How does out-of-pocket spending on and the use of lenalidomide therapy differ between commercially insured patients enrolled in high-deductible health plans (HDHPs) and non-HDHPs?In this cohort study of 3163 adults who initiated lenalidomide therapy between 2013 and 2017, HDHP enrollment was associated with a greater likelihood of paying more than $100 per lenalidomide prescription fill compared with non-HDHP enrollment. However, no meaningful differences in patterns of lenalidomide therapy adherence were observed by enrollment status.Despite higher observed out-of-pocket spending among commercially insured HDHP enrollees, patterns of lenalidomide therapy adherence were similar between HDHP and non-HDHP enrollees. This cohort study assesses the association of high-deductible health plan enrollment with out-of-pocket spending on and adherence to lenalidomide therapy among commercially insured patients with multiple myeloma. High-deductible health plans (HDHPs) require high upfront cost-sharing, which has been associated with suboptimal anticancer medication uptake and adherence. Whether HDHP enrollment has limited the affordability and use of lenalidomide therapy among commercially insured patients with multiple myeloma is unknown.To assess the association of HDHP enrollment with out-of-pocket spending on and adherence to lenalidomide therapy.In this cohort study, data were obtained from the IBM MarketScan Commercial Claims and Encounters Database for adults aged 18 to 64 years with multiple myeloma who newly initiated lenalidomide therapy between April 1, 2013, and June 30, 2017. Quantile regression and modified Poisson regression evaluated out-of-pocket spending, and group-based trajectory models and multinomial logistic regression examined patterns of and factors associated with adherence. Analyses were conducted from April to August 2020.High-deductible health plan enrollment in the 3 months before and 6 months after initiation of lenalidomide therapy.Distribution of out-of-pocket spending, the probability of paying more than $100 for the first and any lenalidomide prescription fill, and monthly lenalidomide therapy adherence using the proportion of days covered (≥80%).Of the 3163 commercially insured patients who initiated lenalidomide therapy , 328 (10.4%) were enrolled in HDHPs and 1769 (55.9%) were women. Among the highest spenders (95th percentile), HDHP enrollees paid $376  and $217  more for their first and any lenalidomide prescription fill, respectively, compared with non-HDHP enrollees in the 6 months after initiation. High-deductible health plan enrollment was also associated with an increased risk of paying more than $100 for the initial  and any  lenalidomide prescription fill. Three adherence trajectory groups were identified: those with high adherence (n = 1273), late nonadherence (n = 1084), and early nonadherence (n = 805). High-deductible health plan enrollment was not associated with adherence group assignment.In this cohort study, HDHP enrollment was associated with higher out-of-pocket spending per lenalidomide prescription fill; however, no statistically significant differences in adherence patterns between HDHP and non-HDHP enrollees were observed. Patient , payer , and clinician  factors may have limited the potential for nonadherence among commercially insured patients who initiated lenalidomide therapy. Eligible patients were aged 18 to 64 years at initiation of lenalidomide therapy, had at least 12 months of continuous health plan coverage in a year with a claim for multiple myeloma, had continuous health plan coverage at least 3 months before and 6 months after their initial lenalidomide prescription fill, and had no evidence of prior treatment with lenalidomide in the 3-month baseline period. Race and ethnicity data were not collected because these data were not available in the data set. This study was deemed exempt from approval and informed consent by the Duke Health Institutional Review Board and conformed to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.In this cohort study, we used the IBM MarketScan Commercial Claims and Encounters Database to identify adults with multiple myeloma  were standardized to a 28-day supply and inflation adjusted to 2017 dollars using the medical component of the Consumer Price Index.23 In accordance with prior research and Pharmacy Quality Alliance thresholds, patients were considered adherent to lenalidomide therapy if the monthly proportion of days covered was at least 80%.24Adherence to lenalidomide therapy was also a primary outcome. We measured adherence using the proportion of days covered, which represents the days a patient had lenalidomide therapy available divided by the days in the study period (each month during a 6-month period).25 and number of unique prescription medications in the 3-month baseline period.The key independent variable was enrollment in an HDHP. Patients were categorized as HDHP enrollees if they were covered by an HDHP throughout the entirety of the study period ; otherwise, they were classified as a non-HDHP enrollee . Covariates included patient demographics , region, quarter and year of lenalidomide therapy initiation, comorbidities ,26 to estimate the probability of paying more than $100 out of pocket for the initial and any lenalidomide prescription fill.We used quantile regression to evaluate the distribution of out-of-pocket spending at the 25th, 50th, 75th, 90th, and 95th percentiles for the initial and any lenalidomide prescription fill and modified Poisson regression with robust error variance29 Specifically, 6 monthly adherence indicators were assessed using logistic regression for group-specific models, with time defined as the months after lenalidomide therapy initiation.29 We estimated models consisting of 2 to 5 adherence groups and used third-order polynomials to model adherence over time.30 We compared fit across models and determined the optimal number of adherence groups with bayesian information criteria31 and estimated group proportions (≥5% of the study population was assigned to each group).32 After determining the most appropriate group-based trajectory model, we used multinomial logistic regression to assess the association between HDHP enrollment and adherence group assignment.31Following previous studies, we evaluated adherence with group-based trajectory models, which simultaneously estimate group assignment probabilities and longitudinal adherence trajectories.P < .05 denoted statistical significance.For all models, we estimated unadjusted and adjusted outcomes with SAS, version 9.4 . Analyses were conducted from April to August 2020. Statistical tests were 2 sided, and 33 Therefore, we included the interaction between quarter of initiation and HDHP enrollment in all models. Second, to account for high antimyeloma treatment costs and variation in out-of-pocket maximums across health plans, we used modified Poisson regression with robust error variance26 to estimate the probability of paying $0 for the first and any lenalidomide prescription fill.We conducted several sensitivity analyses. First, the time of year in which lenalidomide therapy was initiated may have influenced both out-of-pocket costs and adherence for HDHP enrollees .Among the 3163 commercially insured adults who initiated lenalidomide therapy between April 1, 2013, and June 30, 2017 (1769 women [55.9%] and 1394 men [44.1%]) , 328 10. BaselinIn unadjusted analyses, HDHP enrollees paid $1063  and $946  more for their initial lenalidomide prescription fill at the 90th and 95th percentiles of out-of-pocket spending, respectively, relative to non-HDHP enrollees A. SimilaWhen assessing the distribution of unadjusted out-of-pocket costs on any prescription fill of lenalidomide in the 6 months after initiation, HDHP enrollees paid more at the 75th , 90th , and 95th  quantiles of spending than non-HDHP enrollees B. AdjustA larger proportion of HDHP enrollees paid more than $100 for both their first (133 [40.5%] vs 812 [28.6%]) and any (160 [48.8%] vs 1008 [35.5%]) lenalidomide prescription fill vs non-HDHP enrollees . CompareAfter evaluating model fit  paid $0 for any lenalidomide prescription fill in the 6 months after initiation 36 to ensure greater financial security34 for and optimal use of high-value anticancer medications by enrollees.In this cohort study comparing out-of-pocket spending on and use of lenalidomide therapy between commercially insured HDHP and non-HDHP enrollees, we observed that HDHP enrollees were more likely to pay more than $100 per prescription fill in the 6 months after initiation. In addition, HDHP enrollees at the 90th and 95th percentiles of out-of-pocket spending often paid more than $2000 for their initial lenalidomide prescription fill. Because HDHP enrollment is projected to increase dramatically during the next decade33 whereas the highest spenders (90th and 95th percentiles) may have adopted a variety of strategies to aid with monthly prescription expenses. For example, HDHP and non-HDHP enrollees may have used prescription assistance programs ,37 personal or employer-sponsored financial resources ,38 or cost-coping strategies 39 to maintain use of lenalidomide therapy. Advocacy organizations and policy makers should consider expanding access to assistance programs and increasing contributions to employer-sponsored resources to further minimize out-of-pocket costs for anticancer medication fills in each benefit year.Although HDHP enrollment was associated with high out-of-pocket spending, we did not observe statistically significant differences in high adherence between HDHP and non-HDHP enrollees. Out-of-pocket costs among the lowest spenders (25th and 50th percentiles) were perhaps too small ($1 to $53 per fill) to adversely affect continued use of lenalidomide therapy,2 patient- and provider-level factors may have contributed to patterns of high adherence observed among both HDHP and non-HDHP enrollees. In terms of the former, patients’ awareness of lenalidomide therapy on prolonged survival17 and improved quality of life40 may have motivated them to consistently use medication.2 In regard to the latter, high rates (>90%)41 of lenalidomide therapy adherence have been associated with frequent health care appointments,43 which provide an opportunity for clinicians to counsel patients on disease severity and the benefits of continuous treatment,44 monitor and address adverse events or adverse effects,44 and discuss treatment affordability and financial assistance programs.44Aside from out-of-pocket costs and the use of financial resources,18 thus HDHP and non-HDHP enrollees may have been switched to another treatment regimen after lenalidomide therapy intolerance or disease progression.16 Another potential explanation is that HDHP and non-HDHP enrollees may have been prescribed a shorter duration of lenalidomide therapy 45 in preparation for a stem cell transplant—the standard of care among younger adults (younger than 65 years).47 Given the complexities of multiple myeloma treatment, future research is needed to understand how HDHP enrollment influences the affordability of and access to necessary care throughout the disease course.Last, we