New Diagnoses among HIV+ Men and Women in Puerto Rico: Data from the HIV Surveillance System 2003-2014.
Understanding the HIV epidemic -particularly new diagnoses-, continues to advance through innovations in global estimates. Surveillance data for HIV cases can provide an estimate of the number of incident infections within this population and can therefore, help to develop more targeted prevention programs for the at-risk population. In order to have a broader view of new HIV diagnoses in the island, we developed an epidemiological profile of HIV in Puerto Rico. The purpose of this analysis is to describe the age-standardized rates of new HIV diagnosis and compare sex and time disparities of new HIV diagnoses using data from the HIV/AIDS Surveillance System in PR.
Study design and Population
The data source of this study was the PR HIV Surveillance System, which is part of the CDC's National HIV Surveillance System (NHSS) (4). All states and US territories report to NHSS demographic, behavioral, clinical, and laboratory data of persons diagnosed with HIV infection (5). NHSS collected information from hospitals, physicians, public and private clinics, and medical records systems (4). This study comprises data of new HIV diagnosis of persons aged 13 years and older in PR reported from 2003 to 2014. The study protocol was approved by the Institutional Review Board (IRB) of the University of PR Medical Sciences Campus.
To achieve the aims of this publication, the study was divided into 3 periods: (i) 2003 to 2006, (ii) 2007 to 2010 and (iii) 2011 to 2014. Other variables included in the analyses were age (5 categories), sex, and health care regions according to the PR Health Insurance Administration (PRHIA and known as ASES, for its acronym in Spanish) (6). We computed the male to female ratio of new HIV diagnosis, and assessed the trends in new HIV diagnosis using the annual percent change (APC) of the age-standardized rates (ASRs). We estimated the relative risk (RR) with 95% confidence intervals to assess the magnitude of the association between new HIV diagnosis and different demographic characteristics.
The age-standardized rates (ASRs) of new HIV diagnosis (x 100,000 individuals) were calculated using the direct standardization method and the age world population distribution as the reference population (7). Maps were created using the ASRs in tertiles to visualize the regions with the higher and lower ASRs. The ASRs were stratified by sex, and Poisson regression models were used with year of diagnosis as discrete predictor to estimate the APC (8,9).
Stratified analyses were performed and Poisson regression models were used to estimate the RRs of interest by sex, age group and study periods. An interaction assessment of these analyses was performed using the likelihood ratio test. All the statistical analyses were conducted using STATA/SE version 14.0 statistical software.
Male to female ratio
The total number of new HIV diagnoses in persons aged 13 and older from 2003 to 2014 was 10,717. Of those, 7,708 (71.9%) were males and 3,009 (28.1%) females. The overall male to female ratio was 2.56 males per female. Across regions, male to female ratios remains alike of close to 70 males to 30 females. The lowest ratio was 2.12 males per female (Southeast region) while the highest was 3.21 males per female (Southwest region) (Table 1).
Age -Standardized Rates
Figure 1 shows the ASR among males and females for each health care region according to PRHIA (6). PR's health care system is divided into eight health regions: North, Metro North, San Juan, Northeast, East, Southeast, Southwest and West. Overall, the highest rates were observed around the metropolitan area. This trend was similar for previous periods in PR (data not shown). Among males, the San Juan and Northeast regions had the highest ASR for the 2011-2014 period ([greater than or equal to] 40.99 new HIV diagnoses per 100,000 population). Among females, San Juan and Metro North were the regions with the highest ASR ([greater than or equal to] 13.78 new HIV diagnoses per 100,000 population).
