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Education was associated with injuries requiring hospital admission
Journal of Clinical Epidemiology 57 (2004) 945–953
Education was associated with injuries requiring hospital admission Frank J. van Lenthea,*, Ed F. van Beecka, Evelien Geversb, Johan P. Mackenbacha a
Department of Public Health, Erasmus Medical Centre, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands b Prismant, Institute for Health Care Management, PO Box 85200, 3508 AE Utrecht, The Netherlands Accepted 3 November 2003
Abstract Objectives: We describe educational inequalities in the incidence of injuries resulting in hospital admission and explore the contribution of exposure variables and chronic diseases, alcohol consumption, and sedative use to the observed inequalities. Study Design and Setting: Data from the Dutch prospective GLOBE study were linked to the National Hospital Discharge Register after 7 years of follow-up. Results: Significantly higher hazard ratios (HRs) of traffic injuries in lower compared with higher educational groups were substantially reduced after adjustment for differences in the use of cars and mopeds between these groups. Significantly increased HRs in occupational, home, and sports (OHS) injuries in lower compared with higher educational groups were reduced after adjustment for higher prevalence rates of chronic diseases, very excessive alcohol consumption, and sedative use in lower educational groups. Conclusion: Exposure variables, chronic diseases, alcohol consumption, and sedative use contribute to educational inequalities in traffic and OHS injuries resulting in hospital admission. 쑖 2004 Elsevier Inc. All rights reserved. Keywords: Injuries; Socio-economic position; Education; Incidence; Hospital admission; Prospective studies
1. Introduction Injury morbidity puts a high load on health services in many western countries. It is estimated that approximately 20% of the Dutch population needs medical treatment for an injury annually [1]. The majority of these injuries are treated by general practitioners and in emergency outpatient clinics. Approximately 5% of the hospital emergency department visits for injuries result in hospital admission; the percentage rises to over 20% in individuals ⭓65 years of age [2,3]. Taking into account the personal distress and societal consequences of the injuries (e.g., due to absence from work), there is a need for the prevention of injuries [4]. To develop a policy aimed at reducing the incidence of injuries, knowledge of their determinants is essential. For many diseases, an inverse association has been found with educational level and other core indicators of the socioeconomic position (SEP) [5], and studies have been carried out aimed at explaining such inequalities [6,7]. With regard to injuries, evidence of socio-economic inequalities in traffic injuries is well described [8–12], although associations are not necessarily constant over age groups [12,13]. These stud-
* Corresponding author. Tel.: ⫹31-10-408 8220 / 7714; fax: ⫹31-10408 9449. E-mail address: [email protected] (F.J. van Lenthe). 0895-4356/04/$ – see front matter 쑖 2004 Elsevier Inc. All rights reserved. doi: 10.1016/j.jclinepi.2003.11.017
ies have investigated the effect of parental indicators of SEP on the incidence of injuries among children and adolescents. The question of if and why injury morbidity in adults is higher in lower compared with higher socioeconomic groups is left unanswered. Helmkamp and Bone [14] reported higher rates of unintentional injuries resulting in hospital admission for men enlisted in the Navy with lower compared with higher pay grades. In a review study, Cubbin and Smith [15] identified three other studies in which the association between individual SEP and unintentional injuries was investigated [16–18]. The results of these studies are not unequivocal, which can be caused by methodologic problems. Laflamme and Eilert-Petersen [19] showed higher morbidity of nonfatal injuries in lower socioeconomic groups. A methodologic concern for adequately addressing socioeconomic inequalities in injuries is the difficulty of combining valid indicators of SEP with objectively collected information about the occurrence of injuries. Using the personal identification number (PIN) for record linkage, researchers in Sweden were able to construct such databases [9]. In countries lacking the possibility to use a PIN for research tools, studies are carried out based on registers only [20]. In these registers, information about the individual SEP of study participants is often lacking, and therefore area-based measures are used as an indicator of SEP. Recently Cubbin et al. [21] showed an independent effect of area characteristics
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on injury mortality after adjustment for individual socioeconomic characteristics. Thus, it is not clear to what extent such area-based indicators reflect individual or area characteristics. As an alternative, studies are carried out in which information about individual SEP and self-reported injury occurrence is collected. Self-reported morbidity data may result in an underestimation of the prevalence and even more so in lower compared with higher educational groups [22]. Moreover, in these studies, injuries of different severity are included. Especially when it concerns the least severe injuries, differences in help-seeking behavior between socioeconomic groups may result in biased estimates of the socioeconomic gradient [23]. A major issue in injury research in general is the measurement of exposure time. In the interpretation of the association between SEP and injury occurrence, adjustment for exposure time may provide some information about the explanation of observed inequalities: Are higher incidence rates in lower compared with higher socioeconomic groups the result of differences in exposure variables (such as the time spent in traffic) or do they exist after these differences have been taken into account? There is evidence that pedestrian exposure is higher for children in lower compared with higher socio-economic groups and that differences in pedestrian exposure explain part of the socioeconomic inequalities in child pedestrian injuries [24,25]. Injury morbidity differs between socioeconomic groups when differences in exposure time have been taken into account because, generally, risk factors of injuries may be differentially distributed over socioeconomic groups. The occurrence of injuries can be understood in terms of a “manmachine system,” with individuals, their instruments, and their shared environment as components [26]. Thus, a higher prevalence of risk factors at the individual level or at the level of the instruments or environment in lower compared with higher socioeconomic groups may mediate the association between SEP and injuries. A general group of risk factors of injuries at the individual level includes health and health-related behavioral factors, which seem to have in common that they may reduce alertness or the ability to control unexpected circumstances. Among these, alcohol consumption is probably the most obvious risk factor for injuries. There is ample evidence of an association between (excessive) alcohol consumption and injuries [27–30]. Similarly, studies have shown that the use of medication (such as sedative-hypnotic use) and chronic diseases (such as the cerebrovascular accident and Parkinson disease) increase the risk of injuries [31,32]. Because a higher prevalence of chronic diseases and excessive alcohol consumption in lower compared with higher socioeconomic groups is well described [33,34] and because positive and negative associations with SEP are reported for sedative-hypnotic use [35], these factors can potentially mediate the association between SEP and injuries. To our knowledge, studies aimed at explaining socioeconomic inequalities in injuries are almost absent.
The Dutch GLOBE study is a prospective cohort study aimed at explaining socioeconomic inequalities in health in The Netherlands [36]. After 7 years of follow-up, the database was linked to the National Hospital Discharge Register (NHDR). Because of this link, we were able to describe educational inequalities in injuries resulting in hospital admission (further described as “injuries”) in a prospective study with educational level measured at the individual level and objectively collected information of injury morbidity. Moreover, with information obtained in the study we could explore the contribution of exposure time to educational inequalities in injuries and explore the contribution of chronic diseases, alcohol consumption, and sedative use to these inequalities. Specifically, we tested the following hypotheses: 1. The probability of (a) traffic injuries and (b) occupational, home, and sports injuries resulting in hospital admission is higher in lower compared with higher educational groups. 2. Educational inequalities in traffic injuries resulting in hospital admission are partly due to differences in time using means of transport (“exposure variables”) and partly due to a higher prevalence of chronic diseases, higher consumption of alcohol, and higher intake of sedatives in lower compared with higher educational groups. 3. Educational inequalities in occupational, home, and sports injuries are partly due to differences in exposure variables and partly due to a higher prevalence of chronic diseases, higher consumption of alcohol, and higher intake of sedatives in lower compared with higher educational groups.
