BMC Infectious Diseases Regional differences in HIV prevalence among drug users in China: potential for future spread of HIV?

Mirjam Kretzschmar email@example.com
School of Public Health
University of Bielefeld
Bielefeld, Germany

Centre for Infectious Disease Control
RIVM
The NetherlandsBilthoven

Julius Centre for Health Sciences and Primary Care
University Medical Centre Utrecht
The NetherlandsUtrecht

Weidong Zhang email@example.com
School of Public Health
University of Bielefeld
Bielefeld, Germany

National Center for AIDS/STD Control and Prevention
Center for Disease Control and Prevention
BeijingChina, PR China

Rafael T Mikolajczyk
School of Public Health
University of Bielefeld
Bielefeld, Germany

Lan Wang email@example.com
National Center for AIDS/STD Control and Prevention
Center for Disease Control and Prevention
BeijingChina, PR China

Xinhua Sun
Department of Disease Control
Ministry of Health
BeijingPR China

Alexander Kraemer firstname.lastname@example.com
School of Public Health
University of Bielefeld
Bielefeld, Germany

Fan Lv firstname.lastname@example.com
National Center for AIDS/STD Control and Prevention
Center for Disease Control and Prevention
BeijingChina, PR China

BMC Infectious Diseases Regional differences in HIV prevalence among drug users in China: potential for future spread of HIV?
10.1186/1471-2334-8-108BioMed Central Page 1 of 11 (page number not for citation purposes) Research article * Corresponding author †Equal contributors
Background: Drug use and in particular injecting drug use has been at the forefront of the explosive spread of HIV in general populations in many countries in Asia. There is concern that also in China increased HIV incidence in drug users might spark off a generalized epidemic in the wider population. Close monitoring of HIV incidence and risk factors in drug users is therefore important to be able to target interventions effectively. Second generation surveillance was launched to assess HIV prevalence and risk behaviours jointly with the purpose of describing trends and predicting future developments. To assess whether these goals were fulfilled among drug users in China we provide an analysis of risk factors for HIV infection and of regional differences in HIV prevalence.Methods:We analysed data collected in 2005 in 21 drug user second generation surveillance sentinel sites from 14 provinces in China. We used random effects logistic regression to test for risk factors for HIV infection and regional differences.Results:The overall HIV-1 antibody prevalence was 5.4% (279/5128); 4.9% among injecting drug users (IDU) not sharing needles and 3.7% among non-injecting drug users. We found substantial heterogeneity among the surveillance sites with prevalence rates ranging between 0% and 54%. HIV status was strongly affected by the regional prevalence of HIV. Risk behaviours were highly prevalent in regions where HIV prevalence is still low. The distribution of duration of drug use in different sites indicated different stages of the drug use epidemics.Conclusion: ]Regional differences in HIV prevalence in China reflect different stages of the drug use and HIV epidemics rather than differences in risk behaviours. Therefore, outbreaks of HIV among drug users in regions where prevalence is still low can be expected in the future. However, methodological limitations of surveillance embedded into routine systems limit the usability of existing data. More standardized approaches to data collection in secondary generation HIV surveillance are necessary to better understand regional differences in risk behaviour and prevalence and to design targeted intervention for those regions at risk of experiencing outbreaks.

Background

The first recognized outbreak of HIV in China occurred in 1989 in Ruili City, Yunnan Province, among injecting drug users (IDU) [1]. In the years to follow HIV spread explosively among IDU populations in Yunnan province and subsequently in neighbouring provinces. In 1995 a sentinel surveillance system was initiated, the so-called first generation surveillance. Only in 2001 the Chinese government acknowledged the existence of a major epidemic and changed its course to more openness and proactive behaviour to control the increasing HIV/AIDS problem [2]. With international support from the World Health Organization (WHO) and the Joint United Nations Programme on HIV/AIDS (UNAIDS), a first published estimate of the size of the problem was derived combining estimates of the sizes of risk populations in the various provinces with estimates of HIV prevalence per risk group available from national surveillance sites in 2003. The main risk groups considered in this estimation were IDU, men who have sex with men (MSM), paid blood donors infected by unsafe equipment, and commercial sex workers (CSW). It was estimated that there were between 650,000 and 1.2 million people living with HIV in China in 2003. Newer estimates tend to be somewhat lower and put the number of people living with HIV in 2005 between 540,000 and 760,000, the number of new HIV infections in 2005 at 70,000 to 80,000 and 25,000 AIDS deaths [3]. There is substantial regional heterogeneity in HIV prevalence with those provinces bordering to the Golden Triangle, where drug trafficking is common, having the highest HIV prevalences, especially among IDU. In the prefectures of Yunnan and Xinjiang prevalences of up to 80% among IDU have been reported [4,5]. Also in the provinces of Sichuan, Guizhou and Guangxi prevalences of around 50% among IDU have been reported [6].

In recent years the Chinese government has intensified its surveillance programme [7], which consists of: a) case reporting through an internet based surveillance tool, b) sentinel sites where mainly prevalence data is collected, and c) second generation surveillance sites where prevalence and behavioural data are collected, d) in depth epidemiological investigations [8]. The sentinel surveillance has been expanded to include 194 sites in 2003, of which 49 are drug-user sites from 21 provinces. Following recommendations by WHO for implementing second generation surveillance [9], another set of surveillance sites was established specifically for collecting behavioural data aimed at monitoring trends in risk behaviour and the prevalence of other sexually transmitted infections (STI) such as syphilis. At present, there are 105 sites for second generation surveillance. The sites were chosen according to numbers of case reports in the national surveillance and other epidemiological considerations. Concerning drug users, provinces with HIV prevalence above 5% have to have at least one sentinel surveillance site collecting behavioural information; in other provinces sites can be chosen based on factors like local socio-economic level, local health care facilities, and the size of floating populations. In 2005, 21 sites in 14 provinces collected behavioural data in drug users.

Persons identified as drug users in China are registered in a database of the Ministry of Public Security. The numbers of registered drug users have risen from 70,000 in 1989 to 1,140,000 in 2004 [10]. It was also estimated that the total population of drug users in 2004 was around 3.5 million and thus about three times as large as the number of registered drug users [11,12]. It is estimated that among drug users about 54% are injecting drug users and of those about 45% have recently shared needles and other injecting equipment [13,14]. Large regional differences exist, however, with the highest rates of injecting and needle sharing observed in the southwest provinces and Xinjiang. Also, it has been shown that a correlation exists between high risk injecting and high risk sexual behaviour in male injecting drug users [15].

Although up to now the epidemic in China has remained concentrated in risk groups, there is increasing concern that HIV might spread via bridge populations such as female sex workers [16,17] or surplus men [18] into the general population. Many female IDU are also CSWs and commonly engage in unprotected sex [19]. Recently, a molecular epidemiological study has shown increased circulation of sexually transmitted HIV in Yunnan, the province with the first and largest HIV epidemic among IDU [20].

In view of the imminent threat of large scale spread of HIV into the general population of China a surveillance system that allows reliable monitoring of the epidemic and of possible changes in risk behaviour is important. Second generation surveillance of drug users was implemented in 1999 and since then scaled up to the present dimension. Here we analyse data from second generation surveillance among drug users conducted in 2005 with data collected in 21 drug user sentinel sites from 14 provinces. While most studies have focussed on the HIV epidemic in southwest China [14] we report on data from sites all over the country. In this analysis we focus on the geographical differences of HIV prevalence and drug use. The prevalence of syphilis and implications for the further sexual spread of HIV will be analyzed elsewhere. Central aims of second generation HIV surveillance are focussing surveillance on subgroups of the population at highest risk of infection and implementing surveillance that moves with the needs and state of the epidemic [21]. We present results that contribute to assessing whether those aims can be reached with the SGS system as implemented in China at present and what the main limitations of the collected data are in this respect.

