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JAIDS Journal of Acquired Immune Deficiency Syndromes:
doi: 10.1097/QAI.0b013e3181ab6d2e
Epidemiology and Social Science

Time Trends and Regional Differences in the Prevalence of HIV Infection Among Women Attending Antenatal Clinics in 2 Provinces in Cameroon

Kuate, Seraphin PhD, MSc*; Mikolajczyk, Rafael T MD, MSc*; Forgwei, Gideon W†; Tih, Pius M PhD†; Welty, Thomas K MD, MPH†; Kretzschmar, Mirjam PhD*‡§

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Author Information

From the *Department of Public Health Medicine, School of Public Health, University of Bielefeld, Bielefeld, Germany; †Cameroon Baptist Convention Health Board, Bamenda, Cameroon; ‡Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands; and §Centre for Infectious Disease Control, RIVM, Bilthoven, The Netherlands. Seraphin Kuate is now with the Vaccine Branch, National Cancer Institute, NIH, Bethesda, MD 20892.

Received for publication October 1, 2008; accepted April 7, 2009.

S. Kuate and R. T. Mikolajczyk contributed equally to this work and therefore share the first authorship.

Correspondence to: Mirjam Kretzschmar, PhD, Centre for Infectious Disease Control, RIVM, Bilthoven, The Netherland (e-mail: mirjam.kretzschmar@rivm.nl).

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Abstract

Background: HIV prevalence time trends vary in sub-Saharan African countries. In the present study, we studied time trends and regional differences in the prevalence of HIV infection among women attending antenatal care clinics (ANC) in 7 sites located in 2 provinces in Cameroon.

Methodology: As part of ANC, 16,626 women consented to HIV testing from 2000 to 2006. Sociodemographic and risk factor information was collected during the initial 3 years of the study. This information was aggregated within sites and used as site-level covariate in multilevel logistic regression analysis.

Results: HIV prevalence decreased significantly in women younger than 20 years from 13% in 2000 to 5% in 2006. Age-specific prevalence varied among the sites, with a peak prevalence occurring more often at a higher age in 2004-2006 versus 2000-2003, suggesting a reduction of HIV incidence over time. There was a substantial heterogeneity across sites in HIV prevalence, which was lower in sites where women had earlier sexual debut and were less well educated.

Conclusions: ANC surveillance indicates a decreasing trend in HIV prevalence in the studied sites in Cameroon. Cultural differences might have accounted for the heterogeneity of HIV infection observed across sites, which call for tailored interventions.

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INTRODUCTION

Although many countries in the sub-Saharan Africa are above the threshold for a generalized epidemic with a HIV prevalence >1% among pregnant women, there is still considerable heterogeneity in HIV prevalence across countries and within countries.1-9 Cameroon is one of the countries in central Africa most affected by HIV.10-14 The Demographic and Health Survey (DHS) conducted in Cameroon in 2004 found an overall prevalence of 5.5% in a population-based sample in the age group 15-49 years and substantial regional differences in HIV prevalence varying from 8.7% in Northwest Province to 1.7% in North Province.15

Sentinel surveillance methods such as antenatal care (ANC) surveillance have also been used to obtain estimates for HIV prevalence in most African countries. Despite many problems that the estimation of the prevalence of HIV in the population from ANC data face,16-18 the surveillance of sexually transmitted diseases in high-risk populations such as pregnant women remains by far the most affordable method in resource-limited countries. In Cameroon, the sentinel surveillance programs of HIV infection among pregnant women were first implemented in 1989.19 Previous studies indicated an increase in the prevalence of HIV infection among ANC attendees from about 1% in 1990 to about 10% in 2000.20 However, little is known about more recent time trends and regional variation in HIV prevalence among pregnant women, due to the lack of systematic data collection over time.

In the present study, we report trends in the prevalence of HIV in ANC surveillance over 7 years in 7 health facilities in the Northwest and Southwest Provinces in Cameroon. Using multilevel models to combine the disease status information with aggregated risk behavior information collected during the initial 3 years, we demonstrate that there is considerable variation in time trends and distribution of HIV infection between communities in close geographical proximity to each other.

