Late HIV diagnoses (AIDS diagnoses within 12 months of initial HIV infection) is one of the seven core indicators in the US National HIV/AIDS Strategy for monitoring HIV programmes and services [1,2]. According to latest national estimates (end of year 2010), 32% of persons initially diagnosed with HIV were diagnosed late . Late HIV diagnoses represent missed opportunities for treatment and prevention and pose significant public health challenges in the United States .
Persons diagnosed with HIV in the advanced stages (late HIV diagnoses) are less likely to fully benefit from timely HIV treatment [5–7] and more likely to accrue higher treatment-related costs [8,9]. They also are more likely to die from HIV-related causes than those who were diagnosed earlier in the course of infection [10–12]. From a population perspective, late HIV diagnoses represent missed opportunities to prevent onward HIV transmission [13,14]. One study estimated that the transmission rate of HIV among persons not yet diagnosed (and consequently unaware of their HIV status) was approximately four times higher than the rate of those aware . Persons unaware of their HIV status are the source of infection for an estimated 49–66% of individuals newly infected with HIV .
Expanded HIV testing coverage has been associated with increases in the proportion of persons with HIV linked to care, and subsequently, virally suppressed due to earlier HIV treatment initiation [17,18]. Over the long term, successful expansion of HIV testing would be expected to result in earlier HIV diagnoses and, consequently, in a reduction in the number and proportion of new HIV diagnoses that are late [17,19]. In New York City (NYC), there have been multiple recent targeted efforts to expand HIV testing. In the Bronx, following ‘The Bronx Knows’ testing campaign, the proportion of HIV diagnoses concurrent with AIDS (i.e. late HIV diagnoses) declined . However, HIV testing rates improved in other areas across NYC during the same time period as the Bronx Knows campaign. We therefore sought to determine whether expanded HIV testing coverage within neighbourhoods in all five boroughs of NYC was associated with declines in the rate of late HIV diagnosis during 2003–2010 in those same neighbourhoods.
Materials and methods
Data on HIV/AIDS diagnoses during 2003–2010, reported up to 30 September 2011, came from the NYC HIV Surveillance Registry (the Registry), which is a population-based registry of all persons diagnosed with HIV infection and reported to the NYC Department of Health and Mental Hygiene (NYC DOHMH) . Name-based reporting of AIDS diagnoses was mandated by New York State (NYS) law in 1983, followed by HIV reporting in 2000  and laboratory reporting of all positive western blots, viral loads, CD4+ cell counts and nucleotide sequence results in 2005 . All incoming electronic laboratory reports are matched to cases in the Registry, and nonmatching records are sent for field investigation to confirm case status . Only persons residing in NYC at the time of HIV diagnosis were included in the analysis. In addition, we excluded persons who were undomiciled, living in a shelter and those with nonresidential, unknown or missing ZIP codes (representing approximately 3% of remaining HIV diagnoses).
We examined aggregate data stratified by sex on all HIV diagnoses among NYC residents for 171 residential ZIP codes during two year intervals (2003–2004, 2005–2006, 2007–2008 and 2009–2010).
Late HIV diagnoses
We used the CDC's definition of late HIV diagnosis as a CD4+ count test result of 200 cells/μl or less or an AIDS-defining illness within 12 months of the date of HIV diagnosis . We expressed the proportion of persons diagnosed late as a rate per 100 000 population. We calculated the rate of late HIV diagnoses per 100 000 population across ZIP codes in 2-year periods (to improve the stability of rates within ZIP codes with small numbers of events), using Census 2010 as the population denominator to standardize rate calculations. All 2-year rates were divided by two (i.e. annualized) to represent rates per year. The outcome variable, change in the rate of late HIV diagnosis during the study period, represents the absolute difference in rates between 2003–2004 and 2009–2010 within each ZIP code. ZIP codes falling within the top 25 percentile of change in late HIV diagnosis rate (i.e. largest declines in rate) were classified as having ‘large’ declines in the late HIV diagnosis rate. The remaining ZIP codes were classified as having ‘small to no decline’.
