1 Introduction

The COVID-19 pandemic may have changed our lives more than any other event in recent years. It spread quickly worldwide and required strict measures to control it, causing major disruptions to daily life, the economy, and social interactions. Among the numerous factors influencing the pandemic’s impact, age and geography may have been among the most significant.

Age was identified early on as the primary risk factor, with older individuals facing a higher risk of severe illness and mortality (Starke et al. 2021; Liu et al. 2021). This increased vulnerability imposed stricter isolation measures for older adults, further exacerbating feelings of loneliness and anxiety among this demographic (Wu, 2020; Seifert and Hassler 2020). Conversely, younger populations, although less susceptible to severe outcomes, experienced significant disruptions in education, employment, and social activities, leading to different psychological and economic stressors (Luchetti et al. 2020; Varma et al. 2021Steen et al., 2022).

Geography also played a crucial role in shaping the pandemic’s impact. The stark images of empty city streets underscored the profound changes in urban life (Pohl 2022), where high population density and the reliance on public transportation made social distancing more challenging (Tirachini and Cats 2020; Gutiérrez et al. 2021). In contrast, rural areas, characterized by lower population densities, faced less difficulty in maintaining physical distance (Callaghan et al. 2021; Glogowsky et al. 2021). However, these areas also grappled with other challenges, such as limited healthcare infrastructure and an aging population, which compounded the risk factors associated with COVID-19 (Smolić et al. 2022; Henning-Smith, 2021). Thus, it is important to note that rural area residents may face more extensive challenges in accessing healthcare, which may have offset some of the advantages associated with lower population density. Further, in urban areas, where the lifestyle is closely intertwined with density and proximity to others, the response to the virus may have resulted in more pronounced changes in daily life compared to rural areas (Nathan 2020). The lower population density in rural areas naturally facilitates the maintenance of physical distance. Additionally, activities that are more prevalent in urban settings, such as visits to restaurants and cultural events (Glaeser et al. 2001), bore the brunt of the pandemic restrictions. Public transportation, which is more commonly used in urban areas, posed a higher risk compared to car travel, which is more prevalent in rural areas (Nathan 2020). Rural areas also tend to have a higher proportion of older residents, while urban areas tend to have a younger demographic profile (Glasgow and Brown 2012; Rhubart et al 2021; Florida 2002; Gehl 2013).

In general, the pandemic brought about a combination of life restrictions and health risks, resulting in a general decline in individuals’ life satisfaction during this period (Patrick et al., 2020; Brodeur, 2021; Möhring et al. 2021), including in Sweden (Kivi et al., 2020). Many countries implemented strict quarantine measures for their entire populations. In contrast, Sweden opted for a less stringent but more targeted approach, focusing primarily on advising the older adults aged 70 or above to self-quarantine to reduce their risk of infection. Consequently, while the entire population experienced alterations in their daily lives, it is highly probable that the older adults were the most profoundly affected (Public Health Agency of Sweden 2020a, b; Fristedt et al., 2021). These age-targeted pandemic-related restrictions and recommendations affected the older adults in a different way than the rest of the population, including more severe isolation, stigma from being outdoors and seen in public, and difficulties associated with receiving home assistance services and residing and visiting loved ones in care facilities (the latter were also closed to visitors during a significant part of the pandemic (Public Health Agency of Sweden 2020c).

In this work, we integrate age and geography into an analysis aimed at examining their influence on life satisfaction and perceived health status during the pandemic. By examining how these factors affect people differently and similarly, we aim to create a better understanding of how the pandemic may have influenced the life satisfaction and health perceptions of different groups. Based on earlier work, the intersection of age and geography may have complicated the pandemic’s effects.

We focus specifically on those aged 70 years or above since the Swedish pandemic strategy was specifically focused on protecting individuals aged 70 years and older. While Sweden did not implement a lockdown, this age group was instructed to stay at home and avoid contact with others, while individuals aged 69 years and younger did not receive the same instructions to stay isolated. Nursing homes were also closed to visitors. The life situation thereby differed quite extensively for those aged 70 and above compared to those who were younger.