2. Methods 2.1. Design and study population This study was executed within the longitudinal GLOBE study, a prospective cohort study aimed at explaining socioeconomic inequalities in health. Objectives and design of the GLOBE study are presented in detail elsewhere [36]. A random sample of 27,070 noninstitutionalized persons between 15 and 75 years of age and living in or near the town of Eindhoven (in the southern part of the Netherlands) were invited to participate in the baseline measurement in 1991. The response rate was 70.1%, which resulted in 18,973 study participants. No significant differences in response rates were found by age, sex, SES (based on zip codes), marital status, and level of urbanization [36]. The baseline measurement consisted of a postal questionnaire that included questions on education level. As part of the longitudinal study, participants were followed with regard to changes in places of residence and vital status, using annually obtained information from municipal administrations. For the
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present study, this information was available from the start of the study (1 April 1991) until June 1998. The area for the GLOBE study was, among other reasons, chosen because it is in a relatively well-defined coverage area for five hospitals. We selected study participants who lived in the coverage area of these five hospitals, which was defined as the area in which over 90% of the population visits one of the five hospitals in case of hospital admission (n ⫽ 18,843). Information on hospital admission was obtained through record linkage (see below), and therefore participants with missing values for identifying variables for record linkage (date of birth, gender, or zip code) were excluded (n ⫽ 33). Consequently, 18,810 subjects were available for record linkage. 2.2. Variables Information on the highest obtained education level was obtained from a closed question in the postal questionnaire in 1991. Four groups were identified: (1) higher vocational education or university, (2) intermediate vocational or higher secondary general education, (3) lower vocational or lower secondary general education, and (4) primary education. For those still studying, we used the education level at the time of this study. In the Netherlands, education level is considered to be a good indicator of SEP [37]. Of the three core indicators (education, occupation, and income), education is the most individual characteristic, being available for males and females regardless whether they are in paid employment or not. Moreover, it has high reliability and validity [38]. Information about hospital admissions due to injuries was derived from the NHDR, in which all hospital admissions in the Netherlands are registered. The analyses were restricted to unintentional injuries. Diagnoses are classified according to the ninth edition of the International Classification of Diseases, Clinical Modification (ICD9-CM). The External Causes of Injury Supplementation of the ICD-9 allowed us to classify injuries according to their external cause. Two criteria were used to select injuries in the NHDR: (1) main diagnosis at discharge between ICD code 800 and 999, with the exception of ICD codes 905 to 909 (late consequences of injuries, poisoning, toxic influences, other external causes), 958 to 959 (complications of trauma and other unspecified injuries), and 996 to 999 (complications of surgical and medical treatment); and (2) an external cause being responsible for hospital admission (registered as part of the diagnosis during uptake). With regard to the unintentional injuries, traffic injuries could be distinguished from occupational, home, and sports (OHS) injuries in the NHDR. For the latter group of injuries, the registration did not allow further distinctions between injuries occurring at work, during activities at home, or during sports activities. Within the group of OHS injuries,
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accidental falls could be separated from the other OHS injuries. Exposure variables for traffic and OHS injuries were obtained from the postal questionnaire. To obtain such information for traffic injuries, closed questions were used to collect information on the use of a car, motorcycle, or moped (never, sometimes, regularly but ⬍1 hour per day, regularly and ⬎1 hour per day) and an open question regarding the average time (in minutes) spent per day on walking or cycling to work or shops (categorized into never, ⬍1 hour, 1– 2 hours, and ⬎2 hours per day). Information on exposure time of OHS injuries was collected through closed questions in the postal questionnaire. Based on questions on employment status and working conditions (“Do you sometimes have to carry out dangerous work?”), we created a variable for exposure time of occupational injuries with three categories (not employed, employed but never carrying out dangerous work, employed and sometimes carrying out dangerous work). To explore exposure time for injuries occurring in and around house, a closed question asked for the number of hours per week spent in leisure time on working (i.e., fixing, repairing) in or around the house with four answer categories (never, ⬍1 hour, 1–2 hours, and ⬎2 hours per week). To explore exposure time for sports activities, a question asked for the number of hours per week spent on sports activities, with the same four answer categories as described above. The number of chronic diseases from which subjects suffered in the past 5 years was obtained by summing from a list of 23 chronic diseases (yes/no). From questions on the average number of days per week that individuals used alcohol and the average number of glasses used per day, individuals were categorized into five groups for alcohol consumption: total abstainers, light, moderate, excessive, and very excessive alcohol drinkers. Excessive drinking was defined as drinking 6 or more glasses on average per day on at least 3 days per week or 4 to 5 glasses on average per day on at least 4 days per week. Very excessive drinking was defined as drinking 6 or more glasses on average per day on at least 5 days per week [39,40]. A single-item question was used to obtain information on regular sedativehypnotic use, with two answer categories (yes/no). As potential confounding variables, we included sex, age (15–24, 25–44, 45–64, and 65⫹ years of age), level of urbanization (four groups ranging from rural to urban), marital status (married; single and never married; divorced; or widowed), and religious affiliation (none, Roman Catholic, Protestant, other). This information was obtained in the postal questionnaire. 2.3. Record linkage From the NHDR we first selected hospital admissions due to injuries from the five hospitals in the coverage area (using the above-mentioned two criteria) between April 1991 and June 1998. A probabilistic record linkage was carried
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out between the resulting LMR database and the GLOBE database, with the combination of date of birth, sex, and zip code (four numbers and two letters) being the identifiers for linkage. After linkage, we found 32 identical identifiers, which could be due to repeat admissions of the same persons or to admissions of different persons. Comparison of patient numbers showed that these double codes were due to re-admissions; because we studied the incidence, only the first admission was included in the study. 2.4. Statistical analysis Cox proportional hazard models were used to estimate hazard ratios (HRs) of injuries by educational level [41]; time to follow-up stopped after hospital admission, a move out of the coverage area of the hospitals, death of participants, or at the end of the study period. Participants for whom information on zip code and date of move at any time during follow-up were unknown (key variables for record linkage) were excluded from the analysis (n ⫽ 642, 3.4%). Subjects with missing values for educational level and potential confounding variables were excluded (n ⫽ 461, 2.5%). There appeared to be no statistically significantly interactions of educational level with age and sex (P ⬍ .05), and we therefore included both sexes and all age groups in the analyses simultaneously. Exposure variables mediating the association between educational level and injuries should be (1) associated with injuries and (2) differentially distributed over socioeconomic groups. Thus, the association between exposure variables and injuries was determined, adjusted for the potential confounding variables and educational level. For exposure variables associated with injuries with a P value below .10, age- and sex-adjusted prevalence rates of risk groups of these variables were calculated by educational level using the method of direct standardization with the GLOBE study population as the reference population. These factors were added to the model containing educational level and potentially confounding variables. The relative contribution of exposure variables was calculated as follows: 100 × (RRmodel 1 ⫺ RRmodel 2)/RRmodel 1 ⫺ 1) [6], with model 1 including educational level and potential confounding variables and model 2 including exposure variables additionally. Similar analytical steps were taken to investigate the contribution of chronic diseases, alcohol intake, and sedative use to educational inequalities in injuries.