observed no significant differences between HDHP enrollment and assignment to either the early or late nonadherence trajectory groups. One possible explanation for our findings is that patients often require multiple lines of therapy throughout the course of multiple myeloma,48 our cohort primarily consisted of commercially insured patients employed at large firms,48 which limits the generalizability of our findings to those employed by medium or small firms, enrolled in health insurance exchange plans, or covered by public health plans. Second, our analysis focused on a single orally administered anticancer medication. However, the price of lenalidomide therapy is equivalent to other and newly approved anticancer therapies,49 and we would expect similar out-of-pocket spending for patients with cancer. Third, the supply of lenalidomide therapy may vary depending on when the medication was prescribed .50 Although patients with a shorter supply would be categorized as nonadherent in our study, the treatment-recommended supply should not vary by type of health plan coverage. Fourth, our analysis focused on prescription fills and refills; therefore, we could not determine whether patients actually ingested lenalidomide, nor could we account for medication received while hospitalized. Fifth, although group-based trajectory models have summarized longitudinal adherence better than conventional approaches, they assume that within-person correlation is fully explained by the trajectory curve for each patient’s group, which may not be the case.32 Sixth, our analysis focused on a subset of patients who initiated lenalidomide therapy; thus, we did not observe patients who either delayed treatment or never filled a lenalidomide prescription due to financial burden.51 As a result, we may have underestimated differences in out-of-pocket spending by enrollment status. Seventh, to ensure adequate sample size for our analyses, we required the cohort to have continuous health plan coverage in the 3 months before treatment initiation. However, some enrollees may have temporarily discontinued and restarted lenalidomide therapy after this 3-month period, which, in turn, may have resulted in different adherence patterns. Eighth, we were not able to identify clinically indicated reasons for nonadherence ; however, these events should not differ by the generosity of a patient’s health plan. Ninth, we lacked information regarding benefit design  and access to and use of health spending accounts and prescription assistance programs. Last, commercial claims lack information related to known contributors to nonadherence, including sociodemographic factors , patients’ beliefs and behaviors , and clinician characteristics .This study has several limitations. First, although 40 health plans contribute to the IBM MarketScan database,Despite higher observed out-of-pocket costs among commercially insured HDHP enrollees, we did not observe any statistically significant differences in lenalidomide therapy adherence patterns between HDHP and non-HDHP enrollees. Availability of financial resources, frequent contact with the health care system, and patients’ understanding of disease severity and treatment benefits may have contributed to high adherence among enrollees, including those with high upfront out-of-pocket costs. As such, payers, policy makers, and clinicians should consider addressing known barriers to access  to ensure consistent use of high-value anticancer medications.


Phase 3 clinical trials and real-world effectiveness studies showed that China’s two main inactivated COVID-19 vaccines are very effective against serious illness. In November 2021, an outbreak occurred in the Inner Mongolia Autonomous Region that provided an opportunity to assess the vaccine effectiveness (VE) of these inactivated vaccines against COVID-19 caused by the delta variant. We evaluated VE with a retrospective cohort study of close contacts of infected individuals, using a generalized linear model with binomial distribution and log-link function to estimate risk ratios (RR) and VE. A total of 8842 close contacts were studied. Compared with no vaccination and adjusted for age, presence of comorbidity, and time since last vaccination, full vaccination reduced symptomatic infection by 62%, pneumonia by 64% and severe COVID-19 by 90%; reductions associated with homologous booster doses were 83% for symptomatic infection, 92% for pneumonia and 100% for severe COVID-19. There was no significant decline in two-dose VE for any outcome for up to 325 days following the last dose. There were no differences by vaccine brand. Inactivated vaccines were effective against delta-variant illness, and were highly effective against pneumonia and severe COVID-19; VE was increased by booster doses. Coronavirus disease 2019 (COVID-19) has remained a global pandemic for the last three years. Following containment in 2020 and during the period of sustained containment, in August 2021, in response to the spread of the highly transmissible delta variant, China adopted a strategy called “dynamic COVID zero.” The essence of this strategy was to take effective and comprehensive measures to rapidly truncate transmission chains and to end outbreaks in a timely manner [Two ancestral-strain SARS-CoV-2 whole-virus inactivated COVID-19 vaccines developed and produced in China have been widely used domestically and globally, and are listed by the World Health Organization for emergency use during the pandemic: BIBP-CorV, made by the Beijing Institute of Biological Products  and CoronaVac, made by Sinovac, Ltd., Beijing, China . As of 2In China, the dynamic COVID zero policy reduced the number and size of COVID-19 outbreaks. Real-world, domestic evidence of protection with China-produced vaccines has therefore been limited, especially for VE against delta-variant disease. In November 2021, a delta-variant outbreak occurred in the Inner Mongolia Autonomous Region that provided an opportunity to assess primary series and homologous booster dose inactivated vaccine effectiveness against delta-variant-caused illness. We report results of our evaluation and discuss their implications.We used a retrospective cohort study of close contacts of people known to be infected with SARS-CoV-2 during a delta-variant outbreak to estimate primary series and booster dose VE, and the associations between VE, the time since vaccination and the age of the vaccine recipient. Our study took advantage of the public health and social measures used in mainland China during the study period, which included managed quarantine of all close contacts.The outbreak started in the city of Manzhouli in the Inner Mongolia Autonomous Region on 28 November 2021, and spread to neighboring Zhalainuoer district in Hulun Buir prefecture, lasting until 16 December 2021. Manzhouli is located in the northeast of the Inner Mongolia Autonomous Region, sharing a border with two countries, Russia and the State of Mongolia, that had continuous community circulation of SARS-CoV-2 throughout 2021. The two aforementioned county-level cities had a combined registered population of 235,000 people; 13.0% < 18 years, 67.5% 18 to 59 years, and 19.5% ≥ 60 years. At the start of the outbreak, two- and three-dose COVID-19 vaccine coverage levels in the general population were 79.4% and 13.4%, respectively. Among 3–17-year-olds, 18–59-year-olds, and ≥60-year-olds, respective two- and three-dose coverage levels were 37.3% and 0.0%, 87.8% and 18.1%, 81.7% and 6.3%. There were 13,200 people who had received one dose but had not completed full primary vaccination by 28 November 2021.Between 28 November and 16 December 2021, there were 548 COVID-19 cases confirmed in Manzhouli and Zhalainuoer. Among infected individuals, 50.4% were male and 49.6% were female; 47.4% of infections were mild, 46.5% of infected individuals developed pneumonia, 5% of infections were severe, and 1.1% of infections were critical. Eight infections were in children less than three years of age; 26 were in children 3 to 11 years of age; 107 were in children 12 to 17 years of age; 318 were among adults 18 to 59 years of age; and 89 infections were in people 60 years of age or older. Forty-eight infected individuals were unvaccinated, 28 had received one dose, 425 had received two doses, and 47 had received three doses. Of the 500 vaccinated cases, 499 had received inactivated vaccines and one had received an adenovirus type-5 vectored vaccine.We used a retrospective cohort design to determine absolute VE and relative VE (rVE). Close contacts were defined as individuals living in the same residence with an infected person; individuals who had been in the same room, such as a restaurant at the same time with an infected person; and individuals who had been in the same confined space with an infected person up to four days before and after symptom onset of a confirmed case, or four days before and after an RT-PCR positive swab was obtained in an asymptomatically infected person who had not taken effective personal protective measures. We excluded the very few individuals vaccinated with vaccines other than BBIBP-CorV and CoronaVac. Because there were no fully vaccinated 3–11-year-old close contacts, and because there were only 14 (1.5%) unvaccinated 12–17-year-old close contacts see , it was Vaccination histories were obtained by interviewing individuals and then assessing vaccination records from national and provincial immunization information systems (IIS). We considered vaccinations to be valid only if they were documented in the national IIS. Based on vaccination history at time of exposure, subjects were categorized into one of four groups: a 0-dose group (those did not receive any COVID-19 vaccine), a 1-dose group (those who received only one dose), a full 2-dose group , and a 3-dose group . A second dose was considered valid only when given at least 14 days after the first dose; doses with shorter time intervals were considered invalid and not counted in vaccination status assessment.We evaluated VE for protection from three clinical outcomes: symptomatic COVID-19, COVID-19 pneumonia, and severe COVID-19. Case classifications were based on the COVID-19 Diagnosis and Treatment Protocol  and the COVID-19 Prevention and Control Protocol (eighth edition) categories of asymptomatic, mild, moderate, severe, and critically severe infection. A symptomatic COVID-19 outcome included all mild, moderate, severe, and critically severe cases. A COVID-19 pneumonia outcome included moderate, severe, and critically severe cases with evidence of pneumonia. A severe illness outcome included severe and critically severe cases. There were no fatal cases.Symptomatic COVID-19 was defined as SARS-CoV-2 infection with any symptom or sign of COVID-19, regardless of severity. COVID-19 pneumonia was diagnosed based on clinical symptoms and computed tomographic (CT) imaging . Severe illness met any of the following criteria: (a) respiratory distress, or a respiratory rate of 30 breaths/minute or more; (b) a resting oxygen saturation of less than 93%; (c) a ratio of arterial partial pressure of oxygen to fractional inspired oxygen concentration of <300 mmHg ; or (d) patients