To conduct a longitudinal ecological analysis of the distance to and participation in free weekly outdoor physical activity events (parkrun) in England from 2010 to 2019, and related socioeconomic and ethnic inequalities, to inform policies to support participation in physically active community events. We calculate distance to the nearest parkrun event for each English Lower Layer Super Output Area (LSOA) each month from January 2010 to December 2019. We then report the trends in distance to and participation in parkrun by Index of Multiple Deprivation quintile. We also report trends in the Relative Index of Inequality (RII) by deprivation for participation and distance to nearest event. We go on to investigate trends in LSOA level determinants (e.g. deprivation and ethnic density) of parkrun participation between 2010 and 2019, using multivariable Poisson regression models. Mean distance to the nearest parkrun event decreased from 34.1 km in 2010, to 4.6 km in 2019. Throughout the period, parkrun events tended to be situated closer to deprived areas compared to less deprived areas. Participation rates increased superlinearly (greater than linear increase) from 2010 to 2013 before slowing to linear growth. Participation over the period exhibits a clear socioeconomic gradient, with people from deprived areas having consistently lower participation rates over the period. parkrun participation rates became more equal between 2010 and 2013 (RII improved from 189 to 39), before stabilising at an RII between 32.9 and 39.6 from 2014 to 2019. The results of the Poisson regression model validate this finding; the coefficients on IMD score initially increased from -0.050 in 2010 to -0.038 in 2013, and then remained relatively stable to 2019 (-0.035). Over the past 10 years, geodesic distance to the nearest parkrun decreased from a mean of 34 km to 5 km. In 2010, there was equality between the least and most deprived areas but by 2017 the distance of the most deprived areas was 29% that of the least deprived. Participation was shown to have increased over the past 10 years which can be split into two distinct phases: from 2010 to 2013 participation increased super-linearly and inequality in participation fell dramatically; from 2013 to 2019 participation increased linearly, and inequality in participation remained stable. Despite parkrun's ambitions of creating inclusive events and engaging with deprived communities, the socioeconomic gradient in participation rates remained high and stable since 2013. Gaining a better understanding of the reasons why parkrun grew so quickly may be useful for other physical activity movements, while further analysis of the relatively lower participation rates in areas with higher socioeconomic deprivation is important for developing initiatives to encourage physical activity in these communities.

Background: Past workplace exposure to asbestos in combination with tobacco smoking has increased the risk of lung cancer for some residents in an area within the Friuli Venezia Giulia region, Northeast Italy. In light of studies showing that lung cancer screening (LCS) with low-dose computed tomography (LDCT) can reduce mortality, local stakeholders and decision-makers decided to assess the potential benefits, harms and cost-effectiveness of a single round of LCS with LDCT versus standard care among people aged 55 to 80 who were formerly exposed to asbestos and with at least 10 pack-years of smoking. Methods: An economic model was developed using a decision tree connected to a Markov cohort model. The primary outcome was the incremental cost per additional quality-adjusted life year (QALY). Other outcomes included the number of life years saved, the number of deaths averted and overdiagnosis. Results: Per 10,000 people screened, the intervention led to 395 additional QALYs (95% credible interval: 129 to 831) and incremental total costs of EUR 1,086,345 (95% credible interval: −852,607 to 2,155,826). The incremental cost per QALY gained was EUR 2750. There was a probability of cost-effectiveness of 99.5% relative to a threshold of EUR 25,000. Conclusions: The model estimated that the intervention was cost-effective. The model’s simplifications and limitations should be considered when interpreting the findings in relation to policy-making decisions. Further research could include the costs and benefits of incidental findings and could assess the cost-effectiveness of repeated rounds of screening for the same population.

Background: The literature on healthy longevity has typically focused on average values (i.e., healthy life expectancy). Recent studies have started to expand this focus by investigating the whole healthy lifespan distribution, especially the standard deviation of healthy longevity, which captures inter-individual variation. Despite these advancements, research gaps remain on how distributions differ by health indicator and sex. This study aimed to compare healthy longevity distributions at age 60 between different health measures and sexes. Methods: We used data from the Survey of Health, Ageing and Retirement in Europe and the Human Mortality Database. A Markov chain model was used to estimate the first three statistical moments of healthy longevity distributions. The maximum entropy method was then applied to derive the full distributions. The healthy lifespan outsurvival statistic and the Hellinger distance were used to compare distributions between males and females. Results: For most health measures, the probabilities of health loss at younger ages were higher for males than for females, and females had a longer healthy life expectancy. Males had more dispersed distributions with a lower mode. For most health measures, healthy longevity distributions were negatively skewed, with a mode age (i.e., the age with the highest probability of health loss) higher than the healthy life expectancy age. The probability for a man to have a longer healthy lifespan than a female was below 50% for various health measures and was the lowest for living free of cardiovascular disease. In contrast, the probability for a man to live free of arthritis or rheumatism for longer than a female was above 50%. The most similar distributions between males and females were observed with life free of any chronic conditions and life with no more than one chronic condition. Conclusions: This study extended the scope of healthy longevity research by complementing a focus on the statistical moments with observations on the mode of the distributions and with formal comparisons based on the healthy lifespan outsurvival statistic and the Hellinger distance, which are applied for the first time in the healthy longevity field.

Despite extensive research on educational inequalities in healthy life expectancy (HLE), most studies focus on average differences, leaving open how healthy longevity is distributed within educational groups. This study examines sex-specific educational differences in healthy longevity distributions at age 60 in Europe, using multiple health measures. We combined data from the Survey of Health, Ageing and Retirement in Europe (SHARE) with Eurostat life expectancy data (2016-2017). A Markov chain model combined with the maximum entropy method was used to estimate distributions of age at the end of healthy lifespan. Educational differences were formally compared using the healthy lifespan outsurvival statistic and the Hellinger distance. High education was often associated with more negatively skewed healthy longevity distributions. Men with low education had higher healthy lifespan variation than men with high education. Across all health measures for women, and most health measures for men, the probability that a person with low education had a longer healthy lifespan than a person of the same sex with high education was below 50%. However, a lack of statistical significance indicated that this finding did not apply to men for life free from cardiovascular conditions or a broad range of chronic conditions. By moving beyond average values and adopting a distributional perspective, this study highlights important dimensions of health inequality that are not captured by mean-based measures alone such as HLE. The findings also point to the need for further methodological development in the estimation of healthy longevity distributions.