We used estimates of the absolute within-neighbourhood change in the percentage of having recently tested for HIV in the last 12 months. We used the NYC Community Health Survey (CHS), which is an annual, cross-sectional, random-digit telephonic survey on approximately 10 000 noninstitutionalized adults aged 18 years and older administered by the NYC DOHMH since 2002. The survey is based on a stratified sampling design, which enables enumeration of neighbourhood and citywide estimates. In 2009, the survey started sampling respondents with cell phones and landline phones .
The CHS questions are modelled after the CDC's Behavioral Risk Factor Surveillance System (BRFSS) and collect self-reported data across a range of socio-demographic, health and behavioural outcomes such as HIV testing in the past 12 months. The question on HIV testing was first introduced in 2003 and, except for 2004, asked annually through 2010. Data were paired in 2-year periods for improved reliability of estimates. Responses from the earliest (2003–2005) and latest (2009–10) survey years at the time of the study were used to generate the exposure variable, recent HIV testing coverage. The average response rate from the CHS between 2003 and 2010 was 36.6% and the cooperation rate (participation among those reached by phone) was 80.6%.
Due to small CHS sample sizes within ZIP codes, we used United Hospital Fund (UHF) neighbourhood designations (n = 42), which comprise between two and 11 adjacent ZIP codes that represent historical catchment areas of public healthcare facilities [27,28]. For each UHF, the absolute age-adjusted percentage of persons reporting an HIV test in the past 12 months was calculated for 2003–2005 and 2009–2010, separately by sex using weights provided by the NYC DOHMH. CHS poststratification weights were used to adjust for probability of selection while taking into account the respondent's age, sex and race . We classified the neighbourhood changes in recent HIV testing coverage between 2003/2004 and 2009/2010 using tertiles: ‘no/low’, ‘medium’ and ‘large’ increase in recent HIV testing coverage.
Secular trends (decreases) in HIV incidence might result in a reduction in the rate of late HIV diagnosis simply due to a reduction in new HIV infections (i.e. independently of earlier HIV diagnosis), therefore we examined this as a possible alternate explanation for declines in the rate of late HIV diagnoses. HIV incidence trend data were not available at the ZIP code level, so we used change in rate of nonlate HIV diagnoses (i.e. new HIV diagnoses excluding those meeting the above CDC definition of late HIV diagnosis) as a proxy. We calculated the within-neighbourhood change as the difference between rates of late and new HIV diagnoses per 100 000 during 2003–2004 and 2009–2010. We classified neighbourhood change in our proxy for HIV incidence using tertiles: ‘no/low’, ‘medium’ and ‘large’ decrease in incidence.
Borough of residence at HIV diagnosis
HIV testing initiatives as well as jurisdictional HIV testing campaigns have taken place in two of the five boroughs (The Bronx beginning in 2008, and Brooklyn beginning in 2010). We therefore examined borough in our multivariate analysis. We selected Queens as the reference group, as Queens had not been directly targeted by any DOHMH-led HIV testing initiative as of the end of the study period.
Statistical methods and analysis
We examined the trend and statistical significance of the slopes for rates of new HIV, late HIV, nonlate HIV and AIDS diagnoses, HIV-related mortality, median CD4+ cell count and HIV testing coverage. We estimated the relative risks for 2009–2010 vs. 2003–2004 and the relative risk for recent HIV testing coverage for 2009–2010 vs. 2003–2005. We mapped HIV testing coverage, using tertile categories and late HIV diagnoses using quartile categories, to correspond to the categories used in multivariable analysis. Maps are for the earliest and latest year groups and for changes between these two-year groups, by ZIP code. Because HIV testing data from CHS were not available at the ZIP code level, we mapped and examined UHF level testing coverage at the ZIP code level by assigning the same testing coverage for all the ZIP codes within that UHF. Dark blue lines delineate UHFs, while light grey lines are ZIP codes. Maps are based on data for men and women combined.
