The claim that circumcision reduces the risk of sexually transmitted infections has been repeated so frequently that many believe it is true. A systematic review and meta-analyses were performed on studies of genital discharge syndrome versus genital ulcerative disease, genital discharge syndrome, nonspecific urethritis, gonorrhea, chlamydia, genital ulcerative disease, chancroid, syphilis, herpes simplex virus, human papillomavirus, and contracting a sexually transmitted infection of any type. Chlamydia, gonorrhea, genital herpes, and human papillomavirus are not significantly impacted by circumcision. Syphilis showed mixed results with studies of prevalence suggesting intact men were at great risk and studies of incidence suggesting the opposite. Intact men appear to be of greater risk for genital ulcerative disease while at lower risk for genital discharge syndrome, nonspecific urethritis, genital warts, and the overall risk of any sexually transmitted infection. In studies of general populations, there is no clear or consistent positive impact of circumcision on the risk of individual sexually transmitted infections. Consequently, the prevention of sexually transmitted infections cannot rationally be interpreted as a benefit of circumcision, and any policy of circumcision for the general population to prevent sexually transmitted infections is not supported by the evidence in the medical literature.
The earliest report of circumcision status as potential risk factor for sexually transmitted infections (STIs) was published in 1855 by Hutchinson, who noted that in men who were treated for STIs (primarily gonorrhea and syphilis), Jews were less likely to have syphilis [
The claim of reduction of the risk of STIs to justify neonatal circumcision continues today, often supported by selective bibliographies [
To shed some light on this contentious issue and whether the conclusion reached by the committee reflects the information available in the medical literature, this paper will provide a systematic review of the association between male circumcision status and the risk for individual types of STIs (other than human immunodeficiency virus (HIV)) and the overall risk for any STI. While a number of the review articles and systematic reviews of the association between male circumcision and individual types of STIs have been published [
The recommendations of Stroup et al. for the meta-analysis of observational studies were followed [
Articles meeting the inclusion criteria were read to determine the number of circumcised men with the illness, the number of circumcised men without the illness, the number of intact men with the illness, and the number of intact men without the illness. The primary analysis was performed using raw data, when available, for the published studies. In some cases, the raw data were obtained through back calculation with the information available in the article. Where raw data were not available, reported odds ratios, relative risks, and confidence intervals were used.
When distinct strata of the subjects within a study showed differing outcomes, each strata were considered separately in calculating the summary effect.
When data from the same population were published in one or more publications, the study in which the data reported the outcome of interest as a primary result or the most recent report were used.
Analyses of studies assessing disease incidence were conducted separately from studies of disease prevalence.
The impact of the type of study population was determined by separating the studies into those studying high-risk populations, such as attendees of sexually transmitted disease clinics and long-distance truck drivers in Africa, and those studying general populations. The impact of circumcision prevalence in the study population on the association between circumcision status and the prevalence of the various STIs was assessed using meta-regression.
Several studies meeting the inclusion criteria contained obvious forms of differential bias. A number of methods were employed to minimize the bias. Several older studies had inappropriate control groups [
The three randomized clinical trials of adult male circumcision in Africa failed to adjust for lead-time bias. Men in these trials who were assigned to immediate circumcision were instructed to either not engage in sexual activity or use condoms with all sexual contacts until the circumcision healed (approximately, from 4 to 6 weeks). Analyses that included these trials were conducted with the reported data and with the data adjusted for a six-week lead-time bias.
Other adjustments were needed specifically for the studies of HPV. Studies of the prevalence of genital HPV infections were separated into those identifying clinical infections with genital warts and those with diagnosis by culture, serology, biopsy, or polymerase chain reaction. Several studies reported separate data for all HPV infections and for infections with high-risk HPV that are potentially oncogenic. Consequently, two separate analyses were run on the latter group. In both analyses, the data from studies reporting only one set of data were used. In the first analysis, the data on all HPV infections were used, while the second analysis used the data on infections with high-risk HPV.
Previous analyses have found that the studies of HPV were prone to two forms of bias [
For example, in the study published by VanBuskirk et al., if only the glans is sampled, only 66.1% of the intact men with genital HPV would be identified, while only 45.2% of the circumcised men with genital HPV would be identified [
The second is misclassification bias. Studies that rely on the patient report of circumcision status can often inaccurately identify the circumcision status of the participants. This has also been found to be a significant factor in previous analyses of HPV infections [
In one study, two testing methods for syphilis were used: the RPR results were used in this analysis [
For studies of disease prevalence, a general variance-based random-effects model was performed using each study’s exact odds ratios (Proc-LogXact, version 5.0, Cytel Software Corporation, Cambridge, MA) as described previously [
Poisson regression was used to assess studies of disease incidence. Fixed-effects summary results were calculated using Poisson regression. If between-study heterogeneity was significant (
Sensitivity analyses of prevalence data for type of study population were performed through separate analyses for each population type. The impact of the type of study population, performance of a study in Africa, the prevalence of circumcision in the study population, and, for HPV, the sampling only the glans of the penis and determination of circumcision by physical examination was estimated using meta-regression [
To test for potential outliers, the dataset from each publication was individually excluded from the analysis to measure the impact on the chi-square measure of between-study heterogeneity. The exclusion of a study would be justified by a reduction of the between-study heterogeneity chi-square by a statistically significant amount (e.g., for one degree of freedom, a change in the chi-square value of more than 3.84). Sensitivity analysis was performed with each of these studies excluded and with the two most outlying studies excluded.
Publication bias was assessed using funnel graphs and linear regression analysis as described by Egger and associates [
The MEDLINE search identified 91 articles meeting the inclusion criteria. Of these, several reported on redundant study populations [
The characteristics of the studies included for analysis and the types of STIs they studied are listed in Table
Attributes of studies meeting the inclusion criteria.
Study STI studied | Location | When | Population | Type of study | Circumcision status | Method of diagnosis |
---|---|---|---|---|---|---|
Agot [ |
Kenya | From October 1999 to May 2000 | 18–49 y/o sexually active males unaware of HIV status in circumcising and noncircumcising denominations in Luo ethnic community | Cross-sectional | Physical exam | Report |
| ||||||
Auvert et al. [ |
Kisumu, Kenya | From June 1997 to March 1998 | General population | Cluster design to randomly select households | Physical exam and self-report | Serology (HSV and syphilis). Urine DNA (GC and CT) |
| ||||||
Auvert et al. [ |
Orange Farm, South Africa | 2002–2006 | Men interested in a free circumcision | Randomized clinical trial | Intention to treat | Urethral swabs (HPV) |
| ||||||
Aynaud et al. [ |
Paris, France | From March 1991 to September 1992 | Men whose female partners had genital condylomata or intraepithelial neoplasia | Cross-sectional study | Physical exam | Colposcopy, viral culture, and biopsy |
| ||||||
Aynaud et al. [ |
Paris, France | Not documented | Heterosexual HIV-negative men whose female partner has HPV |
Cross-sectional study | Physical exam | Chlamydia by PCR, cultures (GC), and HPV by biopsy and penoscopy |
| ||||||
Bailey [ |
Mbale, Uganda | From April to May 1997 | General population | Single stage cluster sampling cross section | Report | Report |
| ||||||
Baldwin [ |
Tucson, Arizona | From July 2000 to January 2001 | High risk men attending a public STD clinic | Cross-sectional | Physical exam | Swab of glans and sulcus (HPV) |
| ||||||
Barile [ |
Japan | Not documented | US military personnel in Japan |
Case control | Physical exam | Clinically |
| ||||||
Bassett [ |
Sydney, Australia | From December 1990 to May 1991 | STD clinic | Consecutive sample of heterosexual men | Physical exam | HSV2 by serology |
| ||||||
Bleeker [ |
Amsterdam | From April 2002 to November 2002 | 18–75 years old. Group A with female partner without CIN. Group B female partner with CIN. Non-STD hospital population | Consecutive sample of male partners of |
Physical exam | Swab of glans, sulcus, corona, and frenulum (HPV) |
| ||||||
Burundi [ |
Burundi | 2010 | General population from 15 to 49 years old | National representative population survey | Patient report | Patient report |
| ||||||
Buvé [ |
Kisumu, Kenya; Ndola, Zambia; Cotonou, Benin; Yaoundé, Cameroon | From June 1997 to March 1998 | General population from 15 to 49 years old | Cluster design to randomly select households | Physical exam and self-report | Serology (HSV and Lues) |
| ||||||
Bwayo [ |
25 miles from Nairobi | From June 1989 to February 1992 | Truck drivers enrolled at roadside research clinic | Self-selected convenience sample | Not documented | Report (GDS and GUD) and serology (Lues) |
| ||||||
Cameron [ |
Nairobi, Kenya | From March 1986 to December 1987. Followup to March 1988 | STD clinic and men who got STD from a prostitute | Prospective cohort study | Physical exam | Not documented |
| ||||||
Castellsagué [ |
Brazil, Thailand, Philippines, Spain, Columbia | 1985–1993 | Husband or stable partner of woman with cervical cancer or a control woman | Seven separate case-control studies | Physical exam in Brazil, Thailand, and Philippines. Report in Spain and Columbia | PCR for HPV from urethra and glans swabs |
| ||||||
Cook [ |
Seattle, Washington | From January to December 1988 | STD clinic | Chart review of heterosexual men | Chart review (14.3% missing) | Urethral swabs, syphilis by serology, warts, and HSV clinically and warts clinically |
| ||||||
Dave [ |
Great Britain | 2000 | General population | Large-scale, stratified, probability sample survey |
Report | Report |
| ||||||
Dickson et al. [ |
Dunedin, New Zealand | 1999 | Birth cohort from 1972 to 1973 | Prospective cohort repeatedly studied from birth |
Not documented | Serology (HSV) |
| ||||||
Dickson et al. [ |
Dunedin, New Zealand | From 2004 to 2005 | Birth cohort from 1972 to 1973 | Prospective cohort repeatedly studied from birth | Life-time medical records | Life-time medical records |
| ||||||
Dickson et al. [ |
Dunedin, New Zealand | From 2004 to 2005 | Birth cohort from 1972 to 1973 | Prospective cohort repeatedly studied from birth | Life-time medical records | Serology for 6, 11, 16, and 18 |
| ||||||
Dinh [ |
United States | From 1999 to 2004 | Random sample of general population aged from 18 to 59 | National survey NHANES | Patient report using visual aids | Patient report |
| ||||||
Diseker [ |
Baltimore, Denver, Long Beach, San Francisco | From July 1993 to September 1996 | STD clinic | Part of RCT, baseline analysis, and cohort analysis |
Physical exam | GC by culture, chlamydia by urine PCR, and syphilis by serology |
| ||||||
Donovan [ |
Sydney, Australia | From December 1990 to May 1991 | STD clinic | Consecutive sample of heterosexual men |
Physical exam | NGU by clinical picture and microscopy, TPHA for syphilis, HSV by cell culture or clinical criteria, and warts clinically |
| ||||||
Fergusson [ |
Christchurch, New Zealand | From 1998 and 2002 | Birth cohort from 1977 | Prospective cohort repeatedly studied from birth |
Report and medical records | Patient report |
| ||||||
Ferris [ |
Australia | 2005 | 16–64 years old | Representative national sample | Patient report | Patient report |
| ||||||
Gebremedhin [ |
Africa | 2003–2007 | General population | 18 national demographic health surveys | Patient report | Patient report |
| ||||||
Giuliano et al. [ |
Sao Paulo, |
2005 and 2006 | 18–70 years old, no previous warts, and no STD or HIV |
Prospective cohort study | Physical exam | Glans, sulcus, shaft, and scrotum |
| ||||||
Gottlieb [ |