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MATERIALS AND METHODS

Study Population and Data Collection

The Cameroon Baptist Convention Health Board (CBCHB) program for prevention of mother-to-child HIV transmission (PMTCT) collected the data analyzed in this study.21 CBCHB supports PMTCT services in 6 of 10 provinces of Cameroon, but only data from 7 sites in 2 provinces were available for this analysis. The CBCHB Institutional Review Board approved this study.

The health facilities selected for the study represented urban (Banso and Nkwen-Bamenda in the Northwest Province and Mutengene in the Southwest Province) and rural (Bangolan, Belo, Mbingo, and Ndu in the Northwest Province) areas. Pregnant women who attended antenatal clinics were counseled to undergo HIV testing as part of routine ANC (opt-out approach). After their verbal consent, the laboratory performed routine ANC blood tests, including an HIV serology test. For HIV testing, 3 rapid HIV screening tests were used sequentially as previously described.21 Peripartum single-dose nevirapine was provided to HIV-positive mothers and their babies as prophylaxis for HIV infection. Sociodemographic and risk factor information was collected using a standardized interview, including age, number of years of school completed (categorized into education level: 0, no education; 1-7, primary education or less; and >7, secondary education or more), and marital status. Additional variables (the age at first sex, the number of sex partners during the last 3 years, the number of previous pregnancies, and the number of other wives of the patient's husband) were collected only during a limited period (mostly 2000-2003) in the different sites with few missing data during this period. We calculated the proportion of women with given characteristics in each of the sites.

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Statistical Analysis

The data were entered into Epi-info and then exported to SAS for further analyses. Simple tabulation was used for descriptive and χ2 tests for bivariate analysis. Confidence intervals for HIV prevalence stratified by covariates were obtained from binomial distributions. Age-dependent prevalence by site and year of data collection for 2 periods (2000-2003 and 2004-2006) was estimated using nonparametric regression models with probability of the binary outcome as dependent variable using gam library in R.22 To account for the hierarchical structure of the data, random effects logistic regression models were applied for the multivariate analysis of risk factors associated with HIV infection using PROC GLIMMIX in SAS (SAS Institute, Cary, NC). First, only a model with the patient-level covariates age, marital status, years of schooling, and year of data collection (treated as categorical variable) was fitted to the data. Second, each of the site-level covariates (proportion of women below 17 years at first sex, who had more than 1 sexual partner during the last 3 years, with first pregnancy and living in polygynous marriage) were included in the model separately. The overall mean age at first sex was 17.3 (SD = 2.6) years, ranging from 17.0 to 17.7 years in the 7 sites. Third, all second-level covariates, which were significant in the separate analyses at P values <0.1, were included jointly in the final model. This modeling strategy follows general recommendations for logistic regression and multilevel analysis.23,24 To assess the time trends in the prevalence of HIV, we used a random effects logistic regression model with time in years as a continuous variable (centered at 0). First, we tested whether there is an overall trend in prevalence adjusting for age, marital status, and education. Second, we used 2-way interactions between all these variables and the year of data collection to test whether the trends differed between subgroups. Interaction terms that were not significant according to Wald test at P < 0.05 were excluded. Third, we investigated whether time trends differed significantly between different sites in different categories using a 3-way interaction term.

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RESULTS

Characteristics of the Participants and Study Sites

Of 17,223 women who attended ANC between February 2000 and July 2006 in the participating PMTCT sites, 16,626 (97%) consented to HIV testing and were included in the study. In 2000, the number of participants was lower because only 4 health facilities provided PMTCT services in this year. The basic characteristics of the participants are presented in upper part of Table 1, whereas characteristics of sites are shown in lower part of Table 1.