Methods

Choice of second generation surveillance sites and study population

In 2005, based on the HIV prevalence obtained from the HIV/AIDS case reporting system in 2004 and other epidemiological studies, 22 drug user sites from 14 provinces were initially selected to perform a behavioural survey. Each site was asked to recruit between 300 and 400 participants. This number was considered sufficient for descriptive purposes and to estimate prevalence with 95% confidence intervals of +/-5% (for prevalence rates below 30%). Persons to be included were drug users who smoke or intravenously or intramuscularly use heroin, cocaine, opium, marijuana, morphine, methamphetamine hydrochloride, Pethidine, K powder (Ketamine), or Methylenedioxymethamphetamine (MDMA, also known as Ecstasy). In order to avoid prosecution by law and increase participation, anonymity and confidentiality were guaranteed to the drug users when they participated in the survey. Recruitment followed convenience sampling: some participants were recruited from detoxification centres, others in public meeting places where drugs are consumed, and finally in bars and outreach projects. In some cases snowball sampling was also used. The proportions recruited from these settings differed between different sites due to different local circumstances.

The data collection period was from July to September 2005, but could be prolonged in case a site was not able to recruit the requested number of participants within that time period. If the required sample size was not reached by the end of the year, local investigators contacted China Center for Disease Control and Prevention who then decided whether or not to end the sampling process. All participants were asked to complete a questionnaire on demographic and behavioural issues and to provide a blood specimen for HIV antibody testing. One site in Kunming, Yunnan province was excluded from our analysis because information about risk behaviour of drug users was missing. The protocol "HIV Surveillance Protocol at National Comprehensive (second generation surveillance) HIV Sentinel Sites", was approved by the national ethical committee of China [7]. The protocol requires informed consent from participants in behavioural surveillance.

HIV testing

The blood specimens were collected on-site and tested at the provincial CDC. Two Enzyme-linked Immunosorbent Assays (ELISA) were used for testing for HIV antibodies. For blood specimen with a positive result in the first ELISA, a second (different) ELISA assay was performed (first HIV ELISA test: ELISA reagent: (Wantai Biotech, Beijing, China); second HIV ELISA test: ELISA reagent (Vironostika HIV Uniform II Ag/Ab. Akzo Organon Teknika, Netherlands). Specimens with positive results in both ELISA assays were considered positive for HIV.

Behavioural survey

Information asked in the questionnaire included recruitment setting, gender, age, marital status, place of residence, ethnicity, educational background, profession, monthly income, age at first drug use, injecting drug use and needle sharing, number of commercial sex partners in last 12 months, and STI symptoms in last 12 months. In addition to the behavioural risk factors, knowledge about transmission routes of HIV (six questions: "can HIV be recognized from appearance?"; "can HIV be transmitted by blood products?"; "by needle sharing?"; "from mother to child?"; "can transmission be prevented by condoms?", "by selecting uninfected partners?") and participation in prevention programs (needle exchange, methadone replacement, partner education, condom distribution, HIV counseling and testing, and receipt of AIDS/STI brochures) were assessed. Both sets of variables were dichotomized: all answers correct versus other and receipt of any intervention versus no intervention. We collected information about injecting drug use and needle sharing using different recall time intervals. For the analysis reported here we used only the information about ever injecting drugs and ever sharing needles.

Statistical analysis

During data cleaning 649 records were removed. In 621 cases no blood test was performed (blood samples were either insufficient for conducting the HIV test, blood taking was not successful or the participants did not comply with taking a blood sample). Respondents reporting first drug use at age below 14 years (27 cases) were excluded. When the fraction of missing responses for a specific variable was larger than 5% missing responses were coded as a separate category. The final sample size after data cleaning and exclusion of participants with missing responses (for missing values < 5%) was 5128. We assessed the effects of missing values on the results by repeating the analysis with separate categories for missing values (N = 5593).

A Chi-square test with significance level p ≤ 0.05 was used for evaluation of bivariate associations between sociodemographic and behavioural risk factors and HIV status. A joint analysis of all considered risk factors was carried out using random effects multiple logistic regression [22]. First, an analysis including all variables was performed with site as a grouping factor but without any covariates at the site level. Second, we investigated whether the observed regional heterogeneity could be explained by differences in the prevalence of HIV between regions. We generated a variable containing the information about prevalence in each region and included this as a site level covariate in the model. Finally, differences between different study sites were presented as frequencies of the respondents with different characteristics using the sample of 5890 participants with available information. The distribution of duration of drug use was analysed to assess the composition of the regional samples and the epidemiological situation of drug use in the regions.

Results

Characteristics of the total sample

In total, 6539 interviews were conducted in 21 surveillance sites and 5918 blood specimens were tested. About half of the respondents originated from detoxification centres, one third from the community, and the remainder from other recruitment settings ( Table 1). The sample was predominantly male with 5.6:1 male-to-female ratio. Age range was between 14 and 78 years, with a median of 30 years. Eighty percent of the participants initiated drug use before age of 30 years; about half of the sample had never injected drugs. Among the 2717 IDU 44.6% had ever shared needles. Eighty seven percent of the respondents had no commercial sex partners in last 12 months before the interview, 2% had at least one sex partner with a large variation in numbers, and for 11% the information was missing. Among 85 respondents who had commercial sex, 36.8% had used a condom during the last commercial sex (not shown). In the 12 months before the interview 14% of participants had experienced symptoms that could be caused by STI.

Factors associated with HIV in the total sample

The overall HIV-1 antibody prevalence was 5.0%; 6.6% among IDU and 3.5% among non-injecting drug users. Bivariate analysis showed several associations between Overall, the impact of regional differences (as random effects) was large, explaining 87% of the variation in the sample. However, the heterogeneity across sites was substantially reduced after adjusting for regional differences in HIV prevalence (explaining 62% of the total variation). At the same time, the model prediction (and the strength of the association between risk factors and HIV status) considerably improved (area under the receiving operator curve increased from 0.78 to 0.89). Since injecting drugs (with and without needle sharing) was the most prominent risk factor, only the interaction between this variable and regional prevalence was investigated and there was no statistical evidence of interaction (p > 0.05).

Regional diversity in the prevalence of HIV and in risk behaviours

There was a substantial heterogeneity among the surveillance sites ( Table 2). The HIV prevalence was higher in three regions (21% in Guangxi Nanning, 29% in Sichuan Liangshan and 10.8% in Xinjinag Kashi), up to 8% in 10 regions and no HIV cases were found in samples from 8 sites (Figure 1). The fraction of participants recruited in detoxifications centres varied between 0% and 100%, the proportion of participants who had never injected drugs varied between 2% and 98%. On the other hand, between 0.3% and 71% were drug users who had injected drugs and shared needles. Information regarding the number of commercial sex partners in the last 12 months was missing for a substantial fraction of respondents making the assessment of exposure difficult. There were also large differences in the proportions of participants tested in the past for HIV (7%-57%) and who participated in any drug use and HIV related interventions (0%-75%). There was also a substantial difference in the fraction of participants who knew all the correct answers to questions related to HIV ranging from 30% to 80%. Also, in terms of demographic aspects the samples strongly differed with the proportion of female participants ranging from 0% to 49% and the proportion of people from minority ethnic groups from 0% to 98%. There was no clear pattern of differences using cluster analysis; the only correlation between the variables was found for the fraction of respondents tested in the past for HIV and the fraction who participated in any intervention in the past, which is not surprising.