Our findings display that older adults report higher life satisfaction than other age groups, and this remained unchanged during the pandemic. However, perceived health declines with age, and the difference in health status between age groups is even more pronounced during 2020 compared to 2019. More importantly, geography is a significant determinant of well-being, with older individuals in urban areas experiencing a relative decline in life satisfaction during the pandemic, potentially attributed to the greater influence of pandemic restrictions on urban lifestyles. Conversely, older individuals in rural areas reported relatively lower perceived health in 2020, possibly due to limited access to healthcare services in a time when access to healthcare was more important than ever. In summary, our results highlight the significance of both age and geography in assessing overall well-being as well as the effects of the pandemic on well-being, both in terms of life satisfaction and health status.

2 Concepts and theories

Health, happiness, and wellbeing are closely linked to age (Clark and Oswald, 1994). Research has consistently shown that there is a U-shaped relationship between age and subjective well-being, with well-being levels being lowest among middle-aged adults and highest among the youngest and oldest individuals (Deaton 2008; Blanchflower and Oswald, 2008). This tendency has also been observed in the Swedish context, where subjective well-being and happiness levels are lowest for individuals aged 45 to 64 years old, but higher in childhood and older life (Gerdtham and Johannesson 2001). However, it is important to note that the exact age at which well-being is at its lowest may vary across cultures and cohorts (Steptoe et al. 2015; Di Tella et al., 2001). Additionally, factors such as social support, physical health, and cognitive function have been found to play a role in mediating the relationship between age and well-being.

Social isolation and loneliness are considered important social determinants of human well-being and quality of life. Extensive research has demonstrated the relationship between social interaction and health outcomes (Kim et al. 2008), a connection that may be even stronger among older adults (Morrow-Howell and Gehlert 2012). For example, isolation can lead to heightened feelings of loneliness and depression, which was especially the case for older adults during the pandemic (Ammar et al., 2020; Chtourou et al., 2020; Xiao et al., 2020). Research has shown that social isolation and loneliness can have adverse effects on physical health, such as increased risk of cardiovascular disease, weakened immune function, and higher mortality rates (Hawkley & Cacioppo 2010).

Since the emergence of the COVID-19 pandemic, numerous studies have examined its impact on subjective well-being. Usher et al. (2020) discuss the pandemic’s overall negative effect on society, while Philip et al. (2020) find that loneliness, domestic isolation, and social disengagement are linked to poorer physical performance among older adults in the UK. Studies conducted in Italy, Denmark, and the UK indicate that social isolation resulted in reduced physical activity and subsequent declines in health (Plagg et al., 2020; Narici et al., 2020; Moro and Paoli, 2020). Gustavsson and Beckman (2020) conducted an online survey in Sweden during April and May 2020, targeting individuals aged 70 years and older. The survey revealed that up to half of the respondents reported a decline in mental health, including feelings of depression.

The feeling of social isolation may also vary with geography. Putnam (2000) suggests that a general decline in social participation has led to decreased health status among those living in areas with few or no social connections. Henning-Smith et al. (2019) found that rural residents were more likely to report stronger social relationships than their urban counterparts, despite greater distances to neighbors and fewer opportunities for social interaction. Meanwhile, a substantial body of research indicates that cities provide better arenas for social interaction due to higher population density and more numerous, albeit weaker, social ties (Jacobs 1969; Florida 2002; Kavanaugh et al. 2003; Schläpfer et al. 2014). Liu et al. (2021) found higher levels of mental health problems in rural areas compared to their urban counterparts in China during the pandemic.

Many studies highlight the adverse effects of the pandemic on subjective well-being, especially among the older adult population. However, many older adults also saw the pandemic as an opportunity to develop new interests and hobbies. Some also engaged with new technology and learned how to use videoconferencing and social media (Herron et al. 2021). Many participants felt a sense of community and solidarity in the struggle against the pandemic. So, despite the challenging emotional symptoms experienced by some participants due to isolation, a significant portion found ways to adapt and even thrive. Several studies have reported similar findings, with participants successfully adjusting to changes in their lives and mitigating feelings of loneliness and social isolation. Additionally, research has emphasized the value of positivity, community support, and access to outdoor spaces, highlighting broader systemic supports such as health services and public health policies that recognize the value of older adult lives. This underscores the importance of a multi-systemic approach to understanding resilience, one that incorporates material, social, and cultural contexts (Kotwal et al. 2021; Macdonald and Hülür, 2021; Herron et al. 2021).