Table 1
Table 1
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The mean age of ANC attendees who were included in the present study was 25.5 ± 6.2 years. ANC participants from Bangolan (a rural site) were younger and less well educated than women in other communities, had the highest proportion with sexual debut below age 17, and had a high proportion of women living in polygynous marriages. Participants in Ndu, a rural site close to urban Banso, were better educated, mostly monogamous, rarely had more than 1 sexual partner, and had lowest fraction of women having their first sex below 17 years of age. Belo and Mbingo, both rural areas in close proximity to each other, had similar populations in terms of marital status but differed markedly in terms of the fraction of women living in polygynous marriages. Although Mbingo is a rural community, the facility is a larger referral hospital and many women from Bamenda, the provincial capital, travel there for ANC and delivery. Belo has the lowest educated population and highest fraction of parous women. Banso, Nkwen-Bamenda, and Mutengene, all urban or semiurban sites, have a population with highest education. Nkwen-Bamenda has a substantial fraction of women living in polygyny, which is less common in the 2 remaining sites. In contrast, Banso and Mutengene have the highest proportions of women having more than 1 sexual partner in the past 3 years. In all 3 sites, the fraction of women having sex below the age of 17 years was lower than in the rural regions Bangolan and Belo.

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Prevalence of HIV by Different Characteristics (Bivariate Analysis)

The overall prevalence of HIV infection among ANC attendees recruited in the 7 health facilities was 9.9% (Table 2). Prevalence was 7.3% in younger women (<20 years), 11.1% in 20- to 29-year-old women, and 8.4% in women aged 30 years and older. HIV prevalence was considerably higher among single/divorced or widowed women compared with married ones. The former also reported more sexual partners during the last 3 years (mean 2.1 versus 1.4 reported by married women). HIV prevalence increased with educational level and was more than twice higher in women with a secondary education compared with women having no education. In 5 of the 7 sites, the HIV prevalence was around 11% but much lower in the 2 remaining sites Bangolan and Belo, both rural sites.

Table 2
Table 2
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Multivariable Analysis of Risk Behaviors Relevant for HIV Infection

In multilevel multivariable analysis, the risk associated with age followed a similar pattern as in the bivariate analysis: The risk of HIV was highest for the age group 20-29 years and decreased again for women aged 30 years and older. The risk was also higher by 80% for single, divorced, or widowed women compared with married women and increased with the education level (Table 3). When only the above variables were included in the model, there was a significant heterogeneity across sites in relation to the HIV prevalence. After adjustment for the characteristics of the sites, the proportion of women with young age at first sex (<17 years) was negatively associated with HIV prevalence, with lower HIV prevalence observed in communities where the fraction of women with sexual debut before age 17 was larger. The proportion of women with more than 1 sex partner in the last 3 years was not significantly associated with the local HIV prevalence. The same was true for women living in polygynous marriage or for nulliparous ones. However, rural communities with a high proportion of women living in polygynous marriages had a lower HIV prevalence in univariate analysis.

Table 3
Table 3
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Time Trends in HIV Prevalence

The overall prevalence of HIV in the sample significantly decreased during the study period, ranging from about 11% in 2000 to about 8% in 2006 (P value < 0.001). The trend did not differ by marital status or by educational level, but there was a significant difference between age groups. A statistically significant decreasing trend was observed for women in the youngest age group (<20 years) and slightly less pronounced for women aged between 20-29 years (Fig. 1). In the oldest age group (≥30 years), there was some indication of an increasing prevalence over time but the trend was not statistically significant.

Figure 1
Figure 1
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Age-Specific HIV Prevalence in Different Sites

The age-specific HIV prevalence peaked for most sites around the age of 25 years (Fig. 2). Younger women had a lower seroprevalence in the latter period (2004-2006) compared with the earlier period (2000-2003) in all except 2 communities. The maximum prevalence shifted toward a slightly higher age in the latter period in most sites. The difference between the 2 sites with lower prevalence (Belo and Bangolan) and the remaining sites could be explained by the fact that in the former sites prevalence did not increase with age in the period 2000-2003; prevalence at younger ages was only slightly lower than in other sites. In 2004-2006, the prevalence in younger age groups decreased in the remaining sites and approached levels observed in the sites with lower prevalence. Each community had a unique pattern of age-dependent prevalence when the 2 periods were compared.