Distribution of the duration of drug use as an indicator of epidemiologic stage

There were substantial differences with respect to the duration of either any drug use or injecting drug use across the sites (Figures 2 and 3). In several sites the median duration was short (below 5 years) but in a few sites it was substantially longer (approaching 10 years). The distribution of the duration of injecting drug use in most sites followed the duration of any use, but the medians were usually lower.

Discussion

The prevalence of HIV differed markedly between different second generation surveillance sites, ranging from 0% in eight sites to above 20% in two sites (21% in Guangxi Nanning and 29.4% in Sichuan Liangshan). Both of the high prevalence sites lie in provinces bordering Yunnan province -the area where the HIV epidemic among IDU in China first started -or Myanmar. The site in Kunming, Yunnan, which was excluded from the analysis, had a prevalence of 54.0%. Another site with relatively high prevalence was Xinjiang Kashi with 10.8%, which is in the northwest near the border with Afghanistan. In previous years the prevalence in this site was found to be as high as 60% but in 2005 only some of the respondents were actually tested for HIV, so the estimate of 10.8% is probably an underestimate of the true prevalence. These results are similar to the regional distribution of HIV prevalence in China found in earlier surveillance data [3].

Whereas known risk factors like injecting drugs or sharing needles were associated with HIV infection at the individual level in logistic regression, they were not associated with HIV prevalence at the population level. On the contrary, the uncertainty in relating risk behaviour to being infected was greatly reduced when local HIV prevalence was taken into account in the model. This can be seen as an indication of the different phases of the HIV epidemic in different geographical regions, with some regions already heavily affected and others still at the beginning of a large outbreak. Risk behaviours like needle sharing were highly prevalent in many regions, including those with low HIV prevalence, indicating the potential for HIV spread in as yet unaffected regions. Interestingly, the timing of drug use and HIV epidemics differ between sites with some having a well established (injecting) drug use epidemic with low or high HIV prevalence and others where (injecting) drug use and possibly HIV are newly emerging (Figures 2 and 3). In view of the fact that drug users at the beginning of their injecting career are at increased risk of contracting blood borne infections [23][24][25] the distribution of exposure times can provide impor-tant information about the potential for future expansion of the HIV epidemic and for targeting interventions. Interventions were not associated with risk reduction or with a higher knowledge regarding HIV at the regional level. Judging from the distributions of drug use duration in different sites we conclude that sites with mature drug use epidemics lie in the south of the country around Yunnan province, then there is a band with sites where both long Participants were asked whether they were tested in the past for HIV, but not what their results were. Since a positive test result could have been associated with behavioural changes, we did not include current risk behaviour in the risk analysis model. For the same reason the assessment of current risk behaviour in a regional sample with high HIV prevalence could be biased. Therefore, in the analysis of risk factors for HIV we only used the information on ever injecting drugs and ever sharing needles. On the other hand, since there was no assessment of sexual risk behaviour in terms of ever/never, we used the information about the last 12 months. Clearly, this information could be misleading if behavioural changes followed a positive HIV test. The difficulty could be remedied if the questions about having been previously tested for HIV were complemented by asking about time and result of the test. Also having symptoms of sexually transmitted infections in the last 12 months cannot be simply interpreted as a proxy for a behavioural risk factor because these might facilitate the transmission of HIV infection.

A major limitation of the data set is the lack of a systematic sampling frame across the different sites. The marked heterogeneity in recruitment settings, with some samples coming mainly from detoxification centres and others from the drug user community makes any comparison between sites questionable. In particular, if detoxification is enforced by law as reported in [14] the selection of survey participants would be strongly biased. The representativeness of community-based samples is also unknown, although in some settings results obtained from convenience sampling or even snowball sampling can be unbiased [26,27].

Sampling from different subgroups of the drug user population might influence the distribution of duration of drug use. This might explain the striking difference in HIV prevalence between the two sites located in Xinjiang province. Also, the high HIV prevalence in Xinjiang Kashi among drug users who had just started their drug using career could have resulted from a sampling bias. Problems with lack of systematic sampling frames for drug users have been discussed extensively in the literature and the inherent difficulties have to be acknowledged [27]. Drug use is illegal in nearly all countries and stigmatised in most societies [28] so drug users are likely to hide their activities and avoid contacts associated with disclosure of drug use. In order to reduce these kinds of behaviours and increase participation in the study, our data collection procedures ensured that there was no threat of prosecution. Difficulties with reporting of illegal behaviour might also extend to other areas where some responses are socially desirable, resulting in missing values and untruthful responses. This might have occurred in this survey with the reporting of sexual behaviour. Several methods have been proposed for controlling social desirability bias and should be included in further studies of drug users [29].

The distribution of the duration of drug use (age of respondent minus age at first drug use) in sites with low HIV prevalence (< 1%) (Blue boxplots -any drug use, green -injecting drug use among IDU) Figure 2 The distribution of the duration of drug use (age of respondent minus age at first drug use) in sites with low HIV prevalence (< 1%) (Blue boxplots -any drug use, green -injecting drug use among IDU).  Recently, the so-called 'respondent driven sampling' method has been developed to ensure access to drug users who cannot be reached by prevention or health care workers [30,31]. Respondent driven sampling offers the opportunity to develop quantitative estimates of HIV prevalence based on the sample obtained with a sampling frame following strict rules in participant recruitment. However, the method is still being developed and its reliability has to be further investigated [32].

Second generation surveillance for injecting drug users should be further enhanced by surveys assessing knowledge and attitudes about risk behaviour, population size estimates for hidden populations, and hepatitis C surveillance [33]. While questions about knowledge of risk behaviours are already part of second generation surveillance in China, monitoring of hepatitis C could be a valuable addition. As the prevalence of hepatitis C in beginning injectors is seen as a proxy for incidence of new infections, changes in young injectors' hepatitis C prevalence can indicate changes in injecting risk behaviour.

The distribution of the duration of drug use (age of respondent minus age at first drug use) in sites with medium and high HIV prevalence (≥ 1%) (Blue boxplots -any drug use, green -injecting drug use among IDU) Figure 3 The distribution of the duration of drug use (age of respondent minus age at first drug use) in sites with medium and high HIV prevalence (≥ 1%) (Blue boxplots -any drug use, green -injecting drug use among IDU).

Surveillance sites

Conclusion

In conclusion, there are two main findings of this study. First, risk behaviours are prevalent among drug users in China and provide substantial potential for HIV spread in not yet affected regions. Second, methodological limitations of surveillance embedded into routine systems limit the usability of existing data. One of the goals of second generation surveillance according to WHO guidelines is the "better use of surveillance data to increase understanding and to plan prevention and care" [21]. Only when surveillance data lead to an understanding of the epidemic process can effective interventions be implemented. To reach that goal it is advisable that more care be taken to ensure standardized data collection. More capacity and support should be given to surveillance sites to make the investment into second generation surveillance worth the effort.

