Abstract
Current studies have indicated that the number of individuals living with pain has risen in recent years, with nearly half of all adults in some countries living with some form of pain. Such trends have prompted researchers to explore differences in pain across different sociodemographic groups, with a dominant focus on educational attainment. However, much of the studies fail to consider the confounding role of early life characteristics, such as family background. Using data on over 400,000 individuals from the UK Biobank, we look at how educational attainment is associated with nine different domains of pain (headache, facial, neck, back, hip, knee, stomach, all over, and no pain). Ultimately, we find that compared to those with no educational credentials, education is associated with anywhere between a 0.1 to 15% change in the likelihood of reporting pain, depending on pain type and education level, with the greatest change occurring in those with the highest level. Yet, when accounting for family background characteristics in the form of sibling fixed effects, nearly all relationships between education and pain fell by either 50% or were eliminated. We ultimately conclude that failure to consider early life characteristics, such as family background characteristics may lead to inflated estimates of pain, and that future research should delve into early life exposures and their influence on pain in adulthood.
Introduction
Chronic and acute pain are major population health issues, with an estimated quarter of adults globally suffering from pain (Zimmer et al., 2022), including 20 percent of adults in the US and 43 percent in the UK (Dahlhamer et al., 2018; Fayaz et al., 2016). These figures represent significant burdens for individuals, given the accompanying psychological issues that have been found to come with pain, including insomnia, unhappiness, depression, and anxiety (Cheatle et al., 2016; de Heer et al., 2018).
Broadly, previous research has indicated that the number of individuals living with some type of pain has risen steeply in recent years (Case & Deaton, 2017; Stokes et al., 2020; Zajacova et al., 2021). One meta-analysis of papers on chronic pain in the United Kingdom estimated that there was an increase from an average 40.8% reporting pain in the 1990’s to 45% since 2010 (Fayaz et al., 2016). This growing prevalence has led scholars to examine differences in pain by sociodemographic characteristics such as race and ethnicity (Choudhury et al., 2013; Zajacova, Grol-Prokopczyk, et al., 2022) and age (Rustøen et al., 2005). Results suggest adults in mid-life tend to be at the highest risk for reporting pain, due to factors such as reduced satisfaction with life (Blanchflower & Bryson, 2021; Rustøen et al., 2005; Stokes et al., 2020). Also, at all stages of the life course, it has been found that there is a sex gradient with regard to pain, with women reporting higher levels (Grol-Prokopczyk, 2017; Kennedy et al., 2014; Zajacova et al., 2021). Beyond this, there has been an examination into the heritability of pain, with one study finding that genetics attributed to about one-third the prevalence of chronic low back pain (Junqueira et al., 2014).
An increasing body of work has explicitly sought to examine the socioeconomic gradient in pain prevalence, revealing a negative relationship (Johannes et al., 2010; Portenoy et al., 2004). Researchers have shown that people in the lowest socioeconomic class were 2.8 times more likely to feel disabled through pain (Dorner et al., 2011). Investigations of geographic differences in pain across the United States and Canada found hotspots in the deep south and Appalachia, along with parts of the west (Zajacova, Lee, et al., 2022). The authors ultimately find that these trends are partially a result of the worse socioeconomic conditions of these areas.
Additionally, many have opted to look at the educational gradient in pain, finding that those with higher levels of educational attainment see lower pain prevalence. In a study looking at multiple types of pain across 19 European countries, Todd and colleagues (2019) found that compared to those with higher levels of education, those with a medium or low level of education typically had higher levels of pain. Moreover, using longitudinal data over 12 years, other research has found that compared to those with less than a high school education, those with higher educational attainment had a lower likelihood of reporting pain, net of other sociodemographic characteristics like age and sex (Grol-Prokopczyk, 2017). Aside from these examples, other studies have looked into the education and pain relationship and reported similar findings (Großschädl et al., 2016; Kennedy et al., 2014; Zimmer & Zajacova, 2018).
Another paper by Cutler and colleagues (2020) looked at the socioeconomic status gradient in knee pain, and found that less educated individuals reported more pain. To further disentangle the education and pain relationship, they looked at physical demands in occupations, psychological factors, medical treatment, and obesity differences. They ultimately found that physical demands in occupations and obesity both accounted for about one third of the education factor in pain. Furthermore, they also found that obesity is a key component given that physical demands in one’s occupation were associated with pain principally among those who are obese, a finding that other scholars have highlighted in previous years (Glei et al., 2022; Stokes et al., 2020; Zimmer et al., 2022).
Taken together, despite the many studies examining socioeconomic differences in pain, especially those looking at educational disparities, there are limitations in the literature. First, the education and pain relationship may be confounded by early life and family background factors. For instance, those who come from more disadvantaged backgrounds may not be able to acquire more education, and in turn do not have resources to prevent or treat different forms of pain. Second, much of the prior work looks at pain in a broad sense (Todd et al., 2019) or only looks at a single domain of pain (Großschädl et al., 2016; Macfarlane et al., 2014), rather than linkages between socioeconomic status or educational attainment and multiple forms of pain. Third, many of the studies that examine the prevalence of pain focus on the United States, though other developed countries, such as the United Kingdom, have much higher estimates of pain in the adult population than the United States (Fayaz et al., 2016).