Annual percent change (APC)
Figure 2 shows the trend of ASR of new HIV diagnosis stratified by sex. An overall reduction in new HIV diagnoses was observed for both sexes (Table 2). Among males, a reduction of 3.98% in the mid period when compared to the earliest one and a reduction of 2.93% in the latest period when compared to the mid one were observed. Women also showed a reduction in the APC (6.74% in the mid period and 9.13% in the latest period). Across regions, when comparing the earliest and mid periods, the most notable reductions were observed among females in the Southeast (APC: -17.13%), Southwest (APC: -13.48%), and North (APC: -12.13%) regions. Among males, the highest declines were observed in the East (APC: -9.69%), West (APC: -8.80%), and Southeast (APC: -8.78%) regions. When comparing 2007-2010 period with the latest period, the highest declines in new HIV diagnoses among females were observed in the West (APC: -17.23%), East (APC: -12.24%), and Metro North (APC: -10.52%) regions, and in the Southwest (APC: -7.87%), and West (APC: -6.69%) regions among males. All of the aforementioned APCs were statistically different from zero (p-value <0.05).
Magnitude of the association between new HIV diagnosis and sex
In the 2003-2006 period, North, San Juan, Southwest and overall regions had a significant interaction between age and gender. Overall, the highest estimated risk of new HIV diagnosis were on oldest males (55 years or more), when compared with females of the same age group ([[??].sup.(55+).sub.male vs. female] = 3.56; 95% CI: 2.93, 4.32). The same pattern was observed in the North, San Juan, and Southwest regions, with higher risks of new HIV diagnosis in the older age groups for males when compared to females ([[??].sup.(45-54.sub.male vs. female] = 3.58; 95% CI: 2.11, 6.08; [[??].sup.(55+).sub.male vs. female] = 5.04; 95% CI: 3.10, 8.18 and [[??].sup.(55+).sub.male vs. female] = 7.33; 95% CI: 3.29, 16.32) respectively (Table 3).
For the 2007-2010 period, there was no statistical significant interaction between age and sex (p-value>0.05). The lowest and highest estimated risks of new HIV diagnosis among males when compared to females were observed in the Southeast and Southwest regions (adj. [[??].sub.male vs. female] = 2.43; 95% CI: 1.77, 3.33 and adj. [[??].sub.male vs. female] = 3.94; 95% CI: 2.88, 5.39) respectively, after adjusting for age. Both RRs were statistically significant (p-value < 0.05) (Table 3).
During the 2011-2014 period, we observed a significant interaction between ages and sex overall, in the Metro-North, Northeast, and San Juan regions (p-value < 0.05). Overall, the lowest and highest estimated risks of new HIV diagnosis among males when compared to females were observed in the 35-44 age group ([[??].sup.(35-44).sub.male vs. female] = 2.62; 95% CI: 2.2, 3.12) and in the youngest age group ([[??].sup.(35-44).sub.male vs. female] = 4.83; 95% CI: 3.77, 6.19) (Table 3). In the Metro-North, Northeast, and San Juan regions the highest increase in risk of new HIV diagnosis were observed in the younger males ([[??].sup.(25-34).sub.male vs. female] = 4.32; 95% CI: 2.88, 6.47; [[??].sup.(25-34).sub.male vs. female] = 5.48; 95% CI: 3.09, 9.73 and [[??].sup.(13-24).sub.male vs. female] = 6.86; 95% CI: 3.74, 12.58) respectively. In the East, North, West, Southeast, and Southwest regions there were not significant interaction between age and sex (p-value>0.05). In the aforementioned regions, the estimated risks of new HIV diagnosis among males when compared to females were (adj. [[??].sub.male vs. female] = 4.33; 95% CI: 3.29-5.71, adj. [[??].sub.male vs. female] = 2.60; 95% CI: 1.99-3.39, adj. [[??].sub.male vs. female] = 4.69; 95% CI: 3.35-6.56, adj. [[??].sub.male vs. female] = 3.87; 95% CI: 2.67-5.63 and adj. [[??].sub.male vs. female] = 3.61; 95% CI: 2.51-5.19) respectively, after adjusting for age (Table 3).