Table 1 :
1General characteristics of the study population (N = 5128), their bivariate association with HIV-1 antibody status, and results from random effects multiple regression model.Variables
Number
%
HIV +
p*
OR (95% CI)
p

Sample source
Detoxification center
2871
56.0
5.8
0.002
1
Community
1621
31.6
4.0
0.77 (0.55-1.09)
0.143
Others 1
636
12.4
7.6
0.66 (0.46-0.93)
0.019
Gender
Male
4348
84.8
5.5
0.940
1
Female
780
15.2
5.4
0.73 (0.50-1.07)
0.111
Age
<= 30
2676
52.2
6.2
0.012
1.04 (0.80-1.36)
0.750
> 30
2452
47.8
4.6
1
Marital status
live without partner
2821
55.0
5.1
0.195
1
live with partner
2307
45.0
5.9
1.00 (0.79-1.27)
0.991
Place of residence
Local
4393
85.7
5.9
< 0.001
1
Transient
735
14.3
2.5
1.27 (0.64-2.51)
0.490
Nation
Han
3794
74.0
2.9
< 0.001
1
Minority
1334
26.0 12.7
1.38 (0.93-2.04)
0.107
Education
<= 9 years
1710
33.4
9.0
< 0.001
1
> 9 years
3418
66.6
3.7
1.17 (0.89-1.53)
0.250
Profession
white collar
1744
34.0
8.6
< 0.001
1.18 (0.85-1.64)
0.313
blue collar
3384
66.0
3.8
1
Monthly income
<= 600
2147
41.9
6.9
< 0.001
1
601-2000
1127
22.0
4.5
1.56 (1.13-2.15)
0.007
> 2000
313
6.1
1.9
0.51 (0.24-1.08)
0.080
Missing
1541
30.1
4.7
1.05 (0.79-1.39)
0.751
Age at first drug use
<= 30
4189
81.7
5.5
0.05
1
> 30
707
13.8
4.1
0.71 (0.48-1.05)
0.088
Missing
232
4.5
1.6
0.79 (0.38-1.65)
0.532
IDU and sharing needles (ever)
no IDU
2411
47.0
3.7
< 0.001
1
IDU, no sharing of needles
1506
29.4
4.9
1.89 (1.37-2.61)
< 0.001
IDU, sharing needles
1211
23.6
9.5
3.15 (2.29-4.33)
< 0.001
Having commercial sex in last 12 months
No
4455
86.9
5.5
0.148
1
Yes
85
1.7
9.4
1.33 (0.66-2.70)
0.4265
Missing information
588
11.5
4.4
0.73 (0.50-1.06)
0.102
STD symptoms in last 12 months
No
4418
86.2
5.3
0.338
1
Yes
710
13.9
6.2
0.76 (0.55-1.05)
0.100

Table 2 :
2Prevalence and risk factors for HIV infection among drug users in all surveillance sites.Sites
Total
HIV prevalence %
(95% CI)

From
detoxification
center %

IDU and needle-sharing -ever (%)
Number of commercial sex partners in last
12 months (%)

Tested for HIV
in the past %

Participated in any
intervention %

Knows all
answers %

Female %
Ethnic
minority %

IDU without
needle-sharing

IDU with
needle-sharing

None
Any
Missing

1
Hebei
Shijiazhuang

361
0.0
(0.0-1.1)

13.0
47.1
19.8
95.0
0.3
4.7
24.1
17.7
65.1
18.8
3.5

2
Liaoning
Fushun

28
0.0
(0.0-12.1)

26.7
25.0
71.4
92.9
0.0
7.1
39.3
35.7
50.0
0
0

3
Anhui
Huainan

113
0.0
(0.0-3.2)

0
33.6
19.5
89.4
0.9
9.7
51.3
0
65.5
28.3
2.7

4
Anhui
Maanshan

141
0.7
(0.1-3.9)

0
65.0
19.3
92.9
0.7
6.4
36.2
9.9
80.1
33.3
3.6

5
Zhejiang
Hangzhou

267
0.0
(0.0-1.4)

99.3
32.8
20.1
91.8
0.4
7.9
22.5
39.0
68.9
29.6
4.5

6
Hubei
Jingzhou

409
0.5
(0.1-1.8)

98.1
41.9
28.6
89.2
2.9
7.8
21.3
26.2
79.5
14.4
0.2

7
Hubei
Xiaogan

352
0.9
(0.3-2.5)

72.1
37.7
19.1
77.0
0.3
22.7
7.4
66.2
74.7
27.3
2.9

8
Guangdong
Dongguan

407
5.9
(4.0-8.6)

39.2
44.8
21.9
76.9
4.4
18.7
57.0
11.1
37.3
9.8
11.8

9
Guangxi
Nanning

291
21.0
(16.7-26.0)

53.2
44.0
53.6
90.0
2.1
7.9
12.4
75.3
74.2
49.1
17.3

10
Chongqing
368
7.3
(5.1-10.5)

63.7
38.8
52.2
77.4
1.1
21.5
37.2
28.5
62.5
0
.3

11
Guizhou
Qianxinan

363
0.3
(0.1-1.6)

99.8
32.6
22.1
74.7
2.5
22.9
27.5
3.6
60.1
18.7
19.8

12
Guizhou
Tongren

376
4.5
(2.8-7.1)

49.9
30.6
26.9
72.3
1.3
26.3
19.7
37.2
30.3
27.1
53.3

13
Sichuan
Dazhou

223
0.4
(0.1-2.5)

50.0
58.7
33.0
73.1
4.9
22.0
28.3
7.6
57.4
20.6
.5

14
Sichuan
Leshan

189
2.6
(1.1-6.1)

56.5
14.9
61.2
98.4
0.5
1.1
41.3
69.3
51.3
15.9
2.8

15
Sichuan
Liangshan

415
29.4
(25.2-34.0)

76.7
15.4
20.8
90.1
1.9
8.0
26.7
12.0
49.6
6.7
94.9

16
Gansu Dingxi
193
0.0
(0.0-2.0)

64.5
1.0
1.0
100.0
0.0
0.0
38.9
6.2
42.5
3.6
3.2

17
Gansu
Wuwei

357
0.0
(0.0-1.1)

44.6
1.4
0.3
98.3
0.3
1.4
34.2
1.7
56.3
3.6
12.0

18
Qinghai
Haidong

163
0.6
(0.1-3.4)

0
8.1
21.2
96.9
0.0
3.1
10.4
62.0
40.5
7.4
49.1

19
Qinghai Haixi
478
0.0
(0.0-0.8)

62.6
3.6
2.1
95.6
0.4
4.0
50.6
11.3
60.3
3.8
35.4

20
Xinjiang
Kashi

297
10.8
(7.7-14.8)

17.2
12.5
5.1
94.9
3.4
1.7
23.9
14.8
38.0
3.4
98.3

21
Xinjiang Yili
99
0.0
(0.0-3.7)

56.9
46.9
34.7
92.9
0.0
7.1
22.2
41.4
72.7
0
98.0

term and new drug users are found in the samples, and finally there are some sites with mainly new drug users.Sentinel sites for second generation surveillance among drug users in 2005, in brackets the HIV-1 prevalence
Figure 1
Sentinel sites for second generation surveillance among drug users in 2005, in brackets the HIV-1 prevalence.
Green dots mark sites with HIV prevalence < 1%, yellow dots sites with HIV prevalence between 1% and 10%, and red dots
sites with HIV prevalence > 10%. 1 Hebei Shijiazhuang (0%). 2 Liaoning Fushun (0%). 3 Anhui Huainan (0%). 4 Anhui Maanshan
(0.7%). 5 Zhejiang Hangzhou (0%). 6 Hubei Jingzhou (0.5%). 7 Hubei Xiaogan (0.9%). 8 Guangdong Dongguan (5.9%). 9 Guangxi
Nanning (21.0%). 10 Chongqing (7.3%). 11 Guizhou Qianxinan (0.4%). 12 Guizhou Tongren (4.5%). 13 Sichuan Dazhou (0.4%).
14 Sichuan Leshan (2.6%). 15 Sichuan Liangshan (29.4%). 16 Gansu Dingxi (0%). 17 Gansu Wuwei (0%)). 18 Qinghai Haidong
(0.6%. 19 Qinghai Haixi (0%). 20 Xinjiang Kashi (10.8%). 21 Xinjiang Yili (0%).