with pneumonia having >50% lesion progression within 24–48 h on chest imaging. Critical illness met any of the following criteria: (a) respiratory failure or need for mechanical ventilation; (b) shock; or (c) other organ failure that required monitoring and treatment in an intensive care unit .p-values < 0.05 (two-tailed) were considered statistically significant.VE was determined in this cohort of quarantined close contacts by comparing the vaccination status of quarantined individuals who ultimately tested positive for SARS-CoV-2 with those who remained RT-PCR negative. To calculate unadjusted VE, the relative risk (RR) of each outcome was calculated in reference to the 0-dose group using the equation VE = (1 − RR) × 100. We calculated 3-dose relative VE (rVE) in reference to the 2-dose group. For adjusted VE (aVE) and rVE, age, presence of comorbidities, interval between most recent dose and SARS-CoV-2 exposure were considered potentially confounding variables in multivariate analyses. We used a generalized linear model with a binomial distribution and log-link function to calculate adjusted risk ratios (aRR), which were used to calculate aVE and adjusted rVE. Cox proportional hazards models were used to create survival curves adjusted for age, presence of comorbidities, interval between exposure and most recent vaccination date; RR values were tested for significance with a Chi-square test. We used R (version 4.1.2) for drawing charts, statistical analysis, generalized linear model analysis, and Cox proportional hazards analysis. COVID-19 is a Level 2 infectious disease being managed as a Level 1 (highest) infectious disease during the study period. Investigation of outbreaks and assessment of vaccine effectiveness are required activities of disease prevention and control units. Our study used routinely collected data required by the Protocol for Prevention and Control of COVID-19. As an analysis of routinely collected data, the study was exempt from Ethical Review Committee review. Individual-identifying information was not retained in analytic data sets, and informed consent for this retrospective study was not required or obtained.A total of 403 (4.56%) close contacts were unvaccinated; 100 (1.13%) had received one dose; 6396 (72.33%) had received two doses; and 1943 (21.97%) had received three doses (two primary series doses and a booster dose). Medians  from the last dose prior to SARS-CoV-2 exposure were 93.5 days (IQR:38–178 days) for 1-dose subjects, 171 days (IQR:160–188 days) for two-dose subjects, and 15 days (IQR:8–33 days) for 3-dose subjects A.All 373 SARS-CoV-2-infected close contacts met the criteria for symptomatic illness. Among infected individuals, 244 developed pneumonia, 31 developed severe COVID-19 , and nonOur real-world evaluation of the performance of China’s inactivated COVID-19 vaccines during an outbreak showed that these vaccines provided high-level protection against COVID-19 pneumonia and severe COVID-19 caused by the delta variant, similar to vaccine effectiveness levels against illnesses caused by ancestral and previous SARS-CoV-2 strains. Compared with no vaccination and adjusted for age, presence of a comorbidity, and time since vaccination, full primary vaccination reduced symptomatic COVID-19 by 62%, pneumonia by 64%, and severe COVID-19 by 90%; booster-dose vaccination reduced symptomatic COVID-19 by 83%, pneumonia by 92%, and severe COVID-19 by 100%. Adjusted vaccine effectiveness levels were higher among individuals 60 years or older than among 18–59-year-olds. For up to 325 days following the last dose of vaccine, there was no significant decline in 2-dose vaccine effectiveness against any clinical outcome. Vaccine effectiveness did not vary by brand of vaccine. Continued use of these inactivated COVID-19 vaccines is warranted, and the use of booster doses is necessary for optimal protection from severe forms of COVID-19.The study was conducted during full implementation of the dynamic COVID zero policy in mainland China. During this time, there were relatively very few cumulative SARS-CoV-2 infections, implying that the study population was infection-naïve up to the start of the outbreak and therefore lacked hybrid immunity. As a consequence, the vaccine effectiveness estimates are of only vaccine-induced protection, without the influence of prior infection.COVID-19 vaccines made by other technical platforms have similar performance characteristics, showing greatest effectiveness against the more serious forms of delta-variant COVID-19 and requiring booster doses to retain or augment effectiveness . The IntOur results are consistent with other domestic vaccine effectiveness studies of inactivated and other China-produced vaccines against the delta and omicron variants. Earlier in 2021, Ma and colleagues used a delta-variant outbreak in Ruili City of Yunnan Province to assess vaccine effectiveness, finding protection afforded by inactivated and adenovirus 5-vectored vaccines to be 68% to 77% against pneumonia and 100% against severe COVID-19, with no deaths among any subjects . Kang anOur study has program implications. The first implication is that the COVID-19 vaccination campaign should continue, as the vaccines are having measurably strong effectiveness at preventing serious forms of COVID-19. It is the job of the immunization program to use vaccines to protect the population from serious and fatal COVID-19, and the findings from this study show that the vaccines not only provide protection, but that this protection has a reasonable duration of at least 325 days against the delta variant. The second program implication is that booster doses are important for attaining maximal protection, and therefore, the booster dose campaign should continue, attempting to reach all booster dose-eligible individuals. A third program implication is that monitoring the effectiveness and safety of vaccines is essential in the COVID-19 pandemic. Every opportunity must be taken to determine whether the vaccines being used retain effectiveness. The best opportunities in mainland China are in outbreaks such as this one in Inner Mongolia, because outbreaks are generally caused by a single variant, have well-defined exposed populations, and occur over discrete intervals. Variants have been emerging into global prominence at a rapid pace because SARS-CoV-2 is a novel virus, evolving as it traverses the world’s population with a variety of immune selection pressures. The fourth implication is that monitoring the severity of breakthrough infections is critically important. During the dynamic COVID zero policy, the number of infections was small. However, upon lifting the dynamic COVID zero policy, the virus spread rapidly due to limited effectiveness against infection and transmission. Under conditions of a high force of infection, the healthcare system may be overly stressed if the proportion of severe and critical cases is too high. The results of this study show that the proportion of serious/critical infections is low. This information is useful for mathematical modeling for predicting stress on the healthcare system upon lifting the dynamic COVID zero policy. Fifth, the results of this study can be used in communications to help assure the public that the vaccines produced and used in China are working well and providing protection from serious illness, and that the vaccination campaign is providing measurable benefit.The strengths of our study include (1) a study population of individuals with known exposure to delta-variant SARS-CoV-2 who are closely monitored and systematically tested for infection, and ascertainment of vaccination status of the subjects; (2) ascertainment of vaccination status with medical records and documentation, indexed by subjects’ national identification numbers; (3) the study being conducted during the dynamic COVID zero policy in an infection-naïve population, so that the vaccine effectiveness findings represent only vaccine-induced protection, without influence of hybrid immunity; and (4) the ability to estimate both absolute vaccine effectiveness and relative vaccine effectiveness. The unvaccinated group allowed estimation of absolute vaccine effectiveness, with relative VE showing the added protection afforded by the booster dose. When vaccination coverage levels become extremely high, the unvaccinated group will become increasingly different from vaccinated individuals, and different methods may be needed to assess vaccine effectiveness. Thus, the timing of this study makes it possible to estimate absolute vaccine effectiveness.There are several limitations to our study. One limitation is the relatively small size of the outbreak due to application of public health and social measures to stop the outbreak that were in effect during the study period. The small size of the outbreak limited the number of exposed close contacts, which in turn precluded determination of some useful subgroup analyses such as VE by comorbidity. Although we were able to adjust for the presence of one or more comorbidities, we could not determine VE by any specific comorbidity. A second limitation is that we were only able to determine the VE of the inactivated COVID-19 vaccines in common use in China. Due to limited use of non-inactivated COVID-19 vaccines in this population, we were unable to determine VE for other approved COVID-19 vaccines. Third, our study was observational, and therefore reports associations rather than causal inferences. There may have been unmeasured confounding variables that influenced the vaccine effectiveness results. However, observational studies are the most commonly used studies for assessing vaccine effectiveness under real-world conditions, and observational studies do not raise ethical issues of random allocation to vaccine or placebo when the efficacy of the vaccine is known and effective vaccines are widely available. A fourth limitation is that because the booster dose policy during the study period was limited to homologous booster doses, we were unable to determine the differential effectiveness of heterologous versus homologous booster doses following primary series vaccination with inactivated COVID-19 vaccines.In conclusion, the BIBP-CorV and SinoVac inactivated COVID-19 vaccines were highly effective against the delta variant, were most effective against the more severe outcomes and when boosted, and were not associated with a decline in effectiveness up to 325 days. The results of this study add to the growing confidence that population immunity will be established with these vaccines and boosted with the same or other COVID-19 vaccines, and the growing confidence that when used as recommended, these vaccines can greatly decrease severe outcomes from SARS-CoV-2 infection. During the time of China’s dynamic COVID zero policy, it is critically important to attain as high full-series and booster-dose coverage as possible for vaccine-eligible people who do not have valid contraindications, especially the elderly, as they suffer disproportionately from COVID-19. All modest-size or larger COVID-19 outbreaks should be used to evaluate population immunity against circulating SARS-CoV-2 variants.


