HPV-related anal cancer occurs in excess rates among people living with HIV (PLWH) and has been increasing in incidence. The HPV vaccine is an effective and safe approach to prevent and reduce the risk of HPV-related disease. Yet, HPV vaccine programs tailored and implemented in the HIV population are lagging for this high-risk group.A pre-post intervention study design will be used to tailor, refine, and implement the 4 Pillars™ Practice Transformation Program to increase HPV vaccination among PLWH. Guided by the RE-AIM framework, the CHAMPS study will provide training and motivation to HIV providers and clinic staff to recommend and administer the HPV vaccination within three HIV clinics in Georgia. We plan to enroll 365 HIV participants to receive HPV education, resources, and reminders for HPV vaccination. Sociodemographic, HPV knowledge, and vaccine hesitancy will be assessed as mediators and moderators for HPV vaccination. The primary outcome will be measured as an increase in uptake rate in initiation of the HPV vaccine and vaccine completion (secondary outcome) compared to historical baseline vaccination rate (control).The proposed study is a novel approach to address a serious and preventable public health problem by using an efficacious, evidence-based intervention on a new target population. The findings are anticipated to have a significant impact in the field of improving cancer outcomes in a high-risk and aging HIV population.NCT05065840; October 4, 2021. HPV-related anal cancer occurs in excess rates among people living with HIV (PLWH) , and hasThe safety and efficacy of the HPV vaccine has been evaluated in PLWH and is shown to be safe and highly immunogenic against oncogenic HPV types 16 and 18 –8. The HThe United States’ Advisory Committee on Immunization Practices (ACIP) recommends vaccination up to age 26 years and recently FDA (Food and Drug Association) approved up to age 45 years for women and men . ACIP alp < .001) .–28.21–28A pre-post intervention study design is used where HPV vaccination initiation and completion rates are measured before and after the intervention across the same clinics and enrolled participants Fig. . HPV vacThree HIV community clinics for this study were selected due to agreement to participate in the study, granted study access to electronic medical records, and willingness to make office changes to increase vaccination rates. Patients will be recruited from these three clinics and enrolled in the study based on the following eligibility criteria: 1) HIV positive; 2) 18–45 years of age; 3) understand English; 4) capable of informed consent; 5) have not received or completed the three dose HPV vaccine; 6) no contraindications to receiving the HPV vaccine .During the pre-intervention phase of the trial, each clinic site enrolls patients who receive immunizations using the GRITS system. The GRITS system offers a variety of functions for health care providers including recording immunizations, validating immunization history, providing immunization recommendations, producing recall and reminder notices, generating vaccine usage and client reports, and performing data extraction. Clinics will register with the 4 Pillars™ Program and clinic staff will complete a pre-intervention survey that assesses readiness and confidence in increasing HPV vaccination and current vaccination practices. Providers and clinic staff will be asked to participate in a focus group for feedback on tailoring the intervention for their clinic population prior to program implementation.The 4 Pillars™ Program offers providers and clinic staff evidence-based strategies to increase HPV vaccination uptake via training and educational resources. This program will be refined to provide tailored training and motivation to HIV providers and clinic staff to recommend and administer the HPV vaccine to HIV patients at the infectious disease clinic. Providers and clinic staff who are interested in participating will “enroll” online and complete an electronic informed consent before participating in the focus groups and completing the evaluation surveys. Providers and clinic staff are offered an opportunity to attend an in-service training that will provide education, training, and resources to help increase HPV vaccination at their clinic. Participation in the in-service will be offered to the entire clinic with opportunity for continuing education (CE) units to be earned. The in-service component is delivered under the purpose of quality improvement and does not require informed consent to attend.Components of the in-service training consist of education and resources related to the 4 Pillars™ program, epidemiology of HPV-related cancers among HIV positive individuals, ACIP’s guidelines for HPV vaccination for immunosuppressed patients, safety profile of the vaccine, and the importance and effectiveness of delivering evidence-based recommendations for HPV vaccination. Providers and clinic staff who are within scope of practice to administer the HPV vaccine are asked to recommend and administer the HPV vaccine to eligible patients during each routine clinic visit. Consenting providers and clinic staff are asked to complete pre-intervention evaluations, an intervention evaluation every 3 months, and post-intervention evaluations via a secured link to complete online. Alternatively, paper copies will be provided to the providers and clinic staff and administered by an Immunization Champion to those who choose not to access the evaluations by email.Each clinic site identifies an Immunization Champion  who will work and motivate the clinic staff and participate in biweekly updates of progress with the research coordinator. The Immunization Champion (IC) will encourage, remind, and ensure timely documentation of the HPV vaccination within the clinic’s electronic medical records and within GRITS. The IC helps maintain stock and storage of the vaccine and identify and address any issues with the vaccine inventory. The IC assists patients to complete Merck’s Patient Assistance Program to cover vaccination for those who are uninsured and qualify for the program. Lastly, the IC will contact patients on the monthly call list to schedule appointments or assist in reminders to patients to schedule the next visit for the follow up HPV vaccine.Clinics receive a progress report that documents the clinic’s vaccination progress every three months. The research coordinator schedules group feedback sessions via in-person or webinar with the IC every three months to discuss: 1) the intervention evaluations completed by the providers and staff; 2) to learn of any barriers to HPV vaccination at the clinic; 3) brainstorm strategies to overcome the barriers; and 4) quality assurance of intervention fidelity.n = 365) will be part of the intervention group and will receive recommendation for the HPV vaccine from providers and clinic staff. Enrolled participants will also complete a self-administered questionnaire at enrollment on a HIPPA compliant online data management database on a tablet device. The survey questions will include sociodemographic characteristics, knowledge of and attitudes towards HPV, HPV vaccination, and anal cancer, and vaccine hesitancy. Participants will be requested to provide consent for their HPV vaccination history to be verified with electronic EMR and GRITS. Participants will then watch a short video on HPV and HPV vaccination that can be viewed on their phones (or the study’s tablet device) while waiting to be seen. Potential participants will be asked to follow the study’s private Facebook page which offers additional educational information tailored towards individuals with HIV on HPV-related disease and general health promotion, and risk reduction tips. The Facebook page will also utilize Facebook Messenger (commonly known as Messenger). The proposed study will utilize Messenger to send reminders for follow up appointments for the next shot in the series and motivational messaging to encourage and promote receipt of the HPV vaccine. Participants will be contacted to complete a post-evaluation survey administered via online, telephone, or a mailed paper copy at 6–9 months after baseline. Participants will receive $25 incentive for completion of baseline and follow up study activities.Eligible and consenting participants  framework will guide planning, implementation, and evaluation of the 4 Pillars™ program. The study’s reach will be estimated from a quantitative perspective by estimating the target population that was exposed to the intervention. The clinic’s patient census data during the period of the implementation phase will be used to estimate the likely reach of the program across sites. Intervention effectiveness and efficacy will be measured by the change in uptake rate of vaccination . We will calculate the percent change in initiation of the vaccine and percent change in completion of the vaccine from the control phase and 24 months post intervention, after adjusting for demographics differences  in population. Providers and clinic staff will be asked to complete an evaluation of HPV vaccination progress every 3-months. We will also collect qualitative data from feedback sessions with the immunization champions to assess opportunities for and barriers to adoption. The post-evaluation interviews with providers and staff who implemented the program will assess extent of involvement, acceptance of the intervention, implementation fidelity, and extent of organizational spread of the intervention. We will assess the frequency, duration, and the extent to which the intervention was implemented as planned, participation attendance, and costs of implementation, as measured via the intervention and post-intervention evaluations. Intervention sustainability will be measured as the gains or maintenance of HPV vaccination rates post-delivery of the intervention. HPV vaccination rates will be calculated via EMR and GRITS at month 36 (12 months post intervention) and compare to HPV vaccination rates at month 24. Follow up assessments will measure penetration or the extent to which recommendation and administration of the HPV vaccination is integrated within the clinic.For analysis of the primary (HPV vaccination initiation) and secondary endpoints (HPV vaccination completion), the uptake rate of HPV vaccination pre- and post-intervention will be estimated separately with a 95% exact confidence interval using all eligible cases from both phases. The one-sample binomial exact test will be used to test whether the rate after the intervention is higher than the baseline rate (P0 = 13.5%). We will also perform the Chi-square test to compare the rate change between control and interventional phases, which will be exploratory. Logistic regression will be used to further adjust background difference in study population in pre- and post- intervention phases. For the longitudinal data collected from the intervention phase, the data structure holds multi-level information from patients, providers, and clinic levels. The goal of the analyses is to identify the factors that might impact HPV uptake from each level of information, which might lead to the future improvement of implementation strategy. Data will be described using summary statistics  for continuous variables and marginal distribution (frequency and percentage) for categorical variables. The univariate association with the HPV vaccination (yes vs. no) will be tested in logistic regression for each variable separately. The change of survey response between baseline and a follow-up time point will be tested by paired tests . Along with data visualization, all above mentioned descriptive and univariate association analyses will be repeated within each of the three clinics. Data will be pooled to build the multilevel analysis models, we mainly consider using the mixed-effect model and/or Bayesian multilevel modeling, in which the random effect will be considered at provider and clinical levels. We will follow the key modeling considerations listed by J.J. Hox  to identTo assess sustainability, HPV vaccination rates will be calculated at month 36 (12 months post-intervention). The change in HPV vaccination rates between month 24 and month 36 (12 months post-intervention) will be tested by McNemar test. The intervention will be deemed as sustaining its effect if rates of HPV vaccine initiation rates at month 36 remains or increases from month 24’s vaccination rates. To inform future scalability of the program, data from the evaluation and post-evaluation surveys will be described using summary statistics  for continuous variables and marginal distribution (frequency and percentage) for categorical variables. The similar analyses will be repeated within each of the three clinics. Additionally, we will conduct a post-intervention focus group consisting of the providers and clinic staff that participated in the program. The qualitative evaluation explore how implementation took place; the barriers to and facilitators of implementation success; ways to address any problems that may have occurred; and recommendations to refine the intervention for scale-up. The focus session will be recorded and transcribed where themes will be extracted and used for adaptation, scale-up considerations, and future research directions.P < = 13.5% and the alternative hypothesis is P > 13.5%. For the control phase, we will query all eligible subjects from EMR database among the 3 clinics at 18 months pre-intervention, which could be approximately 2300 subjects [N = 317) and intervention phase (N = 317), we will have 81% statistical power to detect an HPV uptake rate difference of 9% (22.5% vs. 13.5%) by two-sided Fishers’ Exact Test and under significance level of 0.05. We anticipate such a difference would be feasible based on our best knowledge and the literature. Regarding the patient- level component of the intervention, all incoming eligible patients will be influenced, and hence 365 participants are the minimum number to capture the follow-up information, the actual number of sample size used for the calculation of HPV vaccination rate will be larger as the vaccination status will be captured automatically in EMR without a consent.Based on our preliminary data, we found that HPV vaccination rate is around 13.5% (P0) in general, and another larger study in the literature found a very similar rate of 13.6% . We poweThe underutilization of HPV vaccination is a national problem that has been identified by the President’s Cancer Panel as a serious but correctable threat to the progress against cancer . HoweverNCT05065840; Registered on October 4, 2021.


