Socioeconomic position indicators and risk of alcohol-related medical conditions: A national cohort study from Sweden

Alexis C Edwards; Sara Larsson Lönn; Karen G Chartier; Séverine Lannoy; Jan Sundquist; Kenneth S Kendler; Kristina Sundquist

doi:10.1371/journal.pmed.1004359

. 2024 Mar 19;21(3):e1004359. doi: 10.1371/journal.pmed.1004359

Socioeconomic position indicators and risk of alcohol-related medical conditions: A national cohort study from Sweden

Alexis C Edwards ^1,^*, Sara Larsson Lönn ², Karen G Chartier ^1,³, Séverine Lannoy ¹, Jan Sundquist ^2,^4,⁵, Kenneth S Kendler ^1,^‡, Kristina Sundquist ^2,^4,^5,^‡

PMCID: PMC10950249 PMID: 38502640

Abstract

Background

Alcohol consumption contributes to excess morbidity and mortality in part through the development of alcohol-related medical conditions (AMCs, including alcoholic cardiomyopathy, hepatitis, cirrhosis, etc.). The current study aimed to clarify the extent to which risk for these outcomes differs as a function of socioeconomic position (SEP), as discrepancies could lead to exacerbated health disparities.

Methods and findings

We used longitudinal Swedish national registries to estimate the individual and joint associations between 2 SEP indicators, educational attainment and income level, and risk of AMC based on International Classification of Diseases codes, while controlling for other sociodemographic covariates and psychiatric illness. We conducted Cox proportional hazards models in sex-stratified analyses (N = 1,162,679 females and N = 1,196,659 males), beginning observation at age 40 with follow-up through December 2018, death, or emigration. By the end of follow-up, 4,253 (0.37%) females and 11,183 (0.93%) males had received an AMC registration, corresponding to overall AMC incidence rates among females and males of 2.01 and 5.20, respectively. In sex-stratified models adjusted for birth year, marital status, region of origin, internalizing and externalizing disorder registrations, and alcohol use disorder (AUD) registration, lower educational attainment was associated with higher risk of AMC in both females (hazard ratios [HRs] = 1.40 to 2.46 for low- and mid-level educational attainment across 0 to 15 years of observation) and males (HRs = 1.13 to 1.48). Likewise, risk of AMC was increased for those with lower income levels (females: HRs = 1.10 to 5.86; males: HRs = 1.07 to 6.41). In secondary analyses, we further adjusted for aggregate familial risk of AUD by including family genetic risk scores for AUD (FGRS_AUD), estimated using medical, pharmacy, and criminal registries in extended families, as covariates. While FGRS_AUD were associated with risk of AMC in adjusted models (HR = 1.17 for females and HR = 1.21 for males), estimates for education and income level remained largely unchanged. Furthermore, FGRS_AUD interacted with income level, but not education level, such that those at higher familial liability to AUD were more susceptible to the adverse effect of low income. Limitations of these analyses include the possibility of false negatives for psychiatric illness registrations, changes in income after age 40 that were not accounted for due to modeling restrictions, restriction to residents of a high-income country, and the inability to account for individual-level alcohol consumption using registry data.

Conclusions

Using comprehensive national registry data, these analyses demonstrate that individuals with lower levels of education and/or income are at higher risk of developing AMC. These associations persist even when accounting for a range of sociodemographic, psychiatric, and familial risk factors. Differences in risk could contribute to further health disparities, potentially warranting increased screening and prevention efforts in clinical and public health settings.

Alexis Edwards and colleagues explore how educational attainment and income level correlate with the risk of developing medical conditions directly related to alcohol consumption.

Author summary

Why was this study done?

Alcohol consumption contributes to worldwide excess morbidity and mortality, including conditions directly related to alcohol (alcohol-related medical conditions, AMCs) such as alcoholic liver disease.
The extent to which socioeconomic position (SEP) is longitudinally associated with AMC risk is unclear, but important to understand as it may contribute to health disparities.

What did the researchers do and find?

We employed a model that follows people over time to estimate their risk of AMC as a function of 2 SEP indicators: educational attainment and income.
The model adjusted for other factors that might also be related to risk of AMC, such as marital status, history of psychiatric illness, and aggregate genetic liability to alcohol use disorder (AUD).
We found that lower educational attainment and lower income were both associated with higher risk of AMC, even after accounting for other predictors.
Individuals with higher genetic liability to AUD were at increased risk for AMC; these individuals were also more vulnerable to the negative effects of low income.

What do these findings mean?

These results indicate that individuals with lower levels of educational attainment or lower incomes might warrant particular clinical attention with respect to screening or evaluation of alcohol consumption, as their risk of AMC is elevated. These differences in risk could exacerbate poor health outcomes among people with lower SEP.
Clinicians should also be aware that individuals with a family history of AUD are at higher risk of alcohol-related medical morbidities.
Limitations include the inability to account for individual differences in alcohol consumption, potential for excluding less severe AUD cases, and inability to extend findings to less wealthy countries.

Introduction

Excessive alcohol use and alcohol use disorder (AUD) contribute substantially to morbidity and mortality worldwide. The World Health Organization estimates that harmful alcohol use accounts for 5.1% of the global burden of disease and injury, resulting in 3 million worldwide deaths per year [1]. In the United States, 9.8% of deaths among working-age adults were attributable to excessive alcohol use [2]. In Sweden, the 2016 prevalence of heavy episodic drinking was 28.0% (12.4% in females and 43.7% in males), slightly lower than in the European Union overall; furthermore, alcohol accounted for 17.6% of unintentional and 21.1% of intentional injuries [3]. The social and psychological consequences of problematic use are considerable, with AUD being linked to higher rates of divorce [4], criminality [5], depression [6], and suicidal behavior [7,8]. Furthermore, excessive alcohol use exacts a steep economic toll: In 2010, the cost of excessive drinking in the United States was estimated at $249 billion [9]. Importantly, alcohol-related medical conditions (AMCs) constituted a substantial proportion of the economic burden of alcohol use, with 11.4% of the 2010 costs attributed to healthcare [9].

Socioeconomic indicators are frequently correlated with alcohol outcomes, including frequency of use, AUD, and alcohol-related morbidity and mortality, and these associations exist within the context of a complex ecosystem of influences [10]. While some studies indicate that drinking frequency and quantity are higher among those with higher socioeconomic position (SEP) [10], others have found that high-frequency/low-quantity drinking is common among those with high SEP, whereas individuals of low SEP drink less frequently but at higher quantities [11–14]. Multiple indicators of lower SEP were associated with increased risk of alcohol-related mortality in a meta-analysis [15], and van Oers [11] reported that lower levels of educational attainment were associated with alcohol-related problems in the Netherlands. In the Swedish Public Health Cohort, lower occupational class was associated with higher risk of alcohol-related health problems, and the effect of heavy episodic drinking was more pronounced among those of lower occupational class [16]. Thus, even when controlling for particularly unhealthy drinking patterns, the extant literature generally suggests that individuals with lower SEP are more likely to suffer severe alcohol-related outcomes.

While many studies report cross-sectional associations between SEP indicators and alcohol outcomes, far fewer have examined longitudinal associations. Clarifying longitudinal associations is particularly important given the possibility of bidirectional relationships [10]: Not only might low SEP lead to poor alcohol outcomes, but alcohol problems might lead to negative economic consequences [17]. Understanding directionality can be informative to prevention and intervention efforts, i.e., if alcohol-related harm-reduction is the goal, it is necessary to confirm that low SEP is prospectively associated with adverse alcohol outcomes to determine whether targeted prevention could be effective within this group.

In the current study, we aimed to clarify the association between SEP and AMC through the use of longitudinal, nationwide Swedish registry data and Cox proportional hazards models. We examined the associations between 2 SEP indicators—namely, educational attainment and income level—and subsequent risk of developing AMC. We sought to substantively contribute to the existing literature on this topic through the use of large, minimally biased, representative data, which also allows for stratification by sex. We account for a range of potentially important sociodemographic and psychological covariates and confounders. Furthermore, we evaluate whether associations between SEP indicators and AMC shift across adulthood. We hypothesized that lower SEP indicators would be associated with higher risk of AMC and that these associations would be attenuated but persist after accounting for covariates and potential confounders.

Materials and methods

Ethics statement

All analyses were approved by the Regional Ethical Review Board of Lund University (no. 2008/409 and later amendments).

Sample

We used several Swedish nationwide registers, linked using the unique 10-digit identification number assigned at birth or immigration to all Swedish residents. The identification number was replaced by a serial number to ensure anonymity. The registry resources are described in S1 Methods, and included the population register, multi-generation register, multiple health care registers, and the Longitudinal Integrated Database for Health Insurance and Labour Market Statistics (LISA), which included information on income and education. To maximize registry coverage based on preliminary analyses, we included females and males, born between 1950 and 1970, and residing in Sweden at age 40, without a prior AMC registration. Due to missing data on education, N = 916 females and N = 845 males from the cohort were excluded; due to missing data on marital status, N = 15 females and N = 11 males were excluded.

