Previous research on post-diagnosis smoking among cancer survivors mainly relied on smoking status, which may not fully reflect the impact of changes in smoking levels. This study aimed to evaluate mortality risk according to smoking trajectories among Korean male cancer survivors, using a trajectory approach to comprehensively capture smoking patterns.The study included 110555 men diagnosed with cancer between 2002 and 2018 from the Korean National Health Information Database. Group-based trajectory modelling was used to identify post-diagnosis smoking trajectories among pre-diagnosis current smokers (n=45331). Cox hazards models were fitted to evaluate mortality risk according to smoking trajectories for pooled cancers, pooled smoking-related cancers, smoking-unrelated cancers, and gastric, colorectal, liver, and lung cancers.Smoking trajectories included light-smoking quitters, heavy-smoking quitters, consistent moderate smokers, and decreasing heavy smokers. Smoking significantly increased all-cause and cancer mortality risks in cancer patients for pooled cancers, pooled smoking-related cancers, and pooled smoking-unrelated cancers. Compared to non-smokers, all-cause mortality risk for pooled cancers significantly increased according to smoking trajectories:, , , and , respectively. Smoking increased all-cause and cancer mortality risks in gastric and colorectal cancer patients and cancer-specific mortality in lung cancer patients. The significant associations of smoking trajectories with all-cause and cancer mortality risks were primarily observed in 5-year survivors but not in short-term survivors. Among heavy smokers, smoking cessation significantly reduced all-cause mortality risk in the long-term.The post-diagnosis smoking trajectory independently predicts cancer prognosis among male cancer patients. Proactive cessation support should be strengthened, particularly for those who smoke heavily. In South Korea, where cigarette smoking remains high among men (36.7%), it was responsible for 20.9% of new cancer cases and 32.9% of cancer deaths in this population2. Smoking-related cancer burden measured in DALYs was estimated at 75117 person-years among men aged ≥40 years3. Notably, while tobacco use is a well-known risk factor for health, up to 38–52% of male cancer patients who smoke continue smoking after diagnosis5. The associations of smoking with the prognosis of cancer patients have been suggested in previous studies6. With medical advances and an ageing population, a foreseen increasing number of cancer survivors can result in an even larger tobacco-induced health burden.The smoking epidemic is one of the biggest threats to public health. According to GLOBOCAN 2020, 8 out of 10 leading cancers worldwide among men are related to smoking in terms of cancer incidence and mortality7. Among patients with head and neck cancer treated with radiotherapy, those who continued smoking have a significantly higher risk of locoregional failure, almost twofold, than those who quit smoking8. It has been suggested that smoking may increase the risk of surgical complications9, reducing treatment response10. Tobacco nitrosamines have been shown to stimulate cancer cell proliferation, suppress apoptosis, induce angiogenesis, and facilitate cell migration, thus accelerating cancer progression and metastasis12. Moreover, smoking may also lead to interactions and adverse effects in and post-cancer treatment and exacerbate comorbidities that negatively impact patients’ quality of life13. Nevertheless, the underlying mechanisms of the association between smoking and cancer prognosis are yet to be fully understood, raising the need for further prospective studies on the impact of post-diagnosis smoking on cancer progression15. Notably, the simultaneous investigation of different cancer types may be helpful to elaborate the findings from studies on specific cancer types.A meta-analysis on prostate cancer reported poor prognosis in terms of overall survival, cancer-specific survival, and recurrence-free survival associated with smoking after cancer diagnosis compared with those for never smokers6.On the other hand, changes in smoking behavior may lead to changes in cancer prognosis. Smoking cessation among colorectal cancer survivors showed a clear improvement of 22% in overall survival in both the short-term (<10 years) and long-term (≥10 years), and 24% in cancer-specific survival in the long-term17, which may not fully reflect the impacts of changes in smoking levels. For instance, the classification of smoking behavior may include three categories of smoking status at diagnosis 17, or four categories of pre- and post-diagnosis smoking status combined 4. As more evidence is needed to emphasize the importance of smoking cessation in cancer survivorship care18, it is meaningful to investigate smoking patterns after cancer diagnosis comprehensively. Trajectory analysis19 is a method that has recently been adopted in epidemiology to track temporal behavior patterns20. To the best of our knowledge, no studies have ascertained smoking trajectories after a cancer diagnosis to date. Therefore, this study aimed to evaluate the association between smoking and mortality risk among South Korean male cancer survivors using trajectory analysis to comprehensively capture smoking patterns after a cancer diagnosis.However, it should be noted that previous studies mainly relied on smoking status to inform behavior changes in cancer patients 21. It includes annually updated data on sociodemographics, healthcare utilization, mortality, and biennially collected health screening data for insured participants and their dependents21.The National Health Information Database (NHID) is a public database of the National Health Insurance Service (NHIS) that covers the whole population of South Korea (>50 million people)22. We further excluded 63864 participants aged <40 or >79 years because individuals who are younger than 40 years have a substantially lower chance of getting cancer related to unhealthy lifestyles, and those aged ≥80 years might already have poorer health conditions that smoking might affect their survival with a different pattern from those in the adult population. After excluding 199137 participants missing data on smoking behavior within six years before or six years after cancer diagnosis and 4 cases with an invalid death date, the study finally included 110555 male cancer patients .This is a population-based cohort study. We used a customized NHIS database of 8968212 individuals who attended the 2002–2003 general health examination and were followed up to 2018. Among 805430 incident cancer cases, we excluded 326567 women due to a low smoking rate (4.2–6.5%)Exposure4. As the trajectory analysis requires at least three measurements, the third smoking measurement was imputed from the post-diagnosis measurement. Smoking patterns after cancer diagnosis were interpreted based on the first two measurements. To examine the robustness of our main analysis findings, we additionally examined the sub-population with three smoking measurements: pre-diagnosis measurement, early post-diagnosis measurement (i.e. at 2 years post-diagnosis), and late post-diagnosis measurement (at 2–6 years post-diagnosis).Data on smoking behavior were collected using a self-administered questionnaire containing information on smoking status  and daily amount of smoking (cigarettes/day). There were six smoking levels: 1) never smokers, 2) former smokers, 3) current smokers, 1–10 cigarettes/day; 4) current smokers, 11–20 cigarettes/day; 5) current smokers, 21–40 cigarettes/day; and 6) current smokers, >40 cigarettes/day. Data on smoking levels were obtained twice: pre-diagnosis (within six years before cancer diagnosis) and post-diagnosis (within six years after cancer diagnosis). The 6-year period was decided, considering the trade-off between the heterogeneity of the study population regarding smoking measurement time-point and the sample size, which is also related to the magnitude of selection bias. In the case of multiple available measurements, the highest pre-diagnosis smoking level and the latest post-diagnosis measurement were selected. Trajectory analysis was conducted only on pre-diagnosis current smokers, given that smoking initiation after diagnosis is not common among pre-diagnosis never and former smokersFor comparison with the trajectory approach, we included two conventional methods of smoking classification. Separate analyses were performed for smoking groups defined by trajectory analysis, smoking status, and baseline smoking levels. In the status-based smoking classification, there were four categories: non-smokers (pre-diagnosis non-smokers), former smokers (pre-diagnosis former smokers), smoking quitters (pre-diagnosis current smokers who quit after diagnosis), and continued smokers (pre-diagnosis current smokers who continued smoking after diagnosis).Covariates2), alcohol consumption frequency , physical activity frequency (0–2 and ≥3 days/week), and Charlson comorbidity index (CCI) (continuous)24. Those covariates were selected to include in Cox hazards models to adjust for confounding effects based on biological plausibility4. Data on covariates were extracted at baseline (2002–2003), except for CCI, obtained within two years before cancer diagnosis.The covariates included age at cancer diagnosis (continuous), household income (quintiles), body mass index (BMI)  and further confirmed by ‘V193’, a special code introduced in 2005 to expand insurance benefits for patients with cancer. The date of cancer diagnosis was defined as the first date of diagnosis with C code. The events of interest included all-cause, cancer, and cancer-specific deaths. We assessed the association between smoking and the risk of these outcomes for pooled cancers of all types, pooled smoking-related cancers, pooled cancers not related to smoking, and four leading cancer types in terms of cancer incidence and mortality in Korean men (2020): gastric, colorectal, liver, and lung cancersDescriptive statisticsData for continuous variables  are presented as mean with standard deviation (SD). The frequency and percentage are used for categorical variables .Trajectory analysis19. This is a semi-parametric finite mixture modeling approach20. Among different trajectory analysis methods commonly used in epidemiology, we adopted this method because it produces less-complex models with more straightforward interpretation and requires less computing time20. We fitted the censored normal model for the smoking level presented in ordinal categories using PROC TRAJ procedure installed in SAS 9.4 26.Group-based trajectory modeling was used to identify smoking patterns after cancer diagnosisModel selection28. We fitted regression models with group numbers from 1 to 7, and