The role of geography and the urban–rural setting on well-being in relation to age can be ambiguous. On average, urban areas tend to attract younger people, which can contribute to better overall health outcomes. However, urban areas can also be more stressful and demanding, potentially negatively affecting the well-being of the residents. Von Humboldt et al. (2015) find that although urban areas offer more opportunities for social interaction and access to medical services, older adults may not derive as much benefit from these activities and resources as younger individuals. In contrast, rural areas often exhibit a stronger sense of community, which can lead to greater social support and lower levels of stress among older adults in particular. However, rural areas may face their own challenges, including limited access to care facilities and services, which can adversely impact the well-being of their residents. It is crucial to recognize the complex interplay between geographical factors and the well-being of older adults, as both urban and rural areas present unique opportunities and challenges in maintaining overall well-being.

Therefore, further research is necessary to understand the role of geography in the well-being of older adults during a crisis such as the COVID-19 pandemic. A United States study demonstrated that the COVID-19 pandemic presented greater risks to the well-being of older adults in rural areas, primarily due to economic challenges and disparities in access to medical resources (Henning-Smith 2020). In another US study, Henning-Smith found no significant differences in mental health outcomes between rural and urban groups during the initial months of the pandemic. However, rural residents exhibited lower concern about COVID-19 and higher engagement with social media, suggesting a differential perception and response to the pandemic risk that may have affected future health outcomes. A Mexican study showed elevated risks for negative mental health outcomes associated with COVID-19 infections and adverse events, with significant variations across demographic groups. Women and those with less education faced heightened risks, illustrating the intersection of COVID-19 impacts with existing sociodemographic vulnerabilities (Gonzalez-Gonzalez et al. 2023).

Earlier studies also find that rural areas or smaller communities offer greater accessibility to nature, providing more opportunities for physical exercise and psychological relaxation (Kaczynski and Henderson, 2007). As a result, residents in rural areas may have been better equipped to mitigate the negative health effects during the pandemic restrictions (Maas,et al. 2010).

Prior to the pandemic, a significant amount of research on subjective well-being focused on the role of income (Easterlin 1974; Luttmer 2005; Deaton 2008; Stevenson and Wolfers 2008; Graham 2012). This research suggests that there is a correlation between income and subjective well-being, both in absolute terms and relative to others, with expectations playing a role that is often income-adjusted. Given that incomes tend to be higher in urban areas (Rauch 1993), it is reasonable to expect a higher level of subjective well-being in larger cities. Florida et al. (2013) confirm this observation, primarily attributing it to higher levels of education.

However, while income and education tend to be higher in urban areas, other factors such as lower population density and a stronger sense of social security in rural areas suggest that subjective well-being may also be higher outside of urban settings (Bell 1992; Lawless and Lucas 2011). This could be due to lower expectations and a more relaxed lifestyle (David and Fine-Davis 1991). Rural areas may also provide more social capital and a stronger sense of community, which contributes to a sense of social safety (Putnam 2000). These findings indicate that subjective well-being is influenced by a complex interplay of factors, including income, education, and social capital, which may vary between urban and rural areas.

This study examines how life satisfaction and perceived health status during the pandemic were influenced by age and geography. By analyzing these factors, we aim to better understand their distinct and overlapping relations, and how the pandemic may have impacted their well-being. Early studies have observed short-term changes in subjective well-being among older adults in Sweden (Kivi et al. 2021). However, a knowledge gap remains regarding whether the pandemic had varying effects on well-being in different age groups, based on their residential rurality or urbanity. This study aims to contribute to this understanding by examining the relationship between residential location and well-being in the wake of the COVID-19 pandemic, with a particular focus on individuals aged 70 and above – specifically targeted by the Swedish pandemic strategy. In our empirical analyses, we utilize survey data from the National SOM (Society, Opinion, and Media) survey developed by the SOM Institute at Gothenburg University for the years 2019 and 2020.

