On October 1, 2014, the Chilean government modified its previous sugar-sweetened beverage (SSB) tax, increasing the tax rate from 13% to 18% on industrialized beverages with high levels of sugar (H-SSBs) (greater than 6.25 grams [g] sugar/100 milliliters [mL]) and decreasing the tax rate from 13% to 10% on industrialized beverages with low or no sugar (L-SSBs) (less than 6.25 g sugar/100 mL). This study examines changes in beverage prices and household beverage purchases following the implementation of the tax reform.We used longitudinal data collected between January 1, 2013, and December 31, 2015, from 2,000 households. We defined the pretax period as January 1, 2013, to September 30, 2014, and the posttax period as October 1, 2014, to December 31, 2015. We conducted a pre–post analysis for changes in prices and purchases, with the latter examined by volume and calories. We compared posttax changes in prices and purchases to a counterfactual, defined as what would have been expected in the posttax period based on pretax trends. All results are stated as comparisons to this counterfactual. We linked beverages at the bar code level to nutrition facts panel data collected by a team of Chilean nutritionists who categorized them by taxation level and beverage subcategory, which included carbonated and noncarbonated H-SSBs and concentrated, ready-to-drink L-SSBs and untaxed beverages. We reconstituted concentrated beverages and analyzed all beverages using as-consumed volumes and calories. Posttax monthly prices of H-SSBs increased, but these changes were small. Prices of carbonated H-SSBs increased by 2.0% , while those of noncarbonated H-SSBs increased by 3.9% (95% CI 1.6%–6.2%). Prices of L-SSB concentrates decreased after the tax by 6.7% (95% CI −8.2%–−4.6%), and prices of ready-to-drink L-SSBs increased by 1.5% (95% CI 0.3%–2.7%). Households decreased monthly per capita purchases of H-SSBs by 3.4% by volume (95% CI −5.9%–−0.9%) and 4.0% by calories (95% CI −6.3%–−1.9%), and this change was greater among high socioeconomic status (SES) households. The volume of household purchases of L-SSBs increased 10.7% (95% CI 7.5%–13.9%), while that of untaxed beverage purchases decreased by 3.1% (95% CI −5.1%–−1.1%). The main limitation of this study was that there was no control group, so we were unable to assess the causal impact of the tax.The modifications of Chile’s SSB tax were small, and observed changes in prices and purchases of beverages after the tax were also small. Our results are consistent with previous evidence indicating that small increases in SSB taxes are unlikely to promote large enough changes in SSB purchases to reduce obesity and noncommunicable diseases (NCDs). Lindsey Smith Taillie and colleagues describe reduced purchases of sugar-sweetened beverages after modifications to the Chilean sugar tax. Sugar-sweetened beverage (SSB) taxes have emerged as a strategy to prevent a continued rise in obesity prevalence and noncommunicable diseases (NCDs).Recent studies in Mexico and the United States have shown that SSB taxes are associated with increased prices of taxed beverages and reduced purchases of those beverages, particularly among households of low socioeconomic status (SES).Little evidence exists on the effects increasing or decreasing an existing SSB tax has on beverage prices and purchases.On October 1, 2014, Chile increased its tax on industrialized beverages with high levels of sugar (H-SSBs) from 13% to 18% and reduced its tax on industrialized beverages with low or no sugar (L-SSBs) from 13% to 10%. Beverages such as plain and flavored milk, 100% fruit juices, and unflavored water remained untaxed.Understanding the effects of changes to existing SSB taxes is critical for informing future SSB tax policy.We used a pre–post design to estimate changes in prices and purchases  of beverages in Chile after the October 1, 2014, modifications of SSB taxes with data collected from January 1, 2013, to December 31, 2015. We compared observed posttax changes in beverage prices and purchases to the counterfactual, or what would have been expected in the posttax period based on pretax trends. All results are stated in terms of a comparison to this counterfactual.Our model links household beverage purchase data to nutrition facts panel data using bar code information to classify each beverage purchased by tax level and subcategory.We found that after the tax increase on H-SSBs, prices of H-SSBs increased, although the increases were small. In contrast, after the tax decrease on L-SSBs, prices decreased in some categories but increased in others. Prices of untaxed beverages also increased.After the tax increase on H-SSBs, purchases of H-SSBs decreased, although the declines were small. After the tax decrease on L-SSBs, purchases of L-SSBs increased. Despite no change in the tax rate, purchases of untaxed beverages decreased.High-SES households showed larger declines in purchases of H-SSBs after the tax than did low-SES households.After a small increase in the tax rate on H-SSBs, changes in prices and purchases of these beverages were small. This is consistent with previous evidence indicating that small increases in SSB taxes are unlikely to produce large changes in SSB purchases. Further evidence is needed to understand the small change in prices due to the tax reform.High-SES households showed larger declines in purchases of H-SSBs than did low-SES households. Further research is needed to understand the differential response to this tax by SES.After a decrease in the tax rate on L-SSBs, purchases of these beverages increased a large amount. Further research is needed to understand the health consequences of the shift toward these beverages, which frequently contain artificial sweeteners. In response to the increasing global burden of obesity and related chronic diseases in the last decade, taxes on industrially produced sugar-sweetened beverages (SSBs) have emerged as a regulatory strategy to prevent the continued rise of obesity ,2. ReseaHowever, limited evidence exists on the impact of tax rate changes to existing SSB taxes. In high-income countries and low- and middle-income countries with rapid income growth, households might be unaware of small changes in tax rates due to higher median incomes, and commercial beverage companies might choose not to switch prices proportionally to the tax change . In addiChile, recently categorized as a high-income country, is an interesting case study to explore changes in prices and purchases after a change in the SSB tax rate. Chile has a high prevalence of obesity and type 2 diabetes –16 and rThe Chilean SSB tax structure is unique for two reasons. First, it creates a price differential between high-sugar and low-sugar SSBs, and second, it taxes beverages with artificial sweeteners (such as diet soft drinks) and flavored unsweetened beverages. In contrast, the SSB taxes in both Berkeley and Mexico apply a single rate to all nondairy and nonalcoholic beverages containing added sugar and do not apply to beverages with zero added sugar or those that contain only artificial sweeteners. The Philadelphia SSB tax is similar to Chile’s in that it also applies to artificially sweetened beverages but different in that it applies a single rate to all taxed beverages, regardless of sugar level. The United Kingdom SSB tax is also two tiered, with high-sugar SSBs taxed at a higher rate than low-sugar SSBs. However, this tax is based on the added sugar content of the beverage, and thus, artificially sweetened beverages containing no added sugar are not taxed. To our knowledge, the Chilean tax is the first that applies a two-tiered rate to SSBs and includes any artificially sweetened or flavored unsweetened beverages.It is unclear how changing an existing SSB tax affects prices or purchases compared to the introduction of a new tax. Small sales taxes on SSBs (mean 5.2%) in jurisdictions across the US suggest that small relative increases in the tax rate may not lead to meaningful changes in sweetened beverage consumption ,19. HoweThe objectives of this study are to (1) analyze whether the average prices of beverage purchases changed after the tax implementation and (2) analyze whether the volumes and calories from beverage purchases changed after the tax implementation, controlling for household covariates and secular trends overall and by SES.We used a longitudinal data set of household food purchases from January 2013 to December 2015 that we obtained from Kantar WorldPanel Chile (see the STROBE Checklist in The data on each beverage purchase were linked at the bar code level to a nutrition facts panel gathered from photographs that a team of Chilean nutrition research assistants collected directly at stores (79.8% of products) –26, MintThe final categorization within each tax group follows. Untaxed beverages: plain water, plain and flavored or sweetened milk, dairy-based ready-to-drink beverages, milk powders and modifiers (such as cocoa), coffee, and tea. Ten percent–taxed beverages: ready-to-drink L-SSBs and L-SSB concentrates. Eighteen percent–taxed beverages: noncarbonated H-SSBs and carbonated H-SSBs . While wFollowing the protocol of the Chilean Association of Market Research, we categorized households into four SES categories using the data Kantar WorldPanel Chile provided . For sim2) as a predictor of economic activity [In addition, because trends in income, economic activity, and other market factors could influence the price and quantity of beverage purchases , we contactivity , the Supactivity .For the price analysis, we sorted brands within each beverage subcategory based on their average market shares  during the 2013–2015 period. We aggregated brands with market shares lower than 3% into a combined brand within each category. Brands with more than a 3% market share remained separate. We then classified products by market share as either low  or high (greater than 10% market share). Next, we defined package sizes for each individual brand. Ready-to-drink beverages we categorized as small  or large . Beverages sold as liquid concentrates, powders, or dry leaves we categorized as small  or large . Hereinafter we refer to a product as the unique combination of brand and package size within each beverage category.Our analysis used data on beverage prices as reported directly by households. Unlike data on prices collected from food stores, prices obtained from purchase data not only reflect changes in prices due to industry behavior but also differences in household preferences for certain beverage types or geographic differences in what products are available in the store  . In otheFirst, we examined sociodemographic characteristics of the households participating in the survey by SES status and separated households that exited the sample at any point between 2013 and 2015. Next, we calculated the average and median adjusted real prices at the