Figure 2
Figure 2
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DISCUSSION

We analyzed time trends in the prevalence of HIV infection among pregnant women attending ANC in 7 health facilities in 2 provinces in Cameroon. The overall prevalence in the sample was 9.9% with significant differences between the study sites. The time trend analysis indicated an overall decreasing prevalence of HIV with an average annual decrease of about 0.5%. The largest decrease in the trends of HIV infection was in women younger than 20 years, while the peak prevalence shifted toward older age groups over time. Furthermore, we found that risk factors on site level (ecological analysis) were associated with HIV prevalence in different ways than individual risk factors as recently reported elsewhere.25 For example, sites with a larger fraction of women with sexual debut before the age of 17 had a lower HIV prevalence than sites where that fraction was smaller. In contrast, early age at first sex was shown to be a risk factor for HIV infection on an individual level in the same study population.21 Similarly, communities with lower HIV prevalence were those with a larger proportion of women living in polygynous marriages, who had more births and lower average levels of education, whereas the fraction of women having more than 1 sexual partner in the last 3 years was not associated with HIV prevalence on the ecological level.

Our findings showing a slow decline of HIV prevalence in younger age groups are in line with observations in other sub-Saharan countries.26-28 This positive finding is important because HIV seroprevalence in the youth is felt to be a proxy measure for incidence.19,29 In a study carried out in 2002 by Meekers et al30 in 2 urban cities in Cameroon, 70%-80% of youth reported consistent use of condoms with casual partners. Although information about condom use is not available for regions where our data were collected, youth-targeted prevention programs, such as “100% Jeune,” have been implemented in Cameroon by the government and non-governmental organizations and have shown to significantly contribute to the reduction of risk behavior among youth.31 Although all women were pregnant at the time of the data collection, they-and their current partners-might have had protected sexual activities in the past (eg, condom use), reducing the risk associated with intercourse leading to the current pregnancy.

The slightly increasing trend in the oldest age group might represent a cohort effect with older women reflecting the risk of HIV infection accumulated over a longer period or it might result from increasing survival due to antiretroviral therapy (ART) as reported in Cameroon and elsewhere.32,33 ART programs have been implemented in many heath facilities nationwide in the frame of PMTCT programs or for other HIV/AIDS treatment programs.21,32,34,35 In 2007, UNAIDS reported a 50%-75% coverage of antiretroviral drugs in Cameroon and women who received ART drugs represented about 65% of people in need.36 In 2008, the national AIDS control committee reported that more than 80% of the districts in Cameroon were covered with the distribution of HIV testing and treatment tools.37 A recent study has reported the benefits of ART in the survival of patients with HIV/AIDS in Far North province in Cameroon.32

Similar to our observations, a DHS conducted in a nationally representative sample in Cameroon in 2004 showed an increasing prevalence of HIV infection with age up to 25-29 years, followed by a decreasing prevalence in older age groups.38 The same pattern was also observed in other western and eastern African countries including Ghana, Burkina Faso, Kenya, and Tanzania38 and probably represents a cohort effect. In our study, this trend is probably contaminated by the selection bias of ANC participants: Women who learned about their HIV status during their first pregnancy might be less likely to have another pregnancy and thus would be underrepresented in women attending ANC. Also, increased mortality among HIV-positive women could lead to their underrepresentation in the study population. Older women who attend ANC can have an a priori lower risk if this is their first pregnancy or an a posteriori lower risk if they were tested HIV negative during their first pregnancy. Therefore, this potential selection bias might lead to the underestimation of the prevalence of HIV infection in older age group. On the other hand, the possibility of detrimental effects of lack of targeted prevention programs, with an unchanged or increasing risk of sexual behavior over time, cannot be ruled out.