Measures

Our outcome variable, AMC, was defined from Swedish medical registers by the following ICD codes, described further in S1 Methods: ICD8: 571.0, ICD9: 357F, 425F, 535D, 571A, 571B, 571C, 571D; and ICD10: E24.4, G31.2, G62.1, G72.1, I42.6, K29.2, K700, K701, K702, K703, K704, K709, K85.2, K86.0, and O35.4. Individuals with AMC registrations prior to age 40 were excluded.

The primary independent variables of interest were education and familial income. Income was assessed at age 40 and categorized based on the income quartiles for the working-age population in Sweden (ages 20 to 65), although analyses included both employed and unemployed individuals. Educational attainment was categorized into low (compulsory school only), mid (upper secondary school), and high (university level), based on the subject’s highest level of school completion. These variables allow us to jointly account for, and distinguish the impact of, indices of individual-level SEP (educational level) and access to resources at the family level (familial income). An individual could have low educational attainment, and/or no personal income, but still have access to resources through a spouse’s income.

Sociodemographic covariates included year of birth, marital status, and region of origin. Marital status was assessed at age 40 and obtained from the total population register. The categories were: unmarried, married, divorced, or widowed. Region of origin was defined by country of birth and categorized into: Finland, Western Countries, Eastern Europe, Latin America and the Caribbean, Middle East and Northern Africa, Africa (excluding Northern Africa), and Asia (excluding Middle East) and Oceania. Finland was examined separately because they represent one of the largest immigrant groups in Sweden and because they have higher AUD rates.

From Swedish medical registers, AUD was defined by the following ICD codes: ICD8: 291, 303; ICD9: 305A, 291, 303; and ICD 10: ICD-10 codes: F10.1, F10.2, F10.3, F10.4, F10.5, F10.6, F10.7, F10.8, and F10.9. Additionally, AUD registrations were identified using the Prescribed Drug Register by retrieving “Drugs used in alcohol dependence” (Anatomical Therapeutic Chemical [ATC] Classification System code N07BB); this includes disulfiram (N07BB01), acamprosate (N07BB03), naltrexone (N07BB04), or nalmefene (N07BB05). Finally, we identified AUD cases as those convicted for or suspected of at least 2 alcohol-related crimes according to law 1951:649, paragraph 4 and 4A and law 1994:1009, Chapter 20, paragraphs 4 and 5 from the Swedish Crime Register, and code 3005 and 3201 in the Suspicion register. Most of these crime types include driving or operating a boat while intoxicated. Among criminal AUD registrations, 99.7% were related to drunk driving; however, only 18.3% of AUD registrations are detected using only in the criminal registries.

Internalizing disorder (ID) was defined using the ICD codes for major depression (MD); ICD-9: 296B, 298A, 300E; and ICD-10: F32 and F33, and anxiety disorder (AD); ICD-10: F41. Externalizing disorder (ED) was defined as criminal behavior including white collar crime, property crime, or violent crime as described in detail elsewhere [18]; or drug use disorder (DUD). DUD was identified from medical registers using codes from ICD-9: 292, 304, and 305C - 305I; ICD-10: F11-F16 and from the Prescribed Drug Register if a person had retrieved (on average) more than 4 defined daily doses a day for 12 months of a drug to treat drug dependence (excluding those suffering from cancer), defined by the following ATC codes: N02A, N05C, N05BA, or N07BC. We further defined DUD from the Suspicion Register by codes 3070, 5010, 5011, and 5012; and the Crime Register if convicted by law 1968:64, paragraph 1, point 6 or and law 1951:649, paragraph 4, subsection 2 and paragraph 4A, subsection 2.

Finally, aggregate genetic liability for AUD, which was used in a series of secondary analyses, was operationalized using the family genetic risk score for AUD (FGRS_AUD), which assesses genetic risk by an examination of first through fifth degree relatives, correcting for cohabitation effects which could make relatives more similar in their AUD risk. This measure is a relative measure which is operationalized as a continuous, standardized indicator of aggregate genetic risk, so that 1 unit corresponds to 1 standard deviation in the population. The details of this measure have been described in detail elsewhere [19]; FGRS have been validated in subsequent research [20].

Statistical analyses

These analyses were not prospectively described in an analysis plan. S1 Methods provides information on the timing of analyses. Cox proportional hazard models were utilized to estimate the time to AMC, censoring at end of follow-up (December 2018), death, or emigration. Models were stratified by sex to facilitate direct comparisons and enhance data transparency [21].

We conducted a series of preliminary analyses to decide whether time-varying coefficients were necessary for the socioeconomic predictors. This was investigated by including a linear interaction between follow-up time and education or income. If the interaction was significant and the modification of the magnitude of the hazard ratio (HR) was potentially meaningful, we elected to allow for a time-dependent coefficient in the final models.

We then pursued model building to estimate the association between education and/or income with AMC and investigated whether observed associations were robust to the inclusion of ID, ED, and AUD, which were included as time-varying variables. These models included birth year, marital status, and region of birth as covariates. In Models 1A and 1B, we tested the association between education or income, respectively, with AMC, otherwise including only the sociodemographic covariates. In Model 2, we jointly estimated these associations, including both education and income. In Model 3, we added registrations for ID and ED, as these could act as important mediators between education/income and AMC. Finally, in Model 4, to provide insight into whether education/income is associated with risk for AMC above and beyond variance that could be accounted for by the association between socioeconomic indicators and AUD, we also included AUD as a time-varying variable.

In a secondary set of analyses, we included genetic risk of AUD, operationalized as FGRS_AUD, to understand how genetic liability to AUD might influence the associations with the investigated risk factors. FGRS_AUD is estimated with greater precision for Swedish born subjects with 2 Swedish-born parents, as these individuals have more family members whose data is available in the Swedish registries. Therefore, this series of analyses included only Swedish-born individuals with 2 Swedish-born parents. We ran the same models as above, excluding region of birth. These models are referred to as Model S1A, S1B, S2, S3, and S4, respectively.

We ran an additional model, Model S5, including an interaction between FGRS_AUD and education and between FGRS_AUD and income, to test whether genetic liability to AUD moderated the associations between education/income and AMC. To be able to interpret the results of the interactions from these multiplicative models on an additive scale, which is often of greater relevance in a public health context [22], we assessed additivity by estimating the relative excess risk due to interaction (RERI) and synergy index (S) [23].

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (S1 STROBE Checklist).

Results

Descriptive statistics

Tables 1 and 2 provide descriptions of the female and male cohorts, respectively, in the sample of N = 2,359,338 individuals. In the female cohort, N = 4,253 individuals had an AMC registration, corresponding to 0.37% of the sample. Among males, N = 11,183 (0.93%) had an AMC registration. For both sexes, the distribution of sociodemographic variables, including the primary predictors of interest, varied significantly across the groups without and with AMC. For example, individuals with AMC were less likely to be married than their counterparts without AMC (females: 42.04% versus 56.42%; males: 32.59% versus 50.75%). In addition, among individuals residing in Sweden who were born elsewhere, AMC registrations were disproportionately common only among those from Finland: 2.81% of females and 2.06% of males in the cohort were Finnish, but Finns represented 6.51% of females and 5.54% of males with AMC. In contrast, immigrants from Asia, Africa, and the Middle East had disproportionately few AMC registrations. Incidences of AMC for each variable, per 10,000 person years, are provided in the S1 Table.

Table 1. Descriptive information on the female cohort, born 1950–1970, with observation beginning at age 40.

	Total female cohort (within column %/SD)¹	Without AMC (within column %/SD)¹	With AMC (within column %/SD)¹	With AMC (within row %/SD)²	Difference test³ p-value across those without versus with AMC
All	1,162,679	1,158,426	4,253	4,253 (0.37%)	n/a
Unmarried	348,653 (29.99%)	347,278 (29.98%)	1,375 (32.33%)	1,375 (0.39%)	<0.00001
Married	655,346 (56.37%)	653,558 (56.42%)	1,788 (42.04%)	1788 (0.27%)
Divorced	152,257 (13.1%)	151,208 (13.05%)	1,049 (24.66%)	1,049 (0.69%)
Widowed	6,423 (0.55%)	6,382 (0.55%)	41 (0.96%)	41 (0.64%)
Low education	126,378 (10.87%)	125,395 (10.82%)	983 (23.11%)	983 (0.78%)	<0.00001
Mid education	551,231 (47.41%)	549,021 (47.39%)	2,210 (51.96%)	2,210 (0.4%)
High education	485,070 (41.72%)	484,010 (41.78%)	1,060 (24.92%)	1,060 (0.22%)
Low income	150,952 (12.98%)	149,596 (12.91%)	1,356 (31.88%)	1,356 (0.9%)	<0.00001
Low-mid income	275,066 (23.66%)	273,897 (23.64%)	1,169 (27.49%)	1,169 (0.42%)
High-mid income	352,095 (30.28%)	351,197 (30.32%)	898 (21.11%)	898 (0.26%)
High income	384,566 (33.08%)	383,736 (33.13%)	830 (19.52%)	830 (0.22%)
Africa	6,372 (0.55%)	6,363 (0.55%)	9 (0.21%)	9 (0.14%)	<0.00001
Asia	21,140 (1.82%)	21,119 (1.82%)	21 (0.49%)	21 (0.1%)
Eastern Europe	43,841 (3.77%)	43,707 (3.77%)	134 (3.15%)	134 (0.31%)
Finland	32,683 (2.81%)	32,406 (2.8%)	277 (6.51%)	277 (0.85%)
Latin America/Caribbean	9,299 (0.8%)	9,279 (0.8%)	20 (0.47%)	20 (0.22%)
Middle East/North Africa	24,546 (2.11%)	24,531 (2.12%)	15 (0.35%)	15 (0.06%)
Sweden	1,003,382 (86.3%)	999,681 (86.3%)	3,701 (87.02%)	3,701 (0.37%)
Western countries	21,416 (1.84%)	21,340 (1.84%)	76 (1.79%)	76 (0.35%)
Internalizing registrations	286,160 (24.61%)	284,452 (24.56%)	1,708 (40.16%)	1,708 (0.6%)	<0.00001
Externalizing registrations	101,851 (8.76%)	100,384 (8.67%)	1,467 (34.49%)	1,467 (1.44%)	<0.00001
AUD registrations	40,717 (3.5%)	38,169 (3.29%)	2,548 (59.91%)	2,548 (6.26%)	<0.00001
Mean follow-up time (years)	18.36 (6.10)	18.36 (6.10)	13.61 (6.51)		<0.00001
Range follow-up time (years)	0.1, 29.0	0.1, 29.0	0.1, 28.9
Mean birth year	1960.12 (6.04)	1960.13 (6.04)	1957.09 (5.32)		<0.00001
Range birth year	1950, 1970	1950, 1970	1950, 1970