market-month level, including the number of products within each beverage category. Finally, we calculated average purchases by household/month both by volume (mL) and calories  purchased per capita per day for each beverage category and the percentage of nonzero household/month observations.We conceived the initial study design in early July 2016 and purchased the Kantar data at the end of the same month. Between July 2016 and May 2017, master’s-level nutritionists cleaned and categorized the data, and we conducted descriptive analyses on volumes of purchases. Prior to any analysis, the initial analysis plan included:Descriptive unadjusted trends in volume, sugar, and calories by taxation category and beverage subcategories and product characteristics.Descriptive unadjusted trends in volume, sugar, and calories by taxation category for each SES subgroup.Longitudinal analysis adjusted for household and macroeconomic covariates using fixed effects models for volume, sugar, and calories by taxation status and beverage subcategory. Pretax trends were to be projected into the posttax period to serve as a counterfactual for observed posttax purchases, with the statistical tests conducted between the observed and the counterfactual posttax average household purchases. This followed the framework laid out in previous studies of the Mexican SSB tax .Using the same longitudinal models, stratify by SES to examine whether there were differences between low- and high-SES households.In July 2017, we introduced two changes to the original analysis plan. First, we realized that, unlike in the Mexican tax evaluation, in Chile, no studies had examined changes in prices due to the tax implementation. Understanding changes in prices seemed critical to explaining any changes in beverage purchases (or lack thereof) after the SSB tax. Thus, we added a price analysis component, which included analysis of how estimated changes vary by product characteristics and market-level SES. Second, for both analyses, we tested several models to determine the best model specification for our data. As a result, we used a correlated random effects tobit model for volumes and a linear random effects model for prices. However, despite these modifications, the goal of the analysis remained the same: to compare observed posttax changes in prices and purchases to a counterfactual, or what would have been observed in the posttax period as predicted by pretax trends.Finally, we introduced a few additional changes as requested during the review process, including the addition of 95% confidence intervals (CIs), sensitivity analyses on model specification, and reorganization of the tables in the main text. As requested, we also included the statistical significance of our results accounting for multiple hypotheses testing.After a preliminary and descriptive analysis of the data set, we developed a pre–post analysis model to address the lack of a proper control group, since the tax was implemented at a national level. The goal of this analysis is to capitalize on changes in the average mean that occur before and after an intervention based on a break in time (the date of tax implementation). This approach is consistent with previous studies of Mexico’s SSB tax ,10,32. IWe conducted all analyses at the product-market level using the natural logarithm of prices . The unit of each observation is the price of each product per 1,000 mL in a market in a given month. We used a linear random effects model with standard errors clustered at the market level. This model uses a tax period indicator variable (pretax versus posttax) to create a break in the average mean purchases before and after the tax implementation. We controlled for linear and quadratic aggregated trends, seasonality, and regional economic covariates. Additionally, we examined whether price changes vary by low versus high market-level SES, package size, and the brand’s market share using interactions with the tax indicator variable.After estimating the model, we calculated the adjusted predicted changes in price level and its corresponding 95% CI, comparing what was observed in the posttax period to the counterfactual (with the tax indicator variable set to zero). To obtain the predicted values, we back-transformed the conditional means, applying Duan smearing factors .For our purchases analysis, the unit of observation was monthly per capita volumes or calories of beverages purchased by the household. We used a tobit model to explicitly recognize the large proportion of nonpurchases of each subcategory and to minimize biases introduced by the large proportion of nonpurchasers. We clustered standard errors at the household level.Additionally, to adjust for unobserved heterogeneity by household (such as underlying differences in household preferences), we allowed for correlated random effects at the household level using the Chamberlain–Mundlak device ,36. We ap-value of a t test on whether the difference in the absolute predicted mean changes between the two groups in the posttax period is statistically different from zero.We conducted several sensitivity analyses to determine the best model specification using information criterion and goodness of fit for model selection. First, we tested different specifications for the trends, time breaks, and seasonality in each model and the significance of the time-varying covariates in each specification. Second, we tested the sensitivity of our estimates to autocorrelation  using the Arellano–Bond estimator . We alsoEighty-five percent of households were in the sample for all 36 months. Our analytic sample includes 1,795 unique households, 64,620 household monthly observations, and 114,003 market-month product observations.Household characteristics in our sample are summarized in Unadjusted average monthly market prices by beverage subcategory showed major differences between subcategories . We founThe distribution of package size and brand size  for all taxed products  are in Monthly adjusted mean prices and purchases (volume) by beverage category are in Compared to the counterfactual, L-SSB concentrates saw a sizable price reduction following the tax cut , while prices of ready-to-drink L-SSBs increased slightly . After the tax, the prices of untaxed beverages increased by 1.8% compared to the counterfactual (95% CI 0.7%–2.9%).p-value = 0.000).We found no significant differences in price changes for H-SSBs or L-SSBs by SES market. However, among untaxed beverages we found a significant increase in price for the low-SES market but no change in price for the high-SES market . While L-SSBs with large market shares increased prices (despite the cut in the tax rate), brands with small market shares experienced a significant decrease in prices.Absolute and relative changes in household beverage purchases by volume and calories are in Tables In contrast, the volume of household purchases of L-SSBs increased 10.7% (95% CI 7.5%–13.9%) relative to the counterfactual. By subcategory, we observed a 9.4% increase in purchase volume of L-SSB concentrates (95% CI 6.0%–12.8%) and a 12.3% increase in ready-to-drink L-SSBs (95% CI 8.4%–16.2%) relative to the counterfactual. Purchases of untaxed beverages decreased 3.1% by volume (95% CI −5.1%–−1.1%) and 5.3% by calories (95% CI −8.1%–−2.5%).p-value = 0.004 for volume and 0.006 for calories). In general, high-SES households showed larger declines in H-SSB purchases after the tax than did low-SES households. Specifically, compared to their respective counterfactuals, high-SES households reduced purchases of H-SSBs by 6.4% by volume (95% CI −9.3%–−3.5%) and 6.5% by calories (95% CI −9.1%–−3.9%), whereas low-SES households showed no change in the volume or calories of H-SSB purchases.We found that posttax changes in H-SSB purchases varied by household SES , though the size of the difference between SES groups was small. Specifically, relative to their respective counterfactuals, high-SES households increased the volume of L-SSB purchases by 10.8% (95% CI 7.8%–13.8%), while low-SES households increased the volume of L-SSB purchases by 9.5% (95% CI 5.8%–13.2%). Finally, there were also statistically significant differences in posttax changes of untaxed beverage purchases in high- versus low-SES households by volume  but not by calories . Specifically, relative to their respective counterfactuals, high-SES households had no change in the volume of untaxed beverages purchased, while low-SES households decreased the volume of untaxed beverage purchases by 4.3% (95% CI −6.6%–−2.0%).We also found that posttax changes in the volume of L-SSB purchases varied by household SES (The results presented in this paper reflect the best model fit based on Akaike information criteria (AIC)  and goodIn October 2014, Chile increased its tax rate on H-SSBs from 13% to 18% and reduced its tax rate on L-SSBs from 13% to 10%, representing an 8% spread between L-SSBs and H-SSBs after tax implementation. This study found that after the 5% tax increase on H-SSBs, prices increased by 2.0% for carbonated H-SSBs and 3.9% for noncarbonated H-SSBs relative to their respective counterfactuals. After the 3% tax decrease on L-SSBs, price changes were heterogeneous, with prices decreasing for concentrated L-SSBs by 6.7% and for ready-to-drink L-SSBs by 1.5% relative to their respective counterfactuals.Commensurate with the small increase in prices of H-SSBs after the tax relative to the counterfactual, we found a small decrease in household purchases of H-SSBs , with most of the declines coming from noncarbonated H-SSBs (−8.2%). Overall household purchases of L-SSBs increased relative to the counterfactual (10.7% for volume), with larger changes among ready-to-drink L-SSBs (14.3%).The price increases of H-SSBs were small  compared to the tax increase of 5% on these beverages. This is different from what was observed in Mexico, where shelf prices of carbonated SSBs increased proportionally to the size of the tax . HoweverWe also found that for H-SSBs and L-SSBs, price changes varied across beverage subcategories when we considered differences in brand size, package size, and market-level SES. While it is outside the scope of this article to explain commercial beverage company behavior, in Chile, companies commonly maintain a complex portfolio of carbonated H-SSBs and carbonated L-SSBs , which allows for a cross-subsidy between tax categories to maintain profits. This could explain the small absolute difference in the prices of ready-to-drink L-SSBs and carbonated H-SSBs after the tax. In contrast, L-SSB concentrates and noncarbonated H-SSBs tend to be manufactured by different companies, preventing them from cost-shifting from one category to another. This may explain why among these beverage types the observed price changes were consistent with the tax changes . Similar industry responses were observed in Mexico, where price changes varied geographically