RR per gram of alcohol consumed was lower for people of older ages. RRs indicated that alcohol use may interact synergistically with other risk factors, in particular with socioeconomic status and other behavioural risk factors, such as smoking, obesity, or physical inactivity. The literature on the impact of genetic constitution on dose–response curves is underdeveloped, but certain genetic variants are linked to an increased RR per gram of alcohol consumed for some diseases. When developing alcohol policy measures, including low-risk drinking guidelines, dose–response relationships must be taken into consideration.

Predicting the Impact of Alcohol Taxation Increases on Mortality—A Comparison of Different Estimation Techniques

Abstract: Abstract Aims To examine how standard analytical approaches to model mortality outcomes of alcohol use compare to the true results using the impact of the March 2017 alcohol taxation increase in Lithuania on all-cause mortality as an example. Methods Four methodologies were used: two direct methodologies: (a) interrupted time-series on mortality and (b) comparing predictions based on time-series modeling with the real number of deaths for the year following the implementation of the tax increase; and two indirect methodologies: (c) combining a regression-based estimate for the impact of taxation on alcohol consumption with attributable-fraction methodology and (d) using price elasticities from meta-analyses to estimate the impact on alcohol consumption before applying attributable-fraction methodology. Results and Conclusions While all methodologies estimated reductions in all-cause mortality, especially for men, there was substantial variability in the level of mortality reductions predicted. The indirect methodologies had lower predictions as the meta-analyses on elasticities and risk relations seem to underestimate the true values for Lithuania. Directly estimated effects of taxation based on the actual mortalities seem to best represent the true reductions in alcohol-attributable mortality. A significant increase in alcohol excise taxation had a marked impact on all-cause mortality in Lithuania.

Estimating effects of health policy interventions using interrupted time-series analyses: a simulation study

Abstract: Abstract Background A classic methodology used in evaluating the impact of health policy interventions is interrupted time-series (ITS) analysis, applying a quasi-experimental design that uses both pre- and post-policy data without randomization. In this paper, we took a simulation-based approach to estimating intervention effects under different assumptions. Methods Each of the simulated mortality rates contained a linear time trend, seasonality, autoregressive, and moving-average terms. The simulations of the policy effects involved three scenarios: 1) immediate-level change only, 2) immediate-level and slope change, and 3) lagged-level and slope change. The estimated effects and biases of these effects were examined via three matched generalized additive mixed models, each of which used two different approaches: 1) effects based on estimated coefficients ( estimated approach), and 2) effects based on predictions from models ( predicted approach). The robustness of these two approaches was further investigated assuming misspecification of the models. Results When one simulated dataset was analyzed with the matched model, the two analytical approaches produced similar estimates. However, when the models were misspecified, the number of deaths prevented, estimated using the predicted vs. estimated approaches, were very different, with the predicted approach yielding estimates closer to the real effect. The discrepancy was larger when the policy was applied early in the time-series. Conclusion Even when the sample size appears to be large enough, one should still be cautious when conducting ITS analyses, since the power also depends on when in the series the intervention occurs. In addition, the intervention lagged effect needs to be fully considered at the study design stage (i.e., when developing the models).