Prevalence of male circumcision in four culturally non-circumcising counties in western Kenya after 10 years of program implementation from 2008 to 2019

Elijah Odoyo-June; Stephanie Davis; Nandi Owuor; Catey Laube; Jonesmus Wambua; Paul Musingila; Peter W Young; Appolonia Aoko; Kawango Agot; Rachael Joseph; Zebedee Mwandi; Vincent Ojiambo; Todd Lucas; Carlos Toledo; Ambrose Wanyonyi

doi:10.1371/journal.pone.0254140

. 2021 Jul 15;16(7):e0254140. doi: 10.1371/journal.pone.0254140

Prevalence of male circumcision in four culturally non-circumcising counties in western Kenya after 10 years of program implementation from 2008 to 2019

Elijah Odoyo-June ^1,^*, Stephanie Davis ², Nandi Owuor ³, Catey Laube ⁴, Jonesmus Wambua ³, Paul Musingila ¹, Peter W Young ¹, Appolonia Aoko ¹, Kawango Agot ⁵, Rachael Joseph ¹, Zebedee Mwandi ³, Vincent Ojiambo ⁶, Todd Lucas ², Carlos Toledo ², Ambrose Wanyonyi ⁷

Editor: D W Cameron⁸

PMCID: PMC8281999 PMID: 34264971

Abstract

Introduction

Kenya started implementing voluntary medical male circumcision (VMMC) for HIV prevention in 2008 and adopted the use of decision makers program planning tool version 2 (DMPPT2) in 2016, to model the impact of circumcisions performed annually on the population prevalence of male circumcision (MC) in the subsequent years. Results of initial DMPPT2 modeling included implausible MC prevalence estimates, of up to 100%, for age bands whose sustained high uptake of VMMC pointed to unmet needs. Therefore, we conducted a cross-sectional survey among adolescents and men aged 10–29 years to determine the population level MC prevalence, guide target setting for achieving the goal of 80% MC prevalence and for validating DMPPT2 modelled estimates.

Methods

Beginning July to September 2019, a total of 3,569 adolescents and men aged 10–29 years from households in Siaya, Kisumu, Homa Bay and Migori Counties were interviewed and examined to establish the proportion already circumcised medically or non-medically. We measured agreement between self-reported and physically verified circumcision status and computed circumcision prevalence by age band and County. All statistical were test done at 5% level of significance.

Results

The observed MC prevalence for 15-29-year-old men was above 75% in all four counties; Homa Bay 75.6% (95% CI [69.0–81.2]), Kisumu 77.9% (95% CI [73.1–82.1]), Siaya 80.3% (95% CI [73.7–85.5]), and Migori 85.3% (95% CI [75.3–91.7]) but were 0.9–12.4% lower than DMPPT2-modelled estimates. For young adolescents 10–14 years, the observed prevalence ranged from 55.3% (95% CI [40.2–69.5]) in Migori to 74.9% (95% CI [68.8–80.2]) in Siaya and were 25.1–32.9% lower than DMMPT 2 estimates. Nearly all respondents (95.5%) consented to physical verification of their circumcision status with an agreement rate of 99.2% between self-reported and physically verified MC status (kappa agreement p-value<0.0001).

Conclusion

This survey revealed overestimation of MC prevalence from DMPPT2-model compared to the observed population MC prevalence and provided new reference data for setting realistic program targets and re-calibrating inputs into DMPPT2. Periodic population-based MC prevalence surveys, especially for established programs, can help reconcile inconsistencies between VMMC program uptake data and modeled MC prevalence estimates which are based on the number of procedures reported in the program annually.

1. Introduction

Voluntary medical male circumcision (VMMC) reduces sexual transmission of HIV from women to men by approximately 60% [1–3]. VMMC is currently implemented as a component of HIV prevention programs in 15 sub Saharan African countries with high HIV prevalence and low historical rates of male circumcision (MC). Since 2008, Kenya has prioritized VMMC services primarily for four counties in western Kenya that are mainly inhabited by the Luo ethnic group that do not practice circumcision culturally and have high HIV prevalence (13.0–19.6%) [4]. Additionally, VMMC is implemented in focal areas of the Rift Valley region and Nairobi where migrant or indigenous non-circumcising populations live. Kenya’s VMMC program achieved 92% of its service delivery target for the first 5-year national strategic plan (2008–2013) and met its annual targets in the second strategic plan (2014–2019) thereby reporting over two million cumulative circumcisions as of September 2018 [5].

Despite excellent performance against annual program targets, lack of accurate population level MC prevalence data by key age bands continued to hamper VMMC program planning and impact assessment in Kenya. Therefore, in 2016 the VMMC decision makers program planning tool version 2 (DMPPT2) was used to model the impact of cumulative circumcisions performed on the prevalence of MC and to guide subsequent annual target setting in four top VMMC priority counties in western Kenya (namely, Siaya, Kisumu, Homa Bay and Migori). DMPPT2 is a compartmental mathematical model that uses the number of men circumcised annually by 5-year age bands, adjusted for age progression, and mortality plus migration, to estimate changes in MC prevalence [6, 7] and models the impact of additional VMMCs on the MC prevalence in a given geographic area. The first DMPPT2 modelling in Kenya conducted in 2016 and published in 2018 [8] generated unexpected high MC prevalence estimates up to and exceeding 100% for some age some age groups in the four counties. Moreover, contrary to the expectation that uptake of VMMC would decline as MC prevalence approached 100%, stable uptake of VMMC services was observed in age bands and geographic area that had achieved or nearly achieved 100% MC prevalence according to DMPPT2. Divergence between sustained high VMMC uptake data and high MC prevalence from DMPPT2 model persisted despite adjustment for known potential confounders. For example, MC prevalence estimates in Homa Bay county remained higher than expected even after adjustment for in-migration from surrounding areas to access VMMC services, duplicate reporting, errors in population estimates and replacement of traditional circumcision with medical circumcisions in the VMMC program. These data issues were first identified in Kenya because of its high baseline circumcision prevalence and availability of reliable data which made it possible to adjust for migration and replacement of traditional circumcision with VMMC in the coverage estimates. The use of DMPPT2 in other African countries, including South Africa and Mozambique, also revealed instances of high MC prevalence above 80%, but the insights on inconsistency between VMMC program uptake and DMPPT2 modeled MC prevalence were revealed by triangulation and a granular analysis of program data in Kenya.

Like immunization coverage estimation [9], VMMC coverage estimation requires reconciliation of service delivery data on the number of procedures performed with population survey data to determine if the number of procedures reported match well with the changes in population coverage.

In order to resolve the discrepancy between sustained VMMC uptake data and high MC prevalence estimates from DMPPT2 model, we conducted a population-based survey to get reference MC prevalence data by age bands for setting realistic program targets and validating DMPPT2 inputs.

As a secondary objective, we sought to assess the accuracy of self-reported circumcision as the primary data source for determining MC prevalence. Although data from previous surveys in Kenya suggest that self-reported circumcision status is generally reliable, those studies were limited by high participant non-response rate [10, 11]. Thus, we also sought to assess the reliability of self-reported circumcision status using physical verification as a reference. The specific objectives of the survey were to 1) Estimate prevalence of self-reported circumcision among adolescents and men 10–14 and 15–29 years of age, 2) Assess the accuracy of self-reported MC status using physically verified circumcision status, and 3) Evaluate the association between circumcision status and demographic characteristics of adolescents and men 10–29 years.

