3.1.1 Introduction

3.1.2 Methods

3.1.3 HIV seroprevalence

3.1.4 HCV seroprevalence

3.1.5 HCV seroprevalence among new injectors

3.1.6 HCV incidence

3.1.7 Discussion

## 3.1.1 Introduction

Measures to prevent HIV infection among people who inject drugs generally focus on preventing blood contact during injection by reducing injection, promoting use of sterile equipment when injecting, or adopting safer injecting practices. Consequently, Needle and Syringe Programs (NSPs) are a key strategy for preventing transmission of HIV infection in several countries, including Australia. In other countries, implementation has been limited by uncertainty about their effectiveness.Randomised trials of the effectiveness of NSP in preventing HIV transmission have not been conducted. Several observational studies have assessed the impact of NSPs on self-reported risk behaviours, in particular use of sterile syringes or re-use of one's own syringe (Drucker et al. 1998). A few studies have compared HIV incidence or HIV, HBV or HCV prevalence in participants and non-participants of NSPs (Bruneau et al. 1997; Des Jarlais et al. 1995; Hagan et al. 1999; van Ameijden et al. 1994). One study compared NSP implementation in countries with sustained low HIV prevalence to those with high HIV prevalence (Hurley et al. 1997). While another used an ecological study design to compare changes in HIV prevalence in cities with and without NSPs (Hurley et al. 1997). The data generally, but not always, show NSPs to be effective in preventing HIV transmission.

In contrast to HIV infection, prevalence of HCV infection among injecting drug users is universally high, regardless of whether the studies were done in cities with or without NSPs (MacDonald et al. 1996). It is likely that HCV prevalence was already very high among injecting drug users before NSPs were introduced. However, there are no studies that quantify the impact of NSPs on HCV infection. In this study, we have repeated the ecological study of change in HIV prevalence in cities with and without NSP because several countries have introduced NSP since the previous study (Hurley et al. 1997). We have also used a similar methodology to assess the effectiveness of NSP for prevention of HCV infection. A discussion on the rationale behind the approach adopted in this study is presented in Appendix B. Top of page

## 3.1.2 Methods

The ecological study design was used to compare HIV and HCV infection among injecting drug users in countries with and without NSPs. Data recorded on HIV and HCV infection included both seroprevalence and seroincidence studies. NSPs were defined as programs distributing needles and syringes, either free or with minimal charge, irrespective of whether they operated from a fixed or mobile site, whether return of a used syringe was mandatory, or the range of other HIV and HCV prevention and treatment services provided.Several sources were used to identify published reports of HIV and HCV prevalence and incidence among injecting drug users and implementation of NSPs. Three electronic databases that indexed relevant journals were searched from January 1984 to June 2001. Both Medline and Embase databases were used because each placed an emphasis on research from different continents, that is, North America and Europe respectively. The Current Contents database was also used because it included literature from Social Science and Psychology journals. Additional studies were obtained from country specific surveillance reports, the HIV/AIDS Surveillance Database (US Census Bureau & UNAIDS, 2000), relevant websites, and through review of the index of journals frequently cited in the electronic searches.

All studies with sample size of at least 50 were included. Cities with HIV prevalence studies were only included if HIV was measured among injecting drug users in two or more calendar years. Studies of HIV or HCV among incarcerated injecting drug users were excluded because very few countries provide NSP during imprisonment.

Studies reported in journals published in languages other than English were only included if sufficient information was provided in the abstract to determine whether the study was suitable for inclusion and all required data points were reported in the abstract. References used in the analysis are provided in Appendix F.

Number of injectors tested per calendar year, percentage with HIV and /or HCV, presence or absence of NSP, and recruitment site were recorded for all studies. If studies reported data aggregated for more than one calendar year, the mid-point of the study period was used as the survey date. Data were also recorded on HIV and HCV prevalence among new and young injectors where available. Studies of HCV incidence were included if they reported numbers of incident HCV infections and person-years of follow-up.

Analysis compared change in HIV and HCV prevalence between cities with and without NSPs at the time of the surveys. For HIV prevalence, city-specific change in prevalence was used in the analysis. For HCV prevalence, however, it was not possible to use city-specific change because relatively few cities had more than one estimate of prevalence.