From this, we ask the following research questions: 1) What is the association between educational attainment and different type of pain? 2) Is this relationship substantially confounded when factoring in family background factors (via sibling fixed effects)? and 3) Is there a gendered gradient driving the findings we see?
Data and Methods
We used data from the UK Biobank (UKB) project. The UKB is a large scale study of over 500,000 individuals recruited between 2006 and 2010, ranging in age from 37 to 74 at the time of the survey. The UKB collected a vast amount of epidemiological, biological, and socioeconomic information from individuals registered with the National Health Service and who lived within 25 mins of one of 22 different assessment centers. One component of the questionnaire was to ask if participants suffered from some type of pain in the past months, from this, they were able to select one or more types of pain that they suffered from. As such, it allows for a more robust examination of types of pain. Of the 502,505 completed responses in the UKB, we excluded those without information on pain, education, body mass index, or genetics (n=98,630), the latter is used to identify siblings. Despite this large number of responses being removed from our analytic sample, it still left us with an analytic sample of 403,875 for the full sample.
Measures
Pain.
Our main dependent variable that we examine is pain. However, rather than a summary measure, we opt to look at nine different measures to test whether or not there may be differential impacts of covariates on specific types of pain. The following types of pain reported in the UKB are: 1) headache pain; 2) facial pain; 3) neck pain; 4) back pain; 5) hip pain; 6) knee pain; 7) stomach pain; 8) all over body pain; and 9) “none of the above” or no pain;. All of these measures are dichotomous outcomes.
Educational Attainment.
Our main independent variable of interest is educational attainment. Educational levels of the UKB participants were measured by mapping each educational qualification from the survey to an International Standard Classification of Education (ISCED) category. From this, the number of years educational equivalent is produced for each ISCED category. This approach has been utilized in prior studies that incorporate a measure of educational attainment from the UKB (Fletcher et al., 2021; Lee et al., 2018).
Sibling Pairs.
Siblings are not initially identified in the UKB database. However, given that the UKB has genetic information on respondents, we used this to identify sibling pairs in the dataset. To do so, we utilized the UKB kinship file, which has information among 100,000 pairs in the full sample of the data. From this, we chose pairs with kinship was more than 20%, reflecting a first-degree biological relative (parents and siblings). To identify the siblings, we then choose to keep the pairs whose age are less than 13 years apart. This left us with about 22,000 sibling pairs. The next step was to then keep only one pair of siblings from families that may have had more than one pair of siblings. This ultimately left us with slightly over 17,000 sibling pairs, further reduced to 16,473 pairs once missing data and information were removed.
Covariates.
Our main covariates we included were sex and age. Sex was included as a covariate in order to see if there were any gendered effects with pain, and to see if the educational attainment of these groups were largely driving any potential findings. Age was also included as a covariate to see how pain operated over time, given that some types of pain have the potential to level off with age, whereas others begin to emerge more in later life. Body mass index was also controlled for in supplemental analyses to see the influence of obesity on the likelihood of reporting pain.
Analytic Strategy
We first summarize our descriptive statistics of our sample by showing the breakdown of the different domains of pain, along with our covariates. Next, we then estimated a series of linear probability models predicting the likelihood of reporting pain, due to the easier interpretation, along with the similar effects that they produced compared to logit models. Furthermore, we assume the relationship between educational attainment and forms of pain to be linear in nature, and after examining the various assumptions of linear probability models, we concluded that it would be the best approach. The first model included our full sample of over 400,000 observations, looking at the relationship between educational attainment and pain. Then, our second model only looked at the relationship between educational attainment and pain among sibling pairs to see if this relationship held in the sub-sample of siblings. The third model then included fixed effects for the sibling pairs to explore the impact of family background confounding. We also examined the interaction between sex and educational attainment in a final set of analyses. For supplementary analyses, we included an analysis that included controlling for body mass index to see if factors such as obesity would explain some of the relationship between education and pain.
Results
Table 1 shows the descriptive statistics of our sample of over 400,000 observations. Among this full sample, the average educational attainment among individuals is 14.78 years (SD=5.13). The average age is 56.89 (SD=8.00) and 54% of our sample is female. Regarding our different measures of pain, they range from as little as 2% to 25% reporting some type of pain. Conversely, 49% of our sample report having no pain at all. For our analysis of sibling pairs, we have over 34,000 observations. To visualize these differences, we show in Figure 1 our different domains of pain by age and education. For simplicity, we compare those who received 10 years of schooling and compare it with those who received 20 years. Across all domains, it is clear that those who received lower education report more pain than those with higher education. One interesting phenomenon is that there is relatively limited evidence of disparities in pain by education past age 40, which may signify the origin of these gradients are in early life.
Table 1.