2. Methods

From July to September 2019 we conducted a cross-sectional household survey among adolescents and men aged 10–29 years in Siaya, Kisumu, Homa Bay and Migori counties in Kenya. We used a structured questionnaire to collect data on demographic characteristics, knowledge of VMMC, service delivery experience of circumcised men and outcome of circumcision status verification. Questionnaire contents were developed under the leadership of the national VMMC technical working group with technical input from various stakeholders including VMMC service providers and researchers. Reported circumcisions were coded as medical if conducted by a health worker otherwise non-medical. The final questionnaire was translated from English into Kiswahili and Luo language and back translated into English to ensure accuracy. The questionnaires were further refined after field pretesting then distributed along with consent forms for use by the trained research assistants (S2 File).

A two-stage cluster sampling approach was used with enumeration areas (EAs) as the primary sampling units from which households were selected. EAs are small counting units of 50–149 households cartographically mapped by the Kenya National Bureau of Statistics (KNBS) to facilitate the 2009 Kenya Population Housing Census [11]. Within each county, the EAs are divided into rural and urban strata as defined in the KNBS Fifth National Sample Survey, and Evaluation Program (NASSEP V) 2009 Kenya Population and Housing Census master sampling frame [12]. In the first stage, a total of 77 rural and 46 urban EAs (clusters) were randomly selected from the four target counties using probability proportional to size of total population sampling methodology in the 2009 Kenya population housing census. This ensured that the survey was designed to produce representative estimates for MC prevalence at county level [13].

A team composed of KNBS staff, a community health volunteer, a village health committee member, and a trained research assistant (RA) visited all sampled EAs, listed all households (new and old) for number of males and females to help update KNBS NASSEP IV, assigned a unique identifier, and their Global Positioning System (GPS) coordinates obtained. The collected household GPS coordinates were used during the survey to relocate sampled households for survey data collection. After updating the households which form the new sampling frame in the selected EA for the second sampling stage, systematic random sampling was then used to select 48–50 households from each EA from the new household listing, thus deduplication was unnecessary. Using findings from the 2009 census indicating that the average household size was four, it was projected that on average, there would be at least one man aged 15–29 years residing in each household, and one younger adolescent boy aged 10–14 years residing in every other household. In order to oversample young adolescents to achieve comparable precisions of estimates between the two target age groups, enrollment was offered to all younger adolescents aged 10–14 years residing in all selected households, and to men aged 15–29 years residing in every other selected household. A resident was defined as a person who lived in the selected household as indicated by the head of the household, or who spent the previous night in the selected household. The head of household was defined as a usual resident member of the household, the key decision maker for the household and the person whose authority was acknowledged by all members of the household.

Trained RAs visited the selected households accompanied by community health volunteers and village health committee members who served as local guides. Households whose eligible occupants were unavailable at the time of first visit were scheduled for up to three follow-up visits on different days. Age eligibility was based on a self-report of 10–29 years. Households found to have no eligible participants were not replaced. Prior to enrollment, resident men aged 18–29 years provided written informed consent, and those 17 years or younger provided written assent in addition to written informed parental or guardian consent. Individuals who were unwilling or unable to provide informed consent or assent, and those with cognitive or hearing disabilities that would undermine participation in the survey were excluded. After enrollment, RAs administered the study questionnaire using password protected tablets installed with Research Electronic Data Capture (REDCap) software version 8.9 [14] to collect all data electronically. The questionnaire design into REDCap employed logic checks to increase data precision and consistency during data collection.

2.1 Verification of circumcision status

After completing the questionnaire, respondents were requested to give a written personal or parental consent plus assent, where applicable, for physical verification of circumcision status. To minimize the possibility of psychological harm or embarrassment from genital exposure and examination, the RAs explained the process to the participants before inviting them to give voluntary informed consent. For respondents who consented to MC status verification, a trained RA examined the penis using a standard job aid to classify circumcision status as: i) fully circumcised if the foreskin was completely absent leaving the glans completely uncovered, ii) partially circumcised if the amount of foreskin present partially covered the glans, or iii) uncircumcised if the foreskin covered the glans fully. For minors 17 years or younger, verification was conducted in the presence or absence of the parent or guardian depending on participant or guardian/parent preference. As part of their training before the survey, RAs practiced physical assessment and classification of MC status under the guidance of a medical officer experienced in VMMC services provision. Photo illustrations of the external male genital anatomy and different sizes of foreskins were presented to the RAs before small group practice sessions on classifying foreskin as fully circumcised, partially circumcised, or uncircumcised. Finally, each RA was given a laminated job aid with photo illustrations of different grades of circumcision as a pocket guide to be used for reference during physical examination of respondents in the field. Uncircumcised participants were offered a flyer with information on the health benefits of VMMC and where to access VMMC services.

2.2 Data management

Collected data was reviewed routinely in the field, by the team lead and centrally by the study data manager for completeness, accuracy, and consistency. Data were imported into Stata version 15 [15] from REDCap for analysis. All data were checked for consistency and multiple imputations done for missing age records.

2.3 Data weighting

Weights were computed and applied during data analysis to adjust for household and individual-level non-response, and accounting for differences in probability of household selection. The design weights incorporated the probabilities of selection of the EAs from the updated 2009 census database, and the probabilities of selection of the households from each of the selected EAs.

The survey cluster weight was calculated using the updated EA selection probabilities for the i-th EA per stratum and also accounting for non-selection, the household weights were calculated using the updated household listing per EA in each stratum accounting for non-selection and lastly individual weights were calculated using the updated household listing for the adolescents aged 10–14 years and men 15–29 years by stratum while adjusting for non-participation. The overall cluster weight was obtained by multiplying the three obtained values at cluster, household and individual levels accounting for design effect. County specific sampling weights were calculated as inverse of the probability of selection of individuals in the EAs including response probability. Selection probabilities were calculated separately for each sampling stage and for each unit of sampling. Survey weights were computed separately for the interview and MC status verification at the county level. The survey final weights were normalized so that the total final weights equal to the total sample size. County population MC prevalence (both verified and self-reported) was calculated. Multiplying the value of each participant’s survey response by the corresponding nonresponse-adjusted weight, then summing up the products across all units (clusters) and finally dividing by the sum of all weights per county. Therefore, we did not employ multiple comparison of the MC prevalence across the counties. However, each county MC prevalence was obtained accounting for survey design.

2.4 Data analysis

The MC status verification rates were computed by dividing the weighted verified MC rates with self-reported rates. Agreement between physically verified and self-reported MC status was calculated for individuals who participated in both the survey and MC verification using weighted Kappa statistics. Univariate and multivariable survey logistic regression were used to assess demographic and social factors associated with circumcision. All analyses were weighted and adjusted for the complex survey design to account for both stratification and clustering. Age and county-specific MC prevalence are reported as point estimates with 95% confidence intervals. All statistical analysis tests were conducted at 5% level of significance.