all groups in quadratic order. As the Bayesian information criterion (BIC) values increased with additional groups, we decided on the optimal group number based on group membership (≥5%), model parsimony, and the models’ ability to present distinct features of interest (i.e. to differentiate smoking patterns according to pre-diagnosis smoking levels). As a result, a model with four groups was selected because of its ability to separate smoking levels at pre-diagnosis (light/moderate/heavy) and differentiate smoking patterns at post-diagnosis (quitting/continuing). To optimize the group order, the non-significant quadratic order (p<0.05) was reduced to linear26. The  model, with the most significant BIC score (negative) among the tested models, was selected  and OCC (≥19.9) values in all groups; therefore, they were considered acceptable for further analysis .The two selected models were evaluated using the average posterior probability of assignment (AvePP) and odds of correct classification (OCC). Despite no definite cut-off values for these diagnostic criteria, AvePP ≥0.7 and OCC ≥5.0 were recommendedDetails of smoking trajectoriesFor the entire study population (n=110555), smoking trajectories for pre-diagnosis current smokers  included: T1.1) light smoking quitters ; T1.2) heavy smoking quitters ; T1.3) consistent moderate smokers ; and T1.4) decreasing heavy smokers (i.e. heavy smokers who decrease smoking level) . The average duration from the time of measurement to cancer diagnosis was 4.0 ± 1.7 years for pre-diagnosis measurement and 4.4 ± 1.5 years for post-diagnosis measurement. For the three-measurement subpopulation (n=43401), the prevalence of smoking at pre-diagnosis was 41.6% (n=18043). The five smoking trajectories among pre-diagnosis current smokers were: T2.1) light smoking quitters ; T2.2) heavy smoking quitters ; T2.3) late heavy smoking quitters ; T2.4) heavy smoking relapse ; and T2.5) consistent heavy smokers . The average duration from smoking measurement to cancer diagnosis was 4.2 (SD=1.7) years for the pre-diagnosis measurement, 2.0 (SD=0.1) years for the early post-diagnosis measurement, and 5.1 (SD=1.0) years for the late post-diagnosis measurement.Cox proportional hazard regressionCox proportional hazards regression was used to evaluate the association between smoking and mortality risk. Time-to-event (years) was estimated as the duration from the date of cancer diagnosis to death or the end of 2018, whichever occurred first. In the analysis of all-cause mortality, the censored cases were cancer survivors at the end of the study period. In the analysis of cancer mortality, censored cases included patients who died from causes other than cancer or survived until the end of 2018. Similarly, for cancer-specific mortality, participants who died of causes other than that specific cancer were censored on their death date, and those who survived at the end of 2018 were censored on 31 December 2018. The variable that indicates the trajectory group in which a participant was assigned was treated as a time-invariant covariate , considering that this variable reflects the smoking pattern over time. The group of non-smokers was used as the reference. All analyses were stratified according to the survival time (<5 and ≥5 years). A linear trend was assessed using the log-rank test, and a p<0.05 was considered significant. The Cox proportional hazards assumption was assessed with the Kaplan-Meier survival curves for categorical covariates and Schoenfeld residuals for continuous covariates, showing the assumption was satisfied for all covariates .29. This approach allows us to estimate the effect of covariates on the occurrence rate of the outcome among participants who are currently event-free29. Specifically, in the cancer mortality analysis, the competing risks included deaths from other causes. In the analysis of cancer-specific mortality, the competing risks were deaths due to other cancer types and causes. We additionally explored all-cause mortality risks according to smoking trajectories for some cancer types (GLOBOCAN cancer dictionary) other than the four cancer types investigated in the main analysis30. An alternative analysis was performed with decreasing heavy smokers (T1.4) as the reference to reveal the implications for behavioral change.We performed competing risk analyses based on the cause-specific hazard function, in which the likelihood function for an event of interest treats all other events as censored at their occurrence timeSensitivity analysisMortality risks according to smoking trajectories were re-assessed with the Cox hazards regression models, including alcohol drinking, physical activity, and body mass index as time-dependent covariates. Data on those variables were collected on the same general health examinations from which we extracted data on smoking behavior. In the trajectory analysis for the entire study population, data imputation for the third measurement assumed participants retained their post-diagnosis smoking behavior throughout the post-diagnosis period. Therefore, we additionally performed trajectory analyses for pre-diagnosis current smokers who had multiple smoking measurements after diagnosis. Five subgroups (each contributing to 10% of the smoking population or above) were selected: participants with at least two, three, four, five, and six measurements after a cancer diagnosis.Study participants were aged, on average, 55.6 ± 8.8 years at baseline and 63.0 ± 8.8 years at their primary cancer diagnosis . At the Among the 110555 cancer patients, the five leading cancer types were stomach (28.2%), colon and rectum (19.1%), prostate (11.9%), liver (6.4%), and lung (6.1%) . During 918546 person-years of follow-up, 23888 deaths occurred. Lung cancer was the most common fatal cancer type (14.6%), followed by liver (12.3%), gastric (7.9%), colorectal (7.3%), and prostate (4.8%) cancers. Moreover, one-third of the deaths were due to causes other than cancer .In the analysis of pooled cancers, compared to non-smokers, smoking significantly increased all-cause and cancer mortality risks, which were observed in all smoking trajectories of pre-diagnosis current smokers in a dose-response pattern (p<0.0001) . SignifiIn the analysis of the three-measurement subpopulation, all-cause mortality risks for pooled cancers and gastric and colorectal cancers  were theCompared to decreasing heavy smokers (T1.4), for pooled cancers, all-cause mortality risk was significantly lower (11–38%) for smoking trajectories among 5-year survivors (Supplementary file Table S10). A risk reduction in heavy smoking quitters (T1.2) for specific cancer types was observed for gastric and colorectal cancers in long-term survival. Former smokers who quit at least 6 years before cancer diagnosis had a consistently lower risk of all-cause mortality in the pooled analysis of all cancers and gastric and colorectal cancers.Mortality risks according to smoking trajectories resulted from analyses with time-dependent covariates that were consistent with the results of the main analyses (Supplementary file Tables S11 and S12). The trajectory analysis performed for the subgroups of multiple post-diagnosis measurements showed that more than 75% of the patients retained their post-diagnosis smoking behavior .4. Supporting our results on elevated all-cause mortality risk in all trajectories of pre-diagnosis current smokers with colorectal cancer, specifically, they found a significant increase of 21–30% in all-cause mortality risk associated with pre-diagnosis smoking, regardless of post-diagnosis smoking status4. In particular, they observed the association of smoking with all-cause mortality restricted to the surgical treatment group4, also reported in another study17. Explanations were suggested, possibly relating to surgery complications17 or lower baseline hazards in the surgical group than in other treatment groups4. A qualitative synthesis indicated that continued smoking might contribute to an elevated incidence of late, but not acute, toxicities8. In our study, the toxicities of smoking in long-term survival and their associations with mortality appeared to be more evident in less fatal cancer types, i.e. in gastric and colorectal cancers  among Korean men, 77.8% and 75.6%, respectively), but not in lung and liver cancers 31. The findings were shown for all-cause and cancer mortality and were supported by competing risk analysis. In particular, significant associations between smoking and mortality risks were also found in the group of cancers unrelated to smoking. Therefore, we suggest that tobacco smoking is an independent risk factor for cancer prognosis among male cancer patients because of its impact on general health and cancer progression. The impact of smoking on mortality risk was not observed in cancer patients who survived <5 years or were diagnosed with fatal cancers, such as liver or lung cancers, which could be explained by the fact that their prognosis might be predominantly affected by other clinical factors such as cancer phase and treatments rather than by behavioral risk factors. In the case of lung cancer, significant associations were observed with cancer-specific death. This might be because lung cancer patients with poor prognosis may be at a higher risk of death from lung cancer itself rather than from other causes. Up to 44.8% of Korean men with lung cancer were diagnosed at advanced stages, with an estimated 5-year relative survival rates of 7.0% only31. More biological evidence on the impact of smoking on cancer progression is necessary; however, this is not limited to smoking-related cancers.This study was conducted among Korean male cancer survivors, eliciting several findings. Current smokers accounted for 41% of participants at pre-diagnosis and 31% continued to smoke, comparable to that reported in a study of Korean men with colorectal cancerReferring to decreasing heavy smokers, heavy smoking quitters were at a significantly lower risk of death from any cause, suggesting the positive effects of complete smoking cessation. Therefore, we strongly recommend that cancer patients to quit smoking as soon as possible after diagnosis. It is never too late for smoking cessation, even for those who smoked heavily before the cancer diagnosis. Smoking cessation services should be strengthened in cancer survivorship care. Proactive support should be provided to heavy smokers to help them quit entirely after cancer diagnosis, as some patients tend to relapse to smoking post-diagnosis. Determinants of smoking continuation among cancer survivors should be further investigated to aid in this.27. This discrepancy might be due to the differences in the study population and objectives. While their study tracked the smoking trajectory over a long period in