Demographic characteristics
Based on our sampling strategy, 1 113 030 residents were included in the study, 30 sample locations were registered according to the degree of economic development (GDP-H, GDP-M, and GDP-L levels, which had 10 points each and contained 390 782, 357 902, and 364 346 residents, respectively). All 1 113 030 subjects were submitted for HIV antibody testing; 406 649 (36.50%) of whom were male, of whom the mean age was 47.29 ± 18.11, 140 397 (12.61%) were ≤24 years of age, 550 465 (49.46%) were 25–54 years of age, and 422 159 (37.93%) were ≥55 years of age (Fig. 1, Table 1).
HIV prevalence by demographics
Of the 1 113 030 people screened for HIV, 310 (2.79/10000) were positive, with a standardized (adjusted for sex and age) rate of 3.45/10000 (95% confidence interval [CI], 3.41–3.48) (Table 1).
Among the male subjects, the crude rate was 5.56/10000, and the standardized rate (adjusted for age) was 5.62/10000 (95% CI, 5.54–5.69). Among the female subjects, the crude rate was 1.20/10000, and the standardized rate (adjusted for age) was 1.17/10000 (95% CI, 1.15–1.20). The HIV prevalence was higher among men than women in all age groups. Compared with male subjects, the risk of HIV infection for females was lower (OR = 0.22, AOR = 0.16; p < 0.05) (Table 2).
As expected, we found significant differences in HIV prevalence by age (Table 1, Fig. 2). The HIV prevalence was significantly lower in subjects < 15 years of age than in older subjects: the crude rate was 0.41/10000, while the standardized rate (adjusted for sex) was 0.42/10000 (95% CI, 0.34–0.5). The HIV prevalence was highest for the 25–34 age group: the crude rate was 5.07/10000, while the standardized rate (adjusted for sex) was 9.30/10000 (95% CI, 9.16–9.44). Compared with the subjects in the <15 age group, those in other groups had a higher risk of HIV infection (AOR range, 2.49–25.69) and those of the 25–34 and 35–44 age groups were extraordinarily high (OR = 12.33, AOR = 25.69, p < 0.05; OR = 11.71, AOR = 18.48, p < 0.05, respectively) (Table 2).
The HIV prevalence was also different at different GDP levels: the GDP-M level was the highest, with a crude rate of 3.38/10000 and a standardized rate (adjusted for sex and age) of 5.28/10000 (95% CI, 4.53–6.04); and the GDP-H level was the lowest, with a crude rate of 2.30/10000 and a standardized rate (adjusted for sex and age) of 2.75/10000 (95% CI, 2.28–3.27). Compared with the GDP-H level, the risk of HIV infection at the GDP-M level was higher (OR = 1.49, AOR = 1.72, 95% CI, 1.30–2.26, p < 0.05); no difference in HIV infection risk was seen between the GDP-H and GDP-L levels (p > 0.05).
To determine the reason for the variation in HIV prevalence at different GDP levels, we further analyzed the HIV prevalence distribution by gender and age and found that the HIV prevalence differences among levels specifically manifested themselves in the male population (p < 0.05). In contrast, no significant differences were seen in the female population (Fig. 3A). We simultaneously found that the HIV prevalence of the 25–34 and 35–44 age groups in the GDP-M level population were significantly higher than those in the GDP-H and GDP-L levels and that there was no difference between the GDP-H and GDP-L levels at any age group (Fig. 3B).
HIV positive population character analysis
All 310 HIV-positive cases were divided into a native group and a migrant group. The average patient age in the native group (163 cases; 52.58%) was 47.28 ± 13.25 years, while that of the migrant group (147 cases; 47.42%) was 34.27 ± 8.56 years. The migrant group was an average of 10 years younger; most of these individuals were in their sexually active periods. Compared with the native group, the migrant group had the following characteristics: more were unmarried (33.98% vs. 12.63%, p < 0.05), less educated (91.3% completed junior high school or less) and worked in migrant positions. We believe that being unmarried, less educated, and a migrant worker may be an important influencing factors for HIV transmission in Zhejiang province.