2.5 Ethical considerations

Ethical approval for this survey was granted by the Maseno University Ethical Review Committee (MUERC). The survey protocol was also reviewed in accordance with the US centers for disease control and prevention (CDC) human research protection procedures and determined to be research. However, CDC investigators did not interact with human subjects or have access to identifiable data or specimens for research purposes.

3. Results

3.1 Sampling and response rate

Overall, 86.8%, (3,569 of 4,113) eligible adolescents and men participated in the survey. Response rate varied from 84.0% to 92.2% across the four counties. All respondents self-reported their circumcision status, and 3,410 (95.5%) consented to physical verification of their circumcision status. Table 1 shows the multistage sampling cascade, participant enrolment and response rate by county. Note: Numbers reported in this table are unweighted.

Table 1. Sampling cascade and response rate in the 2019 male circumcision prevalence survey in four counties in western Kenya.

	County
Variable	Siaya	Kisumu	Homa Bay	Migori	Total
Total EAs by County	1,905	2,003	2,002	1,642	7,552
Sampled EA’s	31	31	31	30	123
Listed HH in all sampled EAs	5,212	7,631	2,995	3,618	19,456
Total sampled HH	1,527	1,548	1,498	1,455	6,028
Sampled HH with eligible men/men 10–29 years	551	570	685	605	2,411
Sampled HH with eligible boys/men 10–14 years	398	357	505	474	1,734
Sampled HH with eligible men 15–29 years	268	342	369	342	1,321
Total number of eligible men 10–29 years	892	907	1,205	1,109	4,113
Number of eligible boys 10–14 years	542	439	675	635	2,291
Number of eligible men 15–29 years	350	468	530	474	1,822
	N (%)	N (%)	N (%)	N (%)	N (%)
Total eligible men interviewed 10–29 years	822 (92.2)	792 (87.3)	1,023 (84.9)	932 (84.0)	3,569 (86.8)
Eligible boys 10–14 years interviewed	514 (94.8)	409 (93.2)	606 (89.8)	566 (89.1)	2095 (91.4)
Eligible men15–29 years interviewed	308 (88.0)	383 (81.8)	417 (78.7)	366 (77.2)	1474 (80.9)
Total with verified MC status 10–29 years	806 (98.1%)	717 (90.5%)	983 (96.1%)	904 (97.0%)	3,410 (95.5%)
Boys 10–14 years with verified MC status	511	392	597	563	2,063
Men 15–29 years with verified MC status	295	325	386	341	1,347
MC status verification rate 10–29 years	98.1%	90.5%	96.1%	97.0%	95.5%

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3.2 Socio demographic characteristics of respondents

Demographic and social characteristic of respondents by county are shown in Table 2. The overall median age of respondents was 14 years (interquartile range = 12–18), 89.6% were of Luo ethnicity; 76.6% had completed primary school; 16.7% secondary school and 6.7% post-secondary education. The majority (93.0%) had never married, 6.9% were married and 0.1% were divorced, separated, or widowed. A minority (13.5%) reported being employed. Under Marital status* 48 participants with no recorded response were excluded; 3 in Homa bay, 4 in Kisumu, 3 in Migori and 38 in Siaya.

Table 2. Demographic characteristics of respondents in a male circumcision survey among 10-29-year-old boys and men from four counties, western Kenya, 2019.

	Counties
Characteristic	Homa Bay	Kisumu	Migori	Siaya	All Counties
Characteristic	N (%)	N (%)	N (%)	N (%)	N (%)
Age (years)
Median (Interquartile Range)	14 (12–18)	14 (12–20)	14 (12–18)	13 (12–18)	14 (12–18)
10–14	606 (59.2)	409 (51.6)	566 (60.7)	514 (62.5)	2095 (58.7)
15–19	226 (22.1)	175 (22.1)	207 (22.2)	158 (19.2)	766 (21.5)
20–24	125 (12.2)	124 (15.7)	99 (10.6)	84 (10.2)	432 (12.1)
25–29	66 (6.5)	84 (10.6)	60 (6.4)	66 (8)	276 (7.7)
Total	1023 (100)	792 (100)	932 (100)	822 (100)	3569 (100)
Marital status*
Never married	959 (94)	721 (91.5)	866 (93.2)	727 (92.7)	3273 (93.0)
Married	60 (5.9)	66 (8.4)	63 (6.8)	56 (7.1)	245 (7)
Divorced, separated or Widowed	1 (0.1)	1 (0.1)	0 (0)	1 (0.1)	3 (0.1)
Total	1020 (100)	788 (100)	929 (100)	784 (100)	3521 (100)
Highest level of education
Primary and below	771 (75.4)	571 (72.1)	732 (78.5)	659 (80.2)	2733 (76.6)
Secondary	173 (16.9)	150 (18.9)	159 (17.1)	114 (13.9)	596 (16.7)
Post-Secondary	79 (7.7)	71 (9)	41 (4.4)	49 (6)	240 (6.7)
Total	1023 (100)	792 (100)	932 (100)	822 (100)	3569 (100)
Religion
Christian	1001 (97.8)	776 (98)	924 (99.1)	811 (98.7)	3512 (98.4)
Other	22 (2.2)	16 (2)	8 (0.9)	11 (1.3)	57 (1.6)
Total	1023 (100)	792 (100)	932 (100)	822 (100)	3569 (100)
Employment
Employed	141 (13.8)	135 (17)	109 (11.7)	96 (11.7)	481 (13.5)
Not employed	882 (86.2)	657 (83)	823 (88.3)	726 (88.3)	3088 (86.5)
Total	1023 (100)	792 (100)	932 (100)	822 (100)	3569 (100)
Ethnic group
Luo	1001 (97.8)	725 (91.5)	685 (73.5)	787 (95.7)	3198 (89.6)
Non-Luo	22 (2.2)	67 (8.5)	247 (26.5)	35 (4.3)	371 (10.4)
Total	1023 (100)	792 (100)	932 (100)	822 (100)	3569 (100)

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3.3 MC prevalence by county

Fig 1 shows the observed MC prevalence by county and two age bands, 10–14 and 15–29 years. The observed MC prevalence for 15-29-year-old men was above 75% in all four counties; Homa Bay 75.6% (95% CI [69.0–81.2]), Kisumu 77.9% (95% CI [73.1–82.1]), Siaya 80.3% (95% CI [73.7–85.5]), and Migori 85.3% (95% CI [75.3–91.7]). For 10–14-year-old boys, the observed prevalence ranged from 55.3% (95% CI [40.2–69.5]) in Migori to 74.9% (95% CI [68.8–80.2]) in Siaya County.

For all age bands, the observed MC prevalence results from this 2019 survey were lower than DMPPT2 modelled estimates for the same year and for 2016 [8]. Among 15–29 year old men, the population survey results were 0.9–12.4% lower than DMPPT2 estimates; 75.6 vs 76.5% in Homabay, 77.7 vs 100% in Kisumu, 90.3 vs 80.3% in Siaya and 85.3 vs 91.2% in Migori. For 10–14 year old boys, the prevalence results from the survey ranged from 55.3% (95% CI [40.2–69.5]) in Migori to 74.9% (95% CI [68.8–80.2]) in Siaya County and were lower than the 2019 DMMPT 2 estimate by 25.1–32.9% across all counties. The differences in 2019 MC prevalence estimates from the population survey and DMPPT2 model are presented in Table 3.

