Incremental Benefits of Male HPV Vaccination: Accounting for Inequality in Population Uptake

<div><p>Background</p><p>Vaccines against HPV16/18 are approved for use in females and males but most countries currently have female-only programs. Cultural and geographic factors associated with HPV vaccine uptake might also influence sexual partner choice; this might impact post-vaccination outcomes. Our aims were to examine the population-level impact of adding males to HPV vaccination programs if factors influencing vaccine uptake also influence partner choice, and additionally to quantify how this changes the post-vaccination distribution of disease between subgroups, using incident infections as the outcome measure.</p><p>Methods</p><p>A dynamic model simulated vaccination of pre-adolescents in two scenarios: 1) vaccine uptake was correlated with factors which also affect sexual partner choice (“correlated”); 2) vaccine uptake was unrelated to these factors (“unrelated”). Coverage and degree of heterogeneity in uptake were informed by observed data from Australia and the USA. Population impact was examined via the effect on incident HPV16 infections. The rate ratio for post-vaccination incident HPV16 in the lowest compared to the highest coverage subgroup (RR_L) was calculated to quantify between-group differences in outcomes.</p><p>Results</p><p>The population-level incremental impact of adding males was lower if vaccine uptake was “correlated”, however the difference in population-level impact was extremely small (<1%) in the Australia and USA scenarios, even under the conservative and extreme assumption that subgroups according to coverage did not mix at all sexually. At the subgroup level, “correlated” female-only vaccination resulted in RR_L = 1.9 (Australia) and 1.5 (USA) in females, and RR_L = 1.5 and 1.3 in males. “Correlated” both-sex vaccination increased RR_L to 4.2 and 2.1 in females and 3.9 and 2.0 in males in the Australia and USA scenarios respectively.</p><p>Conclusions</p><p>The population-level incremental impact of male vaccination is unlikely to be substantially impacted by feasible levels of heterogeneity in uptake. However, these findings emphasize the continuing importance of prioritizing high coverage across all groups in HPV vaccination programs in terms of achieving equality of outcomes.</p></div

Impact of heterogeneity in vaccine uptake on population level outcomes.

<p>(A) Female-only program (50% overall coverage, extreme inequality). (B) Both sex program (50% overall coverage, extreme inequality). “Correlated” uptake refers to a situation where vaccine uptake within the population is correlated with factors which also affect choice of sexual partners. “Unrelated” uptake refers to a situation where vaccine uptake is unrelated to any of these factors. Vaccination was assumed to commence in 2007.</p

Distribution of disease outcomes (incident HPV16 infections) across subgroups (pseudo Lorenz curve).

<p>(A) Higher population coverage (“Australia”; 72.4% overall). (B) Lower population coverage (“USA”; 32.1% overall). (C) 50% overall coverage, extreme inequality. Comparison of the proportion of disease borne by each subgroup with the group's size. The diagonal line represents a situation where there are no inequalities in outcomes between subgroups; the further away a plot of outcomes is from this equality line, the more unequal outcomes are in that scenario. The pseudo Gini coefficient represents twice the area between the pseudo Lorenz curve and the equality line.</p

Impact of heterogeneity of vaccine uptake on subgroup outcomes.

<p>(A) Higher population coverage (“Australia”; 72.4% overall). (B) Lower population coverage (“USA”; 32.1% overall). (C) 50% overall coverage, extreme inequality.</p

Summary of main results, by sex, coverage scenario and program type.

a<p>“Correlated” uptake refers to a situation where vaccine uptake within the population is correlated with factors which also affect choice of sexual partners. “Unrelated” uptake refers to a situation where vaccine uptake is unrelated to any of these factors.</p>b<p>A pseudo Gini coefficient closer to zero represents more equal outcomes between subgroups; a pseudo Gini coefficient closer to the theoretical maximum represents more unequal outcomes between subgroups. Theoretical maxima for pseudo Gini coefficients are 0.8766 (“Australia” scenario), 0.8378 (“USA” scenario) and 0.5 (“extreme inequality” scenario).</p>c<p>RR_L is the risk experienced by the subgroup with the lowest vaccine coverage relative to that in the subgroup with the highest vaccine coverage, obtained by dividing the age-standardised rate of incident HPV16 infections in the subgroup with the lowest vaccine coverage by the corresponding rate in the subgroup with the highest vaccine coverage.</p

Impact of varying model assumptions on inequality of outcomes.

