Bibliographic record
Abstract
When is it appropriate for public health practice to be on the side of an intervention that causes bodily injury? For example, should women with a high genetic risk of breast cancer undergo prophylactic mastectomy? Should tonsillectomies be routinely prescribed to prevent chronic or recurrent acute tonsillitis in children? Should boys be circumcised to protect them from acquiring sexually transmitted diseases (STDs)?1,2 These questions range from the mildly controversial to one of the most intractable questions facing public health practitioners today—namely, the role of male circumcision in curbing HIV transmission in Africa. Few topics touch as raw a nerve as this. Until recently, pediatricians and family physicians in the western world have lived happily with noncommittal guidelines on circumcision, issued by professional societies and intended to inform the physician's dialogue with parents. The decision process was viewed as one in which doctors had the freedom to place their skills at the service of the patients’ best interests, whether these interests stem from religious conviction, family tradition, or health beliefs. This bland tolerance has successfully accommodated a broad range of strong and conflicting opinions. In addition, lest they be accused of overstepping boundaries, practice statements have clearly stipulated that their purview does not extend to male circumcision conducted as religious ritual.3,4 Simply put, it was up to the parents or the consenting-age child to decide after hearing the doctor's views on the procedure. Physicians had only to make sure that they followed sound clinical procedures. Meanwhile, in the background, there has been an emerging knowledge base suggesting health benefits to the circumcised boy in the risk of urinary tract infection, STDs, and even cancer of the penis.5 Even so, with rare exceptions,6 professional societies have carefully avoided to take sides in the polarized debate. Enter the era of acquired immunodeficiency syndrome (AIDS). AIDS has wrought a sense of hopelessness in the public health community as we witness this pandemic evolve in underprivileged areas of the world with little hope of control. It has brought an unparalleled sense of urgency to public health research in seeking promising preventive strategies even before an indisputable body of scientific evidence can be accrued. The urgency has led to bold solutions such as the concept of harm reduction by needle exchange programs and safe injection facilities for illicit drug users in North America and Europe.7,8 Much of the impetus for these interventions has come from compelling observational studies; no randomized, controlled trials have informed these decisions. We can only hope that they will succeed in the long term, rather than leading to unanticipated ill effects—which sometimes happens when, as a society, we apply the precautionary principle. However, implementing these interventions is not a walk in the park, particularly in the United States where legislation has hampered progress.9 There is an ongoing controversy on the benefits of male circumcision in the face of HIV risk,10–13 analogous to the story of needle exchange programs. In both situations, observational studies form the only basis for assessing the validity of the associations. Therein lies the difficulty. It is a daunting job to verify whether a particular behavior or medical intervention truly operates on the etiologic pathway of HIV infection when there are so many confounders along the way. There are many alternative scenarios that are consistent with the observed data. These alternatives need to be shown as implausible before one can establish that male circumcision protects against HIV infection later in life.14 The burden of proof is on the epidemiologist to show that the study design is as free of selection biases as possible, that risk factor information has been measured with the best available instruments, and that careful (even obsessive) statistical analysis has zealously controlled for every possible confounder. Agot and colleagues15 knew this all too well when they set out to conduct yet another observational study on circumcision and HIV risk. How could their study add to the mounting evidence from dozens of ecologic, case-control, and cohort studies that suggest a protective effect for circumcision?16–18 The authors were acutely aware of the difficulty in disentangling the possible biologic effects of circumcision from its inextricable links to religious beliefs, cultural factors, and sexual practices.18 They took great pains to design a study that controlled for the confounding effect of these variables. First, they restricted the survey to a homogeneous group of Kenyan Luo men. Then they recruited these men through a clever sampling scheme that frequency-matched (on geographic proximity) 9 churches from noncircumcising denominations with denominations that preached circumcision. To enhance the comparability, Agot et al.15 restricted the noncircumcising denominations to those favoring polygamy and widow inheritance, as did all 9 circumcising churches. Third, they took into account as pivotal variables in the analysis (1) the churches’ recommendation concerning circumcision, (2) the actual circumcision status of the individual, and (3) an extensive list of empiric confounders at the individual and ecologic level. This highly controlled analysis confirmed a protective effect for circumcision that was convincingly independent from the constellation of factors linked to religion and sexual practices. The study by Agot and colleagues15 is an apt reminder that in our profession, necessity is the mother of invention. In the absence of the opportunity for a randomized intervention trial, many an epidemiologist has had to be creative in designing observational studies that sidestep stubborn biases. By their successful innovation, these researchers have contributed important information to the debate on an urgent public health question. Does this study put an end to the debate? Of course not. However, the quality of the science informing that debate has just moved up a notch. ABOUT THE AUTHOR EDUARDO FRANCO is at McGill University, where he is professor in the Departments of Epidemiology and Oncology, and Director of the Division of Cancer Epidemiology. In his attempts to understand the epidemiology of cancer, human papillomavirus, and HIV infection, he spends a lot of time struggling to disentangle the possible effects of key exposures from inextricably linked confounders. He also worries a lot about measurement error.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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".