Alcohol and infectious diseases: an overlooked causal linkage?
Bibliographic record
Abstract
Alcohol has been identified as a major risk factor for chronic disease and injury 1. However, no infectious disease has been included in the list of alcohol-attributable disease categories, even though several such diseases have consistent associations with alcohol. In July 2008, 25 international experts from eight countries (Australia, Brazil, Canada, China, South Africa, Tanzania, Thailand and the United States) and various disciplines, together with representatives of the World Health Organization (WHO) and Joint United Nations Programme on Human Immunodeficiency Virus/Acquired Immune Deficiency Syndrome (UNAIDS), met in Cape Town to review systematically evidence relating to the linkages between alcohol consumption and HIV disease and tuberculosis (TB) and examine potential causal impacts of alcohol use on both the incidence and course of these infectious diseases. The meeting was hosted by the South African Medical Research Council (MRC) and co-sponsored by the WHO. Over 4 days the participants reviewed data from published and unpublished studies, meta-analyses and other reviews that were prepared specifically for the meeting, as well as information on biological pathways in order to understand more clearly the linkages between alcohol and HIV and TB. Clear criteria were set out for causality to be met, based on common epidemiological standards 2, 3. After reviewing the evidence, there was general consensus that there was conclusive evidence of a causal linkage between heavy drinking patterns and/or alcohol use disorders (AUD) and the incidence of active TB, and that these exposure categories were also linked causally to worsening of the disease course for both TB and HIV. Participants, however, concluded that while alcohol usage was associated consistently with the prevalence 4 and incidence of HIV 5, further research was needed to substantiate causality. Heavy alcohol use and AUD have long been associated with active TB 6. A recent meta-analysis 7 found a threefold increase risk of active TB for consumption of more than 40 g/day or AUD. The causal pathways between heavy consumption and incidence of active TB are both biological and social. Regarding the biological pathways, heavy use of alcohol has an impact on multiple organs and systems, including the weakening of the immune system 8, and thus facilitates the transition to active TB and worsens the disease course. These exposure patterns impact upon both the innate and the adaptive immune system overall, and via breaking immune barriers of specific organs such as the lungs. The second important causal pathway is via social–behavioural consequences of heavy alcohol use and/or alcohol dependence. TB is more prevalent in low-income, densely populated housing areas—settings often associated with high rates of AUD. Heavy use of alcohol has also been shown to disrupt drinkers from seeking medical attention and decrease their adherence to therapeutic schedules. As a result, treatment outcomes for heavy users of alcohol are worse, even after controlling for homelessness and unemployment 9. Finally, heavy alcohol use has been linked to the development of multi-drug-resistant TB 10. Similar causal pathways for TB were identified between alcohol use and worsening the course of the HIV disease. Again, compromised immunity and comorbid conditions, and the impact of heavy alcohol use on treatment adherence, including regular intake of medication, play a crucial role 11. In addition, there are potential interactions with treatment medication for HIV, and also with treatment for common co-morbid conditions such as hepatitis C virus (HCV) infection 12. Regarding a potential impact of alcohol on infection with HIV, it was concluded that ‘the relationship between alcohol use and risky sex is complex, [reflecting] multiple underlying causal and non-causal processes’13. There is a need to answer a number of questions about the potential links, such as: ‘Do people with whom heavy drinkers have sex differ from the partners of people who do not drink heavily?’; ‘Are heavy drinkers more likely to have multiple concurrent partners than people who are not heavy drinkers?; and ‘Are condoms applied less correctly after heavy drinking than otherwise?’. This will require both a re-analysis of already available data as well as new, longitudinal studies to be carried out. In particular, there is a need for a large cohort study on the effects and potential interactions of various risk factors on HIV incidence in countries of high HIV prevalence, including an examination of the contribution of co-infections such as TB. Such research would also be crucial for developing interventions to reduce the risk of new infections. Establishing causality is only the first step. In the next step we need to identify effective interventions to reduce the burden of TB attributable to alcohol. Similarly, interventions to improve TB and HIV treatment outcomes via addressing harmful use of alcohol should be developed. Alcohol-related interventions have been shown to be among the most effective in reducing burden of disease, especially in low- and middle-income countries 14. Linking such interventions to infectious disease control will be a major challenge of the next years. Jurgen Rehm has participated in scientific meetings organized or sponsored by the alcohol industry and received financial support for this participation. Some of his research projects have been supported by the pharmaceutical industry. Charles Parry, Vladimir Poznyak and Robin Room have no conflicts of interest. Hosting of the technical meeting was supported by supplemental funding received under the ‘Cooperative Agreement to the Medical Research Council (MRC) for TB Control and HIV Prevention, Care and Treatment Activities (U51)’, via the US President's Emergency Fund for AIDS Relief (PEPFAR) through the US Centers of Disease Control and Prevention (CDC) (grant number 1U51PS000729-01). The contents of this editorial are solely the responsibility of the authors and do not necessarily represent the decisions or the stated policy of WHO, CDC or PEPFAR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".