3. Results We found a total of 71 traffic injuries and 397 OHS injuries resulting in hospital admission, the latter group including 192 injuries due to accidental falls. HRs of traffic and OHS injuries (but not of accidental falls as a separate group of OHS injuries) were significantly higher for those in
the lower compared with those in the highest educational group (Table1). We investigated whether exposure variables were associated with the risk of traffic and OHS injuries (Table 2). Compared with those who reported never to be in a car, HRs of traffic injuries for participants reporting to use a car were significantly lower. The number of participants in different categories of exposure time for driving a motorcycle was too low to estimate risk of traffic injuries. Substantially increased HRs were found for participants who reported using a moped at least daily compared with those who never used a moped, although confidence intervals (CIs) are wide. No association was found between time spent walking or cycling to work or shops and traffic injuries (results not tabulated). With regard to exposure variables for OHS injuries, we found an increased risk of OHS injuries in participants involved in more than 2 hours of sports activities per week compared with those who were never involved in sports activities. No association could be demonstrated between time working in or around the house and OHS injuries (not tabulated). Employed participants sometimes carrying out dangerous work showed an increased risk of OHS injuries. Table 3 shows the age- and sex-adjusted prevalence rates of exposure variables associated with increased injury risks by educational level. With a decreasing level of education, an increasing percentage of participants does not use a car. The use of mopeds is more frequent in lower educational groups. The percentage of participants involved in sports activities for more than 2 hours per week rose by increasing educational level. No clear pattern was observed for the relation between educational level and doing dangerous work. Table 4 shows the association between chronic diseases, alcohol consumption, and sedative use. There appeared to be an increased risk of traffic injuries for those reporting excessive and very excessive alcohol intake and for those reporting sedative use, but these associations did not reach the significance level. There appeared to be a significantly increased risk of OHS injuries for participants reporting two chronic diseases, very excessive alcohol intake, or sedative use. Table 5 shows the age- and sex-adjusted prevalence rates by educational level for variables showing an increased risk of OHS injuries. The prevalence of two chronic diseases, very excessive alcohol consumption, and sedative use increased by decreasing educational level. Table 6 shows the results of adjustment for exposure variables and chronic diseases, alcohol consumption, and sedative use. The HRs of traffic injuries in the lowest two educational groups were reduced by 28% (HR 2.27, 95% CI 0.87–5.94) (not tabulated) and 22% (HR 2.59, 95% CI 1.06–6.31) (not tabulated), respectively, after adjustment for moped use. These percentages were 33% in the lowest and 16% in the second lowest educational group after adjustment
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Table 1 Hazard ratios (HR) of injuries by educational level Occupational, home, and sports injuries Traffic injuries
Total
Accidental fall
Educationa
Population
nb
HRc
95% CI
n
HR
95% CI
n
HR
95% CI
1 (high) 2 3 4 (low)
3,278 3,948 6,764 3,717
6 11 34 20
1.00 1.50 3.04 2.76
0.55–4.08 1.25–7.38 1.06–7.17
52 66 175 104
1.00 0.98 1.50 1.37
0.68–1.42 1.09–2.07 0.96–1.96
24 29 84 55
1.00 0.90 1.19 1.02
0.52–1.55 0.74–1.92 0.61–1.71
Abbreviation: CI, confidence interval. a 1 ⫽ higher vocational education or university; 2 ⫽ intermediate vocational or higher secondary general education; 3 ⫽ lower vocational or lower secondary general education; 4 ⫽ primary education. b Number of injuries. c Adjusted for age, sex, marital status, religious affiliation, and urbanization.
for car use. Simultaneous adjustment for moped and car use reduced the HR of traffic injuries by 46% in the lowest educational group (HR 1.95, 95% CI 0.98–5.29) and by 31% in the second lowest educational group (HR 2.41, 95% CI 0.98–5.95). Because associations between chronic diseases,
Table 2 Hazard ratios for traffic and occupational, home and sports injuries in the GLOBE study by exposure variables Traffic injuries Exposure variable Use of car No Sometimes ⬍1 h/day ⬎1 h/day Missing valuesb P value Use of moped No Sometimes ⬍1 h/day ⬎1 h/day Missing values P value
HRa
95% CI
1.00 0.49 0.47 0.42 0.79 0.08
0.25–0.97 0.24–0.92 0.20–0.86 0.30–2.08
1.00 1.59 4.97 4.15 1.99 0.00
4. Discussion 0.49–5.16 1.91–12.98 1.45–11.89 0.70–5.64
Occupational, home, and sports injuries Sports participation Never ⬍1 h/wk 1–2 h/wk ⬎2 h/wk Missing values P value Doing dangerous work Not employed Employed, no dangerous work Employed, dangerous work Missing values P value
1.00 0.97 0.95 1.43 1.30 0.05 1.00 0.90 1.59 1.13 0.067
alcohol consumption, and sedative use were not significantly related to traffic injuries, adjustment for these variables did not explain educational inequalities in traffic injuries (not tabulated). Adjustment for exposure variables hardly attenuated the association between educational level and OHS injuries. For example, the HR in the second lowest compared with the highest educational group decreased by 8% but remained significantly increased (HR 1.46, 95% CI 1.05–2.01). Additional adjustment for chronic diseases, alcohol consumption, and sedative use resulted in a reduction of the HRs in the lower socioeconomic groups. For example, the exposure-adjusted HR in the second lowest educational group additionally decreased by 11% after adjustment for chronic diseases, alcohol consumption, and sedative use (HR 1.41, 95% CI 1.01–1.94).
0.54–1.47 0.71–1.28 1.10–1.85 0.66–2.54
0.70–1.18 1.04–2.43 0.64–1.98
Abbreviations: HR, hazard ratio; CI, confidence interval. a Adjusted for age, sex, marital status, religion, urbanization, and educational level. b Missing values were included to investigate whether these were random or selective with respect to the outcome measure.
In this study, we found educational inequalities in traffic and OHS injuries (other than accidental falls) resulting in hospital admission. For traffic injuries, but not for OHS injuries, these inequalities attenuated substantially after adjustment for exposure time. Exposure-adjusted inequalities in OHS injuries were to some extent explained by the prevalence of chronic disease, alcohol consumption, and sedative use. Table 3 Age- and sex-adjusted prevalence of exposure variables associated with increased traffic injury risk by educational level
Car use Never Moped use ⬍1 h/day ⬎1 h/day Sports participation ⬎2 h/day Doing dangerous work Yes
1 (low)
2
3
4 (high)
28.5
16.7
13.8
8.2
1.4 1.4
1.7 1.6
1.9 1.1
0.6 0.3
8.6
16.4
24.9
24.2
3.1
5.6
6.9
3.7
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Table 4 Hazard ratios of injuries by chronic diseases, alcohol consumption, and sedative use in the GLOBE study
Chronic diseases No 1 ⭓2 P value Alcohol Never Light Moderate Excessive Very excessive Missing valuesb P value Sedative use No Yes Missing values P value
Traffic injuries
Occupational, home, and sports injuries
HRa
HR
1.00 0.62 0.92 0.31
95% CI
Table 6 Effect of adjustment for exposure variables, health, and health-related behavior on the association between educational level and traffic and occupational, home, and sports injuries in the GLOBE study
95% CI
0.33–1.15 0.50–1.68
1.00 1.26 1.54 0.026
0.99–1.59 1.20–1.99
1.00 1.19 1.06 1.91 2.00 1.74 0.51
0.62–2.26 0.47–2.38 0.71–5.15 0.54–6.26 0.67–4.50
1.00 0.76 0.92 1.48 2.40 0.89 0.000
0.59–0.99 0.66–1.26 0.97–2.27 1.54–3.75 0.56–1.41
1.00 1.23 2.09 0.44
0.52–2.92 0.65–6.75
1.00 1.65 0.62 0.045
1.20–2.28 0.25–1.50
Basic modela Educationb 1 (high) 2 3 4 (low) 1 (high) 2 3 4 (low) 1 (high) 2 3 4 (low)
Traffic injuries
Occupational, home, and sports injuries
HR 1.00 1.50 3.04 2.76
HR 1.00 0.98 1.50 1.37
95% CI 0.55–4.08 1.25–7.38 1.06–7.17
⫹ exposurec 1.00 1.38 0.50–3.76 2.41 0.73–5.20 1.95 0.98–5.93
95% CI 0.68–1.42 1.09–2.07 0.96–1.96
⫹ exposured 1.00 0.95 0.66–1.37 1.46 1.05–2.01 1.34 0.93–1.93 ⫹ healthe 1.00 0.92 1.41 1.25
0.63–1.32 1.01–1.94 0.86–1.81
a
Adjusted for age, sex, marital status, religion, and urbanization. 1, higher vocational education or university; 2, intermediate vocational or higher secondary general education; 3, lower vocational or lower secondary general education; 4, primary education. c Moped use, car use. d Dangerous work, sport. e Chronic diseases, alcohol intake, use of sedatives. b
Abbreviations: HR, hazard ratio; CI, confidence interval. a Adjusted for age, sex, marital status, religion, urbanization, and educational level. b Missing values were included to investigate whether these were random or selective with respect to the outcome measure.