Although there was no statistically significant difference in the trend among different categories of marital status and educational levels, the prevalence of HIV infection was higher in non-married women compared with married ones, similar to previous studies based on DHS population survey data.39 This difference could be explained by the higher average number of sexual partners in the last 3 years reported by these women. In contrast to our observations, Mmbaga et al33 reported in a rural area in Tanzania a higher risk for HIV infection for married compared with single women. However, they recruited their sample from the general population, including participants who were not sexually active.

The low prevalence of HIV infection observed among women who never attended school compared with other groups has been observed in other studies in Cameroon and elsewhere.40 It was contrary to some previous findings that showed moderate levels of association of lower education with increasing risk of HIV infection.33,41,42 Lower education can be an indicator of a more traditional rural culture, with possibly protective social norms. Although education can provide information on how to avoid risk behavior, it can also dissolve these protective social norms. On the other hand, in resource-limited countries, the risk of contracting HIV infection has been associated with poverty,41,43 which in turn might be associated with low education, although previous reports indicated no association between socioeconomic inequalities and risky sexual behavior for HIV infection among women in South Africa.44

Previous studies have shown that the prevalence of HIV infection can differ even across populations with similar risk behavior.2,45 Other risk factors including forced sex, prevalence of other sexually transmitted diseases, religion, ethnicity, and economical or political factors can also contribute to differences in HIV prevalence between communities. Unfortunately, we did not have information about these risk factors in our study. Rather, we used aggregate information about the occurrence of certain risk factors on the population level as proxies for possible cultural and/or behavioral differences. For example, although Christianity does not allow polygyny, this marriage practice is common in Islam and in many African traditional religions. Although polygyny itself means indirect exposure to more than 1 partner, the risk of infection can be contained when polygynous partners remain faithful to each other-the idea that was behind promoting the “zero grazing” strategy in Uganda.46,47 At the same time, young marriage might explain the young age at the first sex, and given the strong prohibition of premarital sex in Islam, this might result in protection against HIV in this culture. Marriages at younger ages are still common practices in some regions in Cameroon, especially in the northern part of the country and also in the Northwest province where 6 of the 7 PMTCT sites analyzed were located.48 This could explain the high proportion of women with sex debut <17 years attending ANCs in some areas where samples were collected, as more than 85% of women belonging to this study group were married, with 33% of women engaged in polygynous marriages. Higher parity might be associated with higher past sexual exposure, but it can also indicate a more traditional culture. In summary, polygyny, young age at first sex, and high parity could all be associated with traditional or Islamic culture with strong social norms that do not favor the spread of HIV. Unfortunately, the information about religious or cultural background was not collected in the study, and census data do not exist for the study sites. Although information on ethnicity was missing, it is worth to mention that Cameroon is one of the African countries with high ethnic (about 253 ethnics) and language (more than 180 local languages) diversities,49 which might also account for the regional differences in the prevalence of HIV infection observed.

Our study has several limitations. First, the sites that were used for the study were not randomly selected, and therefore, the samples analyzed might not be representative for the entire region where they were collected. Second, some of the variables analyzed were available only for a subset of data and allowed only limited treatment in the analysis. Furthermore, individual information was not collected in the study in a way to allow the identification of subsequent pregnancies of the same woman, which might introduce additional correlation in the data unaccounted for in our analysis. No information was collected on possible nonsexual transmission of HIV such as unsafe injections or traditional medicine that involves cutting or piercing, which may explain some of the differences in seroprevalence in the participating communities.50

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CONCLUSIONS

The prevalence of HIV infection decreased over time especially in younger women. Variations of behavioral factors in these 7 communities are likely related to their culture and traditional ways of living as opposed to more recently adopted lifestyles. Knowledge about these cultural determinants of HIV transmission on a local level is important for designing effective prevention messages and for optimizing the rollout of ART. More research is needed to understand the regional diversity of culture and lifestyle in sub-Saharan African countries and how cultural mores contribute to behaviors that increase or decrease the risk of HIV transmission.