terms of development patterns among young adults, we investigated a cancer population in which cancer diagnosis possibly played a predominant role in affecting behavior change. In particular, we identified groups of pre-diagnosis heavy smokers who delayed quitting or relapsed into smoking after quitting post-diagnosis. The participants in these groups were at a high risk of death, comparable to that of consistent heavy smokers. Those who tended to retain their pre-diagnosis smoking behavior were mostly heavy smokers, suggesting that intensive and continuous support is needed for patients who smoke heavily. In addition, the dose-response association of smoking trajectories with all-cause and cancer mortality risks was shown in the pooled analysis of all cancers and smoking-related cancer types, with the pre-diagnosis smoking levels possibly predicting patient prognosis better than the post-diagnosis smoking status, which was not revealed in the conventional approaches based on smoking status and pre-diagnosis smoking classification.Using trajectory analysis, this study revealed a more comprehensive profile of smoking patterns among cancer survivors when smoking status and amount were considered simultaneously. A study on smoking trajectories identified smoking patterns different from ours, including low steady, rise and fall, lowering, rise and sharp fall, high steady, and very high steady17. In addition, we were able to perform a competing risk analysis given that a large proportion of cancer survivors died from causes other than cancer. The results of the competing risk analysis supported our findings in the primary analysis. Our study also has limitations. First, we could not include people who did not undergo the national health examination post-diagnosis and excluded female patients and those with missing data on smoking behavior within appropriate time, which may lead to selection bias and limit the generalizability of our findings to the cancer male population. Second, this study used claims data that were not collected primarily for research purposes. To provide more conservative results related to the primary cancer diagnosis, we used the code ‘V193’ to confirm cancer cases ascertained using the ICD-code. A recent study validated the utility of primary diagnosis in the NHIS database in cancer research with high sensitivity (>90%) and accuracy (80% consistency with cancer registry data)32. Third, we examined smoking patterns with a limited number of repeated measurements, calling for further longitudinal studies tracking for more time points on patients’ behaviors during survivorship. The imputation of smoking level for the third measurement in the trajectory analysis was a trade-off for the study sample size (n=110555 with imputation vs 43401 without imputation). Nevertheless, the results derived from the three-measurement subpopulation supported our main findings. Furthermore, there might be residual confounding that we could not adjust for. We lacked clinical information, including cancer stage, pathology, and metastasis, which might greatly impact patients’ prognosis. A meta-analysis of cohort studies did not find changes in the estimates of the association between smoking status or cessation and overall or colorectal-specific survival with further adjustment for cancer stage6. Nonetheless, this limitation reduces the ability to make inferences about the mortality risk according to smoking trajectories in our study. The reasons for quitting smoking after a cancer diagnosis might also be confounding. Those who quit for risk reduction while being relatively healthy and those who stop smoking because of poor health might have different prognosis33. Due to data unavailability, this factor was not accounted for in our analysis; this might affect our association estimates. Despite these limitations, we provided robust evidence of the health benefits of smoking cessation among male cancer patients in long-term survival, supported by a recent study on tobacco use-related cancers. They reported protective effects observed over the 5 years following smoking cessation among Non-Hispanic Black patients, marked by the reduced risk of a high neutrophil-to-lymphocyte ratio (i.e. an inflammation biomarker used in cancer prevention and management programs to indicate the influence of smoking cessation)34.This study has several strengths. First, this was a population-based cohort study with a large sample size. A large number of patients with cancer enabled us to analyze the pooled sample of all cancers and some specific cancer types. Second, we used trajectory analysis to comprehensively investigate smoking patterns among cancer survivors, while previous studies on this population were mainly based on smoking statusSmoking significantly increased all-cause and cancer mortality risks among male cancer patients in pooled all cancers, pooled smoking-related cancers, and pooled cancers unrelated to smoking. Smoking also increased all-cause and cancer mortality risks in patients with gastric and colorectal cancers and cancer-specific mortality risks in patients with lung cancer. The significant associations between smoking and mortality risks were presented more clearly among 5-year survivors and those with less fatal cancer types. Smoking cessation post-cancer diagnosis is beneficial for male cancer patients, even among those who smoke heavily.Click here for additional data file.


Since its emergence, the COVID-19 pandemic has compromised the food security both directly by impacting food supply chain and indirectly by overwhelming the individual health and/or personal financial situation. The overarching aim of the current study is to assess aspects of the food security crisis that have arisen due to COVID-19 and to identify which, if any, food security dimensions were specifically compromised.Primary research articles were initially identified through four online databases , with the references of each paper then also reviewed for additional article. The food security status of individuals and the wider community, both before and after the emergence of COVID-19, were examined.Of the 2,057 studies initially identified, a total of ten were included in the final review. The included studies confirmed that COVID-19 had substantially impacted food security, with individuals, households and the wider community experiencing food insecurity. Nine of the included studies aruged that the food accessibility dimension was the most compromised.To address the identified direct and indirect food security issues associated with COVID-19, it is proposed that a combination of prevention practices and proactive food security activities is required. Integrating food security interventions, supporting and facilitating food security resilience, and conducting further studies on the food security of COVID-19 are also recommended.The online version contains supplementary material available at 10.1186/s12889-023-17104-6. Food security is defined as having healthy, nutritious, and physically and economically accessible food available to people in a defined geographic location and which satisfies the individuals’ dietary needs . COVID-1According to the Food and Agricultural Organization (FAO) of the United Nations (UN) , there aA recent article has identified the burden of COVID-19 pandemic on all the four dimensions , while aHow much was food security directly and indirectly affected by COVID-19?Which, if any, dimensions of food security were significantly compromised by the COVID-19 pandemic?To achieve these outcomes, a systematic review was conducted of all studies regarding COVID-19’s impact on food security. This paper examines the extent to which food security was directly and indirectly compromised by COVID-19, and how food security actors recovered from the COVID-19 pandemic induced food insecurity trauma. The application of findings of this systematic review are not necessarily restricted explicitly to COVID-19 as they may also have potential significance in early preparedness of the inevitable future pandemics. The purpose of this article was to summarise the food security impact of COVID-19 and pointing out the critically impacted dimension/s of food security. The specific questions that guided this systematic review were:This systematic review was undertaken in line with the PRISMA 2020 guidelines  and checPrimary research articles conducted on COVID-19’s impact on food security were the focus of the systematic review. However, so as to allow for comparative analysis of the burden of COVID-19, potential articles had to specifically focus on at least one of the four defined dimensions  both prior to and/or following the emergence of COVID-19. No restrictions were placed on study design, with any qualitative, quantitative, and mixed method studies considered. Other types of literature  were excluded from review. The reasons for excluding information sources other than primary research articles were to ensure the goal of identifying information from the primary source, and which were supported with data and followed expected research standards and procedures.It is expected that primary research articles published in different languages on the topic of interest might be available in online information sources (databases), but only studies written in English were included. The timeframe for publication was from the approximate time of emergence of COVID-19 (31 December 2019) through to the end of this systematic review’s literature search (15 May 2022) were included.Four databases  were used as the literature sources. The data from these four databases was identified from the nominal date for COVID-19 pandemic emergence (31/12/2019) through to 15/05/2022. All reference lists from the studies identified through the initial database searches were then explicitly assessed to avoid missing any relevant data source.