Duration of drug use [years]Surveillance sites

Zhejiang
Hangzhou

Xinjiang Yili
Sichuan
Dazhou

Qinghai Haixi
Qinghai
Haidong

Liaoning
Fushun

Hubei
Xiaogan

Hubei
Jingzhou

Hebei
Shijiazhuang

Guizhou
Qianxinan

Gansu
Wuwei

Gansu Dingxi
Anhui
Maanshan

Anhui
Huainan

(page number not for citation purposes)
Outreach programs, contacts of known IDU, unknown. p* Chi-Square
AcknowledgementsWe thank Nicola Low for critically reading an earlier version of the manuscript.Competing interestsThe authors declare that they have no competing interests.Authors' contributionsMK and RTM designed and conducted the analysis, MK wrote the final version of the text. WZ performed data analysis and drafted the first version of the paper. AK laid contact with China CDC and commented on the manuscript. XS, LW, and FL participated in the design of the second generation surveillance in China and helped to develop the questionnaire; LW collected the raw data from all sentinel sites; FL commented and revised manuscript draft. All authors read and approved the final manuscript.Publish with Bio Med Central and every scientist can read your work free of chargePre-publication historyThe pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2334/8/108/pre pub
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Cost of HPV screening at community health campaigns (CHCs) and health clinics in rural Kenya

Jennifer Shen
Easter Olwanda
James G Kahn
Megan J Huchko
Cost of HPV screening at community health campaigns (CHCs) and health clinics in rural Kenya
10.1186/s12913-018-3195-6R E S E A R C H A R T I C L E Open Access
Background: Cervical cancer is the most frequent neoplasm among Kenyan women, with 4800 diagnoses and 2400 deaths per year. One reason is an extremely low rate of screening through pap smears, at 13.8% in 2014. Knowing the costs of screening will help planners and policymakers design, implement, and scale programs. Methods: We conducted HPV-based cervical cancer screening via self-collection in 12 communities in rural Migori County, Kenya. Six communities were randomized to community health campaigns (CHCs), and six to screening at government clinics. All HPV-positive women were referred for cryotherapy at Migori County Hospital. We prospectively estimated direct costs from the health system perspective, using micro-costing methods. Cost data were extracted from expenditure records, staff interviews, and time and motion logs. Total costs per woman screening included three activities: outreach, HPV-based screening, and notification. Types of inputs include personnel, recurrent goods, capital goods, and services. We costed potential changes to implementation for scaling. . After modeling a reduction in staffing, clinic per-screening costs ($25.69)  were approximately equivalent to CHCs.Conclusions: HPV-based cervical cancer screening through community health campaigns achieved lower costs per woman screened, compared to screening at clinics. Periodic high-volume CHCs appear to be a viable low-cost strategy for implementing cervical cancer screening.

Background

Introduction

Invasive cervical cancer is highly preventable through organized screening programs. However, globally over half a million women are diagnosed with the disease each year [1]. Cervical cancer is the most common cancer among women in Sub-Saharan Africa [2]. Nine out of 10 cervical cancer deaths occur in low-resource countries, with an 85% mortality rate in Sub-Saharan Africa [3,4]. While in developed countries a major decline in cervical cancer incidence and mortality occurred after the introduction of cytologic testing, many developing countries still lack effective screening programs [5].

High cancer mortality rates in developing countries may be due to inadequate health facilities and lack of personnel [6][7][8]. Organized screening programs are rare, and screening rates are low in developing countries, where only 19% of women are screened for cervical cancer as compared to 83% in the U.S. [9]. Simplified screening strategies employing testing for high-risk human papillomavirus (HPV), the causative agent in most cervical cancers, have been shown to reduce mortality from cervical cancer when coupled with outpatient treatment of precancerous lesions [5,10,11]. Programs implementing HPV testing in community-based settings may address the infrastructure and personnel limitations that prevent screening in some low-resource settings.

In Kenya, cervical cancer is the most frequent cancer among women, with about 4800 annual diagnoses and over 2400 deaths per year [12]. HPV testing is recommended by Kenya's Ministry of Public Health and Sanitation's Division of Reproductive Health, but is not reliably incorporated into most community or government programs [13]. Visual inspection with acetic acid-based screening is available at some clinics in Kenya. Screening rates are extremely low, at 13.8% nationwide and below 11% in rural areas in 2014 [14]. Even when screening is available, uptake can remain poor, due to low levels of knowledge of cervical cancer [15], and lack of awareness of HPV screening and the risks of cervical cancer [16]. Very few women recognize that routine testing is the primary way to prevent cervical cancer [17].

The community-based health fair is an important strategy to help people in developing countries access important preventive healthcare messaging and services. Community health campaigns (CHCs) occur over a short duration, delivering preventive health services at high-volume close to residential areas. CHCs have been effective in reducing maternal and neonatal mortality [18][19][20], prevention of malaria [21], HIV testing and delivery of antiretrovirals [22,23], tuberculosis detection [24], and increasing mental health care access [25]. A multi-disease CHC in rural Kenya found increased ART coverage and other health benefits with cost savings [26,27]. CHCs are part of a community-based healthcare model, which has gained traction in recent years because it can mobilize a large proportion of a community, and may be less resource-intensive than receiving preventive care at clinics.

In this study, we used costing data collected during a cluster-randomized trial in rural Kenya comparing uptake of HPV-based cervical cancer screening in community health campaigns and government clinics to compare the costs of the strategies. This is the first study to estimate the costs of cervical cancer screening with a community-based health campaign strategy, and furthermore, compares the costs of two cervical cancer screening interventions in Kenya. The cost estimations will help inform future implementers on funding and resource needs for cervical cancer screening.

Study description

This study was conducted as part of a cluster-randomized trial in 12 rural communities in Migori County in the former Nyanza Province of western Kenya between January and September 2016. Two-thirds of citizens of Nyanza live on less than $1 per day, and the province has the highest prevalence of HIV in the country. The target population was women who were eligible for and would benefit from cervical cancer screening per the Kenya Ministry of Health Guidelines: 25 to 65 years old with an intact uterus and cervix. Site selection was based on census data, health facility information, mapping, and demographic data [28]. Each community was a cluster of villages or sub-locations within a defined administrative boundary. The twelve communities were randomized in a 1:1 ratio using an allocation sequence generated by Stata 11 MP (StataCorp, TX). Six communities were randomized to a CHC intervention and six to HPV screening at government clinics. 1 Despite random assignment, women in CHCs were slightly older and more likely to have been screened for cervical cancer in the past [28]. Non-adjacent communities with a population of 4500 to 9000, with at least one health facility were identified. Both arms included three phases for which costs are presented: outreach and mobilization ("Outreach"), HPV-based screening ("Screening"), and notification of results and standard referral ("Notification"). Treatment services were equivalent between the arms, and therefore not costed. More information on the details of the study design can be found in the main outcomes paper, Huchko et al. 2017 [28]. Figure 1a and b summarize the workflows for each of the three phases for CHC communities and clinic communities, respectively. For CHC communities, each of the three phases (Outreach, Screening, and Notification) lasted two weeks. A subset of the CHC team conducted outreach through stakeholder meetings, information sessions, door-to-door mobilization, announcements using a public-address system, and posters. During screening, the entire CHC team traveled to different areas of the CHC community every morning and set up tents. The team then conducted a sequence of activities dedicated to screening for each woman visiting the tents: registration, group education, informed consent, and self-collection of screening specimens using the careHPV test kit. The lab technician then tested whether the woman was HPV-positive using the careHPV™ test system. After two weeks of screening activities, the CHC team moved onto notification of results and standard referral, which again lasted two weeks. Program assistants worked on using text messages and phone calls to notify women of their results, and Community Health Volunteers (CHVs) employed by the program conducted door-to-door home visits to notify women of their status. HPV-positive participants received referral to a treatment site located at Migori County Hospital.