Oral diseases are features of COVID-19 infection. There is, however, little known about oral diseases associated with COVID-19 in adolescents and young adults (AYA). Therefore, the aim of this study was to assess oral lesions’ association with COVID-19 infection in AYA; and to identify if sex and age will modify these associations.Data was collected for this cross-sectional study between August 2020 and January 2021 from 11-to-23 years old participants in 43-countries using an electronic validated questionnaire developed in five languages. Data collected included information on the dependent variables , independent variable (COVID-19 infection) and confounders . Multilevel binary logistic regression was used for analysis.P = 0.02 and < 0.001).Complete data were available for 7164 AYA, with 7.5% reporting a history of COVID-19 infection. A significantly higher percentage of participants with a history of COVID-19 infection than those without COVID-19 infection reported having dry mouth  and taste changes . There was a significant effect modification in the association between COVID-19 infection and the presence of dry mouth and change in taste by age and sex (COVID-19 infection was associated with dry mouth and change in taste among AYA and the strength of this association differed by age and sex. These oral conditions may help serve as an index for suspicion of COVID-19 infection in AYA.The online version contains supplementary material available at 10.1186/s12903-022-02515-5. Oral lesions are associated with several systemic or local infections caused by micro-organisms. These include primary and secondary viral pathological lesions associated with oral infective diseases and dysplastic-neoplastic transformations . These oCOVID-19 infection is associated with oral lesions such as gustatory impairment either due to the effect of the virus on oral tissues or as secondary manifestations resulting from it . Other iThe oral lesions associated with COVID-19 infection have been reported in adults. There are, however, differences in the clinical manifestations of COVID-19 in children compared to adults , 6 whichThis was a secondary analysis of data from cross-sectional study conducted between August 2020 to January 2021. The aim of the primary study was to assess the change in use of regular and electronic cigarettes during the COVID-19 pandemic among AYA .Data was collected from 11 to 23-year old participants recruited through electronic data collection from various countries. Participants were included if they/ their parents consented to participate, if they could read and understand the study tool and if they could access the survey on an electronic device using internet access.. Ethical approval was sought from the Research Ethics Committee of the Faculty of Dentistry, King Abdulaziz University in Saudi Arabia #13–08-20) and Nigeria (IPH/OAU/12/1604). The study was carried out according to the Helsinki Declaration [-20 and NSample size of the original study was determined using the frequency of using regular and electronic cigarettes. This was estimated to range from 4.8 to 36.6% . The samThe administered questionnaire began by explaining the purpose of the study, assuring participant of the confidentiality of their responses, and the freedom to withdraw from the survey at any time. The questionnaire consisted of three sections: The first section collected data on the participant including age , sex at birth  and educational level  as well as on the mother’s and father’s educational level .The second section assessed whether the participant had a history of medical problems (yes/ no) and a history of COVID-19 infection (yes/ no). These questions were adapted from previous research .The third section had participants check a list of possible oral lesions they may have had during the pandemic. A tick of the checkbox indicated that they had the oral lesion(s). Study participants could check multiple responses. These lesions/conditions included gingival inflammation, dry mouth, change in taste, and oral ulcers. These conditions were based on those previously reported to be associated with COVID-19 in adults or older adults .The questionnaire was developed in Arabic and translated by native speakers into French, Malay, Turkish and English. The content validity index (CVI) was calculated for the translated versions . For thep value for the interaction between these terms (age and COVID-19 infection and sex and COVID-19 infection) in the regression models. Adjusted odds ratio (AORs), 95% confidence interval (CIs) and p-values were calculated. Statistical significance was set at p-value less than 0.05.Descriptive statistics as frequencies and percentages were calculated for the study variables. The associations between the presence of oral conditions and COVID-19 infection were assessed using the Chi-square test. Multilevel binary logistic regression modelling using IBM SPSS for Windows version 22.0  was used to assess the associations between the presence of oral conditions including gingival inflammation, dry mouth, change in taste and oral ulcers (dependent variable) and COVID-19 infection (independent variable) controlling for confounders  which were all introduced as fixed effect variables whereas countries were introduced as random effect variables to account for the variability among countries in the dependent variables caused by various factors such as country income level, dentist availability, healthcare systems characteristics and others. The interaction between COVID-19 infection and age and sex respectively was also assessed by calculating the Complete data were available for 7164 participants from 43 countries. Table Table p = 0.008), change in taste  and oral ulcers . A significantly higher percentage of participants with no history of COVID-19 infection than those with an infection reported having no oral conditions during the pandemic .A significantly higher percentage of participants with COVID-19 than those who had no COVID-19 infection reported having dry mouth  and change in taste .Table p = 0.02). Age also significantly modified the association between change in taste and COVID-19 infection: 15–17-year-olds had higher odds of having a change in taste associated with COVID-19 infection than 18–23-year-olds and 11–14-year-olds .Age significantly modified the association between COVID-19 infection and the presence of dry mouth: AYA 18 to 23-year-olds had significantly lower odds of having dry mouth associated with COVID-19 infection than 15 to17-year-olds and 11 to14-years-olds . Sex also modified the association between dry mouth and COVID-19 infection: males had higher odds of having an association than females . In addition, sex modified the association between change in taste and COVID-19 infection: females had higher odds of this association than males .Sex significantly modified the association between the presence of gingival inflammation and COVID-19 infection: males had significantly higher odds of the association than females (AOR = 1.30 vs 1.09; The findings showed that about 4 in 10 AYA reported oral conditions during the pandemic with change in taste, dry mouth and oral ulcers positively associated with COVID-19 infection. The associations of COVID-19 infection with dry mouth and change in taste were modified by age: there was a stronger association between COVID-19 infection, dry mouth and change in taste among 15–17-year-old participants than among other age groups. Sex also modified the associations of COVID-19 infection with dry mouth and change in taste: males had higher odds for having dry mouth than females, while females had higher odds of having a change in taste than males. The null hypothesis of the study was, therefore, not supported.One of the strengths of the study was that we provided information on the possible oral features of COVID-19 infection in AYA aged 11 to 23 years; and identified how sex and age modified these associations. Also, the study recruited a large sample of participants from countries representing various income-levels and regions thereby increasing the generalizability and applicability of findings to various settings. The study, however, has some limitations. The oral conditions were self-reported thereby introducing subjectivity to the reporting with the risk of under-estimation of lesions like gingival inflammation and dry mouth. Although a clinical examination might be considered more objective, it was not possible to conduct clinical assessment of study participants because of the restrictions with contacts during the pandemic. This was the case in most of studies assessing oral lesions during the COVID-19 pandemic . We, addThe association between COVID-19 infection and change in taste and dry mouth had been reported and explained in previous studies –16. HoweThe association between COVID-19 infection and gingival inflammation and dry mouth was stronger in males than females in the present study. While a male predilection was noticed in the current study, previous research showed that these symptoms were more frequent in females than males , 25–27. In the present study, COVID-19 infection had the strongest association with altered taste in 15–17-year-olds and the oldest age group of 18–23-year-old showed the weakest association between COVID-19 infection and reporting dry mouth. Direct comparison with previous studies is difficult because these studies were either case-reports or were conducted in adults , 30. HowParticipants who had COVID-19 infection were more likely to report change in taste, dry mouth and oral ulcers. Sex and age moderated the associations between COVID-19 infection, dry mouth and change in taste in AYA. These findings may be useful for screening for COVID-19 infection among AYA.Additional file 1.Additional file 2.