“Impact” OR “Effect” OR “Burden” AND “COVID 19”) OR “COVID-19 pandemic” OR “SARS COV 2” OR “Coronavirus disease 2019” OR “Coronavirus diseases 19” OR “Severe acute respiratory syndrome coronavirus-2”) AND “Food Security” OR “Food Insecurity” OR “Food Security dimension”. The publication date (31/12/2019—15/05/2022), the publication language (English), and the article type (primary research articles) were used as a filtering mechanism during the searching process. For identifying literature from Google Scholar, the short title of this systematic review  was used, and any potentially relevant articles that were not identified from other databases were included. Details of the search strategy for each database is described as follows.Scopus: ( ALL ( impact) OR TITLE-ABS-KEY ( effect) OR TITLE-ABS-KEY ( burden) AND ALL ( covid 19) OR ALL ( covid-19 AND pandemic) OR TITLE-ABS-KEY ( sars AND cov 2) OR TITLE-ABS-KEY ( coronavirus AND disease 2019) OR TITLE-ABS-KEY ( coronavirus AND diseases 19) OR ABS ( severe AND acute AND respiratory AND syndrome AND coronavirus-2) AND ALL ( food AND security) OR ALL ( food AND insecurity) OR TITLE-ABS-KEY ( food AND security AND dimension)) AND ) AND ) AND ).PubMed: (((((((((((Impact[Title/Abstract]) OR (Effect[Title])) OR (Burden[Title])) AND (COVID 19[Title/Abstract])) OR (COVID-19 Pandemic[Title/Abstract])) OR (SARS COV 2[Title/Abstract])) OR (Coronavirus disease 2019[Title/Abstract])) OR (Coronavirus diseases 19[Title/Abstract])) OR (Severe acute respiratory syndrome coronavirus-2[Title/Abstract])) AND (Food Security[Title/Abstract])) OR (Food Insecurity[Title/Abstract])) OR (Food Security dimension[Title/Abstract]).Web of Science: Impact (All Fields) OR Effect (Abstract) OR Burden (Abstract) AND COVID 19 (All Fields) OR COVID-19 pandemic (All Fields) OR SARS COV 2 (Title) OR Coronavirus disease 2019 (All Fields) OR Coronavirus disease 19 (All Fields) OR Severe acute respiratory syndrome coronavirus-2 (Abstract) AND Food Security (All Fields) OR Food Insecurity (All Fields) OR Food Security dimension (Abstract) and 2020 or 2021 or 2022 (Publication Years) and Article (Document Types) and English (Languages).Google scholar: COVID-19, Food Security, Impact, OR COVID-19, OR Pandemic, OR Food OR security, OR availability, OR accessibility, OR usability, OR stability.A combination of search terms was used. The same terms were used for three databases , and search strings were applied according to the relevant database. As explained in the protocol , the seaThe first author conducted the primary screening, and the same process was repeated by the other team members to ensure the validity and reliability of selection processes. Identification of potentially eligible studies was conducted initially through title and abstract screening, prior to a full text review occurring. A pre-determined process for resolving any disagreements was established, however ultimately there were no disagreements in the selection process among the review authors.Studies selected from the four databases were imported to EndNote X9. Literature searches were undertaken on 01 May 2022 for the first time and repeated 2 weeks later for the second time. The identified articles were de-duplicated using EndNote X9’s unique identifier function. Any redundant studies missed by the EndNote unique identifier were manually de-duplicated. The studies were considered regardless of the studies’ statistical analysis, geographic coverage, and study participants . All COVID-19 related factors that exacerbated the food insecurity of individuals/households, such as COVID-19 prevention restrictions, agricultural production interruptions, job losses, import–export bans, and mortality of productive workforces, were synthesised into the final results.Yes, No, Unclear or Not Applicable answers. The items of the checklist are: 1) the inclusion and exclusion criteria of samples; 2) study subjects and settings; 3) exposure measurement validity and reliability; 4) objective and standard criteria of condition measurement; 5) identification of confounding factors; 6) strategies to deal with confounding factors; 7) validity and reliability of outcome measures; and 8) the use of appropriate statistical analysis in included studies.The Joanna Briggs Institute (JBI) critical appraisal checklist was used for assessing the risk of biases. The eight items of the JBI checklist are designed for assessing the risk of biases in reviewing primary studies with Based on the JBI critical appraisal checklist, each included study was independently appraised by a second author and no disagreement were raised among team members.COVID-19-related food production or supply chain restrictions was considered as impacts on food availability. Food security reductions due to physical or economic constraints during COVID-19 were classified as food inaccessibility. The food quality and diversity reduction during the pandemic was interpreted as the impact of COVID-19 on food usability. Short-term food items availability that cannot be sustained due to the direct (effect on the food supply chain) and indirect  impacts were deemed to be food stability issues. Food insecurity increases during COVID-19 as compared with pre-pandemic food insecurity were expressed in the form of percentages. Overall, one or more food security dimension/s that was/were repeatedly reported by the majority of the included studies was identified as significantly altered dimension by COVID-19 pandemic.Synthesis of this systematic review was focused on investigating COVID-19’s impact on food security and/or its four dimensions . Any included studies that failed to consider relevant confounding food insecurity drivers were critically appraised in the discussion and risk of bias assessment sections of this systematic review.The Agency for Healthcare Research and Quality (AHRQ) tool was used for assessing reporting biases in the studies. As recommended by Berkman et al. , the autOf the initial sample of 2,057 studies identified through the first search, 1,220 were found through database searching and the remaining 837 were from other sources, such as the aforementioned review of reference lists  of the studies used validated food security indicators, while one  of the sTwo studies , 31 exam. [Each reviewed study was assessed using the AHRQ bias assessment criteria. Berkman et al.  noted th.  that use.  was susp. Each study was judged based on the pre-set criteria ia Table . The stuConceptually, food security is a complex and multifaceted issue , with foNine of the included studies reported that COVID-19 negatively affected food security and reported reduced food security status , 31 or iThe largest food insecurity increase 44%) due to COVID-19 was observed in Uganda followed by Kenya  with a 3% due to The majority (9 out of 10) of the included studies confirmed that the most affected food security dimension was food access, which is consistent with other research  that ideIn addition to food accessibility, the availability of food , 33, andThe studies did not necessarily take into account any potential confounding factors, such as illnesses other than COVID-19 , naturalDue to time constraints, only primary research articles were included, with brief reports, unpublished studies, reviews, and studies published in languages other than English excluded. This approach to study selection might have excluded relevant literature. This is important as the time delay from doing a study until results are presented in a journal may have meant that potentially relevant papers relating to a recent issue, such as COVID-19, had not yet had sufficient time to be published. Moreover, it is acknowledged that the initial selection system using title and abstract can also be prone to potentially missing relevant studies .The food security of households and individuals was severely compromised due to COVID-19 emergence. The health, agriculture and socio-economic crises following the COVID-19 pandemic indirectly impacted food security as compared with the pre-pandemic situations. The food accessibility dimension of food security was mainly impacted due to the pandemic. Contrary to the pandemic’s negative impact, integrated food aid interventions by private sectors, and governmental and non-governmental organizations improved food security during COVID-19. Based on this conclusion, the following recommendations were forwarded.To implement either localised or widespread COVID-19 restrictions, such as transport bans, lockdowns, food market chain restrictions, and quarantine, the benefits should outweigh any problems that arise. This is particularly relevant for disadvantaged groups, particularly in low-income countries, where circumstances characterised by ‘hand to mouth’ meant entire communities cannot easily cope with any new food security crises, such as those arising due to COVID-19 prevention measures . TherefoDecisions that are unidirectionally focused  and implemented hastily without due consideration to all possibly factors, may have a catastrophic impact on food security. Unless these decisions are made in integrated manner , emerging crises due to food insecurity might be as devasting as the personal health impact directly from COVID-19. Participating multi-disciplinary professionals, facilitating emergency food aid and SafetyNet programs, and prioritizing the pros and cons of COVID-19 prevention measures before implementation, should all be considered as potential strategies for balancing COVID-19’s prevention measures and arising food security crises.Food insecurity, arising either directly from the pandemic or indirectly due to restrictions applied for its prevention and control, should be mitigated by integrated interventions that are coordinated by governmental and non-governmental organisations, individuals, and institutions. Food security interventions can reduce mortality associated with food insecurity while balancing the need for COVID-19 prevention restrictions . AlthougThis systematic review found that food security was severely affected by COVID-19. This compromised food security needs to be supported to recover, and communities that experience food insecurity should be proactively assisted to overcome both immediate and emerging crises. To achieve food security resilience, communities needed to be provided with the tools and resources to be more independent. Unless cooperative food insecurity resilience is applied, the vulnerable groups' morbidity and mortality due to food insecurity will be exacerbated .There is currently a lack of comparative data examining food security pre and post-COVID-19 emergence, and a more nuanced understanding of these complex issues can only be achieved with a greater focus on these areas. Studies regarding the effect of post-pandemic restrictions on food security, determining the vulnerable groups for food insecurity, identifying the food security resilience methods during and after COVID-19, and investigating the merits and emerging problems arising from COVID-19 prevention measures are all recommended topics for consideration in prospective studies. Considering the limitations of included studies, it is also suggested that future research should consider and evaluate the multiple food security driving factors external to COVID-19 that might increase the food security crisis.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9362949/).This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under the registrations number: CRD42022325475. Protocol for the review process was prepared and the review was conducted as per the pre-planned protocol. The protocol is published at PLOS ONE  and can The protocol of this systematic review  was publAdditional file 1.