We calculated the Global Morans’ I to assess spatial autocorrelation  for HIV testing coverage and the late HIV diagnosis rate. Moran's I evaluates whether a pattern observed is clustered, dispersed or random. A positive Moran's I indicates that high values (i.e. rates or prevalence) spatially cluster near other high values. A statistically significant value indicates a rejection of the null hypothesis that values are randomly distributed. We calculated Morans’ I for HIV testing at the UHF level using a spatial weights matrix with (k = 4) nearest neighbours, and at the ZIP code level for rates of late HIV diagnoses using a spatial weights matrix of (k = 10) nearest neighbours. K-nearest neighbour's specification was chosen because it forces each area-level unit to have the same number of neighbours. We used ArcMap 10.1  to create maps and calculate Moran's I.
The multivariate analyses of factors associated with the largest changes in the late HIV diagnosis rate was conducted on 171 ZIP codes between 2003–2004 and 2009–2010 for each sex. We used generalized estimating equations (GEEs) regression with logit link function and an independent correlation structure using robust variance estimation  to examine whether changes in HIV testing coverage were associated with ‘large’ declines in the rate of late HIV diagnoses, while accounting the hierarchical nature of the data (ZIP codes nested within UHF neighbourhoods). We conducted analyses separately for men and women given known sex differences in HIV testing and late HIV diagnosis . First, we individually assessed the crude association of HIV testing coverage, HIV incidence and NYC borough of residence at HIV diagnoses on the outcome (Model 1). We then estimated the effect of HIV testing coverage adjusting for HIV incidence and borough of residence at HIV diagnosis in a stepwise fashion (Models 2 then 3), and then included all variables in one model simultaneously (Model 4). Statistical analyses were conducted in STATA 10.0 .
City-wide trends in HIV-related measures
During the 7-year period (2003–2010), the rate of new HIV diagnoses significantly declined from 49.69 per 100 000 to 39.80 per 100 000 [relative risk (RR) 0.78, 95% confidence interval (95% CI) 0.76–0.81]. The rate of late HIV diagnoses declined from 14.91 to 10.65 per 100 000 corresponding to a RR of 0.70 (95% CI 0.66–0.74). The rate of nonlate HIV diagnoses declined from 34.78 to 29.15 per 100 000 (RR 0.82, 95% CI 0.79–0.85). The median CD4+ cell count among all persons diagnosed with HIV increased from 228 cells/μl in 2003–2004 to 357 cells/μl in 2009–2010. The age-adjusted rate of mortality attributed to HIV declined during 2003–2004 to 2009–2010 by almost half from 20 to 11 per 100 000 (RR 0.56, 95% CI 0.52–0.59). Recent HIV testing increased from 23% in 2003–2005 to 31% in 2009–2010 (Table 1).
The increasing trend in HIV testing coverage corresponded closely with an increasing trend in median CD4+ cell count among all those diagnosed, especially between 2007–2008 and 2009–2010, and a declining trend in the rates of late HIV diagnosis and HIV-related mortality (Fig. 1).
Temporal changes in late HIV diagnosis rates within ZIP codes
As shown in Fig. 2, ZIP codes with larger increases in HIV testing coverage also had larger decreases in the rate of late HIV diagnoses during 2003–2010. The overall crude association showed that each 10% absolute increase in recent HIV testing coverage was associated with a 2.5 per 100 000 absolute decrease in the late HIV diagnosis rate. The sex-specific plots were suggestive of a stronger association for men (blue) than women (orange). Importantly, ZIP codes with larger numbers (>40) of late HIV diagnoses in 2003–2004 tended to have both larger improvements in recent HIV testing coverage and larger declines in the rate of late HIV diagnosis.