Background: The estimated age-standardized incidence and mortality rates of cervical cancer in Hungary are substantially higher than the European average. In many countries, human papillomavirus (HPV) testing is the first-line method of cervical cancer screening in women >30 years. According to the European guidelines, evidence-based improvement of a national prevention strategy requires the monitoring of representative data.Methods: ThinPrep cervical samples were collected over a period of 8 months at 84 sampling sites, including 4,000 eligible samples with valid laboratory results from the screening target population of females aged 25–65 years, with addresses in the representative geographic area (19 counties and four major settlement types). Genotyping of high-risk HPV (hrHPV) was performed using the Confidence HPV-X (Neumann Diagnostics) and Linear Array HPV Genotyping (Roche) tests. Demographic data were collected using a questionnaire, enabling the analysis of hrHPV genotype distribution by age, geography, education, and HPV vaccination.Results: Overall, 446 samples were hrHPV-positive, showing a prevalence of 11.15% (9.73% age-representative), similar to the world average, higher than the European average, and lower than the Eastern-European average. After age standardization, no significant geographic differences were found, except for low hrHPV prevalence in villages (p = 0.036) and in those with elementary education (p = 0.013). Following genotypes 16 and 31, in order of frequency, certain non-vaccine hrHPV genotypes  showed unexpectedly higher prevalence than international data.Conclusion: Our study provides the first geographically representative genotype-specific hrHPV prevalence baseline database in Hungary to support policy-making efforts. Significant correlations with demographic data have transferable conclusions. The estimated age-standardized incidence and mortality rates of cervical cancer in Hungary  are substantially higher than the European averages . In regional comparisons, Central and Eastern Europe had notably higher incidence and mortality rates  than Western, Southern, and Northern Europe . In addition, the age-standardized overall cancer mortality in Hungary was found to be the least favorable in the European Union, adding a burden to an ageing population, economy, and health care system (Testing for high-risk genotypes of human papillomavirus (hrHPV) has already been widely considered a more effective first-line screening method for preventing cervical cancer than the conventional Papanicolaou Pap-smear. This also requires less frequent screening intervals, owing to its superior sensitivity and negative predictive value . The EurIn addition to optimizing immunization, screening, triage, and therapy procedures, evidence-based improvement of national prevention policy requires the collection and evaluation of representative data to continuously monitor the efficacy of changes, better understand and respond to demographic differences, and focus efforts and resources accordingly. Our study aimed to provide the first geographically representative baseline hrHPV genotype database for Hungary to prepare an update in the guidelines for HPV-based screening, shortly after the introduction of the nonavalent vaccine, replacing the bivalent one, into the nationwide school vaccination program.Our objective was to collect and analyze 4,000 cervical samples from women aged 25–65 years who were inhabitants of settlements geographically representative of Hungary’s such sub-population. A predetermined patient number matrix was prepared for each of Hungary’s 19 counties and stratified according to the four major settlement type levels . This reflected the previously published official demographic data on women aged 25–65 years  into ThinPrep PreservCyt™ liquid-based cytology containers  over a period of 8 months (December 2018–July 2019) by 84 competent sampling sites that were appointed randomly. These included 4,000 eligible samples with valid laboratory results from the screening target population of females aged 25–65 years, with addresses in the representative geographic area (counties and settlement types). We excluded participants with histories of hysterectomies, immunosuppression, recent (within 2 months) childbirths, operations or samplings that affected the cervix, and current menstruation. In each county, there were an average of 2–3 health care institutions and 2–3 registered health visitors who collected samples from women after securing their informed consent, guided by the approved standard recruitment, sampling, and documentation protocol in compliance with the Declaration of Helsinki .in vitro diagnostic tests  using the Confidence HPV-X™  and Linear Array™ HPV Genotyping , which are both clinically validated commercial CE-marked ic tests . The labA logical decision-making algorithm was designed to settle possible inter-test inconsistencies through single repetition of one or each of the tests for consistency or exclusion. The rate of invalid results for the second test, including repeated inconsistencies, was 1.5%. We utilized two consecutive HPV tests for cost-effectiveness; the first test saved costs by group detection in negative samples and only nonavalent genotype positives, so the second genotyping test was needed for a smaller fraction  of the whole sample set.An anonymous questionnaire collecting demographic  and HPV vaccination data was given to each patient, allowing us to understand hrHPV genotype distribution and perform further cross-analyses using subgroups according to geography, age, education, and vaccination.p ≤ 0.05) demonstrated using two-sample t-tests with STATA software . In addition, the relationship between variables was analyzed using analysis of variance and logistic regression models to estimate the odds ratios.A descriptive statistical analysis was used to compute the frequency rates of each variable. Differences between the counties and the national averages were analyzed and statistically  before age standardization. After adjusting for age to better represent female Hungarians aged 25–65 years, it decreased to 9.73% (95% CI: 8.31–11.15).p = 0.028, underlined in The mean age of the 4,000 eligible women was 41.36 years (standard deviation [SD]: 9.972). Of the eligible samples, 31.3% were from the Capital and Central Region, 30.0% from the three western regions, and 38.7% from the three eastern regions, proportional to the population. In total, 29.2% of the population lived in villages. However, there was a wide variation in this proportion among counties (SD = 10.0). It was beyond 2 SD in one of the 19 counties, significantly above country average without capital , while among those with “Less than Elementary” education 6.7%. hrHPV prevalences among women with “Middle education level ,” with “Higher than Elementary education (but without Matura),” and with just “Elementary level (8 years of education)” with OR and p-values are presented in A higher level of education was associated with a higher  prevalence of hrHPV: hrHPV prevalence among women with “High school or University degrees” was 12.8% (p ≤ 0.05) was found only between “Elementary level (8 years of education)” versus “High school or University degrees” , even without the effect of age. Meanwhile, the size of the “Less than Elementary” subgroup was too small (n = 15) to be significant.A significant difference , the hrHPV prevalence representative of the female Hungarian population aged 25–65 years was determined to be 9.73%.During our sample collection, the representativeness to the age distribution of the female population was not a requirement. However, we ensured the validity of the conclusions for the country through statistical adjustments. Analysis of the demographic data in the sample set showed that women under the age of 51 were overrepresented, while those aged 52 years and above were underrepresented see . After ep = 0.211).Based on our data, 174 enrolled women (4.3%) had received vaccination against HPV; among them, 55 (32%) received the quadrivalent vaccine; 47 (27%), the bivalent; 16 (9%), the latest nonavalent vaccine; while 56 (32%) did not know the type. The average age at the time of the survey was 36 years and that at the time of vaccination was 27 years. Among the reported vaccinated women, 11 were hrHPV-positive, and four among them had a genotype matching her previous vaccine. The prevalence of HPV16 and/or HPV18 was 10% among the vaccinated vs. 29% among the unvaccinated hrHPV-positive women  samples. Multiple infections were present in 91 cases (20.4%); with 76 (17.0%) having a double infection, 13 (2.9%) having a triple infection, and two (0.5%) having a quadruple infection with different hrHPV genotypes. The frequencies of the examined hrHPV genotypes are shown in Immediately following genotypes 16 and 31 (together comprising 39% of positives) in order of frequency, certain non-vaccine high-risk HPV genotypes  had the highest prevalence (together comprising 32% of positive cases).The geographic distributions of HPV 16, 31, and 51 are shown in Bivalent vaccine-types (HPV16 and/or HPV18) were present in 110 females (29%), while any or more of the nonavalent vaccine 7 hrHPV genotypes were found in 269 (73%) of the positive samples.As shown in The nationwide prevalence of 11.15% was similar to the world average of 11.7%, based on normal cytology, lower than that of the Eastern-Europe average (21.4%) . Some WeIn Central and Eastern Europe, the mean prevalence of HPV infection was 12.6% among 8,610 women with normal cervical cytology, with HPV16 being the most common . Some coIn Hungary, only a few studies have been previously conducted that covered HPV prevalence. One study in 2001 examined 728 women in more centers and estimated that the overall rate of HPV infection was 17% . AnotherHowever, none of these earlier studies attempted to proportionally represent the urban and rural populations of the whole country, making our recent study the first and unique survey in this aspect.The distribution of local hrHPV prevalence can be compared to the strong geographic inhomogeneity in the morbidity and mortality of cervical cancer . In somep = 0.036) lower (by 23%) at 9.2%. This finding was