Baltimore, Denver, Long Beach, San Francisco | From July 1993 to September 1996 | STD clinic | Part of RCT cohort analysis | Physical exam | Serology (HSV) |
| ||||||
Gray et al. [ |
Rakai, Uganda | From November 1994 to October 1998 | General population | Randomized cluster of general population | Report | Urine PCR (GC and CT), serology (HSV and syphilis), and clinically (GUD) |
| ||||||
Gray et al. [ |
Rakai, Uganda | Completed December 2006 | Men 15–45 who wanted a free circumcision | Randomized controlled trial | Intention to treat | GUD on physical examination |
| ||||||
Hand [ |
US Naval Hospital St. Albans, NY | 1945 | Sailors | Not documented | Not documented | Not documented |
| ||||||
Harbertson [ |
Rwanda | From October 2008 to November 2010 | Active duty soldiers |
Cross-sectional | Patient report | Patient report |
| ||||||
Hart [ |
Australia | 1970 | Soldiers, STD clinic | Cross-sectional | Physical exam | Clinical diagnosis |
| ||||||
Hart [ |
South Australia | From 1988 to 1990 | STD clinic | Consecutive sample | Not documented | Chlamydia by enzyme immunoassay and GC by smear and culture |
| ||||||
Hernandez [ |
Hawaii | From July 2004 to December 2006 | University students |
Convenience sample | Physical exam | HPV swabbed glans, sulcus, shaft, scrotum, and inner foreskin |
| ||||||
Hutchinson [ |
Metropolitan Free Hospital, London |
Past year’s experience | Men with an STD | Not documented | Jew versus Gentile | Clinically |
| ||||||
Kapiga [ |
Moshi, Tanzania | From June to October 2000 | Hotel and bar workers | Randomized sample | Physical exam | Serology (HSV) |
| ||||||
Klavs [ |
Slovenia | 1999–2001 | Men 18–49 years old | National probability sample | Report | Patient report |
| ||||||
Lajous [ |
Mexico | From July 2000 to July 2003 | Healthy military men | Cross-sectional study | Physical exam performed but analysis based on report | HPV DNA |
| ||||||
Langeni [ |
Botswana | 2001 | Men 15–64 who had intercourse | National represented sample | Report | GDS or GDS by report in the past 12 months |
| ||||||
Laumann [ |
United States | 1992 | Men 18–59 years old | National probability sample | Report | Report |
| ||||||
Lavreys [ |
Kenya | From March 1993 to June 1997 | HIV negative-truck drivers | Prospective cohort study and convenience sample | Physical exam | Chlamydia by serology assay, TPHA & RPR (syphilis), HSV by serology assay, warts clinically, and chancroid by serology assay |
| ||||||
Lloyd [ |
Guy’s Hospital, London | From January to June 1932 | STD clinic | Convenience sample | Physical exam | Clinically and soft chancre clinically |
| ||||||
Lu [ |
Tucscon, Arizona; Tampa, Florida | From September 2003 to December 2005 | 18–40 year old sexually active males with no previous genital warts or penile cancer or current STD | Prospective cohort study | Physical exam | Glans, shaft, and scrotum |
| ||||||
Mallon [ |
Chelsea and Westminster Hospital, London | 1994–1997 | Patients referred to a dermatology specialty clinic |
Retrospective case control | Physical exam | Not documented |
| ||||||
Mandal [ |
United Kingdom | STD clinic and men with no evidence of clinical anogenital warts | Cross-sectional | Not documented | Cytology of swabs from urethra, glans, shaft, and anorectal | |
| ||||||
Mattson [ |
Kisumu, Kenya | 2002–2006 | 18–24 year olds who wanted a free circumcision | Randomized clinical trial | Physical exam | PCR for GC and CT and culture for |
| ||||||
Mehta et al. [ |
Kisumu, Kenya | 2002–2006 | Men interested in a free circumcision | Randomized clinical trial | Intention to treat | Urine for GC and chlamydia |
| ||||||
Mehta et al. [ |
Kisumu, Kenya | 2002–2006 | 18–24 year old men who wanted a free circumcision | Randomized clinical trial | Intention to treat | Serology (Lues and HSV) and Clinically identified (GUD) |
| ||||||
Mor [ |
San Francisco | From January 1996 to December 2005 | STD clinic | Consecutive sample | Physical exam | Not documented |
| ||||||
Mujugira [ |
Botswana, Kenya, Rwanda, South Africa, Tanzania, Uganda, Zambia | From November 2004 and April 2007 | HIV-negative partners of women who are HIV and HSV positive |
Discordant couples | Physical exam | Serology (HSV) |
| ||||||
Müller [ |
Alexandra, Johannesburg, South Africa | From December 2006 to July 2008 | 18+ sexually active attending HIV testing clinic | Cross-sectional | Physical exam | Glans, sulcus, and shaft |
| ||||||
Mwandi [ |
Kenya | From August to December 2007 | General population from 15 to 64 years old | Representative samples of households | Patient report | Serology (HSV) |
| ||||||
Nasio [ |
Nairobi | From January to September 1993 | STD clinic | Convenience sample | Physical exam | Report |
| ||||||
Newell [ |
Mwanza Region, Tanzania | From 1990 to 1991 | General population | Random cluster sample survey |
Report | Report (GDS and GUD) and serology (syphilis) |
| ||||||
Ng’ayo [ |
Kisumu, Kenya | Not documented | Fishermen along Lake Victoria |
Random cluster sample cross-sectional survey | Not documented | Serology (HSV) |
| ||||||
Ng’ayo [ |
Kisumu, Kenya | Not documented | Fishermen along Lake Victoria |
Random cluster sample cross-sectional survey | Physical exam | Swab of glans, corona, shaft, scrotum, and perianal |
| ||||||
Nielson [ |
Tucscon, Arizona; Tampa, Florida | 2002–2005 | 18–40 year old sexually active males with no previous genital warts or penile cancer or current STD | Cross-sectional | Physical exam | Swab of glans, sulcus, shaft, scrotum, perianal area, and urethra (optional) |
| ||||||
Obasi [ |
Rural Mwanza Region, Tanzania | May and June 1993 | General population | Nested case-control study within an RCT | Not documented | Type specific ELISA for HSV2 |
| ||||||
Oglivie [ |
British Columbia, Canada | Not documented | STD clinic never MSM | Cross-sectional study | Physical exam | Glans, foreskin, shaft, and scrotum |
| ||||||
Oriel [ |
St. Thomas Hospital, London | From October 1967 to January 1979 | STD clinic | Consecutive sample | Physical exam | Not documented |
| ||||||
Otieno-Nyunya [ |
Kenya | 2007 | General population | Nationally representative population-based serosurvey | Find article | Serology |
| ||||||
Parker [ |
Perth, Australia | From May to September 1981 |
STD clinic | Consecutive sample | Report and physical exam | Cultures and microscopy (NSU, CT, and GC), serology (syphilis) culture (HSV) and warts clinically |
| ||||||
Partridge [ |
Seattle, Washington | From June 2003 to March 2006 recruitment | University of Washington students 18–20 years old history of vaginal intercourse | Prospective cohort study | Physical exam | HPV swabs of penile shaft, glans, foreskin, scrotum, and urine. |
| ||||||
Rakwar [ |
Mombasa, Kenya | Beginning of March 1993 | Long-distance truckers | Cross-sectional | Physical exam | Serology and culture of genital ulcers |
| ||||||
Reynolds [ |
Pune, India | 1993–2000 | STD clinic | Prospective cohort study | Physical exam | Positive gram stain of urethral discharge (GC), RPR, or darkfield (syphilis). Serology (HSV) |
| ||||||
Richters [ |
Australia | From May 2001 to June 2002 | Males 16–59 | National probability sample | Report | Report |
| ||||||
Rodriguez-Diaz [ |
San Juan, Puerto Rico | From October 2009 to December 2011 | STD clinic and Males from 16 to 83 years old | Cross-sectional | Report | Report |
| ||||||
Rombaldi [ |
Caxias do Sul, Brazil | From February 2003 to July 2004 | Sexual partners of women with CIN | Prospective, prevalence study | Physical exam | Penoscopy and sampling of urethra, glans, preputial mucosa, and shaft |
| ||||||
Schneider [ |
Guntur district of Andhra Pradesh, India |
From September 2004 to September 2005 | 15–49 year olds general populations | Random clusters |
Report | Serology |
| ||||||
Schrek [ |
Hines Veteran Hospital, Illinois | 1931–1944 | World War I veterans | Cross-sectional study |
Age at circumcision by report | Report |
| ||||||
Seed [ |
Rwanda | From March to October 1991 | Steady sexual partner of women enrolled in Project San Francisco | Cross-sectional study | Physical exam | Report (GDS and GUD) and RPR (syphilis) |
| ||||||
Shin [ |
Busan, South Korea | From August 29 to September 30, 2002 | University students | Cross section | Report | Continuous swab for HPV DNA from scrotum to top of glans |
| ||||||
Simonsen [ |
Nairobi | From March to December 1986 | STD clinic and men with an STD from a prostitute | Convenience sample | Physical exam | Chancroid by culture |
| ||||||
Smith [ |
US military post | From January 1983 to September 1984 | US Army personnel | Cross section | Physical exam | CDC criteria and culture |
| ||||||
Sobngwi-Tambekou [ |
Orange Farm, South Africa | 2005 | Males interest in a free circumcision | Randomized clinical trial | Intention to treat | Urine samples for GC and CT and serology (HSV) |
| ||||||
Suligoi [ |
Garoua, northern Cameroon | From December 1997 to January 1998 | General medical outpatients without complaints of STD or HIV | Consecutive sample | Report | HSV by ELISA |
| ||||||
Svare [ |
Copenhagen, Denmark | From March to December 1993 | STD clinic | Consecutive sample | Report | Penile swab for PCR |
| ||||||
Talukdar [ |
Kolkata, India | Not documented | Homeless men 18–49 years old | Cluster design among homeless men | Religion | Urine PCR (GC) TPHA (syphilis) |
| ||||||
Taylor [ |
Whitechapel Clinic, The London Hospital | From June 1970 to August 1973 | STD clinic | Consecutive sample with randomly selected controls | Chart review (10.9% missing) | Clinical diagnosis and HSV2 by culture |
| ||||||
Telzak [ |
New York City | 1988–1991 | STD clinic | Prospective cohort study | Patient report | Dark field, RPR, culture, Tzanck smear, and clinical diagnosis |
| ||||||
Thomas [ |
United States Military | From February 1997 to June 2001 | HIV-positive cases and HIV negative controls | Case control | Medical records/patient report | Patient report |
| ||||||
Tobian et al. [ |
Rakai, Uganda | Ended December 2006 | Men 15–45 who wanted a free circumcision | Randomized clinical trial | Physical exam | Swab of glans and sulcus (HPV) and serology (HSV and syphilis) |
| ||||||
Todd [ |
Mwanza, Tanzania | From 1991 to 1994 | General population | Community randomized trial nested case control | Not documented | RPR and VDRL |
| ||||||
Tseng [ |
Los Angeles, California | From May 1975 to October 1985 | Cases of penile cancer and matched controls and mean age 55.9 and 56.2, respectively | Case control | Patient report | Patient report |
| ||||||
Tyndall [ |
Nairobi | Not documented | STD clinic and men with genital ulcers | Convenience sample | Physical exam | Report |
| ||||||
Uganda [ |
Uganda | From February to September 2011 | Adults from 15 to 59 | National representative population-based survey | Patient report | Serology |
| ||||||
Urassa [ |
Mwanza Region, Tanzania | Study 1: 1990-1991. |
Study 1: GP*. |
Study 1: stratified random cluster sample. |
Study 1, 2, 3, and 5: report. |
RPR for syphilis and patient report for any STI |
| ||||||
Vaccarella [ |
Mexico, 27 public clinics in 14 states | From January 2003 to September 2004 | Men attending vasectomy clinic | Consecutive sample |
Physical exam in Mexico |
Complete swab from scrotum to urethra |
| ||||||
VanBuskirk [ |
Seattle, Washington | From June 2003 to March 2006 recruitment |
University of Washington students 18–20 years old history of vaginal intercourse | Prospective cohort study | Physical exam | HPV swabs of penile shaft, glans, foreskin, scrotum, and urine. |
| ||||||
Van Den |
Seattle, Washington | From April 1987 to September 1991 | Men |
Case control | Patient report | Clinical exam |
| ||||||
Van Wagoner [ |
Birmingham, Alabama | Not documented | STD clinic and self-identified heterosexual men | Cross-sectional | Physical exam | Serology and culture |
| ||||||
Vardas [ |
Not reported | 18 countries in Africa, Asia-Pacific, Europe, Latin America, and North America | Heterosexual men from 16 to 24 years with 1 to 5 lifetime female partners | Cross-sectional | Physical exam | Serology for 6, 11, 16, and 18; swab of penis, scrotum, perineum, and perianal (HPV) |
| ||||||
Warner [ |
Baltimore, MD | From 1993 to 2000 | Heterosexual African American men undergoing HIV testing at STD clinics | Chart review | Physical exam documented in medical record | Clinical exam |
| ||||||
Vaz [ |
Maputo, Mozambique | From 1990 to 1991 | Prisoners | Convenience sample | Patient report | RPR and FTA |
| ||||||
Weaver [ |
Seattle, Washington | Part 1: from August 1 to October 30, 2000 and |
Part 1: heterosexual men 18–25 years old attending STD clinic. |
Consecutive sample | Physical exam | HPV swabs of penile shaft, glans, foreskin, scrotum, and urine. |
| ||||||
Weiss [ |
Kisumu, Kenya; Ndola, Zambia; Cotonou, Benin; Yaondé, Cameroon | From June 1997 to March 1998 | General population | Cluster design to randomly select households | Physical exam and self-report | Serology (HSV) |
| ||||||
Wilson [ |
Canada | Not documented | Canadian Army VD treatment center | Convenience sample | Not documented | Not documented |
| ||||||
Wolbarst [ |
New York City | Before 1914 to 1926 | Private patients in urology practice | Convenience sample | Jew versus Gentile | Clinically |
| ||||||
Xu [ |
United States | 1999–2004 | General population | National health survey | Patient report using visual aids | Serology (HSV) |
The results of the analyses of incidence data are shown in Table
Meta-analysis of circumcision status of adult males and incidence of sexually transmitted infections using Poisson regression.