Descriptive Statistics about Baseline Measures
| Variables | N | Mean or % (Std. Dev) | Min, Max |
|---|---|---|---|
| Education (years) | 403,875 | 14.78 (5.13) | 7, 20 |
| 7 | 72,000 | 18% | |
| 10 | 70,596 | 17% | |
| 13 | 21,589 | 5% | |
| 15 | 49,853 | 12% | |
| 19 | 64,753 | 16% | |
| 20 | 125,084 | 31% | |
| Age (years) | 403,875 | 56.89 (8.00) | 39, 73 |
| Female | 403,875 | 54% | |
| BMI | 403,875 | 27.41 (4.76) | 12.12, 74.68 |
| Headache Pain | 403,875 | 20% | |
| Facial Pain | 403,875 | 2% | |
| Neck Pain | 403,875 | 23% | |
| Back Pain | 403,875 | 25% | |
| Stomach Pain | 403,875 | 8% | |
| Hip Pain | 403,875 | 11% | |
| Knee Pain | 403,875 | 21% | |
| All Over Pain | 403,875 | 2% | |
| No Pain | 403,875 | 49% | |
| Family ID | 32,946 |
Figure 1.
Domains of Pain by Age and Education, Full Sample
Table 2 shows the results of our analyses of how education influences the likelihood of headache pain. We use the educational category of “no credentials” as the reference category. Across the full sample, we find evidence that there are statistically significant associations between educational attainment and headache pain. Specifically, we find that compared to those with no education credentials, those with 10 years of schooling have on average a 0.025 lower probability of reporting headache pain, and those with 20 years have a 0.029 lower probability of reporting headache pain. When analyzing the sibling pair sample (column 2), we see a slight increase in the size of the education coefficients and pain, but they still maintain their association. Once we control for sibling fixed effects (column 3) in this sample, we see that the associations between education and headache pain are removed. We see similar findings across other domains of pain (1A-8A in the appendix). Specifically, education is associated with a reduction in the likelihood of reporting pain, and an increase in the likelihood of reporting no pain in the full sample. Among our sibling pair sample, with the expectation of facial pain, the relationship between educational attainment and pain remains, with slight attenuation. However, once we account for sibling fixed effects, the relationship between educational attainment and pain is largely removed, with only a handful of associations between education and pain. Figure 2 summarizes the finds for sibling pairs (columns 2 and 3 of our results) by education level and across all domains of pain for our sibling analysis.
Table 2.
Results for Impact of Education on Headache Pain
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.007*** (0.000) |
−0.008*** (0.000) |
−0.008*** (0.001) |
| Female | 0.073*** (0.001) |
0.075*** (0.004) |
0.062*** (0.006) |
| Education (years) | |||
| 10 | −0.025*** (0.002) |
−0.034*** (0.007) |
−0.010 (0.011) |
| 13 | −0.026*** (0.003) |
−0.049*** (0.011) |
−0.011 (0.016) |
| 15 | −0.028*** (0.002) |
−0.029*** (0.008) |
−0.001 (0.012) |
| 19 | −0.011*** (0.002) |
−0.020** (0.008) |
−0.005 (0.011) |
| 20 | −0.029*** (0.002) |
−0.039*** (0.007) |
0.002 (0.012) |
| Constant | 0.598*** (0.005) |
0.630*** (0.020) |
0.646*** (0.050) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.030 | 0.028 | 0.540 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Figure 2.
Comparison of Education Impact on Pain Domains among Sibling Pairs
To further unpack this relationship between education and pain, we estimated interactions between sex and educational attainment in Tables 1B-8B. In these models, we find a remaining relationship between pain and education in the full sample, but predominantly for males, rather than females. This is largely the same when looking just at our sibling pairs, which still show an effect for education for males, whereas females see few effects across domains of pain. However, this relationship between education and pain is largely confounded once sibling differences are accounted for, similar to prior models. These findings essentially suggest that the relationship between education and different types of pain is substantially weakened once early-family background factors are controlled. As a sensitivity check, we also opted to create a more global measure of pain by summing the number of pain conditions an individual had, along with a factor analysis of the pain domains, both of which mirrored our main results, signifying that the education-pain relationship is attenuated with the addition of sibling fixed effects (see Online Supplement).
Discussion
Previous studies have robustly examined the education gradient in pain during adulthood (Grol-Prokopczyk, 2017; Todd et al., 2019; Zajacova, Lee, et al., 2022). However, much of those studies often focus on one dimension of pain (Großschädl et al., 2016; Macfarlane et al., 2014), or exclusively look at it in the context of the United States (Zajacova et al., 2020). Additionally, research focused on pain has often neglected the potential confounding factors on the education-pain relationship. This paper applies a new way of examining the effects of family background on pain gradients in adulthood by looking at sibling pairs. The analyses conducted in this paper show there are associations between education and pain, but that many of these associations are substantially reduced or completely mediated when factoring in family background characteristics. From this, we have some main takeaways.
First, in our initial models there were associations between all levels of education across all domains of pain. Yet, upon selecting our sample of sibling pairs and controlling for sibling fixed effects, there remained only eight associations out of a possible 45 across domains. Moreover, of these eight associations, the majority of them constituted of either individuals who received 10 years of education, or 20 years of education, which correspond roughly to the US equivalent of a high school and college degree, respectively. This implies potentially that even in the presence of family background characteristics, the major educational milestones may offer some benefit to specific types of pain in adulthood, whereas additional education past the 10 year mark does not necessarily correspond to uniformly better health, as has been found in other studies linking education and health outcomes (Zajacova et al., 2012).