not unprecedented as a similar difference was found in Romania , the prevalence was 11.9%, while in villages, it was significantly . The effect of residence and educational attainment on testing behavior only became significant if women had low levels of HPV knowledge .p = 0.726) from 4% per year  for the entire age range. The use of this simplified model can achieve the aim of filtering out the significant  effect of age.Similar to previous findings , the hrHBased on the literature, the peak of HPV prevalence is in the early 20s, which presumably correlates with the initiation of sexual activity and the number of different partners over a certain period of time. Cervical pre-cancer findings cumulate approximately 10 years after this age, while the peak of cervical cancer appears even later. Only approximately 10% of HPV infections persist over 2 years, and persistence with the same hrHPV genotype indicates a higher risk for cervical neoplasia .As mentioned before, analysis of the demographic data in our sample set showed that women under the age of 51 were overrepresented, while those aged 52 years and above were underrepresented see , which rp = 0.243) without the effect of age differences, suggesting that domestic screening programs should be targeted based on age groups instead of geographic regions. However, the hrHPV prevalence remained significantly lower in village settlement types than in the capital, even after adjusting for age.Among the different counties of Hungary, differences in the hrHPV prevalence from the country average see  were notThe average age at the time of reported vaccination was 27 years in our study group, suggesting that most of the patients received vaccination after engaging in sexual activity. In their cases, there may be incomplete protection because of the potential presence of hrHPV genotypes that were already in the cervix. Indeed, among the reported vaccinated women, 11 were hrHPV-positive, and four of them had a genotype in her cervix against which she received lege artis vaccination in the previous years .p = 0.06)  .n = 8,656, follow-up 12 years), when HPV16 was present in the cervix, the risk of CIN3+  was more than 25%, with HPV18 it was approximately 20%, with HPV31 and HPV33 approximately 15%, and with the rest of the hrHPV genotypes only <5%  had unexpectedly high prevalence in Hungary compared with international data. The latter three were not listed in the five most frequent worldwide, a surprising result that necessitates further research to be understood.Worldwide, the following genotypes were the most prevalent: HPV16 (3.2%), HPV18 (1.4%), HPV52 (0.9%), HPV31 (0.8%), HPV58 (0.7%), and the remaining hrHPV are 0.6% or less . AnotherIn our geographically-representative screening population, the most frequent genotypes were similar to those found in some of the earlier studies in Hungary. Genotype 16, 51 and 31 had the highest prevalence in 2011 among patients with cytological atypia . A studyKey Points: According to Our Results• A hrHPV prevalence of 11.15% (9.73% age-representatively) was found in Hungary, similar to the world average, higher than the European average, and lower than the Eastern-European average;• Domestic screening programs should focus on age group rather than geographic regions, with an attention to the involvement of women above 51;• High education level and urban residence (even after age-standardization) was associated with statistically significantly higher hrHPV prevalence;• Following genotypes 16 and 31, in order of frequency, certain non-vaccine hrHPV genotypes  showed unexpectedly higher prevalence than previous international data, projecting a potential tendency of further growing relative prevalence of these non-vaccine genotypes.Evidence supports the introduction of primary HPV screening in Hungary. Our study provided the first geographically representative genotype-specific hrHPV prevalence baseline database for supporting policy-making efforts. Further follow-ups should reveal probable genotype distribution shifts over time toward the growing prevalence of the non-vaccine genotypes, in conjunction with HPV vaccination and organized cervical screening programs.


Cervical cancer still poses a significant global challenge. Developed countries have mitigated this challenge by the introduction of structured screening programmes and, more recently, the HPV vaccine. Countries that have successfully introduced national HPV vaccination programmes are on course for cervical cancer elimination in a few decades. In developing countries that lack structured screening and HPV vaccination programmes, cervical cancer remains a major cause of morbidity and mortality. The HPV vaccine is key to addressing the disproportionate distribution of cervical cancer incidence, with much to be gained from increasing vaccine coverage and uptake globally. This review covers the history and science of the HPV vaccine, its efficacy, effectiveness and safety, and some of the considerations and challenges posed to the achievement of global HPV vaccination coverage and the consequent elimination of cervical cancer. One of the most extraordinary stories in medical research is that of Henrietta Lacks, an African American woman who died from cervical cancer seventy years ago, at the age of thirty-one. “HeLa cells”, as they are commonly referred to, are the cells which were collected from her cervix in 1951 shortly before her death; these were the first established in vitro immortal cancer cell line [In the originally biopsied HeLa cells, German virologist Harald zur Hausen in the early 1980’s discovered the presence of HPV-18 ,3. It isHPV now forms the basis of both primary and secondary cervical cancer preventative measures. Most cervical screening programmes worldwide utilise HPV detection as a primary screening tool, in keeping with the World Health Organisation (WHO) recommendations . HPV vacThe combined effect of HPV vaccination and cervical screening should effectively make cervical cancer a rare occurrence, yet it remains a leading cause of cancer deaths globally . WorldwiWe review the natural history of HPV infection and its association with cervical cancer, the different types of HPV vaccine and studies looking into their efficacy, effectiveness and safety. We also review the implementation of the HPV vaccine globally, with a focus on the barriers faced by countries with a low vaccine uptake.Papillomaviridae family. It is the most common sexually transmitted infection worldwide and it is estimated that most sexually active individuals will be infected with HPV at least once in their lifetime [HPV is a double-stranded DNA virus which belongs to the lifetime . Infectilifetime   regulates gene expression and replication, the early (E) region encodes for proteins which are required for HPV gene expression, replication and survival, and the late (L) region encodes for viral structural proteins . The virThe majority of women infected with HPV will clear it within a few years. An estimated 10–20% of women will have persistent HPV infection ,21. From® ), a quadrivalent vaccine which targets HPV-6, HPV-11, HPV-16 and HPV-18, conferring protection against genital warts, which are most commonly caused by HPV-6 and HPV-11. In 2007, a bivalent vaccine, Cervarix® , was licensed, which targets HPV-16 and HPV-18. Gardasil 9® ) is a nonavalent vaccine licensed in 2014 that targets HPV-6, HPV-11, HPV-16, HPV-18, HPV-31, HPV-33, HPV-45, HPV-52 and HPV-58 [® , was developed, which utilises Escherichia coli to produce HPV-16 and HPV-18 L1 VLPs [® was licensed for use in China in 2020 and prequalified by the WHO in 2021. Finally, a recombinant bivalent HPV vaccine  targeting HPV-16 and HPV-18 was also licensed for use in China in 2022 and prequalified by WHO in 2022 [This understanding of HPV-induced carcinogenesis alongside the availability of VLPs has allowed various prophylactic HPV vaccines to be developed. The first to be licensed in 2006 was Gardasild HPV-58 ,22. Thes L1 VLPs . Cecolin in 2022 . Table 1 in 2022 .The vaccines have a similar mode of action based on VLPs, a recombinant, noninfectious assembly of the L1 HPV capsid protein. VLPs are antigenically identical to infection-causing HPV virions , such thPrior to licensure, all HPV vaccines underwent clinical trials to determine efficacy, effectiveness and safety. A WHO convention in 2003 determined appropriate HPV vaccine trial endpoints: it was agreed that ethical and time constraints could not allow for cervical cancer to be an appropriate trial endpoint, given that participants under follow-up during the trial period would receive treatment of any cervical precancerous lesions detected . Agreed The phase 3 efficacy trials conducted for HPV vaccines all included younger women aged 15 to 26. These trials were multinational and included thousands of participants, with the trial design and conduct led by the respective manufacturers. The results from these trials are summarised in ® quadrivalent HPV vaccine, with results published in 2007 [The FUTURE I and II trials assessed the efficacy of the Gardasil in 2007 . Over 12 in 2007 .A subset of European participants from the FUTURE II trial were followed up for fourteen years, with no cases of HPV-16- or HPV-18-associated high-grade cervical dysplasia noted in follow-up period (vaccine efficacy of 100%) . SeroposA double-blinded randomised trial of the quadrivalent HPV vaccine in a Japanese population of women aged 18 to 26 similarly found a high efficacy against vaccine-type high-grade cervical disease .® bivalent HPV vaccine [® vaccine or the hepatitis A vaccine. After a three-year follow-up period, vaccine efficacy against HPV-16 or HPV-18-associated high-grade CIN was 92.9% [The PATRICIA trial was the first to assess the efficacy of the Cervarix vaccine ,36. Overas 92.9% .®. After a four-year follow-up period, vaccine efficacy was 89% against HPV-16- or HPV-18-associated CIN2+ [The Costa Rican Vaccine Trial (CVT) was a second, large prelicensure trial investigating the efficacy and safety of Cervarixed CIN2+ . A longeed CIN2+ .® vaccine was also studied in a population of >6000 Chinese women aged 18 to 25 showing a vaccine efficacy of 100% in HPV-naïve participants [The Cervarixicipants .