3 Data and methodology

To examine how age and geography, as well as their interaction, relate to well-being pre-pandemic and during the pandemic, we employ survey data from the National SOM survey for the years 2019 and 2020 (University of Gothenburg, SOM Institute 2019; 2020). The SOM Institute, formally known as the Institute for Opinion Surveys and Media Analysis, is a research institute affiliated with the University of Gothenburg in Sweden. Since 1986, the institute has conducted the annual National SOM survey, which aims to provide a comprehensive understanding of Swedish society by collecting data on a diverse array of topics, including social issues and values. The survey employs a large, random, and representative sample of the Swedish population aged 16–85 years. Questionnaires are distributed in September each year, with the data collection phase concluding 3–4 months later. The response rates were 49 percent in 2019 and 51 percent in 2020. The SOM Institute adheres to stringent methodological standards in survey design, sampling, and data analysis to ensure high-quality data. Annual methodology reports assess the representativeness of the sample compared to the overall Swedish population. These reports indicate a slight underrepresentation in survey responses from foreign-born individuals, younger individuals, and men (particularly younger men) compared to older individuals, Swedish-born individuals, and women (Falk et al. 2019; 2020). After removing individuals with missing values for some of the variables/questions, our sample contains 8,971 observations in the year 2019 and 9,834 observations in 2020. Of these, 1,936 and 2,099 were aged 70 years or older in 2019 and 2020, respectively.

Our dependent variables are the survey questions on self-reported (i) life satisfaction and (ii) overall health status. In the survey, the questions read: (i) “How satisfied are you with your life overall?”, here called “Life Satisfaction,” where 0 = Not at all satisfied and 4 = Very satisfied, and (ii) "How would you assess your health in general?”, here called “Health,” where 0 = Very bad and 10 = Very good.

Our primary variables of interest are the variables on age and geography. Based on previous research and the characteristics of the pandemic, we hypothesize a relationship between combinations of age and geography and well-being. The pandemic significantly impacted everyone’s daily life; however, individuals aged 70 years and older were more vulnerable to the virus and received stricter recommendations on isolation and social distancing. Additionally, individuals in different types of locations and areas were affected in different ways. Social distancing and maintaining a similar lifestyle are likely easier in rural areas compared to urban areas.

The main variable on geography is based on the survey question “In what type of area do you live?”, with the four categories: rural area, smaller agglomeration, city or larger agglomeration, and metropolitan area. Metropolitan area corresponds to living in one of Sweden’s three metropolitan areas: Stockholm, Gothenburg, and Malmo. Given that many official classifications of urban and rural areas are on a municipal level and hence consider the overall status of a relatively large geographic entity, these classifications fail to distinguish between dense and peripheral areas within municipalities. The self-reported measure of place of residence used here incorporates these nuances. However, as a robustness test, we run similar analyses using the Swedish Agency for Growth Policy Analysis’ (2021) official classification of municipalities as our variable on geography. This classification divides the Swedish municipalities into three different categories based on population size, urban population share, and distance to agglomerations: rural, mixed, and urban municipalities (Swedish Agency for Economic and Regional Growth 2021). We also run additional analyses including a variable on NUTS1 statistical regions of Sweden. Sweden can be divided into three “Lands” (NUTS1): north, east, and south. Including this variable in the regressions allows us to examine whether well-being differs depending on where in the country the individual lives, independently of the location’s urban–rural status.

Individuals are divided into four different age categories: 16–29, 30–59, 60–69, and 70–85. We also run sub-analyses where we only include individuals aged 70 years and older; in these analyses, we include the age categories 70–75, 76–80, and 81–85.

In addition, our analyses include control variables for gender (1 = female, 0 = male), household income (12 income groups), higher education (= 1 if the individual has higher education, otherwise 0), housing type (= 1 if the individual lives in a villa/townhouse, otherwise 0), marital status (single, in a relationship, cohabitation, married/registered partnership, and widow/widower), and the month in which the survey was conducted. The summary statistics are available in Table 1 below:

Table 1 Summary statistics
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To investigate the role of age and geography in individual well-being, we run both separate and pooled regressions for the years 2019 (before the pandemic) and 2020 (during the pandemic). In other words, we examine whether the pandemic and the restrictions that followed had diverse effects on the well-being of individuals depending on their age and location (urban–rural setting). Given the ordinal nature of our dependent variables, employing ordered logit regressions allows us to utilize all response categories for our dependent variables. However, when we test for heteroscedasticity using Brant tests, a few independent variables come out significant and thus violate the proportional odds assumption. To ensure that our model gives unbiased estimates, we employ a heteroscedastic generalized ordered logistic model (OGLM) in which the independent variables that show significant heteroscedasticity are specifically analyzed. The OGLM adds a variance equation that considers the differences in residual variability for these independent variables (Williams 2010).