A second takeaway is that educational attainment maintained its relationship net of sibling fixed effects with specific types of pain: knee, back, and no pain. For knee and back pain, these are forms of pain that are often found among those with higher body mass index or those with more physically demanding occupations (Cutler et al., 2020; Hansen et al., 2019). In some supplementary analyses, we include a control for body mass index, where we find attenuation in the education-pain relationship of about 21% for knee pain and 11% for back pain (see Online Supplement). Future research into domains of pain should consider both body mass index and physical demands of occupations to see if this helps explain the education-pain relationship. Regarding the no pain association, that could be the result of the fact that educational attainment has been found to improve one’s overall health, so it would make sense to see an increase in the likelihood of pain for those who completed 10 and 20 years of education. However, there are some limitations with the no pain measure that will be elaborated on later in this section.
There are some important limitations to this study that are in need of being addressed. First, one of the principal limitations is that our examination of family background characteristics is done via sibling fixed effect models, which excludes individuals without siblings. As such, it could be that those individuals without siblings may have had disproportionately different outcomes than those with siblings, and thus our analysis is unable to capture that. However, given that in our analyses of the full sample is similar with those of just the sibling pairs, this suggest that perhaps the effect of early environments still remains, even for non-siblings. Moreover, we have no way of accounting for whether or not the siblings were reared together in the same household, and thus exposed to the same environment and contexts. Another notable limitation of the UK Biobank is that the response rate among those is recruited is rather small, and been found that those who did participate are less deprived socioeconomically, thus giving way to potential selection biases (Allen et al., 2012; Fry et al., 2017). However, given that we find associations among the lowest educated groups in our sample, and the fact that they remain for three of our nine measures of pain, we believe that education coefficients shown here are likely conservative. Furthermore, the large sample size of the UK Biobank gives us one of the largest datasets on siblings that measure different domains of pain, and provides more statistical power to at least examine educational differences in the pain gradient.
A final limitation is that the “no pain” measure included in this study results from individuals who answered “none of the above” on the lists of pain they have. Thus, it could be they have a form of pain to report, but that it was not captured in the survey instrument. Likewise, it could also be the case that it means they did not have any pain to report and thus, selected the “none of the above” option. Our results indicate that there is a positive relationship between the metric with education, which would lead us to believe that the majority of individuals selecting that option selected under the guise of reporting no pain. Nevertheless, given this limitation, it is imperative that potential results for this domain of pain be interpreted with caution.
Despite these limitations, this research is among the first to utilize sibling models to examine the education-pain relationship across different types of pain. Results ultimately point to and suggest that when family background factors are accounted for, most associations dissipate, implying that the origins of these gradients in education and pain are in early life. For those associations that do remain, they are substantially confounded by nearly 50% on average. Without better measures of family background, we can only speculate on the sources of these effects. In this list, we would highlight parental health, parental economic resources, and parental educational attainment as well as broader environmental factors that vary by household resources. For example, poor parental health could limit children’s education attainment and also be transmitted genetically and be reflected in the child’s future (i.e. adult) pain outcomes. The level of household economic resources likely affects the child’s educational attainment and also the types of occupations, and associated pain risk, for the child. Parental educational attainments are strongly linked with children’s education and may also affect the occupational choice and other adult outcomes related to pain. Our findings reveal that failing to factor in early life factors in the form of family background characteristics may lead to inflated estimates between educational attainment and pain in adulthood.
Supplementary Material
Table 3.
Results for Impact of Education on Headache Pain, Sex and Education Interaction
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.007*** (0.000) |
−0.008*** (0.000) |
−0.008*** (0.001) |
| Female | 0.069*** (0.003) |
0.070*** (0.010) |
0.057*** (0.014) |
| Education (years) | |||
| 10 | −0.028*** (0.003) |
−0.039** (0.012) |
−0.007 (0.016) |
| 13 | −0.033*** (0.005) |
−0.056*** (0.017) |
−0.013 (0.022) |
| 15 | −0.021*** (0.004) |
−0.025 (0.013) |
0.002 (0.017) |
| 19 | −0.022*** (0.003) |
−0.037*** (0.011) |
−0.019 (0.015) |
| 20 | −0.030*** (0.003) |
−0.035*** (0.010) |
−0.002 (0.015) |
| 10 X Female | 0.006 (0.004) |
0.008 (0.015) |
−0.004 (0.021) |
| 13 X Female | 0.012* (0.006) |
0.012 (0.022) |
0.004 (0.029) |
| 15 X Female | −0.012** (0.005) |
−0.006 (0.016) |
−0.005 (0.022) |
| 19 X Female | 0.025*** (0.004) |
0.035* (0.015) |
0.028 (0.021) |
| 20 X Female | 0.001 (0.004) |
−0.008 (0.013) |
0.007 (0.018) |
| Constant | 0.599*** (0.005) |
0.632*** (0.020) |
0.647*** (0.051) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.030 | 0.029 | 0.540 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Highlights.
Explores socioeconomic gradient across different types of pain.
Uses the UK Biobanks large number of sibling pairs to control for family background.
Family background confounds or eliminates the education-pain relationship.
Body mass index further attenuates the education-pain relationship.