and by package size ,39. FutuWith regard to household purchases of H-SSBs, after the tax, there was an overall decline of 3.4% by volume relative to the counterfactual. This reflected substantial heterogeneity by beverage subcategory, with an 8.2% decline in volume for noncarbonated H-SSBs and a 2.6% decline in volume for carbonated H-SSBs. Although these posttax declines in volume purchased are larger than the corresponding changes in price, these findings are consistent with previous studies of price elasticities  of SSBs in Chile, which range from −1.30 to −1.37 . In otheDespite these declines in purchases, however, this tax is likely to have a small impact on preventing excess caloric intake or adverse health effects associated with SSB intake. This is because the absolute reductions in H-SSB purchases were small. For example, compared to the counterfactual, the overall volume of purchases of H-SSBs decreased by 108 mL per person per month . Similarly, calories purchased decreased by 3.0% and 8.9%, respectively, for carbonated H-SSBs and noncarbonated H-SSBs compared to their respective counterfactuals. This reflects absolute reductions of only 34 calories per person per month for carbonated H-SSBs and 21 calories per person per month for noncarbonated H-SSBs compared to their respective counterfactuals. To put this in context, Mexico’s SSB tax was about 10%, which translated to a 6% decline in volume of purchases relative to the counterfactual in the first year after tax implementation but represented a larger absolute decline of 360 mL per person per month compared to the counterfactual  [On the contrary, purchases of L-SSBs increased considerably in the posttax period . This translates into absolute increases of 281 mL per person per month (or roughly three-fifths of a can of soda) beyond what would be expected based on pretax trends. We were unable to estimate changes in calories purchased for L-SSBs, because many of them had very few or no calories. While the absolute changes in volume of L-SSB purchases were small, the relative percentage increase may be concerning, particularly for high consumers, given the lack of evidence regarding the long-term health effects of consumption of artificial sweeteners .There are several possibilities that explain the smaller declines in H-SSB purchases after Chile’s tax compared to Mexico’s tax. First, as noted above, the estimated relative price increases on H-SSBs were smaller in Chile than in Mexico. Second, in Mexico, the SSB tax was accompanied by a prolonged, multiyear advocacy campaign that included coalition-building across key scientific and consumer advocacy organizations and paid and earned media campaigns, public demonstrations, press conferences, scientific forums, and civil society forums . Such caEven though the Chilean tax modification did not affect previously untaxed beverages, such as milk or 100% fruit and vegetable juices, this study found that prices of these beverages increased 1.8% in the posttax period relative to the counterfactual. Our estimates suggest that the changes in the relative prices of taxed beverages affected the markets of untaxed products, particularly in low-SES markets .Commensurate with this price increase, household purchases of untaxed beverages decreased in the posttax period .Interestingly, the largest reduction in calories purchased after the tax change came from untaxed products rather than taxed H-SSBs. One possibility is that consumers substituted L-SSBs for untaxed beverages, which is consistent with the finding that after the tax decrease on L-SSBs, untaxed beverages became relatively more expensive. Because different untaxed beverage subcategories have varying amounts of sugar (such as lactose in plain milk and added sugar in sweetened flavored milk) and beneficial nutrients, like calcium in milk or vitamin C in 100% juice, it will be useful for future research on SSB taxes to examine the changes in purchases that occur across various untaxed beverage subcategories and the potential implications of these changes for health.This study found significant differences in changes in price after the tax by market-level SES for untaxed beverages but not for H-SSBs or L-SSBs. It was not clear why changes in the price of untaxed beverages would vary by market-level SES. We were unable to tell whether these differences in the change in price by SES market were due to differential price changes by retailers and manufacturers or differences in consumer behavior, as noted earlier. For example, if low-SES households change how often they buy products or favor bulk discounts on large products, it could lead to variations in the price changes across markets. More research is needed to explore differential price changes by SES market after the tax to understand the implications for both consumer behavior and industry behavior.We found that high-SES households had a larger decline in the volume of H-SSB purchases (−6.4%) than did low-SES households (−1.6%) relative to their respective counterfactuals. This was mainly driven by differences in purchases of carbonated H-SSBs. While high-SES household volume purchases of H-SSBs decreased by 7.2%, low-SES household volume purchases did not change. Low-SES households showed larger declines in noncarbonated H-SSBs than high-SES households, but the difference was not statistically significant. In addition, it is important to note that carbonated H-SSBs represent a larger proportion of SSB purchases than do noncarbonated H-SSBs, so the SES differences for carbonated H-SSBs are especially important.The result—that high-SES households had bigger changes in purchases of H-SSBs—contrasts with the Mexican first-year SSB tax evaluation, which found a 9.2% decline in low-SES households compared to a 5.6% decline for high-SES households . Also, lFinally, we found differences in changes in posttax purchases by household SES. Low-SES households showed larger reductions in untaxed beverage purchases by volume, but high-SES households showed larger reductions in untaxed purchases by calories. These differences in response by household SES could be the result of changes in the types of untaxed beverages purchased, since this category is very heterogenous and includes beverages with calories, such as milk and 100% juice, but also beverages with no calories, such as plain bottled water. We were unable to examine differences by beverage subcategory to better explain these findings due to the low amount of purchases in some of these subcategories.The most important limitation of this study is our inability to assess a causal relationship between the tax modifications and changes in prices or purchases due to the potential presence of other simultaneous trends affecting underlying preferences and due to the inability of household food purchase data to capture all beverages consumed (particularly those consumed out of the home). While we did adjust for economic factors and secular trends, we cannot rule out shifts in preferences or norms that may have occurred concurrently with the tax implementation. To address our inability to capture all beverages using household purchase data, we will conduct future research using dietary recall data to capture the full range of beverages consumed. In the meantime, we note that the trends observed here are consistent with those in Euromonitor sales data, which reflect all beverages sold in the country . In termAn additional limitation of this study is that, as previously noted, the prices reflected products purchased and not those available in stores . However, we were able to address this issue using models that consistently captured variability in prices, allowing us to accurately estimate the average market changes while also exploring heterogeneity across product and brand characteristics. We also explicitly accounted for the censored nature of purchasing data by estimating a tobit model that predicts both the choice to purchase and the amount purchased. Finally, we were able to examine differential changes in both prices and purchases by SES (at the market level for price and at the household level for purchases). An understanding of differences in tax responses by SES is important for understanding how the tax may affect existing disparities in diet and health.Additional research is needed to understand the observed changes in prices and purchases and the differential responses by SES. For example, one possibility is that companies changed their marketing strategies to mitigate the effects of the price increase. A second possibility is that companies could have changed the nutritional formulation of products to avoid the tax. We also want to note that a subsequent Chilean law in July 2016 implemented front-of-package warning labels, restrictions on marketing to children, and restrictions on sales in schools for all foods and beverages containing high levels of sugar, sodium, saturated fat, or calories, which could have had additional influence on both commercial beverage industry behavior and consumer behavior in the time period leading up to the implementation of those regulations. Given the substantial regulatory changes underway in Chile’s food environment since 2014, a critical question for future research is whether this small SSB tax modification, along with the newer marketing and media controls and front-of-package labels, will result in sustained changes in Chileans’ dietary intake with potential downstream effects for SES disparities in obesity and NCDs.Finally, given the large and increased consumption of ready-to-drink L-SSBs and L-SSB concentrates, we recommend that the effects of these beverages on health be further investigated in the Chilean population, especially considering that they are relatively cheaper than their high-sugar alternatives and often bear significantly more front-of-package marketing, such as nutrition and health claims .The small increase (5%) in the Chilean tax rate on H-SSBs was followed by small decreases in prices and purchases of these beverages, with high-SES households showing larger declines in purchases than low-SES households. The small decrease (3%) in the tax on L-SSBs was followed by heterogeneous changes in prices and increases in purchases of these beverages. Prices of untaxed beverages increased, and purchases of these beverage decreased, despite the lack of a tax or a change in the tax rate on these beverages. Our results are consistent with previous evidence indicating that small tax rates on SSBs are unlikely to promote the changes in SSB purchases needed to reduce obesity and NCDs.S1 Table(DOC)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigH-SSBs and ready-to-drink L-SSBs on left axis, L-SSB concentrates on right axis. H-SSB, industrialized beverage with high levels of sugar; L-SSB, industrialized beverage with low or no sugar.(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.