<p>(A) Higher population coverage (“Australia”; 72.4% overall). (B) Lower population coverage (“USA”; 32.1% overall). A higher value of the pseudo Gini coefficient represents more unequal outcomes. (F) denotes the value for the pseudo Gini coefficient relating to outcomes in females; (M) denotes the value for the pseudo Gini coefficient relating to outcomes in males. SA =  sensitivity analysis. * Switched heterogeneity: Higher heterogeneity used for Australia scenario ((equivalent to heterogeneity in main USA scenario); Lower heterogeneity used for USA scenario (equivalent to heterogeneity in main Australia scenario).</p

Risk of COVID-19 death for people with a pre-existing cancer diagnosis prior to COVID-19-vaccination: a systematic review and meta-analysis

While previous reviews found a positive association between pre-existing cancer diagnosis and COVID-19-related death, most early studies did not distinguish long-term cancer survivors from those recently diagnosed/treated, nor adjust for important confounders including age. We aimed to consolidate higher-quality evidence on risk of COVID-19-related death for people with recent/active cancer (compared to people without) in the pre-COVID-19-vaccination period. We searched the WHO COVID-19 Global Research Database (20 December 2021), and Medline and Embase (10 May 2023). We included studies adjusting for age and sex, and providing details of cancer status. Risk-of-bias assessment was based on the Newcastle-Ottawa Scale. Pooled adjusted odds or risk ratios (aORs, aRRs) or hazard ratios (aHRs) and 95% confidence intervals (95% CIs) were calculated using generic inverse-variance random-effects models. Random-effects meta-regressions were used to assess associations between effect estimates and time since cancer diagnosis/treatment. Of 23 773 unique title/abstract records, 39 studies were eligible for inclusion (2 low, 17 moderate, 20 high risk of bias). Risk of COVID-19-related death was higher for people with active or recently diagnosed/treated cancer (general population: aOR = 1.48, 95% CI: 1.36-1.61, I2 = 0; people with COVID-19: aOR = 1.58, 95% CI: 1.41-1.77, I2 = 0.58; inpatients with COVID-19: aOR = 1.66, 95% CI: 1.34-2.06, I2 = 0.98). Risks were more elevated for lung (general population: aOR = 3.4, 95% CI: 2.4-4.7) and hematological cancers (general population: aOR = 2.13, 95% CI: 1.68-2.68, I2 = 0.43), and for metastatic cancers. Meta-regression suggested risk of COVID-19-related death decreased with time since diagnosis/treatment, for example, for any/solid cancers, fitted aOR = 1.55 (95% CI: 1.37-1.75) at 1 year and aOR = 0.98 (95% CI: 0.80-1.20) at 5 years post-cancer diagnosis/treatment. In conclusion, before COVID-19-vaccination, risk of COVID-19-related death was higher for people with recent cancer, with risk depending on cancer type and time since diagnosis/treatment. </p

Additional file 1: of Trends in genital warts by socioeconomic status after the introduction of the national HPV vaccination program in Australia: analysis of national hospital data

Supplementary data. Table S1. Summary of procedure and diagnosis codes used to define subcategories. Table S2 Admission rates & estimated post-vaccination reductions, by sex, age, and sociodemographic features – sensitivity analyses. Figure S1. Admission rate ratio (admission rate in July 2010–June 2011 relative to three-year prevaccination mean (July 2004–June 2007)) estimated from the full dataset, SES subset, and remoteness area subset, by age and sex. (DOCX 34 kb

List of primary HPV screening strategies evaluated.

<p>List of primary HPV screening strategies evaluated.</p

Model predicted average lifetime number of (a) screening/follow-up episodes and (b) number of colposcopies examinations.

<p>Model predicted average lifetime number of (a) screening/follow-up episodes and (b) number of colposcopies examinations.</p