In contrast to CHCs, each of the three phases were conducted concurrently at the six clinic communities for nine months total. After an initial series of key stakeholder meetings and information sessions prior to launching screening, one to two CHVs at each clinic conducted outreach through door-to-door mobilization and posters. During screening, CHVs, with supervision from the program coordinator and assistance from the data manager and program administrator, conducted all the screening activities at the clinic sites. They conducted group education, acquired informed consent, and collected HPV self-collection test kits. Notification also occurred throughout the nine months of screening at each clinic site. Since CHVs attended clinics fulltime and were in charge of notifying women who decided to visit clinics to receive test results, the CHVs were not involved with home visit notification, in contrast to CHCs. Instead, a team of program assistants would conduct home visits (1-3 days per month for each clinic) to notify women of their test results. The program assistants issued text messages and phone calls. HPV-positive women were referred for treatment at Migori County Hospital. More detailed information on the CHC and clinic community workflows is in the Additional file 1.

Methods

Micro-costing procedures, a method of valuation in health economics that involves the "direct enumeration and costing of every input consumed in the treatment of a particular patient" (Siegel et al. 1996), were applied to quantify resources used for HPV testing in each arm (clinic and CHC). Recommendations on economic evaluations of health system interventions made by Drummond and Jefferson (1996) and Weinstein et al. (1996) guided our analyses [29,30]. Direct costs were estimated from the health system perspective through a prospective analysis. We conducted an economic analysis of costs, where all inputs were costed, whether donated or subsidized, and costs of labor or wage rates were based on market wages and salaries. The micro-costing method was a hybrid of top-down and bottom-up, depending on the cost-item and data collected. For most cost items, we enumerated the item (based on numbers used for the community), multiplied the number by the cost of each item whether purchased or donated, and estimated unit (per-screening) costs from the sum of costs. Fig. 1 a Personnel team and activities of CHCs across 3 phases: outreach, screening, and notification. b Personnel team and activities of Clinics across 3 phases: outreach, screening, and notification Capital goods cost estimation was based on total costs from expenditure records. Capital goods are tangible assets including vehicle for transportation, tents for CHCs, and the careHPV ™ test system, which is a rapid batch diagnostic test designed for HPV-testing in low-resources settings (Qiagen, Cost Gaithersburg, Maryland). Costs of capital items were amortized on a straight-line basis over five years assuming no salvage value. For the vehicle and tents, the costs were allocated evenly across the 6 CHC communities. For the careHPV ™ test system, costs were allocated on a per-screening basis in each of the 12 communities. Communities with more screenings had higher costs allocated for the test system. More information on capital goods costing is in the Additional file 2.

A B

Personnel costs were estimated based on salary records. Costs of personnel for CHCs were based on the monthly salaries and the amount of time spent on each phase of the CHC. Since all phases for CHCs lasted two weeks, the personnel cost for a phase was the personnel's estimated salary for two weeks. Since clinic personnel worked on all six clinic communities concurrently, the total per-clinic personnel costs were estimated based on the number of women screened at each clinic, i.e. the proportion of women screened in each clinic community over the total number of women screened at clinics. More information on personnel costing is in the Additional file 2.

The primary data used to measure the costs of each intervention activity were expenditure records, interviews, time and motion logs, direct counts made by the costing lead, market rates, salary records, and estimations based on government data (MOH/NAS-COP) for facility space costs. Tables 1 and 2 of the (Additional file 3: Table S1 and Additional file 4: Table S2) show in-depth information on the data sources for quantity, price, and percent allocation towards program or non-program activities at CHC and clinic communities. The cost data was collected manually and subsequently electronically recorded on Excel workbooks. The cost items were classified under five input types: personnel, recurrent goods, services, capital goods, and facility overhead. The number of units and price of each input was recorded. Number of input units and price were converted into a total economic cost. All costs reported are in 2016 U.S. dollars, converted from Kenyan shillings at an exchange rate of 95 Kenyan shillings per U.S. dollar [31].

Once total economic cost of each item was calculated, each cost item was further allocated to program and non-program purposes. For example, time spent on administering pre-and post-screening surveys was a research activity, and was omitted from program cost estimates. To arrive at a unit cost (per completed screening), total economic cost of each item that was designated for program purposes was divided by the number of women screened at each community. The Additional file 2 provides further detail on how each category of cost (personnel, capital goods, facility, recurrent goods, services) was recorded and estimated.

Using the unit-cost estimations, the micro-costing data was then aggregated to estimate total costs per woman screened at a CHC and clinic community. In the costing analyses, we compared costs across the six CHC communities, and conducted a similar comparison across the six clinic communities. The costs per woman screened at each of the 12 communities were broken down by type of costing input (personnel, facility, services, recurrent goods, capital), and phase (outreach, screening, and notification). We did not include cost of treatment because the treatment model was identical between the CHC and clinic arms.

Sensitivity analysis

A series of sensitivity analyses were conducted to assess whether costs per screening estimated are sensitive to changes in implementation. First, cost estimates for CHCs were conducted under two alternate transportation In the second sensitivity analysis, we explored a scenario where clinic personnel are used more efficiently, and therefore measure the costs of reducing the number of personnel dedicated to clinic screening. The research team, based on observation and experience, believed that each clinic could have hired another CHV who was dedicated to conducting home visits for the notification phase, rather than using program assistants. During the study, clinics each had five program assistants conduct home visits 1-3 times a month, which may have driven up personnel costs for notification at clinics. The team also believed that the program coordinator and data manager could have reduced their time spent on clinic activities, and delegated more responsibility to a program assistant, who would manage operations and data collection. The maximum number of clinics a program assistant could have managed was two clinics. Therefore, in the sensitivity analysis, a program assistant would dedicate full time to monitoring screening and notification at two clinics, where he or she would oversee the progress of the CHV and make appointments with certain patients who need more attention. The program coordinator would commit 5% time to each clinic, and lab technicians would dedicate time to each clinic based on the number of women screened (same as before, average 10% time across clinics). Each clinic would therefore only have four team members: CHV, program assistant, program coordinator, and lab technician. The personnel ratio per clinic would reduce from 2 to 1.65.

Finally, we also implemented cost regressions to estimate the fixed cost for the screening program and the marginal costs of each screening. We estimated total cervical cancer screening costs, one from each community, and regressed total costs on numbers of women screened in each community. Each cost regression has 6 observations, one from each CHC and clinic community.

Results

Between January and September 2016, 2899 women were screened at the six CHC communities and 2042 at the six clinic communities. In Tables 1 and 2, the site-by-site cost breakdown per woman screened is presented. The mean cost per woman screened was $25.00 for CHCs and $29.56 for clinics. The mean number of women screened was 482 per CHC and 340 per clinic. 3 Differences between CHCs and clinics Figure 2a and b depict cost breakdowns of CHC and clinic community costs, by cost type and phase, respectively. Personnel costs were higher for clinics ($14.27 per screening) compared to CHCs ($11.26 per screening). Capital costs were also higher for clinics ($5.55) compared to CHCs ($2.68), due to the need for clinics to pay for renting space at facilities. 4 Services were almost equivalent ($2.48 for CHCs and $2.29 for clinics). Recurrent goods were the only input type that was slightly higher for CHCs, at $8.59 per screening compared to $7.45 for clinics; this reflects additional transportation costs associated with executing a CHC (e.g. fuel costs and transport reimbursements).