There was no difference in either OS or PFS compared to those who did not receive BV before AHSCT. We compared BV treatments based on their indication . There was statistically significant difference in the 5-year PFS based on the inication of BV therapy. Recovery rates of our R/R HL patient population, who underwent AHSCT, improved significantly. Our positive results can be attributed to the PET/CT directed, response-adapted treatment approach, and the widespread use of BV.Autologous stem cell transplantation (ASCT) is the standard treatment of primary refractory or relapsed Hodgkin-lymphoma, which can provide a cure rate of about 50%. The aim of our study was to analyze the data of 126 HL patients undergoing AHSCT in Hungary between 01/01/2016 and 31/12/2020. We assessed the progression-free and overall survival, the prognostic role of PET/CT performed before transplantation and effect of brentuximab vedotin (BV) treatment on survival outcomes. The median follow-up time from AHSCT was 39 (1–76) months. The 5-year OS comparing PET- and PET + patients was 90% v. 74% ( Today, 80–90% of Hodgkin lymphoma (HL) patients can be cured , 2. HoweThis study aimed to assess the progression-free (PFS) and overall survival (OS) of all HL patients who underwent AHSCT at the four national transplantation centers in Hungary, and also to investigate the prognostic role of PET/CT performed before transplantation, with particular emphasis on pre-AHSCT disease status and use of BV therapy. We compared our results with other studies in adult Hodgkin lymphoma, regarding progression-free and overall survival after transplantation, the depth of remission before transplantation, and the timing of brentuximab vedotin treatment.We retrospectively analyzed the data of HL patients undergoing AHSCT in Hungary between 01/01/2016 and 31/12/2020. Patients were treated in accordance with the evidence- and consensus-based practice guidelines of the Hungarian Society of Haematology and Transfusion . EligiblAll patients received ABVD  polychemotherapy as a first-line treatment per the national guidelines. R/R patients received two cycles of DHAP polychemotherapy as the first salvage treatment, followed by a PET/CT scan. PET/CT-negative patients (Deauville score 1–3) underwent AHSCT at any time after cycle 2, while PET/CT-positive (Deauville score 4–5) patients were administered further salvage therapy. The second salvage protocols were two cycles of BV-based, combined immunochemotherapy, including BV-bendamustine, BV-ICE, and BV-IGEV. A dedicated PET/CT scan was performed after the second cycle of BV-based therapy. AHSCT was performed in case of PET/CT negativity. In PET/CT-positive cases, further salvage treatment or receiving AHSCT (with the determination of further treatment after transplantation) was the transplant center’s sovereign decision. Stem cell mobilization and collection, also the administration of standard supporting treatment, were performed according to institutional guidelines.Maintenance BV treatment was applied if one or more of the following criteria were met, according to the modified AETHERA criteria:  primary p < 0.05. Statistical analyses were performed using SPSS 26.0 .The overall survival (OS) was calculated from the day of AHSCT to the last follow-up visit or death. Progression-free survival (PFS) was defined as the time from AHSCT to disease progression or death. Continuous variables are given as their medians and ranges, while categorical variables are with frequencies and percentages. Data normality was evaluated by the Kolmogorov–Smirnov test. Life-table estimates for OS and PFS were calculated by the Kaplan–Meier method, and the log-rank test was used to compare survival curves between groups. The level of statistical significance was considered at A total of 126 HL patients did undergo AHSCT during the examined period and were therefore eligible for inclusion in our study . Relapse rates were also favorable in PET/CT scan negative patients, with 23% of them experiencing relapse post-AHSCT, compared to 43% among the PET/CT scan-positive population (p = 0.005). Altogether, pre-AHSCT BV therapy did not affect the outcome of HL patients. There was no difference in the 5-year OS or PFS between BV-recipients and those who did not receive BV-based salvage therapy before AHSCT . Regarding subgroups determined by not just the presence but the indication of BV treatment , there was no difference in the 5-year OS . However, the 5-year PFS of these subgroups was different  . A total of 35 patients (27%) relapsed after AHSCT, 25 (71%) of them within 12 months, and 7 further (5%) within 24 months. Twenty-two patients out of 28 patients undergoing AHSCT with PET/CT-positivity underwent PET/CT during the first follow-up visit scheduled 100 days post-procedure. Half of them (55%) presented without metabolically active tumor burden. Twenty patients who relapsed after AHSCT received programmed cell death-1 (PD1) inhibitor treatment. Application of PD1 inhibitors is not routinely financed by the National Health Insurance Fund of Hungary (NHIFH). Pre-transplant (pembrolizumab) or post-transplant (nivolumab and pembrolizumab) application of checkpoint inhibitors depends on previous ineffectivity or intolerance of brentuximab vedotin and application of an individual aid request by the NFIFH. A total of 11 patients died; however, only 2 deaths were due to the progression of HL. We lost 5 further patients to HL-related, non-relapse mortality, while 2 patients died of unrelated causes to HL. The 18 patients (14%), who did not receive BV treatment at all, were at low risk based on the modified AETHERA criteria. Among them, no relapse occurred, and only one patient died due to alcoholic cardiomyopathy. In addition, high-risk patients who received BV-maintenance after AHSCT (whether they received BV before AHSCT or not) experienced superior PFS compared to those who did not continue with BV-maintenance treatment after AHSCT  of PET/CT-negative patients was above 80%, compared to 29% in patients with PET/CT positivity . Today, CR rate is between 17 and 75% with traditional salvage treatments  , but theThe AETHERA trial proved that maintenance therapy with brentuximab vedotin improves PFS, compared to placebo, in patients at high risk for relapse after AHSCT. The 5-year PFS was 59% in the BV group, compared to 41% in the placebo group. Altogether, 33% of these patients relapsed, 20% of them within the first year. By the nature of the study, there were no BV-pretreated patients in the AETHERA trial. However, since then, BV has been incorporated into pre-ASCT salvage treatments and emerged to the first-line setting in advanced stage HL based on the results of the ECHELON-1 trial.A few clinical studies have confirmed the improved survival after AHSCT, but there was a difference between studies based on the use of BV –29.As a result of this process, the proportion of HL patients receiving BV therapy before AHSCT is growing by the day. Veilleux et al. analyzed the data of 89 R/R HL patients who underwent AHSCT between 2007 and 2019 . The medThe study of Marouf et al. presented a similar result after analyzing the data of 115 patients with HL undergoing AHSCT . The 2-yChung et al. demonstrated similarly good overall survival as we did. Although the exact number of their patients receiving BV pre-AHSCT is not known in the 2015–2020 period, and post-AHSCT BV was used in 71% of patients, which is a higher proportion compared to our data .Spinner et al.  and ShahThe vast majority of our patients in this study received BV treatment before or after transplantation or both. The number of patients receiving maintenance therapy alone was relatively low. Nearly half of the patients treated with a BV-based salvage regimen before AHSCT have also received BV maintenance therapy. We did not observe any difference in the outcome of patients who proceeded to AHSCT with or without prior BV treatment.There was difference in progression-free survival rates between high-risk patients who received BV maintenance treatment and those high-risk patients who did not get, but there was no survival difference, who did not require such treatment. Therefore, in our opinion, this result can be considered a validation of the indications of post-AHSCT BV therapy established via the AETHERA trial in a real-world setting.p = 0.004). The 3- year PFS was 52.5% (BV therapy only before AHSCT), 64.4% , 80.5% (only BV consolidation treatment), and 88.9% (BV before and after AHSCT). 36.5% of patients received BV before transplantation, 34.6% were treated ≥ 2 or more salvage treatments, and 64.7% had negative PET/CT before AHSCT [Patients who received one line of salvage therapy before AHSCT had no survival benefit compared to patients treated with two or more salvage regimens. This observation is in line with the results of the study by Massaro and colleagues . The incre AHSCT . These rIn the majority of publications analyzing a similar group of patients, ABVD was the first-line treatment choice by a dominant margin , 29–32. The complex task of balancing efficacy in disease control with the risk of side effects in Hodgkin lymphoma (HL) often leads to variations in clinical practice. The utilization of BEACOPP and escBEACOPP protocols is generally limited to well-defined, high-risk patients . However, in our region, these protocols have not been widely adopted due to concerns regarding both early and late side effects , as well as challenges in procuring procarbazine. Notably, HL predominantly affects young and working-age individuals, making the incidence of secondary malignancies and infertility problems associated with BEACOPP protocols a critical concern for this specific social group. This challenge becomes more pronounced in light of the growing availability of modern biological therapies, which offer significantly lower toxicity profiles. Overall, the future role of BEACOPP in HL treatment is largely uncertain .We acknowledge the limitations of this non-randomized study, including the retrospective nature of data collection and the relatively low number of patients receiving only BV maintenance therapy. However, in our opinion, real-world data is of particular importance in patient populations with unusual disease settings. Also, the inclusion of patients from all age groups with no regard to co-morbidities can be considered the main strength of the current report. To adequately attribute any outcome to the administration of BV in the pre-AHSCT setting, further information needs to be acquired in the form of sufficiently powered randomized control trials.In our present study, the recovery rates of our R/R HL patient population, who underwent AHSCT, improved significantly. Our positive results can be attributed to the PET/CT directed, response-adapted treatment approach, and the widespread use of BV.In conclusion, our results strongly underline the importance of achieving CR before AHSCT in HL, regardless of the number and type of previous treatments. The depth of remission before AHSCT affects the success of transplantation and the patients’ chances of recovery. PET negativity is an extremely important prognostic factor before transplantation, so this should always be the main goal of therapy, even with the use of novel agents . If the patient is not PET-negative before transplant, clinicians are urged to plan the post-transplant treatment  before or at the time of transplantation. Furthermore, AHSCT can provide prolonged disease control, which can be improved with the adequate use of BV therapy. It is important to use maintenance brentuximab vedotin treatment if the patient is at high risk of relapse, based on the modified AETHERA criteria.