I values for notified BP-PTB ranged from 0.19 to 0.49 (P < 0.001). Anselin’s local Moran’s I analysis showed that the high–high (HH) cluster for notified BP-PTB incidence was mainly located in the southernmost region. The primary spatiotemporal cluster for notified BP-PTB  occurred in the central region of the Guanzhong Plain in 2011. In total, 116,447 BN-PTB cases were notified, with an average annual incidence rate of 38.38 per 100,000 persons between 2011 and 2018. Global Moran’s I values for notified BN-PTB ranged from 0.39 to 0.69 (P < 0.001). The HH clusters of notified BN-PTB were mainly located in the north between 2011 and 2014 and in the south after 2015. The primary spatiotemporal cluster for notified BN-PTB  occurred in the mountainous areas of the southernmost region from 2014 to 2017. Spatiotemporal clustering of BP-PTB and BN-PTB was detected in the poverty-stricken mountainous areas of Shaanxi, Northwest China. Our study provides evidence for intensifying PTB control activities in these geographical clusters.Pulmonary tuberculosis (PTB) is a major health issue in Northwest China. Most previous studies on the spatiotemporal patterns of PTB considered all PTB cases as a whole; they did not distinguish notified bacteriologically positive PTB (BP-PTB) and notified bacteriologically negative PTB (BN-PTB). Thus, the spatiotemporal characteristics of notified BP-PTB and BN-PTB are still unclear. A retrospective county-level spatial epidemiological study (2011–2018) was conducted in Shaanxi, Northwest China. In total, 44,894 BP-PTB cases were notified, with an average annual incidence rate of 14.80 per 100,000 persons between 2011 and 2018. Global Moran’s  China had the second largest burden of new TB cases in the world in 2020, with an estimated 842,000 new cases, accounting for 8.5% of the global incidence1. According to the Fifth Chinese Epidemiological Survey in 2010, the prevalence of active PTB and bacteriologically positive (BP-PTB) in West China was 695 per 100,000 persons and 198 per 100,000 persons, respectively, which are higher than those for Central and East China2. The northwest region of West China is regarded as a high-risk area for PTB3. Thus, the identification of PTB epidemic characteristics in this area and promotion of targeted control is urgent5.Pulmonary tuberculosis (PTB) is a major public health problem, with an estimated 9.87 million new cases worldwide in 20208. The global Moran’s I test is used most frequently to assess the presence of an overall area-wide spatial autocorrelation. The local Moran’s I is used to detect the location and types of spatial clusters9. In addition, Kulldorff’s space–time scan statistic is the most commonly employed method to detect spatiotemporal clusters10. Due to their different methods of testing, areas identified as hotspots by different methods are not identical. Increasingly, the use of multiple cluster analysis methods is recommended and their overlap is used to identify a truly high-risk area10.Spatiotemporal cluster analysis has been applied to detect infectious disease hotspots in recent years12. BP-PTB is the most infectious type of PTB and even contributes to outbreaks in crowds. It includes smear-positive PTB and sputum smear-negative but culture-positive PTB13. A few studies have examined the spatiotemporal characteristics of smear-positive PTB14. However, they did not analyse the spatiotemporal characteristics of sputum smear-negative but culture-positive PTB. Similar to sputum smear-positive cases, sputum culture-positive cases are treated as confirmed cases and have the same treatment protocol and cure criteria as sputum smear-positive cases. Combining smear-positive PTB and sputum smear-negative but culture-positive PTB to analyse the spatiotemporal nature of notified BP-PTB would facilitate unified resource allocation.Most of the previous studies on the spatiotemporal patterns of PTB only considered all PTB cases as a whole and did not distinguish notified BP-PTB and notified bacteriologically negative PTB (BN-PTB)15. However, it is difficult to diagnose. Additionally, half of BN-PTB cases are predicted to convert to BP-PTB without treatment16, which increases the difficulty of ending this epidemic. Therefore, proper diagnosis and early treatment are essential for BN-PTB. Information on the spatiotemporal clustering of notified BN-PTB would facilitate early detection and active case identification in high-risk regions.Although BN-PTB is less infectious than BP-PTB, it accounts for 50–70% of all PTB cases17. An epidemiological characteristics analysis found that notified BP-PTB and notified BN-PTB incidence exhibited spatial and temporal heterogeneity3. In the current study, we conducted a case study in Shaanxi to explore the spatiotemporal patterns of notified BP-PTB and notified BN-PTB. Using county-level notification data between 2011 and 2018, we carried out spatial variation pattern analyses, spatial autocorrelation analyses and space–time scans. The aim of this study was to clarify the spatiotemporal distribution characteristics of notified BP-PTB and notified BN-PTB in Northwest China, thus providing evidence to inform interventions and improve PTB control.Shaanxi is a less-developed area of Northwest China that has a serious PTB epidemic. In 2018, approximately 21,442 PTB cases were reported in Shaanxi, with an overall notification rate of 55.49 cases per 100,000 individuals2, with mountains and loess plateaus constituting 76% of the territory. The altitude varies tremendously, ranging from 170 to 3,767 m. The nominal gross domestic product (GDP) in 2019 was 2.58 trillion RMB (US$ 399.79 billion). The GDP per capita in 2019 was 66,649 RMB 18, which was lower than the national average19.Shaanxi  is a province in Northwest China, with 108 counties. It covers an area of 205,624 km16. PTB cases were diagnosed through pathogen detection, X-rays, and pathologic diagnoses according to the National TB Diagnosis and Treatment Guideline of China. PTB case information from 1 January 2011 to 31 December 2018, including patient home address, date of diagnosis, smear microscopy results, and diagnosis type, was acquired from the Shaanxi Centre for Disease Control and Prevention. Four types of PTB cases were diagnosed: sputum smear-positive PTB, sputum smear-negative PTB, sputum smear-negative but culture-positive PTB, and PTB without a sputum smear test. Notified BP-PTB cases included sputum smear-positive PTB and sputum smear-negative but culture-positive PTB. Since 1 July 2017, rifampicin-resistant tuberculosis (RR-TB) information has been added to the national infectious disease surveillance system; thus, notified BP-PTB in 2017 and 2018 also included RR-TB cases. PTB cases without sputum smear tests mainly refer to patients who had slight symptoms and did not cough up sputum. Population data were obtained from the Shaanxi Statistical Yearbook (2011–2018).PTB is listed as a class B notifiable infectious disease in China. Medical institutions are required to report PTB cases to the China Centres for Disease Control and Prevention (CCDC) via the National Notifiable Infectious Diseases Reporting Information System (NIDRS)Mycobacterium tuberculosis antibody test; or (5) abnormalities consistent with active PTB on chest radiography and TB lesions confirmed by extra-pulmonary histopathological examination20.Notified BN-PTB cases refer to clinically diagnosed patients whose sputum smear and culture were both negative. According to the National TB Diagnosis and Treatment Guidelines of China, a case is diagnosed as BN-PTB after other lung diseases have been excluded by differential diagnosis and one of the following criterion is met: (1) abnormalities consistent with active PTB on chest radiography and clinical features of PTB; (2) abnormalities consistent with active PTB on chest radiography and moderately positive or strongly positive tuberculin test; (3) abnormalities consistent with active PTB on chest radiography and positive interferon-gamma release; (4) abnormalities consistent with active PTB on chest radiography and a positive 21. The diagnosis of BN-PTB was made by a team of physicians from the respiratory, laboratory and radiology departments. Doctors made the diagnosis according to the uniform criteria mentioned above. Therefore, there was generally no bias. Furthermore, specialists in PTB from provincial or municipal hospitals appointed by the provincial centre for disease control and prevention regularly reviewed and assessed medical records of BN-PTB cases diagnosed at county hospitals13. The checklist included information about symptoms, diagnosis  examinations, immunological examinations, and differential diagnosis information), and treatment regimens. During the monitoring process, misdiagnosis of BN-PTB cases was promptly identified and corrected. Therefore, bias was minimised as much as possible. In addition, PTB cases were notified according to their current address instead of the address of the hospital where they were diagnosed12. The geographical distribution of PTB cases was not affected by the location of doctors who attended them.A range of measures was employed to ensure the diagnostic quality of BN-PTB cases. First, BN-PTB cases were diagnosed and treated according to the National TB Diagnosis and Treatment Guidelines of China, and these diagnostic criteria for BN-PTB are consistent with that of the World Health Organization (WHO)To protect the privacy of patients, the names of patients were anonymized and numbered. Then, the information of each patient was sorted. The current address information in the PTB case information database was collated, and the county to which each patient's current address belonged was extracted. A total of 149 cases were eliminated due to incomplete or untraceable addresses.Descriptive analysisThe age-specific incidence rate of notified BP-PTB and notified BN-PTB was calculated. A line chart of monthly cases was constructed to show the temporal distribution of notified BP-PTB cases and notified BN-PTB cases. The annual incidence rates of notified BP-PTB and notified BN-PTB were calculated. Then, the annual incidence was linked to each county on a digital map of Shaanxi to display the spatial variation.Spatial autocorrelation analysisI was calculated to examine the spatial autocorrelation of notified BP-PTB and notified BN-PTB in Shaanxi. Moran's I is calculated based on the deviation from the means of two neighbouring values22. The value of Moran’s I ranges between − 1 (the maximum negative association) and 1 (the maximum positive association). A high positive value indicates a strong spatial autocorrelation, and vice versa for a negative value23. The significance of the estimated Moran’s I was examined by the Z test . The PTB incidence was regarded as statistically spatially clustered when the Z score was greater than or equal to 1.969. The global Moran’s I was calculated to examine the overall spatial autocorrelation. Anselin’s local Moran’s I was used to assess the local spatial autocorrelation and determine the locations of the clusters. The high–high (HH), low–low (LL), and outlying local cluster (high–low (HL) and