® 9 vaccine was assessed in a multinational double-blind trial in women aged 16 to 25 [® HPV vaccine. Vaccine efficacy against high-grade cervical disease associated with HPV-31, HPV-33, HPV-45, HPV-52 and HPV-58  was 97.1%; the number of cases per 10,000 person-years was 0.5 in the nonavalent group versus 18.1 in the quadrivalent group. The incidence of abnormalities associated with HPV-6, HPV-11, HPV-16 and HPV-18  was comparable between both groups of participants. Furthermore, the immunogenicity of the nonavalent vaccine with respect to HPV-6, HPV-11, HPV-16 and HPV-18 was comparable to that of the quadrivalent vaccine [The efficacy of the nonavalent Gardasil16 to 25 . Over 14 vaccine , with an vaccine . The aut® vaccine recruited over 7000 female participants aged 18 to 45, in multiple centres in China between 2012 and 2013. The control group received the hepatitis E vaccine. Vaccine efficacy against high-grade genital disease and persistent infection associated with HPV-16 or HPV-18 were 100% and 97.3%, respectively [® vaccine is ongoing in Bangladesh and Ghana, with results expected in 2023 (NCT04508309).The efficacy trial for the bivalent Cecolinectively ,44. Part® vaccine and the bivalent Cervarix® vaccine have both been trialled in older females, a group likely to have reduced efficacy since vaccine efficacy declines with prior HPV exposure. The FUTURE III trial evaluated the efficacy of the quadrivalent vaccine in over 3800 women aged 24 to 45. Vaccine efficacy against vaccine-type CIN1+ was 88.7% in HPV-naïve women and 30.9% in all women [® vaccine’s efficacy in women aged > 25. Vaccine efficacy against combined endpoint of vaccine-type 6-month persistent infection and CIN1+ was 90.5% in the per protocol group and 86.5% in the total vaccinated cohort. Estimated vaccine efficacy against vaccine-type CIN2+ was high but insignificant due to low numbers [The quadrivalent Gardasilll women . Anotherll women . The VIV numbers .While these studies support the use of the HPV vaccine in older females, the effectiveness of the vaccine in this group is less than in adolescents: because the incidence of HPV infection declines with age , older f® vaccine and the quadrivalent Gardasil® vaccine [In many countries, there has been a move to HPV-based cervical screening, meaning that many women will already be known to have had a previous HPV infection. It is therefore important to define the efficacy of the HPV vaccine in this group. Most of the HPV vaccine clinical trials included in the total vaccinated cohort participants who, at the time of recruitment, were positive for HPV DNA or seropositive for HPV antibodies. Unsurprisingly, these groups had lower vaccine efficacy compared to HPV-naïve participants. Vaccine efficacy in seropositive, DNA-negative participants, i.e., those with previous but no current HPV infection, was summarised in a meta-analysis which pooled data from trials on the bivalent Cervarix vaccine . Against vaccine . These fPeople with HIV are known to have lower levels of immunity, which make them more susceptible to persistent HPV infection. A few studies have investigated the efficacy and safety of the HPV vaccine in this population, showing a 100% seroconversion rate following vaccination with no adverse outcomes . HoweverIn males, HPV is associated with a range of anogenital and oropharyngeal diseases—over 90% of anal cancers, over 70% of oropharyngeal cancers and up to 48% of penile cancers are associated with HPV . HPV vac® vaccine, the nonavalent Gardasil 9® vaccine did not undergo clinical trials in men prior to licensure. Rather, immunogenicity studies were done which showed that the nonavalent vaccine elicited immune responses similar to the quadrivalent vaccine against HPV-6, HPV-11, HPV-16 and HPV-18 [® has been licensed for use in men [® vaccine against persistent oral HPV infection [Unlike the quadrivalent Gardasild HPV-18 . Based oe in men . In the nfection .The prelicensure HPV vaccine trials explored protection conferred by the vaccines against nonvaccine subtypes, i.e., cross-protection, with all vaccines demonstrating partial cross-protection against nonvaccine-type infection and disease. The FUTURE I and II trials of the quadrivalent vaccine assessed cross-protection against ten other HPV subtypes—31, 33, 35, 39, 45, 51, 52, 56, 58 and 59. These showed some cross-protective effect against HPV infection and low-grade CIN, most marked against HPV-31, but not against high-grade CIN .® also assessed the cross-protective effects of the bivalent vaccine against nonvaccine-type persistent infection and CIN2+ [® trials indicate cross-protective effect lasting at least 11 years [The PATRICIA and Costa Rica trials of Cervarixnd CIN2+ . A high nd CIN2+ ,61. Long11 years ,63.® and nonavalent Gardasil® vaccines [As with all vaccines, the safety of the HPV vaccine was evaluated in prelicensure trials and continues to be evaluated in surveillance systems worldwide following licensure. Data on HPV vaccine safety are robust and have consistently shown no concerns ,65,66,67vaccines . In vaccvaccines . Followivaccines .® and quadrivalent Gardasil® vaccines in various countries showed a low rate of serious adverse events [In vaccine clinical trials, there was no difference in rates of serious adverse events between vaccine and control groups ,67. Natie events . Most ofe events . There he events ,68.The HPV vaccine is not currently recommended in pregnancy, but safety data from this group have been obtained from prelicensure trials, clinical trials and surveillance systems where women were vaccinated during pregnancy or became pregnant shortly after vaccination. None of these found any association between vaccine administration and adverse pregnancy outcomes including spontaneous abortion, foetal loss and congenital abnormalities . FurtherFinally, the safety of the vaccine with regards to the development of autoimmune disease, venous thromboembolism and neurological conditions has been studied in various contexts, with no association found between the HPV vaccine and these conditions . The eviDespite these robust safety data, concerns about vaccine safety have impacted vaccine uptake in several countries. A notable example of this is Japan, who introduced HPV vaccination into their routine national immunisation schedule in April 2013. Due to physical symptoms including pain and motor impairment reported by vaccinated girls, the vaccination programme was suspended ten weeks later in June 2013. The vaccine remained available; however, its proactive recommendation was discontinued causing a reduction in vaccine coverage from 70% to less than 1% . A modelIn the initial ten years following the HPV vaccine introduction, several population-level studies assessed the real-world effectiveness of the vaccine on HPV-16/HPV-18 infection, anogenital warts and high-grade CIN. In 2019, findings from these were summarised in a systematic review and meta-analysis . Data fr® HPV vaccine nationally in 2009, and was one of the first countries, in 2020, to report on cervical cancer outcomes following vaccine introduction. Their national data showed a remarkable 88% reduction in cervical cancer incidence in women who had been vaccinated prior to age 17, and a 53% reduction in cervical cancer incidence in women vaccinated between the age of 17 and 30 [Ultimately, the aim of the HPV vaccine is to reduce the burden of HPV-associated cancers, and with longer-term population data increasingly emerging, a few studies have been able to report on this outcome. Sweden introduced the quadrivalent Gardasil7 and 30 .In the UK, the bivalent vaccine was introduced in England in 2008, and data on vaccine effectiveness against cervical cancer were published in 2021. Population data from England showed an 87% reduction in cervical cancer incidence in women vaccinated at age 12 to 13, a 62% reduction in women vaccinated at age 14 to 16 and a 34% reduction in women vaccinated at age 16 to 18 . The autAnother national study in Denmark published in 2021, where the HPV vaccine was introduced into the childhood vaccination programme in 2009, reported a high vaccine effectiveness against cervical cancer. The incidence rate ratio was 0.14 in females vaccinated under the age of 17, and 0.32 in females vaccinated between age 17 and 20, when compared to unvaccinated females. Mirroring vaccine efficacy data, the effectiveness of the vaccine was reduced when administered at an older age .HPV vaccines were initially licensed with a recommended three-dose schedule, and the prelicensure trial protocols included three vaccine doses, which demonstrated a high efficacy against vaccine-type CIN2+ and vaccine-type persistent HPV infection. This led to a three-dose regimen being recommended by the WHO and national governing bodies.