Magnitude of the association between new HIV diagnosis and study period
Table 4 shows a reduction in risk of new HIV diagnosis on every age strata for both sexes except among males aged 13-24 years. In this group, we observed a significant increased risk in new HIV diagnosis of 53% in 2011-2014 period when compared to 2003-2006 (p-value < 0.05). This pattern was also observed in the East (RR: 1.61; 95% CI: 1.08-2.4), Northeast (RR: 2.87; 95% CI: 1.83-4.51), North (RR: 2.85; 95% CI: 1.47-5.56), and San Juan (RR: 1.81; 95% CI: 1.28-2.57) regions (data not shown).
This study explores the rates of new HIV diagnoses from the HIV/AIDS Surveillance System in PR from 2003 to 2014. We observed sex and time period disparities with regards to HIV diagnosis. A higher number of new HIV diagnoses were observed among males when compared to females; also men were at higher risk of new HIV diagnosis. An overall reduction in new HIV diagnoses was observed for both sexes; however, the decrease rate of new diagnoses was lower in men (as compared to women). These diagnoses were higher around the metropolitan area for both sexes. Furthermore, youth males (13-24 years) were at higher increase risk of new HIV diagnoses in the most recent period when compared to earlier periods.
We found that the overall male to female ratio was 3:1 males per female, or a proportion of about 70 cases among males for every 100 cases reported. This finding is similar to what have been observed in other surveillance systems (10,11). Regarding the ASR across the health care regions, the highest rates were observed in the metropolitan area. This behavior remains similar across previous periods in PR, and is similar to US HIV geographic distribution, in where HIV cases are mainly concentrated in urban areas (12,13). Because HIV diagnosis is not evenly distributed across the municipalities in PR, other strategies should be implemented, such as the inclusion of new policies to intensify HIV prevention efforts in the regions where HIV is more heavily concentrated and allocation of funds according to the geographic distribution of the HIV disease (14).
Overall, the annual rates of new HIV diagnoses decreased for both sexes from the 2007 period onward. This decreasing trend in new HIV diagnoses was observed across all healthcare regions. In regard to sex disparities, the decreased trend was more notably in females (9%) in the last period (2011-2014) and males (4%) in the mi d period of study (2007-2010). This result is consistent with other studies in US that have reported a higher decreasing trend in females when compared to males in the periods of 2006 to 2009 (15), 2008 to 2013 (16), and 2005 to 2014 (1). Furthermore, we found that the risk of getting a new HIV diagnosis was significantly higher among males when compared to females, ranging from an increased risk of more than 50% to almost 5-fold. Moreover, in the most recent period (2011-2014) there was a trend of an increased risk in males as age decreases for the overall health region. However, for the earlier period of 2003-2006, the risk increased in males as the age increased. Additionally, we observed that the males 13 to 24 years of age have the highest risk of new HIV diagnosis in the most recent period (2011-2014), when compared to previous study periods. It is in the most recent period that the effect of new cases varies by age and sex. These findings suggest a shift from older to younger males in the risk of getting a HIV diagnosis.
A possible explanation for the increase in HIV diagnoses in young males could be that HIV spread among MSM might be increasing (17) and the majority of new transmissions come from this population (18-21). Although in this study the incidence rates by main mode of transmission for this sample cannot be calculated, studies show that 75% of new diagnoses among Hispanic/Latino in US are in MSM (22).
These new transmissions in young MSM could be explained because youth populations have high risk factors such as low rates of HIV screening, low rates of condom use, have multiples sexual partners, and substance use before their last sexual intercourse that predispose them to a greater likelihood of contracting the infection (20). Another study documented that having a main partner may be linked to having unprotected sexual intercourse, less visits to the primary health care provider, and less HIV testing (23). Moreover, most of these relationships with main partners had a duration of less than six months on average.