Intra-household dynamics play crucial roles in utilisation of healthcare services for children. We investigated the influence of household relationships on healthcare seeking behaviour for common childhood illnesses in four sub-Sahara African regions.Data on 247,061 under-five children were extracted from recent Demographic and Health Surveys conducted between 2012 and 2016 in 25 countries. Data were combined and analysed per sub-region. Dependent variables (DVs) were uptake of health facility care for diarrhea and Acute Respiratory Tract Infection (ARI) symptoms. The main independent variable (IV) was household relationship which was represented by maternal marital profile  and maternal relationship to household head. Mixed effects logit models were fitted to assess independent relationship between the IVs and DVs with adjustment for relevant demographic and socio-economic characteristics at 5% significance level.The percentage of children who received care for diarrhea and ARI symptoms from health facilities across sub-regions was: Western Africa (WA) 42.4, 44.1%; Central Africa (CA) 32.6, 33.9%; Eastern Africa (EA) 41.5, 48.7% and Southern Africa (SA) 58.9, 62.7%. Maternal marital profile was not associated with healthcare seeking behaviour for diarrhea and ARI symptoms in any of the sub-regions. Children whose mothers were daughter/daughter-in-law to household head were significantly less likely to be taken to health facility for diarrhea treatment in Eastern Africa . Having a mother who is the head of household was significantly associated with higher odds of facility care for ARI symptoms for children from Western  and Southern Africa .The type of relationship between mother of under-fives and head of households affect health seeking behaviour for treatment of diarrhea and ARI symptoms in Eastern, Western and Southern Africa. Countries in these regions need to adapt best practices for promoting healthcare utilisation for children such that household relationship does not constitute barriers.The online version of this article (10.1186/s12913-019-4142-x) contains supplementary material, which is available to authorized users. Though there has been steady progress in reduction of childhood deaths from infectious causes, the magnitude is still substantial in sub-Saharan Africa region. Approximately, 50% of global under-five deaths were from the region . GloballMeanwhile, the second target under the third Sustainable Development Goal (SDG 3) is aimed at ending preventable neonatal and under-five deaths by the year 2030. For this goal to be achieved, efforts need to be geared towards improving healthcare seeking behaviour for childhood illnesses. In poor resource settings such as those found in many sub-Saharan Africa communities, household relationships constitute a challenge to healthcare utilisation. Household relationships are not static; they are subject to the prevailing socio-economic and development ecosystem in different countries –6. SimilLiterature has shown that childhood mortality, morbidity, and healthcare seeking behaviour share similarities in terms of associated factors –12 in whSome previous studies, mostly qualitative in design, have shown evidence to underscore the importance of the family environment in care-seeking for childhood illnesses –28. Two In this study, we explored these issues using available nationally representative survey data in sub-Saharan Africa, bearing in mind the diversities across different sub-regions. Further investigation of factors affecting care-seeking behaviour for childhood illnesses is imperative for two reasons. First, the roles of families and communities remain central as seen in the strategic revision to the World Health Organisation’s (WHO) guidelines on Integrated Management of Neonatal and Childhood Illnesses . Two, thThis study involved secondary analysis of cross-sectional data extracted from the most recent Demographic and Health Surveys (DHS) conducted in 25 sub-Saharan African countries between 2012 and 2016  symptoms. The variables were dichotomous, coded Good  and PoorCare-seeking for ARI symptoms was derived in similar manner to diarrhea. The first question posed to a mother was “Has child had an illness with a cough at any time in the last 2 weeks?” If mother answered ‘Yes’, she was further asked “When child had an illness with a cough, did he/she breath faster than usual with short, rapid breaths or have difficulty breathing?”. Children for whom the answer to these two questions was ‘Yes’ were categorised as having ARI symptoms. The third and fourth question which relate to care-seeking was “Did you seek advice or treatment for the illness from any source?” and “where did you seek advice or treatment?”Two explanatory variables were employed to represent household relationships. These were maternal marital profile and relationship to household head. Maternal marital profile has six categories that captured the marital status, family type and number of marriages a mother has had. The categories were: 1- never married; 2- first order monogamy, 3- first order polygyny, 4- higher order monogamy, 5- higher order polygyny and 6- formerly married . Order of a marriage is the number of times a woman has been married. Therefore, first order means that a woman has been married only once while higher order implied being married two or more times. Monogamy is the situation whereby a woman report that her partner do not have other wives while polygyny implied that there are other co-wives. Relationship between the mother of under-five and household head was categorised as 1-head of household , 2- wife, 3- daughter or daughter-in-law and 4- others.Based on existing literature, we adjusted for other background variables representing the demographic and socio-economic characteristics of the mother and household , 16, 37.Analysis was done for sub-Saharan Africa as a whole and for each sub-region . To provide a description of the study variables, weighted percentage distributions were estimated. In order to account for the complex sampling scheme employed in the DHSs and the hierarchical structure of the data, three-level mixed effects logit models were fitted to explore the independent association between household relationships and healthcare seeking behaviour for diarrhea and ARI symptoms. In this study, children-mother pairs were nested in clusters or communities (primary sampling unit) which were in turn nested within countries. Thus, for the three-level mixed effects model, mother-child pairs represents level 1, clusters are level 2, while countries represent level 3. Estimation models for each sub-region were based on the equation below:Where:Pijk= probability of good healthcare seeking behaviour by mother-child pair i, in community j and country k.Xijk = vector of explanatory variables.β = regression coefficient representing effects of respective explanatory variable on probability of good health seeking behaviour.jk= community-level variance.μvk = country-level variance.Models were fitted in two stages. In the first model, each of the variables for household relationship was used to estimate unadjusted effects. Thereafter, to obtain adjusted effects, demographic and socio-economic variables were added to the model. We did not use any variable selection technique because we were interested in an explanatory rather than a predictive model. The explanatory model was aimed at clarifying the independent association between household relationship and healthcare seeking behaviour.Mixed effects models have two components – random and fixed. Random effects were estimated at cluster (community) and country levels. Fixed effects of explanatory variables were estimated as Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI). Model fitness was assessed and all anayses were conducted using Stata MP version 14.Of a weighted sample of 247,061 under-five children, 36,865 (14.9%) were reported to have had diarrhea and 23,135 (9.4%), ARI symptom. Healthcare seeking behaviour is presented in Table In terms of household relationship


Dynamic hand gesture recognition has attracted increasing attention because of its importance for human–computer interaction. In this paper, we propose a novel motion feature augmented network (MFA-Net) for dynamic hand gesture recognition from skeletal data. MFA-Net exploits motion features of finger and global movements to augment features of deep network for gesture recognition. To describe finger articulated movements, finger motion features are extracted from the hand skeleton sequence via a variational autoencoder. Global motion features are utilized to represent the global movements of hand skeleton. These motion features along with the skeleton sequence are then fed into three branches of a recurrent neural network (RNN), which augment the motion features for RNN and improve the classification performance. The proposed MFA-Net is evaluated on two challenging skeleton-based dynamic hand gesture datasets, including DHG-14/28 dataset and SHREC’17 dataset. Experimental results demonstrate that our proposed method achieves comparable performance on DHG-14/28 dataset and better performance on SHREC’17 dataset when compared with start-of-the-art methods. Hand gesture provides an efficient and natural way for human–computer interaction (HCI) due to its flexibility and expressiveness. Hand gesture recognition has great potentials for applications in sign language recognition, remote control and virtual reality and has attracted great research interest in past decades ,12,13,14Existing methods on dynamic hand gesture recognition usually take RGB images and depth images ,17 as inDe Smedt et al.  proposedIn this paper, we propose a Motion Feature Augmented Network (MFA-Net) for skeleton-based dynamic hand gesture recognition. We extract finger articulated features from the hand skeleton by a variational autoencoder (VAE), which is an efficient and concise representation of the finger articulated movements. To describe the global movements of the hand, we extract the global rotation and global translation of the hand. A distance adaptive discretization scheme is exploited to better model the amplitude of the gestures. The finger motion features and global features along with the skeleton sequence are fed into a RNN to predict the class of input gesture. Experiments on the publicly available skeleton-based DHG-14/28 dataset  demonstrA preliminary version of this paper is presented in . This paThe remainder of this paper is organized as follows. In In this section, we briefly review recent methods on hand gesture recognition, which are broadly categorized into static hand gesture and dynamic hand gesture recognition. For dynamic hand gesture recognition, we briefly review related work on RGB-D based and skeleton-based methods. We also review some recent methods of feature augmentation. More comprehensive reviews on hand gesture recognition are found in ,37,38,39Static hand gesture recognition aims to predict the gesture label for a single image. Ren et al.  proposedDynamic hand gesture recognition from RGB-D frames has been actively researched for decades ,47,48,49As the recent progress of fast and accurate hand pose estimation algorithms ,22,23,24De Smedt et al.  proposedThere are arising trends to use deep learning methods for skeleton-based dynamic hand gesture recognition. Núñez et al.  adopted Different from above existing methods, our proposed method takes advantages of both hand-crafted features and deep learning methods to obtain optimal features for hand gesture recognition.There have been some attempts to enhance the capability of deep neural network by fusing hand-crafted features into the network. Sadanandan et al.  proposedInspired by these methods, we exploited motion features to augment the neural network for better performance of skeleton-based dynamic hand gesture recognition.The framework of our proposed motion feature augmented network (MFA-Net) is shown in Firstly the global motion features and finger motion features are extracted from the input skeleton sequence. The global movement of a dynamic hand gesture can be represented by the global translation and rotation of the hand. For finger motion features, we explore two kinds of representations in this paper: kinematic features and variational autoencoder (VAE) features. The hand skeleton can be directly and effectively represented by a kinematic hand model whose parameters are the angles of bones, the global translation and global rotation ,21. TherWe exploit the recurrent neural network (RNN) to model temporal information for its great successes in temporal sequences recognition tasks ,56. Moret, T is the number of frames of this sequence and J is the number of joints for hand skeleton.In this section, we describe how to extract finger motion features The global motion features  are important for dynamic hand gesture. Typically, the global status of the hand can be determined by the wrist joint, palm joint and metacarpophalangeal (MCP) joints, which are denoted by lgorithm  to inferM bins using the threshold The amplitudes of hand gestures differ from person to person for the same gesture. Therefore, previous work  ignored e Scheme ,58, whicresholds:∫0ηig(x)dSimilar to previous work , we use The dynamic pose represents the difference of global features between current frame and several previous frames:t.There features can enhance the representability of the global motion of the hand and thus can model the temporal information of dynamic hand gesture. All above features are concatenated to form the global motion features For finger motion features, we explore two kinds of representations, namely kinematic features and variational autoencoder (VAE) features, which are presented in For many dynamic hand gestures, the articulated movements of fingers are critical because the global movements may be insignificant, especially for fine-grained gestures. We use 20 DoFs (degree of freedoms) to model the finger articulation movements. For the MCP joints, there are 2 DoFs for each joint. For proximal interphalangeal (PIP) and distal interphalangeal (DIP) joints, 1 DoF is used to describe the angle of bone. These kinematic parameters retain rich information for the shape of the hand skeleton. We use Similarly, we use dynamic pose t.These features are concatenated to obtain the kinematic finger motion features The pose variational autoencoder (PoseVAE) consists of an encoder ,,st and tThere are several benefits of using VAE