® bivalent vaccine, carried out a post hoc analysis investigating the vaccine efficacy in women who received less than three doses of the bivalent HPV vaccine. Interestingly, they found that women who received less than three doses of the vaccine had a similar vaccine efficacy against persistent HPV-16/HPV-18 infection, relative to the women who received the per-protocol three doses; this protection lasted for up to ten years [The Costa Rica Vaccine trial group, who conducted a prelicensure trial for the Cervarixen years ,79,80. Aen years .These findings were of huge significance, as a reduced dosing schedule has important public health implications, easing both financial and logistical barriers to the introduction of HPV vaccination programmes. A single-dosing schedule would significantly reduce supply and storage issues and increase compliance. Even with a modest assumed single-dose vaccine efficacy of 80%, there would be (projected) millions of averted cases of cervical cancer . Thus, sA group In India, the Indian HPV vaccine study group, designed a randomised trial to compare vaccine efficacy between one-, two- and three-dosing schedules. Trial plans were interrupted in 2010 when the Indian government suspended HPV vaccination. Consequently, the design was changed to an observational cohort study consisting of >12,000 participants aged 10 to 18. There were over 4000 participants in each dose category . Early dAnother study, the ESCUDDO trial, is an ongoing randomised double-blind design comparing the vaccine efficacy of a one- versus two-dosing schedule of the bivalent and the nonavalent HPV vaccines . Over 20Similar randomised controlled trials are ongoing in various African countries to investigate the efficacy of a single-dosing vaccine schedule including the DoRIS trial in Tanzania (NCT02834637), the HANDS trial in Gambia (NCT03832049) and the KEN SHE trial in Kenya (NCT03675256) . PrelimiWith the evidence pointing towards the high efficacy of a single dose of the HPV vaccine, the Joint Committee on Vaccination and Immunisation (JCVI) in the UK has recently recommended a single HPV vaccine dose in their routine adolescent and MSM vaccination programmes . The WHOEven following treatment for CIN, women remain susceptible to reinfection with HPV and have been shown to be at increased risk of developing recurrent CIN and HPV-associated cancers ,95. TherA systematic review was recently published, which evaluated 22 studies reporting on HPV infection and the risk of HPV-associated disease following surgical management of HPV-associated genital disease in vaccinated individuals . AlthougSince HPV vaccines became available in 2006, countries have progressively introduced the vaccine into their national immunisation schedules. The WHO initially recommended HPV vaccines in 2009, using a three-dosing schedule to girls aged 9–14 years . In 2014As of March 2022, 60% of WHO member states have introduced the HPV vaccine into their national immunisation schedule . The majStrategies to deliver the HPV vaccine include school-based programmes, healthcare facility-based programmes, and outreach/campaign programmes. School-based HPV vaccination programmes have been successful at achieving a high HPV vaccine coverage as demonstrated in several countries. Australia was one of the first countries to implement a national HPV vaccination programme in 2007. This was a government-funded, school-based immunisation programme, offering three doses of the quadrivalent vaccine to 12-to-13-year-old girls. In 2013, the programme was expanded to include boys, and in 2018, changed to two doses of the nonavalent vaccine . Since tThe UK similarly introduced a government-funded school programme aimed at 12-to-13-year-old girls in 2008. The triple dosing schedule was changed to a two-dose schedule in 2014, and the vaccination of 12-to-13-year-old boys was included in 2019 . Prior tIn the United States, routine HPV vaccination for girls aged 11 to 12 was recommended as a three-dosing schedule in 2006. This also included catch-up vaccination for women up to the age of 26. In 2011, routine vaccination was recommended for males aged 11 to 12. In 2016, the standard dosing schedule was reduced to two doses . HPV vacUnsurprisingly, many LLMICs have not been able to introduce the HPV vaccine into national immunisation schedules, due to financial and infrastructural constraints. As of March 2022, 114 out of 145 (78.6%) high-income and upper-middle-income countries have introduced HPV vaccination, whereas only 20 out of 80 (37.5%) low-income and low-middle-income countries have introduced HPV vaccinations nationally . Many ofIn Asia, several countries have been able to add HPV vaccination to their national programmes, such as Thailand, Sri Lanka, Bhutan and the Maldives . In ChinAs of 2022, gender-neutral HPV vaccination schedules are present in 39 WHO member states, and many high-income countries have shifted/are shifting into an era of equity of access to HPV vaccination for all genders ,114. DueThe impact of the HPV vaccine on cervical cancer incidence can only be realised with a high vaccine coverage, and several barriers continue to hinder this. Unfortunately, the countries with the highest cervical cancer burden and therefore in most need of the HPV vaccine face the greatest barriers. Addressing these barriers are key to achieving the full benefit of the vaccine.Vaccine cost is a significant barrier, especially in LLMICs where the introduction of national HPV vaccination programmes is not financially feasible without external support. Gavi, the vaccine alliance, is a global public–private partnership which works with manufacturers to reduce the cost of vaccines to burdened countries, particularly LLMICs. They introduced a programme in 2012 to enable Gavi-eligible countries to introduce HPV vaccination programmes nationally at a negotiated cost of <$5 per dose, versus the usual >USD 100 per dose . There w® vaccine and Walvax Biotechnology’s vaccine, and it is anticipated that this, alongside a ramp-up of production of currently licenced vaccines, will help reverse vaccine shortages by 2023 [Since 2018, there has been a worldwide shortage of HPV vaccines resulting in significant supply constraints. This resulted in adjustments to planned vaccine introduction programmes, which particularly affected LLMICs. The shortages also led WHO’s SAGE to suspend recommendations to vaccinate males and multiage cohorts, in order to reduce the impact of the vaccine shortage . These a by 2023 ,118.Another huge barrier to HPV vaccination which considerably affects LLMICs is the requirement for cold-chain preservation for current HPV vaccines, which adds to already high costs. One way to address this is via lyophilization, which would dehydrate vaccine components and allow transfer of a frozen powder form at higher temperatures . Alterna® in children aged 4 to 8 years (NCT05329961).The HPV vaccine is targeted at adolescent/school-age children, who are a harder group to reach compared to the age group targeted by childhood immunisation programmes. The use of school-based vaccination programmes helps to circumvent this; however, many LLMICs do not have funded school health programmes, which require additional costs and manpower . FurtherGlobally, the COVID-19 pandemic has affected existing HPV vaccination programmes and halted the introduction of new programmes via the closure of schools and suspension of routine immunisation programmes. This has affected other vaccination programmes, and many are still recovering from the impact of the pandemic .Vaccine hesitancy has a huge impact on HPV vaccine uptake globally. Misinformation on safety concerns about the vaccine, and an unfounded association of HPV vaccine use with sexual promiscuity are common drivers behind vaccine hesitancy . A combiDespite the high efficacy of prophylactic HPV vaccines, worldwide prevalence of high-risk HPV infection remains high, mainly due to the low global vaccine uptake and pre-existing HPV infection. This means the elimination of HPV infections and associated diseases could take decades still. Due to their mechanism of action, by inducing a humoral response to prevent an initial HPV infection, prophylactic HPV vaccines cannot have a reliable therapeutic effect . This haListeria-based ADXS11-001 [There are no licensed therapeutic vaccines yet, although several have undergone phase 2 and 3 clinical trials for use in the treatment of CIN and cervical cancer. VGX-3100 is one of such, a DNA-based therapeutic vaccine targeting the E6 and E7 proteins of HPV-16 and -18. Results from a phase 2 clinical trial showed histopathological regression from CIN2/3 to CIN 1 in 49% of vaccinated participants at six months . None ofXS11-001  are in lXS11-001 .In trial settings, therapeutic HPV vaccines have also been combined with other anticancer agents, such as antibodies against programmed death-ligand 1 (PD-L1) and programmed death-1 receptor (PD-1), in the treatment of advanced and recurrent cervical cancer. PD-L1 is an immune checkpoint inhibitor peptide which binds to its receptor PD-1. PD-1 and PD-L1 are expressed by many tumour cells, promoting immune evasion, and they are thus important immunotherapy targets . A phaseTherapeutic vaccines have shown moderate effectiveness against CIN and HPV-associated cancers, but this has been less than anticipated, especially given the high effectiveness of the prophylactic vaccine . One proWe are now 16 years following the licensure of the first prophylactic HPV vaccine, and the efficacy, effectiveness and safety of the vaccine is no longer in question. The main factor limiting the vaccine’s impact is a deficient population coverage . The addAnother interesting concept called FASTER suggests combining HPV vaccination with HPV-based screening in women aged up to 50 . Using t®, is already helping to drive vaccine costs down and increase availability. In the long term, the development of additional cheaper vaccines with less cold-chain dependence will increase HPV vaccine coverage and success. Furthermore, the gradual endorsement of single-dosing schedules will further reduce costs and improve full-dose coverage globally.Equity of access to the HPV vaccine, particularly in LLMICs, is a prerequisite for advancing sustainable HPV vaccination globally. Affordability, political buy-in and addressing cold-chain challenges are all ways to achieve this. The local development of biosimilar vaccines, such as CecolinFrom a racial and ethnic perspective, there remain concerns regarding vaccine equity given ethnic and racial variations in HPV genotype prevalence, which may limit the effectiveness of the vaccine in certain nonwhite populations . The comCervical screening of vaccinated women is still recommended, as there remains the potential for disease caused by nonvaccine oncogenic HPV subtypes. Where it was previously theorised that vaccination against the common oncogenic HPV subtypes would result in an increase in the prevalence of the less common, nonvaccine oncogenic subtypes, early studies indicated that there was no evidence of “type replacement”, i.e., the replacement of the common oncogenic HPV subtypes with the less common ones . Due to The comprehensive approach to cervical cancer control consists of a triad of primary, secondary and tertiary preventative measures. With the highly efficacious prophylactic vaccine, existing cervical screening measures and an anticipated effective therapeutic vaccine, alongside established treatment methods, achieving cervical cancer elimination is within reach. A focus on increasing the coverage and uptake of the HPV vaccine globally will certainly accelerate the attainment of this goal.