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¹For reported percentages, the numerator is the N listed in the cell, and the denominator is the N in the top row of the same column. For example, there were N = 348,653 unmarried females, which corresponds to 29.99% of the sample (348,653/1,162,679).

²For reported percentages, the numerator is the N listed in the cell, and the denominator is the N in the first column of the same row. For example, there were N = 1,375 unmarried females with AMC, which corresponds to 0.39% of the unmarried females in the sample (1,375/348,653).

³For categorical variables, this is a Chi-square; for continuous variables, this is a t test.

AMC, alcohol-related medical conditions; AUD, alcohol use disorder; SD, standard deviation.

Table 2. Descriptive information on the male cohort, born 1950–1970, with observation beginning at age 40.

	Total male cohort (col %/SD)¹	Without AMC (col %/SD)¹	With AMC (col %/SD)¹	With AMC (row %/SD)²	Difference test³
All	1,196,659	1,185,476	11,183	11,184 (0.93%)	n/a
Unmarried	475,629 (39.75%)	470,065 (39.65%)	5,564 (49.75%)	5,564 (1.17%)	<0.00001
Married	605,286 (50.58%)	601,641 (50.75%)	3,645 (32.59%)	3,645 (0.6%)
Divorced	113,865 (9.52%)	111,920 (9.44%)	1,945 (17.39%)	1,945 (1.71%)
Widowed	1,879 (0.16%)	1,850 (0.16%)	29 (0.26%)	29 (1.54%)
Low education	188,732 (15.77%)	185,959 (15.69%)	2,773 (24.80%)	2,773 (1.47%)	<0.00001
Mid education	580,381 (48.50%)	574,971 (48.50%)	5,410 (48.38%)	5,410 (0.93%)
High education	427,546 (35.73%)	424,546 (35.81%)	3,000 (26.83%)	3,000 (0.7%)
Low income	199,405 (16.66%)	194,905 (16.44%)	4,500 (40.24%)	4,500 (2.26%)	<0.00001
Low-mid income	284,353 (23.76%)	281,694 (23.76%)	2,659 (23.78%)	2,659 (0.94%)
High-mid income	375,414 (31.37%)	373,129 (31.47%)	2,285 (20.43%)	2,285 (0.61%)
High income	337,487 (28.2%)	335,748 (28.32%)	1,739 (15.55%)	1,739 (0.52%)
Africa	7,735 (0.65%)	7,698 (0.65%)	37 (0.33%)	37 (0.48%)	<0.00001
Asia	15,810 (1.32%)	15,739 (1.33%)	71 (0.63%)	71 (0.45%)
Eastern Europe	33,009 (2.76%)	32,759 (2.76%)	250 (2.24%)	250 (0.76%)
Finland	24,624 (2.06%)	24,004 (2.02%)	620 (5.54%)	620 (2.52%)
Latin America/Caribbean	8,718 (0.73%)	8,664 (0.73%)	54 (0.48%)	54 (0.62%)
Middle East/North Africa	35,680 (2.98%)	35,552 (3%)	128 (1.14%)	128 (0.36%)
Sweden	1,047,228 (87.51%)	1,037,379 (87.51%)	9,849 (88.07%)	9,849 (0.94%)
Western countries	23,855 (1.99%)	23,681 (2%)	174 (1.56%)	174 (0.73%)
Internalizing registrations	177,998 (14.87%)	174,804 (14.75%)	3,194 (28.57%)	3,194 (1.79%)	<0.00001
Externalizing registrations	276,156 (23.08%)	269,886 (22.77%)	6,270 (56.07%)	6,270 (2.27%)	<0.00001
AUD registrations	109,221 (9.13%)	101,499 (8.56%)	7,722 (69.05%)	7,722 (7.07%)	<0.00001
Mean follow-up time (years)	18.17 (6.03)	18.01 (6.11)	13.47 (6.55)		<0.00001
Range follow-up time (years)	0.1, 29.0	0.1, 29.0	0.1, 28.9
Mean birth year	1960.17 (6.03)	1860.20 (6.03)	1957.02 (5.26)		<0.00001
Range birth year	1950, 1970	1950, 1970	1950, 1970

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¹For reported percentages, the numerator is the N listed in the cell, and the denominator is the N in the top row of the same column. For example, there were N = 475,632 unmarried males, which corresponds to 39.75% of the sample (475,632/1,196,663).

²For reported percentages, the numerator is the N listed in the cell, and the denominator is the N in the first column of the same row. For example, there were N = 5,564 unmarried males with AMC, which corresponds to 1.17% of the unmarried males in the sample (5,564/475,632).

³For categorical variables, this is a Chi-square; for continuous variables, this is a t test.

AMC, alcohol-related medical conditions; AUD, alcohol use disorder; SD, standard deviation.

The polychoric correlation between income quartile and education level for females was 0.185 (SE-0.001) and for males was 0.198 (SE = 0.001), suggesting that while these socioeconomic measures are related, they also provide nonoverlapping information. The S2 and S3 Tables provide cross-tabulations for females and males, respectively, of income quartile and education level.

Crude models

In crude models unadjusted for any covariates, females with lower educational attainment were at higher risk of AMC (low versus high: HR = 4.48, 95% CIs = 3.66, 5.47, Chi-square p < 0.001; mid versus high: HR = 1.98, 95% CIs = 1.66, 2.34, p < 0.001); the same was true for males, at lower effect sizes (low versus high: HR = 2.27, 95% CIs = 2.01, 2.55, p < 0.001; mid versus high: HR = 1.46, 95% CIs = 1.32, 2.61, p < 0.001). Likewise, females in lower income quartiles had higher risk of AMC (income quartile 1 versus 4: HR = 11.52 [9.30, 14.27], p < 0.001; income quartile 2 versus 4: HR = 3.90 [3.12, 4.87], p < 0.001; income quartile 3 versus 4: HR = 1.60 [1.26, 2.03], p = 0.001). Results for males were comparable (income quartile 1 versus 4: HR = 11.95 [10.41, 13.72], p < 0.001; income quartile 2 versus 4: HR = 3.32 [2.86, 3.86], p < 0.001; income quartile 3 versus 4: HR = 1.63 [1.39, 1.91], p < 0.001).

Multivariable models

We next estimated the association between educational attainment (in Model 1A) or income at age 40 (in Model 1B) with risk of AMC, adjusting only for sociodemographic covariates. We provide snapshots of the HRs for education and income at 4 time points: time 0 (age 40), after 5 years of observation (age 45), after 10 years of observation (age 50), and after 15 years (age 55 or older). As shown in the Fig 1, in which estimates from Models 1A and 1B are superimposed for ease of illustration, individuals with lower levels of education or income at age 40 were at substantially increased risk for AMC. Complete results, including for all covariates, are provided in S4 and S5 Tables.

Among females (Fig 1 and S4 Table), having the lowest level of education was associated with a nearly 5-fold increase in risk of AMC in Model 1A (HR = 4.67; 95% CI 3.82, 5.72; p < 0.001). Relative to individuals with the highest level of education, those with mid- or low-level education at age 40 were still at increased risk of AMC 15 years later (HRs = 1.68 [95% CIs 1.56, 1.81], p < 0.001; and 2.84 [95% CIs 2.60, 3.11), p < 0.001, respectively). HRs for the 3 lower quartiles of income were also elevated (Model 1B; Fig 1 and S5 Table). The magnitude of effect declined across observation time, most notably for the lowest quartile (from HR = 11.26 at observation onset to HR = 3.86, 15 years later), and was more pronounced for income than for education. We observed a general dose-response pattern for levels of education/income and for time elapsed since the start of observation; i.e., the strongest effects were observed at the start of observation, and at the lowest education and income levels.