features to represent the finger articulated motion. Firstly, learning latent representations for hand skeletons by PoseVAE has the potential to obtain more representative features than hand-crafted features such as kinematic features. Secondly, PoseVAE reduces the noises in hand skeletons that are introduced by inaccurate annotations. As shown in In this section, we show the experimental results of our proposed method. Firstly, the datasets used for the experiments and some implementation details are briefly introduced. Secondly, comparisons with state-of-the-art methods and ablation studies are shown and discussed.M was set to The proposed framework was implemented in Keras . We usedAlthough there are many dynamic hand gesture datasets, e.g. Chalearn IsoGD , Nvidia DHG-14/28 [On DHG-14/28 dataset, we followed the same experimental setup as previous work ,62,63,64Grab and Pinch is severe, due to the high similarity of these two gestures. However, our algorithm does improve the performance of these two gestures compared with those of SoCJ + HoHD + HoWR [We compared our work with several state-of-the art methods ,62,63,64D + HoWR , with 60D + HoWR . It can D + HoWR .As shown in t method . The conSHREC’17 dataset  was firsFollowing the evaluation protocol of SHREC’17 track , we traiAs shown in ent work .The confusion matrices for 14 gestures and 28 gestures recognition on SHREC’17 dataset are shown in To verify the contributions of different modules of our proposed method, we conducted several self-comparison experiments on DHG-14 dataset, which has 14 gestures.Since DHG-14 exploits LOOCV strategy for evaluation, there are totally 20 splitting protocols for 20 subjects. Existing studies only report the average classification accuracy of these 20 experiments, which is not sufficient to evaluate the performance and robustness of hand gesture recognition algorithms for different participants. In the ablation studies, we report the worst, best and average results of 20 different splitting protocols as well as the standard deviation, which is a more comprehensive metric for taking the inter-subject effects into account.Skeleton) only took the skeleton sequences as input and adopted LSTM for gesture recognition. The second baseline (MF(Kinematic)) only took motion features as input and removed the third branch of the framework shown in We conducted several baseline experiments to explore how the motion feature augmentation strategy affects the accuracy of dynamic hand gesture recognition. The first baseline (Skeleton + MF (Kinematic) has better average accuracies for fine, coarse and all gestures, which verify the effectiveness of the proposed strategy of augmenting LSTM with motion features.As shown in the first three rows of We then explored the effects of using VAE features or kinematic features for finger motion feature extraction. Kinematic features were exploited in our preliminary version of MFA-Net  and it aIn Equation . As showk-NN, an enhanced k-NN algorithm (Centroid Displacement-Based k-NN) [k-NN, the performances are good when compared with state-of-the-art methods. For example, the best prior performance on 14 gestures classification is 90.48% by Boulahia et al. [k-NN obtains recognition rate of 90.60% and CD k-NN achieves 90.85%. On 28 gestures classification task, the highest recognition accuracy of existing methods is 81.90% [k-NN and CD k-NN achieve the accuracy of 86.07%. The considerably good performances of these classifiers demonstrate that the features produced by our proposed MFA-Net are quite discriminative for hand gesture recognition. To better understand this, we used t-SNE [In the proposed MFA-Net, fully connected (FC) layers are utilized to classify gestures from deep features from LSTM blocks, as shown in ed k-NN)  and randed k-NN)  are showa et al. , while ked t-SNE  to visuaWe evaluated the inference speed of MFA-Net on a computer equipped with 3.4 GHz i7-4770 CPU and Nvidia Tesla K40c GPU. MFA-Net takes about 1.47 ms to extract motion features for one hand skeleton and about 83.2 ms to predict the gesture for an input dynamic hand gesture sequence with 64 skeletons. Therefore, on average MFA-Net takes 2.77 ms to process one hand skeleton. In other words, MFA-Net can process 361 skeletons per second, which is sufficient for real-time performance.This paper proposes the motion feature augmented network (MFA-Net) to recognize skeleton-based dynamic hand gestures. Finger motion features are extracted via a variational autoencoder from the hand skeleton sequence to describe finger articulated movements. Global motion features are utilized to represent the global movements of hand skeleton. The motion features, along with the skeleton sequence, are fed into three branches of RNN to predict the label of input gesture. Experiments demonstrate that our proposed MFA-Net achieves comparable performance with state-of-the-art methods on the public DHG-14/28 dataset and best performance on SHREC’17 dataset. Future work may focus on a hierarchical coarse to fine framework to achieve better classification performance.


Connective tissue disease associated with interstitial lung disease (CTD-ILD) and interstitial lung disease (ILD) alone have same pathological and imaging backgrounds. However, the differences between lung cancer development and the mortality risk between these two conditions are unclear. Incidence of primary lung cancer and all-cause mortality were studied between interstitial lung disease patients with and without connective tissue disease.Data were extracted from the Korean National Health Insurance Research Database in 2009. A total of 12,787 cases of ILD without idiopathic pulmonary fibrosis and 2491 cases of CTD-ILD were diagnosed in 2009. The cohort was followed up until June 30, 2014. Incident lung cancers and all-cause mortality were ascertained.The overall incidence of lung cancer was 165.7 and 161.8 per 10,000 person-years in the CTD-ILD and ILD-only, respectively . CTD-ILD patients in the 40–49 and 50–59 years old age groups had lung cancer incidence rates of 92.5 and 139.2, which were 2.0 and 1.7 times higher than those in the ILD-only, respectively. All-cause mortality was significantly higher in the CTD-ILD group compared to ILD-only group in patients aged 50–79 years. All-cause mortality of women in the 50–59, 60–69 and 70–79 age groups was 2.0, 1.8, and 1.4 times higher in the CTD-ILD group than in the ILD-only group, respectively.CTD-ILD patients aged < 60 years had a higher lung cancer incidence than ILD-only patients in the same age group. Furthermore, CTD-ILD patients aged 50–79 years had higher all-cause mortality than ILD-only patients in the same age group. Connective tissue diseases (CTDs) can result in the development of pulmonary manifestations and considered as major organ involvement. CTD may be accompanied by interstitial lung disease (ILD) due to rheumatoid arthritis (RA) in 20–62% of cases –4, systeVarious CTDs share similar lung pathologies . The numPatients with CTD are also prone to developing lung cancer. Systemic sclerosis, dermatomyositis/polymyositis, rheumatoid arthritis, and a variety of CTDs have been associated with a high incidence of lung cancer –22. MoreTo identify a meaningful number of patients with lung cancer from among those with orphan lung diseases , large data sets must be utilized, such as entire national insurance databases. Korea has only one health insurance system, the National Health Insurance (NHI) service. Inclusion of a large number of patients with CTD-ILD and ILD with follow-up data may allow investigation of the relationship between CTD-ILD and lung cancer according to age and sex after controlling for the effects of ILD alone.Thus, this nationwide, 5-year, longitudinal, population-based study investigated the incidence of lung cancer and all-cause mortality among patients with CTD-ILD compared to those with ILD among patients without idiopathic pulmonary fibrosis (ILD-only) to help clarify the disease behavior.This closed longitudinal cohort study included data collected from the National Health Insurance (NHI) system based on the entire population. Since each citizen in Korea has a unique resident registration number, data duplication was avoided. The NHI service—the only public insurance system operated by the Ministry of Health and Welfare in Korea—is compulsory and covers the entire population, without exceptions for seasonal or part-time workers or unemployed persons . The curAccording to the National Statistical Office of Korea, the mid-year population of people ≥40 years of age in 2009 was 22,280,691. Patients with ILD only were selected as control subjects. Patients with CTD-ILD or ILD only between January and December 2009 were enrolled. The International Classification of Diseases, 10th revision (ICD-10) codes were used as a key reference not only for disease diagnosis but also within the NHI database.Specific codes for each CTD and IPF (J84.1A) have been implemented by the NHI service since (2009). Patients classified under autoimmune diseases including CTD, IPF, and lung cancer are eligible for a copayment reduction from the NHI; this is in line with the government’s improved policy regarding enhanced support for orphan diseases since 2009. Specific code for primary lung cancer (C34) has been implemented since 2005. When a patient is registered with CTD, IPF, or lung cancer, the physician should send the necessary eligibility documents to the NHI.The cases and controls were identified between January and December 2009 and were followed up until June 2014. The diagnostic codes for primary lung cancer cases diagnosed before 2009 were maintained on the NHI database. New lung cancer cases were identified by counting new cases registered during the calendar year after excluding preexisting lung cancer. We counted newly developed lung cancer cases and death after 1 month of recruiting CTD-ILD and ILD-only cases to exclude cases simultaneously diagnosed as both ILD and lung cancer.Death were identified using the NHI database. Deceased person is dropped off insurance coverage.Based on the ICD-10 definitions, ILD-only was defined by code J84 for other ILD, excluding drug-induced ILD, interstitial emphysema, and lung diseases caused by external agents. In order to identify ILD without specific related diseases, connective tissue disease-associated ILD, hypersensitivity pneumonitis, sarcoidosis, and COPD were not included in the cohort. Code J84.x excluding J84.0, chronic pulmonary fibrosis due to inhalation of chemicals, gases, fumes, or vapors or that occurred following radiation therapy during the study period , which has been implemented since 2009, in much the same way as the new diagnostic criteria introduced in 2011 .Every patient with CTD, lung cancer, and IPF sends a document to the NHI that aligns with the diagnostic criteria of each CTD. All CTD patients registered in the NHI. All patients with cancer are registered in a separate national cancer database maintained by the National Cancer Center (NCC) and strictly validated before final registration. If patients change their lung cancer diagnosis, they are later dropped off from the cancer code.P-values are two-sided, and those less than 0.05 are considered statistically significant. All analyses were performed using SPSS version 21  and the statistical software system R, version 3.5.2.Age is reported using five-year units in the NHI data. We decided to divide the age groups by 10 years to account for the number of patients. The incidence rates of lung cancer per 10,000 person-years were calculated for patients with ILD with and without CTD according to the sex and age groups . The incWomen were predominant 71.1%) in the CTD-ILD group, whereas men were predominant (54.5%) in the ILD-only group. The median age was approximately 10 years less in patients with CTD-ILD compared to patients with ILD-only  patients , 30. HowThe incidence of lung cancer is high in men with RA –33. In lOne objective of this study was to estimate the contribution of CTD to lung cancer development. In the younger age group (40 to 49 years), the lung cancer risk was 2.3 times higher in the CTD-ILD than in the ILD-only group, but the risk decreases with age. This phenomenon is commonly observed in both men and women. Based on these results, the following hypotheses can be considered. First, lung cancer development potential due to autoimmune responses is reduced as the patient ages. The second hypothesis is that autoimmunity combined with other factors contributes to an increased risk of lung cancer development at a young age. Third, the development of autoimmune lung disease related cancer may overlap with other powerful lung cancer-causing factors, such as the aging process; thus, lung cancer risk related to autoimmunity can be neglected.The presence of fibrosing interstitial pneumonia in RA patients is associated with a lower survival rate than that of RA alone , and CTDWe could not control for the confounding effects of unmeasured smoking on the development of lung cancer, because the NHI database did not include smoking history data. ILD carries an increased risk of lung cancer, even after correcting for or considering the smoking status , 41. TheWe identified CTD-ILD based on the ICD-10 code. However, no specific ICD-10 code is available for CTD-ILD, and no clear consensus diagnosis exists concerning what constitutes CTD-ILD based on the ICD-10 code. Additionally, the ICD-10 code does not include histological findings. The patient data are anonymized, which makes it impossible to trace information back to a medical record. Therefore, we did not perform further analyses according to lung cancer histology.We excluded patients with a diagnosis of lung cancer before and 1 month after the index date. However, patients with lung cancer could be miscounted due to the occurrence of new cases in the early period of the cohort. Additionally, other risk factors, such as pulmonary function and high-resolution computed tomography (CT) findings for ILD, could not be evaluated, because the NHI primarily included medical claims.We observed increased lung cancer incidence in younger (less than 60 years old) patients with CTD-ILD when compared to ILD-only. In addition, we found that all-cause mortality rate was higher in older CTD-ILD patients (50 to 79 years old) than in those ILD-only, especially in women. These results imply that a physician may pay more attention to lung cancer screening and management strategies for CTD-ILD patients.