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ACKNOWLEDGMENTS

The antenatal and obstetric staff at all participating facilities worked tirelessly to implement the PMTCT Program and to document the work they did. Nkfusai Joseph and Nshom Emmanuel, the CBCHB PMTCT program directors, and the CBCHB PMTCT coordinators made difficult trips through mud and dust to support this program in remote health centers that have no electricity or telephone. The mothers had the courage to consent to HIV testing because of their concern for the health of their babies. Dr. Marc Bulterys, Division of HIV/AIDS Prevention, National Center for HIV/STD/TB Prevention, Centers for Disease Control and Prevention, helped to design, implement, and evaluate this program. We also thank Marc Bulterys for critically reading an earlier version of this article. Tancho Sam, CBCHB laboratory supervisor, provided training and quality assurance of rapid HIV testing at all sites. Dr. Anne Nlend and Dr. Landry Tsague, National PMTCT Directors; Dr. Akwe Samuel, AIDS Coordinator of the Southwest Province; Dr. Mayer Magdalene, AIDS Coordinator of the Northwest Province; Dr. Nchifor Simon, Director, Northwest Provincial Hospital; and Dr. Akam Wilfred, AIDS Coordinator, Limbe Provincial Hospital, provided enthusiastic support. Dr. Pratima Raghunathan, Country Director, Centers for Disease Control and Prevention-Cameroon, provided analytic support. We thank Elizabeth Glaser Pediatric AIDS Foundation for financial support and the technical and administrative support provided by their staff.

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REFERENCES

1. Bello GA, Chipeta J, berle-Grasse J. Assessment of trends in biological and behavioural surveillance data: is there any evidence of declining HIV prevalence or incidence in Malawi? Sex Transm Infect. 2006;82(Suppl 1):i9-i13.

2. Buve A, Carael M, Hayes RJ, et al. Multicentre study on factors determining differences in rate of spread of HIV in sub-Saharan Africa: methods and prevalence of HIV infection. AIDS. 2001;15(Suppl 4):S5-S14.

3. Cleland J, Ali MM. Sexual abstinence, contraception, and condom use by young African women: a secondary analysis of survey data. Lancet. 2006;368:1788-1793.

4. Hallett TB, berle-Grasse J, Bello G, et al. Declines in HIV prevalence can be associated with changing sexual behaviour in Uganda, urban Kenya, Zimbabwe, and urban Haiti. Sex Transm Infect. 2006;82(Suppl 1):i1-i8.

5. Kilian AH, Gregson S, Ndyanabangi B, et al. Reductions in risk behaviour provide the most consistent explanation for declining HIV-1 prevalence in Uganda. AIDS. 1999;13:391-398.

6. Mahomva A, Greby S, Dube S, et al. HIV prevalence and trends from data in Zimbabwe, 1997-2004. Sex Transm Infect. 2006;82(Suppl 1):i42-i47.

7. UNAIDS. Report on the global AIDS epidemic: Executive Summary. Available at: http://data unaids org/pub/Global Report/2006/2006_GR-ExecutiveSummary_en.pdf. Accessed February 12, 2009.

8. Urassa W, Kaaya S, Mwakagile D, et al. Evidence of a substantial decline in prevalence of HIV-1 infection among pregnant women: data from 1995 to 2003 in Dar es Salaam, Tanzania. Scand J Public Health. 2006;34:272-278.

9. Welz T, Hosegood V, Jaffar S, et al. Continued very high prevalence of HIV infection in rural KwaZulu-Natal, South Africa: a population-based longitudinal study. AIDS. 2007;21:1467-1472.

10. Mboudjeka I, Bikandou B, Zekeng L, et al. Genetic diversity of HIV-1 group M from Cameroon and Republic of Congo. Arch Virol. 1999;144:2291-311.

11. Mboudjeka I, Zekeng L, Takehisa J, et al. HIV type 1 genetic variability in the northern part of Cameroon. AIDS Res Hum Retroviruses. 1999;15:951-956.

12. Takehisa J, Zekeng L, Miura T, et al. Triple HIV-1 infection with group O and group M of different clades in a single Cameroonian AIDS patient. J Acquir Immune Defic Syndr Hum Retrovirol. 1997;14:81-82.