Study | Intact infections/patient years | Circumcised infections/patient years | Relative risk | 95% confidence interval |
---|---|---|---|---|
GDS | ||||
Tobian; unadjusted | 60/2790 | 53/2740 | 1.1118 | 0.7684–1.6086 |
Tobian; 6 weeks* | 60/2790 | 53/2423.85 | 0.9835 | 0.6797–1.4230 |
Chlamydia | ||||
Diseker | 36/346 | 88/1109 | 1.3073 | 0.8872–1.9267 |
Mehta | 101/2091 | 88/2027.5 | 1.1128 | 0.8362–1.4810 |
Mehta; 6 weeks* | 101/2091 | 1875.43 | 1.0294 | 0.7735–1.3700 |
SOB | 32/1541.75 | 19/1550.5 | 1.6938 | 0.9601–2.9880 |
SOB; 6 weeks* | 32/1541.75 | 19/1448.27 | 1.5820 | 0.8968–2.7910 |
Summary |
|
|
||
Summary; 6 weeks* |
|
|
||
Gonorrhea | ||||
Diseker | 36/346 | 83/1109 | 1.3903 | 0.9402–2.0557 |
Mehta | 74/2102 | 70/2065 | 1.0385 | 0.7490–1.4399 |
Mehta; 6 weeks* | 74/2102 | 70/1912.92 | 0.9620 | 0.6938–1.3339 |
SOB | 91/1541.75 | 89/1550.5 | 1.0283 | 0.7677–1.3772 |
SOB; 6 weeks* | 91/1541.75 | 89/1448.27 | 0.9605 | 0.7171–1.2864 |
Summary |
|
|
||
Summary; 6 weeks* |
|
|
||
GUD | ||||
Mehta | 101/1950 | 51/1912 | 1.9418 | 1.3800–2.7191 |
Mehta; 6 weeks* | 101/1950 | 51/1753.81 | 1.7812 | 1.2720–2.4940 |
Tobian | 75/2790 | 48/2740 | 1.5349 | 1.0681–2.2045 |
Tobian; 6 weeks* | 75/2790 | 48/2581.92 | 1.4460 | 1.0065–2.0774 |
Summary |
|
|
||
Summary; 6 weeks* |
|
|
||
Syphilis | ||||
Diseker | 4/347 | 6/1109 | 2.1306 | 0.5560–7.5504 |
Mehta | 6/1976 | 7/1897.5 | 0.8230 | 0.2766–2.4490 |
Mehta; 6 weeks* | 6/1976 | 7/1741.73 | 0.7558 | 0.2539–2.2481 |
Tobian | 45/4286 | 50/4166 | 0.8748 | 0.5848–1.3087 |
Tobian; 6 weeks* | 45/4286 | 50/3925.65 | 0.8243 | 0.5511–1.2334 |
Summary |
|
|
||
Summary; 6 weeks* |
|
|
||
HSV | ||||
Dickson | 19/2235 | 13/1512 | 0.9888 | 0.4883–2.0019 |
Mehta | 100/1628.5 | 86/1493.5 | 1.0664 | 0.7993–1.4226 |
Mehta; 6 weeks* | 100/1628.5 | 86/1379.73 | 0.9852 | 0.7384–1.3145 |
SOB | 35/1003 | 23/995 | 1.5095 | 0.8921–2.5546 |
SOB; 6 weeks* | 35/1003 | 23/929.39 | 1.4100 | 0.8332–2.3862 |
Tobian | 153/2906.5 | 114/2888.5 | 1.3338 | 1.0466–1.6998 |
Tobian; 6 weeks* | 153/2906.5 | 114/2704.81 | 1.2489 | 0.9800–1.5917 |
Summary |
|
|
||
Summary; 6 weeks* |
|
|
||
HPV | ||||
Auvert | 144/1086.25 | 90/1125.25 | 1.5132 | 1.1651–1.9650 |
Auvert; 6 weeks* | 144/1086.25 | 90/1051.06 | 1.4134 | 1.0883–1.8355 |
Auvert ADJ† | 217.50/1086.25 | 193.03/1125.25 | 1.0657 | 0.8786–1.2924 |
Auvert ADJ; 6 weeks*† | 217.50/1086.25 | 193.03/1051.06 | 0.9953 | 0.8207–1.2072 |
Dickson | 54/7830 | 41/5220 | 0.8780 | 0.5851–1.3177 |
Lajous | 37/174 | 8/36 | 1.0451 | 0.4867–2.2441 |
Lu | 7/25.4 | 56/243.3 | 1.1967 | 0.5454–2.6256 |
Partridge | 32/2486 | 132/7840 | 0.7645 | 0.5196–1.1249 |
Tobian | 80/574 | 42/466 | 1.5464 | 1.0644–2.2466 |
Tobian; 6 weeks* | 80/574 | 42/412.23 | 1.3679 | 0.9416–1.9873 |
Tobian ADJ† | 120.83/574 | 90.08/466 | 1.0889 | 0.8289–1.4306 |
Tobian ADJ; 6 weeks*† | 120.83/574 | 90.08/412.23 | 0.9633 | 0.7333–1.2655 |
VanBuskirk | 45/124 | 142/412 | 1.0530 | 0.7530–1.4724 |
Summary |
|
|
||
Summary; 6 weeks* |
|
|
||
Summary ADJ† |
|
|
||
Summary ADJ; 6 weeks*† |
|
|
||
Any STI | ||||
Dickson | 70/2991 | 47/1296 | 0.9591 | 0.6627–1.3879 |
Diseker | 135/356 | 475/1109 | 0.8853 | 0.7313–1.0718 |
Fergusson | 37/2848 | 7/1232 | 2.2864 | 1.0194–5.1289 |
Mattson | 17/265.5 | 27/235 | 0.5583 | 0.3043–1.0244 |
Summary |
|
|
The results of the analyses of prevalence data are shown in Tables
Studies of the association between circumcision status and the prevalence of genital ulcerative disease versus genital discharge syndrome.
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratio | Exact 95% |
---|---|---|---|---|---|---|
Cameron | 56/23 | 94/120 | 3.11 | 1.78–2.63 | 3.0961 | 1.7301–5.6797 |
Hutchinson | 165/107 | 11/47 | 6.59 | 3.27–13.27 | 6.5517 | 3.1747–14.6549 |
Nasio | 58/20 | 373/207 | 1.61 | 0.94–2.75 | 1.6083 | 0.9226–2.9055 |
Warner | 492/2316 | 1836/14352 | 1.66 | 1.49–1.85 | 1.6606 | 1.4863–1.8531 |
Wolbarst | 330/420 | 203/547 | 2.12 | 1.71–2.63 | 2.1161 | 1.6959–2.6444 |
Random effects summary effect: | 2.2368 | 1.63–2.24 |
Heterogeneity chi-square (
Studies of the association between circumcision status and the prevalence of genital discharge syndrome.
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratio | Exact 95% |
---|---|---|---|---|---|---|
Agot | 207/237 | 184/210 | 1.00 | 0.76–1.31 | 0.9968 | 0.7525–1.3206 |
Bailey | 58/118 | 65/79 | 0.60 | 0.38–0.94 | 0.5984 | 0.3657–0.9654 |
Burundi | 48/1612 | 22/864 | 1.17 | 0.70–1.95 | 1.1693 | 0.6870–2.0486 |
Bwayo | 88/88 | 376/383 | 1.02 | 0.73–1.41 | 1.0186 | 0.7238–1.4336 |
Gray et al. [ |
156/4443 | 33/875 | 0.92 | 0.64–1.36 | 0.9310 | 0.6312–1.4097 |
Gray et al. [ |
503/3967 | 97/728 | 0.93 | 0.64–1.36 | 0.9516 | 0.7528–1.2123 |
Lavreys | 47/48 | 297/354 | 1.17 | 0.76–1.80 | 1.1668 | 0.7404–1.8383 |
Newell | 77/1279 | 58/588 | 0.61 | 0.43–0.87 | 0.6105 | 0.4222–0.8866 |
Seed | 236/358 | 136/107 | 0.52 | 0.38–0.70 | 0.5159 | 0.3790–0.7095 |
Tyndall | 86/92 | 311/321 | 0.96 | 0.69–1.35 | 0.9649 | 0.6818–1.3646 |
Warner | 2316/2849 | 14352/21054 | 1.19 | 1.12–1.26 | 1.1925 | 1.1239–1.2653 |
Random effects summary effect: | 0.8902 | 0.7277–1.0891 |
Heterogeneity chi-square (
Studies of the association between circumcision status and the prevalence of nongonococcal urethritis.
Study | Uncircumcised +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratio | Exact confidence interval |
---|---|---|---|---|---|---|
Aynaud | 56/106 | 9/39 | 2.29 | 1.04–5.06 | 2.2811 | 0.9954–5.7489 |
Cook | 161/379 | 721/1515 | 0.89 | 0.73–1.10 | 0.8927 | 0.7225–1.0996 |
Dave | 169/4664 | 39/943 | 0.88 | 0.61–1.25 | 0.8762 | 0.6106–1.2843 |
Donovan | 55/60 | 81/104 | 1.18 | 0.74–1.88 | 1.1763 | 0.7177–1.9282 |
Ferris | 34/1567 | 136/2209 | 0.35 | 0.24–0.52 | 0.3525 | 0.2333–0.5198 |
Laumann | 21/1097 | 35/1414 | 0.77 | 0.45–1.34 | 0.7735 | 0.4252–1.3751 |
Lavreys | 15/80 | 81/570 | 1.32 | 0.73–2.40 | 1.3189 | 0.6724–2.4469 |
Parker | 138/452 | 236/493 | 0.64 | 0.50–0.82 | 0.6380 | 0.4946–0.8211 |
Richters | 150/3367 | 369/5092 | 0.61 | 0.51–0.75 | 0.6148 | 0.5025–0.7492 |
Smith | NA | NA | 0.61 | 0.50–0.73 | 0.61 | 0.50–0.73 |
Taylor | 100/207 | 42/62 | 0.71 | 0.45–1.13 | 0.7137 | 0.4405–1.1624 |
Wilson | 140/860 | 45/259 | 0.94 | 0.65–1.35 | 0.9370 | 0.6449–1.3807 |
Random effects summary effect: | 0.76 | 0.63–0.92 |
Heterogeneity chi-square (
Studies of the association between circumcision status and the prevalence of
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratio | Exact confidence interval |
---|---|---|---|---|---|---|
Auvert | 9/340 | 3/132 | 1.32 | 0.31–4.37 | 1.1644 | 0.2848–6.7884 |
Aynaud | 8/154 | 1/47 | 2.44 | 0.30–20.03 | 2.4335 | 0.3125–110.6090 |
Cook | 34/506 | 147/2089 | 0.95 | 0.65–1.40 | 0.9549 | 0.6293–1.4145 |
Dave | 72/4761 | 12/970 | 1.22 | 0.66–2.26 | 1.2224 | 0.6554–2.4849 |
Diseker | 72/212 | 240/622 | 0.88 | 0.65–1.20 | 0.8803 | 0.6382–1.2057 |
Fergusson | NA | NA | 2.50 | 0.73–8.53 | 2.50 | 0.73–8.53 |
Ferris | 30/1571 | 59/2294 | 0.74 | 0.48–1.16 | 0.7425 | 0.4596–1.1774 |
Gray et al. [ |
71/2131 | 17/421 | 0.83 | 0.48–1.42 | 0.8252 | 0.4751–1.5104 |
Gray et al. [ |
53/2589 | 15/462 | 0.63 | 0.35–1.13 | 0.6306 | 0.3466–1.2152 |
Hart | 251/2725 | 330/4686 | 1.31 | 1.10–1.55 | 1.3079 | 1.0979–1.5567 |
Laumann | 0/1118 | 36/1413 | 0.02 | 0.00–0.28 | 0.0246 | 0–0.1368 |
Lavreys | 15/33 | 31/36 | 0.53 | 0.24–1.15 | 0.5308 | 0.2238–1.2241 |
Parker | 37/553 | 45/684 | 1.02 | 0.65–1.59 | 1.0170 | 0.6303–1.6322 |
Richters | 74/3392 | 116/5218 | 0.98 | 0.73–1.32 | 0.9813 | 0.7206–1.3295 |
Rodriguez-Diaz | 41/405 | 20/194 | 0.98 | 0.56–1.72 | 0.9820 | 0.5452–1.8198 |
Random effects summary effect: | 0.9099 | 0.72–1.15 |
Heterogeneity chi-square (
Studies of the association between circumcision status and the prevalence of gonorrhea.
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratio | Exact confidence interval |
---|---|---|---|---|---|---|
Aynaud | 1/161 | 0/48 | 0.90 | 0.04–22.47 | 0.2963 | 0.0076–[11.5518] |
Bailey | 58/118 | 55/89 | 0.80 | 0.50–1.26 | 0.7959 | 0.4892–1.2944 |
Cook | 87/453 | 175/2061 | 2.26 | 1.72–2.98 | 2.2616 | 1.6436–3.0031 |
Dave | 53/4780 | 15/967 | 0.71 | 0.40–1.27 | 0.7148 | 0.3950–1.3714 |
Diseker | 110/212 | 294/622 | 1.09 | 0.84–1.44 | 1.0977 | 0.8299–1.4474 |
Donovan | 8/107 | 19/166 | 0.65 | 0.28–1.55 | 0.6541 | 0.2388–1.6331 |
Ferris | 29/1573 | 52/2302 | 0.82 | 0.52–1.29 | 0.8162 | 0.4972–1.3164 |
Gray et al. [ |
25/2177 | 3/435 | 1.67 | 0.50–5.54 | 1.6649 | 0.5046–8.6526 |
Gray et al. [ |
29/2613 | 4/473 | 1.31 | 0.46–3.75 | 1.3123 | 0.4578–5.1610 |
Hand (black) | 473/250 | 71/51 | 1.36 | 0.92–2.01 | 1.3585 | 0.8987–2.0434 |
Hand (white) | 399/388 | 123/82 | 0.69 | 0.50–0.94 | 0.6858 | 0.4947–0.9474 |
Hart | 56/2920 | 48/4968 | 1.98 | 1.35–2.93 | 1.9848 | 1.3217–2.9904 |
Laumann; 1–4 partners | 9/440 | 12/542 | 0.92 | 0.39–2.21 | 0.9239 | 0.3405–2.4141 |
Laumann; 5–20 partners | 64/380 | 58/480 | 1.39 | 0.95–2.04 | 1.3934 | 0.9361–2.0775 |
Laumann; 21+ partners | 37/153 | 55/178 | 0.78 | 0.49–1.25 | 0.7831 | 0.4743–1.2826 |
Lavrey | 14/81 | 88/563 | 1.11 | 0.60–2.04 | 1.1056 | 0.5541–2.0721 |
Lloyd | 203/178 | 75/43 | 0.65 | 0.43–1.00 | 0.6544 | 0.4161–1.0203 |
Parker | 54/536 | 43/686 | 1.61 | 1.06–2.44 | 1.6067 | 1.0385–2.4983 |
Reynolds | 110/1197 | 7/184 | 2.42 | 1.11–5.27 | 2.4145 | 1.1090–6.2430 |
Richters | 85/3471 | 112/5338 | 1.17 | 0.88–1.55 | 1.1671 | 0.8669–1.5666 |
Rodriguez-Diaz | 59/387 | 28/186 | 1.01 | 0.63–1.64 | 1.0127 | 0.6122–1.7074 |
Schrek (white) | 22/130 | 10/26 | 0.44 | 0.19–1.04 | 0.4423 | 0.1750–1.1733 |
Schrek (black) | 50/73 | 19/26 | 0.94 | 0.47–1.87 | 0.9376 | 0.4447–2.0000 |
Smith | NA | NA | 1.14 | 0.92–1.41 | 1.14 | 0.92–1.41 |
Talukdar | 19/345 | 10/92 | 0.51 | 0.23–1.13 | 0.5075 | 0.2157–1.2662 |
Taylor | 72/235 | 21/83 | 1.21 | 0.70–2.09 | 1.2104 | 0.6846–2.2069 |
Wilson | 640/360 | 229/75 | 0.58 | 0.44–0.78 | 0.5825 | 0.4291–0.7847 |
Random effects summary effect: | 1.0272 | 0.86–1.23 |
Heterogeneity chi-square (
Studies of the association between circumcision status and the prevalence of genital ulcerative disease.