Targeted strategies should be implemented to address the need of HIV/AIDS prevention and draw the attention to young males. Studies have documented an increasing HIV prevalence trend in younger age groups (23,24) and showed that the percentage of HIV testing in youths (13 to 24 years) is low, particularly in males (25). The effect of age and sex as contributors to disparities in young males, is mostly attributed because this population may have limited experience and might undergo barriers with obtaining care from healthcare systems (26). Strategies should be directed to educate this population regarding sexual health, making effective interventions to educate youths in schools, universities and community-based organizations, learn approaches to condom access, and increase the amount of HIV testing and treatment clinics in order to reach this population (25,27). Given the importance of targeting this group of young men, national strategies should contemplate youth as a targeted group, aligned with innovative and emerging strategies (14). Moreover, strategies should focus on incorporating social media, including mobile technologies and social networking sites (such as grinder), since they are being used increasingly as part of social and sexual networking (28). These technologies could also be used as tools to disseminate prevention messaging and treatment opportunities, particularly among young men.
The main limitation of our study was that it was not possible to measure the incidence by mode of transmission. Therefore, this limitation did not allow us to quantify which high risk group owns the highest proportion of new HIV diagnoses. Although we acknowledge that different methodologies and techniques have been used worldwide (29-35), this effort is beyond the scope of this study. This limitation affects our ability to explain, which subgroup specifically is attributed the new HIV cases in PR. Although similar to the US, an increase in HIV cases are recently observed among MSM, surpassing IDU transmission since 2012 (36), additional factors such as cultural differences can increase the risk by other modes of transmission, such as heterosexual of high risk and persons who inject drugs (22). This did not allow us to quantify which high risk group owns the highest proportion of new HIV diagnoses. Furthermore, the use of data from a surveillance system has limitations of its own, such as reporting delay and migration, which could lead to underestimating the incidence rates. Additional methods are necessary to better identify new HIV cases. For example, a study in Rome identified recent infections in individuals newly diagnosed with HIV by using an algorithm that combines clinical and serological information and the application of an avidity assay with serological test for recent infections (37). Since 2008, 25 US jurisdictions have an HIV incidence Surveillance System as a component of the National HIV Surveillance system, which is a system that collects HIV testing and antiretroviral use history. This data are combined using a statistical approach to identify trends of new HIV infections (38). Another method used to quantify the HIV epidemic was the development of the geographic area-based rate, which measures the number of new infections or cases in one year using the unit land area as denominator (39). Other studies in US used molecular HIV surveillance data with nucleotide sequences to assess and explain better the spread of HIV among risk groups (19,40).
In summary, the annual rates of new HIV diagnosis decreased throughout the study period. The risk of getting a new HIV diagnosis was significantly higher among males compared to females. We observed that the group of males ages 13 to 24 have the highest risk of new HIV diagnoses in the most recent period (2011-2014), when compared to other study periods. This finding suggests a shift in the risk of getting a HIV diagnosis from older to younger males. Targeted strategies should be implemented to address the need of HIV/AIDS prevention in this age groups. Moreover, future research efforts should consider the implementation of methods to estimate the population size of high-risk groups for the estimation of incidence rates, as well as to contemplate other socioeconomic health disparities that might have an impact on new HIV infections in the island.
This project was fully supported by 5U1BPS003245. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Centers for Disease and Control. Funding source: This project was fully supported by 5U1BPS003245. The authors have no conflict of interest to disclose. The views expressed in this paper are those of the authors and should not be interpreted to reflect the views or policies of the Centers for Disease Control and Prevention (CDC) and the Puerto Rico Department of Health (PRDH).