The major strength of this study is the linkage of a core indicator of individual SEP to objectively collected data on injury morbidity. Using less valid indicators of SEP (e.g., area-based indicators) or injuries (e.g., self-reported data) may have resulted in an underestimation of inequalities in injuries in previous studies [11,20]. Further, we used a prospective design, which is rare in research on inequalities in injuries. Explanatory mechanisms for educational inequalities in health can generally be divided in a selection mechanism (in which health determines the SEP) and a causal mechanism (in which SEP determines health). Thus, to distinguish these mechanisms, prospective studies are required [36]. By linking the GLOBE database to the NHDR, we also had information about exposure, the prevalence of chronic diseases, alcohol consumption, and sedative use, which allowed us to explore the explanation of the inequalities in
Table 5 Age- and sex, adjusted prevalence of chronic diseases, alcohol and sedative use associated with increased nontraffic injury risk by educational level
Chronic diseases ⭓2 chronic diseases Alcohol Very excessive Sedative use Yes
1 (high)
2
3
4 (low)
12.6
15.8
19.9
25.1
2.2
2.6
3.1
3.4
4.2
5.6
5.9
10.0
injuries. Finally, using patient numbers of the NHDR, we were able to exclude re-admissions, which is generally not possible from studies using hospital discharge data for the determination of incidence of injuries resulting in hospital admissions [42]. Our study has several methodologic limitations. First, we had to use broad categories of injuries. The NHDR did not allow us to distinguish more specific groups, in particular for the OHS injuries, and other hospital admission databases are not available in the Netherlands. Even if such databases were available, prospective research of more specific injuries groups does not seem feasible because it requires large sample sizes or a long period of follow-up before sufficient patients are included. Thus, the heterogeneity of injuries is an inherent problem in injury research, not just restricted to this study. Indeed, injury studies with a different topic also used similar broad injury categories [43]. Using broad categories may have masked some effects in our study. For example, the probably higher incidence of sports injuries in higher compared with lower educational groups may have resulted in an underestimation of the educational gradient in OHS injuries. Second, we assessed the incidence of injuries using hospital discharge data. Thus, we were not able to include prehospital deaths due to injuries. If we can extrapolate the suggestion that educational inequalities in injuries are increasing with the severity of the injuries [16], this would imply higher incidence rates of such fatal injuries in lower compared with higher educational groups. Although such
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data are not available for injuries, it is known that prehospital deaths due to other diseases (i.e., acute myocardial infarction) occur more frequently in lower compared with higher socioeconomic groups [44]. Thus, indirect evidence suggests that exclusion of fatal injuries before hospital admission may have resulted in an underestimation of the educational gradient in the incidence of severe injuries. Third, we were only able to explore the role of exposure variables because they were derived from single-item questions (although being able to adjust for these variables may be regarded a strength of the study in comparison to previous work). Using these questions may have resulted in less precise assessments of exposure and therefore perhaps to an underestimation of the association between exposure and injury risk; it is unclear if such an underestimation would differ between educational groups. Further, this information was gathered only at baseline and we assumed this to reflect exposure during follow-up. Despite these shortcomings in the measurement of exposure variables, their association with injuries and their differential distribution over socioeconomic groups showed the need to include information about exposure time in (explanatory) studies of inequalities in injuries. In future research, exposure variables should be measured in more detail. Finally, information on health and health-related behavior was also based on self-reported information. Using selfreported information to assess the prevalence of chronic diseases may result in an underestimation, and this may be more so in lower compared with higher educational groups [22]. Consequently, we may have underestimated the contribution of chronic diseases to the explanation of educational inequalities in the incidence of injuries. There is less evidence of misclassification by educational level for the included health-related behavioral variables [45]. We found that adjustment for exposure variables reduced educational inequalities in traffic injuries, which was particularly due to differences in car use. Perhaps surprisingly, those who never used a car were at increased risk of traffic injuries. It is likely that this group is particularly vulnerable to pedestrian and cycle injuries, even though exposure time for walking and cycling was not related to traffic injuries in our study. The exposure-adjusted inverse association of OHS injury risk with educational level was to some extent explained by chronic diseases, alcohol consumption, and sedative use. However, HRs in lower educational groups remained significantly increased after adjustment for these variables. Hence, other factors must be responsible for the reported inequalities. At the individual level, psychosocial factors, such as risk seeking behavior, may contribute to inequalities in serious injuries. At the level of the instruments, material factors are a potentially important group of mediating factors. We have previously shown the importance of material factors for educational inequalities in incidence and mortality of acute myocardial infarction [7,46]. With respect to injuries, material factors may reflect the ability to have
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access to safe products, such as smoke detectors and bicycle helmets. There is also evidence of the importance of factors at the level of the shared environment: Studies have shown an association between neighborhood socioeconomic characteristics and injury mortality [21]. More research should be devoted to investigating the contribution of individual, instrumental, and environmental factors and their interactions to educational inequalities in injuries. We found no evidence of educational inequalities in the risk of accidental falls, which is in accordance with the conclusion drawn from a review of available evidence on this association [27]. In our study, a higher probability of accidental falls was found in subjects ⭓65 years of age, particularly in women, a pattern reported by others for the risk of fractures [47]. At these ages, relatively unhealthy subjects in lower educational groups may have died from other diseases. Hence, those in the lower educational groups in our population could be a more healthy selection of the total population with risk factors for accidental falls more equally distributed between socioeconomic groups. Alternatively, accidental falls may occur during activities (e.g., sports activities) that are more frequent among persons from higher socio-economic groups. Further investigation of the determinants of accidental falls is needed because of their relative importance as a cause of injuries increased in past decades [48]. In conclusion, we found educational inequalities in traffic and OHS injuries resulting in hospital admission in Dutch adults in a prospective study. Despite the limitations in measurement, our study implies that educational inequalities in traffic injuries can be substantially explained by differences in exposure variables between educational groups, whereas chronic diseases, alcohol consumption, and sedative use partly explained educational inequalities in OHS injuries. Our data show that a reduction of educational inequalities in injuries resulting in hospital admission can be achieved if injury prevention programs are able to reach those in the lower educational groups. It has recently been shown that lower socio-economic groups could benefit more from specific injury prevention measures than higher socio-economic groups [49]. More knowledge is required about the explanation of these inequalities before implementing prevention programs. Acknowledgments The GLOBE study is carried out by the Department of Public Health of the Erasmus University Rotterdam, in collaboration with the Public Health Services of the city of Eindhoven and region South-East Brabant. The authors are indebted to Michel Provoost, Ilse Oonk, and Roel Faber for constructing the database. The present study is supported by grants of the Ministry of Public Health, Welfare and Sport and the Health Research and Development Council (ZON). Dr van Lenthe is supported by a grant from the Netherlands Organisation for Scientific Research (904-66-104).