13. Takehisa J, Zekeng L, Ido E, et al. Various types of HIV mixed infections in Cameroon. Virology. 1998;245:1-10.

14. Yamaguchi J, McArthur CP, Vallari A, et al. HIV-1 group N: evidence of ongoing transmission in Cameroon. AIDS Res Hum Retroviruses. 2006;22:453-457.

15. DHS. Cameroon Demographic and Health Survey. 2004. Available at: http://www.measuredhs.com/countries/metadata.cfm?surv_id=232&ctry_id=4&SrvyTp=ctry&cn=Cameroon. Accessed April 22, 2008.

16. Ghys PD, Kufa E, George MV. Measuring trends in prevalence and incidence of HIV infection in countries with generalised epidemics. Sex Transm Infect. 2006;82(Suppl 1):i52-i56.

17. Glynn JR, Buve A, Carael M, et al. Factors influencing the difference in HIV prevalence between antenatal clinic and general population in sub-Saharan Africa. AIDS. 2001;15:1717-1725.

18. Gregson S, Terceira N, Kakowa M, et al. Study of bias in antenatal clinic HIV-1 surveillance data in a high contraceptive prevalence population in sub-Saharan Africa. AIDS. 2002;16:643-652.

19. Kaptue L, Zekeng L, Salla R, et al. Setting up a sentinel surveillance system for HIV infection in Cameroon. Int Conf AIDS. 1990;6:230. Abstract no F.C. 597.

20. UNAIDS/WHO. Update. Cameroon. Epidemiological facts sheets on HIV/AIDS and sexually transmitted infections. 2004. Available at: http://www.who.int/GlobalAtlas/predefinedReports/EFS2004/EFS_PDFs/EFS2004_CM.pdf. Accessed April 10, 2008.

21. Welty TK, Bulterys M, Welty ER, et al. Integrating prevention of mother-to-child HIV transmission into routine antenatal care: the key to program expansion in Cameroon. J Acquir Immune Defic Syndr. 2005;40:486-493.

22. Hastie TJ. Generalized additive models. In: Chambers SJM, Hastie TJ, eds. Statistical Models. S. Pacific Grove, CA: Wadsworth & Brooks/Cole; 1991:249-307.

23. Bryk A, Raudenbush SW. Hierarchical Linear Modelsfor Social and Behavioral Research: Applications and Data Analysis Methods. Newbury Park, CA: Sage; 1992.

24. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New York, NY: Wiley-Interscience; 2000.

25. Msisha WM, Kapiga SH, Earls FJ, et al. Place matters: multilevel investigation of HIV distribution in Tanzania. AIDS. 2008;22:741-748.

26. Stringer E, Chintu N, Levy J, et al. Declining HIV prevalence among young pregnant women in Lusaka, Zambia, 2002-2006. Bull World Health Organ. 2008;86:697-702.

27. Sandoy IF, Kvale G, Michelo C, et al. Antenatal clinic-based HIV prevalence in Zambia: declining trends but sharp local contrasts in young women. Trop Med Int Health. 2006;11:917-928.

28. Michelo C, Sandoy IF, Fylkesnes K. Marked HIV prevalence declines in higher educated young people: evidence from population-based surveys (1995-2003) in Zambia. AIDS. 2006;20:1031-1038.

29. Batter V, Matela B, Nsuami M, et al. High HIV-1 incidence in young women masked by stable overall seroprevalence among childbearing women in Kinshasa, Zaire: estimating incidence from serial seroprevalence data. AIDS. 1994;8:811-817.

30. Meekers D, Klein M, Foyet L. Patterns of HIV risk behavior and condom use among youth in Yaounde and Douala, Cameroon. AIDS Behav. 2003;7:413-420.

31. Plautz A, Meekers D. Evaluation of the reach and impact of the 100% Jeune youth social marketing program in Cameroon: findings from three cross-sectional surveys. Reprod Health. 2007;4:1.