HRs were lower, but still consistently >1, for males (Fig 1 and S4 Table). In males, being in the lowest education category (Model 1A) was associated with a 2-fold increase in risk of AMC (HR = 2.04, 95% CIs 1.81, 2.30; p < 0.001) at the start of observation. Over time, these estimates declined, but remained above 1 (HRs = 1.26 to 1.57 for those with mid-level and low-level education). Similar to females, the effect size for being in the lowest income quartile was high: HR = 10.21 (95% CIs 8.86, 11.77; p < 0.001; Model 1B; Fig 1 and S5 Table). Individuals in the 2 middle quartiles were at lower, but still elevated, risk of AMC. Also consistent with females, the HRs for education were more stable over time than those for income.

In Model 2 (S6 Table), education and income were jointly included as predictors. The HRs were attenuated, but only slightly, indicating that these measures of socioeconomic position account for independent components of AMC risk. We next adjusted for psychiatric illness using time-varying covariates, in Model 3 (S7 Table). Among females, HRs for ID and ED were associated with an approximately 2.6- to 3.7-fold increase in AMC risk. We observed a slight attenuation in the effect sizes of education and income from Model 2. The lowest income quartile remained at high risk of AMC at the start of observation (HR = 7.80, 95% CIs 6.24, 9.75; p < 0.001), declining to HR = 2.88, 15 years later. A similar pattern was observed for males, among whom HRs were approximately 2.1 to 3.1 for ID and ED. For males, having a mid- or low-level education was associated with a 1.21- to 1.63-fold increase in AMC risk. Being in the lowest income quartile was still strongly associated with AMC (HR = 7.76, 95% CIs 6.73, 8.95; p < 0.001 at start of observation), though the effects were attenuated as time progressed and being in the third quartile 15 years after the onset of observation was not significantly associated with higher AMC risk.

Finally, in Model 4, we adjusted for AUD as a time-varying covariate. AUD was prominently associated with AMC risk: the associated HRs were HR = 34.00 (95% CIs 31.73, 36.42; p < 0.001) for females and HR = 10.71 (95% CIs 10.28, 11.15; p < 0.001) for males (S8 Table). For both sexes, the inclusion of AUD led to lower HRs for education and income relative to previous models. However, with the exception of the estimate for individuals in the third income quartile, 15 years after the start of observation, all estimates remained significantly >1. We continued to observe a dose-response pattern for income quartiles and as a function of time elapsed since onset of observation in most cases, the exception being that the effect of having mid-level education at age 40 was relatively stable across the observation period. This pattern was clearer among males.

Secondary analysis

We pursued a secondary set of analyses that corresponded to the original models, replacing region of origin with FGRS_AUD, as described in the Methods. In Models S1A/S1B through Model S4, the effect of FGRS_AUD ranged from HR = 1.17 to 1.39 for females and HR = 1.21 to 1.36 for males (S9–S13 Tables) per standard deviation increase. The HRs for education level and income did not differ substantially from the corresponding HRs from the original Models 1 to 4. We added a model with an interaction term between education/income and FGRS_AUD, Model S5 to evaluate the possibility that level of aggregate genetic liability to AUD leads to differential susceptibility to the risky effects of lower education or income. The main effects of all predictors and covariates are reported in S14 Table, and we report the RERI and S (estimates of interactions on the additive scale) in S15 Table. For females, there was no evidence that FGRS_AUD moderated the association between education level and AMC risk. However, RERI and S estimates were consistent with an interaction between FGRS_AUD and the first and second income quartiles, such that individuals at higher genetic liability to AUD were more susceptible to the adverse impact of lower income on risk for AMC. Similarly, in males, we observed no interaction between FGRS_AUD and education level, but the association between income and AMC was significantly moderated by FGRS_AUD.

Discussion

In the current study, we used nationwide longitudinal Swedish registry data to evaluate a research question of clinical and social importance: whether 2 socioeconomic indicators, education and income, are related to risk of alcohol-related medical conditions. Even after accounting for other sociodemographic measures, psychopathology, and the role of AUD itself, individuals with lower levels of education and income had an increased risk of AMC. For education, the associations were slightly higher among females. For both sexes, the magnitude of effect was higher for lower income level than for lower education, and generally decreased across the observation period but in nearly every case remained elevated even 15 years after the start of observation. Furthermore, the adverse impact of low income was exacerbated among females and males with higher genetic liability to AUD. These findings demonstrate that lower education and income, though related, have at least partially independent and persistent associations with risk of AMC, which could contribute to exacerbated health disparities related to SEP. This effect is only slightly mediated by AUD, suggesting that individuals of lower SEP may warrant particular clinical attention, even within the already high-risk group of individuals with AUD.

The magnitude of effect for income was considerably higher at the onset of observation (age 40) than the corresponding effect of education, and its attenuation was more pronounced across the observation period. The change over time is likely due at least in part to shifts in income during middle to later adulthood: For example, if income increases after age 40, the effect of one’s prior income on subsequent health may be superseded by that of their more recent income. However, income at the point of AMC registration might not be the most relevant measure, as AMC is typically the result of chronically high alcohol consumption that began years before.

As noted above, previous research has found that drinking patterns vary by SEP, with those of lower SEP more likely to engage in heavy consumption [11–14,16], which is strongly linked to risk of AMC [24–26]. Information on consumption levels is not available in the Swedish nationwide registries, precluding our ability to test whether the associations between education/income remain when accounting for chronic heavy drinking, for example. However, our finding that these associations persist when controlling for AUD—itself closely associated with consumption levels [27,28]—is consistent with prior evidence that individuals of lower SEP are at disproportionately high risk of AMC even after accounting for heavy drinking [16].

With respect to the discrepancy between effect sizes for education and income, some prior research suggests that education is a more potent risk factor for the onset of chronic health conditions, while income more strongly predicts their progression [29]. A study that included the current cohort reported more protective effects between education and AUD than between income and AUD [30], though differences were not as pronounced as observed in the current study. Although these nuances remain to be investigated in future research, the current study clearly indicates that education and income are independently associated with AMC risk, though in assessing future risk, clinicians should pay particular attention to income.

What might contribute to this unequal risk across socioeconomic strata? Some studies indicate that individuals of lower SEP are less likely to engage in preventive health care behaviors [31]. Even in countries with socialized medical care, where access should be less of a barrier, these discrepancies may be due to health literacy [32] and less expendable time [33]. A study of large Finnish and British cohorts found that the association between lower SEP and poor health outcomes was driven in part by psychiatric and substance use disorders, which in turn led to downstream medical problems [34]. Still, accounting for other AUD registrations in the current study resulted in only slight attenuations to the observed associations, providing evidence that SEP indicators do not merely exert their effects through AUD.

In addition to the effects of our primary predictors of interest, we note 3 incidental findings that merit further investigation in the context of models specifically designed to replicate our observed associations. First, in fully adjusted models (Model 4 and Model S4), unmarried females were at reduced risk of AMC relative to married females, while among males, married individuals were consistently at lowest AMC risk. Prior studies have supported a protective effect of marriage for alcohol use, AUD, and other psychiatric outcomes [35–39], suggesting that AMC is a relative outlier, and only for females. This could be due to a stronger negative impact of husbands’ alcohol use on wives’ alcohol-related outcomes [40], an exposure that would not apply to unmarried females. The dynamics at play are likely to be challenging to dissect using registry data, but merit follow-up.

Second, with the exception of those from Finland, immigrants were at lower risk of AMC than their Swedish-born counterparts. One potential explanation is that immigrants from many regions have lower levels of alcohol consumption than Swedes and many other European nations [41]. A study of immigrant cohorts in Sweden showed that foreign-born Swedes from Asia and the Middle East (males and females) and from African and Eastern Europe (females) had lower rates of AUD than the general population [42]. Finns, who constitute one of the largest immigrant groups in Sweden, had the highest AUD rates (males and females). Finland is included in the so-called “vodka belt,” i.e., those Northern countries where the use of stronger alcoholic beverages (spirits) and binge drinking are more common than in other countries [43]. Prior studies also document Finns’ excess mortality in Sweden across multiple medical conditions [44] and their self-reported poor health compared to other Swedes and to Finns living in Finland [45], which together suggests their greater risk of AMC signals wider health inequities. Additionally, immigrant groups may be less likely to use medical care, even in nations where it is broadly accessible. For example, foreign-born patients are less likely than Swedish-born patients to pick up their medication for treating AUD from the pharmacy [46]. It then is possible that registry data could potentially underrepresent the extent of AMC among immigrants. If this is the case, additional outreach may be necessary to ensure that immigrants are adequately screened for risky drinking and treated for AUD to prevent the development of AUD and AMC.