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Education was associated with injuries requiring hospital admission Frank J. van Lenthea,*, Ed F. van Beecka, Evelien Geversb, Johan P. Mackenbacha a
Department of Public Health, Erasmus Medical Centre, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands b Prismant, Institute for Health Care Management, PO Box 85200, 3508 AE Utrecht, The Netherlands Accepted 3 November 2003
Abstract Objectives: We describe educational inequalities in the incidence of injuries resulting in hospital admission and explore the contribution of exposure variables and chronic diseases, alcohol consumption, and sedative use to the observed inequalities. Study Design and Setting: Data from the Dutch prospective GLOBE study were linked to the National Hospital Discharge Register after 7 years of follow-up. Results: Significantly higher hazard ratios (HRs) of traffic injuries in lower compared with higher educational groups were substantially reduced after adjustment for differences in the use of cars and mopeds between these groups. Significantly increased HRs in occupational, home, and sports (OHS) injuries in lower compared with higher educational groups were reduced after adjustment for higher prevalence rates of chronic diseases, very excessive alcohol consumption, and sedative use in lower educational groups. Conclusion: Exposure variables, chronic diseases, alcohol consumption, and sedative use contribute to educational inequalities in traffic and OHS injuries resulting in hospital admission. 쑖 2004 Elsevier Inc. All rights reserved. Keywords: Injuries; Socio-economic position; Education; Incidence; Hospital admission; Prospective studies
1. Introduction Injury morbidity puts a high load on health services in many western countries. It is estimated that approximately 20% of the Dutch population needs medical treatment for an injury annually [1]. The majority of these injuries are treated by general practitioners and in emergency outpatient clinics. Approximately 5% of the hospital emergency department visits for injuries result in hospital admission; the percentage rises to over 20% in individuals ⭓65 years of age [2,3]. Taking into account the personal distress and societal consequences of the injuries (e.g., due to absence from work), there is a need for the prevention of injuries [4]. To develop a policy aimed at reducing the incidence of injuries, knowledge of their determinants is essential. For many diseases, an inverse association has been found with educational level and other core indicators of the socioeconomic position (SEP) [5], and studies have been carried out aimed at explaining such inequalities [6,7]. With regard to injuries, evidence of socio-economic inequalities in traffic injuries is well described [8–12], although associations are not necessarily constant over age groups [12,13]. These stud-
* Corresponding author. Tel.: ⫹31-10-408 8220 / 7714; fax: ⫹31-10408 9449. E-mail address: [email protected] (F.J. van Lenthe). 0895-4356/04/$ – see front matter 쑖 2004 Elsevier Inc. All rights reserved. doi: 10.1016/j.jclinepi.2003.11.017
ies have investigated the effect of parental indicators of SEP on the incidence of injuries among children and adolescents. The question of if and why injury morbidity in adults is higher in lower compared with higher socioeconomic groups is left unanswered. Helmkamp and Bone [14] reported higher rates of unintentional injuries resulting in hospital admission for men enlisted in the Navy with lower compared with higher pay grades. In a review study, Cubbin and Smith [15] identified three other studies in which the association between individual SEP and unintentional injuries was investigated [16–18]. The results of these studies are not unequivocal, which can be caused by methodologic problems. Laflamme and Eilert-Petersen [19] showed higher morbidity of nonfatal injuries in lower socioeconomic groups. A methodologic concern for adequately addressing socioeconomic inequalities in injuries is the difficulty of combining valid indicators of SEP with objectively collected information about the occurrence of injuries. Using the personal identification number (PIN) for record linkage, researchers in Sweden were able to construct such databases [9]. In countries lacking the possibility to use a PIN for research tools, studies are carried out based on registers only [20]. In these registers, information about the individual SEP of study participants is often lacking, and therefore area-based measures are used as an indicator of SEP. Recently Cubbin et al. [21] showed an independent effect of area characteristics
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on injury mortality after adjustment for individual socioeconomic characteristics. Thus, it is not clear to what extent such area-based indicators reflect individual or area characteristics. As an alternative, studies are carried out in which information about individual SEP and self-reported injury occurrence is collected. Self-reported morbidity data may result in an underestimation of the prevalence and even more so in lower compared with higher educational groups [22]. Moreover, in these studies, injuries of different severity are included. Especially when it concerns the least severe injuries, differences in help-seeking behavior between socioeconomic groups may result in biased estimates of the socioeconomic gradient [23]. A major issue in injury research in general is the measurement of exposure time. In the interpretation of the association between SEP and injury occurrence, adjustment for exposure time may provide some information about the explanation of observed inequalities: Are higher incidence rates in lower compared with higher socioeconomic groups the result of differences in exposure variables (such as the time spent in traffic) or do they exist after these differences have been taken into account? There is evidence that pedestrian exposure is higher for children in lower compared with higher socio-economic groups and that differences in pedestrian exposure explain part of the socioeconomic inequalities in child pedestrian injuries [24,25]. Injury morbidity differs between socioeconomic groups when differences in exposure time have been taken into account because, generally, risk factors of injuries may be differentially distributed over socioeconomic groups. The occurrence of injuries can be understood in terms of a “manmachine system,” with individuals, their instruments, and their shared environment as components [26]. Thus, a higher prevalence of risk factors at the individual level or at the level of the instruments or environment in lower compared with higher socioeconomic groups may mediate the association between SEP and injuries. A general group of risk factors of injuries at the individual level includes health and health-related behavioral factors, which seem to have in common that they may reduce alertness or the ability to control unexpected circumstances. Among these, alcohol consumption is probably the most obvious risk factor for injuries. There is ample evidence of an association between (excessive) alcohol consumption and injuries [27–30]. Similarly, studies have shown that the use of medication (such as sedative-hypnotic use) and chronic diseases (such as the cerebrovascular accident and Parkinson disease) increase the risk of injuries [31,32]. Because a higher prevalence of chronic diseases and excessive alcohol consumption in lower compared with higher socioeconomic groups is well described [33,34] and because positive and negative associations with SEP are reported for sedative-hypnotic use [35], these factors can potentially mediate the association between SEP and injuries. To our knowledge, studies aimed at explaining socioeconomic inequalities in injuries are almost absent.
The Dutch GLOBE study is a prospective cohort study aimed at explaining socioeconomic inequalities in health in The Netherlands [36]. After 7 years of follow-up, the database was linked to the National Hospital Discharge Register (NHDR). Because of this link, we were able to describe educational inequalities in injuries resulting in hospital admission (further described as “injuries”) in a prospective study with educational level measured at the individual level and objectively collected information of injury morbidity. Moreover, with information obtained in the study we could explore the contribution of exposure time to educational inequalities in injuries and explore the contribution of chronic diseases, alcohol consumption, and sedative use to these inequalities. Specifically, we tested the following hypotheses: 1. The probability of (a) traffic injuries and (b) occupational, home, and sports injuries resulting in hospital admission is higher in lower compared with higher educational groups. 2. Educational inequalities in traffic injuries resulting in hospital admission are partly due to differences in time using means of transport (“exposure variables”) and partly due to a higher prevalence of chronic diseases, higher consumption of alcohol, and higher intake of sedatives in lower compared with higher educational groups. 3. Educational inequalities in occupational, home, and sports injuries are partly due to differences in exposure variables and partly due to a higher prevalence of chronic diseases, higher consumption of alcohol, and higher intake of sedatives in lower compared with higher educational groups.