Table 3. Comparing MC prevalence data from 2019 population survey with DMPPT2 modelled estimates the same year.

	Siaya		Kisumu		Homabay		Migori
Age	MC Prev Survey	DMPPT2 estimate	MC Prev Survey	DMPPT2 estimate	MC Prev Survey	DMPPT2 estimate	MC Prev Survey	DMPPT2 estimate
10–14 yrs	74.9	100.0	67.1	100.0	63.9	89.0	55.3	96.4
15–29 yrs	80.3	90.3	77.9	100.0	75.6	76.5	85.3	91.2

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Granular details including the prevalence of MC by socio demographics characteristics of the respondents are presented in Table 4.

Table 4. MC prevalence among boy and men aged 10–29 years across selected demographics characteristics by county.

	Homa Bay		Kisumu		Migori		Siaya		Total
Characteristic	Unweighted	Weighted	Unweighted	Weighted	Unweighted	Weighted	Unweighted	Weighted	Unweighted	Weighted
Characteristic	n/N	Prev. % (95% CI)	n/N	Prev. % (95% CI)	n/N	Prev. %	n/N	Prev. %	n/N	Prev. %
Age
10–14	393/606	63.9(55.8–72.1)	271/409	67.1(60.7–73.5)	298/566	55.3(40.5–70.1)	396/514	74.9(68.5–81.3)	1358/2095	64.3(59.3–69.3)
15–29	318/417	75.6(69.7–81.5)	297/383	77.9(69.8–86.0)	315/366	85.3(76.6–94.1)	254/308	80.3(68.5–81.3)	1184/1474	79.2(76.2–82.3)
Total	711/1023	66.7(58.3–75.2)	568/792	72(67.1–77.0)	613/932	63.5(52.7–74.2)	650/822	76.5(72.5–80.4)	2542/3569	68.8(64.5–73.1)
Age (years)
10–14	393/606	63.9(55.8–72.1)	271/409	67.1(60.7–73.5)	298/566	55.3(40.5–70.1)	396/514	74.9(68.5–81.3)	1358/2095	64.3(59.3–69.3)
15–19	178/226	78.1(71.5–84.8)	135/175	76.5(70.0–83.0)	178/207	83.8(70.4–97.1)	136/158	85.5(80.4–90.6)	627/766	80.4(76.6–84.1)
20–24	101/125	79(70.1–87.9)	101/124	86(70.8–100)	90/99	91.6(86.3–96.8)	75/84	90.1(80.7–99.5)	367/432	85.7(81.0–90.4)
25–29	39/66	60.5(51.7–69.3)	61/84	69.8(63.7–76.0)	47/60	79.5(63.3–95.6)	43/66	58.8(44.5–73.2)	190/276	66.8(60.9–72.8)
Total	711/1023	66.7 (58.3–75.2)	568/792	72(67.1–77.0)	613/932	63.5(52.7–74.2)	650/822	76.5(72.5–80.4)	2542/3569	68.8(64.5–73.1)
Marital status
Never married	669/959	66.7(58.1–75.4)	518/721	72.1(66.6–77.5)	559/866	62.2(50.7–73.7)	584/727	78.5(73.6–83.4)	2330/3273	68.7(64.1–73.3)
Married	38/60	64.2(54.5–74.0)	47/66	72.3(54.1–90.6)	52/63	88.2(74.2–100)	42/56	63.4(30.5–96.3)	179/245	72.1(62.8–81.5)
Divorced, Separated or Widowed	1/1	100 (.—.)	1/1	100 (.—.)	0/0	.(.—.)	0/1	.(.—.)	2/3	42.4(0.0–100)
Total	708/1020	66.6(58.3–75.0)	566/788	72.1(67.2–77.0)	611/929	63.5(52.6–74.3)	626/784	77.6(73.5–81.7)	2511/3521	68.9(64.5–73.2)
Education level
Primary and below	516/771	64.6(56.2–73.1)	386/571	68.2(61.5–74.8)	438/732	59.2(46.2–72.1)	504/659	74.3(69.2–79.4)	1844/2733	65.7(61.1–70.4)
Secondary	127/173	74.9(68.0–81.8)	122/150	81(74.3–87.7)	139/159	85.7(75.6–95.9)	105/114	91.1(86.3–95.8)	493/596	82(78.4–85.6)
Post-Secondary	68/79	87.8 (76.6–99.1)	60/71	83.6(37.0–100)	36/41	92.5(78.0–100)	41/49	79.6(59.4–99.8)	205/240	85.7(74.4–97.1)
Total	711/1023	66.7(58.3–75.2)	568/792	72(67.1–77.0)	613/932	63.5(52.7–74.2)	650/822	76.5(72.5–80.4)	2542/3569	68.8(64.5–73.1)
Religion
Christian	693/1001	66.9(59.3–74.5)	556/776	71.9(67.2–76.6)	606/924	63.3(52.4–74.1)	640/811	76.5(72.3–80.7)	2495/3512	68.8(64.7–72.9)
Other	18/22	59.2(0.0–100)	12/16	78.9(32.0–100)	7/8	93.5(55.7–100)	10/11	73.4(3.0–100)	47/57	69.1(39.0–99.1)
Total	711/1023	66.7(58.3–75.2)	568/792	72(67.1–77.0)	613/932	63.5(52.7–74.2)	650/822	76.5(72.5–80.4)	2542/3569	68.8(64.5–73.1)
Employment status
Employed	98/141	69.4(64.8–74.0)	101/135	72(66.3–77.7)	89/109	83.5(75.6–91.3)	72/96	66.4(55.0–77.7)	360/481	72(67.9–76.0)
Not employed	613/882	66.3(56.7–75.9)	467/657	72(66.6–77.5)	524/823	61.6(49.8–73.4)	578/726	77.6(73.3–81.8)	2182/3088	68.3(63.5–73.2)
Total	711/1023	66.7(58.3–75.2)	568/792	72(67.1–77.0)	613/932	63.5(52.7–74.2)	650/822	76.5(72.5–80.4)	2542/3569	68.8(64.5–73.1)
Ethnicity
Luo	690/1001	66.3(58.1–74.6)	510/725	70.8(66.3–75.4)	477/685	69.3(59.0–79.6)	619/787	76.3(72.1–80.4)	2296/3198	69.8(65.4–74.1)
Non-Luo	21/22	97(89.9–100)	58/67	84.5(67.4–100)	136/247	48.1(26.0–70.2)	31/35	85.3(66.5–100)	246/371	58.9(40.2–77.7)
Total	711/1023	66.7(58.3–75.2)	568/792	72(67.1–77.0)	613/932	63.5(52.7–74.2)	650/822	76.5(72.5–80.4)	2542/3569	68.8(64.5–73.1)

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3.4 Contribution of non-medical circumcision to observed MC prevalence

The contribution of non-medical circumcision to the total circumcisions reported by county for the 10-14-year age band ranged from 2.1% in Siaya to 6.9% in Migori but was higher for the 15-29-year age band ranging from 3.1% in Homa bay to 28.4% in Migori (Table 5).

Table 5. Contribution of non-medical circumcision to the observed MC prevalence by age band and county.