Background: Persons living with HIV may be at risk of more severe forms of COVID-19 infection and minimizing health risks largely depends on their acceptance of the COVID-19 vaccinations.Objective: This study examined the correlates and predictors of COVID-19 vaccine hesitancy among persons living with HIV in Trinidad and Tobago.2 tests examined the associations between study variables and COVID-19 vaccine hesitancy, and multivariable logistic regression analyses examined its predictors.Methods: A cross-sectional survey using a structured interview was conducted. Data were compiled on patient socio-demographics, diagnosed chronic diseases, psychological factors, and decisions to take the COVID-19 vaccine. Pearson χResults: In this study, 84% were virally suppressed, i.e., HIV viral load <1000 copies/ml. COVID-19 vaccine hesitancy was found to be 39%. Univariate analysis showed that higher vaccine hesitancy was significantly associated with females  and patients of mixed ethnicity . In our multivariable analysis, psychological factors namely, confidence in the COVID-19 vaccine , the perceived benefits of the vaccine , and cues to action  were observed as predictors of COVID-19 vaccine hesitancy.Conclusion: Psychological factors such as confidence in the COVID-19 vaccine, perceived benefits of the vaccine, and cues to action were possible predictors of COVID-19 vaccine hesitancy. This study underscored the continued need for strategies to increase confidence and knowledge about the benefits of taking the COVID-19 vaccine among persons living with HIV. In the Caribbean, the twin island republic of Trinidad and Tobago has an estimated population of 1.39 million persons (2021 mid-year estimate) with the ethnic composition comprising 35.4% of East Indian origin, 34.2% of African ancestry, 23% of mixed races, and 8.4% of other ethnic groups  [The World Health Organization (WHO) defined vaccine hesitancy as a behavior influenced by several factors including issues of confidence, i.e., mistrust in the vaccine or healthcare provider; complacency, i.e., a low perceived need and value attached to the vaccine; and convenience, i.e., access and availability of vaccines . VaccineBeginning in mid-2020, several cross-sectional studies were conducted examining COVID-19 vaccine willingness and hesitancy in the general population in Trinidad and Tobago. In the last quarter of 2020, one of the first studies which included Trinidad and Tobago was conducted by the Johns Hopkins Centre for Communication Programs, which reported that 59% of persons were COVID-19 vaccine-hesitant . FollowiWhile the generalizability of these cross-sectional studies was limited, the prevalence of vaccine hesitancy on average was found to be approximately 60% among adults in the general population . VaccineThe WHO recommended that persons living with HIV (PLHIV) should be prioritized for early COVID-19 vaccinations as HIV infection was reported to be a significant independent risk factor for severe COVID-19 presentation on hospital admission, i.e., PLHIV were hospitalized at higher rates and presented a higher mortality risk than non-PLHIV . FindingStudy design and participantsThis cross-sectional survey was implemented in August 2021, targeting PLHIV in Trinidad and Tobago, through structured face-to-face interviews. The Medical Research Foundation of Trinidad and Tobago (MRFTT) facilitated the study. The MRFTT is recognized as the largest HIV Treatment and Care Centre in Trinidad and Tobago. Adhering to the national COVID-19 guidelines, the MRFTT implemented procedures to alleviate crowded waiting rooms. Therefore, patient appointments fell considerably each month such that approximately 800 patients comprised the available target population for the duration of the study. Using a margin of error of 5%, a confidence level of 95%, and a 60% vaccine hesitancy rate based on the average reported in the general population, this gave a minimum sample size of n = 253 participants. The final sample comprised n = 272 patients meeting the eligibility criteria and giving their consent to participate in the study.The eligibility criteria included PLHIV in Trinidad and Tobago, aged 18 years and older. These persons were provided with details about the study and participated after their written consent was provided. Nurses at the MRFTT were trained in data collection and given the inclusion criteria for the study. Participant confidentiality was maintained throughout the survey. Physical copies of the completed questionnaires were secured at the MRFTT, and the resulting database was stored on a password-protected computer, with only members of the research team having access to this data.Study questionnaire and data collectionA structured questionnaire was used during interviews and consisted of the following sections: 1) sociodemographic characteristics; (2) presence of chronic diseases; (3) self-rated health status; (4) exposure to COVID-19; (5) COVID-19 vaccination hesitancy; (6) psychological factors; (7) length of time since HIV diagnosis; and (8) viral suppression. Each interview took approximately 15 minutes to complete. Table  sociodemPsychological factors were scored on a 4-point Likert scale, with a score of one (1) indicating strong disagreement and a score of four (4) indicating strong agreement. Item 4: “The medications I am currently taking can protect me from contracting COVID-19 infections”, of the perceived susceptibility scale was reverse coded to maintain internal consistency. An overall score was then created to measure each construct using the sum of individual scores of the responses from the Likert scales. COVID-19 vaccine hesitancy was a dichotomous variable such that “0 = not hesitant to take the COVID-19 vaccine" and “1 = hesitant to take the COVID-19 vaccine”.Reliability of psychological constructsThe questionnaire was pre-tested. Medical doctors and medical research experts at the MRFTT also reviewed the questionnaire design, content, words, comprehension, and ease of completion.The Cronbach α coefficients for the psychological components showed low to high internal consistency. The reliability statistics were presented in Table Statistical analysis2 tests for categorical variables with Bonferroni correction for multiple hypothesis testing. Logistic regression was used to examine unadjusted and adjusted odds ratios (OR) with 95% confidence intervals (CI) for associations between COVID-19 vaccine hesitancy with psychological factors and sociodemographic characteristics. Psychological measures were used as possible predictors of COVID-19 vaccine hesitancy while controlling for sociodemographic confounders in the multivariable regression analysis. Analyses included only non-missing data. Statistical significance was set at p < 0.05.All data were cleaned, coded, and analyzed using Statistical Product and Service Solutions (SPSS)  . DescripEthical approvalThe study protocol, data collection instruments, and informed consent form were approved by the Campus Research Ethics Committee of the University of the West Indies, St. Augustine, Trinidad, approval number CREC-SA.1063/06/2021.Characteristics of participants living with HIVTable Most of the participants  reported not being previously exposed to COVID-19. More than one-quarter of the patients  reported having been diagnosed with chronic diseases, and almost 80% rated themselves as being in good health.Almost half of the participants  indicated that they were confident in the COVID-19 vaccine. Overall, 106 patients (39.0%) were hesitant to take the COVID-19 vaccine. Most PLHIV  were virally suppressed, with patients being initiated on antiretroviral therapy (ART) for an average of 8.73 ± 6.02 years since the time of initial diagnosis.Differences in COVID-19 vaccine hesitancy and sociodemographic characteristicsTable No statistically significant differences in COVID-19 vaccination hesitancy were reported for gender (p = 0.005), marital status (p = 0.523), education (p = 0.224), employment status (p = 0.164), sexual orientation (p = 0.047), prior experience with COVID-19 (p = 0.824), whether family/friends contracted COVID-19 (p = 0.138), presence of chronic diseases (p = 0.560), self-rated health status (p = 0.919) and viral suppression (p = 0.955) based on Bonferroni-adjusted p-values for significance. There were also no significant differences when age (p = 0.304) and length of time since HIV diagnosis and initiation on ART (p = 0.486) were used as independent variables.Associations between COVID-19 vaccine hesitancy and sociodemographic characteristicsTable There were no significant associations between COVID-19 vaccine hesitancy with marital status, education level, employment status, experience with COVID-19, family/friends contracting COVID-19, existing chronic diseases, self-rated health status, viral suppression, age, or length of time since HIV diagnosis and initiation on ART and COVID-19 vaccine hesitancy.COVID-19 vaccine hesitancy and psychological factorsTable Predictors of COVID-19 vaccine hesitancy in persons living with HIVTable Gender, ethnicity, sexual orientation and the perceived susceptibility of contracting COVID-19, the perceived severity of COVID-19, and perceived concerns/barriers of the COVID-19 vaccine constructs were not found to be significant predictors of COVID-19 vaccine hesitancy in this study.The objective of our study was to examine the correlates and predictors of COVID-19 vaccine hesitancy among patients with HIV in Trinidad and Tobago. The study included patients’ sociodemographic characteristics, existing chronic diseases, self-rated health status, COVID-19 experience, and psychological factors.Our results showed that COVID-19 vaccine hesitancy was 39% among PLHIV in this sample, which was lower than in studies conducted among persons in the general population  ,7. TheseIn this sample, 84% of the patients were virally suppressed (VL <100 copies/ml), demonstrating high levels of treatment success. Over 75% of our sample reported having no experience with the COVID-19 virus, i.e., had not been clinically diagnosed with COVID-19. Over 75% also reported that they were in good health, with no diagnosed chronic diseases.Since the first confirmed case of the COVID-19 virus in Trinidad and Tobago in March 2020, the MRFTT implemented several measures such as six-month ART dispensing, telehealth, and community ART pick-up, reducing barriers to care during the COVID-19 pandemic and ensuring patients maintained their medication regimens and remained virally suppressed. These strategies may have contributed to the high levels of viral suppression among patients in our sample. This study was also implemented six months after the COVID-19 vaccine was made available to the public , during which time, information on the consequences of the COVID-19 virus