2. Methods 2.1. Design and study population This study was executed within the longitudinal GLOBE study, a prospective cohort study aimed at explaining socioeconomic inequalities in health. Objectives and design of the GLOBE study are presented in detail elsewhere [36]. A random sample of 27,070 noninstitutionalized persons between 15 and 75 years of age and living in or near the town of Eindhoven (in the southern part of the Netherlands) were invited to participate in the baseline measurement in 1991. The response rate was 70.1%, which resulted in 18,973 study participants. No significant differences in response rates were found by age, sex, SES (based on zip codes), marital status, and level of urbanization [36]. The baseline measurement consisted of a postal questionnaire that included questions on education level. As part of the longitudinal study, participants were followed with regard to changes in places of residence and vital status, using annually obtained information from municipal administrations. For the
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present study, this information was available from the start of the study (1 April 1991) until June 1998. The area for the GLOBE study was, among other reasons, chosen because it is in a relatively well-defined coverage area for five hospitals. We selected study participants who lived in the coverage area of these five hospitals, which was defined as the area in which over 90% of the population visits one of the five hospitals in case of hospital admission (n ⫽ 18,843). Information on hospital admission was obtained through record linkage (see below), and therefore participants with missing values for identifying variables for record linkage (date of birth, gender, or zip code) were excluded (n ⫽ 33). Consequently, 18,810 subjects were available for record linkage. 2.2. Variables Information on the highest obtained education level was obtained from a closed question in the postal questionnaire in 1991. Four groups were identified: (1) higher vocational education or university, (2) intermediate vocational or higher secondary general education, (3) lower vocational or lower secondary general education, and (4) primary education. For those still studying, we used the education level at the time of this study. In the Netherlands, education level is considered to be a good indicator of SEP [37]. Of the three core indicators (education, occupation, and income), education is the most individual characteristic, being available for males and females regardless whether they are in paid employment or not. Moreover, it has high reliability and validity [38]. Information about hospital admissions due to injuries was derived from the NHDR, in which all hospital admissions in the Netherlands are registered. The analyses were restricted to unintentional injuries. Diagnoses are classified according to the ninth edition of the International Classification of Diseases, Clinical Modification (ICD9-CM). The External Causes of Injury Supplementation of the ICD-9 allowed us to classify injuries according to their external cause. Two criteria were used to select injuries in the NHDR: (1) main diagnosis at discharge between ICD code 800 and 999, with the exception of ICD codes 905 to 909 (late consequences of injuries, poisoning, toxic influences, other external causes), 958 to 959 (complications of trauma and other unspecified injuries), and 996 to 999 (complications of surgical and medical treatment); and (2) an external cause being responsible for hospital admission (registered as part of the diagnosis during uptake). With regard to the unintentional injuries, traffic injuries could be distinguished from occupational, home, and sports (OHS) injuries in the NHDR. For the latter group of injuries, the registration did not allow further distinctions between injuries occurring at work, during activities at home, or during sports activities. Within the group of OHS injuries,
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accidental falls could be separated from the other OHS injuries. Exposure variables for traffic and OHS injuries were obtained from the postal questionnaire. To obtain such information for traffic injuries, closed questions were used to collect information on the use of a car, motorcycle, or moped (never, sometimes, regularly but ⬍1 hour per day, regularly and ⬎1 hour per day) and an open question regarding the average time (in minutes) spent per day on walking or cycling to work or shops (categorized into never, ⬍1 hour, 1– 2 hours, and ⬎2 hours per day). Information on exposure time of OHS injuries was collected through closed questions in the postal questionnaire. Based on questions on employment status and working conditions (“Do you sometimes have to carry out dangerous work?”), we created a variable for exposure time of occupational injuries with three categories (not employed, employed but never carrying out dangerous work, employed and sometimes carrying out dangerous work). To explore exposure time for injuries occurring in and around house, a closed question asked for the number of hours per week spent in leisure time on working (i.e., fixing, repairing) in or around the house with four answer categories (never, ⬍1 hour, 1–2 hours, and ⬎2 hours per week). To explore exposure time for sports activities, a question asked for the number of hours per week spent on sports activities, with the same four answer categories as described above. The number of chronic diseases from which subjects suffered in the past 5 years was obtained by summing from a list of 23 chronic diseases (yes/no). From questions on the average number of days per week that individuals used alcohol and the average number of glasses used per day, individuals were categorized into five groups for alcohol consumption: total abstainers, light, moderate, excessive, and very excessive alcohol drinkers. Excessive drinking was defined as drinking 6 or more glasses on average per day on at least 3 days per week or 4 to 5 glasses on average per day on at least 4 days per week. Very excessive drinking was defined as drinking 6 or more glasses on average per day on at least 5 days per week [39,40]. A single-item question was used to obtain information on regular sedativehypnotic use, with two answer categories (yes/no). As potential confounding variables, we included sex, age (15–24, 25–44, 45–64, and 65⫹ years of age), level of urbanization (four groups ranging from rural to urban), marital status (married; single and never married; divorced; or widowed), and religious affiliation (none, Roman Catholic, Protestant, other). This information was obtained in the postal questionnaire. 2.3. Record linkage From the NHDR we first selected hospital admissions due to injuries from the five hospitals in the coverage area (using the above-mentioned two criteria) between April 1991 and June 1998. A probabilistic record linkage was carried
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out between the resulting LMR database and the GLOBE database, with the combination of date of birth, sex, and zip code (four numbers and two letters) being the identifiers for linkage. After linkage, we found 32 identical identifiers, which could be due to repeat admissions of the same persons or to admissions of different persons. Comparison of patient numbers showed that these double codes were due to re-admissions; because we studied the incidence, only the first admission was included in the study. 2.4. Statistical analysis Cox proportional hazard models were used to estimate hazard ratios (HRs) of injuries by educational level [41]; time to follow-up stopped after hospital admission, a move out of the coverage area of the hospitals, death of participants, or at the end of the study period. Participants for whom information on zip code and date of move at any time during follow-up were unknown (key variables for record linkage) were excluded from the analysis (n ⫽ 642, 3.4%). Subjects with missing values for educational level and potential confounding variables were excluded (n ⫽ 461, 2.5%). There appeared to be no statistically significantly interactions of educational level with age and sex (P ⬍ .05), and we therefore included both sexes and all age groups in the analyses simultaneously. Exposure variables mediating the association between educational level and injuries should be (1) associated with injuries and (2) differentially distributed over socioeconomic groups. Thus, the association between exposure variables and injuries was determined, adjusted for the potential confounding variables and educational level. For exposure variables associated with injuries with a P value below .10, age- and sex-adjusted prevalence rates of risk groups of these variables were calculated by educational level using the method of direct standardization with the GLOBE study population as the reference population. These factors were added to the model containing educational level and potentially confounding variables. The relative contribution of exposure variables was calculated as follows: 100 × (RRmodel 1 ⫺ RRmodel 2)/RRmodel 1 ⫺ 1) [6], with model 1 including educational level and potential confounding variables and model 2 including exposure variables additionally. Similar analytical steps were taken to investigate the contribution of chronic diseases, alcohol intake, and sedative use to educational inequalities in injuries.