	Siaya			Kisumu			Homabay			Migori
Age	MC Prev %	Medical (%)	Non-Medical (%)	MC Prev %	Medical (%)	Non-Medical (%)	MC Prev %	Medical (%)	Non-Medical (%)	MC Prev %	Medical (%)	Non-Medical (%)
10-14yrs	74.9	97.9 [95.1–99.1]	2.1 [0.9–4.9]	67.1	95.1 [90.6–97.5]	4.9 [2.5–9.4]	63.9	97.8 [94.8–99.1]	2.2 [0.9–5.2]	55.3	93.1 [83.1–97.4]	6.9 [2.6–16.9]
15-29yrs	80.3	89.8 [68.2–97.3]	10.2 [2.7–31.8]	77.9	92.2 [83.7–96.4]	7.8 [3.6–16.3]	75.6	96.9 [93.5–98.6]	3.1 [1.4–6.5]	85.3	71.6 [56.6–83.0]	28.4 [17.0–43.4

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3.5 Agreement between self-reported and physically verified MC status

Overall, 95.5% (3,410/3,569) of the respondents consented to and were examined for physical verification of their circumcision status after completing the structured questionnaire. The MC status verification cascade is shown in Table 6. There was no difference in the proportion who consented to physical verification among self-reported circumcised and uncircumcised respondents (95.8% vs. 95.0%; p-value = 0.39). Among 2,434 respondents who self-reported being circumcised, genital examination revealed that 99.5% (2,421/2,434) were circumcised, 0.2% (5/2,434) were partially circumcised and 0.3% (8/2,434) were uncircumcised. Similarly, of the 976 participants who reported they were uncircumcised and were examined, 98.6% (962/976) were confirmed to be uncircumcised, 0.4% (4/976) were found to be partially circumcised and 1.0% (10/1,027) were circumcised.

Table 6. Self-reported circumcision status versus physically verified circumcision status among 3,569 boys and men in western Kenya, 2019.

	Self-reported MC Status
Physical Verification outcome	Circumcised	Uncircumcised	Total
Declined verification*	108	51	159
Verified Circumcised	2421	10	2431
Verified Uncircumcised	8	962	970
Verified Partially Circumcised*	5	4	9
Total	2542 (71.2%)	1027(28.8%)	3569

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There was no difference between the overall MC prevalence based on self-report compared to physical verification in all counties.

In total, nine respondents (0.3%) were found to be partially circumcised and four self-identified as uncircumcised while five self-identified as circumcised. Partially circumcised respondents were excluded from the analysis for agreement between self-reported and physically verified MC status. Overall agreement between self-reported and verified circumcision status was 99.2% (kappa agreement p <0.0001) with no significant differences across the four counties (not shown). Respondents who declined verification* or were found to be partially circumcised* were not included in the analysis for agreement between self-reported and physically verified MC status.

3.6 Predictors of circumcision

In bivariate analysis shown on Table 7, age, education, and county of residence were all significantly associated with verified circumcision status. Multivariate analysis revealed that men 15–19 years had 2.05 times higher odds of being circumcised compared to younger adolescents 10-14-year (95% CI [1.45, 2.89], p-value <0.001), while those aged 20–24 years had 2.33 times greater odds of being circumcised (95% CI [1.25, 3.65], p-value <0.006). Additional significant predictors of verified MC status included level of education, ethnicity and county of residence. Although ethnicity did not emerge as significantly associated with MC status in the bivariate analysis, it did when controlling for other covariates in the multivariate analysis.

Table 7. Demographic predictors of verified male circumcision in four counties in western Kenya, 2019.

	Univariate analysis				Multivariable analysis
Covariate	OR	95% CI	P-value	Global P-value	OR	95% CI	P-value	Global P-value
Age (n)
10–14 years	Ref (1)
15–19 years	2.53	[1.82, 3.51]	<0.001	<0.001	2.05	[1.45, 2.89]	<0.001	<0.001
20–24 years	3.16	[2.05, 4.88]	<0.001		2.13	[1.25, 3.65]	0.006
25–29 years	1.05	[0.74, 1.51]	0.774		0.68	[0.44, 1.05]	0.085
Marital Status (n)
Never Married	Ref (1)
Married	1.05	[0.61, 1.81]	0.858	0.663
Separated/Divorced/Widowed	0.33	[0.03, 3.84]	0.373	0.663
Education completed
Primary & below	Ref (1)			<0.001				<0.001
Secondary	2.72	[1.95, 3.79]	<0.001		1.77	[1.31, 2.39]	<0.001
Post-secondary	2.87	[1.50, 5.51]	0.002		2.53	[1.10, 5.80]	0.029
Employment status
Employed	Ref (1)			0.276
Unemployed	0.85	[0.64, 1.14]	0.276	0.276
Religion
Christian (ref)	Ref (1)			0.979
Other	1.02	[0.30, 3.42]	0.979	0.979
Ethnicity
Luo	1.72	[0.87, 3.38]	0.118	0.118	1.82	[0.95, 3.49]	0.07	0.07
Non-Luo (ref)	Ref (1)			0.118
County
Homa Bay (ref)	Ref (1)			0.011				0.023
Kisumu	1.31	[0.92, 1.85]	0.128		1.19	[0.84, 1.69]	0.312
Migori	0.85	[0.50, 1.45]	0.547		0.97	[0.58, 1.61]	0.891
Siaya	1.64	[1.16, 2.33]	0.006		1.64	[1.17, 2.31]	0.005

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4. Discussion

This population-based survey provided the latest MC prevalence data for boys and men aged 10–29 years in four non-circumcising counties with established VMMC programs in western Kenya. Consequently, the observed estimates are critical for setting new realistic targets for achieving the national goal of attaining and sustaining MC prevalence at 80% or more in the four counties.

Using the observed MC prevalence from this survey as a reference, we concluded that the DMPPT2 model overestimated the MC prevalence in the four counties. The observed MC prevalence data across the two age bands (10–14 and 15–29 years) were 0.9–32.9 percentage points lower than the DMPPT2 modeled estimates for the same year. The survey results were also lower than the initial DMPPT2 estimates in 2016 by 5–10% [8]. This is despite additional VMMCs performed in the intervening period between the initial DMPPT2 modeling and this survey. The sustained high uptake of VMMC in age bands that had attained close to 100% MC prevalence by the DMPPT2 model three years earlier, supports lower MC prevalence as observed in this survey. By revealing that the DMPPT2 model overestimated the MC prevalence, this survey has resolved the inconsistency between sustained high uptake of VMMC and high MC prevalence from the 2016 modeling results published in 2018 [8]. Reasons for overestimation by the DMPPT2 model remain unclear but might include lack of precision in the age-specific baseline circumcision estimates and misreporting of client ages or the number circumcised in the program. The discrepancy between the MC prevalence estimates from the survey and DMPPT 2 was wider for 10-14-year age band (25.1–32.9%) than for 15-29-year-old clients (0.9–12.4%). This is a pointer to the possibility less accurate reporting of the ages for younger VMMC clients. Errors in the demographic model may also result in imprecision in the estimates; for example, if DMPPT2 is underestimating population growth. Furthermore, the DMPPT2 model assumes that the background rate of non-program circumcision remains constant over time. Errors in adjusting the model inputs to account for any changes in the contribution of traditional circumcision to the total numbers can result in overestimation of prevalence.