and on the vaccine’s safety and efficacy for use among PLHIV were still evolving. This may have contributed to the patient’s hesitancy to take the vaccine. Therefore, it was important for HIV treatment facilities like the MRFTT to continue to implement educational campaigns tailored to the specific concerns of PLHIV and COVID-19 vaccination.Based on our univariate analysis, among the sociodemographic characteristics in our study, females had twice the odds of being vaccine-hesitant compared to males and patients of mixed ethnicity also had greater odds of being hesitant compared to patients of African descent. Patients identifying as non-heterosexuals  had lower odds of being hesitant when compared to heterosexuals. These study findings were consistent with research on COVID-19 vaccine hesitancy and the associated demographic factors ,15. HowePsychological factors related to patients’ beliefs and perceptions about the COVID-19 vaccine were included to investigate the associations with COVID-19 vaccine hesitancy. Our univariate analysis presented significant associations among four of the psychological factors namely, confidence in the COVID-19 vaccines, perceived severity of COVID-19 disease, perceived benefits of the COVID-19 vaccine, and perceived barriers to the COVID-19 vaccine. It was found that lower scores on perceived benefits were associated with higher vaccine hesitancy among patients in our study. Emerging studies supported these findings as persons with a higher level of perception of the benefits of the vaccine had greater odds of being vaccinated ,18. OverIn this study, there were no significant associations between the presence of diagnosed chronic diseases and COVID-19 vaccine hesitancy. This finding was not consistent with the previous studies which found that patients with underlying comorbid disease conditions were more likely to take the COVID-19 vaccine than those without underlying conditions ,23. The In the multivariable analysis, patients’ confidence in the COVID-19 vaccine, the perceived benefits of the COVID-19 vaccine, and cues to action were observed as possible predictors of COVID-19 vaccine hesitancy. Over the course of the pandemic, there was early evidence of the storm of misinformation that developed around COVID-19, largely from social and alternative media sources, and coupled with mistrust of government and other authority figures, eroded confidence in COVID-19 containment measures and contributed to vaccine hesitancy .COVID-19 vaccine hesitancy might have also resulted from skepticism about the reliability and competence of the health service system, and mistrust of healthcare providers ,26. In aOverall, the results of this study highlighted the importance of boosting confidence in the COVID-19 vaccines' safety and effectiveness. In the context of PLHIV, it was critical to provide tailored messages and implement strategies to reinforce both the personal and the social/collective benefits of getting vaccinated against the COVID-19 virus. Strategies to build vaccine literacy and acceptance, addressing the specific concerns of PLHIV and their knowledge of the vaccine’s benefits and the reliability of the healthcare system were required. Strategies should also be implemented to build vaccine literacy and dispel misconceptions, especially as they related to PLHIV.With the announcement of the availability of COVID-19 vaccinations to the public, clinicians at the MRFTT  began educating and counseling patients about COVID-19 vaccinations during their visits to the MRFTT. Information about the benefits of the vaccine was further reinforced through the distribution of information brochures specifically addressing the patients' concerns about the potential for drug interactions between the COVID-19 vaccine and their HIV treatment regimens, and the overall safety of PLHIV. Information about the benefits of the COVID-19 vaccinations was also shared via a dedicated social media page and on television displays at the clinic during the daytime and evening clinic hours. Targeted messages to patients were further provided using peer-to-peer educators who served as advocates to reduce COVID-19 vaccine hesitancy among patients. An important role for patient peers was to provide personal testimonials about their experience with the COVID-19 virus and/or on their receipt of the COVID-19 vaccination. Patients' peers also provided group counseling to further dispel the myths and misconceptions around the COVID-19 vaccinations, side-effects, reduce stigma and alleviate patients' fears about contracting the virus. In October 2021 (following the completion of the survey), the MRFTT became a designated COVID-19 vaccination site approved by the Ministry of Health of Trinidad and Tobago. The intention was to increase access and availability of COVID-19 vaccinations for patients enrolled in care at the MRFTT. COVID-19 vaccinations have since been integrated with the overall vaccine prevention program for patients enrolled in care, who also benefitted from the Flu, Pneumovax, Hepatitis B, and HPV vaccines.The results of the survey highlighted the need to upscale targeted strategies to increase the uptake of COVID-19 vaccinations among HIV-positive women and among ethnic groups. These results additionally highlighted the need to increase patients' knowledge of the personal and collective benefits of COVID-19 vaccinations such as not spreading the virus to others, and reducing the community spread of the COVID-19 virus.Study limitationsThe study was conducted during the period when COVID-19 vaccines were made available to the public. This corresponded with public mistrust and concerns about the safety and efficacy of the vaccines. During the period of COVID-19 restrictions, several measures were implemented to reduce the potential risk of the spread of COVID-19 infections by patients and staff. Risk reduction interventions such as the use of telehealth, community ART delivery, and six-month ART dispensing substantially reduced the number of in-person visits. Therefore, the study sample may not be representative of the entire PLHIV population in Trinidad and Tobago. Information regarding COVID-19 vaccinations and HIV were still in its early stages during the time of the study, thus contributing to uncertainty over vaccine safety among this subpopulation in Trinidad and Tobago. Unmeasured factors could have also influenced vaccine hesitancy. These included sources of information and trust in the healthcare professionals and the health system. As it pertained to psychological factors, the study did not suggest a strategy for changing health-related actions. Additionally, the cross-sectional nature of the study could potentially leave it susceptible to reverse causality.Our study underscored the ongoing need to further develop and continue existing strategies to increase patients’ confidence and knowledge of the benefits of the COVID-19 vaccines since psychological factors including confidence in the COVID-19 vaccine, perceived benefits of the vaccine, and cues to action were observed to be possible predictors of COVID-9 vaccine hesitancy among PLHIV. It remains important for persons to comprehend the psychological factors associated with COVID-19 vaccine hesitancy and facilitate the necessary avenues by which this at-risk subpopulation is provided with the opportunity to share their personal and collective experiences with COVID-19 vaccination.


This cross-sectional study investigates rates of prescription of low-dose minoxidil after of publication of a newspaper article on this treatment. On August 18, 2022, The New York Times published an article describing successful treatment experiences of several dermatologists and results of a small observational study of women with hair loss who received low-dose oral rather than topical minoxidil.5 We used prescription drug data to investigate changes in prescribing of oral minoxidil after the article, which was covered broadly by news and social media.6Media coverage of health-related news may be associated with changes in clinician practices and patient behaviors.STROBE reporting guideline for cross-sectional studies, including description of study design, setting, data sources, and statistical methods.In this cross-sectional study, we identified adults prescribed oral minoxidil from January 1, 2021, to December 31, 2022, in the Truveta database, which holds electronic health records from member US health care systems. Included patients were treated in 8 health systems, primarily resided in 13 states, and received first-time oral minoxidil before (January to July 2022) or after (August to December 2022) publication of the article. Data were deidentified, and the Providence Institutional Review Board determined that this study was not human participants research. The study follows the t tests to test for a difference in means for 8 weeks before vs after article publication. P values reflected α = .025 in each tail. We made comparisons vs first-time low-dose finasteride hair loss medication  and antihypertensive medications (given that minoxidil is an antihypertensive).We calculated the weekly rate of first-time oral minoxidil prescriptions (No. prescriptions/No. outpatient encounters/week) for 2.5-mg and 5-mg tablets . We conducted interrupted time-series analyses of weekly prescription rates, accounting for autocorrelation using an autoregressive, integrated moving-average model. We used 2-sample Among 6541 patients with first-time oral minoxidil prescriptions , 2846 individuals received prescriptions in the 7 months before and 3695 individuals in the 5 months after publication, respectively . The proP < .001) and for males  and females   . After tThis cross-sectional study found that after a newspaper publication describing use of low-dose oral minoxidil to treat hair loss, there was an immediate increase in prescribing. Importantly, the article did not report new research findings or large-scale randomized evidence. Our findings suggest that media coverage alone, even without new research or with limited evidence, may be associated with immediate changes in prescribing, although they may not be sustained. Underlying factors associated with this change in prescription behavior from patients, doctors, or both and differences between males and females are important to understand. Socioeconomic factors, such as access to health care and education and income levels, may be associated with individuals seeking low-dose oral minoxidil after article publication. Study limitations include lack of generalizability to populations not studied, the possibility of an unobserved contemporaneous event associated with increased prescriptions of oral minoxidil, and the potential for misclassification of patients who divided 10-mg minoxidil tablets to treat hair loss.