3. Results We found a total of 71 traffic injuries and 397 OHS injuries resulting in hospital admission, the latter group including 192 injuries due to accidental falls. HRs of traffic and OHS injuries (but not of accidental falls as a separate group of OHS injuries) were significantly higher for those in
the lower compared with those in the highest educational group (Table1). We investigated whether exposure variables were associated with the risk of traffic and OHS injuries (Table 2). Compared with those who reported never to be in a car, HRs of traffic injuries for participants reporting to use a car were significantly lower. The number of participants in different categories of exposure time for driving a motorcycle was too low to estimate risk of traffic injuries. Substantially increased HRs were found for participants who reported using a moped at least daily compared with those who never used a moped, although confidence intervals (CIs) are wide. No association was found between time spent walking or cycling to work or shops and traffic injuries (results not tabulated). With regard to exposure variables for OHS injuries, we found an increased risk of OHS injuries in participants involved in more than 2 hours of sports activities per week compared with those who were never involved in sports activities. No association could be demonstrated between time working in or around the house and OHS injuries (not tabulated). Employed participants sometimes carrying out dangerous work showed an increased risk of OHS injuries. Table 3 shows the age- and sex-adjusted prevalence rates of exposure variables associated with increased injury risks by educational level. With a decreasing level of education, an increasing percentage of participants does not use a car. The use of mopeds is more frequent in lower educational groups. The percentage of participants involved in sports activities for more than 2 hours per week rose by increasing educational level. No clear pattern was observed for the relation between educational level and doing dangerous work. Table 4 shows the association between chronic diseases, alcohol consumption, and sedative use. There appeared to be an increased risk of traffic injuries for those reporting excessive and very excessive alcohol intake and for those reporting sedative use, but these associations did not reach the significance level. There appeared to be a significantly increased risk of OHS injuries for participants reporting two chronic diseases, very excessive alcohol intake, or sedative use. Table 5 shows the age- and sex-adjusted prevalence rates by educational level for variables showing an increased risk of OHS injuries. The prevalence of two chronic diseases, very excessive alcohol consumption, and sedative use increased by decreasing educational level. Table 6 shows the results of adjustment for exposure variables and chronic diseases, alcohol consumption, and sedative use. The HRs of traffic injuries in the lowest two educational groups were reduced by 28% (HR 2.27, 95% CI 0.87–5.94) (not tabulated) and 22% (HR 2.59, 95% CI 1.06–6.31) (not tabulated), respectively, after adjustment for moped use. These percentages were 33% in the lowest and 16% in the second lowest educational group after adjustment
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Table 1 Hazard ratios (HR) of injuries by educational level Occupational, home, and sports injuries Traffic injuries
Total
Accidental fall
Educationa
Population
nb
HRc
95% CI
n
HR
95% CI
n
HR
95% CI
1 (high) 2 3 4 (low)
3,278 3,948 6,764 3,717
6 11 34 20
1.00 1.50 3.04 2.76
0.55–4.08 1.25–7.38 1.06–7.17
52 66 175 104
1.00 0.98 1.50 1.37
0.68–1.42 1.09–2.07 0.96–1.96
24 29 84 55
1.00 0.90 1.19 1.02
0.52–1.55 0.74–1.92 0.61–1.71
Abbreviation: CI, confidence interval. a 1 ⫽ higher vocational education or university; 2 ⫽ intermediate vocational or higher secondary general education; 3 ⫽ lower vocational or lower secondary general education; 4 ⫽ primary education. b Number of injuries. c Adjusted for age, sex, marital status, religious affiliation, and urbanization.
for car use. Simultaneous adjustment for moped and car use reduced the HR of traffic injuries by 46% in the lowest educational group (HR 1.95, 95% CI 0.98–5.29) and by 31% in the second lowest educational group (HR 2.41, 95% CI 0.98–5.95). Because associations between chronic diseases,
Table 2 Hazard ratios for traffic and occupational, home and sports injuries in the GLOBE study by exposure variables Traffic injuries Exposure variable Use of car No Sometimes ⬍1 h/day ⬎1 h/day Missing valuesb P value Use of moped No Sometimes ⬍1 h/day ⬎1 h/day Missing values P value
HRa
95% CI
1.00 0.49 0.47 0.42 0.79 0.08
0.25–0.97 0.24–0.92 0.20–0.86 0.30–2.08
1.00 1.59 4.97 4.15 1.99 0.00
4. Discussion 0.49–5.16 1.91–12.98 1.45–11.89 0.70–5.64
Occupational, home, and sports injuries Sports participation Never ⬍1 h/wk 1–2 h/wk ⬎2 h/wk Missing values P value Doing dangerous work Not employed Employed, no dangerous work Employed, dangerous work Missing values P value
1.00 0.97 0.95 1.43 1.30 0.05 1.00 0.90 1.59 1.13 0.067
alcohol consumption, and sedative use were not significantly related to traffic injuries, adjustment for these variables did not explain educational inequalities in traffic injuries (not tabulated). Adjustment for exposure variables hardly attenuated the association between educational level and OHS injuries. For example, the HR in the second lowest compared with the highest educational group decreased by 8% but remained significantly increased (HR 1.46, 95% CI 1.05–2.01). Additional adjustment for chronic diseases, alcohol consumption, and sedative use resulted in a reduction of the HRs in the lower socioeconomic groups. For example, the exposure-adjusted HR in the second lowest educational group additionally decreased by 11% after adjustment for chronic diseases, alcohol consumption, and sedative use (HR 1.41, 95% CI 1.01–1.94).
0.54–1.47 0.71–1.28 1.10–1.85 0.66–2.54
0.70–1.18 1.04–2.43 0.64–1.98
Abbreviations: HR, hazard ratio; CI, confidence interval. a Adjusted for age, sex, marital status, religion, urbanization, and educational level. b Missing values were included to investigate whether these were random or selective with respect to the outcome measure.
In this study, we found educational inequalities in traffic and OHS injuries (other than accidental falls) resulting in hospital admission. For traffic injuries, but not for OHS injuries, these inequalities attenuated substantially after adjustment for exposure time. Exposure-adjusted inequalities in OHS injuries were to some extent explained by the prevalence of chronic disease, alcohol consumption, and sedative use. Table 3 Age- and sex-adjusted prevalence of exposure variables associated with increased traffic injury risk by educational level
Car use Never Moped use ⬍1 h/day ⬎1 h/day Sports participation ⬎2 h/day Doing dangerous work Yes
1 (low)
2
3
4 (high)
28.5
16.7
13.8
8.2
1.4 1.4
1.7 1.6
1.9 1.1
0.6 0.3
8.6
16.4
24.9
24.2
3.1
5.6
6.9
3.7
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Table 4 Hazard ratios of injuries by chronic diseases, alcohol consumption, and sedative use in the GLOBE study
Chronic diseases No 1 ⭓2 P value Alcohol Never Light Moderate Excessive Very excessive Missing valuesb P value Sedative use No Yes Missing values P value
Traffic injuries
Occupational, home, and sports injuries
HRa
HR
1.00 0.62 0.92 0.31
95% CI
Table 6 Effect of adjustment for exposure variables, health, and health-related behavior on the association between educational level and traffic and occupational, home, and sports injuries in the GLOBE study
95% CI
0.33–1.15 0.50–1.68
1.00 1.26 1.54 0.026
0.99–1.59 1.20–1.99
1.00 1.19 1.06 1.91 2.00 1.74 0.51
0.62–2.26 0.47–2.38 0.71–5.15 0.54–6.26 0.67–4.50
1.00 0.76 0.92 1.48 2.40 0.89 0.000
0.59–0.99 0.66–1.26 0.97–2.27 1.54–3.75 0.56–1.41
1.00 1.23 2.09 0.44
0.52–2.92 0.65–6.75
1.00 1.65 0.62 0.045
1.20–2.28 0.25–1.50
Basic modela Educationb 1 (high) 2 3 4 (low) 1 (high) 2 3 4 (low) 1 (high) 2 3 4 (low)
Traffic injuries
Occupational, home, and sports injuries
HR 1.00 1.50 3.04 2.76
HR 1.00 0.98 1.50 1.37
95% CI 0.55–4.08 1.25–7.38 1.06–7.17
⫹ exposurec 1.00 1.38 0.50–3.76 2.41 0.73–5.20 1.95 0.98–5.93
95% CI 0.68–1.42 1.09–2.07 0.96–1.96
⫹ exposured 1.00 0.95 0.66–1.37 1.46 1.05–2.01 1.34 0.93–1.93 ⫹ healthe 1.00 0.92 1.41 1.25
0.63–1.32 1.01–1.94 0.86–1.81
a
Adjusted for age, sex, marital status, religion, and urbanization. 1, higher vocational education or university; 2, intermediate vocational or higher secondary general education; 3, lower vocational or lower secondary general education; 4, primary education. c Moped use, car use. d Dangerous work, sport. e Chronic diseases, alcohol intake, use of sedatives. b
Abbreviations: HR, hazard ratio; CI, confidence interval. a Adjusted for age, sex, marital status, religion, urbanization, and educational level. b Missing values were included to investigate whether these were random or selective with respect to the outcome measure.