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Vivian Colon-Lopez, PhD * ([dagger]); Sandra Miranda-De Leon, MPH ([double dagger]); Mark Machin-Rivera, MPH *; Roxana Soto-Abreu, MS *; Edna L. Marrero-Cajigas, MS ([double dagger]); Yadira Rolon-Colon, MS ([double dagger]); Ileska M. Valencia-Torres, BS *; Erick L. Suarez-Perez, PhD ([section])
* Population Sciences Division, PR Comprehensive Cancer Center, San Juan, PR; ([dagger]) Department of Health Services Administration, Graduate School of Public Health, Medical Sciences Campus, University of PR, San Juan, PR; ([double dagger]) PR HIV Surveillance System, Department of Health, San Juan, PR; ([section]) Department of Biostatistics and Epidemiology, Graduate School of Public Health, Medical Sciences Campus, University of PR, San Juan, PR
The author/s has/have no conflict/s of interest to disclose.
Address correspondence to: Vivian Colon-Lopez, PhD, MPH, PO Box 365067, San Juan, PR 00936-5067. Email: email@example.com
Caption: Figure 1. Age-standardized rates of new HIV diagnosis among males and females living with HIV/AIDS, PR, 2011-2014
Caption: Figure 2. Age-Standardized Rate of New HIV Diagnosis, PR, 2003-2014
Table 1. Number of new cases by Healthcare regions, Overall Region Total Male Female Male:Female n n (%) n (%) Ratio East 1,283 939 (73.2) 344 (26.8) 2.73:1 Metro-North 2,234 1,588 (71.1) 646 (28.9) 2.46:1 Northeast 1,543 1,079 (69.9) 464 (30.1) 2.33:1 North 904 637 (70.5) 267 (29.5) 2.39:1 West 1,004 765 (76.2) 239 (23.8) 3.20:1 San-Juan 2,355 1,689 (71.7) 666 (28.3) 2.54:1 Southeast 628 427 (68.0) 201 (32.0) 2.12:1 Southwest 766 584 (76.2) 182 (23.8) 3.21:1 Total 10,717 7,708 (71.9) 3,009 (28.1) 2.56:1 Note: Percentages per row Table 2. Annual Percent Change of HIV Incidence, 2003-2014 Region APC2003-06 vs. 2007-10 APC2007-10 vs. 2011-14 Male Female Male Female Overall -3.98 * -6.74 * -2.93 * -9.13 * East -9.69 * -11.16 * -3.99 -12.24 * Metro-North -5.69 * -6.26 * -3.20 -10.52 * Northeast -3.57 -8.68 * -1.12 -6.53 North -5.08 -12.13 * -1.60 -1.18 West -8.80 * -5.85 -6.69 * -17.23 * San Juan 5.31 * 5.23 * -1.98 -9.82 * Southeast -8.78 * -17.13 * 1.01 -8.90 Southwest -8.06 * -13.48 * -7.87 * -7.64 APC indicates annual percent change. * Statistically different from zero (p < 0.05) Table 3. Relative Risk of HIV stratified by health region and age group, 2003-2014 2003-2006 2007-2010 Region Age Group