Third, we found that individuals with higher genetic liability to AUD were more susceptible to the adverse impact of low income, though this was not the case for those with lower levels of educational attainment. This could reflect the stronger overall effect size of income level, and potentially due to the fact that the estimate is based on year 0, which is when the effect of income was most pronounced; replication in other samples, and perhaps using complementary methods (e.g., molecular polygenic scores) is needed. However, these results do suggest that clinicians should be attentive to the potentially combinatorial effects of low-income and dense family history of AUD: Patients subject to both conditions may warrant additional screening to ensure that their alcohol use does not result in adverse health outcomes.

Strengths of the current analysis include the use of nationwide, longitudinal databases with minimal bias; comparison of 2 related measures of SEP with unique contributions to AMC risk; and our ability to control for important covariates such as comorbid psychopathology and genetic liability to AUD. Our findings must still be viewed in the context of several limitations. First, income can change frequently, but to facilitate modeling we focused on income at the beginning of observation (age 40). Including repeated measures of income could have resulted in different findings, e.g., the effect size of income might decline less rapidly than in the present study. Second, while individuals with an AMC registration are likely to meet criteria for AUD, approximately 30% to 40% of those with AMC registrations did not have another registration for AUD (e.g., through the ICD-10 F10 codes), suggesting inconsistency in how clinicians record patients’ AUD. Third, due to the nature of FGRS_AUD, these scores are less precise among immigrants, precluding our ability to confidently estimate the extent to which aggregate genetic liability to AUD contributes directly to AMC and/or mediates/moderates the effects of income or education among those born outside of Sweden. Fourth, we were unable to directly account for differences in alcohol consumption, which is correlated with AUD, and which may differ by SEP. The medical community’s perspective on the impact of alcohol consumption at different levels is variable and subject to revision. For example, while many studies support a J-shaped association between consumption and certain health outcomes [47], other considerations (e.g., the climate-based impacts of the alcohol industry) have led some entities to recommend abstinence [48]. These issues are beyond the scope of the current study but warrant further consideration for public health considerations.

Fifth, we cannot firmly ascribe the observed associations to a causal path: Education and income are correlated with an array of psychosocial factors that could act as confounders, and complementary methodological approaches are necessary to provide additional insight. Finally, alcohol consumption varies across countries and cultures, and the current findings might not generalize to other contexts. For example, although the drinking age is as low as 18 in Sweden, alcohol access is constrained by relatively high costs and by the Systembolaget, which controls liquor sales and has limited hours of operation. Greater alcohol accessibility in other countries, especially where costs are lower, could exacerbate the risks of AMC associated with lower SEP. Furthermore, the sex differences observed in the current study might not be replicated in societies with lower sexual egalitarianism. Additional research in other cultural contexts is therefore essential.

In summary, we provide evidence that individuals with lower levels of education and/or income are more likely to suffer from AMC, even after accounting for differences in AUD, comorbid psychopathology, and aggregate genetic liability. Income generally plays a more prominent role, though the effects of both SEP indicators persist across time. Secondary observations suggest that more research is needed to better understand differences related to sex, marital status, and region of origin. These findings contribute to a growing body of literature on health disparities as a function of socioeconomic resources and suggest that individuals with fewer such resources could benefit from additional clinical attention regarding the risks associated with problematic alcohol use.

Supporting information

S1 Methods. Description of registry resources and analytic methods.

(DOCX)

pmed.1004359.s001.docx^{(15.5KB, docx)}

S1 Table. Incidence rates of alcohol-related medical conditions, reported as number of new cases per 10,000 person years, for variables that are constant over time.

(DOCX)

pmed.1004359.s002.docx^{(14.4KB, docx)}

S2 Table

Cross-tabulation of income quartile and education level for females in the full sample (top panel) and among those with an alcohol-related medical condition (AMC; bottom panel).

(DOCX)

pmed.1004359.s003.docx^{(13.7KB, docx)}

S3 Table

Cross-tabulation of income quartile and education level for males in the full sample (top panel) and among those with an alcohol-related medical condition (AMC; bottom panel).

(DOCX)

pmed.1004359.s004.docx^{(13.7KB, docx)}

S4 Table. Complete results for Model 1A for females and males, testing the association between education level and alcohol-related medical conditions.

Hazard ratios, 95% confidence intervals, and p-values from Chi-square tests are presented. The primary predictor of interest (here, education level) was modeled using a time-varying coefficient, with a linear term for time. Below, we provide snapshots of hazard ratios for education at 4 time points: at the beginning of observation (time 0), after 5 years, after 10 years, and after 15 years.

(DOCX)

pmed.1004359.s005.docx^{(15.5KB, docx)}

S5 Table. Complete results for Model 1B for females and males, testing the association between income and alcohol-related medical conditions.

Hazard ratios, 95% confidence intervals, and p-values from Chi-square tests are presented. The primary predictor of interest (here, income) was modeled using a time-varying coefficient, with a linear term for time. Below, we provide snapshots of hazard ratios for income at 4 time points: at the beginning of observation (time 0), after 5 years, after 10 years, and after 15 years.

(DOCX)

pmed.1004359.s006.docx^{(16.1KB, docx)}

S6 Table. Complete results for Model 2 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

Hazard ratios, 95% confidence intervals, and p-values from Chi-square tests are presented. The primary predictors of interest (education level and income) were modeled using time-varying coefficients, with a linear term for time. Below, we provide snapshots of hazard ratios for education and income at 4 time points: at the beginning of observation (time 0), after 5 years, after 10 years, and after 15 years.

(DOCX)

pmed.1004359.s007.docx^{(16.7KB, docx)}

S7 Table. Complete results for Model 3 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s008.docx^{(16.9KB, docx)}

S8 Table. Complete results for Model 4 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s009.docx^{(17.1KB, docx)}

S9 Table. Complete results for Model S1A for females and males, testing the association between education level and alcohol-related medical conditions.

Hazard ratios, 95% confidence intervals, and Chi-square p-values are presented. The primary predictor of interest (here, education level) was modeled using a time-varying coefficient, with a linear term for time. Below, we provide snapshots of hazard ratios for education at 4 time points: at the beginning of observation (time 0), after 5 years, after 10 years, and after 15 years. These secondary analyses were limited to the subsample born in Sweden with 2 Swedish-born parents to improve the precision of the family genetic risk score for alcohol use disorder; accordingly, region of interest is excluded as a covariate.

(DOCX)

pmed.1004359.s010.docx^{(15.1KB, docx)}

S10 Table. Complete results for Model S1B for females and males, testing the association between income and alcohol-related medical conditions.

Hazard ratios and 95% confidence intervals are presented. The primary predictor of interest (here, income) was modeled using a time-varying coefficient, with a linear term for time. Below, we provide snapshots of hazard ratios for income at 4 time points: at the beginning of observation (time 0), after 5 years, after 10 years, and after 15 years. These secondary analyses were limited to the subsample born in Sweden with 2 Swedish-born parents to improve the precision of the family genetic risk score for alcohol use disorder; accordingly, region of interest is excluded as a covariate.

(DOCX)

pmed.1004359.s011.docx^{(15.6KB, docx)}

S11 Table. Complete results for Model S2 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

Hazard ratios, 95% confidence intervals, and Chi-square p-values are presented. The primary predictors of interest (education level and income) were modeled using time-varying coefficients, with a linear term for time. Below, we provide snapshots of hazard ratios for education level and income at 4 time points: at the beginning of observation (time 0), after 5 years, after 10 years, and after 15 years. These secondary analyses were limited to the subsample born in Sweden with 2 Swedish-born parents to improve the precision of the family genetic risk score for alcohol use disorder; accordingly, region of interest is excluded as a covariate.

(DOCX)

pmed.1004359.s012.docx^{(16.4KB, docx)}

S12 Table. Complete results for Model S3 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s013.docx^{(16.6KB, docx)}

S13 Table. Complete results for Model S4 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

Hazard ratios, 95% confidence intervals, and Cho-square p-values are presented. The primary predictors of interest (education level and income) were modeled using time-varying coefficients, with a linear term for time. Below, we provide snapshots of hazard ratios for education level and income at 4 time points: at the beginning of observation (time 0), after 5 years, after 10 years, and after 15 years. These secondary analyses were limited to the subsample born in Sweden with 2 Swedish-born parents to improve the precision of the family genetic risk score for alcohol use disorder; accordingly, region of interest is excluded as a covariate.

(DOCX)

pmed.1004359.s014.docx^{(16.8KB, docx)}

S14 Table. Complete results for Model S5 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s015.docx^{(16.8KB, docx)}

S15 Table. Model S5 includes an interaction term between FGRS_AUD and each level of education and income (main effects are provided in S11 Table).

We present these as interactions on the additive scale through the use of the relative excess risk due to interaction (RERI) and synergy index (S), along with corresponding 95% confidence intervals; the p-value is based on a Chi-square test of the interaction term. These terms are estimated based on the effect of education and income at time 0.

(DOCX)

pmed.1004359.s016.docx^{(14.7KB, docx)}

S1 STROBE Checklist. STROBE checklist.