Acknowledgments
The authors gratefully acknowledge use of the facilities of the Center for Demography of Health and Aging at the University of Wisconsin-Madison, funded by NIA Center Grant P30 AG017266.
Appendix
Table 1A.
Results for Impact of Education on Facial Pain
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.000*** (0.000) |
−0.000*** (0.000) |
−0.000 (0.000) |
| Female | 0.012*** (0.000) |
0.012*** (0.002) |
0.009*** (0.002) |
| Education (years) | |||
| 10 | −0.002* (0.001) |
−0.002 (0.002) |
0.006 (0.004) |
| 13 | −0.002 (0.001) |
−0.003 (0.004) |
0.006 (0.006) |
| 15 | −0.003** (0.001) |
−0.002 (0.003) |
0.009* (0.004) |
| 19 | −0.001 (0.001) |
−0.001 (0.003) |
0.001 (0.004) |
| 20 | −0.003*** (0.001) |
−0.005* (0.002) |
0.007 (0.004) |
| Constant | 0.033*** (0.002) |
0.037*** (0.007) |
0.016 (0.017) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.002 | 0.003 | 0.510 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 2A.
Results for Impact of Education on Neck Pain
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.001*** (0.000) |
−0.001*** (0.000) |
−0.002 (0.001) |
| Female | 0.021*** (0.001) |
0.009* (0.005) |
0.008 (0.007) |
| Education (years) | |||
| 10 | −0.053*** (0.002) |
−0.063*** (0.008) |
−0.026* (0.012) |
| 13 | −0.075*** (0.003) |
−0.075*** (0.012) |
−0.019 (0.017) |
| 15 | −0.056*** (0.002) |
−0.070*** (0.008) |
−0.017 (0.013) |
| 19 | −0.034*** (0.002) |
−0.034*** (0.008) |
−0.005 (0.013) |
| 20 | −0.094*** (0.002) |
−0.091*** (0.007) |
−0.020 (0.013) |
| Constant | 0.330*** (0.006) |
0.345*** (0.021) |
0.329*** (0.053) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.007 | 0.006 | 0.525 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 3A.
Results for Impact of Education on Back Pain
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.001*** (0.000) |
−0.001** (0.000) |
−0.001 (0.001) |
| Female | −0.021*** (0.001) |
−0.027*** (0.005) |
−0.022** (0.007) |
| Education (years) | |||
| 10 | −0.058*** (0.002) |
−0.062*** (0.008) |
−0.030* (0.013) |
| 13 | −0.086*** (0.003) |
−0.077*** (0.012) |
−0.025 (0.018) |
| 15 | −0.063*** (0.003) |
−0.062*** (0.009) |
−0.017 (0.014) |
| 19 | −0.048*** (0.002) |
−0.040*** (0.008) |
−0.020 (0.013) |
| 20 | −0.110*** (0.002) |
−0.108*** (0.007) |
−0.046*** (0.013) |
| Constant | 0.391*** (0.006) |
0.381*** (0.022) |
0.363*** (0.054) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.008 | 0.008 | 0.533 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 4A.
Results for Impact of Education on Hip Pain
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | 0.002*** (0.000) |
0.003*** (0.000) |
0.003*** (0.001) |
| Female | 0.038*** (0.001) |
0.039*** (0.004) |
0.043*** (0.005) |
| Education (years) | |||
| 10 | −0.032*** (0.002) |
−0.033*** (0.006) |
−0.013 (0.010) |
| 13 | −0.047*** (0.002) |
−0.036*** (0.009) |
−0.003 (0.013) |
| 15 | −0.033*** (0.002) |
−0.035*** (0.006) |
−0.002 (0.010) |
| 19 | −0.022*** (0.002) |
−0.026*** (0.006) |
0.001 (0.010) |
| 20 | −0.057*** (0.002) |
−0.055*** (0.005) |
−0.016 (0.010) |
| Constant | −0.013** (0.004) |
−0.021 (0.016) |
−0.067 (0.041) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.013 | 0.012 | 0.527 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 5A.
Results for Impact of Education on Knee Pain
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | 0.002*** (0.000) |
0.002*** (0.000) |
0.002** (0.001) |
| Female | −0.017*** (0.001) |
−0.028*** (0.005) |
−0.027*** (0.007) |
| Education (years) | |||
| 10 | −0.054*** (0.002) |
−0.041*** (0.008) |
−0.008 (0.012) |
| 13 | −0.078*** (0.003) |
−0.074*** (0.011) |
−0.027 (0.017) |
| 15 | −0.059*** (0.002) |
−0.052*** (0.008) |
−0.015 (0.013) |
| 19 | −0.037*** (0.002) |
−0.027*** (0.008) |
−0.006 (0.012) |
| 20 | −0.099*** (0.002) |
−0.092*** (0.007) |
−0.042*** (0.013) |
| Constant | 0.150*** (0.005) |
0.137*** (0.020) |
0.103* (0.052) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.011 | 0.011 | 0.532 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 6A.