For easier interpretation, we display the results as odds ratios, where coefficients above 1 signify a positive correlation and coefficients below 1 suggest a negative correlation. We cluster the standard errors at the municipality level to account for potential dependence of individuals within the same municipality.

4 Results

We now move on to the results of our analysis. We examine the relationship between life satisfaction and perceived health status on one hand, and age and geography on the other. We do this for the years 2019 (before the pandemic) and 2020 (during the pandemic). We also conduct sub-analyses where we focus specifically on older adults.

4.1 Life satisfaction—Full sample analyses

The results from the full-sample analysis of life satisfaction in 2019 and 2020 are presented in Table 2. The table displays results for the variables of interest, but we also control for individual characteristics (gender, income, education, marital status, health, housing type, and the month in which the survey was conducted). In addition, independent variables on age, income, and health show significant heteroskedasticity and are included also in a variance equation in the OGLM model. The full table, Table 5 in the Appendix, displays the results for the control variables and variance parameters.

Table 2 OGLM estimates: Life satisfaction (dep. var.)
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First and foremost, the results in Table 2, column 1, where we pool the 2019 and 2020 data, convey that subjective life satisfaction is not significantly different in 2020 compared to 2019. The survey question on life satisfaction is broad and represents overall satisfaction with one’s life as a whole.

Secondly, individuals in the age categories of 60–69 years (in 2020) and 70–85 years (in 2019 and 2020) are significantly more likely to report higher levels of life satisfaction compared to the baseline category of 16–29-year-olds across estimations. Individuals aged 30–59 years reported the lowest level of life satisfaction, in accordance with the U-shaped relationship between age and life satisfaction (Columns 1–3). Based on the point estimates, the difference between the age category of 70–85 years and the base category of 16–29 years has increased during the pandemic. This indicates that the gap in life satisfaction between the youngest and oldest age groups has widened in 2020 compared to 2019. However, t-tests show that there is no statistically significant difference in coefficients in 2019 and 2020 (Columns 2–3).

Examining the role of geography, we find that individuals living in rural areas (the baseline category) are more likely to report higher levels of life satisfaction compared to those living in urban areas, both before and during the pandemic (Columns 1–3). Based on the point estimates, the differences were further pronounced during the pandemic, with individuals living in metropolitan areas (Stockholm, Gothenburg, and Malmö) being even less likely to report higher levels of life satisfaction than those residing in rural areas in 2020 compared to 2019. However, t-tests convey that there is no statistically significant difference in the relationship between geography and life satisfaction in 2020 compared to 2019 if we choose a 5% significance level.

The inclusion of geography-age category interaction variables in the estimations in Columns 4 (2019) and 5 (2020) results in the overall relationship between life satisfaction and geography becoming statistically insignificant. Instead, we identify a few significant interaction variables. Based on the results for 2020, we can see that the lower likelihood of reporting higher life satisfaction in urban areas (Columns 1–3) is driven by the older adult population (Column 5). That is, individuals in the age category 70–85 are significantly less likely to report higher life satisfaction levels if they live in metropolitan areas, cities, or larger agglomerations than if they live in rural areas.

During the pandemic year 2020, we see that the older age categories, 70–85 years, who live in urban areas (cities, larger agglomerations, or metropolitan areas) experienced significantly lower levels of life satisfaction compared to their peer group residing in rural regions. This indicates that although older people generally have a higher degree of life satisfaction, during the pandemic in particular, older adults in urban environments experienced lower life satisfaction than older adults in rural areas.

Based on the variance parameters provided by the OGLM (see Appendix Table 5), it is clear that individuals in the reference age category, aged 16–29, are more variable in their life satisfaction scores than older individuals. Furthermore, the higher the household income or subjective health score, the less variable the respondents are in their life satisfaction scores.