(DOCX)

pmed.1004359.s017.docx^{(37.2KB, docx)}

Abbreviations

AD: anxiety disorder
AMC: alcohol-related medical condition
ATC: Anatomical Therapeutic Chemical
AUD: alcohol use disorder
DUD: drug use disorder
ED: externalizing disorder
HR: hazard ratio
ID: internalizing disorder
MD: major depression
RERI: relative excess risk due to interaction
SEP: socioeconomic position

Data Availability

Data cannot be shared publicly because they are only available through application to Statistics Sweden (http://www.scb.se/) for researchers who meet the criteria for access.

Funding Statement

This project was supported by grant AA023534 from the US National Institutes of Health to KK and KS, and grants from the Swedish Research Council to JS (2020-01175) as well as ALF funding from Region Skåne awarded to KS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1004359.r001

Decision Letter 0

Philippa C Dodd

7 Nov 2023

Dear Dr Edwards,

Thank you for submitting your manuscript entitled "Lower educational attainment and income level are independently associated with risk of alcohol-related medical conditions: A Swedish national cohort study" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by Nov 09 2023 11:59PM.

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email me at pdodd@plos.org (or the team at plosmedicine@plos.org) if you have any queries relating to your submission.

Kind regards,

Pippa

Philippa Dodd, MBBS MRCP PhD

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1004359.r002

Decision Letter 1

Philippa C Dodd

7 Dec 2023

Dear Dr. Edwards,

Thank you very much for submitting your manuscript "Lower educational attainment and income level are independently associated with risk of alcohol-related medical conditions: A Swedish national cohort study" (PMEDICINE-D-23-03228R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am pleased to tell you that we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

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We expect to receive your revised manuscript by Dec 28 2023 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

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Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Best wishes,

Pippa

Philippa Dodd, MBBS MRCP PhD

PLOS Medicine

plosmedicine.org

pdodd@plos.org

-----------------------------------------------------------

COMMENTS FROM THE ACADEMIC EDITOR

A major revision seems reasonable. I had just two thoughts to add:

1. Maybe I missed this, but why limit the lower end of the age range to 40 years? The listed alcohol-related medical conditions do occur in younger populations. Is it a sample size problem?

2. I wonder if the authors might consider making a conceptual model to visually depict the relationships and confounders they include in the models. I understand that it isn't a fully causal DAG, but I find that such figures tend to help readers a lot. This is not a mandatory comment, just a suggestion for interpretability/readability.

COMMENTS FROM THE EDITORS

GENERAL

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Please kindly include as stated, per our previous email correspondence.

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We note and thank the reviewer (please see below) for their interest in the genetic risk score and for the comments regarding its reference in title. We agree that it is very interesting, but as it forms part of your secondary analyses, we don’t think it necessary for the title to specifically detail this or for extensive additional details to be added to the abstract.

ABSTRACT

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‘…even after adjusting for all other covariates and potential confounders…’ as above please provide details of the important dependent variables adjusted for.

We don’t think that it is necessary to present both crude and adjusted analyses in the abstract (but it certainly doesn’t hurt to do so) so, if necessary, to conserve space during your revisions you could present only the adjusted results (and the factors adjusted for).

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INTRODUCTION

Final paragraph – suggest revising/moving the sentence beginning ‘Our approach…’ which seems better suited to the discussion as written as written. Reference to stratification by sex should remain in the introduction.

Penultimate sentence, suggest, ‘We account for a range…’

METHODS and RESULTS

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As above for the abstract, please quantify the main results with 95% CIs and p values. Please report p as <0.001 and where higher the exact p value as p=0.002, for example. Please use commas as opposed to hyphens to separate upper and lower CI bounds as the latter can be confused with reporting of negative values. Suggest reporting statistical information as follows, ‘(HR 4.71; 95% CI [3.85, 5.77]; p</=)’. Please check and amend throughout (including supporting information) to ensure consistency of reporting.

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DISCUSSION

Please also see reviewer comments

REFERENCES

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COMMENTS FROM THE REVIEWERS

Reviewer #1: See attachment

Michael Dewey

Reviewer #2: Thank you for the invitation to review this manuscript. I guess it is because I have performed related research previously.

The study assesses the association of socioeconomic position with alcohol-related medical conditions. The interplay between different measures of alcohol, SEP measures and different health outcomes related to alcohol and mortality has been studied extensively. The authors do not try to oversell the importance of the paper, they are sober, and argue that the study is an important contribution to the literature because of how they made use of different information from registry data. I don't how a complete overview of comparable studies, but I am impressed by how the authors have used the different registries to define alcohol use disorder, alcohol-related medical conditions, and other study variables, as well as the family genetic risk score for AUD. I can see that these registries and the different definitions has been used previously by the authors.

The manuscript appears to me as being well written and I haven't found anything that I with confidence can say is wrong, although there are some things I would like the authors to clarify. Perhaps the most important is related to the measure of income and the use risk estimates from the final model for predefined covariates, such as to say something about how the risk of AMC differ according to biological sex and background.

Comment 1

Quoting: "The primary independent variables of interest were education and familial income. Income was assessed at age 40 and categorized based on the income quartiles for the working population, aged 20 to 65, in Sweden."

Are the analyses restricted to individuals who were working or restricted to this population when income is included in the model? Or does the authors mean that income was assessed for individuals in the age range 20 to 65, an age range where people tend to work? I would have liked that the authors commented on why they didn't include "working yes or no" or something related to employment as a covariate.

The authors write in the manuscript that income is "familial income", which I assume is the total income of the household, including both the index person and the partner? Please make this much clearer in the manuscript than what it is in the current. Related to the question above, does this mean that a person could be unemployed, but have an income in the 4th quartile if they have a partner that earns well? Please clarify.

Consider including some type of discussion regarding the use of familial income versus personal income as a measure of SEP. For example, the authors have chosen personal education level, not total educational level, so there must be a good reason for using household/familial income?

Comment 2:

The authors highlights from the analyses results based on risk estimates of covariates. In other words, they start asking more research questions, such as: Does unmarried women have a higher risk of AMC then married women? Do Swede-Fins have a higher risk of AMC? These research questions or additional analyses should be mentioned earlier in the manuscript, as they are presented as important findings, but were not predefined.

The authors write that unmarried females were at reduced risk of AMC relative to married females. Now I am not an expert on causal pathways, DAGs, statistics and causality, but I have been told not to take a model specified to answer a causal research question (in this study, what is the association of SEP with AMC), and answer other research questions using the risk estimates from the covariates. If the model is set up for prediction (all variables in, then this is okey I was told).

I'm not saying it is wrong in this occasion, but the advice is probably to avoid ending up in situations where the model is not well suited to answer research questions pertaining to other variables than the exposure of interest. The other research question here is whether AMC risk differs by marital status, which is answered by a model set up to answer whether the risk of AMC differ by SEP. Please give it a thought, for example, consider whether adjusting the association between marital status and AMC risk for familial income is ok, as this is what is being done now I think. I guess it would place a lot of unmarried women without a partner in the "low income" section, and I don't know if that would be the best SEP indicator.

Comment 3:

Harmful and problematic alcohol consumption. Alcohol is defined as a Group 1 carcinogen (IARC). So, in that sense, any alcohol intake is harmful in a dose-dependent manner. How it interacts with mental health and emotions is well, difficult, bi-directional and perhaps multidirectional. I mean, alcohol is probably a part of celebrations as well as a bad coping mechanism.

The "not harmful" version stems from epidemiological studies of the traditional sort, showing that individuals reporting light to moderate, and in some cases also quite frequent intake, has a lower HR of ischemic heart disease in comparison with no/low/infrequent drinking. And because ischemic heart disease caused so many premature deaths among men in particular, and still does, but less so because of primary prevention (smoking and diet) and better treatment (medication and PCI), this association propagated into a lower risk of all-cause death in similar studies. Whether alcohol has a beneficial effect on ischemic heart disease is much more debated and controversial today, mainly because studies using instrumental variables does not find a beneficial association of alcohol with IHD (at least that was the case the last time I checked a couple of years ago), creating a discrepancy between results from "traditional" and "modern" observational studies. In the now published Nordic nutrition recommendations (revised every 8 years), "no alcohol intake" is promoted as the intake level that is associated with the lowest health risk of alcohol, taking into account alcohols effect on cancer, heart, as well as accidents and violence and drunken driving etc. I haven't found the background paper on this, but the conclusion seems to be based on the work with the Canadian low drinking guidelines, where the conclusion is as follows: "the most recent and highest quality systematic reviews showing that drinking a little alcohol neither decreases nor increases the risk of ischemic heart disease, but it is a risk factor for most other types of cardiovascular disease, including, hypertension, heart failure, high blood pressure, atrial fibrillation and hemorrhagic stroke"…

Paradis, C., Butt, P., Shield, K., Poole, N., Wells, S., Naimi, T., Sherk, A., & the Low-Risk Alcohol Drinking Guidelines Scientific Expert Panels. (2023). Canada's Guidance on Alcohol and Health: Final Report. Ottawa, Ont.: Canadian Centre on Substance Use and Addiction

Why am I writing this? The consensus might be moving in the direction that "any alcohol intake is harmful", which would be the case if there really is no beneficial effect of alcohol on the development of ischemic heart disease. Because the manuscript starts with referring to data from WHO regarding harmful alcohol use, and also use the word "problematic" alcohol use, which is in the same category, the authors states that one type of alcohol intake is harmful and indirectly that another type is not harmful.

