Among the subpopulation of the Masaka District, the relative risk of fertility was 1.2 to 2.3 times higher for HIV-1-negative women, compared with HIV-1-positive women, for all age groups, with the exception of the 15–19 years age group. For the 15–19 years age group, HIV-1-positive women had a higher fertility rate than HIV-1-negative women. As a consequence, when the antenatal clinic data were extrapolated to the general female population, a marked increase in HIV-1 prevalence from the 15–19 years to the 20–24 years age group was observed in our study area. The overall age-residence standardized HIV-1 prevalence was estimated to have decreased from 25.4% in the period 1993–1994 to 17.8% in the period 1996–1997 (from 30.4 to 24.5% in the Gulu Municipality and from 24.6 to 16.7% in other areas of Gulu District).
The overall age–residence standardized HIV-1 prevalence among the general female population was approximately 1.22 and 1.28 times higher in the periods 1993–1994 and 1996–1997, respectively, compared with that among antenatal clinic attendees aged 15–39 years.
Using the trend of HIV-1 prevalence among pregnant women for monitoring the epidemic could lead to inaccurate conclusions. Differences in mortality, fertility, migration, and other HIV-1-related factors between HIV-1-positive and HIV-1-negative women could obscure a high incidence in the presence of a stable or decreasing prevalence [1,10]. However, the temporal trend of HIV-1 prevalence can provide useful indications on the dynamics of the infection when restrictive assumptions on these HIV-1-related factors in the reference population can be made [10–14].
In our study, the trend of decrease in HIV-1 prevalence appears to be consistent with data from other population groups of the study area, such as secondary-school students (HIV-1 prevalence declined from 2.0% in 1994 to 0.8% in 1998), and with data reported in other antenatal clinics of Uganda (HIV-1 prevalence declined from 26.6% in 1993 to 14.6% in 1997 among antenatal clinic attendees at Nsambya Hospital in Uganda's capital, Kampala) [15,16]. Moreover, HIV-1 prevalence was found to be very high, and it was similar to that observed in Kampala . The general decrease seemed to be mainly due to the decreasing prevalence observed in the younger age groups until 1995. Assuming that the differences in mortality, fertility, and migration between HIV-1-positive and HIV-1-negative women were negligible for the younger age groups [18,19], this decrease could reflect a decreasing incidence among these young women [11,20]. HIV-1 prevalence appeared to be higher and more stable over time among women aged 25–34 years with respect to younger women, whereas the lowest prevalence was observed among those aged 35–39 years, when the effect of AIDS-related mortality probably becomes more consistent. This picture is consistent with the beginning of the mature stage of the epidemic in Uganda .
Women living in the Gulu Municipality were found to have a higher risk of HIV-1 than those living in rural areas of the Gulu District, confirming the results of other studies conducted in Uganda [22,23]. However, a significant decrease in HIV-1 prevalence was observed over time for both groups, suggesting that there are no geographic differences in the dynamics of the infection.
It is unlikely that the overall decrease in HIV-1 prevalence among antenatal clinic attendees observed between 1994 and 1995 can be totally explained by changes in incidence and differences in mortality and fertility by HIV-1 serostatus. In fact, assuming a null incidence in the 1994–1995 period and that the differences in mortality and fertility rates between HIV-1-negative and HIV-1-positive women aged 15–39 years are equal to those estimated in the Masaka District (personal communication, ), the observed decrease in prevalence is higher than expected .
Another factor potentially contributing to the observed decrease was the fact that, due to commercial availability, different types of ELISA kits were used to detect HIV-1 during the study period, possibly accounting for an increased test specificity and a consequent biased reduction in prevalence, especially considering the greater variations in the specificity of ELISA kits when used on African sera .
No differences with respect to age structure or area of residence were observed among the antenatal clinic attendees among the years tested, and there were no changes in testing policy during the study period, although the number of women tested was sometimes randomly reduced because of the availability of HIV-1 ELISA kits. Finally, the turnover of the laboratory personnel, supervisors, and data-entry clerks was limited and was unlikely to have caused major biases.
The importance of addressing differences in fertility by HIV-1 serostatus was confirmed by the finding that, in the periods 1993–1994 and 1996–1997, the age-residence standardized HIV-1 prevalence among the general female population aged 15–39 years was, respectively, 1.22 and 1.28 times greater than that observed among pregnant women.
Using data on the level of fertility of HIV-1-positive and HIV-1-negative women in a rural subcounty of the Masaka District for estimating the HIV-1 prevalence among the Gulu District's general female population aged 15–39 years could have introduced a bias in our estimates if these data are not similar to those for women in our study area. With respect to our study population, pregnant women in the reference area, located in south-western Uganda, showed lower HIV-1 prevalence (9.5% in the overall period 1990–1996, compared with 17.8% in the period 1993–1997 for our study population) and lower total fertility rate (TFR; TFR = 6.1 in 1996 compared with TFR = 6.7 in 1997 in our study area; unpublished data).