Objectives: This study aimed to prospectively investigate gender-specific relationship between hyperuricemia and all-cause mortality among Chinese older adults.Methods: The study was based on the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2008–2018, a prospective nationwide cohort of older adults in China. Multivariate Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs for all-cause mortality. Restricted cubic splines (RCS) were conducted to explore the dose-response relationship between SUA levels and all-cause mortality.Results: For older women, compared to the participants in the third quartile of SUA level, those in the highest quartile of SUA was associated with significantly higher risk of all-cause mortality in the fully adjusted model . No significant associations between SUA levels and all-cause mortality were observed in older men. The present study further found a U-shaped non-linear relationship between SUA levels and all-cause mortality in both sexes of older population (P for non-linear <0.05).Conclusions: This study provided prospective epidemiological evidence for the predictive role of SUA on all-cause mortality among the Chinese aging population over 10 years of follow-up, while revealing considerable gender-related differences. Serum uric acid (SUA), the final enzymatic product of purine metabolism, is a biological marker for the diagnosis of hyperuricemia. Increasing evidence linked hyperuricemia to a variety of adverse health outcomes, including hypertension , 2, obesHigher SUA levels in men than in women at all ages have been widely demonstrated . Most prConsidering those mentioned above, the aim of the present study was to examine the prospective associations between hyperuricemia and all-cause mortality in Chinese older adults over 10 years of follow-up. To our knowledge, few studies have examined the modified effect of gender on the longitudinal association between hyperuricemia and all-cause mortality among Chinese older adults. Therefore, this study further examined the gender differences in the relationship between hyperuricemia and all-cause mortality in the Chinese aging population.The CLHLS is the first and largest nationwide, community-based, longitudinal prospective cohort survey in China. The CLHLS were performed by randomly selected from counties or cities in 23 provinces across China, covering about 85% percent of the Chinese population . The CLHInitially, A total of 2035 participating older adults provided blood sample data in the 5th wave (2008–2009) of CLHLS. According to the following exclusion criteria, we retained 1,067 Chinese older adults in this study after applying the following exclusion criteria. 1) We excluded participants who had no participant ID and who were younger than 60 years old. 2) We excluded people with missing SUA and other biochemical data. 3) We excluded participants who were unable to match the 2008–2018 follow-up longitudinal survey. 4) We excluded participants due to their unknown information. The flow chart of the study population was shown in Fasting venous blood samples were collected by trained medical personnel from all willing participant who had fasted overnight. These samples were collected into 5 mL heparin anticoagulant vacuum tubes and centrifuged at 20°C and 3000 The outcome of our study was all-cause mortality from 1 January 2008 to 31 December 2018. Mortality status was obtained from a publicly available dataset of the CLHLS, which captured the vital status of survey participants from baseline to 31 December 2018. If a participant missed the follow-up visit, the survival time was defined as the interval between baseline and time of missed visits. Survival time was calculated from the survey date at baseline until the date of death, or the interview date of follow-up survey, or lost visit date, whichever came first.a priori based on previous studies examining risk factors for serum uric acid levels and all-cause mortality. The sociodemographic information included age, sex , residence  and economic income (low/high). Health characteristics included lifestyle and history of chronic diseases. Lifestyle characteristics contained smoking (yes/no), drinking (yes/no), physical activity (current or former/none) and diet . After the household interview, all participants were asked to undergo anthropometric measurements, which included systolic blood pressure (mmHg), diastolic blood pressure (mmHg) and body mass index (kg/m2). History of chronic disease including self-reported and/or hospital diagnosed hypertension, diabetes, heart disease, stroke or cerebrovascular disease and chronic nephritis. Blood biochemistry tests were analyzed in this study, included BUN (mmol/L), plasma creatinine (mmol/L), SUA (μmol/L), plasma glucose (mmol/L), TC (mmol/L), HDL-C (mmol/L), LDL-C (mmol/L), TG (mmol/L).Covariates were collected information using a standardized questionnaire administered by the trained interviewers during an household interview. The present study adjusted for sociodemographic variables, health characteristics, and confounding biomarkers in the model, which were selected Continuous variables were presented as mean  or median , and categorical variables were presented as counts (%).For continuous variables that followed normal distribution, we used two independent sample t-test and ANOVA to compare the characteristics of participants in two and multiple groups, respectively. Tukey’s post-hoc test or Games-Howell post-hoc test was used to conduct pairwise comparisons for multiple groups. For the continuous variables with skewed distribution, Mann-Whitney U test and Kruskal-Wallis test with Dunn post-hoc test were used to compare the difference between two groups and multiple groups, respectively. Characteristics of the two and multiple groups were compared using chi-square test for categorical variables.Multivariate Cox proportional hazards models were used to investigate relationships between SUA with all-cause mortality, using the third quartile (Q3) of SUA levels as a reference, and the results are presented as hazard ratios (HRs) and 95% confidence intervals (CIs). We performed analyses of the association between SUA and all-cause mortality by sex using four models based on previous studies as follows , 9, 25: In addition, subgroup analyses and their interactions were conducted to further explore whether gender would modify the relationship between SUA levels and all-cause mortality. Furthermore, multivariate Cox regression models with restricted cubic splines (RCS) methods were performed to explore the potentially non-linear relationship between SUA level and hazard ratio of all-cause mortality, using SUA level as a continuous variable. To balance best fit and overfitting in the main splines for all-cause mortality, the optimal model is selected based on the knot (ranging from 3 to 10) corresponding to the lowest value of the Akaike Information Criterion (AIC). In present study, we used restricted cubic splines methods with three knots to analyze the non-linear relationship.Sensitivity analyses were performed to evaluate the robustness of our results: 1) using diagnostic values for clinically defined hyperuricemia by gender  instead of quartiles; 2) We excluded participants who were younger than 80 years old and further explored the relationship between SUA levels and all-cause mortality in the oldest-old adults.p < 0.05 was considered to indicate statistical significance.The statistical analysis was performed using the R 4.0.3 Statistical Package . A two-sided n = 608). Participants who had higher SUA level tended to be older, lived in city or town areas, had higher income, drink alcohol, and eat meat and fish. In addition, participants with higher SUA levels had high levels of BUN, plasma creatinine, LDL-C and TG. The baseline characteristics of participants who remained in the cohort and were lost from the cohort due to lost to follow-up or death are summarized in Demographics, lifestyle, diet, history of chronic disease, and biochemical characteristics of all study participants were compared according to SUA level quartiles . In briep < 0.001) (p = 0.320)  . However= 0.320) .Time-dependent ROC analysis was conducted to compare the ability of the Model 4 to predict all-cause mortality between male and female. The AUC values of female in the total follow-up time were greater than those of male, which showed relatively good predictive accuracy in female .p-value for gender was 0.011, which indicated that the association between SUA levels and all-cause mortality was significantly different by gender after adjusting for a range of covariates , and the lowest risk was observed to be within the third quartile SUA levels for men and women, respectively  in the study population was not available during baseline information collection, which might influence the relationship between hyperuricemia and all-cause mortality. Finally, exploration of the potential relationship between serum uric acid levels and cause-specific mortality could not be performed because of the unavailable cause-specific mortality data.In summary, the relationship between SUA levels and all-cause mortality was modified by gender among Chinese older adults. This longitudinal finding of 10-year follow-up was pronounced in older women participants, but not in older men participants. In addition, our study provided the new epidemiologic evidence that the U-shaped non-linear relationship between SUA levels and all-cause mortality was present across gender among Chinese older adults. Further studies are needed to elucidate the actual role of SUA in all-cause mortality among older adults as well as potential biological mechanisms.