The major strength of this study is the linkage of a core indicator of individual SEP to objectively collected data on injury morbidity. Using less valid indicators of SEP (e.g., area-based indicators) or injuries (e.g., self-reported data) may have resulted in an underestimation of inequalities in injuries in previous studies [11,20]. Further, we used a prospective design, which is rare in research on inequalities in injuries. Explanatory mechanisms for educational inequalities in health can generally be divided in a selection mechanism (in which health determines the SEP) and a causal mechanism (in which SEP determines health). Thus, to distinguish these mechanisms, prospective studies are required [36]. By linking the GLOBE database to the NHDR, we also had information about exposure, the prevalence of chronic diseases, alcohol consumption, and sedative use, which allowed us to explore the explanation of the inequalities in
Table 5 Age- and sex, adjusted prevalence of chronic diseases, alcohol and sedative use associated with increased nontraffic injury risk by educational level
Chronic diseases ⭓2 chronic diseases Alcohol Very excessive Sedative use Yes
1 (high)
2
3
4 (low)
12.6
15.8
19.9
25.1
2.2
2.6
3.1
3.4
4.2
5.6
5.9
10.0
injuries. Finally, using patient numbers of the NHDR, we were able to exclude re-admissions, which is generally not possible from studies using hospital discharge data for the determination of incidence of injuries resulting in hospital admissions [42]. Our study has several methodologic limitations. First, we had to use broad categories of injuries. The NHDR did not allow us to distinguish more specific groups, in particular for the OHS injuries, and other hospital admission databases are not available in the Netherlands. Even if such databases were available, prospective research of more specific injuries groups does not seem feasible because it requires large sample sizes or a long period of follow-up before sufficient patients are included. Thus, the heterogeneity of injuries is an inherent problem in injury research, not just restricted to this study. Indeed, injury studies with a different topic also used similar broad injury categories [43]. Using broad categories may have masked some effects in our study. For example, the probably higher incidence of sports injuries in higher compared with lower educational groups may have resulted in an underestimation of the educational gradient in OHS injuries. Second, we assessed the incidence of injuries using hospital discharge data. Thus, we were not able to include prehospital deaths due to injuries. If we can extrapolate the suggestion that educational inequalities in injuries are increasing with the severity of the injuries [16], this would imply higher incidence rates of such fatal injuries in lower compared with higher educational groups. Although such
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data are not available for injuries, it is known that prehospital deaths due to other diseases (i.e., acute myocardial infarction) occur more frequently in lower compared with higher socioeconomic groups [44]. Thus, indirect evidence suggests that exclusion of fatal injuries before hospital admission may have resulted in an underestimation of the educational gradient in the incidence of severe injuries. Third, we were only able to explore the role of exposure variables because they were derived from single-item questions (although being able to adjust for these variables may be regarded a strength of the study in comparison to previous work). Using these questions may have resulted in less precise assessments of exposure and therefore perhaps to an underestimation of the association between exposure and injury risk; it is unclear if such an underestimation would differ between educational groups. Further, this information was gathered only at baseline and we assumed this to reflect exposure during follow-up. Despite these shortcomings in the measurement of exposure variables, their association with injuries and their differential distribution over socioeconomic groups showed the need to include information about exposure time in (explanatory) studies of inequalities in injuries. In future research, exposure variables should be measured in more detail. Finally, information on health and health-related behavior was also based on self-reported information. Using selfreported information to assess the prevalence of chronic diseases may result in an underestimation, and this may be more so in lower compared with higher educational groups [22]. Consequently, we may have underestimated the contribution of chronic diseases to the explanation of educational inequalities in the incidence of injuries. There is less evidence of misclassification by educational level for the included health-related behavioral variables [45]. We found that adjustment for exposure variables reduced educational inequalities in traffic injuries, which was particularly due to differences in car use. Perhaps surprisingly, those who never used a car were at increased risk of traffic injuries. It is likely that this group is particularly vulnerable to pedestrian and cycle injuries, even though exposure time for walking and cycling was not related to traffic injuries in our study. The exposure-adjusted inverse association of OHS injury risk with educational level was to some extent explained by chronic diseases, alcohol consumption, and sedative use. However, HRs in lower educational groups remained significantly increased after adjustment for these variables. Hence, other factors must be responsible for the reported inequalities. At the individual level, psychosocial factors, such as risk seeking behavior, may contribute to inequalities in serious injuries. At the level of the instruments, material factors are a potentially important group of mediating factors. We have previously shown the importance of material factors for educational inequalities in incidence and mortality of acute myocardial infarction [7,46]. With respect to injuries, material factors may reflect the ability to have
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access to safe products, such as smoke detectors and bicycle helmets. There is also evidence of the importance of factors at the level of the shared environment: Studies have shown an association between neighborhood socioeconomic characteristics and injury mortality [21]. More research should be devoted to investigating the contribution of individual, instrumental, and environmental factors and their interactions to educational inequalities in injuries. We found no evidence of educational inequalities in the risk of accidental falls, which is in accordance with the conclusion drawn from a review of available evidence on this association [27]. In our study, a higher probability of accidental falls was found in subjects ⭓65 years of age, particularly in women, a pattern reported by others for the risk of fractures [47]. At these ages, relatively unhealthy subjects in lower educational groups may have died from other diseases. Hence, those in the lower educational groups in our population could be a more healthy selection of the total population with risk factors for accidental falls more equally distributed between socioeconomic groups. Alternatively, accidental falls may occur during activities (e.g., sports activities) that are more frequent among persons from higher socio-economic groups. Further investigation of the determinants of accidental falls is needed because of their relative importance as a cause of injuries increased in past decades [48]. In conclusion, we found educational inequalities in traffic and OHS injuries resulting in hospital admission in Dutch adults in a prospective study. Despite the limitations in measurement, our study implies that educational inequalities in traffic injuries can be substantially explained by differences in exposure variables between educational groups, whereas chronic diseases, alcohol consumption, and sedative use partly explained educational inequalities in OHS injuries. Our data show that a reduction of educational inequalities in injuries resulting in hospital admission can be achieved if injury prevention programs are able to reach those in the lower educational groups. It has recently been shown that lower socio-economic groups could benefit more from specific injury prevention measures than higher socio-economic groups [49]. More knowledge is required about the explanation of these inequalities before implementing prevention programs. Acknowledgments The GLOBE study is carried out by the Department of Public Health of the Erasmus University Rotterdam, in collaboration with the Public Health Services of the city of Eindhoven and region South-East Brabant. The authors are indebted to Michel Provoost, Ilse Oonk, and Roel Faber for constructing the database. The present study is supported by grants of the Ministry of Public Health, Welfare and Sport and the Health Research and Development Council (ZON). Dr van Lenthe is supported by a grant from the Netherlands Organisation for Scientific Research (904-66-104).
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