We also run ordered logit estimates and the results are robust (see Table 6 in the Appendix). In addition, we add a control for NUTS1 region in the analyses presented in Table 7. There is no significant relationship between life satisfaction and NUTS1. The results on location (rural area, smaller agglomeration, etc.) remain the same. Thus, the density of the location seems to matter more for life satisfaction than where in the country one lives. Table 8 in the Appendix displays results on the relationship between life satisfaction and location, using the Swedish Agency for Growth Analysis’ municipal classification as the determinant of individuals’ type of location. The results are similar to those in Table 2; however, some significance levels vary between the analyses. The lower life satisfaction in urban areas compared to rural areas in 2020 compared to 2019 is more evident when we employ this location variable in the analyses. The gap seems to have widened between the different types of municipalities in 2020 compared to 2019. The interactions between age and location show similar relationships as in Table 2, but they are not statistically significant in Table 8.

Table 3 OGLM estimates: Health status (dep. var.)
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Table 4 OGLM estimates: Life satisfaction and health status (dep. var.). Restricted sample 70 years old and above
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In summary, older individuals and those living in rural areas reported higher levels of life satisfaction before and during the pandemic, with this pattern being slightly reinforced during the pandemic year. In 2020, older adults in urban areas had significantly lower life satisfaction compared to older adults in rural areas. Finally, the type of area one lives in seems to matter more for life satisfaction than where in the country one lives.

Our finding that overall life satisfaction has not been severely affected by the pandemic in the short run is in consensus with previous literature, which emphasizes that subjective life satisfaction is not a quick or temporary judgment based on momentary influences and feelings but a relatively stable phenomenon (e.g., Pavot et al. 1991). Presumably, individuals base their life satisfaction on perceptions of their past and future and believe that the pandemic will not have long-term consequences on their life situations.

Taken together, our results suggest that younger individuals may have experienced a different kind of stress and uncertainty than older individuals, albeit to a somewhat lesser extent from public restrictions. For instance, high school and university students received their education entirely remotely. Additionally, other age groups may have experienced greater stress due to potential negative effects on their employment opportunities, while the older generation’s pensions remained unchanged.

One reasonable explanation for the fact that older adults in urban environments experienced lower life satisfaction than those in rural areas may be that the urban lifestyle was more significantly affected by the restrictions. For example, older adults in urban areas presumably had a harder time going outdoors while following the recommendations and may therefore have stayed indoors and more isolated than their rural counterparts. Higher population density in urban areas may also have caused more fear of being infected. Furthermore, it is more difficult to maintain distance on public transport or to get around by car. Additional factors could include variations in the sense of community or living conditions (size of accommodation/garden).

4.2 Health—full sample analyses

Turning to our results on the relationship between subjective health, age, and geography before and during the pandemic, we observe a somewhat different pattern. The results from the full-sample analysis of health in 2019 and 2020 are presented in Table 3. As in Tables 2, 3 displays results for the variables of interest, but we also control for individual characteristics (gender, income, education, marital status, life satisfaction, housing type, and the month in which the survey was conducted) and heteroskedasticity for the variables significant in Brant tests: age, geography, income, life satisfaction, education, gender, and housing type. The full table displays the results for the control variables and variance parameters (see Appendix Table 9).

In contrast to the results on life satisfaction, the results in Table 3, column 1, where we pool the 2019 and 2020 data, convey that subjective health is significantly lower in 2020 compared to 2019. The pandemic appears to have had a significant effect on individuals’ perceptions of their health status; however, we cannot distinguish whether these perceptions correspond to actual declines in “objective” health conditions.

Furthermore, all older age categories are more likely to report worse health status compared to the younger age group (base category 16–29 years), which is expected. However, the age groups 30–59 and 60–69 report similarly lower levels of health scores relative to the reference category, while the age group 70–85 reports significantly lower levels.

In columns 1 and 2, we investigate the relationships between health, age, and geography separately for the years 2019 (column 2) and 2020 (column 3). In relative terms, the health gap between the oldest and the youngest age categories remains similar in the two years. On the contrary, based on the point estimates, the age gap between middle-aged individuals and the youngest individuals appears to have increased slightly in 2020 compared to 2019 (the corresponding 2019 and 2020 coefficients are not statistically different from each other in t-tests). Additionally, we find that individuals residing in metropolitan areas (2019 and 2020) and cities or larger agglomerations (2020) are more likely to report higher health scores compared to those living in rural areas.