Spatial analysis of temporal changes
The maps in Fig. 3 show that HIV testing coverage was strongly clustered across pockets of areas within the UHF neighbourhoods in 2003–2005 (Map a), which corresponded to Moran's I = 0.62, z = 7.47, P < 0.001. The clustering pattern persisted in 2009–2010 (Map c), corresponding to a Moran's I = 0.49, z = 6.1, P < 0.001. Results from neighbourhoods (especially those with increases in HIV testing) (Map e) showed weak evidence of clustering as indicated by the small and nonsignificant Moran's I coefficient (Moran's I = 0.12, z = 1.74, P = .08).
Rates of late HIV diagnoses showed strong evidence of spatial clustering across ZIP codes in 2003–2005 (Map b), with Moran's I = 0.58, z = 12.8, P < 0.001, and in 2009–2010 (Map d), with Moran's I = 0.53, z = 11.8, P < 0.001. Neighbourhoods with the largest changes (declines) in rates of late HIV diagnoses (Map f) were geographically clustered (Moran's I = 0.18, z = 4.17, P < 0.001) as indicated by the red-shaded regions (Table 2).
Multivariable analysis of change in HIV testing coverage and change in late HIV diagnosis rates
ZIP codes with the largest changes in HIV testing coverage among men (mean change = +18%) had three times higher odds of having the largest decline (top quartile) in late HIV diagnosis rates among men (mean change = −17 per 100 000) as compared with ZIP codes with no or small changes in HIV testing coverage (mean change = +0.09%) [Model 1, crude odds ratio (OR) = 3.1, 95% CI 1.3–7.0). The association persisted after adjusting for changes in the rate of nonlate HIV diagnoses [Model 2, adjusted odds ratio (aOR) = 3.2, 95% CI 1.3–7.4) and borough of residence at HIV diagnosis (Model 3, aOR = 4.1, 95% CI 1.7–10.1) and (Model 4, aOR = 3.9, 95% CI 1.6–9.6).
ZIP codes with the largest changes in HIV testing coverage among women (mean change = +15.6%) did not have a significantly higher odds of having the largest declines (top quartile) in late HIV diagnosis rates among women (mean change = −14 per 100 000) as compared with ZIP codes with no or small changes in HIV testing coverage (mean change = +0.05%) in crude (Model 1) or adjusted analyses (Models 3 and 4).
Our study found that, during 2003–2010, larger increases in the proportion of persons tested for HIV during the last 12 months in NYC neighbourhoods were associated with statistically significant declines in the rate of late HIV diagnoses in the ZIP codes within those neighbourhoods among men but not women. Among men, the association held after adjusting for changes in nonlate HIV diagnoses (a proxy for HIV incidence), and NYC borough of residence at HIV diagnosis. Among women, the relatively higher levels of both HIV testing coverage and lower levels of late HIV diagnoses at the start of the study period may explain the weaker and nonsignificant association between within-neighbourhood changes in HIV testing and changes in late HIV diagnosis rates. Alternatively, HIV testing campaigns may have been better able to reach persons with undiagnosed HIV among men more easily than women.
The higher proportion of HIV testing among women compared with men may be partly explained by a greater propensity among women to seek healthcare and/or voluntary HIV testing, and the fact that women in New York have received universal HIV screening during pregnancy since the 1990s. National estimates show that 62% of women were screened during pregnancy for HIV in 2002 . In 2009–2010, CHS data indicated that among NYC women, 61% reported ever testing for HIV compared with 57% among men. Also, there may have been a floor effect of testing on the late diagnosis rate among women, as the rate of late diagnosis among women in 2003 (4.73 per 100 000) was already lower than what it would become for men in 2009/2010 (7.97 per 100 000) (Table 1).
Bronx neighbourhoods experienced the largest overall decline in the rates of late HIV diagnoses and the largest increase in HIV testing coverage, followed by Manhattan and Brooklyn. The findings within Bronx were expected given the reported impact of the DOHMH's ‘The Bronx Knows’ borough-wide HIV testing initiative [36,37]. However, the findings of decreasing late HIV diagnosis rates in Manhattan associated with HIV testing could have been due to a spillover effect whereby residents from Manhattan neighbourhoods, particularly on the bordering areas with the Bronx, were exposed to ‘The Bronx Knows’ programme via social media, other outreach initiatives and high inter-borough mobility of NYC residents .