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratios | Exact confidence interval |
---|---|---|---|---|---|---|
Agot | 133/312 | 93/301 | 1.38 | 1.01–1.88 | 1.3792 | 1.0020–1.9032 |
Barile | 32/3 | 15/32 | 22.76 | 6.00–86.29 | 21.7418 | 5.5279–128.4283 |
Burundi | 63/1597 | 17/869 | 2.02 | 1.17–3.47 | 2.0160 | 1.1564–3.6993 |
Bwayo | 58/118 | 179/583 | 1.60 | 1.12–2.29 | 1.6000 | 1.0988–2.3141 |
Gray et al. [ |
297/4302 | 65/843 | 0.90 | 0.68–1.18 | 0.8954 | 0.6751–1.2021 |
Gray et al. [ |
383/4087 | 64/761 | 1.11 | 0.85–1.47 | 1.1143 | 0.8428–1.49261 |
Lavreys | 13/82 | 46/605 | 2.09 | 1.08–4.02 | 2.0825 | 0.9888–4.1281 |
Newell | 52/1304 | 18/628 | 1.39 | 0.81–2.40 | 1.3911 | 0.7926–2.5488 |
Seed | 142/452 | 41/202 | 1.55 | 1.05–2.27 | 1.5470 | 1.0407–2.3358 |
Simonsen | 23/47 | 35/196 | 2.74 | 1.48–5.07 | 2.7297 | 1.4016–5.2706 |
Telzak | 239/105 | 211/203 | 2.19 | 1.62–2.96 | 2.1876 | 1.6056–2.9905 |
Tyndall | 48/130 | 150/482 | 1.19 | 0.81–1.73 | 1.1862 | 0.7937–1.7548 |
Warner | 492/4673 | 1836/33570 | 1.93 | 1.73–2.14 | 1.9250 | 1.7300–2.1378 |
Random effects summary effect: | 1.6760 | 1.3926–2.0170 |
Heterogeneity chi-square (
Studies of the association between circumcision status and the prevalence of syphilis.
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratio | Exact confidence interval |
---|---|---|---|---|---|---|
Bailey | 7/169 | 6/138 | 0.95 | 0.31–2.90 | 0.9528 | 0.2673–3.5173 |
Buvé: Kismu | 17/393 | 0/156 | 13.92 | 0.83–232.90 | 9.4870 | 1.1611–[55.8609]† |
Buvé: Ndola | 57/480 | 7/48 | 0.81 | 0.35–1.88 | 0.8146 | 0.3442–2.2355 |
Bwayo RPR* | 14/162 | 42/729 | 1.50 | 0.80–2.81 | 1.4993 | 0.7380–2.8812 |
Bwayo TPHA* | 38/75 | 106/351 | 1.68 | 1.07–2.62 | 1.6761 | 1.0403–2.6740 |
Cook | 20/520 | 13/2223 | 6.69 | 3.27–13.70 | 6.5705 | 3.0874–14.4743 |
Dave | 10/4823 | 3/979 | 0.68 | 0.19–2.46 | 0.6767 | 0.1738–3.8332 |
Diseker | 10/212 | 18/622 | 1.62 | 0.74–3.69 | 1.6290 | 0.6608–3.7908 |
Donovan | 1/114 | 2/183 | 0.80 | 0.07–8.95 | 0.8032 | 0.0135–15.5915 |
Gray et al. [ |
482/4117 | 93/815 | 1.03 | 0.81–1.30 | 1.0260 | 0.8091–1.3116 |
Gray et al. [ |
446/3917 | 96/709 | 0.84 | 0.67–1.06 | 0.8410 | 0.6629–1.0750 |
Hand | 420/1090 | 108/219 | 0.78 | 0.60–1.01 | 0.7815 | 0.6007–1.0204 |
Laumann | 12/1106 | 13/1312 | 1.10 | 0.50–2.41 | 1.0950 | 0.4546–2.6147 |
Lavreys | 11/84 | 48/603 | 1.65 | 0.82–3.29 | 1.6438 | 0.7400–3.3705 |
Lloyd | 81/300 | 25/93 | 1.00 | 0.61–1.66 | 1.0044 | 0.5937–1.7413 |
Mor | 192/13838 | 384/36290 | 1.31 | 1.10–1.56 | 1.3112 | 1.0956–1.5651 |
Newell | 125/1229 | 45/597 | 1.35 | 0.95–1.92 | 1.3491 | 0.9379–1.9695 |
Otieno-Nyunya | NA | NA | 2.2 | 1.3–3.7 | 2.2 | 1.3–3.7 |
Parker | 9/581 | 3/726 | 3.75 | 1.01–13.91 | 3.7452 | 0.9292–216041 |
Reynolds | 128/1639 | 9/151 | 1.31 | 0.65–2.63 | 1.3101 | 0.6510–2.9907 |
Rodriguez-Diaz | 68/378 | 37/177 | 0.86 | 0.56–1.33 | 0.8608 | 0.5448–1.3756 |
Schneider | 107/5049 | 25/986 | 0.84 | 0.54–1.30 | 0.8359 | 0.5335–1.3562 |
Schrek white | 10/142 | 1/35 | 2.46 | 0.31–19.90 | 2.4557 | 0.3293–109.9779 |
Schrek black | 19/104 | 6/39 | 1.19 | 0.44–3.19 | 1.1863 | 0.4155–3.9024 |
Seed | 24/570 | 10/233 | 0.98 | 0.46–2.08 | 0.9811 | 0.4438–2.3363 |
Talukdar | 25/339 | 8/94 | 0.87 | 0.38–1.98 | 0.8668 | 0.3640–2.2979 |
Todd | 187/354 | 39/137 | 1.86 | 1.25–2.76 | 1.8541 | 1.2310–2.8387 |
Uganda | 149/6643 | 37/2452 | 1.49 | 1.03–2.14 | 1.4864 | 1.0276–2.1990 |
Urassa | 775/3282 | 155/772 | 1.18 | 0.97–1.42 | 1.1761 | 0.9707–1.4311 |
Vaz | 74/748 | 29/433 | 1.48 | 0.95–2.31 | 1.4767 | 0.9321–2.3934 |
Wilson | 90/910 | 10/294 | 2.91 | 1.49–5.66 | 2.9059 | 1.4838–6.3494 |
Random effects summary effect: | 1.3036 | 1.1103–1.5306 |
Heterogeneity chi-square (
Studies of the association between circumcision status and the prevalence of genital herpes/herpes simplex virus type 2.
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratio | Exact confidence interval |
---|---|---|---|---|---|---|
Bassett | 69/36 | 125/70 | 1.07 | 0.65–1.34 | 1.0731 | 0.6348–1.8283 |
Buvé | 136/199 | 36/96 | 1.82 | 1.17–2.83 | 1.8202 | 1.1512–2.9206 |
Cook | 49/491 | 205/2031 | 0.99 | 0.70–1.37 | 0.9887 | 0.6977–1.3795 |
Dave | 48/4785 | 11/971 | 0.89 | 0.46–1.71 | 0.8855 | 0.4514–1.8979 |
Dickson | 19/241 | 13/162 | 0.98 | 0.47–2.04 | 0.9825 | 0.4460–2.2295 |
Donovan | 21/94 | 27/158 | 1.31 | 0.70–2.44 | 1.3061 | 0.6618–2.5514 |
Ferris | 28/1594 | 55/2317 | 0.74 | 0.47–1.17 | 0.7401 | 0.4499–1.1930 |
Gottlieb | 27/295 | 74/724 | 0.90 | 0.56–1.42 | 0.8956 | 0.5424–1.4419 |
Gray | 395/160 | 76/43 | 1.40 | 0.92–2.12 | 1.3961 | 0.8962–2.1560 |
Kapiga | 3/8 | 57/138 | 1.10 | 0.28–4.30 | 0.9083 | 0.1499–3.9591 |
Laumann | 9/1109 | 22/1427 | 0.53 | 0.24–1.15 | 0.5265 | 0.2125–1.1955 |
Lavreys | 20/28 | 32/33 | 0.74 | 0.35–1.56 | 0.7386 | 0.3237–1.6679 |
Mallon | 8/267 | 0/82 | 5.24 | 0.30–91.81 | 3.3651 | 0.5116–[22.1343]* |
Mujugira | 669/358 | 760/483 | 1.19 | 1.00–1.41 | 1.1875 | 0.9966–1.4156 |
Mwandi | 396/546 | 1320/4355 | 2.39 | 2.07–2.76 | 2.3925 | 2.0671–2.7677 |
Ng’ayo | 146/86 | 14/4 | 0.49 | 0.15–1.52 | 0.4863 | 0.1129–1.6142 |
Obasi | 25/77 | 5/25 | 1.62 | 0.56–4.69 | 1.6179 | 0.5292–5.9872 |
Parker | 60/530 | 44/685 | 1.76 | 1.18–2.64 | 1.7617 | 1.1535–2.7079 |
Reynolds | 178/1096 | 14/111 | 1.29 | 0.72–2.30 | 1.2875 | 0.7157–2.4867 |
Richters | 68/3476 | 138/5347 | 0.76 | 0.57–1.02 | 0.7580 | 0.5566–1.0241 |
Rodriguez-Diaz | 48/398 | 28/186 | 0.80 | 0.49–1.32 | 0.8014 | 0.4755–1.3715 |
Schneider | 278/4878 | 80/931 | 0.66 | 0.51–0.86 | 0.6633 | 0.5101–0.8702 |
Suligoi | 2/5 | 18/57 | 1.27 | 0.23–7.10 | 1.2628 | 0.1112–8.5647 |
Taylor: UK | 102/180 | 20/59 | 1.67 | 0.95–2.93 | 1.6694 | 0.9283–3.1007 |
Taylor: caribbean | 50/70 | 2/12 | 4.29 | 0.92–20.00 | 4.2480 | 0.8863–40.7423 |
Taylor: other | 36/56 | 4/33 | 5.30 | 1.73–16.24 | 5.2451 | 1.6604–22.0974 |
Van Wagoner ≤ 25 years old | 13/31 | 64/130 | 0.85 | 0.42–1.74 | 0.8524 | 0.3820–1.8147 |
Van Wagoner ≥ 26 years old | 45/8 | 77/55 | 4.02 | 1.76–9.19 | 3.9905 | 1.6878–10.5916 |
Weiss: Cotonou | 1/9 | 102/751 | 0.81 | 0.10–6.52 | 0.8183 | 0.0185–6.0119 |
Weiss: Yaoundé | 1/7 | 235/644 | 0.39 | 0.05–3.20 | 0.3918 | 0.0087–3.0759 |
Weiss: Kisumu | 161/264 | 41/117 | 1.74 | 1.16–2.61 | 1.7387 | 1.1418–2.6833 |
Weiss: Ndola | 194/359 | 22/32 | 0.79 | 0.44–1.39 | 0.7863 | 0.4294–1.4627 |
Xu | 146/919 | 323/2462 | 1.21 | 0.98–1.49 | 1.2109 | 0.9747–1.4993 |
Random effects summary effect: | 1.1522 | 0.95–1.40 |
Heterogeneity chi-square (
Studies of the association between circumcision status and the prevalence of chancroid.
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratio | Exact confidence interval |
---|---|---|---|---|---|---|
Hand; black | 60/663 | 18/104 | 0.57 | 0.30–0.92 | 0.5234 | 0.2908–0.9807 |
Hand; white | 55/732 | 5/200 | 3.01 | 1.19–7.61 | 3.0030 | 1.1890–9.7420 |
Hart [ |
NA | NA | 4.76 | 3.45–7.14 | 4.76 | 3.44–7.14 |
Lavreys | 10/46 | 91/259 | 0.62 | 0.30–1.28 | 0.6194 | 0.2674–1.3100 |
Lloyd | 9/372 | 1/117 | 2.83 | 0.35–22.58 | 2.8263 | 0.3846–125.1128 |
Rakwar | NA | NA | 0.82 | 0.50–1.16 | 0.82 | 0.50–1.16 |
Random effects summary effect: | 1.33 | 0.52–1.33 |
Heterogeneity chi-square (
Studies of the association between circumcision status and the prevalence of genital warts.