RR(95% CI) RR(95% CI) Overall 13-24 1.58 (1.31-1.92) 2.10 (1.7-2.58) 25-34 2.32 (2.04-2.63) 2.88 (2.48-3.34) 35-44 2.92 (2.6-3.28) 2.89 (2.51-3.32) 45-54 2.78 (2.41-3.2) 2.98 (2.55-3.48) 55+ 3.56 (2.93-4.32) 2.74 (2.24-3.34) Age-Adjusted RR 2.62 (2.45-2.79) * 2.78 (2.58-2.99) East 13-24 1.64 (0.99-2.71) 1.48 (0.84-2.6) 25-34 3.03 (2.1-4.36) 3.87 (2.41-6.22) 35-44 2.45 (1.79-3.35) 3.13 (2.07-4.75) 45-54 2.61 (1.81-3.77) 3.24 (1.99-5.27) 55+ 3.27 (1.81-5.91) 3.06 (1.53-6.16) Age-Adjusted RR 2.57 (2.15-3.06) 2.98 (2.38-3.73) Metro-North 13-24 1.97 (1.31-2.97) 2.59 (1.62-4.14) 25-34 2.65 (2.02-3.48) 3.23 (2.29-4.56) 35-44 3.40 (2.64-4.37) 2.50 (1.86-3.35) 45-54 2.41 (1.8-3.23) 2.47 (1.74-3.51) 55+ 2.93 (1.92-4.46) 2.21 (1.42-3.44) Age-Adjusted RR 2.75 (2.39-3.16) 2.62 (2.23-3.08) Northeast 13-24 1.18 (0.67-2.08) 2.67 (1.53-4.66) 25-34 2.27 (1.58-3.25) 2.63 (1.78-3.88) 35-44 2.50 (1.89-3.31) 3.39 (2.31-4.97) 45-54 1.97 (1.4-2.77) 2.20 (1.48-3.29) 55+ 3.15 (1.92-5.17) 2.87 (1.73-4.74) Age-Adjusted RR 2.24 (1.9-2.65) 2.74 (2.26-3.33) North 13-24 0.90 (0.41-1.96) 2.22 (1.06-4.66) 25-34 1.81 (1.15-2.84) 2.71 (1.55-4.76) 35-44 2.72 (1.86-3.98) 3.64 (2.15-6.16) 45-54 3.58 (2.11-6.08) 3.48 (1.9-6.36) 55+ 2.20 (1.26-3.84) 3.01 (1.62-5.58) Age-Adjusted RR 2.32 (1.86-2.9) * 3.07 (2.35-4.01) West 13-24 2.28 (1.19-4.36) 2.35 (1.17-4.74) 25-34 2.55 (1.72-3.78) 3.04 (1.83-5.05) 35-44 4.46 (2.97-6.7) 2.99 (1.82-4.93) 45-54 3.79 (2.3-6.26) 3.11 (1.91-5.07) 55+ 4.95 (2.64-9.3) 2.47 (1.3-4.67) Age-Adjusted RR 3.51 (2.83-4.35) 2.87 (2.24-3.67) San Juan 13-24 1.76 (1.13-2.75) 1.58 (1.05-2.4) 25-34 1.94 (1.44-2.62) 2.56 (1.94-3.38) 35-44 3.40 (2.53-4.55) 2.77 (2.12-3.62) 45-54 3.67 (2.58-5.24) 3.19 (2.35-4.31) 55+ 5.04 (3.1-8.18) 3.55 (2.35-5.35) Age-Adjusted RR 2.91 (2.48-3.4) * 2.72 (2.36-3.13) Southeast 13-24 1.22 (0.66-2.26) 2.46 (0.88-6.9) 25-34 1.78 (1.11-2.83) 1.85 (0.96-3.56) 35-44 1.70 (1.11-2.61) 1.59 (0.92-2.73) 45-54 1.47 (0.84-2.6) 5.55 (2.6-11.85) 55+ 2.82 (1.3-6.13) 2.69 (1.03-6.99) Age-Adjusted RR 1.69 (1.33-2.14) 2.43 (1.77-3.33) Southwest 13-24 1.40 (0.73-2.7) 3.04 (1.3-7.11) 25-34 2.29 (1.51-3.48) 4.56 (2.3-9.05) 35-44 2.95 (1.91-4.55) 5.29 (2.76-10.11) 45-54 6.11 (3.3-11.31) 3.93 (2.07-7.48) 55+ 7.33 (3.29-16.32) 2.44 (1.14-5.2) Age-Adjusted RR 3.18 (2.52-4.02) * 3.94 (2.88-5.39) 2011-2014 Region Age Group RR(95% CI) Overall 13-24 4.83 (3.77-6.19) 25-34 4.78 (3.93-5.8) 