This cross-sectional analysis of US Medicaid files from 29 states investigates the prevalence of and risk factors associated with concurrent stimulant and opioid use among Medicaid-enrolled adults with attention-deficit/hyperactivity disorder. What are the risk factors for and prevalence of long-term concurrent stimulant and opioid prescriptions among adults with attention-deficit/hyperactivity disorder?In this multiyear, cross-sectional study of 66 406 Medicaid-enrolled adults with attention-deficit/hyperactivity disorder, long-term concurrent stimulant and opioid use was common (5.4%) and increased from 1999 to 2010. Prevalent long-term stimulant-opioid use was significantly associated with older age, non-Hispanic white race/ethnicity, southern US residency, or a pain, anxiety disorder, or substance use disorder diagnosis.The common long-term stimulant-opioid use observed among adults with attention-deficit/hyperactivity disorder warrants further investigation to understand the association between this drug combination and patient health outcomes. There exist limited data on the long-term concurrent use of stimulants and opioids among adults with attention-deficit/hyperactivity disorder (ADHD), a population at risk for prescription drug abuse.To assess the prevalence and secular trends of and the factors associated with long-term concurrent use of stimulants and opioids among adults with ADHD.This cross-sectional study assessed Medicaid Analytic eXtract data from 29 states between 1999 and 2010. Medicaid fee-for-service enrollees aged 20 to 64 years with ADHD who were continuously enrolled for more than 12 months after receiving an ADHD diagnosis were included. One 12-month continuous enrollment period was randomly selected as an observation unit for each enrollee. Multivariable regression models were used to determine secular trends in the prevalence of and the potential risk factors associated with long-term concurrent stimulant-opioid use. Analyses were conducted between January 1 and December 31, 2017.Risk factors measured during the first half of the 12-month observation unit.Prevalence of long-term use of stimulants and opioids overlapping for at least 30 consecutive days was measured during the second half of the randomly selected 12-month observation unit.Of the 66 406 Medicaid-enrolled adults with ADHD who were identified as eligible, 35 670 (53.7%) were 20 to 30 years old, 37 155 (56.0%) were women, and 52 551 (79.1%) were non-Hispanic white individuals. Of these 66 406 adults with ADHD, 21 723 (32.7%) used stimulants, and 3590 (5.4%) were long-term users of stimulants and opioids. Long-term opioid use was more common among adults with ADHD who used stimulants (3590 of 21 723 [16.5%]) than among those with ADHD who did not use stimulants (5826 of 44 683 [13.0%]). Long-term concurrent stimulant-opioid use increased between 1999 and 2010 . Compared with patients aged 20 to 30 years, the prevalence of long-term concurrent stimulant-opioid use was higher among patients in their 30s  and was further increased among patients in their 40s  and 50s . Other strongly associated risk factors included being non-Hispanic white , living in the southern United States , and receiving a diagnosis of substance abuse disorder , depression , anxiety disorder , chronic pain , chronic obstructive pulmonary disease , or cardiovascular disease .Long-term concurrent use of stimulants and opioids among adults with ADHD is common. This study suggests that clinical and research priorities should be made toward understanding the benefits and risks of long-term coadministration of stimulants and opioids in the management of ADHD and co-occurring pain conditions. The present study provides evidence of the prevalence and secular trends of and the factors associated with long-term concurrent stimulant-opioid use among adult Medicaid enrollees with ADHD.The prevalence of ADHD diagnosis and treatment has dramatically increased in the past decade among adults, especially among those insured by Medicaid.International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) billing codes. Beneficiary demographics, enrollment status, and mortality information are provided in the MAX personal summary file. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies. The University of Florida, Gainesville, and the Centers for Medicare & Medicaid Services (CMS) institutional review and privacy boards approved this study with a waiver of informed consent and a waiver of Health Insurance Portability and Accountability Act authorization.This multiyear, cross-sectional study used Medicaid Analytic eXtract (MAX) files of 29 states from 1999 to 2010. The MAX files contain beneficiary billing records for inpatient and outpatient encounters and pharmacy-filled prescriptions. The inpatient and outpatient records include details on procedures and diagnoses coded using the The majority of the data were obtained before the CMS suppressed all substance use disorder–related encounter claims for its beneficiaries under the Confidentiality of Alcohol and Drug Abuse Patient Records Regulations. The regulations underwent a change in early 2017, revoking redaction of substance use disorder claims. Although we are awaiting CMS approval for a new data user agreement for more recent data (up to 2013), we used MAX data from 1999 to 2010 for the present study to provide timely reports on long-term concurrent use of stimulants and opioids among adults with ADHD. Data analyses were conducted between January 1 and December 31, 2017.ICD-9-CM codes 314.xx) during 1999 and 2010.21 To address enrollment gap issues ,22 we randomly selected for each patient one 12-month continuous enrollment period following receipt of an ADHD diagnosis. We used the first half of each 12-month observation period to determine baseline demographic and clinical characteristics and used the second half  to ascertain long-term concurrent use of stimulants and opioids (outcome). This approach that randomly selects an observation period for each patient has been used in previous studies to generate population-based estimates of medication exposure.24To construct the study sample, we first identified adult patients enrolled in Medicaid fee-for-service plans who were aged 20 to 64 years and had at least 1 inpatient or 2 outpatient visits that were coded with an ADHD diagnosis . We included the following opioid agents approved for use in the United States by 2010: buprenorphine, butorphanol, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, levorphanol, nalbuphine, meperidine, methadone, morphine, opium, oxycodone, oxymorphone, pentazocine, propoxyphene, tapentadol, and tramadol.25 Among adults with ADHD, we assessed the proportion of patients having concurrently used stimulants and opioids for at least 30 consecutive days. The minimum of 30 days was used to capture scenarios in which both stimulants and opioids appeared to be used long term, thus increasing the risk for substance use disorder and prescription drug abuse.19 To assess the robustness of our findings, we conducted a sensitivity analysis using a different cutoff period to define concurrent use of stimulants and opioids .We examined daily stimulant or opioid exposure based on the number of days’ supply of prescription claims during the 6-month follow-up of patients who received a diagnosis of ADHD. A grace period of 7 days was used to account for delayed prescription fills.26 and represent potential factors associated with prescription opioid use.27 Both mental and physical comorbidities were ascertained based on the presence of at least 1 relevant diagnosis code in any position on inpatient and outpatient encounter claims . The race/ethnicity classification was based on the information available from the MAX data. Because of the small sample size, Hispanic, Asian, Pacific Islander, and Native American individuals were classified as “other.” The 29 states of residence were categorized into 4 census regions  no opioid use, (2) concurrent short-term opioid use (1-29 days), and (3) concurrent long-term opioid use  during the 6-month follow-up. Sensitivity analysis was performed using a shorter overlap of 15 days or more to define concurrent stimulant-opioid use. Among those who were long-term concurrent users, we further analyzed types of stimulant and opioid combinations, with opioids grouped as only short-acting opioids, only long-acting opioids, and both short- and long-acting opioids. Patients who had multiple episodes of concurrent stimulant-opioid use could contribute to more than 1 type of drug combination.P < .05 was considered statistically significant.We used multivariable modified Poisson regression models to analyze risk factors  associated with long-term concurrent use of prescription stimulants and opioids  among adults with ADHD. The independent variables included age, sex, race/ethnicity, rural residency, region, mental comorbidity , and physical comorbidity . To test secular changes in the prevalence of long-term concurrent stimulant-opioid use, we included each calendar year as a dummy variable in the models. The coefficients of these annual dummy variables represented changes in the prevalence of long-term concurrent use for a given year compared with the reference year 1999. We expressed associations as prevalence relative ratios (PRRs) and reported associated 95% CIs. All analyses were performed using SAS, version 9.4 (SAS Institute Inc). A 2-sided We identified 66 406 eligible adult patients with at least 1 inpatient or 2 outpatient visits coded for a diagnosis of ADHD between 1999 and 2010. The final study sample and the number of individuals excluded are shown in the study flowchart . In the Of the 21 723 adults (32.7%) with ADHD who used stimulants during follow-up, 13 129 (60.4%) received no opioids, 5004 (23.0%) received prescription stimulants and opioids for a short term (1-29 days), and 3590 (16.5%) used these 2 classes of medications concurrently for at least 30 days. Of the 44 683 patients with ADHD who did not use stimulants, 27 476 (61.5%) received no opioids, 11 381 (25.5%) used