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratio | Exact confidence interval |
---|---|---|---|---|---|---|
Cook | 51/489 | 412/1824 | 0.46 | 0.34–0.63 | 0.4618 | 0.3326–0.6307 |
Dave | 175/4659 | 37/945 | 0.95 | 0.66–1.37 | 0.9594 | 0.6646–1.4173 |
Dinh | 28/1127 | 133/2822 | 0.53 | 0.35–0.80 | 0.5272 | 0.3356–0.8026 |
Donovan | 20/95 | 30/155 | 1.09 | 0.58–2.02 | 1.0874 | 0.5517–2.1075 |
Ferris | 45/1578 | 107/2263 | 0.60 | 0.42–0.86 | 0.6032 | 0.4135–0.8677 |
Lavreys | 3/92 | 16/635 | 1.29 | 0.37–4.53 | 1.2937 | 0.2370–4.6463 |
Mallon | 29/246 | 7/75 | 1.26 | 0.53–3.00 | 1.2623 | 0.5139–3.5538 |
Mandal | 22/66 | 6/11 | 0.61 | 0.20–1.85 | 0.6142 | 0.1815–2.2693 |
Parker | 45/545 | 52/677 | 1.07 | 0.71–1.63 | 1.0749 | 0.6931–1.6617 |
Richters | 110/3429 | 194/5297 | 0.88 | 0.69–1.11 | 0.8759 | 0.6840–1.1171 |
Rodriguez-Diaz | 54/392 | 40/174 | 0.60 | 0.38–0.94 | 0.5997 | 0.3753–0.9640 |
Tseng: cases | 5/56 | 8/31 | 0.35 | 0.35–1.15 | 0.3499 | 0.0825–1.3348 |
Tseng: controls | 3/46 | 3/48 | 1.04 | 0.20–5.44 | 1.0430 | 0.1328–8.1928 |
Van Den Eeden | 14/72 | 25/126 | 0.98 | 0.48–2.00 | 0.9801 | 0.4656–2.1052 |
Wilson | 18/982 | 0/304 | 11.47 | 0.96–190.85 | 7.8664 | 1.3505–[45.8203]* |
Random effects summary effect: | 0.8225 | 0.65–1.04 |
Studies of the association between circumcision status and the prevalence of genital human papillomavirus infection.
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio |
95% confidence interval | Exact odds ratio | Exact confidence interval |
---|---|---|---|---|---|---|
Aynaud et al. [ |
383/354 | 119/144 | 1.31 | 0.99–1.74 | 1.3089 | 0.9773–1.7551 |
Aynaud et al. [ |
93/69 | 20/28 | 1.89 | 0.98–3.62 | 1.8812 | 0.9354–3.8412 |
Baldwin | 46/112 | 46/186 | 1.66 | 1.04–2.66 | 1.6585 | 1.0074–2.7333 |
Bleeker; group A | A: 18/52 | 3/10 | 1.15 | 0.29–4.67 | 1.1519 | 0.2562–7.2315 |
Bleeker; group B | 93/67 | 8/2 | 0.35 | 0.07–1.69 | 0.3489 | 0.0350–1.8257 |
Castellsagué; Brazil | 40/63 | 1/5 | 3.17 | 0.36–28.18 | 3.1470 | 0.3344–153.8345 |
Castellsagué; Columbia | 52/183 | 0/4 | 2.57 | 0.14–48.60 | 1.4849 | 0.1824–[12.0884]* |
Castellsagué; Philippines | 2/20 | 12/221 | 1.84 | 0.38–8.81 | 1.8362 | 0.1870–9.1816 |
Castellsagué; Spain | 37/278 | 1/36 | 4.79 | 0.64–35.99 | 4.7785 | 0.7551–199.6211 |
Castellsagué; Thailand | 35/ 136 | 2/35 | 4.50 | 1.03–19.64 | 4.4810 | 1.0600–40.2844 |
Giuliano et al. [ |
NA | NA | 0.97 | 0.68–1.39 | 0.97 | 0.68–1.39 |
Giuliano et al. [ |
NA | NA | 0.93 | 0.63–1.33 | 0.93 | 0.63–1.33 |
Hernandez | 14/44 | 52/144 | 0.88 | 0.44–1.74 | 0.8816 | 0.4113–1.8042 |
Lajous | 365/465 | 28/67 | 1.88 | 1.18–2.98 | 1.8770 | 1.1631–3.0978 |
Mandal | 22/66 | 6/11 | 0.61 | 0.20–1.85 | 0.6142 | 0.1815–2.2693 |
Müller; any HPV† | 125/29 | 35/19 | 2.34 | 1.17–4.66 | 2.3092 | 1.0971–4.8997 |
Müller; high risk# | 80/74 | 23/31 | 1.46 | 0.78–2.72 | 1.4545 | 0.7454–2.8691 |
Müller; low risk | 124/30 | 34/20 | 2.43 | 1.23–4.81 | 2.4198 | 1.1521–5.0476 |
Ng’ayo | 136/96 | 8/10 | 1.77 | 0.67–4.65 | 1.7667 | 0.6028–5.3567 |
Nielson; any HPV† | 38/36 | 199/190 | 1.01 | 0.61–1.66 | 1.0078 | 0.5942–1.7117 |
Nielson; high risk# | 23/51 | 112/227 | 0.91 | 0.53–1.57 | 0.9142 | 0.5062–1.6133 |
Oglivie; any HPV† | 89/41 | 94/38 | 0.88 | 0.52–1.49 | 0.8780 | 0.4997–1.5397 |
Oglivie; high risk# | 25/105 | 38/94 | 0.59 | 0.33–1.05 | 0.5902 | 0.3160–1.0877 |
Oriel | 151/69 | 40/28 | 1.53 | 0.87–2.68 | 1.5295 | 0.8362–2.7769 |
Rombaldi | 47/42 | 7/3 | 0.48 | 0.12–1.97 | 0.4830 | 0.0758–2.2850 |
Shin | 3/40 | 29/296 | 0.77 | 0.22–2.63 | 0.7660 | 0.1429–2.6506 |
Svare | 84/89 | 4/20 | 4.78 | 1.57–14.55 | 4.6869 | 1.4852–19.6399 |
Vaccarella | 62/470 | 6/241 | 5.30 | 2.26–12.42 | 5.2905 | 2.2526–15.1850 |
Vardas; any HPV | 417/1598 | 247/905 | 0.96 | 0.80–1.14 | 0.9561 | 0.7981–1.1469 |
Vardas; high risk | 161/1854 | 115/1037 | 0.78 | 0.61–1.01 | 0.7831 | 0.6050–1.0161 |
Weaver STD clinic | 0/3 | 10/17 | 0.24 | 0.01–5.08 | 0.4826 | [0.0478]* –4.8700 |
Weaver University students | 17/42 | 82/176 | 0.87 | 0.47–1.62 | 0.8691 | 0.4365–1.6708 |
Random effects summary effects: | Any HPV | 1.2411 | 1.02–1.51 | |||
High-risk HPV | 1.1661 |
0.94–1.45 | ||||
Selective HPV | 1.0128 | 0.80–1.2 |
Any HPV: Heterogeneity chi-square (
High-risk HPV: Heterogeneity chi-square (
Selective HPV: Heterogeneity chi-square (
Studies of the association between circumcision status and the prevalence of any sexually transmitted infection versus no sexually transmitted infections.
Study | Intact +ve/−ve | Circumcised +ve/−ve | Odds ratio | 95% confidence interval | Exact odds ratio | Exact confidence interval |
---|---|---|---|---|---|---|
Auvert | 82/279 | 19/122 | 1.89 | 1.10–3.25 | 1.8850 | 1.0763–3.4396 |
Aynaud | 30/132 | 7/41 | 1.33 | 0.54–3.26 | 1.3294 | 0.5208–3.8565 |
Burundi | 101/1559 | 33/853 | 1.67 | 1.12–2.50 | 1.6743 | 1.1091–2.5848 |
Cook | 342/198 | 1449/787 | 0.94 | 0.77–1.14 | 0.9382 | 0.7685–1.1472 |
Dave | 522/4311 | 109/873 | 0.97 | 0.78–1.21 | 0.9698 | 0.7769–1.2187 |
Diseker | 291/212 | 869/622 | 0.98 | 0.80–1.21 | 0.9825 | 0.7967–1.2128 |
Ferris | 166/1458 | 409/1965 | 0.55 | 0.45–0.66 | 0.5471 | 0.4483–0.6654 |
Gebremedhin | 1178/21926 | 2623/43363 | 0.89 | 0.83–0.95 | 0.8882 | 0.8269–0.9536 |
Harbertson | 185/360 | 371/304 | 0.42 | 0.33–0.53 | 0.4214 | 0.3311–0.5353 |
Klavs | 38/682 | 2/35 | 0.98 | 0.23–4.21 | 0.09751 | 0.2348–8.6761 |
Langeni | 6931/173523 | 927/35099 | 1.51 | 1.41–1.62 | 1.5124 | 1.4106–1.6229 |
Laumann 1–4 partners | 15/434 | 26/528 | 0.70 | 0.37–1.34 | 0.7021 | 0.3411–1.3960 |
5–20 partners | 80/364 | 102/436 | 0.94 | 0.68–1.30 | 0.9395 | 0.6696–1.3152 |
21+ partners | 61/129 | 95/138 | 0.69 | 0.46–1.03 | 0.6875 | 0.4502–1.0457 |
Parker | 350/240 | 404/325 | 1.17 | 0.94–1.46 | 1.1730 | 0.9360–1.4707 |
Richters | 487/3460 | 929/4736 | 0.72 | 0.12–0.81 | 0.7176 | 0.6362–0.8086 |
Rodriguez-Diaz | 293/153 | 157/57 | 0.70 | 0.48–1.00 | 0.6956 | 0.4752–1.0104 |
Schrek white | 26/126 | 10/26 | 0.54 | 0.23–1.25 | 0.5385 | 0.2176–1.4070 |
Schrek black | 58/65 | 22/23 | 0.93 | 0.47–1.85 | 0.9333 | 0.4451–1.9595 |
Seed | 378/216 | 177/66 | 0.65 | 0.47–0.91 | 0.6529 | 0.4620–0.9159 |
Taylor | 251/56 | 87/17 | 0.88 | 0.48–1.59 | 0.8761 | 0.4521–1.6293 |
Thomas | 1.08 | 0.52–2.26 | 1.08 | 0.52–2.26 | ||
Urassa 1 | 117/1239 | 70/572 | 0.77 | 0.56–1.05 | 0.7717 | 0.5592–1.0711 |
Urassa 2 | 291/1854 | 84/374 | 0.70 | 0.54–0.91 | 0.6989 | 0.5322–0.9246 |
Urassa 3 | 85/262 | 58/119 | 0.67 | 0.45–0.99 | 0.6662 | 0.4392–1.0133 |
Urassa 4 | 29/799 | 23/723 | 1.14 | 0.65–1.99 | 1.1408 | 0.6308–2.0853 |
Urassa 5 | 355/4409 | 101/987 | 0.79 | 0.62–0.99 | 0.7869 | 0.6220–1.0023 |
Random effects summary effect: | 0.8627 | 0.7368–1.0102 | ||||
—without Langeni | 0.8248 | 0.7358–0.9245 |
Heterogeneity chi-square (
The results of testing an individual publication’s impact on between-study heterogeneity are shown in Table
Impact of removing outlying studies on between-study heterogeneity and summary effects.