For each city, the annual rate of change of HIV seroprevalence was estimated by fitting a regression line on a logit scale, with calendar years centred to 1990. The annual rate of change of HIV seroprevalence was also estimated using regressions weighting the comparison of cities with and without NSPs according to one over the variance of the regression estimator (Hurley et al. 1997). The effect of NSPs was assessed by comparing the annual rate of change in HIV seroprevalence in cities that had ever introduced NSPs with cities that had never introduced NSPs. Analyses of HIV seroprevalence were performed comparing all cities, and also in the subset of cities which had an initial HIV seroprevalence of less than 10%, and had results from at least three surveys available over at least three years. Analyses were repeated using regressions weighted according to survey sample size, and also excluding cities in developing countries.

A random effects regression model was used for analyses of HCV seroprevalence because few cities had data points before and after NSPs were introduced, and to allow appropriately for within and between city effects. The analysis model fits regression equations of the form:

Logit(HCV prevalence) = a + b*(calendar year) + y*(year since NSPs started)

The parameter estimate for y can then be directly interpreted as the modifying effect of NSPs on logit(HCV prevalence) levels per year. The effect of NSPs on HCV prevalence was estimated using all data from all cities, excluding studies that used blood stored since 1981, and for cities that introduced NSP between the first and last available study. A random effects regression model was also used to estimate the effect of NSPs on HCV prevalence using data available for people reporting less than three years of drug injection. Other regression models, such as ML random effects, and GEE, were also used on the sampled HCV prevalences and gave identical results (data not reported).

Two sets of analyses were performed to assess the effect of NSPs on HCV incidence. In the first set of analyses, random effects and GEE negative-binomial models were used to compare cohorts in cities with and without NSPs, allowing for within and between city effects in the analysis and for over-dispersion effects. In the second analysis, an overall incidence rate was calculated for each city by summing the numbers of incident infections and person-years of follow-up. Straightforward negative-binomial regression models were then used to compare cities with and without NSPs. Top of page

## 3.1.3 HIV seroprevalence

There were 778 calendar years of data from 103 cities with HIV seroprevalence measurements from more than one year and information on NSP implementation. Studies were from 67 cities without NSP, 23 cities that implemented NSP between the first and last study, and 13 cities that already had NSP when the studies were carried out (Table 3.1.1).HIV prevalence ranged from zero to 79% at the first data point for each city (median 18%), with 53 cities reporting first HIV prevalence 10% or less. Data were reported from 1978 to 1999. Studies with first HIV prevalence 10% or less were available from 23 cities without NSP, 19 cities that implemented NSP between the first and last study, and 13 cities that already had NSP when the studies were carried out

The fitted HIV prevalence regression lines are presented for those cities that had never introduced NSPs in Figure 3.1a, and for those cities that had ever introduced NSPs in Figure 3.1b. To illustrate the fitting procedure, the fitted regression lines and the reported HIV seroprevalence survey results are shown for two sites (Songkla Province, Thailand and Sydney, Australia) in Figure 3.1c and Figure 3.1d respectively.

The overall comparison of annual rates of change of HIV seroprevalence in cities that never introduced NSPs with cities that did introduce NSPs are summarised in Table 3.1.2. Cities that introduced NSPs had a mean annual 18.6% decrease in HIV seroprevalence, compared with a mean annual 8.1% increase in HIV seroprevalence in cities that had never introduced NSPs (mean difference –24.7% [95% CI: –43.8%, 0.5%], p=0.06).

In cities with an initial HIV prevalence less than 10% and with sero-surveys over a period of at least three years, the mean annual decrease in HIV prevalence was 4.0% in cities that introduced NSPs, compared with a mean annual 28.6% increase in cities without NSPs (mean difference –25.3% [95% CI:-50.8%, 13.3%], p=0.2).

Variability of the point estimate was markedly reduced and statistical significance markedly increased when the analyses for all cities and cities with HIV prevalence less than 10% were weighted according to one over the regression estimate (The better fit implies a smaller variance, and therefore its reciprocal is larger, representing a larger weight). However, a disadvantage of the weighted analyses is that it tends to put much greater weight on the few cities in which the linear regression gives a very good fit to the available HIV seroprevalence estimates. For this reason, and because the unweighted results are qualitatively very similar and, for all cities, the point estimate is smaller than the weighted analysis, estimates of NSP effectiveness were based on the unweighted analysis.