NYC experienced a major increase in median CD4+ cell count among persons newly diagnosed with HIV during the study period. We estimated an annual change of 18.4 cells/μl per year among all persons newly diagnosed with HIV (228–357 cells/μl over the entire 7-year study period). This is substantially higher than the increase of 1.5 cells/μl per year reported in a meta-analysis study that used longitudinal clinic-based data that spanned 20 years , and also marginally higher than those reported in a study that used population-based data from Washington, District of Columbia, USA  (6.6 cells/μl per year; 346–379 cells/μl over the 5-year period 2005–2009 compared with 8.4 cells/μl per year in our study (315–357 cells/μl over the same 5-year period).
In the most recent time period in our analysis (2009–2010), we found significant geographic disparities at the neighbourhood level (ZIP code) in the rates of late HIV diagnoses. The highest rates were in East Flatbush within Central Brooklyn and in the South Bronx. These areas are characterized by relatively high levels of socioeconomic disadvantage and a high proportion of racial/ethnic minorities. Our findings correspond to findings from a recent study that showed low socioeconomic status, high proportion of racial/ethnic minorities, high prevalence and incidence of HIV tend to geographically cluster within the same neighbourhoods .
Our study has some limitations. First, there might have been factors other than improvements in HIV testing coverage that could partially explain the declines in late HIV diagnoses rates. General declines in HIV incidence could result in fewer people being diagnosed overall as well as late, independently of increasing HIV testing coverage. Although our multivariate analyses attempted to control for this, the proxy measure we used for HIV incidence (nonlate HIV diagnoses) is imperfect, as some persons who were nonlate could have been living with HIV for several years without progressing to CD4+ cell count less than 200 cells/μl. Reliable population-based data HIV incidence, however, are not available at the ZIP code level in NYC.
Second, there could have been reporting bias of HIV-testing behaviours. We used a self-reported measure from a household survey, which included only residents with landline phones through 2009. From 2009 onwards, the CHS started sampling residents with both landline and cell phones. However, initial evaluations of prevalence estimates of health and behavioural outcomes showed that the HIV testing prevalence was not markedly different between cellphone and landline respondents with who were primarily landline users vs. those who were primarily cell phone users .
Third, our unit of analysis, ZIP codes, may not accurately represent or differentiate neighbourhoods; however, ZIP codes were the smallest unit of aggregation we had HIV surveillance data. We also did not have sufficiently precise data on HIV testing coverage at the ZIP code level to match with HIV surveillance data, resulting in less granularity and possibly 'scale effects’ whereby variance in association between HIV testing and late HIV diagnoses may be different at other levels of aggregation .
Lastly, our findings may not apply to newly diagnosed persons who were homeless or undomiciled or living in a shelter or where residence of ZIP code was unknown.
However, our study also has some strengths. First, due to the population-based nature of the data sources used in our analysis, our results are generalizable to all NYC neighbourhoods. Second, we were able to examine and quantify the association of changes of HIV testing coverage with changes in late HIV diagnoses longitudinally within ZIP codes throughout NYC, facilitating an ecological assessment of the potential effects that may have resulted from varying degrees of changes in HIV testing coverage.
Our study suggests that expanded HIV testing has resulted in a reduction in the rate of late HIV diagnoses among men in NYC. Given that sex and geographic disparities in the rates of late HIV diagnosis persisted in 2009/2010, our findings underscore the need for ongoing expansion of HIV testing because of its potential to reach those areas and populations in which recent HIV testing coverage remains low and the rates of late HIV diagnosis remain high.
Future research should examine whether other behavioural and socioeconomic area-level factors, such as income inequality, social capital and racial residential segregation are associated with late HIV diagnoses, as well as the population-level impact that disparities in late HIV diagnoses have on other outcomes such as mortality attributed to HIV and HIV incidence.
Conflicts of interest
There are no conflicts of interest.
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