Results for Impact of Education on Stomach Pain
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.003*** (0.000) |
−0.002*** (0.000) |
−0.003*** (0.001) |
| Female | 0.021*** (0.001) |
0.017*** (0.003) |
0.017*** (0.004) |
| Education (years) | |||
| 10 | −0.017*** (0.001) |
−0.021*** (0.005) |
−0.015 (0.008) |
| 13 | −0.026*** (0.002) |
−0.030*** (0.008) |
−0.006 (0.011) |
| 15 | −0.023*** (0.002) |
−0.016** (0.006) |
0.001 (0.009) |
| 19 | −0.013*** (0.002) |
−0.008 (0.005) |
−0.001 (0.008) |
| 20 | −0.030*** (0.001) |
−0.030*** (0.005) |
−0.003 (0.008) |
| Constant | 0.241*** (0.004) |
0.221*** (0.014) |
0.253*** (0.035) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.008 | 0.006 | 0.518 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 7A.
Results for Impact of Education on Pain All Over the Body
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.000*** (0.000) |
−0.000 (0.000) |
−0.000 (0.000) |
| Female | 0.004*** (0.000) |
0.003 (0.001) |
0.003 (0.002) |
| Education (years) | |||
| 10 | −0.016*** (0.001) |
−0.012*** (0.002) |
−0.003 (0.004) |
| 13 | −0.019*** (0.001) |
−0.013*** (0.003) |
−0.005 (0.005) |
| 15 | −0.017*** (0.001) |
−0.010*** (0.002) |
−0.004 (0.004) |
| 19 | −0.015*** (0.001) |
−0.011*** (0.002) |
−0.007 (0.004) |
| 20 | −0.023*** (0.001) |
−0.020*** (0.002) |
−0.007 (0.004) |
| Constant | 0.034*** (0.002) |
0.028*** (0.006) |
0.025 (0.016) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.004 | 0.003 | 0.520 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 8A.
Results for Impact of Education on reporting No Pain
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | 0.003*** (0.000) |
0.003*** (0.000) |
0.004*** (0.001) |
| Female | −0.024*** (0.002) |
−0.011 (0.006) |
−0.009 (0.008) |
| Education (years) | |||
| 10 | 0.091*** (0.003) |
0.092*** (0.009) |
0.041** (0.013) |
| 13 | 0.123*** (0.004) |
0.133*** (0.013) |
0.048* (0.020) |
| 15 | 0.098*** (0.003) |
0.095*** (0.010) |
0.023 (0.015) |
| 19 | 0.066*** (0.003) |
0.057*** (0.009) |
0.021 (0.013) |
| 20 | 0.152*** (0.002) |
0.144*** (0.008) |
0.047** (0.014) |
| Constant | 0.174*** (0.006) |
0.154*** (0.025) |
0.150* (0.062) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.012 | 0.011 | 0.538 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Appendix
Table 1B.
Results for Impact of Education on Facial Pain, Sex and Education Interaction
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.000*** (0.000) |
−0.000*** (0.000) |
−0.000 (0.000) |
| Female | 0.010*** (0.001) |
0.012*** (0.003) |
0.010* (0.005) |
| Education (years) | |||
| 10 | −0.003** (0.001) |
−0.003 (0.004) |
0.006 (0.005) |
| 13 | −0.004** (0.002) |
−0.002 (0.006) |
0.007 (0.007) |
| 15 | −0.003** (0.001) |
−0.004 (0.004) |
0.003 (0.005) |
| 19 | −0.002* (0.001) |
0.002 (0.004) |
0.007 (0.005) |
| 20 | −0.004*** (0.001) |
−0.005 (0.003) |
0.007 (0.005) |
| 10 X Female | 0.003 (0.001) |
0.002 (0.005) |
−0.000 (0.007) |
| 13 X Female | 0.004 (0.002) |
−0.001 (0.007) |
−0.000 (0.010) |
| 15 X Female | 0.001 (0.002) |
0.003 (0.006) |
0.010 (0.007) |
| 19 X Female | 0.003* (0.001) |
−0.006 (0.005) |
−0.012 (0.008) |
| 20 X Female | 0.002 (0.001) |
0.000 (0.004) |
0.001 (0.006) |
| Constant | 0.034*** (0.002) |
0.038*** (0.007) |
0.016 (0.017) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.002 | 0.003 | 0.510 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 2B.
Results for Impact of Education on Neck Pain, Sex and Education Interaction
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.001*** (0.000) |
−0.001*** (0.000) |
−0.002 (0.001) |
| Female | 0.000 (0.003) |
−0.030** (0.011) |
−0.045** (0.017) |
| Education (years) | |||
| 10 | −0.056*** (0.004) |
−0.079*** (0.013) |
−0.057** (0.019) |
| 13 | −0.087*** (0.005) |
−0.089*** (0.018) |
−0.053* (0.026) |
| 15 | −0.066*** (0.004) |
−0.101*** (0.014) |
−0.059** (0.020) |
| 19 | −0.049*** (0.003) |
−0.066*** (0.011) |
−0.034* (0.017) |
| 20 | −0.111*** (0.003) |
−0.123*** (0.011) |
−0.063*** (0.017) |
| 10 X Female | 0.008 (0.005) |
0.030 (0.016) |
0.054* (0.023) |
| 13 X Female | 0.023*** (0.007) |
0.025 (0.023) |
0.060 (0.032) |
| 15 X Female | 0.020*** (0.005) |
0.052** (0.017) |
0.071** (0.025) |
| 19 X Female | 0.032*** (0.005) |
0.059*** (0.016) |
0.051* (0.023) |
| 20 X Female | 0.032*** (0.004) |
0.055*** (0.014) |
0.078*** (0.020) |
| Constant | 0.338*** (0.006) |
0.364*** (0.022) |
0.358*** (0.054) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.007 | 0.007 | 0.525 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 3B.