### Table 3.1.1 Location of cities and sites of recruitment for cities with at least two HIV prevalence studies according to NSP status from the time of first to last study

Table 3.1.1 is separated into 2 smaller tables in this HTML version for accessibility reasons. It is presented as one table in the PDF version.#### Location of studies

Location of studies | Number of cities without NSP | Number of cities with & without NSP | Number of cities with NSP |
---|---|---|---|

Asia - China | 3 | 0 | 0 |

Asia - India | 1 | 1 | 0 |

Asia - Malaysia | 4 | 0 | 0 |

Asia - Myanmar | 4 | 0 | 0 |

Asia - Nepal | 0 | 0 | 1 |

Asia - Thailand | 22 | 2 | 0 |

Asia - Vietnam | 0 | 1 | 0 |

Australia | 0 | 2 | 8 |

Canada | 0 | 3 | 0 |

Europe - Austria | 0 | 1 | 0 |

Europe - Czech Republic | 1 | 0 | 0 |

Europe - Denmark | 0 | 1 | 0 |

Europe - France | 0 | 0 | 1 |

Europe - Germany | 0 | 1 | 0 |

Europe - Greece | 0 | 1 | 0 |

Europe - Israel | 1 | 0 | 0 |

Europe - Italy | 10 | 0 | 0 |

Europe - Netherlands | 0 | 0 | 2 |

Europe - Spain | 3 | 0 | 0 |

Europe - Switzerland | 0 | 1 | 0 |

South America - Argentina | 0 | 1 | 0 |

South America - Brazil | 5 | 0 | 0 |

United Kingdom | 0 | 4 | 1 |

United States | 13 | 4 | 0 |

Total cities | 67 | 23 | 13 |

#### Recruitment sites

Recruitment sites | Number of cities without NSP | Number of cities with & without NSP | Number of cities with NSP |
---|---|---|---|

Deceased | 0 | 4 | 0 |

Detoxification/rehabilitation | 226 | 18 | 0 |

Drug treatment agency | 95 | 72 | 8 |

Entry to treatment | 33 | 17 | 0 |

Field & snowball | 16 | 25 | 7 |

Health service | 0 | 2 | 0 |

HIV testing centre | 6 | 17 | 0 |

Infectious diseases hospital | 14 | 1 | 0 |

Multiple sites | 15 | 61 | 6 |

NSP/pharmacy | 0 | 27 | 35 |

Sexual health clinics | 4 | 12 | 2 |

Other/not reported | 26 | 25 | 3 |

Total studies | 435 | 281 | 61 |

### Table 3.1.2 Estimated annual rate of change in HIV seroprevalence according to weighting of analysis and sample selection for cities without and with NSPs

Table 3.1.2 is separated into 4 smaller tables in this HTML version for accessibility reasons. It is presented as one table in the PDF version.#### No weighting of analysis - all cities

Cities without NSPs | Cities with NSPs | |
---|---|---|

Number | 63 | 36 |

Mean | 8.1% | -18.6% |

(95% CI) | (-2.8%, 20.1%) | (-42.6%, 15.3%) |

**Mean difference (95%CI)**: -24.7% (-43.8%, 0.5%), p=0.057

#### No weighting of analysis - cities with initial HIV prevalence <10% and three calendar years of data

Cities without NSPs | Cities with NSPs | |
---|---|---|

Number | 19 | 25 |

Mean | 28.6% | -4% |

(95% CI) | (-4.9%, 73.8%) | (-28.5%, 29%) |

**Mean difference (95%CI)**: -25.3% (-50.8%, 13.3%), p=0.165 Top of page

#### Weighting of analysis - all cities

Cities without NSPs | Cities with NSPs | |
---|---|---|

Number | 63 | 36 |

Mean | 5.1% | -29.2% |

(95% CI) | (1.4%, 9.1%) | (-30.8%, -27.6%) |

**Mean difference (95%CI)**: 32.7% (-37.5%, -27.6%), p=<0.001

#### Weighting of analysis - cities with initial HIV prevalence <10% and three calendar years of data

Cities without NSPs | Cities with NSPs | |
---|---|---|

Number | 19 | 25 |

Mean | 32.1% | 7.8% |

(95% CI) | (22.1%, 42.8%) | (-4.8%, 22%) |

**Mean difference (95%CI)**: -18.4% (-32.0%, -2.0%), p=0.030

### Figure 3.1a Fitted HIV prevalence in cities without NSPs

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#### Text version of Figure 3.1a

Figures in this description are approximate as they have been read from the graph.This diagram shows fitted HIV prevalence in cities without NSPs between 1977 and 2000. There was no overall trend pattern (patterns included linearly increasing, linearly decreasing and exponentially growing). The values ranged from 0 to 1, and data for each city started and finished at varying years.