When comparing CHCs and clinics across activities, per-screening costs were higher for CHCs only in outreach ($3.34 vs $0.17 at clinics), whereas per-screening costs were higher for clinics for screening ($17.59 for CHCs and $21.88 for clinics) and notification ($4.18 for CHCs and $7.51 for clinics). CHCs had higher costs for outreach because they conducted more intensive outreach activities than clinics, including door-to-door mobilization, public address systems, and meetings with chief elders. A larger team of personnel was involved with CHC outreach activities. At clinics, the only outreach conducted was door-to-door mobilization by CHVs. During door-to-door mobilization at clinics, CHVs visited homes one-by-one and provided information on cervical cancer screening conducted at clinics. Costs of mobilization were low for clinics because daily compensation of CHVs is lower than wage rates for personnel staff. In clinic communities, CHVs also put up posters with further information about screening. The outreach activities at CHCs generated higher numbers of women screened at CHCs compared to clinics (483 vs. 340) in a shorter period of time (10 days vs. nine months). Figure 3 shows further breakdown of personnel costs by cadre. While CHCs had higher costs for data management and other miscellaneous personnel costs (e.g. driver, security, tent setup team), clinics had higher personnel costs associated with hired program assistants (type 1 was involved with monitoring screening at the clinics, and type 2 were involved with home visits during notification). and compensation rates do not change much across CHCs, but per-screening personnel costs for the screening phase among CHCs range from $4.80 to $9.00. Similarly, for capital goods, the cost of capital goods such as the careHPV test system, tablet, and pipettes, do not change for different CHC communities. Per-screening capital goods costs for the screening phase therefore vary because of differences in numbers of women screened at each community. Variation in recurrent goods ($8.09 to $8.82 per screening during the screening phase) occurred across communities because of difference in fuel costs and amount of supplies required for screening. Variation in services offered for notification (texts, calls, and home visits) occurred because of differences in notification options selected at each CHC. Variation in cost per screening for clinics was primarily due to differences in costs of facility space. For most clinics, facility rent was not entirely allocated for cervical cancer screening, because other health services such as HIV testing were conducted in the same space. One clinic, however, had considerably higher facility costs because 100% of a clinic room was allocated to CCSP screening. Clinic personnel designated to screening activities shifted across the six clinics, which is why there was slight variation in clinic-level personnel costs. The team of personnel involved with notification did not change across clinics, and therefore, personnel costs for notification did not vary.

Differences across sites, for CHCs and clinics

For capital goods, all six communities used the central careHPV Test System and shared two motorbikes. Similar to personnel, clinic-level costs of capital goods were estimated based on proportion of women screened at each clinic, and therefore did not vary much across the clinics.

Sensitivity analyses

In the sensitivity analysis in which transportation strategies were varied, the average cost per screening for CHCs under both transportation scenarios declined very slightly, at $23.12 per screening with a range of $20.53 to $27.51 for the transport reimbursement scenario, and at $24.98 per screening with a range of $21.68 to $30.42 for the rented vehicle scenario. The CHC cost per screening estimates are therefore not very sensitive to changes in transportation type used for the program.

In the second sensitivity analysis, personnel costs at clinics were estimated with an adjustment to team structure. During the program, a total of 11 individuals comprised clinic personnel: eight working on screening, and nine working on notification, with a personnel ratio of about two per clinic. Additionally, each clinic had one CHV working on both screening (trained to help women self-sample using HPV self-test kits) and notification. Table 3 shows the results of the sensitivity analysis. Average cost per screening reduced to $25.69 for clinics, with a range of $20.57 to $32.12. The results measured a change in personnel efficiency. The average cost per screening from the sensitivity analysis was similar to per-screening costs of CHCs, differing by only 69 cents.

Finally, we conducted two cost regressions for the six CHCs and six clinic communities to empirically estimate the program fixed cost per community screened and the marginal cost of a screening. For CHCs, the regression results showed an intercept of $5368.24 representing fixed costs, and marginal costs of $13.89, representing variable costs. For clinic communities, the regression results showed an intercept of $982.27 representing fixed costs, and marginal costs of $26.68, representing variable costs. CHCs had higher fixed costs than clinics, and clinics had higher marginal costs than CHCs.

Discussion

This study determined and compared the costs of HPV based cervical cancer screening for women in Kenya through two distinct strategies: HPV self-sampling offered in brief community health campaigns (CHC) designed to rapidly screen women, or in government clinics. The study found that the CHC was less expensive ($25.00 per screening) than HPV based-screening at government clinics ($29.56 per screening), at a difference of $4.56 per screening. The findings can help inform the planning and design of future cervical cancer screening programs implemented in similar low-and middle-income regions with high-burden of disease. Notes: Estimated per-screening costs at each of the 6 control clinics based on a reduction in number of personnel involved with screening and notification. Each clinic is designated 1 CHV dedicated to both screening and notification. One program assistant dedicates 100% of their time to monitoring screening and notification at 2 clinics. The program coordinator will commit 5% time to each clinic, and lab technicians dedicate time to each clinic based on the number of women screened (same as before, average 10% time). Each clinic will therefore only have 4 team members: CHV, HE, program coordinator, and lab technician. The personnel ratio per clinic will reduce from 2 to 1.65. No other changes were made to estimated costs Etiologies of increased costs throughout the cervical cancer prevention cascade can help identify areas to target and develop more cost-efficient strategies for future evaluation. Costs per screening differed between CHCs and clinics for two main reasons. First, CHCs may have had lower per-screening costs than clinics because of higher numbers of women screened on average in CHC communities. CHCs were likely more successful at recruiting women for cervical cancer screening because of more intensive outreach and mobilization efforts. Outreach and mobilization for CHCs involved a larger team than clinics, and included several more high-visibility promotion activities including involvement of community elders and public-address systems. Since CHCs screening activities were placed in prominent locations within each community, the excitement surrounding a community health "event" may have also encouraged more women to get screened. The more intensive recruitment efforts dedicated to CHCs brings up the question of whether more intensive recruitment for government clinics would have led to more screenings in control communities. The CHC was mobile, switched locations each day of the 10-day campaign, and was more integrated with communities. Thus, it is difficult to attribute the higher numbers of women screened to simply more outreach, or the combination of outreach and CHC integration and visibility within communities. Future studies could incorporate equivalent outreach efforts for government health clinics to identify the effect of a screening site that is mobile and integrated within the community.

The second possible reason for higher costs per screening in clinics is that there was too much staffing allocated for clinics during the study, especially with the lower numbers of women screened. Given the possibility that personnel size for implementation at clinics was larger than necessary, we simulated costs under the scenario of a smaller team dedicated to clinic screening activities (18% decline in personnel) in the sensitivity analysis. For this hypothetical scenario, average costs per screening were approximately the same as average costs per screening at CHCs ($25.69 versus $25.00). It is difficult to conclude that all other activities at the clinic, including the number of women who visited the clinic for screening, would have been exactly equivalent under this hypothetical personnel structure in the sensitivity analysis. Future studies could explore effectiveness of clinic screening with fewer personnel involved with screening and notification, and the cost models are helpful adjuncts to discussions with program planners.

