Table 2 below shows the weighted percentage distribution of the characteristics of the study participants. The full sample consisted of 6,067 women.

The table shows the sample distribution of the study’s explanatory variable. Herein, 36% of women reported undergoing CCS at least once in their lifetime, while 64% reported never undergoing CCS in their lives. It also shows the sociodemographic characteristics of the sample. By age, women aged 50 and older constituted the greatest proportion of participants at 31%, followed by women aged 15–19 years at 12%, 25–29 years at 12%, 20–24 years at 11%, 30–34 years at 10%, 35–39 years at 9%, and 40–44 years at 8%, while women aged 45–49 years constituted the lowest proportion at 7%. In terms of provinces, women from KwaZulu-Natal constituted the greatest proportion of participants at 16%, followed by women from Limpopo at 14%, Eastern Cape at 13%, Mpumalanga at 11%, Free State at 11%, North West at 9%, Gauteng at 9%, and Northern Cape at 9%, while women from Western Cape constituted the least 8%.

By area of residence, women from urban areas constituted the greatest proportion at 55%, while women from rural areas constituted the least at 45%. In terms of marriage, women who reported being married and not being married constituted an equal proportion of 50% each. By educational attainment, women with secondary education constituted the greatest proportion of the sample at 64%, followed by women with primary education (17%), while women with no education and higher education both constituted 9%. By employment, unemployed women constituted the greatest proportion of the sample at 71%, while employed women constituted the smallest proportion at 29%.

The table concludes with the health/risk factor characteristics of the sample. In terms of frequency of cigarette smoking, women who reported that they did not smoke constituted the greatest proportion of the sample at 92%, followed by those who reported smoking every day at 7%, while women who reported smoking sometimes constituted the lowest proportion at 1%. By alcohol consumption, women who reported that they did not consume alcohol constituted the greatest proportion of the sample at 74%, while women who reported consuming alcohol constituted the lowest proportion at 26%.

In terms of perception of their own health, women who reported being in good health constituted the greatest proportion of the sample at 41%, followed by women who reported their health as average (33%), while women who reported being in excellent and poor health both constituted the lowest proportion at 13%. Regarding health insurance, women who reported having no health insurance constituted the greatest proportion of participants at 86%, while women with health insurance constituted the smallest proportion at 14% (Fig. 1).

Levels of HIV among women aged 15 and older in South Africa

The study found that 19% of women aged between 15 years and older tested positive for HIV, while 81% of women in the sample tested negative for the disease.

Figure 2 below shows that the hazard of WLHIV undergoing CCS began at the age of approximately 19 years. Conversely, women who were diagnosed with HIV did not undergo CCS before the age of 19, as represented by the horizontal hazard line.

Time (age in years) to CCS among WLHIV in SA

After the age of 19 years, the hazard line begins to change, showing that the event of interest (CCS) has occurred. The highest hazard of undergoing CCS among WLHIV was between the ages of 19–58. Approximately 74% of women within this age range had the probability of undergoing CCS. Thereafter, the hazard of undergoing CCS by WLHIV began to decrease at 58 years. In other words, the hazard of undergoing CCS started declining once WLHIV turned 58 years old. The hazard of undergoing CCS among WLHIV remained low from the age of 58 years. Conversely, the hazard of not undergoing CCS was higher among WLHIV from the age of 58 years and older compared to women who underwent CCS in the same age range. The hazard of not undergoing CCS among WLHIV dropped to approximately 73% at the age of 60 years.

Table 3 below shows the unadjusted and adjusted hazard ratios or risk of HIV by the characteristics of women aged 15 years and older. The effect of the study’s explanatory variable (CCS) was found to have a significant influence on HIV diagnosis. Herein, the study found that the hazard of CCS was 19% (Model 1) and 26% (Model 2) lower among WLHIV compared to women WLHIV who did not undergo CCS (UHR: 0.81; P < 0.05; CI: 0.72–0.91; AHR: 0.74; P < 0.05; CI: 0.65–0.85). Compared to Black/African women, the hazard of HIV diagnosis was decreased by 68% and 70% in Models 1 and 2, respectively, among women of white ethnicity (UHR: 0.42; P < 0.05; CI: 0.30–0.59; AHR: 0.30; P < 0.05; CI: 0.21–0.43). In terms of provinces, Model 2 showed that the hazard of HIV increased among women from the Free State (AHR: 1.47; P < 0.05; CI: 1.07–2.01) and KwaZulu-Natal (AHR: 1.45; P < 0.05; CI: 1.07–1.98) compared to women from the Western Cape.

Conversely, compared to the Western Cape, the hazard of HIV increased among women from Gauteng (UHR: 1.52; P < 0.05; CI: 1.15–2.02; AHR: 1.47; P < 0.05; CI: 1.06–2.03) and Mpumalanga (UHR: 1.38; P < 0.05; CI: 1.05–1.82; AHR: 1.73; P < 0.05; CI: 1.25–2.38). By area of residence, Model 1 revealed that the hazard of HIV decreased by 13% among women from rural areas compared to women from urban areas. Compared to women who reported not being married, the hazard of HIV decreased by 57% and 50% in Models 1 and 2, respectively. By education, the hazard of HIV increased among women with primary (UHR: 1.77 & AHR: 1.77), secondary (UHR: 3.88 & AHR: 4.18), and higher education (UHR: 3.16 & AHR: 4.44) compared to women with no education.

Compared to unemployed women, the hazard of HIV increased among employed women (UHR: 1.38; P < 0.05: CI: 1.21–1.57). In terms of health/risk factors, women who reported consuming alcohol had higher risks of HIV than women who reported not consuming alcohol in both Models 1 and 2 of the study. Compared to women who perceived their own health as poor, the hazard of HIV increased with higher perceptions of personal health among WLHIV in both models. Finally, in terms of health insurance, the hazard of HIV decreased by 19% and 25% in Models 1 and 2, respectively, among women with health insurance compared to women who reported not having health insurance.