low–high (LH)) locations were visualized with local indicators of a spatial association (LISA) cluster map.Moran’s Spatiotemporal cluster analysisRRs) and log-likelihood ratios (LLRs) were calculated to identify the primary cluster and the secondary clusters24. The maximum length of the cluster radii was set to 100 km. A similar method of setting cluster radii was used in another spatiotemporal cluster study25. The criterion for reporting secondary clusters was no geographical overlap.To identify geographic areas and time periods with high BP-PTB and BN-PTB incidence, space–time scan statistics for notified BP-PTB cases and notified BN-PTB cases were performed. SaTScan  was used, and a discrete Poisson regression model was constructed. The scan window was a cylinder, with a circular geographical base and height corresponding to time. The mean relative risks  was used for data management and demographic characteristic analyses. GeoDa  was used to create the incidence map, calculate Moran’s The research protocol was approved by the Institutional Review Board of Xi’an Jiaotong University Health Science Centre. This study was performed in accordance with the Declaration of Helsinki. PTB data were collected through routine TB surveillance activities. Personal information, such as the patient’s name and identity number, was not required. This study only analysed aggregated data at the county level. Therefore, the need for written informed consent was waived.A total of 180,512 PTB cases were included in this study. The numbers of notified BP-PTB cases and BN-PTB cases were 44,894 and 116,447, respectively. The average notified annual incidence rates of PTB, BP-PTB and BN-PTB were 59.50 per 100,000 persons, 14.80 per 100,000 persons, and 38.38 per 100,000 persons between 2011 and 2018, respectively. The notified incidence rates of BP-PTB and BN-PTB between 2011 and 2018 are presented in Table Both the notified incidence rates of BP-PTB and BN-PTB showed an overall increasing trend with age, peaking in the 80–84-year-old group. The one exception was that the notified peak of BN-PTB for females occurred in the 75–80-year-old group. In addition, the notified incidence rates of BP-PTB and BN-PTB in men were higher than those in women in all age groups Fig. .Figure 1As shown in Fig. The spatial distributions of the notified incidence rates of BP-PTB and BN-PTB cases at the county level are shown in Figs. I > 0) in the notified incidence rate of BP-PTB and BN-PTB were identified between 2011 and 2018  was located in the central plain in 2011. It involved thirty-nine counties and 4,885 cases, with an RR of 1.77. The coordinates of the cluster centre are 34°71'N and 108°94'E. Cluster A2 was mainly distributed in the middle of the southern region from 2011 to 2012. This cluster included 2,077 cases, with an RR of 1.94. Other clusters were located in the northern, southwestern and southeastern regions . This cluster occurred from 2014 to 2017, with an RR of 1.33. More details on the other clusters are displayed in Table The space–time scan statistics for notified BN-PTB cases identified 13 significant clusters Fig. . The priThis study explored the spatiotemporal patterns of notified BP-PTB and BN-PTB cases using advanced spatiotemporal statistics in Shaanxi, Northwest China. Clusters of both notified BP-PTB and BN-PTB were located in the mountainous areas in the southernmost region. This study also found that the primary spatiotemporal clusters of notified BP-PTB and BN-PTB were located in different regions. This difference was partly due to an early detection policy. Our study provides evidence for intensifying PTB control activities in these geographical clusters.26. The implementation of this policy and subsequent interventions were effective, and therefore the incidence rate decreased between 2012 and 2016. The significant upwards trend in the BP-PTB incidence after 2017 was attributable to improved laboratory testing capacity, which is mainly due to the availability of molecular detection techniques. The proportion of designated hospitals capable of performing sputum culture and those performing Xpert Mycobacterium tuberculosis/rifampicin (MTB/RIF) assays were 55.56% (60/108) and 49.07% (53/108) in 2017, respectively, compared to 12.04% (13/108) and 5.56% (6/108) in 2014, respectively27. Other molecular detection techniques, such as loop-mediated isothermal amplification (LAMP) and cross-priming amplification (CPA), were also applied. The molecular detection techniques are more accurate than traditional sputum smears in diagnosing BP-PTB29, thus increasing the rate of BP-PTB cases detected. Generally, January was one of the months with the highest number of notified BP-PTB cases. Given the impact of improved detection techniques, a peak in patients occurred in January 2018.The fluctuations in the incidence rate of BP-PTB were caused by the early detection policy and the application of advanced testing techniques. The early detection policy established in the twelfth Five-Year (2011–2015) National TB Integrated Control Plan, which was initiated in 2011, facilitated the detection of BP-PTB31. Vitamin D deficiency due to reduced sunlight exposure, increased transmission due to household crowding and poorer ventilation in winter could increase PTB infection33. In chronic infectious diseases, time is needed for the symptoms to develop and become evident. In addition, there is a delay between the onset of symptoms in patients and the care-seeking period34. Moreover, avoiding healthcare appointments during the Chinese Lunar New Year festival is traditional and likely contributed to the lower number of reported cases in February compared with that in January and March. Furthermore, undetected cases persisting from February may increase the number of cases identified in March. All these factors contribute to the notification peak that occurs in early spring.The notification peaks of BP-PTB and BN-PTB mainly occurred in March. Similar temporal trends have been reported in other studiesI was higher in notified BN-PTB cases than in BP-PTB cases, indicating that the spatial autocorrelation of BN-PTB was stronger than that of BP-PTB. Wubuli also found a similar phenomenon; specifically, Moran’s I of smear-negative PTB was higher than that of smear-positive PTB between 2011 and 2013 in Xinjiang, China16. Furthermore, the global Moran’s I of BN-PTB showed a significant increasing trend, which indicated escalated regional differences for BN-PTB. The expanded LL clusters indicated effective BN-PTB control. However, the HH clusters of notified BN-PTB remained relatively stable, indicating that it is difficult to control the BN-PTB epidemic in these areas. Therefore, the HH clusters should be considered priority regions in the future.In every year, the global Moran's 26. The primary cluster of notified BN-PTB cases was characterized by relatively low socioeconomic status and limited geographic access to health care. This cluster was located deep in the mountainous areas, and five of the eight counties were considered high-poverty counties. In addition, Cluster B2 was located in the eastern region of the north. This area is more mountainous and experienced slower economic development than the western region of the north, which may have led to it becoming a high-risk area. TB is common in poor and deprived populations36. Barriers to public transportation and care deep in the mountains also delayed care seeking and diagnosis37. Efforts to mitigate poverty and enhance healthcare services, as well as measures to improve living conditions, should be promoted to eliminate TB38.The early detection policy in 2011 facilitated detection of the primary cluster (Cluster A1) for notified BP-PTB. Cluster A1 was located in a well-developed region with a high population density. Case detection was improved due to advanced healthcare services and adequate human resources, thus forming the primary cluster. A study in Yunnan also suggested that early case identification during the implementation of the national PTB control plan facilitated BP-PTB case detectionDue to changes in PTB control policies and the application of advanced testing techniques, the spatial and temporal characteristics of BP-PTB and BN-PTB appear inconsistent. However, the second cluster of BP-PTB (Cluster A2) was located in the mountainous areas of the southernmost region. Thus, the cluster location was mostly consistent with the primary BN-PTB cluster. According to the different spatial analyses, the high epidemic rates of BP-PTB and BN-PTB in the southernmost region and the eastern region of the north were stable during the study period.40.Easy access to public transportation and prosperous trade facilitate contact with infectious patients and thus favour transmission across provincial boundaries. Located in a hub of commercial activities, Cluster B6 bordered three provinces with a high TB burden. Individuals frequently crossed the border from these provinces to Shaanxi for economic and social reasons, which could contribute to maintenance of the long-term epidemic41.Cluster B8 (Beilin district) occurred in a major business centre with a high education base in Xi’an city, the provincial capital. High infection rates among migrants and college students contributed to this cluster. There were approximately 12,000 college students per year in the cluster. The proportion of college students in the cluster was 14%, which was almost twice the average proportion (7.19%) of college students among the entire population in Xi’an city. Other clusters that lasted for short periods, from one month to four months, were associated with occasional screenings or active case detections38. Along with the improved economic situation, people have become more aware of health care and are more likely to take the initiative to seek medical treatment for their illnesses, which leads to early detection and treatment, thus reducing the spread of PTB. Second, because the epidemic in the northern region has been more serious, PTB control has received increasing attention by the local government, including PTB screening for key groups such as elderly individuals and close contacts of PTB patients and students12. The local government also provides financial support for PTB screening and treatment. All these PTB prevention and treatment efforts contributed to the decline in PTB incidence in the north.Overall, both notified BP-PTB and BN-PTB epidemics in the northern region showed a decreasing trend during the study period. The following facts may explain these changes. First, the rapid economic development in the northern region, especially in the northwest, has led to a decline in the epidemic due to the improvement of living standards and better housing and nutritional conditionsThis was the first case study to explore the spatiotemporal patterns of notified BP-PTB and BN-PTB at the county level in Northwest China. Multiple spatial cluster analyses were explored to determine the cluster times, locations, and relative risks of high-risk areas. Poor mountainous areas have a heavy burden of notified BP-PTB and BN-PTB, and these individuals have poor access to health services. Therefore, the intensification of PTB control activities among high-risk populations in this area should receive more attention.This study also had some limitations. First, overdiagnosis or misdiagnosis of BN-PTB is an issue. However, due to the lack of an aetiological gold standard, it is difficult to avoid the overdiagnosis or misdiagnosis of BN-PTB. Second, our results indicated that socioeconomic development and the application of molecular detection techniques may be related to the notified incidence rate of BP-PTB and BN-PTB. The associations between various ecological factors and PTB clusters will be explored in future studies. Third, BP-PTB and BN-PTB incidences may be underestimated because this study did not include cases that were not reported in the official system. Fourth, the cylindrical window of the space–time scan statistics  did not allow for irregular cluster shapes.This study reported notable spatial and temporal heterogeneity in notified BP-PTB and BN-PTB incidences across Shaanxi, Northwest China. The primary cluster of notified BP-PTB was located in the central plain in 2011. The primary cluster of notified BN-PTB was located in the mountainous areas of the southernmost region from 2014 to 2017. Our study provides evidence for intensifying PTB control activities in these geographical clusters. Further study is needed to explore the association between various ecological factors and the detected spatiotemporal distribution of BP-PTB and BN-PTB.