This paper considers the optimal sensor placement problem for angle-of-arrival (AOA) target localization in the 2D plane with a Gaussian prior. Optimal sensor locations are analytically determined for a single AOA sensor using the D- and A-optimality criteria and an approximation of the Bayesian Fisher information matrix (BFIM). Optimal sensor placement is shown to align with the minor axis of the prior covariance error ellipse for both optimality criteria. The approximate BFIM is argued to be valid for a sufficiently small prior covariance compared with the target range. Optimal sensor placement results obtained for Bayesian target localization are extended to manoeuvring target tracking. For sensor trajectory optimization subject to turn-rate constraints, numerical search methods based on the D- and A-optimality criteria as well as a new closed-form projection algorithm that aims to achieve alignment with the minor axis of the prior error ellipse are proposed. It is observed that the two optimality criteria generate significantly different optimal sensor trajectories despite having the same optimal sensor placement for the localization of a stationary target. Analysis results and the performance of the sensor trajectory optimization methods are demonstrated with simulation examples. It is observed that the new closed-form projection algorithm achieves superior tracking performance compared with the two numerical search methods. In target tracking and localization problems, target–sensor geometries are known to play a significant role in determining the localization and tracking performance. In this paper, we focus on optimal target–sensor geometries for angle-of-arrival (AOA) localization in the 2D plane using a single moving sensor. First, the localization problem is cast as a Bayesian estimation problem, which assumes the availability of prior information in the form of a Gaussian prior for the unknown target location. For this problem, optimal sensor placement results are developed using approximate estimation bounds. Next, the Bayesian estimation problem is extended to target tracking using the Kalman filter, and optimal sensor trajectories are developed to track a manoeuvring target.Optimal sensor placement has been researched for several decades. Early works included ,2,3, wheA comprehensive analysis of the 2D optimal AOA sensor placement problem for a stationary target was presented in ,12. OptiThis paper develops optimal sensor placement results for a single AOA sensor at a fixed distance from the mean of the Gaussian prior. To do this, the Bayesian FIM is approximated by replacing the expectation of the contribution form measurement data with its instantaneous value calculated at the mean of the Gaussian prior. It is argued that this approximation is valid when the covariance of the Gaussian prior is relatively small compared with the target range. The optimal sensor placements for the D- and A-optimality criteria are shown to be identical and align with the minor axis of the error ellipse of the prior covariance. In the context of bearings-only manoeuvring target tracking, numerical search methods based on the D- and A-optimality criteria and a new closed-form projection algorithm that attempts to achieve alignment with the minor axis of the prior covariance error ellipse are proposed for sensor trajectory optimization subject to turn-rate constraints. It is observed that the D- and A-optimality criteria yield markedly different optimal sensor trajectories even though they produce identical optimal sensor placement for the localization of a stationary target. The projection algorithm is shown to outperform the other two methods in simulation studies.This paper is organized as follows. In tracking problems with a state space that can be modelled or approximated as a Gauss–Markov process, the Kalman filter has been extensively used to compute the Gaussian prior for state estimates from the noisy sensor measurements available in each recursion in the form of the predicted state estimate and predicted state covariance. Starting with the Gaussian prior criteria ,22,23, wThe bearing measurements collected by the sensor located at r, w∼N0,σ is the bThe optimality criteria considered in this paper employ estimation bounds obtained from the FIM or CRLB. In Bayesian estimation problems that involve random unknown parameters, these bounds are replaced by the Bayesian FIM (BFIM) or Bayesian CRLB (BCRLB). The BFIM for the single-sensor AOA localization problem is defined as (3)Φ=K0+Equation  can be rThe approximation in  is validHere, tr denotes trace. Applying the same line of reasoning to ude that  is the cude that .D-optimality criterion aims to maximize the determinant of the Fisher information matrix (FIM). For the Bayesian estimation problem considered here, the FIM is replaced by the Bayesian FIM (BFIM). Considering the optimization problem described in The ribed in maxs|Φ|wNoting that in  K0 is a wo terms (18)|Φ|≈d, the optimization problem in +State Prediction: k given all measurements up to time k. The EKF replaces the nonlinear measurement Equation Φk=49), the Note that as  apply),  does notUsing , the A-o P0 into .Different from the D-optimality criterion in , 55) de de55) deThe D- and A-optimality solutions for determining optimal sensor waypoints described above can be computationally expensive, especially if the numerical search must be carried out over a large number of candidate waypoints in the set The idea behind the projection algorithm is illustrated in This behaviour makes sense because the optimality of a target–sensor geometry is improved at the maximum rate by moving the sensor directly towards the optimal sensor location for the given Gaussian prior with mean Algorithm 1 Projection algorithm.Input: s   Output:       Calculate    Calculate    Calculate    If        Else       End   If     Return    Else    Calculate ∠ denotes bearing angle)    Calculate     If      Return     Else     Calculate      Return     End   EndA detailed description of the projection algorithm is provided in Algorithm 1.x-axis. The AOA sensor is allowed to be located on a circle of radius This section presents simulation examples to verify the optimization results and to demonstrate the performance of the sensor trajectory optimization algorithms developed in given by  and 3),,|Φ| and To confirm that  is a valThe optimal target–sensor geometry for the simulated scenario is depicted in In the next set of simulations, we consider sensor trajectory optimization in a bearings-only manoeuvring target tracking problem. The algorithms in , 55) an an55) anThe time interval between successive EKF recursions is set equal to The simulated sensor trajectory for the D-optimality criterion in  is depicThe simulation results for the projection algorithm in Algorithm 1 are shown in Optimal sensor placement for the Bayesian AOA target localization problem was considered. The concept of updating prior information with data from measurements was extended to the Kalman filter tracking problem. Using an approximation of the BFIM, the optimal sensor placement for a given Gaussian prior was shown to be aligned with the minor axis of the prior error ellipse for both D- and A-optimality criteria. By way of simulations, this result was shown to match the optimal sensor placement result for the exact BFIM, which was numerically calculated using Monte Carlo simulations. The D- and A-optimality criteria were adopted for optimal sensor guidance in target tracking applications. Simple methods requiring a numerical search over sensor heading were developed and demonstrated in simulations. A new method, called the projection algorithm, was also developed predicated on the optimal sensor placement result for the D- and A-optimality criteria. The efficacy of the projection algorithm in achieving fast minimization of tracking error was confirmed by numerical simulations. Even though the D- and A-optimality criteria share the same optimal sensor placement result for a stationary target, they generate quite different sensor trajectories in a target tracking setting where optimal target–sensor geometries cannot be realized instantaneously because of constrained sensor dynamics. The A-optimality method was observed to favour circular motion around the target initially. This results in delays in minimizing the tracking error to a desirable level.