This was the first study to measure costs of self-sampling in Kenya using cost data captured directly from program implementation, including not just the screening, but the essential mobilization and notification components. Furthermore, this study was the first to compare the costs of two different implementation strategies for cervical cancer screening in Kenya, a low income, and high-burden country. Prior to this study, cervical cancer costs in Kenya were only measured using a predictive model [32] and through a time and motion study of integrating cervical cancer screening at one HIV clinic [33]. The costs of implementation were comprehensively estimated through collection of program data, which can be used as reference for future cervical cancer screening programs in Kenya.

This analysis had several limitations. The first is the low number of communities where CHCs were implemented; Six CHC communities were compared with six control communities. Including more communities in the study may have allowed for more robust comparisons in costs per screening. The second, an efficiency curve occurred for CHCs that may have caused the first campaigns to be more expensive. The final consideration is whether CHCs can feasibly be scaled-up as a nationwide program, and whether government or donor support could fund regular CHCs.

Conclusion

This study compared six short-term cervical cancer screening community health campaigns with six communities offering cervical cancer screening at clinics in rural Kenya. Results show that mean costs per woman screened at the six CHC communities were slightly lower than mean costs per woman screened at the six clinic communities ($25.00 compared to $29.56). In our experience, cervical cancer screening at community health campaigns increased screening coverage and lowered per-women screening costs as a result. Future cervical cancer screening programs should consider the option to integrate screening activities at community health campaigns, with an emphasis on increasing the efficacy of outreach to increase screening numbers and improve efficiency. Endnotes 1 The 6 CHC communities were Oyani, Ongito, Nyarongi, Nyamanga, Obware, and Ageng'a. The 6 clinic communities were Diruma, God Jope, Got Kachola, Nyamasare, Ondong, and Winjo. There were 6 communities where HPV screening was conducted through clinics, but a total of 10 clinics. 2 We assume that each liter of gasoline translates to 96 Kenyan shillings. 3 The costing table for each of the 10 clinics is in the Additional file 5: Table S3. 4 In this figure, facility costs incurred at clinics were labeled as a capital cost, since rent represents payment towards a capital investment.

Variation in costs per woman screened across CHCs can largely be attributed to differences in numbers of women screened at each CHC. For example, personnel team size A B Fig. 2 a and b CHC and clinic average cost per screening estimates, color coded by cost type and phase Fig. 3 Bar graph of CHC and clinic personnel cost estimate breakdowns, by cadre

Table 1
1Cost estimations, in 2016 USD, by phase and cost type, per woman screened with self-collected HPV in community health campaigns in six communities in 2016Community Health Campaign Community #

1
2
3
4
5
6

Outreach

Capital Goods
0.51
0.92
0.67
0.72
0.64
0.53

Personnel
2.00
3.18
2.32
2.26
1.66
1.18

Recurrent Goods
0.01
0.02
0.02
0.56
0.24
0.25

Services
0.32
0.51
0.35
0.40
0.43
1.01

Outreach Subtotal
2.84
4.63
3.36
3.94
2.97
2.97

Screening

Capital Goods
1.18
1.82
1.38
1.47
1.59
1.15

Personnel
5.09
9.00
7.72
6.40
4.91
4.80

Recurrent Goods
8.11
8.82
8.42
8.70
8.21
8.09

Services
2.36
2.31
1.16
1.48
1.30
1.46

Screening Subtotal
16.74 21.95 18.69 18.05 16.02 15.51

Notification

Capital Goods
0.51
0.92
0.67
0.72
0.64
0.53

Personnel
3.47
2.17
3.47
3.28
3.66
2.78

Recurrent Goods
0.08
0.02
0.08
0.07
0.04
0.05

Services
0.33
0.51
0.12
0.13
0.48
0.22

Notification Subtotal 4.40
3.63
4.34
4.21
4.82
3.58

Women screened
602
337
461
430
483
586

Cost per screening
23.98 30.21 26.39 26.21 23.81 22.06

Table 2
2Cost estimations, in 2016 USD, by phase and cost type, per woman screened with self-collected HPV in clinics, in six communities in 2016Clinic Community #

1
2
3
4
5
6

Outreach

Personnel
0.10
0.19
0.19
0.14
0.12
0.20

Recurrent Goods
0.01
0.02
0.02
0.01
0.01
0.02

Outreach Subtotal
0.11
0.21
0.21
0.15
0.13
0.22

Screening

Capital Goods
1.89
2.20
1.33
1.66
2.09
3.57

Facility
10.78 2.49
3.32
2.17
0.41
2.09

Personnel
8.10
6.72
6.63
6.63
6.75
6.83

Recurrent Goods
7.24
7.73
6.43
6.86
7.98
9.70

Services
1.70
2.04
2.28
1.84
1.55
2.82

Screening Subtotal
29.73 21.17 19.99 19.17 18.78 25.01

Notification and Standard Referral

Personnel
5.82
8.00
7.96
6.85
6.31
8.29

Services
1.42
0.26
0.00
0.09
0.05
0.00

Notification Subtotal
7.24
8.26
7.96
6.94
6.35
8.30

# women screened 326
502
338
450
269
157

Cost per screening
37.08 29.64 28.16 26.26 25.27 33.53

Notes: Community #2 had a total of 3 separate clinics; Community #3 had a
total of 2 separate clinics; and Community #4 had a total of 2 separate clinics

Table 3
3Sensitivity analysis with modeled costs per screening based on a reduction in personnel responsible for screening and notification at clinicsClinic Community # Observed Clinic
Costs (from Table 2)

Per-screening costs at
each clinic after personnel
adjustment

1
$37.08
$30.93

2
$29.64
$26.62

3
$28.16
$24.89

4
$26.26
$22.27

5
$25.27
$20.57

6
$33.53
$32.12

Average cost per
screening

$29.56
$25.69

AcknowledgementsWe are grateful to the National Institutes of Health, National Cancer Institute for funding support. We are thankful for the program officers and costing team at the Kenya Medical Research Institute, specifically Evans Otieno, who was the costing assistant, for their dedication and ensuring the success of the program. We also acknowledge Dr. Elizabeth Bukusi for her scientific guidance, Katelyn Bryant-Comstock for providing research support and reviewing the manuscript, and members of the IHPS and PCRF Works-in-Progress research seminar for their helpful comments and suggestions.FundingThis work was supported by the National Institutes of Health, National Cancer Institute (ClinicalTrials.gov primary registry and trial identifying number: NCT02124252).Availability of data and materialsThe datasets generated and analyzed are available in the Harvard Dataverse repository, https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/ DVN/HACSMB Ethics approval and consent to participate We obtained IRB approval from the Kenya Medical Research Institute (KEMRI), Duke University, and University of California -San Francisco. The identification for KEMRI is SERU 2918, for Duke is Pro0007742, and for UCSF is CHR 14-13,698. We obtained written informed consent from all participants.Competing interestsThe authors declare that they have no competing interests.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Additional filesAdditional file 1: Detailed information on CHC and Clinic Workflow: Includes in-depth information on how screening was set-up and facilitated by the implementation team and providers at both CHCs and clinics. (DOCX 17 kb) Additional file 2: Further detail on cost estimation methods of each cost type (personnel, capital, facility, recurrent goods, and services): Includes in-depth information on the sources, data collection method, and cost estimation method of each of the major cost category types. Includes how personnel costs were allocated across sites, and how capital costs were amortized. (DOCX 16 kb) Additional file 3:Table S1. Major cost items for each cost type for CHCs, with data source information on quantity, unit and total costs, and % allocation across purposes. (DOCX 19 kb) Additional file 4:Table S2. Major cost items for each cost type for Clinics, with data source information on quantity, unit and total costs, and % allocation across purposes. (DOCX 18 kb) Additional file 5:
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