The gaps in MC prevalence revealed by this survey across the VMMC priority counties provide a good basis for refocusing program geographically based on unmet need. Although the VMMC program target of 80% MC prevalence was reached or exceeded among 15–29 year old men in Siaya (80.3%) and Migori counties (85.3%), and nearly achieved in Kisumu (77.9%) and Homa Bay (74.6%), the prevalence among young adolescents aged 10–14-years was lower in all counties surveyed. Therefore, Kenya requires sustained investment in VMMC to address need for men aged 15 years or more years and to maintain services for progressively larger annual cohorts young adolescents who become eligible for VMMC annually due to the youth bulge demographic phenomenon [16, 17].

Although this survey showed MC prevalence of 55.3–74.9% among young adolescents aged 10–14 years, this is bound to decline over time if the country refocuses VMMC services towards older males as recommended by WHO guidelines of 2020. Both WHO and PEPFAR have deemphasized VMMC services for boys below 15 years due to concerns over safety and challenges of consenting [18, 19]. This policy shift, coupled with the youth bulge demographic phenomenon, likely lead to a progressive increase in the absolute number and proportion of boys turning 15 years before being circumcised. Consequently, Kenya will need to set progressively higher VMMC targets for the 15-19-year-old adolescents to minimize the dilution effect of the increasing number of uncircumcised boys graduating into this age band.

As expected, the observed MC prevalence was higher in the 15–29 than 10-14-year age group in all counties with the prevalence gap ranging from 5–30 percentage points. The prevalence gap between the two age groups was largest in Migori at 30.0% compared to 5.4–11.7% in the remaining three counties. A likely explanation for the steeper MC prevalence increase in Migori after 14 years is the preference by culturally circumcising residents for men to be circumcised between 15 and 18 years. Unlike Kisumu, Siaya and Migori, three out of the nine sub-counties in Migori are inhabited partially by ethnic group (the Kuria) who prefer that men be circumcised from 15 to 18 years. The observed contribution of non-medical circumcision to the overall MC prevalence for men in the 15-29-year age group ranged from 3.1% in Homa bay to 28.4% in Migori. Similarly preliminary results of 2018 Kenya population-based impact assessment (KENPHIA 2018) [4] also showed that the contribution of non-medical circumcision to the overall MC prevalence was highest in Migori(24.4%) compared to 2.7–6.6% in the remaining three counties. These observations underscore the importance of accounting for the contribution of non-medical circumcision and preferred age of circumcision for different subgroups when assessing the effect of VMMC on the population MC prevalence in counties occupied by both circumcising and non-circumcising subgroups.

A secondary objective of this survey was to assess the level of agreement between self-reported and physically verified circumcision status. There was high participation in physical verification of MC status, and high agreement of 99.2% between self-reported and physically verified MC status. An earlier study conducted in the same counties in 2014–2015 found comparable agreement of 98.6% among 24–39 year-old men [9]. Compared to this study, in which the overall response rate was 84.6% with 95.5% of the respondents consenting to physical verification, the earlier survey had a low participation rate (58.3%), but comparable uptake of physical verification (97.8%). Our findings support the use of self-reported circumcision status as a reliable source of data for estimating the population prevalence of MC.

On physical examination, we found nine respondents who were partially circumcised, but it was uncertain if these were naturally short or incompletely removed foreskins. Four partially circumcised respondents who self-identified as uncircumcised and were presumed to be cases of naturally short foreskin while five who reported that they were circumcised were presumed to have had incomplete removal of the foreskin during circumcision. Though rare, incomplete removal of the foreskin is a significant adverse event [20] because it can theoretically lower the HIV prevention efficacy of VMMC [21]. Programs should guard against incomplete excision of the foreskin because it can erode public confidence in the VMMC program besides undermining its HIV prevention benefits. Strategies for preventing insufficient removal of the foreskin include consistent supervision and support for health workers through refresher training. Use of male circumcision devices such as ShangRing may also standardize the amount of foreskin removed and minimize the risk of insufficient skin removal [22]. Partial circumcisions were randomly distributed across the counties regardless of the prevalence of non-medical circumcision and with no clustering among non-medically circumcised respondents compared to those circumcised medically.

Partial circumcision also has practical implications for surveys that involve physical verification of MC status because of the need to distinguish between the incomplete removal of the foreskin, which is preventable through proper circumcision techniques, and naturally short foreskin. Such surveys should include questions on the date and place of circumcision procedure to permit review of patient records, if needed, to ascertain any history of past circumcision.

In the multivariable model, age, level of education, ethnicity and county of residence were significant independent predictors of circumcision status. Specifically, having a higher level of education, being aged 15–24 years and being of Luo ethnic community were associated with being circumcised. These findings are consistent with the results of a similar survey conducted in the same counties in 2014–2015 among men aged 25–29 years [23]. In that study, higher education and having employment were associated with being circumcised, and having ever been married (currently married, divorced, separated, or widowed) was associated with being uncircumcised. Our results support the need for tailored education on benefits of VMMC and mobilization targeting men with lower education and other demographic subgroups with lower likelihood of being circumcised.

This study had some limitations; 1). There were only two response options for self-reported circumcision status (circumcised or uncircumcised) but physical verification included a third outcome of partial circumcision. Fortunately, partial circumcisions were few (9 out of 4,010) and were excluded from the analysis of agreement between self-reported and physically verified MC status without affecting the overall results. The observed MC prevalence data in this survey conducted in 2019 were compared with DMPPT2 modelled estimates of 2016 and found to be lower. The margin of MC prevalence overestimation in DMPPT2 would probably be larger had we compared the results with DMPPT2 modeled estimates for 2019. Necessary adjustments for migration and replacement for DMMPT2 inputs beyond 2016 were not completed in time for consideration in this analysis. 3). It is possible that some clients who had incomplete removal of the foreskin failed to disclose their history of previous circumcision due to embarrassment. 4). The study was primarily conducted among the non-circumcising Luo ethnic group where MC has been promoted as a medical intervention for HIV prevention. The results of agreement between self-reported and physically verified circumcision status may not be generalizable to settings where VMMC program is not fully embraced because low social desirability and social disapproval of MC may discourage respondents from disclosing their correct status.

5. Conclusion

Using the observed population prevalence of MC from this survey as a reference, we have demonstrated that the initial DMPPT2 modeling performed in Kenya in 2016 and published in 2018 provided inflated estimates of MC prevalence especially for men aged 15–29 years. Regardless of the basis for the previous overestimates, this survey has provided up to date MC prevalence data which form a good reference for setting realistic VMMC program targets and re-calibrating inputs into DMPPT2. Similar population-based MC prevalence surveys conducted periodically, especially for mature programs, can help reconcile inconsistencies between VMMC program uptake data and modeled MC prevalence estimates which are based on the number of procedures reported in the program annually.

Supporting information

S1 File. Appendix-7 questionnaire English.

(PDF)

Click here for additional data file.^{(208.6KB, pdf)}

S2 File. Appendix 7_questionnaire_Kiswahili.

(PDF)

Click here for additional data file.^{(263.6KB, pdf)}

S3 File. Appendix 7_questionnaire_Dholuo.