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Opioid-related mortality continues to rise across North America, and mortality rates have been further exacerbated by the COVID-19 pandemic. This study sought to provide an updated picture of trends of opioid-related mortality for Ontario, Canada between January 2003 and December 2020, in relation to age and sex.Using mortality data from the Office of the Chief Coroner for Ontario, we applied Bayesian Poisson regression to model age/sex mortality per 100,000 person-years, including random walks to flexibly capture age and time effects. Models were also used to explore how trends might continue into 2022, considering both pre- and post-COVID-19 courses.From 2003 to 2020, there were 11,633 opioid-related deaths in Ontario. A shift in the age distribution of mortality was observed, with the greatest mortality rates now among younger individuals. In 2003, mortality rates reached maximums at 5.5 deaths per 100,000 person-years  for males around age 44 and 2.2 deaths per 100,000 person-years (95% CI: 1.5–3.2) for females around age 51. As of 2020, rates have reached maximums at 67.2 deaths per 100,000 person-years (95% CI: 55.3–81.5) for males around age 35 and 16.8 deaths per 100,000 person-years (95% CI: 12.8–22.0) for females around age 37. Our models estimate that opioid-related mortality among the younger population will continue to grow, and that current conditions could lead to male mortality rates that are more than quadruple those of pre-pandemic estimations.This analysis may inform a refocusing of public health strategy for reducing rising rates of opioid-related mortality, including effectively reaching both older and younger males, as well as young females, with health and social supports such as treatment and harm reduction measures. Opioid-related mortality remains a crucial public health issue in North America, which has been further exacerbated by the ongoing COVID-19 pandemic. In the United States, the number of opioid-related deaths nearly quadrupled between 2003 and 2019 . OntarioLarge increases in morbidity and mortality involving opioid drugs were initially attributed to over-prescribing of opioids for pain . As presThis study sought to provide an updated picture of how opioid-related mortality has changed in Ontario between 2003 and 2020, in relation to age and sex, through an application of novel Bayesian modelling. Previous studies have been limited by modelling age in groups; however, binning data into age groups leads to the creation of artificial age breaks, to which the results can be sensitive, especially when mortality distribution by age changes over time. In addition, age groups can mask important trends and features in the data, such as peaks or troughs in mortality rates for particular ages. Our Bayesian modelling approach is able to utilize continuous age data that are sparse, allowing flexible patterns for age-time interaction by sex. We also used the model to explore how mortality trends might continue into 2022, considering both pre-COVID-19 and post-COVID-19 courses, to provide 20-year pictures of the opioid mortality crisis in Ontario. These results may provide insight for tailored public health interventions to more effectively reduce rising rates of opioid-related mortality in Ontario or other demographically similar regions.Data on opioid-related mortality in Ontario between January 2003 and December 2020 was obtained from the Office of the Chief Coroner. Following the Ontario Coroners Act, all deaths from non-natural causes are investigated by physicians at the Office of the Chief Coroner. The cause and manner of death are determined by the death investigation and a post-mortem examination by a pathologist, informed by interpretation of toxicology testing , 21, 25.Only accidental opioid-related deaths were included in the cohort as they represented the vast majority of deaths, and interventions to reduce mortality may differ for accidental versus other manners of death . We furtUse of these data was approved in writing by the research ethics board at Public Health Ontario (ID: 2017–065.01). The de-identified data were transferred to Public Health Ontario in SAS dataset format (.sas7bdat) through a secured Microsoft SharePoint site that only select project members had access to. The analysis was done using R v.4.11 and results were written back out to the secure SharePoint site.We applied Bayesian mixed-effects Poisson regression to model opioid-related mortality over time and by age, with person-years (derived from population estimates) accounted for as an offset in the model. The time trend was modelled as changing monthly , and the age effect varying by single-year age groups , with random walk models encouraging the time trend and age effect to vary smoothly. Although monthly mortality counts were often zero for some ages, the random walk models borrow strength across nearby ages and time points to produce smooth risk estimates when counts are low. The interaction of age and time was modelled as a two-dimensional random walk, which allowed mortality rates to change more quickly for some ages than others in a smooth manner . The smoothness or roughness of the age and time effects, as well as their interaction, is governed by the variance parameters, which were estimated from the data . If the To visualize the results, estimated rates were plotted by age over time for each sex using 2D heat maps as an overview. In order to better understand how mortality changed over time, estimated mortality distributions over age were plotted for selected years. We further plotted relative risk over time for selected ages to understand how the rate of increase in mortality differed by age for each sex, and this information was also summarized using 2D interaction heat maps.We then used the model to explore mortality trends up to December 31, 2022 in order to examine how opioid-related mortality might change over the next 2 years. This exploration was done assuming trends will continue without disturbance, meaning that all parameters estimated from the model using the observed data would remain constant except for time. To assess the impact of the COVID-19 pandemic on mortality rates, we re-fit the model to pre-pandemic data only (January 2003 to February 2020), and used the model to explore mortality up to December 31, 2022 again as comparison.During the 18-year study period, there were a total of 11,633 opioid-related deaths that met our inclusion criteria, and 72% were male . AccidenWe plotted modelled mortality rates against observed mortality rates in order to visualize the model fit. In the face of sparse data, we plotted the model estimates for a given age and sex overlaid with observed mortality rates for the same sex, but instead for the 5-year age group surrounding the given age and at a lower temporal resolution by binning the time at 3-month intervals. An example of the model fit for males aged 35  can be found in exp[Xtβi + Ui(a) + Vi(t) + Wi + Zit] in the Opioid-related mortality rates have increased substantially for both sexes and across all ages over time, shown by the colour gradation in We also observed spikes in mortality in late-2017 to early-2019, as shown through dark patches in the heat maps of The age distribution for mortality has shifted over time for both sexes. In order to better visualize these shifts, cross-sections of exp[Wi] in the exp[Vi(t) + Wi + Zit − Zi1] the To understand how temporal changes in mortality differ by age, in Based on the observed data and resulting parameters from the models, our models estimate that opioid-related mortality among the younger population will continue to grow. Specifically, age distributions of mortality rates are expected to become less bimodal, and instead peak among individuals in the 25 to 44-year age group, assuming trends continue without disturbance. This was observed for both sexes, as well as both excluding  and inclFrom the models we also estimate based on pre-pandemic data that relative rates for both males and females might have remained relatively stable over the next 2 years , as mortality records from the cohort are more likely to have missing or inaccurate residential information compared to mortality records from the general population. Data availability also rendered us unable to examine simultaneous trends in opioid use disorder by age, sex and time in Ontario, which may have provide further insights into the opioid-related mortality trends we observed , 38. SecThis analysis may inform a refocusing of public health strategy to address the opioid overdose crisis—programs and policies for reducing opioid-related mortality should take into consideration its changing age distribution. Targeting individuals in the 25 to 44-year age group is of greater importance now than was the case previously, thus strategies should be developed to effectively reach younger adults with health and social supports such as harm reduction measures and treatment for opioid use disorder. Older males in the 55 to 69-year age group experienced relative increases in opioid-related mortality rates greater than that of their younger counterparts, thus they also represent an important cohort and may require alternate strategies tailored to their age group.S1 Fig(a) Males. (b) Females.(ZIP)Click here for additional data file.S2 Fig(a) random walk on age. (b) iid random effect for time. (c) random walk on time. (d) two-dimensional random walk for age-time interaction. Note: red dashed lines = priors; black solid lines = posterior distributions of standard deviations.(ZIP)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.


