What Do Demographics Have To Do With It? An Oaxaca-Blinder Decomposition of Changes over Time in Inequalities in Alcohol, Narcotics and Tobacco-Related Ill Health in Sweden
Bibliographic record
Abstract
The purpose of this study was to document historical trends and socioeconomic inequalities in ill health outcomes related to alcohol consumption, narcotics use and tobacco smoking over the seventeen years prior to the implementation of the Swedish government's first strategy for alcohol, narcotics, doping and tobacco (ANDT) in 2011. We also sought to explain the changes over time in terms of changes in the population distribution of selected demographic and socioeconomic characteristics. Our two key research questions, for each of alcohol, narcotics and smoking were: 1) How have trends in a) consumption, inpatient care and deaths, and b) income-related inequalities therein developed over time? 2) To what extent can demographic (gender, age, civil status, foreign background), socioeconomic (parental education, own education) and social characteristics (social isolation, proportion of welfare recipients in the municipality) explain the trends in a) levels of consumption, inpatient care and deaths, and b) income-related inequalities therein? For consumption, we investigated the prevalence of heavy drinking and smoking; data on narcotics use were not available. We used International Classification of Diseases (ICD) codes to identify inpatient care and deaths related to alcohol, narcotics and smoking. In our main analyses we used income as a measure of socioeconomic rank. We performed sensitivity analyses to investigate: i) the use of education as an alternative socioeconomic rank, ii) differences between measures of relative and absolute inequality, and iii) sex-differences in the trends over time. We document increasing pro-poor socioeconomic-related inequalities in all of our outcomes except heavy drinking (which was concentrated among higher income individuals, and did not change significantly) during the study period. This reflects an increasing concentration of smoking, and inpatient care and deaths related to alcohol, narcotics and smoking among low income individuals. We are able to explain some of the change over time by demographic and socioeconomic changes (i.e changes in the distribution of our sample by age, foreign background and educational attainment). However, our findings suggest that most of the change observed was due to external factors, such as changing norms and behaviours, and policy or macroeconomic conditions affecting certain groups more than others. In order to achieve the goal of equality in health, ANDT as a policy area must address the increasing concentration of alcohol-, narcotics- and smoking-related outcomes among the poorest and least educated in our society.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".