(PDF)

Click here for additional data file.^{(283.5KB, pdf)}

S1 Table. 2019 Kenya MC survey data dictionary.

(PDF)

Click here for additional data file.^{(95.2KB, pdf)}

S2 Table. 2019 Kenya MC survey dataset.

(CSV)

Click here for additional data file.^{(178.3KB, csv)}

Acknowledgments

This survey was jointly implemented by Jhpiego, Kenya’s Ministry of Health and the national VMMC technical working group. The County Governments of Siaya, Kisumu, Homa bay and Migori and their respective implementing partners, namely, CHS, UCSF, EGPAF and UMB. The Kenya National Bureau of statistics (KNBS) provided technical assistance in sampling and data weighting. We are grateful to all these agencies, the survey personnel and the residents of Siaya, Kisumu Homa bay and Migori Counties for their diverse contributions towards the success of this survey.

Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the funding agencies.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This survey was funded by the President’s Emergency Plan for AIDS Relief (PEPFAR) through the U.S. Centers for Disease Control and Prevention (CDC) under the terms of Cooperative Agreement # GH001469. "The author(s) received no specific funding for this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0254140.r001

Decision Letter 0

Deborah Donnell

16 Feb 2021

PONE-D-20-37607

Prevalence of male circumcision in four culturally non-circumcising counties in western Kenya after 10 years of program implementation from 2008 to 2019

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362 GH001469 and was jointly implemented by Jhpiego and Kenya’s Ministry of Health."

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Additional Editor Comments:

Overall, this is a nicely summarized piece of work providing important data on current prevalence of male circumcision, that is an important reality check for mathematical modelling and better understanding of demand for services. The authors are to be congratulated on high quality of study design and execution, and the appropriate statistical analysis for the design.

Overall

1. There are several places where the text is not clear about the role and predictions of the DMPPT2 modelling estimates. In the back ground and introduction much greater clarity about the mathematical modelling issues is needed to better understand the context for the work.

• Second sentence of abstract introduction needs revision – this reviewer could not understand

• No context for the “lower population-based MC prevalence” is given in the abstract – I think the context of overly optimistic model projections is needed

• Introduction: More precision in the language is needed to make it clear when estimates are “model estimates” and when estimates are from observed data. For example mention of “VMMC program saturation” – it is not clear whether this is estimated saturation or field (observed) saturation.

2. Another area that needs clarity is whether “MC” combines both traditional and VMMC. It appears the MC reported is almost all VMMC, but this needs to be discussed.

Methods: “none selection” should be “non-selection”

Statistical: The sampling design and the sample size were very similar in different regions. It seems surprising that the 95%CI for 10-14 yo in Migoro was ~ +/- 15%, but for Siaya was +/- 8%. Typically the SE would not change so dramatically for prevalence in the range ~55% to ~75%.

Tables:

• In Table 1 and Table 2 row% should be replaced by col%, as there is little interest in comparison between regions.

• In Table 1: Please change to % interviewed within each subgroup; similarly the last two rows could be entered as % fof the two rows above rather than as separate rows.

Discussion:

• Several instances of problems with references and words that were left in from editing “(insert coverage…)”

• Line 274-278: This is the first time details are provided about the modelled estimates. It would be helpful to include this in the introduction. The statement that the observed estimates were lower than the modelled estimates is incorrect – because they are within the range provided for the 10-14 yos, simply on the lower end. The range is, of course very wide (so wide as to be not very useful?).

• Line 287ff. This reviewer understood that traditional MC in the South African is typically partial, and there were extremely few partial circumcisions reported. It would be helpful in the commentary in the discussion about the potential role of traditional circumcision in Migori if this could be clarified, as it seems somewhat in conflict with the explanation offered.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Odoyo-June et al. describe the results of a survey of circumcision prevalence among boys and men in four counties of Kenya. The manuscript is well-written. I have only minor suggestions for how to improve the clarity and flow of the manuscript.

Title

Comparing the title to the abstract, it was difficult to tell whether the purpose of the study was to ascertain VMMC prevalence in four counties of Kenya (title) or to validate the DMPPT2 (abstract). It is fine for there to be a secondary purpose but best to be consistent between title and abstract as to the main purpose of the study.

Abstract

The abstract is inconsistent as described above. Introduction implies that the purpose is to validate DMPPT2, but Results only discuss VMMC prevalence in the four counties and Conclusion does not help readers interpret the results in terms of whether DMPPT2 was validated.

The abstract should be revised to ensure it makes sense as a stand-alone document. I recommend reading it with fresh eyes and revising for clarity. Here are some issues that stood out to me as a new reader:

"80% saturation" -- what does this mean? Why is 80% a point of saturation?

"Estimates from the DMPPT2 included unlikely MC coverage approaching 100% without commensurate decline in VMMC uptake." -- I could not understand the meaning of this sentence

From reading conclusions I could not tell whether the survey results did or did not agree with the DMPPT2 estimates. Clearly state this and discuss possible reasons why or why not.

Introduction

"The program achieved 92% of its target" -- please clarify what the target is.

Methods

line 109 "using probability proportional to size sampling" is unclear. Is this probability in proportion to the total population, males age 10+, land area? Please specify and, if possible, provide a reference to the complete method.

line 111 "All households in the sampled EAs were listed to help update NASSEP IV" is unclear. Did a staff member manually enumerate each HH? Was this list used directly or merged with the NASSEP list? If merged, how was it deduplicated?

The survey weights description was excellent and very clear except for this part on Lines 173-174: "Finally, the weights were adjusted to ensure consistency with the projected population figures." It was not clear to me what this meant methodologically.

The long sentence starting on Line 176 is missing a subject, and could generally be re-written for readability/clarity:

County population MC prevalence (both verified and self-reported) was calculated by multiplying the value of each participant’s survey response by the corresponding nonresponse-adjusted [***MISSING SUBJECT***] summing up the products across all units (clusters and strata) and finally dividing by the sum of all weights.

Results

Given the claim that the survey resolved discrepancies with DMPPT, Results should also include results from DMPPT and whether they agree with the data given sampling uncertainties in the survey. Some of the DMPPT results are included in Discussion instead which makes for an awkward flow.

Table 3 is the "meat" of the paper and it is very large and difficult to navigate. A figure and a map would be welcome. Maybe a map of counties colored by prevalence, overlayed with dots representing EAs also colored by prevalence.

Discussion

As best I understand, PEPFAR no longer recommends circumcision for ages 10-14 due to higher risk of adverse events. The paper should address this. For example, the observation that 15-19yo boys are 2x more likely to become circumcised than 10-14yo boys should be discussed in the context of the recent guidance.

The Discussion section has multiple instances of "(insert coverage for these counties)" and "Error! Bookmark not defined."

DMPPT results should be in Results instead of Discussion.

A large section of the Discussion focuses on incomplete circumicision. This part of the paper is both confusing and concerning. It speaks of nine individuals with short but not entirely removed foreskins, says four of them stated they were uncircumcised, and for some reason deduces that four of them were partially circumcised. Why four and not five?