Outlier studies | Chi-square ( |
Adjusted odds ratio |
Heterogeneity chi-square |
---|---|---|---|
GUD versus GDS |
|
|
|
Warner | 7.85 (.0051) | 2.65 (1.67–4.18) | 9.35 (3, .0250) |
Hutchinson | 10.08 (.0015) | 1.92 (1.53–2.40) | 7.12 (3, .0681) |
Hutchinson and Warner | 14.87 ( |
2.15 (1.68–2.74) | 2.33 (2, .3121) |
GDS |
|
|
|
Bailey | 6.223 (.0125) | 0.9209 (0.75–1.13) | 41.13 (8, <.0001) |
Newell | 9.94 (.0016) | 0.9279 (0.76–1.13) | 37.42 (8, <.0001) |
Seed | 23.54 (<.0001) | 0.9614 (0.81–1.13) | 23.82 (8, .0025) |
Warner | 26.08 (<.0001) | 0.8482 (0.70–1.03) | 21.28 (8, .0065) |
Warner and Seed | 37.30 ( |
0.91.47 (0.79–1.07) | 10.06 (7, .1850) |
NSU |
|
|
|
Lavreys | 4.02 (.0450) | 0.74 (0.61–0.89) | 35.76 (10, <.0001) |
Donovan | 4.24 (.0394) | 0.74 (0.61–0.92) | 35.54 (10, .0001) |
Cook | 5.94 (.0148) | 0.75 (0.61–0.92) | 33.84 (10, .0002) |
Aynaud | 6.25 (.0124) | 0.73 (0.61–0.88) | 33.53 (10, .0002) |
Ferris | 12.92 (.0003) | 0.80 (0.68–0.95) | 26.86 (10, .0027) |
Ferris and Aynaud | 18.78 ( |
0.77 (0.66–0.91) | 21.00 (9, .0126) |
Chlamydia |
|
|
|
Hart [ |
10.83 (.0010) | 0.8605 (0.67–1.10) | 24.70 (12, .0163) |
Laumann | 18.43 (<.0001) | 0.9920 (0.85–1.16) | 17.10 (12, .1460) |
Laumann and Hart | 27.78 ( |
0.9362 (0.87–1.00) | 7.75 (11, .7357) |
Gonorrhea |
|
|
|
Lloyd | 5.23 (.0222) | 1.05 (0.88–1.26) | 83.58 (24, <.0001) |
Hand | 9.16 ( |
1.04 (0.86–1.25) | 79.55 (23, <.0001) |
Hart [ |
9.42 (.0021) | 0.99 (0.83–1.19) | 79.39 (24, <.0001) |
Wilson | 18.26 (<.0001) | 1.07 (0.90–1.26) | 70.55 (24, <.0001) |
Cook | 30.10 (<.0001) | 0.99 (0.84–1.15) | 58.71 (24, .0002) |
Cook and Wilson | 44.32 ( |
1.03 (0.89–1.19) | 44.49 (23, .0669) |
GUD |
|
|
|
Tyndall | 4.21 (.0402) | 1.7326 (1.43–2.10) | 26.88 (10, .0027) |
Warner | 6.48 (.0109) | 1.6492 (1.33–2.05) | 24.61 (10, .0109) |
Barile | 7.64 (.0057) | 1.6308 (1.38–1.92) | 23.45 (10, .0057) |
Gray | 10.33 (.0013) | 1.7581 (1.48–2.09) | 20.76 (10, .0228) |
Gray and Warner | 12.61 ( |
1.7385 (1.40–2.17) | 18.48 (9, .0300) |
Gray and Barile | 17.73 ( |
1.7334 (1.51–1.99) | 13.36 (9, .1470) |
Syphilis |
|
|
|
Todd | 4.18 (.0409) | 1.2766 (1.08–1.50) | 63.52 (27, <.0001) |
Wilson | 4.98 (.0256) | 1.2704 (1.08–1.49) | 62.72 (27, .0001) |
Buvé; Kismu | 5.24 (.0221) | 1.2804 (1.10–1.50) | 62.46 (27, .0001) |
Otieno | 5.37 (.0204) | 1.2685 (1.08–1.49) | 62.33 (27, .0001) |
Gray | 9.05 (.0026) | 1.3439 (1.14–1.58) | 58.65 (27, .0004) |
Hand | 10.95 (.0009) | 1.3467 (1.15–1.58) | 56.75 (27, .0007) |
Cook | 17.52 (<.0001) | 1.2304 (1.07–1.42) | 50.18 (27, .0043) |
Cook and Hand | 27.55 ( |
1.2704 (1.11–1.45) | 40.15 (26, .0377) |
Chancroid |
|
|
|
Lloyd | 4.83 (.0279) | 1.5961 (0.5418–4.7022) | 54.88 (4, <.0001) |
Hand | 11.69 ( |
1.4490 (0.4186–5.0152) | 48.02 (3, <.0001) |
Rakwar | 14.74 (.0001) | 1.5289 (0.4499–5.1956) | 44.97 (4, <.0001) |
Hart [ |
52.23 (<.0001) | 0.8177 (0.5092–1.3134) | 7.48 (4, .1128) |
Herpes; simplex virus |
|
|
|
Van Wagoner | 6.24 ( |
1.1293 (0.93–1.38) | 145.89 (31, <.0001) |
Laumann | 4.76 (.0291) | 1.1771 (0.97–1.43) | 147.37 (31, <.0001) |
Ferris | 5.68 (.0172) | 1.1735 (0.96–1.43) | 146.45 (31, <.0001) |
Richters | 13.61 (.0002) | 1.1761 (0.97–1.27) | 138.52 (31, <.0001) |
Schneider | 26.18 (<.0001) | 1.1851 (0.98–1.43) | 1.25.95 (31, <.0001) |
Mwandi | 86.28 (<.0001) | 1.0944 (0.94–1.27) | 65.85 (31, .0003) |
Mwandi and Schneider | 99.97 ( |
1.1311 (0.98–1.30) | 51.16 (30, .0073) |
Genital Warts |
|
|
|
Oriel | 5.46 (.0194) | 0.7792 (0.69–0.98) | 31.61 (13, .0027) |
Wilson | 6.76 (.0093) | 0.7885 (0.64–0.98) | 30.31 (13, .0042) |
Cook | 11.89 (.0006) | 0.8696 (0.70–1.08) | 25.18 (13, .0218) |
Cook and Wilson | 18.13 ( |
0.8365 (0.69–1.01) | 18.93 (12, .0901) |
HPV any |
|
|
|
Lajous | 4.09 (.0431) | 1.1962 (0.98–1.45) | 35.89 (23, .0424) |
Vardas | 5.56 (.0184) | 1.2912 (1.05–1.59) | 34.42 (23, .0593) |
Vaccarella | 8.29 (.0040) | 1.1806 (0.99–1.40) | 31.69 (23, .1068) |
Vaccarella and Vardas | 12.77 ( |
1.2320 (1.02–1.48) | 27.21 (22, .2033) |
HPV high risk |
|
|
|
Oglivie | 4.01 (.0451) | 1.2162 (0.98–1.51) | 41.26 (23, .0111) |
Svare | 4.02 (.0450) | 1.1323 (0.92–1.40) | 41.25 (23, .0111) |
Lajous | 4.86 (.0274) | 1.1192 (0.90–1.39) | 40.41 (23, .0138) |
Vardas | 8.04 (.0046) | 1.2186 (0.98–1.52) | 37.23 (23, .0307) |
Vaccarella | 8.77 (.0031) | 1.1049 (0.91–1.35) | 36.50 (23, .0366) |
Vardas and Vaccarella | 15.74 ( |
1.1602 (0.95–1.41) | 29.53 (22, .1303) |
HPV high risk selective |
|
|
|
Aynaud et al. [ |
4.62 (.0316) | 0.9727 (0.76–1.25) | 24.20 (14, .0433) |
Vardas | 4.20 (.0404) | 1.0564 (0.82–1.37) | 24.62 (14, .0385) |
Vaccarella | 9.92 (.0016) | 0.9553 (0.79–1.15) | 18.90 (14, .1689) |
Vaccarella and Aynaud I | 15.60 ( |
0.8747 (0.74–1.03) | 13.22 (13, .4306) |
Vaccarella and Vardas | 13.13 ( |
1.0073 (0.82–1.23) | 15.69 (13, .2664) |
Any STI |
|
|
|
Auvert | 4.55 (.0329) | 0.8425 (0.7182–0.9884) | 298.45 (25, <.0001) |
Seed | 5.63 (.0177) | 0.8732 (0.7432–1.0266) | 297.37 (25, <.0001) |
Uganda | 5.87 (.0154) | 0.8410 (0.7164–0.9873) | 297.13 (25, <.0001) |
Urassa | 15.68 ( |
0.8846 (0.7379–1.0605) | 287.32 (21, <.0001) |
Gebremedhin | 10.48 (.0012) | 0.8638 (0.7186–1.0383) | 292.52 (25, <.0001) |
Richters | 29.28 (<.0001) | 0.8713 (0.7394–1.0268) | 273.72 (25, <.0001) |
Ferris | 35.50 (<.0001) | 0.8822 (0.7542–1.0319) | 2267.50 (25, <.0001) |
Harbertson | 49.14 (<.0001) | 0.8923 (0.7665–1.0389) | 253.86 (25, <.0001) |
Langeni | 203.41 (<.0001) | 0.8248 (0.7358–0.9245) | 99.59 (25, <.0001) |
Langeni and Ferris | 221.08 ( |
0.8442 (0.7554–0.9434) | 81.92 (24, <.0001) |
Langeni and Harbertson | 234.61 ( |
0.8519 (0.7700–0.9426) | 68.39 (24, <.0001) |
Any STI without Langeni |
|
|
|
Richters | 5.92 (.0150) | 0.8344 (0.7374–0.9442) | 93.67 (24, <.0001) |
Gebremedhin | 7.28 (.0070) | 0.8250 (0.7236–0.9406) | 92.31 (24, <.0001) |
Auvert | 7.32 (.0068) | 0.8078 (0.7219–0.9039) | 92.27 (24, <.0001) |
Parker | 9.79 (.0017) | 0.8071 (0.7195–0.9053) | 89.80 (24, <.0001) |
Burundi | 10.37 (.0013) | 0.8030 (0.7182–0.8978) | 89.22 (24, <.0001) |
Ferris | 17.67 (<.0001) | 0.8442 (0.7554–0.9434) | 81.92 (24, <.0001) |
Harbertson | 31.20 (<.0001) | 0.8519 (0.7700–0.9426) | 68.39 (24, <.0001) |
Harbertson and Burundi | 40.87 ( |
0.8317 (0.7544–0.9157) | 58.72 (23, <.0001) |
Harbertson and Ferris | 51.08 ( |
0.8761 (0.794–0.9602) | 48.51 (23, .0014) |
Sensitivity analyses were not performed for the evaluation of risk of GUD versus GDS or chancroid because of the small number of studies. Sensitivity analysis comparing disease prevalence in studies of high-risk populations and general population is shown in Table
Sensitivity analysis of high-risk and general populations of studies of the association between circumcision status and various sexually transmitted infections.
Random-effects odds ratio | 95% confidence interval | Heterogeneity chi-square (df) | |
---|---|---|---|
Genital discharge syndrome | |||
High-risk populations | 1.18 | 1.11–1.25 | 2.14 (3) |
General populations | 0.77 | 0.59–0.99 | 16.78 (5) |
NSU | |||
High-risk populations | 0.95 | 0.73–1.55 | 13.90 (6) |
General populations | 0.61 | 0.48–0.76 | 11.76 (4) |
Chlamydia | |||
High-risk populations | 1.02 | 0.83–1.26 | 9.49 (6) |
General populations | 0.77 | 0.46–1.31 | 22.40 (6) |
Gonorrhea | |||
High-risk populations | 1.09 | 0.86–1.40 | 77.07 (15) |
General populations | 1.02 | 0.88–1.18 | 11.12 (10) |
GUD | |||
High-risk populations | 1.91 | 1.50–2.43 | 15.63 (6) |
General populations | 1.34 | 1.13–1.59 | 3.78 (4) |
Syphilis | |||
High-risk populations | 1.40 | 1.06–1.85 | 39.45 (12) |
General populations | 1.22 | 1.00–1.49 | 27.96(15) |
Herpes simplex | |||
High-risk populations | 1.20 | 0.99–1.46 | 23.15 (14) |
General population | 1.06 | 0.78–1.45 | 124.28 (17) |
Genital warts | |||
High-risk populations | 0.91 | 0.58–1.44 | 28.16 (7) |
General populations | 0.78 | 0.63–0.96 | 8.61 (6) |
Any HPV | |||
High-risk populations | 1.24 | 0.85–1.82 | 14.13 (8) |
General populations | 1.23 | 0.97–1.55 | 24.10 (15) |
High-risk HPV | |||
High-risk populations | 1.08 | 0.72–1.63 | 15.82 (8) |
General populations | 1.21 | 0.92–1.58 | 28.65 (15) |
Any STD | |||
High-risk populations | 0.96 | 0.79–1.17 | 6.29(4) |
General populations | 0.84 | 0.69–1.02 | 296.71 (20) |
General populations; no Langerin | 0.79 | 0.69–0.90 | 43.59 (19) |
Meta-regression was not performed for the evaluation of risk of GUD versus GDS, chancroid, or the studies of disease incidence because of the small number of studies.
Meta-regression methods found that the population type (general versus high risk) was notable (
No significant differences were seen for chlamydia, gonorrhea, syphilis, HSV, genital warts, HPV, or any STI (either with or without the study by Langeni [
Meta-regression methods found that having a study carried out in Africa as opposed to elsewhere was notable (
No significant difference was seen in with NSU, gonorrhea, syphilis, genital warts, high-risk HPV, or any STI.
A statistically significant impact of circumcision prevalence on the natural logarithm of the odds ratio of the association between circumcision status and prevalence of disease was found for GDS (
Natural logarithm of odds ratio as a function of the prevalence of circumcision in the population when estimating the prevalence of genital discharge syndrome by circumcision status in adult men. Solid triangles represent individual populations. Circles represent estimates and 95% confidence intervals using meta-regression.
Natural logarithm of odds ratio as a function of the prevalence of circumcision in the population when estimating the prevalence of gonorrhea by circumcision status in adult men. Solid triangles represent individual populations. Circles represent estimates and 95% confidence intervals using meta-regression.
Natural logarithm of odds ratio as a function of the prevalence of circumcision in the population when estimating the prevalence of genital ulcerative disease by circumcision status in adult men. Solid triangles represent individual populations. Circles represent estimates and 95% confidence intervals using meta-regression.
Natural logarithm of odds ratio as a function of the prevalence of circumcision in the population when estimating the prevalence of syphilis by circumcision status in adult men. Solid triangles represent individual populations. Circles represent estimates and 95% confidence intervals using meta-regression.
Natural logarithm of odds ratio as a function of the prevalence of circumcision in the population when estimating the prevalence of genital warts by circumcision status in adult men. Solid triangles represent individual populations. Circles represent estimates and 95% confidence intervals using meta-regression.