prescription opioids for a short term, and 5826 (13.0%) used prescription opioids for a long term. Differences were noted between individuals who used opioids and individuals who did not with regard to all characteristics studied among adults with ADHD who used stimulants, as well as among those who did not use stimulants. Overall, long-term opioid use was more common among adults who used stimulants (16.5%) than among those who did not use stimulants (13.0%) .Among the 3590 adults with ADHD who used stimulants and opioids concurrently for a long term, 4 of every 5 adults (81.8%) used short-acting opioids, whereas only 1 of every 5 adults (20.6%) used long-acting opioids along with the stimulants. Nearly a quarter of the adults who used stimulants and opioids concurrently for a long term (23.2% [n = 832]) had prescriptions for both short- and long-acting opioids .We observed a significant increasing prevalence of long-term concurrent stimulant-opioid use over time  . CompareTo our knowledge, the present study is among the first to provide population-based data on the long-term concurrent use of stimulants and opioids among adults with ADHD using Medicaid MAX files from 29 states. Our results comprising drug use data from the last decade indicated substantial and increasing long-term use of these 2 types of controlled prescription medications. Overall, 5% of adults with ADHD had concurrently used both prescription stimulants and opioids for at least 30 days during the 6-month follow-up period. The proportion was even higher among adults with ADHD who used stimulants, with 16.5% of these adults using both types of medications concurrently. The probability of long-term stimulant-opioid use compared with non–long-term use  increased by 12% between 1999 and 2010. Our findings suggest that long-term concurrent use of stimulants and opioids has become an increasingly common practice among adult patients with ADHD.29 and the discrepancies may be due to differences in pain measurement, population, and time frame studied. The mechanism underlying ADHD symptoms and muscle pain is not entirely clear, but hypotheses have been proposed. Because of attention-deficit issues and increased risk-taking behavior, patients with ADHD are more prone to accidents causing physical injuries30 and, thereby, may be more likely to develop musculoskeletal chronic pain. Alternatively, some researchers have postulated that muscle pain is the long-term consequence of ADHD-associated motor problems .31 Given that the hypothesis involving increased pain prevalence is currently supported only by self-report survey data and small-scale clinical findings,31 formal observational studies are needed to further examine the association between ADHD and pain. An alternative hypothesis is inherent in reported associations between stimulant use and increased risk for substance use disorder,19 which may in turn result in increased prescription opioid use.Our data also suggest that an increasing number of adults with ADHD may have developed moderate to severe chronic pain that required aggressive pain management. However, we observed only 2.2% of adults with ADHD who had received a diagnosis of any pain condition during the 6-month baseline, with the percentage higher among those with long-term opioid use (6.4% of adults who used stimulants and 7.2% of those who did not use stimulants). Our estimates are lower than self-reported data (from 18.8% to 76.7%) in surveys involving adults with ADHD,33 Both opioids and stimulants are prescription drugs commonly misused by adults.36 National data have shown an increase in the use of these medications, and the increasing use has contributed to an increasing number of emergency department visits and overdose deaths associated with both drug classes.32 Of particular concern is our finding that a high proportion (81.8%) of adults who use both drugs concurrently for a long term are prescribed stimulants and short-acting opioids. Short-acting opioids are recommended for acute pain, but their long-term use has been discouraged by clinical guidelines because of the risk of abuse, opioid tolerance, and dose escalation.39 The common long-term use of stimulants and opioids, especially short-acting agents, observed among adults with ADHD deserves further investigation to understand the association of the use of this drug combination with patient health outcomes.Although the concurrent use of stimulants and opioids may initially have been prompted by ADHD symptoms and comorbid chronic pain, continued use of opioids alone or combined with central nervous system stimulants may result in drug dependence and other adverse effects  because of the high potential for abuse and misuse.40 These prevalence estimations from the general adult population, which did not require a minimum 30 days’ supply, were much lower than those of long-term opioid use observed among adults with ADHD (16.5% of those who used stimulants and 13.0% of those who did not use stimulants) in the present study. Furthermore, our findings of higher long-term concurrent stimulant-opioid use among non-Hispanic white adults compared with non-Hispanic black adults and among residents from the South compared with all other US regions are consistent with previous studies on prescription opioid use.40Our study also showed several important social and clinical factors associated with long-term concurrent stimulant-opioid use among adults with ADHD. We noted a significant increase in stimulant-opioid use with increasing age, even after adjusting for aging-related chronic health conditions. Our finding is consistent with a recent study of any prescription opioid use, with a reported prevalence of 8.1% among adults older than 40 years compared with a prevalence of 4.7% among adults aged 20 to 39 years.44 which may explain the increase in long-term stimulant-opioid use among adults with ADHD who have these comorbid conditions. Patients with cardiovascular disease (vs without) are more likely to experience pain,45 and patients with COPD symptoms are at high risk for pain and for nicotine dependence, the latter of which is an independent risk factor for substance use.46 Our study contributes to the understanding of the potential risk factors associated with long-term concurrent stimulant-opioid use among adults with ADHD. Identifying these high-risk patients allows for early intervention and may reduce the number of adverse events associated with the long-term use of these medications.We also observed that adults with ADHD and comorbid depression, anxiety disorder, substance use disorder, cardiovascular disease, or COPD tended to use stimulants and opioids concurrently. Depression, anxiety disorder, and substance abuse disorder are strongly associated with long-term chronic opioid use,Several strengths of this study are noteworthy. Our study provided important information on the treatment of adults with ADHD, a population that is increasing but has received limited research attention. Using more than a decade of administrative data that include detailed prescription information from 29 states allowed us to examine secular changes in concurrent stimulant-opioid use. We also explored a broad range of potentially associated factors, such as physical comorbidities, which have rarely been considered in the literature for stimulant-opioid polypharmacy.47 our reported prevalence of concurrent stimulant-opioid use may be too low. In addition, the data provided no information on whether prescribed opioids were intended for regular use or as needed. Using strict definitions of overlapping treatment periods that were defined based on the dispensed days’ supply (assuming daily use) might have underestimated long-term concurrent use. Furthermore, our data did not allow us to determine the intended clinical indications for each prescribed medication, which limited our ability to assess the appropriateness of the observed concurrent use of stimulants and opioids. We identified the study sample of patients with ADHD based on the disease diagnosis codes for inpatient or outpatient encounters, which may have excluded patients with ADHD who did not seek medical care. Our findings from the 1999 to 2010 MAX data may not reflect recent changes in clinical opioid prescribing practice because several initiatives were implemented after 2010 to educate physicians to reduce unsafe opioid prescribing. Our study results are only generalizable to the Medicaid fee-for-service plans of 29 states. We selected these states based on absolute Medicaid enrollment numbers, resulting in the capture of more than 80% of all Medicaid beneficiaries. However, restrictions to fee-for-service plans resulted in varying representation of individual states and study years (because of growing managed care penetration over time). Future studies that extend to other states’ populations of adults with ADHD in more recent time periods will contribute to our understanding of the differences in rates and patterns of concurrent stimulant-opioid use as well as to the association of concurrent use with adverse outcomes.The present study shares limitations common to observational studies based on administrative data. First, MAX data captured only prescription medications filled and reimbursed by Medicaid, without information on medications obtained through other channels, such as paying for prescription fills with cash or obtaining medications through illicit sources. Considering that opioid prescription fills are commonly paid out of pocket,Among Medicaid-enrolled adults with ADHD, long-term concurrent use of stimulants and opioids was common and increased over time. Prevalent long-term stimulant-opioid use was associated with older age, non-Hispanic white race/ethnicity, southern US residency, and a diagnosis of substance abuse disorder, depression, anxiety disorder, chronic pain, COPD, or cardiovascular disease. Clinical and research priorities should be made toward understanding the benefits and risks of the long-term coadministration of stimulants and opioids in the management of ADHD and co-occurring pain conditions.