In order to gain further insight into the relationship between age and location in relation to health, we include interaction variables between age and place of residence in columns 4 (2019) and 5 (2020), while controlling for the same individual characteristics. The results of our analysis reveal that the relationship between age and health remains consistent with our earlier findings, with older individuals reporting lower perceived health than the reference group (16–29 years), especially during the pandemic year of 2020. When we include these interaction variables, the relationship between geography and health becomes insignificant, and the negative relationship between the two oldest age categories and health becomes larger in magnitude in 2020. While the interaction variables are statistically insignificant, the point estimates in Table 3, column 5, suggest that the geographical variation in subjective health score is driven by older adults in urban areas perceiving their health to be better than their peers in rural areas.

Based on the variance parameters provided by the OGLM, we find that individuals aged 70–85 are more variable in their health scores than younger individuals (reference category, 16–29). Furthermore, individuals in smaller agglomerations, cities, or larger agglomerations are less variable in their health scores compared to individuals in rural areas (full results available in Appendix Table 9). We also run ordered logit estimates and the results are robust (see Table 10 in the Appendix). In Table 11 in the Appendix, we add a control for the NUTS1 region in the analyses. There is a slight positive relationship between living in the South (rather than the North) and health status in 2019 but not in 2020. No other NUTS1 coefficient is significant, and the results for the other variables remain the same when this variable is controlled for. Thus, the rurality/density of the location seems to matter more, both for health and life satisfaction, than where in the country one lives. In Table 12 in the Appendix, the Swedish Agency for Growth Analysis’ municipal classification is used as a determinant of individuals’ type of location. The results are similar to those in Table 3; however, the relatively higher health score in urban areas compared to rural areas in 2020 compared to 2019 is more evident when we employ this location variable in the analyses. The gap seems to have widened between the different types of municipalities in 2020 compared to 2019. The similar results in Tables 2 and 8, and Tables 3 and 12, employing different measures of type of location (self-reported location and municipal classification), indicate that the results are robust.

In summary, older individuals and those living in rural areas reported lower perceived health status, which was further reinforced during the pandemic year.

4.3 Life satisfaction and Health – sub-sample analyses

To investigate more in depth the relationship between geography/location and well-being for older adults, we run a subsample analysis including only individuals aged 70–85 years. This sub-analysis allows us to examine if the overall relationships between geography and well-being differ for this age group. The results are displayed in Table 4 (full table available in Appendix Table 13). We also control for age in this subsample, using the following age categories: 70–75, 76–80, and 81–85, with 70–75 forming the reference group.

Starting with the results on life satisfaction in the years 2019 (column 1) and 2020 (column 2), we can see that there is significant variation in life satisfaction between individuals aged 70–75 and older individuals in 2020. That is, individuals 70–75 are significantly less likely to report higher life satisfaction in comparison to individuals 76–85 during the pandemic but not before the pandemic. In consensus with the findings in Table 2 (i.e., the geographical variation in life satisfaction is driven by the variation in life satisfaction of older adults across space), the results in Table 4 display that older adults living in metropolitan areas (2019 and 2020) and cities or larger agglomerations (2020) are significantly less likely to report higher life satisfaction. In other words, in 2019, older individuals in the metropolitan areas (Stockholm, Gothenburg, and Malmö) felt that they had a lower degree of life satisfaction than the reference group who lived in the countryside. During the pandemic year, this also applied to those who lived in other urban environments (cities and larger agglomerations). That is, the older age group of 70–85 years living in urban environments experienced a lower degree of life satisfaction during the pandemic in comparison to 2019 (the difference in coefficients between 2019 and 2020 is statistically significant in t-tests, p-value = 0.07).

Table 4, columns 3 (year 2019) and 4 (2020), display the results on the relationship between subjective health, age, and geography. The results show a significant negative relationship between age and health, as expected, and this relationship is similar in 2019 and 2020. Regarding geography, we do not find any significant results for the year 2019, i.e., the year before the pandemic, for these older individuals. However, in the pandemic year 2020, we observe a significantly higher likelihood of higher perceived health in urban settings compared to rural areas.

It is possible that the restrictions had a more pronounced effect on the age group 70–75 than on those who were older, as individuals in the younger group are more likely to engage in a wide range of cultural and social activities compared to their older counterparts. From a geographical perspective, it could be that people in cities had closer contact with healthcare during the pandemic, as accessibility to healthcare is significantly better in cities than in rural areas. Although it is unlikely that the actual health status of urban older adults improved compared to rural older adults within a year, it is clear that perceived health status was higher in urban areas than in rural areas in 2020 compared to 2019. This may have been a result of the frequent emphasis on the connection between being older and being in a higher-risk group in 2020, which led older individuals in rural areas to perceive themselves as even less healthy than those in cities at a time when health status was in focus. We also ran the analysis using an ordered logit regression, and the results are robust (see Appendix Table 14).