Despite these differences, we were not able to determine whether the estimated relative risks of fertility of HIV-1-positive women versus HIV-1-negative women living in the Masaka District were similar to those of our study population, although a recent review of different studies on fertility by HIV-1 serostatus suggests that these relative risks tend to be lower in populations with higher HIV-1 prevalence . However, data from other areas of Uganda and Africa have shown similar patterns of differences in fertility by age group, suggesting a substantial geographical homogeneity [19,25].
Another possible bias could be due to the fact that we assumed no differences between rural and urban areas in the relative risks of fertility (HIV-1-positive versus HIV-1-negative women), given that no reference data were available for urban areas. All of the women belonging to the reference population lived in rural areas, compared with 86.1% of the women aged 15–39 years in the Gulu District [4,6]. Thus, the HIV-1 prevalence among the general female population of the Gulu Municipality could represent an underestimate if the relative risk of fertility is lower in urban areas (relatively high prevalence with respect to rural areas) .
Finally, a bias in the time-trend analysis could be due to the implicit assumption that the relative risks of fertility remained constant over time, whereas it could be possible that the overall and age-specific relative risks of fertility changed during the study period, as a consequence of changes in behavioural and biological factors influencing the association between HIV-1 infection and fertility (e.g., increasing age at start of sexual activity, increasing use of condoms and decreasing breast-feeding among HIV-1- positive women, and increasing treatment of sexually transmitted diseases other than HIV-1). The effects of these changes are not the same, and they could affect the relative risks of fertility in different ways, toward an increase or a decrease .
However, the prevalence rates estimated for the general female population aged 15–39 years of the Gulu District were consistent with those of other areas of Uganda with similar HIV-1 prevalence among pregnant women [3,26], partially validating the obtained results and the assumed representativeness of the relative risks of fertility from the Masaka District used in this analysis.
The distribution of the age-specific prevalence rates indicates that young girls, who are just beginning to become sexually active and for whom changes in prevalence more closely reflect incidence, are at a high risk of becoming infected.
The slight increase in HIV-1 prevalence observed in 1997 could be indirectly due to the increased civil strife. In fact, conditions of insecurity have resulted in a dramatic reduction, especially for rural areas, in all activities for health education and prevention. Furthermore, the fact that many people live in protected camps as a result of population displacement may have contributed to increasing the risk of HIV-1 infection .
In conclusion, the overall reduction in HIV-1 prevalence in the period 1993–1997 could be related to many factors, such as incidence, mortality, and migration. However, assuming that the effect of mortality and migration is negligible among young pregnant women, this downward trend could be partially interpreted, as suggested in recent studies , in terms of a reduction in risk behaviour and a consequent reduction in incidence, possibly in part due to prevention and control measures.
Finally, when extrapolating HIV-1 prevalence data from antenatal clinic attendees to the general female population of reproductive age, different fertility rates between HIV-1-positive and HIV-1-negative women should be taken into account to avoid underestimating the HIV-1 prevalence among this sub-group of the population.
This manuscript is dedicated to Dr Matthew Lukwiya, Medical Superintendent of Lacor Hospital, who died of ebola in December 2000 helping his people during this dramatic outbreak. All the staff involved in the Uganda AIDS Project, who have had the pleasure of knowing him, will remember his professionalism, intelligence, gaiety and availability in promoting and improving the health status of the population of the Gulu District.
The authors are grateful to Claudio Blè, Jacob Ouma, Proscovia Akello, and Florence Atim for data collection, blood testing and data entry; to Mark Kanieff for editorial assistance; to Ernesto Costabile for bibliographic research; and to Alessia Ranghiasci, Patrizia Tancredi, Maria Grazia Dente, and Alessandro Di Vincenzo for their continuous support to the project. The authors also thank L. M. Carpenter and A. J. Nunn for providing helpful information in the preparation of this paper.
1. Wawer MJ, Serwadda D, Gray RH. et al. Trends in HIV-1 prevalence may not reflect trends in incidence in mature epidemics: data from the Rakai population-based cohort, Uganda.
AIDS 1997, 11: 1023 –1030.
2. Boisson E, Nicoll A, Zaba B, Rodrigues LC. Interpreting HIV seroprevalence data from pregnant women.
J Acquir Immune Defic Synd Hum Retrovirol 1996, 13: 434 –439.
3. Gray RH, Wawer MJ, Serwadda D. et al. Population-based study of fertility in women with HIV-1 infection in Uganda.
Lancet 1998, 351: 98 –103.