35-44 2.62 (2.2-3.12) 45-54 2.78 (2.34-3.29) 55+ 2.86 (2.3-3.54) Age-Adjusted RR 3.35 (3.07-3.65) * East 13-24 7.24 (3.46-15.16) 25-34 5.50 (2.96-10.21) 35-44 3.60 (1.98-6.56) 45-54 3.47 (2.07-5.83) 55+ 3.17 (1.53-6.57) Age-Adjusted RR 4.33 (3.29-5.71) Metro-North 13-24 3.44 (2.07-5.71) 25-34 4.32 (2.88-6.47) 35-44 1.78 (1.22-2.6) 45-54 2.49 (1.71-3.63) 55+ 2.58 (1.56-4.26) Age-Adjusted RR 2.77 (2.3-3.34) * Northeast 13-24 4.42 (2.53-7.73) 25-34 5.48 (3.09-9.73) 35-44 3.01 (1.96-4.61) 45-54 1.87 (1.23-2.85) 55+ 5.25 (2.63-10.51) Age-Adjusted RR 3.38 (2.71-4.23) * North 13-24 3.68 (1.69-8.02) 25-34 2.46 (1.46-4.16) 35-44 3.58 (1.92-6.66) 45-54 2.70 (1.53-4.75) 55+ 1.49 (0.82-2.73) Age-Adjusted RR 2.60 (1.99-3.39) West 13-24 5.69 (1.97-16.39) 25-34 9.78 (3.89-24.57) 35-44 2.99 (1.5-5.95) 45-54 5.87 (2.75-12.49) 55+ 3.19 (1.72-5.9) Age-Adjusted RR 4.69 (3.35-6.56) San Juan 13-24 6.86 (3.74-12.58) 25-34 5.12 (3.45-7.61) 35-44 2.82 (2.01-3.96) 45-54 2.50 (1.8-3.46) 55+ 3.38 (2.2-5.18) Age-Adjusted RR 3.51 (2.95-4.17) * Southeast 13-24 6.81 (2.04-22.76) 25-34 5.30 (2.59-10.85) 35-44 2.08 (1.09-3.96) 45-54 6.86 (2.39-19.72) 55+ 2.10 (0.7-6.27) Age-Adjusted RR 3.87 (2.67-5.63) Southwest 13-24 3.81 (1.56-9.31) 25-34 5.90 (2.48-14.03) 35-44 2.47 (1.08-5.64) 45-54 3.68 (1.81-7.48) 55+ 2.76 (1.2-6.34) Age-Adjusted RR 3.61 (2.51-5.19) RR indicates relative risk. Reference group: Females. * Significant interaction terms between age and gender in the Poisson model (p < 0.05) Table 4. Relative Risk of HIV diagnosis by study period, stratified by sex and age group, Overall Sex Period RR (95%CI) Age Group 13-24 25-34 Female 2003-2006 Reference Reference 2007-2010 0.82 (0.65 -1.03) 0.70 (0.59 -0.82) 2011-2014 0.50 (0.38 -0.66) 0.40 (0.33 -0.50) Male 2003-2006 Reference Reference 2007-2010 1.09 (0.92 -1.28) 0.87 (0.78 -0.96) 2011-2014 1.53 (1.31 -1.79) 0.83 (0.75 -0.93) Sex Period RR (95%CI) Age Group 35-44 45-54 Female 2003-2006 Reference Reference 2007-2010 0.73 (0.62 -0.85) 0.78 (0.66 -0.94) 2011-2014 0.50 (0.42 -0.60) 0.69 (0.57 -0.83) Male 2003-2006 Reference Reference 2007-2010 0.72 (0.65 -0.79) 0.84 (0.75 -0.94) 2011-2014 0.45 (0.40 -0.51) 0.68 (0.61 -0.77) Sex Period RR (95%CI) Age Group 55+ Female 2003-2006 Reference 2007-2010 0.94 (0.75 -1.2) 2011-2014 0.75 (0.58 -0.95) Male 2003-2006 Reference 2007-2010 0.73 (0.63 -0.84) 2011-2014 0.60 (0.51 -0.70) RR indicates relative risk