Did wait times increase differentially for Black and Hispanic veterans compared with White veterans receiving care from the US Veterans Health Administration during the COVID-19 pandemic?This cross-sectional study including 1 162 148 US veterans found that wait time disparities increased significantly from the pre–COVID-19 period to the COVID-19 period for Black and Hispanic veterans.These results suggest that Black and Hispanic veterans experienced greater access barriers to care compared with their White counterparts during the COVID-19 pandemic. This cross-sectional study including US veterans compares wait times for Black and Hispanic veterans with those for White veterans for outpatient orthopedic and cardiology services from fiscal years 2019 to 2021. Prior studies indicate that Black and Hispanic vs White veterans wait longer for care. However, these studies do not capture the COVID-19 pandemic, which caused care access disruptions, nor implementation of the US Department of Veterans Affairs (VA) Maintaining Internal Systems and Strengthening Integrated Outside Networks Act (MISSION), which is intended to improve care access by increasing veterans’ options to use community clinicians.To determine whether wait times increased differentially for Black and Hispanic compared with White veterans from the pre–COVID-19 to COVID-19 periods given concurrent MISSION implementation.This cross-sectional study used data from the VA’s Corporate Data Warehouse for fiscal years 2019 to 2021 . Participants included Black, Hispanic, and White veterans with a new consultation for outpatient cardiology and/or orthopedic services during the study period. Multivariable mixed-effects models were used to estimate individual-level adjusted wait times and a likelihood ratio test of the significance of wait time disparity change over time.Overall mean wait times and facility-level adjusted relative mean wait time ratios.P < .001). During the COVID-19 period, significant disparities were evident for orthopedic services  but not for cardiology services. Although there was variation in wait time ratios across the 140 facilities, only 6 facility wait time ratios were significant during the pre–COVID-19 period and 26 during the COVID-19 period.The study included 1 162 148 veterans . Significant wait time disparities were evident for orthopedic services  in the pre–COVID-19 period, but not for cardiology services. Mean wait times increased from the pre–COVID-19 to COVID-19 periods for both services for all 3 racial and ethnic groups . Wait time disparities for Black veterans  and Hispanic veterans  vs White veterans  increased significantly from the pre–COVID-19 to COVID-19 periods (These findings suggest that wait time disparities increased from the pre–COVID-19 to COVID-19 periods, especially for orthopedic services for both Black and Hispanic veterans, despite MISSION’s goal to improve access. Facility-level analyses identified potential sites that could be targeted to reduce disparities. In a national sample of adults in the US, an estimated 40.9% reported delayed care or avoided care because of the COVID-19 pandemic, with delayed care significantly higher for Black and Hispanic adults.15The Institute of Medicine defines access to care as having the timely use of personal health services to achieve the best outcomes.17 In response, the VHA adopted several policies aimed at improving care access, most significantly establishing the Veterans Choice Program (VCP) in 2014. The VCP expanded opportunities for eligible veterans to receive care from community clinicians paid for by the VHA (hereinafter referred to as community care). Since implementation of VCP, wait times have declined,20 and by 2017, the VHA generally had shorter wait times than those found in the private sector.19The US Veterans Health Administration (VHA), which provides health care services to 9 million veterans via VHA medical facilities (hereinafter referred to as facilities) across the country, is not immune to care access challenges. The 2014 wait time scandal revealed excessive delays in care for veterans at some VHA facilities and highlighted the need for the VHA to improve timely access to care, especially for outpatient specialty care services.20 However, we additionally found wait time disparities: Black and Hispanic veterans had longer mean wait times for all 5 outpatient specialty care services studied. Other studies conducted both within and outside the VHA22 have noted similar care access disparities by race and ethnicity. However, these studies, like our own, do not capture the more recent operating environment, specifically the disruption in care access due to the COVID-19 pandemic and the implementation of the 2018 US Department of Veterans Affairs (VA) Maintaining Internal Systems and Strengthening Integrated Outside Networks Act (MISSION), which further expanded veterans’ options to use community care. Although several studies have examined how COVID-19 altered health care patterns and utilization among VA-enrolled veterans,27 to our knowledge, no study has examined the timeliness of care during COVID-19 or the potential influence of COVID-19 on care access disparities. Thus, the degree to which COVID-19 reduced or exacerbated existing wait time disparities in the post-MISSION era needs further study.Our own work examining wait times for 5 VHA outpatient specialty care services in 2015 and 2018 also reported declining wait times.In this study, we evaluated changes in wait times for Black and Hispanic veterans compared with White veterans for outpatient orthopedic and cardiology services from fiscal years 2019 to 2021 . We assessed whether there were changes in overall and facility-level wait time disparities during the COVID-19 period) compared with the pre–COVID-19 period. We expected that despite potential care access improvements introduced under MISSION, both wait times and wait time disparities would increase overall during the study period and that the number of facilities with statistically significant disparities would likewise increase.STROBE) reporting guideline.This cross-sectional study was deemed as non–human participant research by the VA Boston Healthcare Research Administration, which waived the need for approval and informed consent. We followed the Strengthening the Reporting of Observational Studies in Epidemiology  obtained from the consultation schema in the VA’s Corporate Data Warehouse. We focused on cardiology and orthopedic services because they are 2 of the most frequently used outpatient specialty services in the VHA and community care. To identify new-patient consultations, we used VHA stop codes  and additional free-text searching when needed to clarify type of service. We included data for services delivered in the VHA and those purchased through community care.29 We extracted veterans’ county code and rurality status from the Planning Systems Support Group’s file, combining rural and highly rural and removing those living in an area categorized as an island. County-level physician workforce by specialty supply was obtained from the Area Health Resource File based on the veterans’ county code in the Planning Systems Support Group’s file. Information on social risks (homelessness and food insecurity) was obtained from the VA’s Health Factor table. We used the 3- to 5-digit institution code associated with each consultation to associate it with a specific VHA facility.We obtained veterans’ sociodemographic information , and Nosos risk score  for the fiscal year in which a consultation occurred. The Nosos risk score was developed to characterize the disease burden of the veteran population for the purpose of predicting costs.18 An individual veteran could be included in the analysis up to 2 times per fiscal year if they had 1 consultation for cardiology and 1 for orthopedics.Our study sample consisted of veterans who received a new-patient consultation for 1 or both outpatient specialty services during the study period. We followed recommended methods for measuring VHA wait times, which defines a new-patient consultation as a consultation for a veteran who has not had an encounter within the same stop code in the prior 24 months at the same VA Medical Center.18 Because means and regression coefficients are sensitive to large outliers, we windsorized consultation wait times, specifically, wait times in the top 1% of the distribution for all 3 fiscal years combined across the 2 stop codes .The primary outcome measure was consultation wait time, defined as the number of days from when a veteran’s clinician requested a consultation for the service to when the appointment for that consultation occurred, which is the recommended method for measuring VHA wait times.30 we defined the pre–COVID-19 period as October 1, 2018, to March 10, 2020, and the COVID-19 period as March 11, 2020, to September 30, 2021. Although the VHA began implementing MISSION in June 2019, full-scale nationwide implementation occurred in 2020,31 closer to when the pandemic began.Two independent variables were of primary interest: (1) veterans’ race and ethnicity and (2) time period (pre–COVID-19 period vs COVID-19 period). We classified veterans as Hispanic, non-Hispanic Black (hereinafter Black), and non-Hispanic White (hereinafter White) based on records. Since the World Health Organization declared COVID-19 a pandemic on March 11, 2020,35 Priority group indicates a veteran’s priority level for enrollment in the VHA. It is based on specific eligibility criteria, including severity of service-connected disabilities and income. Veterans in the lowest priority groups have the highest enrollment priority and are exempt from copayments.28 Priority levels were grouped into 3 categories . A final variable in the model was physician supply (defined as the number of cardiologists and orthopedists in the county where the veteran lived), which is also associated with wait times.37Additional covariates associated with wait times were also included in the models: age (mean-centered), sex (men and women), marital status , social risk (defined as having housing instability and/or food insecurity), rurality status , concurrent Nosos risk score , and priority group.38 Because of large sample sizes, all P values are statistically significant (P < .001).Although the unit of analysis was a veteran new-patient consultation, we also report descriptive statistics on the characteristics of veterans in our sample and compare the characteristics of White veterans with those of Black and Hispanic veterans. We report means and counts along with the standardized mean differences  to compare subgroups. We interpreted effect sizes as small (0.20), medium (0.50), and large (0.80).P < .05 to be statistically significant. To determine whether disparities changed between periods, we reran the above models with a single combined variable for race or ethnicity and COVID-19 instead of an interaction term. We then used a likelihood ratio test to check whether the more complex model (with the interaction term) fit better than the simple model without the interaction term. If the likelihood ratio test was not significant (P > .05), then we would conclude there was no change in disparities between the 2 periods.We ran mixed-effects linear regression models to calculate the adjusted wait times separately for each of the 2 services and 2 minority groups . The dependent variable was the number of days  to consultation. Each model included a random slope for the combined race or ethnicity variable interacted with COVID-19 and a random intercept for facility. We considered 2-sided 39 except we calculated adjusted relative mean ratios (not relative risk ratios). For each service and starting with the Black vs White veteran model, we calculated the estimated wait time for each veteran separately for the pre–COVID-19 and COVID-19 periods, first assuming the veteran was White and then assuming the veteran was Black (and then similarly for the Hispanic vs White veteran model). The adjusted mean ratio for the Black vs White or Hispanic vs White veteran analysis was the ratio of the mean wait time under the assumption all veterans were Black or Hispanic divided by the mean wait time under the assumption all veterans were White.  To determine whether the relative adjusted mean ratios were statistically significant , we calculated 1000 cluster bootstrapped samples for each stop code by race or ethnicity combinations, using an extension of the approach used by Centers for Medicare & Medicaid Services when profiling facility performance.40 If the set of bootstrapped adjusted mean ratios did not contain 1, then we considered it statistically significant at the 95% level. We used R Studio, version 4.1.2  to perform the analysis.To compare adjusted mean wait times for White veterans with those of Black and Hispanic veterans, we followed the approach described by Kleinman and Norton,Because of small sample sizes at some facilities and the problem of multiple comparisons, the power to detect facility relative mean ratios as statistically significant was low. For these reasons, we interpreted statistical significance not as a formal test of a hypothesis but as a signal suggesting that more in-depth monitoring or evaluation of a specific facility would be useful.A total of 1 162 148 veterans with 1 306 383 distinct consultations  were included in the analysis. Black and Hispanic veterans were younger  compared with White veterans  and more likely to reside in counties with higher physician supply. For example, White veterans resided in counties with a mean (SD) of 39.1 (79.3) cardiologists compared with 81.9 (122.2)  for Black veterans and 80.1 (129.2)  for Hispanic veterans. A higher percentage of White veterans resided in rural areas (44.2%) compared with Black veterans  and Hispanic veterans .Our sample included a total of 1 306 383 new patients across 140 facilities . Of the P < .001 for both).Adjusted mean wait times during the pre–COVID-19 period for cardiology services did not differ significantly between White vs Black or Hispanic veterans . In contP < .001 for all). Adjusted mean wait time for orthopedic services followed a similar pattern, increasing 3.75  days for White veterans, 4.10  days for Black veterans, and 4.40  days for Hispanic veterans . Adjusted mean wait time disparities for Black and Hispanic veterans compared with those for White veterans also increased significantly for both services. For cardiology services, the wait time disparity grew by 0.27 days  for Black veterans and 0.60 days  for Hispanic veterans vs White veterans; for orthopedic services, the disparity increase was 0.36 days  for Black veterans and 0.69 days  for Hispanic veterans (P < .001), indicating that the disparity change was significant from the pre–COVID-19 to COVID-19 periods  days for White veterans, 4.74  days for Black veterans, and 5.09  days for Hispanic veterans (veterans . The likP < .001 for both).Despite these increases, as in the pre–COVID-19 period, mean wait times for Black and Hispanic veterans vs White veterans differed significantly only for orthopedic services. Black and Hispanic veterans’ mean wait times exceeded those of White veterans by 2.45  days for Black veterans and 1.98  days for Hispanic veterans during the COVID-19 period  but only modest disparities for cardiology (<1-day difference). The relatively modest increase in disparities for cardiology could be due to the rapid expansion in the VHA’s use of telehealth early in the pandemic.26 Some studies reported higher rates of telehealth use among Black and Hispanic compared with White patients during the COVID-19 pandemic,50 leading to reduced racial and ethnic disparities.53 Regardless, any wait time disparity is concerning, and it will be important for future work to monitor these trends, understand their sources, and implement appropriate interventions as needed.Second, we found a significant increase in overall wait time disparities for both Black and Hispanic veterans vs White veterans from the pre–COVID-19 to COVID-19 periods. This finding was consistent with our expectation that despite MISSION, wait time disparities would increase, given the disproportionate impact of COVID-19 on Black and Hispanic communities.54 or clinician factors .55Third, our national and facility-level findings differed in some respects. For example, nationwide, we did not observe wait time disparities among Black vs White veterans for cardiology during either the pre–COVID-19 or COVID-19 periods. However, we identified 4 facilities during the pre–COVID-19 period and 14 facilities during the COVID-19 period where the mean ratios for cardiology services were significantly different than 1. This finding underscores the critical importance of facility-level analyses for highlighting opportunities to reduce disparities and target quality improvement efforts. It will be important for further work to determine the extent to which these disparities are driven by patient 57 and community care is associated with comparatively longer wait times than the VHA.59 Thus, facilities where White veterans wait longer than Black or Hispanic veterans may indicate needed improvements in access to community care.The facility-level analyses also identified facilities where the relative mean ratio was significant, but in the opposite direction—that is, where White vs Black or Hispanic veterans waited longer. This finding also suggests a need for further inquiry and interventions. For example, White veterans disproportionately use community care compared with those who identify as Black or other races or ethnicities,20 Second, our measure of wait times was based on VHA administrative data. Although this provided an objective measure of wait times, we were unable to account for veterans’ care perceptions, which could yield different results. Third, although we identified changes in facility-level performance, the measure we used (mean ratios) is one of several measures that could be used to flag sites where interventions may help to reduce wait time disparities.This study has several limitations. First, we examined only wait time disparities for 2 outpatient specialty services; findings for other outpatient services within the VHA may differ. In previous analyses, however, we found that wait times followed similar trends across 5 outpatient specialty services, suggesting there may be consistencies in overall trends.In this cross-sectional study, wait time disparities for outpatient cardiology and orthopedic services increased for Black and Hispanic veterans from the pre–COVID-19 to COVID-19 periods compared with White veterans. Specific facilities were identified where wait time disparities were higher, signaling potential opportunities for further investigation and intervention.