Results for Impact of Education on Back Pain, Sex and Education Interaction
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.001*** (0.000) |
−0.001** (0.000) |
−0.001 (0.001) |
| Female | −0.012*** (0.003) |
−0.015 (0.011) |
−0.012 (0.017) |
| Education (years) | |||
| 10 | −0.038*** (0.004) |
−0.032* (0.013) |
0.003 (0.019) |
| 13 | −0.080*** (0.005) |
−0.060** (0.018) |
−0.041 (0.027) |
| 15 | −0.059*** (0.004) |
−0.050*** (0.014) |
−0.008 (0.021) |
| 19 | −0.048*** (0.003) |
−0.047*** (0.012) |
−0.029 (0.018) |
| 20 | −0.107*** (0.003) |
−0.101*** (0.011) |
−0.038* (0.018) |
| 10 X Female | −0.033*** (0.005) |
−0.046** (0.016) |
−0.050* (0.024) |
| 13 X Female | −0.010 (0.007) |
−0.028 (0.024) |
0.026 (0.034) |
| 15 X Female | −0.008 (0.005) |
−0.021 (0.018) |
−0.016 (0.025) |
| 19 X Female | 0.003 (0.005) |
0.019 (0.016) |
0.020 (0.024) |
| 20 X Female | −0.006 (0.004) |
−0.011 (0.014) |
−0.014 (0.021) |
| Constant | 0.384*** (0.006) |
0.370*** (0.022) |
0.353*** (0.055) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.008 | 0.009 | 0.533 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 4B.
Results for Impact of Education on Hip Pain, Sex and Education Interaction
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | 0.002*** (0.000) |
0.003*** (0.000) |
0.003*** (0.001) |
| Female | 0.028*** (0.002) |
0.028*** (0.008) |
0.038** (0.013) |
| Education (years) | |||
| 10 | −0.032*** (0.003) |
−0.044*** (0.010) |
−0.030* (0.014) |
| 13 | −0.052*** (0.004) |
−0.036*** (0.014) |
0.009 (0.019) |
| 15 | −0.039*** (0.003) |
−0.043*** (0.010) |
−0.012 (0.015) |
| 19 | −0.032*** (0.002) |
−0.033*** (0.009) |
0.001 (0.013) |
| 20 | −0.063*** (0.002) |
−0.064*** (0.008) |
−0.015 (0.013) |
| 10 X Female | 0.001 (0.003) |
0.018 (0.012) |
0.026 (0.018) |
| 13 X Female | 0.008 (0.005) |
−0.000 (0.018) |
−0.021 (0.024) |
| 15 X Female | 0.012** (0.004) |
0.014 (0.013) |
0.018 (0.020) |
| 19 X Female | 0.022*** (0.003) |
0.012 (0.012) |
−0.001 (0.018) |
| 20 X Female | 0.011*** (0.003) |
0.015 (0.011) |
−0.001 (0.016) |
| Constant | −0.009* (0.004) |
−0.015 (0.017) |
−0.062 (0.042) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.013 | 0.012 | 0.526 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 5B.
Results for Impact of Education on Knee Pain, Sex and Education Interaction
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | 0.002*** (0.000) |
0.002*** (0.000) |
0.003** (0.001) |
| Female | −0.021*** (0.003) |
−0.025* (0.010) |
−0.043** (0.017) |
| Education (years) | |||
| 10 | −0.043*** (0.003) |
−0.028* (0.012) |
−0.010 (0.019) |
| 13 | −0.078*** (0.005) |
−0.061*** (0.018) |
−0.045 (0.026) |
| 15 | −0.057*** (0.004) |
−0.054*** (0.013) |
−0.031 (0.020) |
| 19 | −0.038*** (0.003) |
−0.020 (0.011) |
−0.008 (0.018) |
| 20 | −0.109*** (0.003) |
−0.097*** (0.010) |
−0.059*** (0.017) |
| 10 X Female | −0.017*** (0.004) |
−0.020 (0.015) |
0.006 (0.023) |
| 13 X Female | −0.001 (0.006) |
−0.021 (0.023) |
0.030 (0.032) |
| 15 X Female | −0.003 (0.005) |
0.004 (0.017) |
0.027 (0.025) |
| 19 X Female | 0.004 (0.004) |
−0.014 (0.015) |
0.002 (0.023) |
| 20 X Female | 0.020*** (0.004) |
0.010 (0.013) |
0.032 (0.020) |
| Constant | 0.149*** (0.006) |
0.134*** (0.021) |
0.111* (0.053) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.012 | 0.011 | 0.532 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 6B.