For GUD, population type, a study being performed in Africa, and circumcision prevalence were all statistically significant factors. When multiple factors are added to the regression model, only a study being performed in Africa was statistically significant. A model with a general population performed in Africa found a random effects summary odds ratio of 1.33 (95% CI = 1.02–1.71).
With the studies of any type of HPV, sampling only the glans trended toward being a factor (
With high-risk HPV studies, sampling only the glans trended toward being a factor (
A funnel graph, which plots the precision (the inverse of variance) on the
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of genital discharge syndrome by circumcision status in adult men. Empty triangles represent published studies.
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of nonspecific (nongonococcal) urethritis by circumcision status in adult men. Empty triangles represent published studies.
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of genital infections with
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of gonorrhea by circumcision status in adult men. Empty triangles represent published studies.
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of genital ulcerative disease by circumcision status in adult men. Empty triangles represent published studies. Solid squares represent likely unpublished studies using the “trim and fill” method.
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of syphilis by circumcision status in adult men. Empty triangles represent published studies.
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of genital herpes/herpes simplex virus type 2 by circumcision status in adult men. Empty triangles represent published studies.
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of genital warts by circumcision status in adult men. Empty triangles represent published studies. Solid squares represent likely unpublished studies using the “trim and fill” method.
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of genital human papillomavirus of any type by circumcision status in adult men. Empty triangles represent published studies in which the entire penis was sampled and circumcision status was determined by physical examination. Shaded triangles represent published studies in which only the glans was sampled and circumcision status was determined by physical examination. Inverted shaded triangles represent published studies in which entire penis was sampled and circumcision status was determined by patient report. Shaded circles represent published studies in which only the glans was sampled and circumcision status was determined by patient report. Solid squares represent likely unpublished studies using the “trim and fill” method.
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of genital human papillomavirus of focussing on those at high-risk oncogenic potential by circumcision status in adult men. Empty triangles represent published studies in which the entire penis was sampled and circumcision status was determined by physical examination. Shaded triangles represent published studies in which only the glans was sampled and circumcision status was determined by physical examination. Inverted shaded triangles represent published studies in which entire penis was sampled and circumcision status was determined by patient report. Shaded circles represent published studies in which only the glans was sampled and circumcision status was determined by patient report. Solid squares represent likely unpublished studies using the “trim and fill” method.
Funnel graph of precision (1/variance) by the natural logarithm of the odds ratio of studies estimating the prevalence of any sexually transmitted infection versus no sexually transmitted infection by circumcision status in adult men. Empty triangles represent published studies. An outlier study [
Methods to determine the presence of publication bias use a
Evaluation of publication bias in studies evaluating the association between sexually transmitted diseases and circumcision status in adult males using the methods described by Egger et al. [
Begg | Begg’s alternative | Egger | Egger’s weighted | Macaskill | Macaskill’s pooled variance | |
---|---|---|---|---|---|---|
Genital discharge syndrome | 0.9287 | 0.3252 | 0.0213 | 0.0056 | 0.0127 | 0.0171 |
Nonspecific urethritis | 0.0549 | 0.0397 | 0.1301 | 0.3893 | 0.1322 | 0.0917 |
Chlamydia | 0.8695 | 0.9563 | 0.0855 | 0.0003 | 0.1172 | 0.2961 |
Gonorrhea | 0.2801 | 0.0404 | 0.3403 | 0.5653 | 0.1124 | 0.1764 |
Genital ulcerative disease | 0.6547 | 0.7884 | 0.3795 | 0.1073 | 0.1804 | 0.3424 |
Syphilis | 0.7356 | 0.6258 | 0.1429 | 0.8972 | 0.1023 | 0.6316 |
Genital herpes | 0.2646 | 0.0137 | 0.1627 | 0.0014 | 0.3803 | 0.1324 |
Genital warts | 0.5862 | 0.6918 | 0.1782 | 0.9378 | 0.5768 | 0.7383 |
Human papillomavirus: any type | 0.9627 | 0.5592 | 0.1461 | 0.0035 | 0.0639 | 0.0857 |
Human papillomavirus: oncogenic type | 0.8153 | 0.5911 | 0.2465 | 0.0889 | 0.0531 | 0.0913 |
Any sexually transmitted infection | 0.0913 | 0.3482 | 0.1286 | 0.0900 | <0.0001 | <0.0001 |
Any sexually transmitted infection: without Langeni | 0.8084 | 0.7079 | 0.9552 | 0.0765 | 0.2035 | 0.3550 |
Using the “trim and fill” technique, no adjustments were needed for studies of the prevalence of GDS, NSU, gonorrhea, syphilis, HSV, HPV (in which there was complete sampling and circumcision status that was determined by physical examination), and any STI.
For genital infections with chlamydia, the “trim and fill” technique indicated two unpublished studies. By adding these two studies, the summary odds ratio, adjusted for publication bias, is 0.88 (95% CI = 0.69–1.11). The addition of one study was indicated for GUD, the addition of which yield a summary odds ratio, adjusted for publication, of 1.64 (95% CI = 1.34–2.01). For genital warts, the technique indicated two unpublished studies, whose addition would yield a summary odds ratio, adjusted for publication bias, of 0.76 (95% CI = 0.60–0.97). For both analyses of the prevalence of HPV infections (any type and high-risk types), two unpublished studies would be expected. The summary odds ratio, adjusted for publication, would be 1.19 (95% CI = 0.97–1.46) for any HPV types and 1.11 (95% CI = 0.88–1.39) for using high-risk HPV types.
The comparisons of men diagnosed with GUD and GDS are consistent with findings that intact men are more prone to GUD and circumcised men are more prone to GDS. Consequently, there is no surprise here.
The prevalence of GDS shows a moderate trend toward being less common in intact men (OR = 0.89 and 95% CI = 0.73–1.09). The finding in general populations is statistically significant (OR = 0.77 and 95% CI = 0.59–0.99). The only study of incidence found no significant difference [
The prevalence of NSU is significantly lower in intact males (OR = 0.76 and 95% CI = 0.63–0.92). Between-study heterogeneity is a concern as five of the twelve studies contributed significantly to the between-study heterogeneity, but exclusion of any these studies did not change the significance of this finding (Table
Other than the problems with between-study heterogeneity and these analysis indicates a fairly robust, significant association between a lower prevalence of NSU in intact males.
There was no significance difference in the prevalence of genital chlamydia infections but a trend toward a lower prevalence in intact men. None of the studies of incidence found a significant difference (whether adjusted for lead-time bias or not). When studies of incidence are adjusted for lead-time bias and combined, there is no significant association.
Only two outliers were identified (Table
The funnel graph indicates a clear outlier (Figure
The analysis indicates a trend toward a lower prevalence of chlamydia in intact men, especially in Africa and in the general population. No difference was seen in the incidence studies.
No significant association between the incidence or the prevalence of gonorrhea and circumcision status of males was found. This was seen in both high-risk and general populations. There was significant between-study heterogeneity, and five potential outliers were identified. The prevalence of circumcision in the population studied was significantly associated with odds ratio reported in the study (
Only one measure of publication bias was positive, and the funnel graph (Figure
The data indicate that the incidence and the prevalence of gonorrhea are not affected by circumcision status as much as by the prevalence of circumcision within the community studied.
Incidence and prevalence of GUD were consistently positively associated with intact men, even when subjected to sensitivity analysis and meta-regression. Between-study heterogeneity was significant even after adjusting for four “outlying” studies. Meta-regression found significant associations for population type, whether studies were performed in Africa and circumcision prevalence in the populations studied (Figure
In the funnel graph, there is a study in the right lower portion that is not balanced in the left lower portion (Figure
GUD, which is more commonly seen in developing countries, has a propensity for mucosal surfaces. Most of the studies of HSV have looked at seroconversion rates for herpes simplex virus type 2. This will not capture recurrences. Since GUD is a clinical measure that includes HSV recurrences and ulcers for which no causative agent can be identified; one would expect a higher rate in intact men because more than half of the mucosal surface of the penis is removed with circumcision. Herpes simplex viruses, including type 1 and type 2, also have a propensity for junctional tissues. This is why cold sores recur in the corner of the mouth and on the facial lips. If one were to amputate facial lips, one would see a lower recurrence rate of herpes simplex virus type 1. To follow this analogy, circumcision removes all of the junctional tissue of the prepuce [
The data on syphilis present quite a farrago. On the one hand, there is a positive association between the prevalence of syphilis and intact genitalia, but, on the other hand, the incidence of syphilis, even before adjusting for lead-time bias, indicates a negative, albeit nonsignificant, association. The positive association is seen primarily in populations at high risk for acquiring STIs, while in the prevalence in general populations found no statistically significant difference (depending on the calculation method used such as general variance-based method: OR = 1.23 and 95% CI = 1.0064–1.49; meta-regression method: OR = 1.25 and 95% CI = 0.96–1.60). Seven prevalence studies had statistically significant contributions to the between-study heterogeneity. The between-study heterogeneity improves when only studies of general populations are considered but does not resolve completely. The prevalence of syphilis by circumcision status is also significantly associated with the prevalence of circumcision in the population studied (Figure
The funnel graph clearly looks asymmetric (Figure
With the mixed results between incidence and prevalence, the lack of a significant association in general populations, the number of studies that could be considered outliers, the significant association with circumcision prevalence in the population studied, and the asymmetry of the funnel graph, one cannot accurately conclude that the risk of syphilis is significantly associated with circumcision status.
While there was a trend for the prevalence of HSV to be greater in intact men, the association was not statistically significant. When adjusted for lead-time bias, none of the studies that looked at the incidence of herpes simplex virus type 2 found a statistically significant association. When the studies are combined, there is no statistically significant association but a slight trend toward higher risk for intact men.
There was significant between-study heterogeneity for the prevalence studies. Six outliers were identified. Exclusion of these studies individually and the two largest contributors did not bring the between-study heterogeneity within an acceptable range and did not yield a summary effect that was statistically significant. In both high-risk and general populations, the summary effect was not statistically significant, and between-study heterogeneity remained significant. Using meta-regression, there was a trend (
The funnel graph indicates some asymmetry with a cluster of studies in the lower right portion that is not balanced on the left side (Figure
While there is a trend toward higher incidence and prevalence of HSV in intact men, the finding is persistently not statistically significant despite a number of adjustments. The high level of between-study heterogeneity, which could not be shed despite several attempts, presents a problem in making any recommendation regarding circumcision’s impact on HSV.
An earlier meta-analysis of HSV prevalence and circumcision had failed to include two of the populations included in this analysis [
As an aside, there have been a number of systemic and fatal herpetic infections reported following ritual circumcision in which the person performing the circumcision puts his mouth around the penis after the foreskin has been amputated [
The paucity of studies, the reliance on clinical identification in all but one of these studies, and the high degree of between-study heterogeneity make it difficult to comment on the impact of circumcision on this illness, yet the lack of good evidence did not keep the 2012 AAP Task Force from including a discussion of circumcision’s impact on the prevalence of chancroid [
The data do not support the claim by Weiss et al. that “circumcised men are at lower risk of chancroid” [
The prevalence of genital warts has a strong trend towards being lower in intact males. In general populations, the association is statistically significant (OR = 0.78 and 95% CI = 0.63–0.96) and did not have evidence of between-study heterogeneity (chi-square = 8.61 (df = 6) and
The funnel graph indicates some paucity of studies in the left lower region (Figure
The evidence in favor of a lower prevalence of genital warts in intact males is supported by the finding in studies of general populations, which were surprisingly free of between-study heterogeneity and the summary result after adjusting for publication bias. The odds ratios in studies were, however, impacted by the prevalence of circumcision in the population studied.
A systematic review of the incidence and prevalence of genital HPV infections as they relate to circumcision status in males is fraught with a variety of pitfalls. This may explain why several systematic reviews with meta-analysis have been published with inconsistent results [
Previous analyses have found that sampling bias and patient report of circumcision status significantly effect the odds ratio reported in a study [
Finally, the two randomized clinical trials that reported their results on HPV infection both failed to adjust for sampling only the glans and to adjust for lead-time bias.
The incidence of HPV infections was barely statistically significantly different based on circumcision status before adjustment for sampling bias and lead-time bias (RR = 1.16 and 95% CI = 1.0097–1.34). After adjustment for these sources of bias, the relative risk is 0.96 (95% CI = 0.85–1.09).
Prevalence of HPV in the first analysis (any HPV) was higher in intact men (OR = 1.24 and 95% CI = 1.02–1.50), but the statistical significance of this finding is tenuous. When sensitivity analysis comparing studies of high-risk populations and studies of general populations, the result in neither group is statistically significant. When two of the identified “outliers” are individually excluded from the analysis, the results are not statistically significant.
When meta-regression is used to adjust for sampling bias, and misclassification bias the summary odds ratio is 1.08 (95% CI = 0.93–1.24).
The funnel graph for the first analysis of HPV (any HPV) shows a clear paucity of studies in the left lower portion (Figure
Prevalence of HPV in the second analysis (high-risk HPV) was not significantly different on the basis of circumcision status (OR = 1.17 and 95% CI = 0.94–1.45). Significant difference was found in neither high-risk populations nor general populations.
Five outliers were identified. Excluding them individually from the analysis or excluding the two studies that contributed the most to between-study heterogeneity did not result in providing evidence of statistically significant difference. Excluding the two studies did bring between-study heterogeneity to within an acceptable range (
Using meta-regression to adjust for sampling bias and misclassification bias the summary odds ratio was 1.01 (95% CI = 0.84–1.22).