It then discusses how these might be VMMC adverse events and may erode confidence in VMMC programs and discusses the potential benefits of ShangRing in avoiding partial removal. Although the authors may be trying to speak in hypotheticals, this really feels like jumping to conclusions. It may be best to omit this paragraph in order to not cast doubt on the Kenyan VMMC program, since these results may be spurious. Follow-up with this handful of partially circumcised participants seems warranted in order to verify partial circulcision, interview respondents regarding their history of circumcision procedures, and alert the VMMC program if there are quality issues.

Finally, the authors claim that "Partial circumcisions have practical implications for surveys." However, it seems that the number of partially circumcised individuals in this study was extremely small, so much so that there may not be significant implications for surveys from this finding. It seems important to point that out. To address this formally, the authors could perform sensivitity analyses to determine (1) if the partially circumcised participants would have any meaningful impact on the survey results, and (2) if partial circumcision would meaningfully reduce the coverage of VMMC. If it is very rare and not a driver of survey bias or coverage, then it does not seem appropriate to single it out as a main topic of Discussion.

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Reviewer #1: Yes: Anna Bershteyn

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PLoS One. 2021 Jul 15;16(7):e0254140. doi: 10.1371/journal.pone.0254140.r002

Author response to Decision Letter 0

10 Mar 2021

All review comments have been addressed.

Summary of responses is uploaded under filename Response to Reviewers.

The manuscript has been edited in response to review comments. A tracked and clean version uploaded.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(46.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0254140.r003

Decision Letter 1

D W Cameron

27 Apr 2021

PONE-D-20-37607R1

Prevalence of male circumcision in four culturally non-circumcising counties in western Kenya after 10 years of program implementation from 2008 to 2019

PLOS ONE

Dear Dr. Odoyo-June,

Ed: Please accommodate the remaining minor reviewers suggestions, and I will be happy to accept your MS for publication.

Please submit your revised manuscript by Jun 11 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Journal Requirements:

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Additional Editor Comments (if provided):

Please accommodate the remaining reviewers suggestions, and I will be happy to accept your MS for publication.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: The paper is much improved, addressing all of my concerns. I have just one minor remaining concern.

In the re-written manuscript, the DMPPT estimates are stated in the text as % over-estimation relative to the survey for each of the Counties in the study. The DMPPT estimates themselves are not listed anywhere. For ease of reference, the VMMC results table should include a column with the DMPPT estimate and can then have a column with the % over-estimation relative to the survey. Otherwise, it is too difficult for a reader to pull these figures out of the text without having a table to refer to.

Reviewer #2: This is a revision of an analysis investigating the prevalence of male circumcision and associated characteristics in four counties in Kenya. This manuscript has been previously reviewed and it appears in the response to reviewers that the authors have addressed the concerns with the changes reflected in the edited manuscript. I recommend reviewing the tables and suppressing cells less than 5 to protect privacy.

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Reviewer #1: Yes: Anna Bershteyn

Reviewer #2: No

PLoS One. 2021 Jul 15;16(7):e0254140. doi: 10.1371/journal.pone.0254140.r004

Author response to Decision Letter 1

11 Jun 2021

Response to comments by Reviewer No 1.

We have included a table that compares the 2019 DMPPT 2 modelled MC prevalence estimates with the population survey results for the same year (Table 3 on page 16; Line 256-258).

Response to comments by Reviewer No 2

Tables 2 and 3 have marital status variable which has three categories; “Divorced, separated or Widowed” which have cell values less than 5. Kindly note that this category cannot be merged with either of the two remaining categories because Never married and Married are standalone. Therefore, this category (Divorced, separated or Widowed) of marital status with cell values less than 5 can only remain as is and only used in descriptive and weighting cases only.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0254140.r005

Decision Letter 2

D W Cameron

21 Jun 2021

Prevalence of male circumcision in four culturally non-circumcising counties in western Kenya after 10 years of program implementation from 2008 to 2019

PONE-D-20-37607R2

Dear Dr. Odoyo-June,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

D W Cameron, MD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Good work.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: All comments have been addressed and the paper appears ready for publication. Table 3 makes it much easier to compare the estimates to the model projections. Thank you for adding it.

Reviewer #2: Thank you for the opportunity to review this manuscript. All my comments have been addressed. It is both well designed and clearly written.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Anna Bershteyn

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0254140.r006

Acceptance letter

D W Cameron

24 Jun 2021

PONE-D-20-37607R2

Prevalence of male circumcision in four culturally non-circumcising counties in western Kenya after 10 years of program implementation from 2008 to 2019

Dear Dr. Odoyo-June:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Professor D W Cameron

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 File. Appendix-7 questionnaire English.

(PDF)

Click here for additional data file.^{(208.6KB, pdf)}

S2 File. Appendix 7_questionnaire_Kiswahili.

(PDF)

Click here for additional data file.^{(263.6KB, pdf)}

S3 File. Appendix 7_questionnaire_Dholuo.

(PDF)

Click here for additional data file.^{(283.5KB, pdf)}

S1 Table. 2019 Kenya MC survey data dictionary.

(PDF)

Click here for additional data file.^{(95.2KB, pdf)}

S2 Table. 2019 Kenya MC survey dataset.

(CSV)

Click here for additional data file.^{(178.3KB, csv)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(46.5KB, docx)}

Attachment

Submitted filename: Response to Reviewers.pdf

Click here for additional data file.^{(154.1KB, pdf)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

Prevalence of male circumcision in four culturally non-circumcising counties in western Kenya after 10 years of program implementation from 2008 to 2019

Elijah Odoyo-June

Stephanie Davis

Nandi Owuor

Catey Laube

Jonesmus Wambua

Paul Musingila

Peter W Young

Appolonia Aoko

Kawango Agot

Rachael Joseph

Zebedee Mwandi

Vincent Ojiambo

Todd Lucas

Carlos Toledo

Ambrose Wanyonyi

Roles

Abstract

Introduction

Methods

Results

Conclusion

1. Introduction

2. Methods

2.1 Verification of circumcision status

2.2 Data management

2.3 Data weighting

2.4 Data analysis

2.5 Ethical considerations

3. Results

3.1 Sampling and response rate

Table 1. Sampling cascade and response rate in the 2019 male circumcision prevalence survey in four counties in western Kenya.

3.2 Socio demographic characteristics of respondents

Table 2. Demographic characteristics of respondents in a male circumcision survey among 10-29-year-old boys and men from four counties, western Kenya, 2019.

3.3 MC prevalence by county

Fig 1. Observed prevalence of MC by age group and county in western Kenya, 2019.

Table 3. Comparing MC prevalence data from 2019 population survey with DMPPT2 modelled estimates the same year.

Table 4. MC prevalence among boy and men aged 10–29 years across selected demographics characteristics by county.

3.4 Contribution of non-medical circumcision to observed MC prevalence

Table 5. Contribution of non-medical circumcision to the observed MC prevalence by age band and county.

3.5 Agreement between self-reported and physically verified MC status

Table 6. Self-reported circumcision status versus physically verified circumcision status among 3,569 boys and men in western Kenya, 2019.

3.6 Predictors of circumcision

Table 7. Demographic predictors of verified male circumcision in four counties in western Kenya, 2019.

4. Discussion

5. Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Deborah Donnell

Roles

Author response to Decision Letter 0

Decision Letter 1

D W Cameron

Roles

Author response to Decision Letter 1

Decision Letter 2

D W Cameron

Roles

Acceptance letter

D W Cameron

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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