To test the main hypothesis that anticoagulation reduces risk of hospitalization, intensive care unit (ICU) admission and death in COVID-19. Nested case–control study among patients with atrial fibrillation (AF) in Stockholm. COVID-19 cases were matched to five disease-free controls with same sex, born within ± 1 years. Source population was individuals in Stockholm with AF 1997–2020. Swedish regional and national registers are used. National registers cover hospitals and outpatient clinics, local registers cover primary care. Records were linked through the personal identity number assigned to each Swedish resident. Cases were individuals with COVID-19 . The AF source population consisted of 179,381 individuals from which 7548 cases were identified together with 37,145 controls. The number of cases (controls) identified from hospitalization, ICU admission or death were 5916 , 160 (750) and 1472 . The proportion of women was 40% for hospitalization and death, but 20% and 30% for admission to ICU in wave one and two, respectively. Primary outcome was mortality, secondary outcome was hospitalization, tertiary outcome was ICU admission, all with COVID-19. Odds ratios  for antithrombotics were 0.79 (0.66–0.95) for the first wave and 0.80 (0.64–1.01) for the second wave. Use of anticoagulation among patients with arrythmias infected with COVID-19 is associated with lower risk of hospitalization and death. If further COVID-variants emerge, or other infections with prothrombotic properties, this emphasize need for physicians to ensure compliance among vulnerable patients.The online version contains supplementary material available at 10.1007/s10654-023-00967-9. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes Coronavirus disease 2019 (COVID-19). Since 31 December 2019 until today (December 2022) over 650 million cases and approximately 6.5 million deaths have been reported and it continues to be a global health threat. The Stockholm Region in Sweden was hit hard by the pandemic with a surge in cases at the end of March and beginning of April of 2020.The infection principally causes respiratory symptoms, ranging from intermittent coughing, via dyspnea, to life threatening acute respiratory distress syndrome . AdditioNaturally, speculation as to if pre-medication with oral anticoagulants (OAC) is beneficial for patients later infected with SARS-CoV-2 has been prevalent. Results from investigations vary, the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) registry found oral anticoagulants to be associated with lower risk of death and other outcomes . In contAlso, diabetes/antidiabetics –13, cardIn patients with pre-existing atrial fibrillation (AF) we performed a nested case–control study, testing the main hypothesis that oral anticoagulation as a class would confer a reduced risk of COVID-19 as measured by hospitalization, ICU admission or death in individuals with AF. We also tested secondary hypotheses of altered COVID-19 risks for other drug classes and comorbidities. All hypotheses were tested pooled and by wave for the first two COVID-19 waves in Sweden.A case–control study design was chosen since it allows for investigation of several risk factors and is suitable for establishing associations within new fields . This neThe Stockholm regional healthcare data warehouse (VAL-database), containing information from both hospitals and outpatient clinics on preexisting diagnoses according to the International Classification of Diseases (ICD) and Swedish codes (KVÅ) for procedures.The Swedish Prescribed Drug Register (PDR) on filled prescriptions coded by anatomical therapeutic classification (ATC) codes [C) codes .The Swedish National Patient register (NPR), was used in addition to the VAL-database. It too has information from hospitals and outpatient clinics, with ICD codes regarding preexisting diagnoses and KVÅ-codes for procedures.The Swedish Cause of Death Register (SCR) allowed us to identify COVID-19 related deaths [d deaths .The Swedish intensive care register (SIR) was used to track ICU admissions.SmiNet [SmiNet —a nationRegisters and records were linked through the unique personal identity number assigned to each Swedish resident at birth or immigration .The source population (nest) consisted of all individuals with a recorded diagnosis of atrial fibrillation  between January 1st 1997 and December 31st 2020 residing in the Stockholm region.All individuals recorded with a diagnosis of COVID-19  or a COVID-19 related procedure (KVÅ: ZV100) together with either actions concerning notifiable communicable diseases , admission to inpatient care (KVÅ: XS100) or oxygen treatment  in NPR or the VAL-database, recorded as treated in intensive care for COVID-19 in SIR or recorded as dead with COVID-19  as underlying or contributory cause in SCR were selected as cases for hospitalization, ICU admission or death, respectively.before their index date.For COVID-19 hospitalization the first date retrieved from NPR or the VAL-database was used as index date. For COVID-19 in intensive care the first date recorded in SIR was identified as index date. For COVID-19 deaths the date of death was the index date. Only cases with a history of AF were included, i.e. only cases who had an AF-diagnosis recorded Controls were matched to cases by sex and age. Up to five controls  per caseThe primary outcome was mortality with confirmed COVID-19 infection. The secondary outcome was hospitalization, and the tertiary outcome was intensive care unit (ICU) admission with confirmed COVID-19 infection.Exposure was defined via ATC-codes from filled prescriptions. The included exposures were the drug classes (ATC-code): drugs used in diabetes (A10), antithrombotic agents (B01), cardiac therapy (C01), antihypertensives (C02), diuretics (C03), beta blocking agents (C07), calcium channel blockers (C08), agents acting on the renin-angiotensin system (C09), lipid modifying agents (C10), sex hormones and modulators of the genital system (G03), corticosteroids for systemic use (H02), and endocrine therapy (L02). For a complete list of drugs included in each class, see Supplementary table 1. An individual was assumed to be exposed to a drug class if they had supply of a drug within that class at the index date. Supply was assessed from number of filled defined daily doses (DDDs) plus a grace period of 30 days for the prescription filled closest before the index date. The DDD is defined by WHO as the assumed average maintenance dose per day for a drug used for its main indication in adults .Each outcome  was modelled separately by two COVID-19 waves and pooled using conditional logistic regression adjusted for all exposures and identified confounders. The COVID-19 waves were defined as first wave between January 1st 2020 and August 31st 2020, second wave between September 1st 2020 and March 31st 2021. It was important to analyze the two pandemic waves separately due to the massive surge during the first one. Confounders included: ischemic heart disease, heart failure/cardiomyopathy, valve disorder, ischemic stroke/ transient ischemic attack (TIA)/systemic thromboembolism, hemorrhagic/unspecified stroke, other vascular disease, arrhythmia (other than AF/flutter), lung disease, renal disease, liver disease, venous thromboembolism, and malignancies . See SupNo patients were actively involved or asked for advice in the current study.The source population consisted of 179,381 individuals from which 7548 cases with COVID-19 overall were identified together with 37,145 matched controls. The number of included cases (controls) for hospital admission, ICU admission and death were 5916 , 160 (750) and 1472 (7360), respectively. Figure Table Figure In this nested case–control study from the Stockholm Region the use of antithrombotic drugs was associated with lower risk of hospitalization, ICU admission and death during infection with SARS-CoV-2 in patients with arrythmias.We hypothesized that anticoagulation reduces risk of adverse outcomes, measured by risk of hospitalization, ICU admission and death, in COVID-19-patients. Similar assumptions have been tested by other research groups. However, evidence regarding medication with antithrombotic drugs before exposure- and admission for COVID-19 is plagued by an inability to properly adjust for confounding. Additionally, the field has a relative lack of large-scale investigations and high-resolution data. This triad of issues contributes to the confusion; findings with regards to pre-COVID-antithrombotic medication range from associations with higher mortality, via no discernable effects, to improved outcomes including lower mortality. Sub-analysis of the HOPE COVID-19 registry, a cohort of 1002 patients and 110 of which were on oral anticoagulation, showed higher mortality risk compared to propensity score matched patients . In a siIn total, 360 patients were hospitalized for COVID-19 and 160 patients presented the composite outcome, but this did not differ significantly from the comparator group. This composite outcome is problematic, since ICU admission in Stockholm during the first wave was prone to triage/patient selection due to the massive surge of patients; described in detail in a recent publication . TherefoArrythmias, including atrial fibrillation, are coupled with other more severe cardiac diseases and frequently with significant non-cardiac comorbidities . This maThis study has strengths and weaknesses. We have access to high resolution data on medications, co-morbid conditions, COVID-infections, hospital and ICU admission and date of death. This allowed us to create a large dataset using only AF-patients. Specifically, this was done to address issues of confounding by indication. The detailed data was paramount for the matching procedure as described in the methods. Moreover, it made it possible to adjust for confounding factors to an extent that is unusual. Weaknesses include an inability to know why some patients with atrial fibrillation were unmedicated. Reasons are likely spanning from a spectrum of non-compliant/non-health seeking behavior, often associated with elevated risks of adverse outcomes, to the polar opposite: low risk patients where their doctors’ choice was not to medicate, due to only intermittent AF. Therefore, untreated AF might be a source of unmeasured confounding. Moreover, one can never be sure that the prescribed and collected medication actually is used by every single subject. Another weakness has been mentioned; using ICU admission as an outcome during the first wave in Sweden is problematic. Data on ICU admission is of high quality, but the March and April 2020 surge of patients means that this outcome was distorted by patient selection issues: elderly patients with high illness severity, i.e., more comorbidities or co-medication, were often not admitted . This isWe conclude that the use of anticoagulation therapy among patients with COVID-19 and arrythmias is associated with lower risk of hospitalization and death. This data should significantly alter how medication and risk-reward is evaluated in these patients. If further COVID-variants emerge, or in the event of other infectious diseases with prothrombotic properties, this further emphasize the need for physicians to ensure compliance among these vulnerable patient groups, such as those with arrythmias.Supplementary file1 (DOCX 21 KB)Supplementary file2 (DOCX 16 KB)Below is the link to the electronic supplementary material.