The funnel graph for the second analysis also shows a paucity in the left lower portion (Figure
Prevalence of HPV in the third analysis (selective HPV) was nearly identical in intact and circumcised men (OR = 1.01 and 95% CI = 0.80–1.28). Three studies were identified as outliers. Exclusion of the study with the largest contribution to the between-study heterogeneity [
There are several messages from the three analyses performed on the HPV prevalence studies. Sampling bias and misclassification bias have a significant differential effect on the odds ratios reported in studies where these forms of bias are suspected. There is no significant difference in the incidence or the prevalence of HPV (especially oncogenic HPV) on the basis of circumcision status. While circumcision proponents repeatedly laud circumcision as preventive for HPV infections, the data do not support this claim. When their own studies are adjusted for lead-time bias and sampling bias, their treatment effect disappears [
Several studies of HPV and circumcision status warrant additional comment because of their serious methodological flaws. One study compiled data collected from seven studies in five countries from three continents. A fatal flaw in the study was the small number of circumcised men in four of the countries and the small number of intact men in the fifth country. Of the twenty data cells that make up the two-by-two tables from the five countries, seven had five or fewer subjects. The authors used parametric statistical methods, which are notably unreliable in this situation, to report the statistics on the combined data [
The study published by Lajous et al. is problematic in that fourteen men were identified as circumcised on physical examination, while 95 men identified themselves as being circumcised. Although physical examination is considered the gold standard for assigning circumcision status, instead of using physical examination as the measure of circumcision status, the study published the association between HPV infection and self-report of circumcision. Eighty-eight of the 95 men who reported themselves as circumcised were not circumcised on the basis of physical examination [
This inability of researchers in Mexico to accurately identify the circumcision on physical examination may call into question other studies from Mexico. For example, the study by Vaccarella of Mexican men undergoing vasectomy reported a circumcision rate of 31.7% and was identified as an outlier [
Perhaps most concerning is the results reported from the group of researchers from Johns Hopkins, who have after publication of their studies become vociferous advocates of the benefits of circumcision [
Their randomized clinical trial ended in December 2006. In 2004, Weaver et al. published a study that demonstrated the clear differential between intact and circumcised males regarding the likelihood of HPV detection based on sampling the shaft or the glans of the penis [
Research published in December 2008 had demonstrated the HPV viral load varied significantly by anatomic site with the penile shaft having the highest viral loads and being the preferred site for HPV-16 (the most prevalent oncogenic HPV type) replication [
The pertinent question as it relates to a systematic review of the medical literature and meta-analysis is whether studies that report only on cultures taken from the glans of the penis should be included in an analysis and adjusted for or be completely dismissed as invalid?
A couple for studies have indicated that the clearance of HPV takes longer from the intact penis [
Finally, the data from the randomized clinical trial of adult male circumcision in Kismu, Kenya, were published in 2012. While swabs were taken from the penile shaft and the glans and the data on circumcision status were collected, the authors failed to report the overall rates of HPV infection by circumcision status [
This is the first systematic review of the medical literature looking at the incidence and the prevalence of any STI as opposed to not acquiring an STI based on circumcision status. This analysis indicates that prevalence of acquiring any STI is lower in intact men. Three of the four studies of incidence are consistent with the prevalence date, while one study from New Zealand indicated a significant protective effect. Overall, the incidence data indicate a trend that intact men have a lower incidence of any STI.
When looking at the funnel graph for any STI, the study by Langeni [
Langeni may also be justifiably excluded because the study reported participant self-report of either GUD or GDS, which might exclude several types of STIs and relied on self-diagnosis in Botswana.
With Langeni excluded, the prevalence of any STI is significantly lower in intact men. When only high-risk populations are considered, the trend is in the same direction, but the difference is not statistically significant. The funnel graph, with the exclusion of Langeni, is fairly symmetric. “Trim and fill” analysis found that no studies needed to be added whether or not Langeni was included.
STIs with genital discharges are more common than genital ulcers, which may explain why the prevalence of any STI is lower in intact males. The ratio of the two general types of STIs within a community may also influence the impact of circumcision on overall risk of having any STI. Differences in these ratios in different populations may also contribute to the between-study heterogeneity.
Identifying and quantitating “any STI” may be problematic as the outcome of interest varied between studies. In some studies, collected data were the recollection of any STI in one’s lifetime, while, in others, it was the recollection of any STI within the past 12 months. The range of infections tested for or queried about also varied between studies. Likewise, determinations needed to be made regarding what was an STI. Is a yeast infection a sexually transmitted infection or the result of an imbalance of normal flora? In this analysis, candidal infections as well as infections with
It is clear that despite these methodological concerns that the impact of circumcision on the overall risk of contracting any STI is to increase the overall risk of infection. Because of the hodgepodge of data included in this analysis and disparate results on the incidence of infection, more studies specifically designed to answer this question are needed.
Several consistencies in the analyses deserve comment. All of the prevalence analyses showed significant between-study heterogeneity. This reflects the variety of populations, settings, diagnostic methods, and ways of determining circumcision status. Some would argue that given this degree of between-study heterogeneity, any meta-analysis that follows is not worthy of publication. Because of the between-study heterogeneity, one cannot sufficiently emphasize a disclaimer of
The summary effect for the prevalence of every disease was greater in studies of high-risk populations than in studies general populations. This consistent finding, which was often statistically significant, has public policy implications. Calls for population-wide implementation of male circumcision on the grounds that it prevents STIs are not supported by the findings of these analyses. These analyses indicate that if male circumcision has any role (which these analyses also dispute) in reducing the incidence and prevalence of STIs, it should be implemented in easily identifiable high-risk populations. A major problem with infant circumcision is the lack of an accurate method of identifying which infants will find themselves in high-risk population when they become sexually active. Similarly, meta-regression analysis of the studies of HIV incidence and prevalence has found that there is no significant association in general populations but only in high-risk populations [
In several analyses, the summary effect of the prevalence of a disease was significantly and positively associated with circumcision prevalence in the population studied. A similar finding has been identified in studies of HIV incidence and prevalence [
The lack of a significant association between high-risk HPV infections and circumcision status undermines the argument made by the few who believe that circumcision reduces cancer risk [
The results of these analyses also further undermine the argument of how the increased risk of HIV infection in intact men is biologically plausible. The plausibility argument is based on several assumptions, all of which are purely speculative. The first is that the inner mucosa of the foreskin is thinner and more prone to abrasions. The second is that the subpreputial space is a breeding ground for sexually transmitted viruses. The third is that the Langerhans cells on the mucosal surface act like HIV-virus magnets pulling the virus into the body [
There are several studies that reported results that could not be incorporated into the analyses. For example, Urassa et al. reported that they did not find a significant difference in GDS or GUD prevalence in males based on circumcision status but gave no further details [
Because circumcision status based on country of origin is inexact, a Dutch study was excluded that found that men born in the Netherlands, where circumcision is an uncommon practice, had lower rates of STIs than men who immigrated from Turkey, where circumcision is nearly uniformly practiced (one or more STI: OR = 0.30 and 95% CI = 0.12–0.72; HSV: exact OR = 0.37and 95% CI = 0.007 infinity; early syphilis: exact OR = 0.20 and 95% CI = 0.06–0.63; gonorrhea: OR = 0.20 and 95% CI = 0.06–0.63; chlamydia: OR = 0.42 and 95% CI = 0.14–1.37) [
Of historical interest, a study of the cause of deaths in New York City in 1931 found that death from syphilis and related diagnoses was lower in Jews than non-Jews (Poisson regression RR = 0.66 and 95% CI = 0.51–0.86). When only males are considered, the results are similar (Poisson regression RR = 0.66 and 95% CI = 0.49–0.88). If circumcision was a contributing factor, beyond that seen for ethnicity alone, one would expect a significant interaction between ethnicity and gender in which Jewish men would have a lower rate of syphilis than Jewish women. Such an interaction could not be demonstrated (
This paper did not review the literature for HIV infections for two reasons. First, such a review would be lengthy and best left to another article. Second, most of the study of HIV and circumcision status has taken place in Africa. In that setting that is estimated 20% or more of infections are not spread through sexual contact [
A drawback seen in some observational studies is having a small number of patients with a specific outcome. When this occurs, the parametric assumptions that allow one to make accurate inferences may no longer be valid, resulting inaccurate estimates for odds ratios and 95% confidence intervals. Since these inaccurate calculations of odds ratios and variance can bias summary effects and estimates of variance, including studies with small cell populations can result in inconsistent summary estimates depending on the calculation method used [
Some adjustments in the composition of control groups were necessary to provide consistency of methodology between studies. For example, Wilson compared seasoned soldiers to new recruits [
In Mallon et al., British men referred to a dermatology specialist for penile problems were compared to a control group of patients without penile problems cared for by the same dermatologists [
Using a control group of men without any STI is problematic. First, men without a detectable STI differ in several ways from men who have an STI and introduce a “Berksonian bias” [
Second, using a disease-free control group discards data collected on men who had an STI other than the infection of interest. Those who participate in medical research allow their medical information to be used and their privacy to be violated. Violating a subject’s privacy to collect data and then not use the information excludes useful information and is ethically suspect. Every participant’s information should contribute to a study, and so serious deliberation needs to be undertaken before this information is arbitrarily excluded from analysis. If the aim of a study is to consider a specific infection, the data on all patients meeting the inclusion criteria should be incorporated into the analysis. For example, in a cross-sectional study, the characteristics of men with the disease of interest would be compared to the characteristics of men without the illness, regardless if they happen to have a different type of infection.
Finally, it provides a method of comparison that is consistent with the other studies included in the meta-analysis.
Many prefer to use individual patient data in meta-analyses for a variety of reasons [
One of the most important tasks in performing the literature review is looking for forms of bias and making adjustments to minimize the impact of differential bias. Bias happens, and it is hard to identify and control. Most forms of bias are insidious and difficult to measure. Circumcision status, which is linked to socioeconomic status, may impact healthcare seeking behaviors. If, for example, circumcised men are more likely to visit an STD clinic for reassurance purposes, they would be more likely to be placed in a no disease only control group thus increasing the odds ratio for those intact men and the illness of interest [
Lead-time bias was present in all of the data coming from the randomized clinical trials of adult male circumcision in Africa. Because men randomized to immediate circumcision were not exposed to STIs for four to six weeks following their procedures, their exposure to disease was not the same as men who were assigned to later circumcision. While a six-week adjustment to trials scheduled to last from 21 to 24 months wound not appear to be substantial, when the reduced exposure time is accounted for, several of the associations found that these trials were no longer statistically significant. If these findings were robust, adjusting for lead-time bias should not have influenced the interpretation of the results.
What is more concerning is that potential for lead-time bias was overlooked in the planning, funding, analysis, and reporting phases of these projects. The potential for lead-time bias in any cohort study or clinical trial is taught and emphasized in the most basic classes on research design. How was this potential source of bias missed by the highly regarded researchers at Johns Hopkins, the reviewers who approved funding for these studies at the National Institutes of Health, and the editors and peer-reviewers at highly regarded medicals journals such as
The need to adjust for sampling bias in the studies of HPV is quite apparent. Multiple studies have found that the location of HPV on the penis is differentiated by circumcision status [
Nondifferential misclassification is a concern as the correlation between circumcision status based on patient report and physical examination can vary widely depending on the population studied. [
Reliance on the patient report to document an STI introduces a potential for recall bias and may underestimate the incidence of STIs. This would only introduce bias if a differential ability to recall and report medically diagnosed sexually transmitted disease was linked to circumcision status [
Searching for sources of bias also occurs in a meta-analysis, particularly for those involving observational studies, when looking at the impact of various factors on between-study heterogeneity. Some consider accounting for contributions to between-study heterogeneity is an obligation for the investigator and the most important task in performing a meta-analysis [
Most of the between-study heterogeneity can likely be attributed to methodological limitations in the source studies and the inherent biases in study design. Many of the studies included in these analyses reported information collected at STD clinics. While these clinics provided concentrated clinical material at one location, their clientele does not reflect the characteristics and risk factors for disease seen in the general population and may introduce a selection bias that unduly influences the results generated [
Meta-analysis is an inexact tool and best applied to randomized controlled trials. It has inherent weaknesses when applied to observational studies, so guidelines on how to undertake this process have been published proposed [
The analyses presented in this paper used a random-effects model to determine summary effects and confidence intervals. The alternative, fixed-effects models assume a single true effect common to all studies. Any variation would be attributed only to sampling error. Random-effects models allow for a true random component as a source of variation in effect size between studies as well as sampling error [
One limitation of this systematic review, or any systematic review, is the inability to find all sources of data using any search strategy. All search strategies have an ascertainment bias: the goal is to diminish this bias by finding as many relevant studies as feasible. So, there may be published and unpublished studies that were not included.
The measures of publication bias are a mathematical attempt to quantify the gestalt of looking a funnel graph and determining if it looks like an inverted funnel. Each measure of publication bias has its strengths and weakness [
The results of these meta-analysis should be taken with caution. The trials they are based on come from a number of sources with a number of different methodologies. Some studies employed exemplary methodology, while others were published in high-profile medical journals, such as the
Most specific STIs are not impacted significantly by circumcision status. These include chlamydia, gonorrhea, HSV, and HPV. Syphilis showed mixed results with prevalence studies suggesting intact men were at great risk and incidence studies suggesting the opposite. Intact men appear to be greater risk for GUD while at lower risk for GDS, NSU, genital warts, and the overall risk of any STIs. It is also clear that any positive impact of circumcision on STIs is not seen in general populations. Consequently, the prevention of STIs cannot be rationally interpreted as a benefit of circumcision, and a policy of circumcision for the general population to prevent STIs is not supported by the evidence currently available in the medical literature.