I leave it up to the authors to decide whether they want to address this or not, but I encourage them to at least consider it. However, as a reader, I would prefer if the authors were more specific when they refer to literature regarding measures of alcohol intake and measures of health outcomes. When referring to intake as "harmful", what is the definition in the study? When referring to intake as "excessive", what is meant? When referring to a paper on heavy episodic drinking, what is the definition?

Comment 4:

Introduction, second paragraph: I think I understand what is meant with the term "lower occupational class", but mayhaps "it is better to write "lower socioeconomic position measured by occupation"?

Comment 5:

Although I understand the need to be short and there could be a word limit, consider whether "alcohol outcomes" is a term that is used extensively as an umbrella term for measures of alcohol intake and measures of health outcomes that is associated with different measures of alcohol intake.

Comment 6:

Individuals that died before the age of 40 years were not allowed to participate in this study, by design. It is perhaps worth mentioning and it should be commented on whether the authors think it is relevant for the association under study.

Comment 7:

Figure: Would it be correct to state that you have superimposed the hazard lines for each predictor?

Figure legend. There is no information in the figure legend that the hazard ratio is the risk of AMC. It now states that the hazard ratio is for education level and income.

There is no information on what is included in the models, so in order to make use of the figure, the reader must go back to method section. I have always included this information in the legends, but can't find anything about this in strobe guidelines or journal style that says what is correct. You choose I guess. Might be that this part is not included in the pdf I have in front of me.

Regarding the figures. At glance, I can't see any differences between the 4 figures for women or the 4 for men. If there is a difference, I need to use a ruler on the screen I think. The authors might consider if it is really necessary to include all 2x4 figures to show the same thing? If adding more variables to the equation did not materially alter HR for the predictors, then simply stating this in the text would be adequate for me at least. At least give it a thought.

Comment 8:

Supplemental tables: Wouldn't it be nice for the reader to have exact information on what is included in the model instead of having to twist and turn? There is no reason to not be super informative and helpful, given that the manuscript is being considered for an online journal, and this is supplemental information.

Comment 9:

Result section:

Quoting: "We next estimated the association between lifetime educational attainment and income at age 40 in Models 1A and 1B, respectively"

This is not correct, is it? The outcome is AMC?

Quoting: "Among females (Figure and Supplementary Table 1), having the lowest level of

education was initially associated with a nearly 5-fold increase in risk of AMC (HR=4.71; 95% CI

3.85, 5.77)."

Consider whether referring to the exact model is more precise than using "initially".

Comment 10:

The reporting and use of interactions on an additive and multiplicative scale:

I am not a statistician and have to admit that I have had and still have problems wrapping my head around the use of reporting additive interactions as an alternative to multiplicative interaction:

The researchers ask the question of whether the association between education and income with the risk of AMC differs according to familial risk of AUD. Such a question is most often answered in a multiplicative model with interaction terms, which is what the researchers write that they have done.

If the interaction term shows a difference, this answers the research question. It is important to show the results of the test, and stratified HRs is often included to help the reader get an overview of how the nature of the interaction plays out for each group in comparison to each other, and in comparison with the results of the main model. That is what I expected to see, but the authors chose to report interaction on the additive scale from the multiplicative model. That is probably ok, but I have some minor comments.

Is it correct that results in table S11 are the same as S10, only with the interaction term? Seems to be materially the same, with slight variation.

The combination of what is presented in table S11 and S12 and the manuscript text, would you say that it provides the reader with a good understanding of how the association between income and AMC differs? The text explains it to me, and I can see from table S12 that the RERI and S looks different for education and income, but there is no guidance for me to understand the magnitude of the difference between the groups. That doesn't mean its wrong or anything, but perhaps help the reader a bit more in this regard.

In addition, I agree with the authors that it is important not to make a big fuzz about small stuff. For example, if the group with familial AUD is for example very small, then it is not that important (from a public health perspective) that the association between low education or low income with the risk of AMC is more pronounced, in comparison with a scenario where this group is large. From a clinical point of view, and if it were treatment types or drugs involved, it would perhaps be clinical important for a practitioner to know about the interaction, even though the interaction involves only a very small group. As the authors includes this argument, I would really like to see how many that have a family risk of AUD calculated by the family matrix. Or maybe I missed it?

Comment 11:

Quoting: "As noted above, previous research has found that drinking patterns vary by SEP, with

those of lower SEP more likely to engage in heavy consumption (11, 12, 13, 14, 16), which is

strongly linked to risk of AMC (22, 23, 24). Information on consumption levels is not available in

the Swedish nationwide registries, precluding our ability to test whether the associations

between education/income remain when accounting for heavy episodic drinking, for example.

However, our finding that these associations persist when controlling for AUD - itself closely

associated with consumption levels (25, 26) - is consistent with prior evidence that individuals

of lower SEP are at disproportionately high risk of AMC even after accounting for heavy drinking

(16)."

Heavy episodic drinking or binge drinking is the consumption of a lot of alcohol per occasion, normally defined as 5+ or 6+ units, corresponding to 60+ grams of pure alcohol per occation. To qualify as HED, the person has to drink about 2 grams per day on average, which corresponds to 5+ units once a month. Heavy episodic drinking exist as a drinking pattern from this lower bound and up until the average daily consumption moves into the area of chronic heavy drinking or heavy drinking, which may be defined as >60 grams per day, which in practice means that the person can have a heavy episode drinking every day.

My point of writing this, is that the authors refers to heavy episodic drinking and heavy drinking/AUD without making a clear distinction about how they are defined. I also commented on this earlier with regards to the introduction. Chronic heavy drinking is probably much more strongly related to AMC, because the amount of alcohol is considerable and takes a big toll on the metabolizing organs of the body. You woudn't expect the same thing for a person binge drinking once a week?

HED is very important in relation to alcohol-related accidents, and therefore also relevant for alcohol related mortality, but whether a given amount of alcohol consumed in the form of HED or spread over several days conveys a different effect on organs, and AMC, is not a research question that has not been studied extensively with good methods.

Perhaps better to only focus on AUD/heavy drinking/alcohol intake measured as the quantity, rather than mixing in HED in the discussion. Consider. I think maybe that this discussion regarding alcohol intake should be made after the discussion that adjustment for AUD registrations did only slightly moderate the association of SEP with AMC. Could it be that the measurement of AUD is not very good a picking up alcohol intake that can lead to AMC? That is my interpretation. Any other interpretation would imply a link between SEP and AMC via another route than alcohol?

Comment 12:

Quoting: "Third, we found that individuals with higher genetic liability to AUD were more

susceptible to the adverse impact of low income, though this was not the case for those with

lower levels of educational attainment. This could reflect the stronger overall effect size of

income level, and replication in other samples, and perhaps using complementary methods

(e.g., molecular polygenic scores) is needed."

Consider including the information that the test for interaction was performed at year 0, when the magnitude of the association of income with AMC was most pronounced.

Comment 13:

Quoting: "Finally, alcohol consumption varies across countries and cultures; the current findings might not generalize to other contexts."

I agree that this study is irrelevant for cultures where alcohol is not consumed, but this is obvious and does not leave the reader from another country with any meaningful insight about whether this study applies to their country. And alcohol consumption is not the only thing that can influence the external validity of these findings. A thorough discussion regarding the external validity of the findings is lacking in the current manuscript.

Take women for example, as the results are stratified by gender. They are likely to vary more between countries then men in terms of their alcohol intake, their educational attainment at age 40, and whether they work or not at age 40, for example depending on the access to kindergarten at a subsidized price. In Canada for example, the price for child care at day time is not the same as in Norway, which could contribute to more women choosing to stay home for more years. These are only thoughts.

Reviewer #3: This is a useful study based on a very large national cohort. The conclusions are well-supported by the data, and the strengths are many.

The authors refer to the outcome variable as "alcohol-related medical conditions". This is technically correct, but to add precision, I would recommend using the term "fully alcohol-attributable disorder or disease". As far as I could see from the list of diagnoses in the text, all values would qualify to be fully AADDs. That way, nobody would think that the article is about AUDs or broader health conditions that could be influenced by alcohol, such as cancer or stroke.

In the abstract, the authors present the findings that are not based on genetic risk first, then the ones that are. However, the interaction with genetic risk is more cutting edge and has less uncertainty, so I would recommend that these are given focus in both the abstract and the title.

The analysis treats death and emigration as similar to regular censoring. This will lead to biased estimates. The standard today is to use competing risks models, the type that was first developed by Fine and Gray (see Austin et al 2022 for an updated discussion). A competing risks regression model may not be estimable with such a large dataset, but perhaps the authors can run an analysis on a random sample of the population to estimate the influence of death. However, without considering death as a competing risk, the decline in risk associated with low education could be a complete artefact: it just seems that the risk declines over time, because those with low income and low education die disproportionately at a younger age, and are no longer at risk of AMD. I may be completely wrong here, but if so, I think the authors should explain why.

