Trend, disparities, and projection analysis of public data on shoulder fractures in Sweden: a retrospective analysis of two hundred and sixty two thousand, four hundred and forty four fractures

The findings of this study show differences in distribution across age and sex groups, seasonal variations, and projected trends of shoulder fractures in Sweden. Men older than 80 years and women 65 years and older were two group at risk for shoulder fractures which is in line with previous data [10, 11]. These results were not surprising given that shoulder fractures are a common injury in elderly women due to osteoporosis, increased tendency for falls and higher frailty in this population [12, 13]. The next 15 years, according to our analysis, will display slightly lower incidence in shoulder fracture incidence in people over 65 years of age and in older women. The COVID-19 pandemic had also impacted the shoulder fracture incidence numbers in Sweden during 2020–2022, which affected the analysis.

There is another validated register for fractures in Sweden, the Swedish register for fractures, the ‘Svenska Frakturregistret’ [14]. However, the Swedish register for fractures suffers from poor data completeness as it is not mandatory for regions to participate in its reporting. The NPR includes all registered citizens in Sweden and therefore provides a more complete image of fracture incidence than the Swedish register for fractures and has been externally validated [15]. However, the NPR does not contain information about treatments or subtypes of fractures which hinders further analysis. Additional studies are therefore required to examine potential variations in patient characteristics or injury mechanisms among different subtypes of shoulder injuries. Unfortunately, due to the limitations in the available dataset, described above, this analysis was not feasible using our dataset.

The COVID-19 pandemic period (2020–2022) presented unique challenges that likely influenced the trends observed in shoulder fracture incidence, particularly among the elderly population. During this period, Sweden, like many other countries, experienced significant increases in mortality rates, particularly among those aged 65 and older, due to the direct and indirect effects of the pandemic. To account for the potential impact of this increased mortality on our analysis, we adjusted the population at risk on a yearly basis using age-specific mortality data.

It is important to recognize that the healthcare-seeking behavior and overall lifestyle changes during the pandemic, such as reduced physical activity and altered social interactions due to lockdowns and restrictions, may have contributed to the observed trends. Reductions in both incidence rates and number of shoulder fractures during 2020 has previously been reported in numerous publications in other studies [16,17,18]. Similar trends have been observed for other injuries and diseases during the pandemic [19]. However, to our knowledge this is the first nationwide observational study on shoulder fractures during the pandemic. Although Sweden notably avoided implementing stringent lockdown measures. This, coupled with alterations in activity levels, could account for the observed decline in fracture incidence during the pandemic years.

In our projection analysis, we observed a decrease in fracture incidence among the elderly population, which might seem counterintuitive given the expected increase in this demographic. This trend could be explained by several factors. Improved preventive measures, such as better osteoporosis management and fall prevention programs, alongside advancements in healthcare, likely contribute to reducing fracture rates [20, 21]. Additionally, behavioural changes, such as increased physical activity and better overall health among the elderly, may play a role in mitigating fracture risk. In our analysis, the exclusion of COVID-19 years significantly impacted the projected incidence of shoulder fractures in individuals under 65, with a notably higher expected incidence when these years were excluded. This suggests that the pandemic had a substantial effect on fracture trends in younger populations, likely due to changes in behaviour or healthcare access during this period. In contrast, projections for those over 65 remained consistent regardless of COVID-19 data inclusion, indicating that the pandemic had a less pronounced impact on this age group’s long-term fracture trends. However, caution is warranted in interpreting these projections as our projection model may not fully account for the impact of demographic changes. In particular the expected increase in elderly population which may skew incidence numbers [22].

The seasonal variance in fracture incidence, with a statistically significant increase during winter months. The higher incidence of fractures during the winter months can be attributed to several factors. In Sweden, winter is characterized by icy and slippery conditions, which increase the likelihood of falls, especially among the elderly. Additionally, reduced daylight hours and colder temperatures may limit outdoor activity, potentially leading to decreased physical fitness and balance, further heightening fracture risk. This seasonal trend is consistent with previous studies that have documented similar patterns [23, 24]. Further investigation into the reasons behind this seasonality is warranted in order to facilitate targeted preventive measures during the winter months to protect vulnerable populations, particularly the elderly.

While this study provides insights, certain limitations should be acknowledged. The retrospective observational nature of the analysis relies on accurate and complete reporting in publicly available healthcare records. We were also not able to distinguish between certain subgroups of shoulder fractures as the ICD-10 codes we analyzed does not distinguish between fractures. Nevertheless, we believe our findings provide important data in the expected incidence of shoulder fractures in the population in Sweden using easily available and stratified data.

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