5 Conclusion

In this study, we combine age and geography to examine their impact on life satisfaction and perceived health status during the pandemic, aiming to better understand how these elements uniquely and combined influenced life satisfaction and perceived health status. Previous research suggests that both age and location may have influenced the pandemic’s impact. We investigate these relationships in Sweden in 2019 (before the pandemic) and 2020 (during the pandemic).

Our Swedish survey is unique in its kind, and with no exact international comparison, our results contribute to an ongoing discussion about the impacts of COVID-19 on older adults. However, it can still be related to other international studies.

Our findings show, perhaps somewhat unexpectedly, that the life satisfaction of older adults does not appear to have been significantly impacted by the pandemic—at least not when compared to other age groups. However, other international studies have found similar results. For instance, studies from Germany and Poland (Kivi et al. 2021; Bidzan-Bluma et al., 2020) found that older adults were less susceptible to declines in life satisfaction due to COVID-19. This observation could be attributed to the relatively short-term disruption that the pandemic was (see e.g., Pavot et al. 1991), but also to the fact that many older adults adapted well to the pandemic by leveraging their technology knowledge to communicate remotely with family and friends (Herron et al. 2021), which might have mitigated the negative impacts on their life satisfaction. Further, the untouched nature of their primary source of income—the pension—compared to the younger group, which faced a greater risk of unemployment. Furthermore, the prolonged closure of high schools disproportionately affected the younger generation, impeding their social opportunities. However, it is also worth considering that many older adults may have appreciated the pandemic strategies that prioritized their protection.

In terms of geography, we find that older adults (70–85) residing in metropolitan areas (Stockholm, Gothenburg, and Malmo) had a lower degree of life satisfaction compared to those living in the countryside, both in 2019 and 2020. However, during the pandemic year 2020, older adults in other urban areas (cities or larger agglomerations) also experienced significantly lower levels of life satisfaction than older adults living in rural areas. This indicates that the pandemic resulted in a larger gap in life satisfaction between older adults in urban environments and those in rural areas. This may be due to the urban lifestyle being more affected by the restrictions and a lower sense of community in cities.

These findings partially align with Henning-Smith’s (2021) findings that older adults in rural areas in the US face more physical health risks associated with COVID-19. This might suggest that while rural older adults face greater health risks, they may maintain or even increase their life satisfaction relative to their urban counterparts. This also underscores the complex relationship between health risks, life satisfaction, and age, where geography plays a critical role. Additionally, the findings are in line with Liu et al. (2021). Their study shows how urban residents in China experienced more mental health problems and a lower level of life satisfaction during COVID-19, and more so in urban than in rural settings. Also, Helliwell (2020) found higher levels of life satisfaction in rural areas among older adults. This may suggest that our results are observable across different national contexts.

Regarding perceived health status, we did not find any significant results for geography in 2019 for older individuals. However, in the pandemic year 2020, perceived health was significantly lower in rural settings compared to urban areas. This could be attributed to the generally lower health status in rural areas, relatively less accessibility to healthcare, and the heightened focus on health during the pandemic, as discussed in earlier research concerning rural challenges in healthcare provision (Henning-Smith 2020). Overall, these findings highlight the importance of considering both age and geography when examining life satisfaction and health, especially during times of crisis such as the COVID-19 pandemic. Additionally, our results indicate that people’s perceived health progressively deteriorates with age, but this is not the case with life satisfaction. While individuals’ perceived health declines gradually as they age, in general, older adults are more satisfied with life.

These results should be viewed as interim findings, and the analysis comes with limitations. The impact of the pandemic may have longer-lasting effects that could evolve with time, which this study would not be able to capture. Another limitation is that our analysis builds on self-reported survey data, which may be influenced by personal outlook and understanding of the questions. There may also be other variables affecting our results that we are unable to capture, such as family support or pre-existing health conditions. We therefore encourage additional research into the effects of pandemic restrictions on life satisfaction and the role of geographic factors in shaping these outcomes.