Viral infection is an exogeneous factor for primary Sjogren’s syndrome (pSS). This study investigated the association between human papillomavirus (HPV) infections and pSS through a nationwide population based cohort study.Patients with HPV infections between January, 1999 and December, 2013 were included. The incidence of new-onset pSS in patients with HPV infections and non-HPV controls were derived. The multiple Cox regression model derived the risk of pSS in patients with HPV infections. Subgroup analysis and sensitivity analysis were performed to validate the association.During a follow-up period of 12 years, the adjusted hazard ratio (aHR) of pSS in patients with HPV infections was significantly higher than that in non-HPV controls . The risk of pSS increased with age and the risk increased by 2.64-fold (95% CI= 2.37-2.93) for those older than 45 years. The significant association between HPV infections and the risk of pSS persisted in the sensitivity analysis restricted in HPV infections that lasted over 12 months . Subgroup analyses revealed that both male  and female  patients with HPV infections and HPV-infected patients aged between 16 and 45 years  and over 45 years  were associated with a significantly greater risk of pSS.Patients with HPV infections presented with a significantly higher risk of pSS, regardless of age and sex. Primary Sjogren’s syndrome (pSS) is a chronic autoimmune disorder manifested as lymphocytic infiltration, which causes inflammation of the exocrine glands. The destructed glands result in dryness of the eyes and mouth, pain of the joints and fatigue . 30 to 4Human papillomaviruses (HPVs) are transmitted through sexual intercourse or direct contact with macerated skin and they exclusively invade the epithelium and mucous membranes . The HPVA recent study indicates that female patients with rheumatic diseases have higher risk for HPV infections, which might be due to disarranged immune system or immunosuppressive effect of treatments . HoweverWe used the Longitudinal Health Insurance Database 2000 (LHID2000) as our data source, which is a subset of the National Health Insurance Research Database (NHIRD) , 15. TheTo clarify the association between HPVs and the risk of pSS, we designed a retrospective cohort study. The exposure group and the controlled group were followed up from the index date to the date of pSS onset, study subjects withdrawal, or study completion. The index date was defined as the date of HPV diagnosis. The observational period was set between January, 1999 and December, 2013. We also selected several comorbidities to investigate their effects on the incidence of pSS. The HPV group included patients with new-onset HPV infections between 2002 and 2012; while the non-HPV group included patients who did not have HPV infections during the study period. Propensity score matching of 1:4 by sex, year of birth and the index date was used to select non-HPV controls. For both cases and controls, individuals of any missing demographic data, patients who developed pSS before index date, aged below 16 in 2002, or had HPV infections before 2002 and new-onset HPV infections after 2012, were excluded.After excluded 362 patients with pSS diagnoses prior to HPV infections, 6,817 patients with missing demographic data, 185,298 patients younger than 16 years in 2002, 8,664 patients with previous HPV diagnoses before 2002, and 3,491 patients with HPV diagnoses after 2012, a total of 47,302 patients with first HPV diagnoses  between 2002 and 2012 were enrolled in the HPV group Figure 1748,066 patients without HPV diagnoses between 2002 and 2012 were enrolled as the comparison group and matched to patients in the HPV group by sex, year of birth and the index date at a ratio of 1:4. As a result, there were 47,300 patients in the HPV exposure group and 189,200 matched-controls in the control group at baseline Figure 1New-onset pSS was set as the primary endpoint of this study and the diagnoses were based on the American College of Rheumatology (ACR)-the European League Against Rheumatism (EULAR) classification criteria . To ensuPropensity score matching –25 was uIn total, 47,300 HPV-infected patients and 189,200 non-HPV individuals were enrolled at the baseline. The mean age at the time of diagnosis of HPV was 42.59 ± 16.43 years, and 59.24% of HPV patients were between the age of 16 and 45. The female to male ratio was slightly greater than 1 (50.03% vs. 46.97%). After propensity score matching, baseline demographics and comorbidities were balanced Table 1.A total of 493 HPV patients and 1,081 controls were diagnosed with pSS, which corresponded to incidence rates of 13.61 and 7.53 per 100,000 person-months in the total follow-up period of 3,622,659 and 14,359,439 person-months for the HPV group and control group Table 2.Subsequent risk of pSS was estimated by using Cox proportional hazard regression Table 3.After excluded individuals with at least 12 months of loss of follow-up, the aHR of pSS in HPV patients was 1.63 (95% CI=1.45-1.83) Table 4.The etiology of pSS is still inconclusive. Currently, the genetic background, the environmental stimulation on epigenetics, and the use of immunotherapeutic agents –40 are pDuring the infected stage, the HPV deoxyribonucleic acid (DNA) in the infected cells may activate T cells or B cells through MHC pathways , 42. MajIn Taiwan, pSS is one of the catastrophic illness specified by the National Health Insurance program. Attending physicians must submit related clinical information including pSS patient histories, laboratory, and pathological data to the National Health Insurance Administration (NHIA) to apply for a catastrophic illness certificate (CIC). A committee under the NHIA would review all applications in according to the criteria of the American-European Consensus Group (AECG) for pSS . PatientThere are limitations in this study. Firstly, the diagnoses of HPV infections may not be fully accurate. About 75% of females with history of sexual intercourse were susceptible to HPV infections, but the infection symptoms were not always clinically identifiable in patients with HPV infections, which could cause an underestimated rate of HPV-infected patients in this survey. Secondly, the diagnosis of HPV infection in Taiwan mainly depended on pap smear methods and viral DNA types analysis. Smear tests were convenient, but their like-hood ratios (LRs) of HPV infections were as low as 50%. Southern blot analysis, PCR, and other molecular level laboratory analysis were usually performed to increase the true positive rates and to identify specific type of HPV DNA . FurtherOur study employed a large, nationwide sample with high external validity to neutralize deviation from selection bias. In the future, more clinical data are required to reflect a more accurate disease course of HPV infections. Genetic or cytological analyses of lesions that specify HPV genotypes and phenotypes of the infections and biomarkers with high LR values are key prognosis factors of HPV infections, which may solidify our knowledge on the relationship between HPV infections and the risk of pSS. On the other hand, this would help with future disease screenings, patient educations , and earIn this population-based cohort study, patients with HPV infections presented with significantly higher risk of new-onset pSS. RA, COPD, pneumonia, dental caries, chronic liver disease, calculus of kidney, urinary tract infection, and arthralgia were also independent risk factors for pSS.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.H-HC and KM contribute to conception and design of the study. KM and W-JC organized the database and performed the statistical analysis. H-HC wrote the first draft of the manuscript. KM and CD wrote sections of the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.