Results for Impact of Education on Stomach Pain, Sex and Education Interaction
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.003*** (0.000) |
−0.002*** (0.000) |
−0.003*** (0.001) |
| Female | 0.008*** (0.002) |
0.007 (0.007) |
0.006 (0.010) |
| Education (years) | |||
| 10 | −0.023*** (0.002) |
−0.028*** (0.008) |
−0.027* (0.012) |
| 13 | −0.035*** (0.003) |
−0.043*** (0.012) |
−0.020 (0.016) |
| 15 | −0.028*** (0.002) |
−0.014 (0.009) |
0.002 (0.013) |
| 19 | −0.026*** (0.002) |
−0.019* (0.007) |
−0.004 (0.011) |
| 20 | −0.038*** (0.002) |
−0.036*** (0.007) |
−0.015 (0.011) |
| 10 X Female | 0.011*** (0.003) |
0.012 (0.010) |
0.018 (0.015) |
| 13 X Female | 0.016*** (0.004) |
0.023 (0.015) |
0.024 (0.020) |
| 15 X Female | 0.009** (0.003) |
−0.003 (0.011) |
−0.000 (0.016) |
| 19 X Female | 0.028*** (0.003) |
0.021* (0.010) |
0.003 (0.015) |
| 20 X Female | 0.014*** (0.003) |
0.011 (0.009) |
0.021 (0.013) |
| Constant | 0.246*** (0.004) |
0.226*** (0.014) |
0.259*** (0.035) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.008 | 0.006 | 0.519 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 7B.
Results for Impact of Education on All Over Pain, Sex and Education Interaction
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | −0.000*** (0.000) |
−0.000 (0.000) |
−0.000 (0.000) |
| Female | 0.004*** (0.001) |
−0.002 (0.003) |
−0.000 (0.006) |
| Education (years) | |||
| 10 | −0.015*** (0.001) |
−0.014*** (0.004) |
−0.004 (0.006) |
| 13 | −0.019*** (0.001) |
−0.016** (0.005) |
−0.005 (0.007) |
| 15 | −0.016*** (0.001) |
−0.016*** (0.004) |
−0.010 (0.006) |
| 19 | −0.017*** (0.001) |
−0.015*** (0.003) |
−0.010 (0.006) |
| 20 | −0.023*** (0.001) |
−0.023*** (0.003) |
−0.009 (0.005) |
| 10 X Female | −0.001 (0.001) |
0.004 (0.005) |
0.002 (0.007) |
| 13 X Female | −0.000 (0.002) |
0.006 (0.007) |
0.001 (0.010) |
| 15 X Female | −0.002 (0.001) |
0.010 (0.005) |
0.011 (0.008) |
| 19 X Female | 0.003* (0.001) |
0.008 (0.005) |
0.004 (0.008) |
| 20 X Female | 0.000 (0.001) |
0.005 (0.004) |
0.004 (0.006) |
| Constant | 0.033*** (0.002) |
0.030*** (0.006) |
0.027 (0.016) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.004 | 0.003 | 0.520 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Table 8B.
Results for Impact of Education on No Pain, Sex and Education Interaction
| VARIABLES | (1) Full Sample |
(2) Sibling Pairs |
(3) Sibling Pairs |
|---|---|---|---|
| Age | 0.003*** (0.000) |
0.003*** (0.000) |
0.004*** (0.001) |
| Female | 0.000 (0.004) |
0.017 (0.013) |
0.019 (0.017) |
| Education (years) | |||
| 10 | 0.086*** (0.004) |
0.086*** (0.015) |
0.038 (0.021) |
| 13 | 0.140*** (0.006) |
0.140*** (0.021) |
0.075* (0.030) |
| 15 | 0.107*** (0.004) |
0.107*** (0.016) |
0.041 (0.022) |
| 19 | 0.085*** (0.004) |
0.085*** (0.013) |
0.034 (0.019) |
| 20 | 0.177*** (0.003) |
0.173*** (0.012) |
0.082*** (0.019) |
| 10 X Female | 0.004 (0.005) |
0.003 (0.019) |
−0.000 (0.025) |
| 13 X Female | −0.032*** (0.008) |
−0.013 (0.027) |
−0.047 (0.037) |
| 15 X Female | −0.018** (0.006) |
−0.020 (0.020) |
−0.030 (0.028) |
| 19 X Female | −0.038*** (0.005) |
−0.054** (0.018) |
−0.022 (0.025) |
| 20 X Female | −0.048*** (0.005) |
−0.052** (0.016) |
−0.063** (0.023) |
| Constant | 0.165*** (0.007) |
0.144*** (0.025) |
0.137* (0.062) |
| Observations | 403,875 | 32,946 | 32,946 |
| R-squared | 0.012 | 0.012 | 0.538 |
| Sibling FE | No | No | Yes |
| Sibling Pairs | 16,473 | 16,473 |
p<0.001
p<0.01
p<0.05; Standard errors in parentheses.
Column 1 shows the results of our full analytic sample. Column 2 shows results restricted to sibling pairs. Column 3 shows results of sibling pairs with sibling fixed effects added.
Footnotes
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Contributor Information
Michael Topping, Department of Sociology, Center for Demography and Ecology, Center for Demography of Health and Aging, University of Wisconsin-Madison.
Jason Fletcher, La Follette School of Public Affairs, Center for Demography of Health and Aging, University of Wisconsin-Madison.
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