4. Carpenter LM, Nakiyingi JS, Ruberantuari A, Malamba SS, Kamali A, Whitwhort JAG. Estimates of the impact of HIV infection on fertility in a rural Ugandan population.
Health Transition Rev 1997, 7 (suppl. 2): 113 –126.
5. Nicoll A, Stephenson J, Griffioen A, Cliffe S, Rogers P, Boisson E. The relationship of HIV prevalence in pregnant women to that in women of reproductive age: a validated method for adjustment.
AIDS 1998, 12: 1861 –1867.
6. Ministry of Finance and Economic Planning (MFEP), Statistics Department. The 1991 Population and Housing Census (District Summary Series) – Gulu District.
Entebbe: MFEP; 1997.
7. Skow T, Deddens J, Petersen M, Endahl L. Prevalence proportion ratios: estimation and hypothesis testing.
Int J Epidemiol 1998, 27: 91 –95.
8. Norosis MJ. SPSS for Windows version 6.1.3.
Chigago: SPSS inc.; 1995.
9. StatCorp. Stata Statistical Software: Release 5.0.
College Station, TX: Stata Corporation; 1997.
10. 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.
11. Heyward WL, Osmanov S, Saba J. et al. Preparation for phase III HIV vaccine efficacy trials: methods for the determination of HIV incidence.
AIDS 1994, 8: 1285 –1291.
12. Gregson S, Donnelly CA, Parker CG, Anderson RM. Demographic approaches to the estimation of incidence of HIV-1 infection among adults from age-specific prevalence data in stable endemic conditions.
AIDS 1996, 10: 1689 –1697.
13. Podgor MJ, Leske MC. Estimating incidence from age-specific prevalence for irreversible diseases with differential mortality.
Stat Med 1986, 5: 573 –578.
14. Ades AE. Serial HIV seroprevalence surveys: interpretation, design, and role in HIV/AIDS prediction.
J Acquir Immune Defic Synd Hum Retrovirol 1995, 9: 490 –499.
15. Fabiani M, Accorsi S, Corrado B, et al
. HIV prevalence trends among population groups in the Gulu district (Northern Uganda). XIth International Conference on AIDS and STDs in Africa.
Lusaka. September 1999 [abstract 13PT31-14].
16. STD/AIDS Control Programme:HIV/AIDS surveillance report – March 1998.
Entebbe: Ministry of Health; 1998.
17. Asiimwe-Okiror G, Opio AO, Musinguzi J, Madraa E, Tembo G, Carael M. Change in sexual behaviour and decline in HIV infection among young pregnant women in urban Uganda.
AIDS 1997, 11: 1757 –1763.
18. Boerma JT, Nunn AJ, Whitworth JAG. Mortality impact of the AIDS epidemic: evidence from community studies in less developed countries.
AIDS 1998, 12 (suppl. 1): S3 –S14.
19. Zaba B, Gregson S. Measuring the impact of HIV on fertility in Africa.
AIDS 1998, 12 (suppl. 1): S41 –S50.
20. Mertens T, Carael M, Sato P, Cleland J, Ward H, Smith GD. Prevention indicators for evaluating the progress of national AIDS programmes.
AIDS 1994, 8: 1359 –1369.
21. Stoneburner RL, Low-Beer D, Tembo GS, Mertens TE, Asiimwe-Okiror G. Human Immunodeficiency virus infection dynamics in east Africa deduced from surveillance data.
Am J Epidemiol 1996, 144: 682 –695.
22. Wawer MJ, Serwadda D, Musgrave SD, Konde-Lule JK, Musagara M, Sewankambo NK. Dynamics of spread of HIV-1 infection in a rural district of Uganda.
BMJ 1991, 303: 1303 –1306.
23. Serwadda D, Wawer MJ, Musgrave SD, Sewankambo NK, Kaplan JE, Gray RH. HIV risk factors in three geographic strata of rural Rakai District, Uganda.
AIDS 1992, 6: 983 –989.
24. Thorstensson R, Andersson S, Lindback S. et al. Evaluation of 14 commercial HIV-1/HIV-2 antibody assays using serum panels of different geographical origin and clinical stage including a unique seroconversion panel.
J Virol Methods 1998, 70: 139 –151.
25. Desgrees du Lou A, Msellati P, La Ruche G, Welffens-Ekra C, Ramon R, Dabis F. Estimation of HIV-1 prevalence in the population of Abidjan by adjustment of the prevalence observed in antenatal centres.
AIDS 1999, 13: 526 –527.
26. Kilian AHD, 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.
27. Kayita J, Kyakulaga JB. HIV/AIDS status report, Uganda, July 1997.
Kampala: Uganda AIDS Commission; 1997.
Keywords:© 2001 Lippincott Williams & Wilkins, Inc.
HIV-1 prevalence; trend; pregnant women; general population; Uganda