Owing to their high similarity to humans, non-human primates (NHPs) provide an exceedingly suitable model for the study of human disease. In this Review, we summarize the history of transgenic NHP models and the progress of CRISPR/Cas9-mediated genome editing in NHPs, from the first proof-of-principle green fluorescent protein-expressing monkeys to sophisticated NHP models of human neurodegenerative disease that accurately phenocopy several complex disease features. We discuss not only the breakthroughs and advantages, but also the potential shortcomings of the application of the CRISPR/Cas9 system to NHPs that have emerged from the expanded understanding of this technology in recent years. Although off-target and mosaic mutations are the main concerns in CRISPR/Cas9-mediated NHP modeling, recent progress in genome editing techniques make it likely that these technical limitations will be overcome soon, bringing excellent prospects to human disease studies. Summary: This Review discusses the history and development of genome editing in non-human primates, as well as the challenges and prospects facing this technology now and in the future. Macaca fascicularis (crab-eating macaque or cynomolgus), Macaca mulatta (rhesus monkey) and Callithrix jacchus (common marmoset)  have many advantages over any other species as a prime model system for the study of human diseases. NHPs feature high similarity to humans in many properties, including genetics, physiology, developmental biology, social behaviors and cognitive ability. DNA sequence similarity between NHPs and humans can be as high as 98.77%  comparedarmoset) .Fig. 1.Traditionally, methods for disease modeling in NHPs have included the observation of spontaneously occurring disease, chemical (drug) induction and surgical procedures. In recent years, methods that utilize precise gene modification have been widely applied. Here, we summarize the history of such transgenic models, the progress of NHP genome editing using the clustered regularly interspaced short palindromic repeat (CRISPR)-associated nuclease Cas9 (CRISPR/Cas9) system, and the most recent developments of this technology's application in NHPs.in vitro  receptor, induces hypercholesterolemia if highly expressed. The authors showed that the six NHPs with PCSK9 knocked out had a normal concentration of serum LDL. This research highlighted safety considerations for gene therapy of hypercholesterolemia  to infect monkey kidney cells maintained in vitro . Since tin vitro , lentiviin vitro  and adenin vitro . Retroviin vitro A targetierolemia . In addiHTT which would lead to Huntington's disease. The animal showed both genetic modification and the associated neurodegenerative phenotypes  transgene, which was introduced by injecting a replication-defective retroviral vector carrying this gene into the perivitelline space of rhesus monkey oocytes . In the enotypes . In 2009enotypes . Furtherenotypes .The major drawback of lentivirus-based gene editing is the possibility of random insertion into the host genome. As a result of this uncertainty, non-targeted loci can be influenced by the insertion and lead to unexpected phenotypical consequences, regardless of whether they insert in coding or non-coding regulatory regions. Therefore, genetic models of some diseases, especially those caused by specific mutations, have been difficult to generate. Besides, it is often necessary to disrupt a target gene or artificially replace a gene sequence in order to analyze the functions of the genes of interest and their relevance to diseases in detail . Thanks There are three main types of CRISPR systems, among which those belonging to the type II are most commonly used in gene editing applications. One such type II system is CRISPR/Cas9, which mediates genome editing via Watson-Crick complementary base pairing. A single guide RNA (sgRNA) recognizes a target DNA sequence and guides the endonuclease component, Cas9, which cuts the DNA. In more detail, the working mechanism of this system can be split into the following steps. First, long primary pre-CRISPR RNA (pre-crRNA) is transcribed from the CRISPR loci in bacteria, then pre-crRNA pairs with trans-activating crRNA (tracrRNA) to be processed by RNase III. Simultaneously, Cas9 catalyzes the formation of a crRNA-tracrRNA complex, followed by the generation of shorter mature crRNA. Finally, this mature crRNA and the tracrRNA form a double-stranded RNA structure that directs Cas9 towards a target locus where it generates double strand breaks (DSBs) in the DNA . This syThe development of this precise gene editing system that does not require customized protein synthesis for targeted nuclease activity offered a new and elegant way to generate animal models of genetic defects, enabling detailed characterization of the functional consequences of specific genetic changes. Furthermore, precise genome modification technologies allow efficient generation of isogenic cell lines with and without disease mutations, which can then be differentiated into somatic cell types for disease or cell therapy research. Until now, precise genetic engineering in mammalian species has been most common and successful in mice, whereby the main associated methods include zygote-based microinjection approaches for transgenesis and gene knockout (KO)/knock-in (KI) approaches using embryonic stem cells .Nr0b1 , Ppar-γ (Pparg) and Rag1, with targeting efficiencies of 26.6%, 46.7% and 60% in the embryo, respectively. Similarly, in 2014, Wan et al. generated a Tp53 biallelic mutant cynomolgus monkey (DMD), mutations of which cause Duchenne muscular dystrophy (DMD)  (HBB) gene as the test locus for CRISPR/Cas9 to assess the targeting efficiencies, specificities, timing and genetic mosaicism outcomes in rhesus monkeys, and determined a high targeting efficiency (80-100%) in embryos  . The new(AHC-HH) . Two yea embryos . In 2018 editing . In a on in 2016 , gene ta in 2016 . However in 2016 .Table 1At a target genomic site, the CRISPR/Cas9 system can accurately and effectively introduce a DSB that triggers two basic repair systems. If this DNA damage is repaired through NHEJ, it will likely result in a target gene KO with random indels. Conversely, if the DSB is repaired by homology-directed repair (HDR), a desired fragment could be introduced into the target site as long as the donor template DNA that harbors a homologous sequence corresponding to the genomic DNA around the DSB site is provided A. PrecisInfluencing non-targeted genes (the off-target effect) has been a controversial issue since the first reports of the CRISPR/Cas9 system. A significant number of experiments revealed undesired cleavage by Cas9 at off-target genome sites at which the DNA sequence was partly homologous (with one or more mismatches) to the 20-base sgRNA. The specificity of the CRISPR/Cas9 system mainly depends on the number of homologous off-target sites. Thus, for species with large genomes such as humans and NHPs, avoiding off-target Cas9 activity is the major challenge in the application of this technology. Furthermore, it is critical for clinical safety that all off-target effects are eliminated when the system is used as a therapeutic reagent. Many research groups have focused efforts on reducing this phenomenon. For example, scientists have designed mutant Cas9 nucleases with higher specificities than that of the wild-type protein, such as enhanced specificity Cas9 (eSpCas9) , Cas9-hiin vivo off-targets’ (VIVO), a highly sensitive two-step strategy that can robustly identify genome-wide off-target effects of Cas nucleases  to discover off-target cleavage sites. They then screened the genomes of edited animals for these potential off-target mutations. This technique was shown to be highly sensitive, avoided potential confounding effects associated with cell-based off-target screening assays and could successfully identify supersets of sites that include bona fide off-targets in cultured human cells. Although several approaches can identify off-target sites, VIVO is, up to now, the most reliable method. Fortunately, the research on gene modification by CRISPR/Cas9 in NHPs described above and in Most recently, Pinar and coworkers described a new method called ‘verification of ucleases . In thisAlthough all the results of gene editing in NHPs appear to be positive thus far, more attention should still be devoted to identifying and minimizing potential off-target effects in NHPs, especially considering the complexity of their genome, their significance for human disease research and the specific ethical considerations for research in these species.In addition to off-target effects, genetic mosaicism, in which cells within an organism have different genomes, is another issue that may hinder the application of genome editing for disease modeling, as components of the CRISPR/Cas9 system can repeatedly edit target genes at different stages of embryonic development. This repeated editing induces problems in two ways: the founding animals may carry both mutant and wild-type alleles, and different mutations may be induced in different cells of an animal. Both of these effects lead to genetic mosaicism. Owing to the long time for sex maturation (4-5 years) and limited reproductive capacity (1-2 offspring per year), it is especially important to avoid mosaicism in genetically modified NHPs.Arntl and 91% for Prrt2) in the embryos and obtained a complete Prrt2 KO monkey in a single step. Importantly, they performed whole-genome sequencing on the positive samples at a sufficient depth to detect off-target mutations and found no indels among the 1947 potential off-target sites. The above results indicate that it is possible to obtain single homozygous mutations in monkey, thus avoiding a mosaic genotype.The prolonged expression of Cas9 mRNA was thought to be a potential mosaicism-inducing element. However, direct injection of the Cas9 protein into cells also leads to mosaic mutations . For exaOverall, advances in the CRISPR/Cas9 genome editing technique have made it possible to generate genetically modified NHPs, which provide a new and exciting way to explore gene functions, disease mechanisms and human therapy strategies in modern translational medical research. As mentioned above, almost all the studies revealed that CRISPR/Cas9 creates mosaic mutations in NHP embryos, which has also recently been seen in non-viable human embryos derived from human tripronuclear zygotes injected with CRISPR/Cas9 . BecauseOne of the ultimate goals of translational medical research is to use experimental animals to substitute the human body to learn how an illness develops, progresses and affects the organism. In addition, all potential disease treatments, such as drugs, vaccines or technologies, must pass the animal experiment phase before any clinical application. Overall, all efforts are made to improve human health and lengthen the lifespan of humans, while simultaneously ensuring animal welfare. We are mindful of the ethical considerations and are adopting the appropriate 3R  guidelines. As species that are closely related to humans, NHPs bring great hope for improving our understanding of human diseases and finding the appropriate treatment strategy. It is also worth mentioning that commonly used NHP species in biomedical research  are smalNowadays, the CRISPR/Cas9 system is being constantly improved. Besides the evolution of the technology itself, CRISPR/Cas9 has also been combined with other methodologies, such as genetically modified stem cells for cell therapy, chimeric antigen receptor T cells (CAR-T) for cancer therapy, and somatic cell nuclear transfer (SCNT) for generating disease models, with the potential to bring groundbreaking progress to modern biomedical research. In an example of research enabled by the CRISPR/Cas9-mediated genome editing system, breakthroughs in NHP SCNT research , the tecin vitro and, when combined with the SCNT procedure, a genetically defined monkey model could be generated within 1-2 years. Although the efficiency of a cloned monkey could be increased to 33.3% (live birth) by the injection of a critical histone demethylase of H3K9me3, lysine demethylase 4D (KDM4D), the authors of this study still observed some issues such as post-implantation defects and abnormal placentas (Although a human disease model could be generated by direct injection of CRISPR/Cas9 components into zygotes, several problems still exist, including mosaicism , random lacentas , indicatOverall, the development of genome editing has enabled the generation of NHP and other large animal models, such as pig  and shee


We aimed to better understand the profile of people living alone with mild-to-moderate dementia in the UK and to identify any systematic differences between those living alone and those living with others. We analysed cross-sectional data from 1541 people with mild-to-moderate dementia participating in the IDEAL cohort at the first wave of assessment. There were 285 participants (18.5%) living alone and 1256 (81.5%) living with others, usually a spouse/partner. Among those living alone, 145 (50.9%) had no care partner participating in the study, and 56 (19%) had received no help from a relative or friend in the past week. People living alone were older on average than those living with others, reported fewer functional difficulties, had slightly smaller social networks, engaged in fewer cultural activities, and experienced slightly more loneliness. People living alone had lower satisfaction with life scores, but quality of life scores did not differ between the groups.