32. Sieleunou I, Souleymanou M, Schonenberger AM, et al. Determinants of survival in AIDS patients on antiretroviral therapy in a rural centre in the Far-North Province, Cameroon. Trop Med Int Health. 2009;14(1):36-43.

33. Mmbaga EJ, Hussain A, Leyna GH, et al. Prevalence and risk factors for HIV-1 infection in rural Kilimanjaro region of Tanzania: implications for prevention and treatment. BMC Public Health. 2007;7:58.

34. Labhardt ND, Manga E, Ndam M, et al. Early assessment of the implementation of a national programme for the prevention of mother-to-child transmission of HIV in Cameroon and the effects of staff training: a survey in 70 rural health care facilities. Trop Med Int Health. 2009;14:288-293.

35. Mbanya D, Sama M, Tchounwou P. Current status of HIV/AIDS in Cameroon: how effective are control strategies? Int J Environ Res Public Health. 2008;5:378-383.

36. UNAIDS. Report on the global AIDS epidemic. 2008. Available at: http://www.who.int/hiv/pub/towards_universal_access_report_2008.pdf. Accessed March 5, 2009.

37. National AIDS Control Committee Cameroon. Mise en Oeuvre de la Déclaration D'engagement sur le VIH/SIDA; Rapport de Progress No 3; UNGASS. 2008. Available at: http://data unaids org/pub/Report/2008/cameroon_2008_country_progress_report_fr.pdf. Accessed February 12, 2009.

38. de-Walque D. Who gets AIDS and how? The determinants of HIV infection and sexual behaviors in Burkina Faso, Cameroon, Ghana, Kenya and Tanzania. 2006. Available at: http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2006/02/03/000016406_20060203104911/Rendered/PDF/wps3844.pdf. Accessed April 10, 2008.

39. Adair T. HIV status and age at first marriage among women in Cameroon. J Biosoc Sci. November 2007:1-18.

40. Swai RO, Somi GG, Matee MI, et al. Surveillance of HIV and syphilis infections among antenatal clinic attendees in Tanzania-2003/2004. BMC Public Health. 2006;6:91.

41. Tladi LS. Poverty and HIV/AIDS in South Africa: an empirical contribution. SAHARA J. 2006;3:369-381.

42. Tohon Z, Mamadou S, Mainassara HB, et al. HIV seroprevalence surveys in Nigerien pregnant women: a comparison between 2002 and 2006. Trans R Soc Trop Med Hyg. 2007;101:1101-1105.

43. Mbirimtengerenji ND. Is HIV/AIDS epidemic outcome of poverty in sub-Saharan Africa? Croat Med J. 2007;48:605-617.

44. Booysen FR, Summerton J. Poverty, risky sexual behaviour, and vulnerability to HIV infection: evidence from South Africa. J Health Popul Nutr. 2002;20:285-288.

45. Ferry B, Carael M, Buve A, et al. Comparison of key parameters of sexual behaviour in four African urban populations with different levels of HIV infection. AIDS. 2001;15(Suppl 4):S41-S50.

46. Elangot F. Uganda: an AIDS control programme. AIDS Action. 1987;(1):6.

47. Green EC, Halperin DT, Nantulya V, et al. Uganda's HIV prevention success: the role of sexual behavior change and the national response. AIDS Behav. 2006;10:335-346.

48. United States Bureau of Citizenship and Immigration Services. Information on forced or arranged marriage in Cameroon. 2002. Available at: http://www.unhcr.org/refworld/country,USCIS,QUERYRESPONSE,CMR,456d621e2,3f51ec864,0.html. Accessed February 14, 2009.

49. Tsala JP. [The lift and the stairs-the fight against AIDS in Cameroon]. SAHARA J. 2004;1:139-156.

50. Apetrei C, Becker J, Metzger M, et al. Potential for HIV transmission through unsafe injections. AIDS. 2006;20:1074-1076.

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Keywords:

Cameroon; HIV/AIDS; pregnant women; regional differences; trends

© 2009 Lippincott Williams & Wilkins, Inc.

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