The authors write that "The polychoric correlation between income quartile and education level for females was 0.185 (SE-0.001) and for males was 0.198 (SE=0.001), suggesting that while these socioeconomic measures are related, they also provide non-overlapping information." This is not a very formal test of the degree of dependence or independence of the two variables. I think I get that the authors want to argue that both variables should be considered (and I agree), while they also want to acknowledge that both come under a common heading, SES (again - I agree). But if they want to provide a statistical argument for degree of dependence, I would want something stronger.

Reference

Austin PC, Putter H, Lee DS, Steyerberg EW. Estimation of the Absolute Risk of Cardiovascular Disease and Other Events: Issues With the Use of Multiple Fine-Gray Subdistribution Hazard Models. Circ Cardiovasc Qual Outcomes. 2022.

Any attachments provided with reviews can be seen via the following link:

[LINK]

Attachment

Submitted filename: edwards.pdf

pmed.1004359.s018.pdf^{(52.2KB, pdf)}

PLoS Med. 2024 Mar 19;21(3):e1004359. doi: 10.1371/journal.pmed.1004359.r003

Author response to Decision Letter 1

30 Jan 2024

Attachment

Submitted filename: responses-to-reviewers-2024-01-29.pdf

pmed.1004359.s019.pdf^{(235.9KB, pdf)}

PLoS Med. doi: 10.1371/journal.pmed.1004359.r004

Decision Letter 2

Philippa C Dodd

9 Feb 2024

Dear Dr. Edwards,

Thank you very much for re-submitting your manuscript "Socioeconomic position indicators and risk of alcohol-related medical conditions: An observational Swedish national cohort study" (PMEDICINE-D-23-03228R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by 2 reviewers including the statistical reviewer. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

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Philippa Dodd, MBBS MRCP PhD

Senior Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

COMMENTS FROM THE ACADEMIC EDITOR:

I agree with the decision to proceed.

I had just one more suggestion: Might it be helpful to clarify in the abstract that these results are from a high-income country? I would hypothesize that these risks would be very different in low-income countries. Sweden is a unique country in many ways.

COMMENTS FROM THE EDITORS:

GENERAL

Thank you for your detailed and considered responses to previous editor and reviewer comments. Please see below for further comments which we require you address prior to publication.

TITLE

Thank you for revising your title, we suggest, ‘Socioeconomic position indicators and risk of alcohol-related medical conditions: A national cohort study from Sweden.’

ABSTRACT

Abstract methods and findings - ‘N=4253 (0.37%) of females and N=11,183 (0.93%) of males…’ please remove the ‘N=’ and the ‘of’ on both occasions here to improve reader accessibility.

AUTHOR SUMMARY

Line 3 – suggest ‘…excess morbidity and mortality…’ instead.

Please add a final bullet point after line 24 of ‘What Do These Findings Mean?’, which briefly describes the main limitations of the study in non-technical language.

INTRODUCTION

Page 6, line 5 – should the colon be a full stop?

METHODS and RESULTS

Results page 12 onwards - Multivariable models – if possible, we think that the presentation/description of your results could be made more concise to improve reader accessibility. It is not difficult to get a bit lost in the detail. Please revise for the purpose of improved brevity.

SOCIAL MEDIA

To help us extend the reach of your research, if not already done so, please detail any X (formerly Twitter) handles you wish to be included when we tweet this paper (including your own, your coauthors’, your institution, funder, or lab) in the manuscript submission form when you re-submit the manuscript.

COMMENTS FROM THE REVIEWERS:

Reviewer #1: The authors have addressed my points.

Michael Dewey

Reviewer #2: To the authors,

I did not identify or raise any major concerns about the integrity of the study when reading the manuscript for the first time but had a list of questions and comments for the authors to clarify and consider, respectively. Most of which were of minor importance. The authors have addressed them thoroughly in their rebuttal and made changes to the manuscript text and supplemental methods and tables as a result, which I find improves clarity. I have not identified any new concerns when reading the revised manuscript and have no further comments or suggestions on how to improve the manuscript.

Eirik Degerud

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2024 Mar 19;21(3):e1004359. doi: 10.1371/journal.pmed.1004359.r005

Author response to Decision Letter 2

13 Feb 2024

Attachment

Submitted filename: Responses to Editorial Comments.pdf

pmed.1004359.s020.pdf^{(87.9KB, pdf)}

PLoS Med. doi: 10.1371/journal.pmed.1004359.r006

Decision Letter 3

Philippa C Dodd

14 Feb 2024

Dear Dr Edwards,

On behalf of my colleagues and the Academic Editor, Dr David Flood, I am pleased to inform you that we have agreed to publish your manuscript "Socioeconomic position indicators and risk of alcohol-related medical conditions: A national cohort study from Sweden" (PMEDICINE-D-23-03228R3) in PLOS Medicine.

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Thank you again for submitting to PLOS Medicine, it has been a pleasure handling your manuscript. We look forward to publishing your paper.

Kind regards,

Pippa

Philippa C. Dodd, MBBS MRCP PhD

PLOS Medicine

pdodd@plos.org

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Methods. Description of registry resources and analytic methods.

(DOCX)

pmed.1004359.s001.docx^{(15.5KB, docx)}

S1 Table. Incidence rates of alcohol-related medical conditions, reported as number of new cases per 10,000 person years, for variables that are constant over time.

(DOCX)

pmed.1004359.s002.docx^{(14.4KB, docx)}

S2 Table

Cross-tabulation of income quartile and education level for females in the full sample (top panel) and among those with an alcohol-related medical condition (AMC; bottom panel).

(DOCX)

pmed.1004359.s003.docx^{(13.7KB, docx)}

S3 Table

Cross-tabulation of income quartile and education level for males in the full sample (top panel) and among those with an alcohol-related medical condition (AMC; bottom panel).

(DOCX)

pmed.1004359.s004.docx^{(13.7KB, docx)}

S4 Table. Complete results for Model 1A for females and males, testing the association between education level and alcohol-related medical conditions.

(DOCX)

pmed.1004359.s005.docx^{(15.5KB, docx)}

S5 Table. Complete results for Model 1B for females and males, testing the association between income and alcohol-related medical conditions.

(DOCX)

pmed.1004359.s006.docx^{(16.1KB, docx)}

S6 Table. Complete results for Model 2 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s007.docx^{(16.7KB, docx)}

S7 Table. Complete results for Model 3 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s008.docx^{(16.9KB, docx)}

S8 Table. Complete results for Model 4 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s009.docx^{(17.1KB, docx)}

S9 Table. Complete results for Model S1A for females and males, testing the association between education level and alcohol-related medical conditions.

(DOCX)

pmed.1004359.s010.docx^{(15.1KB, docx)}

S10 Table. Complete results for Model S1B for females and males, testing the association between income and alcohol-related medical conditions.

(DOCX)

pmed.1004359.s011.docx^{(15.6KB, docx)}

S11 Table. Complete results for Model S2 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s012.docx^{(16.4KB, docx)}

S12 Table. Complete results for Model S3 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s013.docx^{(16.6KB, docx)}

S13 Table. Complete results for Model S4 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s014.docx^{(16.8KB, docx)}

S14 Table. Complete results for Model S5 for females and males, testing the associations between education level and income with alcohol-related medical conditions.

(DOCX)

pmed.1004359.s015.docx^{(16.8KB, docx)}

S15 Table. Model S5 includes an interaction term between FGRS_AUD and each level of education and income (main effects are provided in S11 Table).

(DOCX)

pmed.1004359.s016.docx^{(14.7KB, docx)}

S1 STROBE Checklist. STROBE checklist.

(DOCX)

pmed.1004359.s017.docx^{(37.2KB, docx)}

Attachment

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pmed.1004359.s018.pdf^{(52.2KB, pdf)}

Attachment

Submitted filename: responses-to-reviewers-2024-01-29.pdf

pmed.1004359.s019.pdf^{(235.9KB, pdf)}

Attachment

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pmed.1004359.s020.pdf^{(87.9KB, pdf)}

Data Availability Statement

Data cannot be shared publicly because they are only available through application to Statistics Sweden (http://www.scb.se/) for researchers who meet the criteria for access.

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PERMALINK

Socioeconomic position indicators and risk of alcohol-related medical conditions: A national cohort study from Sweden

Alexis C Edwards

Sara Larsson Lönn

Karen G Chartier

Séverine Lannoy

Jan Sundquist

Kenneth S Kendler

Kristina Sundquist

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Materials and methods

Ethics statement

Sample

Measures

Statistical analyses

Results

Descriptive statistics

Table 1. Descriptive information on the female cohort, born 1950–1970, with observation beginning at age 40.

Table 2. Descriptive information on the male cohort, born 1950–1970, with observation beginning at age 40.

Crude models

Multivariable models

Fig 1. HRs between education level and/or income with risk of AMCs, across time, from Models 1A/1B–Model 4.

Secondary analysis

Discussion

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Philippa C Dodd

Roles

Decision Letter 1

Philippa C Dodd

Roles

Author response to Decision Letter 1

Decision Letter 2

Philippa C Dodd

Roles

Author response to Decision Letter 2

Decision Letter 3

Philippa C Dodd

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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