### Figure 3.1b Fitted HIV prevalence in cities with NSPs

#### Text version of Figure 3.1b

Figures in this description are approximate as they have been read from the graph.This diagram shows fitted HIV prevalence in cities with NSPs between 1977 and 2000. There was no overall trend pattern (patterns included linearly increasing, linearly decreasing, steady state and exponentially growing). The values ranged from 0 to 1 and data for each city started and finished at varying years. Top of page

### Figure 3.1c HIV seroprevalence in injecting drug users per year of survey for a city without NSP, Songkla Province, Thailand (Lines represent fitted values from the logistic regression model)

#### Text version of Figure 3.1c

Figures in this description are approximate as they have been read from the graph.The fitted values of the logistic regression model for Thailand linearly increases from about 0.27 in 1991 to about 0.42 in 1999. The actual values range from 0.24 to 0.48.

### Figure 3.1d HIV seroprevalence in injecting drug users per year of survey for a city with NSP, Sydney, Australia (Lines represent fitted values from the logistic regression model)

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#### Text version of Figure 3.1d

Figures in this description are approximate as they have been read from the graph.The fitted values of the logistic regression model for Sydney slowly decreases from about 0.021 in 1985 to about 0.018 in 2000. The actual values fluctuate greatly from 1985 until 1993 with variances from 0 to 0.15. The actual values from 1993 onwards fall closely above and below the fitted line.

## 3.1.4 HCV seroprevalence

There were 190 calendar years of HCV seroprevalence data from 101 cities. Data were from 41 cities without NSP, 9 cities that implemented NSP between the first and last study, and 51 cities that already had NSP when the studies were carried out (Table 3.1.3). There were 71 cities with data available for one calendar year, 13 cities with data for two calendar years and 17 cities with data for three or more calendar years. In the 30 cities with HCV seroprevalence data available for more than one year, 60% had already implemented NSPs before the first year of measurement and 30% introduced NSP between the first and last year of measurement.Median HCV prevalence was 75% (range 24% to 96%) in studies from cities without NSP and 60% (range 17% to 98%) in cities with NSP (NPtrend p=0.01). Data were reported from 1973 to 2000 (Figure 3.2). HCV results from stored samples collected between 1973 and 1989 were reported by 21 cities. There were 44 cities with their first study carried out between 1990 and 1994 and 36 cities with their first study between 1995 and 1999.

Overall the results indicated little change in HCV prevalence before NSPs were introduced, followed by a decline after introduction of NSPs (Table 3.1.4). If HCV prevalence was 75% or 50% respectively before NSPs were introduced, the results correspond to around a 1.5% or 2% decline in HCV prevalence per annum.

Similar results were obtained when two studies based on samples from the 1970s and one from 1980 were excluded from analysis and when analysis was limited to nine cities that implemented NSP between the first and last study (Table 3.1.4). Other analyses, using different regression models, such as ML random effects, and GEE, gave similar results (data not presented).

### Table 3.1.3 Location of cities and sites of recruitment for cities with HCV prevalence studies according to NSP status from the time of first to last study

Table 3.1.3 is separated into 2 smaller tables in this HTML version for accessibility reasons. It is presented as one table in the PDF version.#### Location of studies

Location of studies | Number of cities without NSP | Number of cities without & with NSP | Number of cities with NSP |
---|---|---|---|

Asia - China | 2 | 0 | 0 |

Asia - Bangladesh | 1 | 0 | 0 |

Asia - India | 0 | 0 | 1 |

Asia - Japan | 3 | 0 | 0 |

Asia - Malaysia | 1 | 0 | 0 |

Asia - Nepal | 0 | 0 | 1 |

Asia - Taiwan | 1 | 0 | 0 |

Asia - Thailand | 3 | 0 | 1 |

Australia | 0 | 2 | 10 |

Canada | 0 | 0 | 1 |

Europe - Austria | 1 | 1 | 0 |

Europe - Belgium | 1 | 0 | 2 |

Europe - Croatia | 1 | 0 | 0 |

Europe - Denmark | 0 | 0 | 1 |

Europe - France | 1 | 0 | 4 |

Europe - Germany | 1 | 0 | 2 |

Europe - Greece | 1 | 0 | 0 |

Europe - Hungary | 1 | 0 | 0 |

Europe - Iceland | 1 | 0 | 0 |

Europe - Israel | 1 | 0 | 0 |

Europe - Italy | 7 | 0 | 1 |

Europe - Luxembourg | 0 | 0 | 1 |

Europe - Netherlands | 0 | 0 | 1 |

Europe - Norway | 1 | 0 | 0 |

Europe - Poland | 2 | 0 | 0 |

Europe - Portugal | 0 | 1 | 0 |

Europe - Saudi | 1 | 0 | 0 |

Europe - Slovenia | 0 | 0 | 1 |

Europe - Spain | 3 | 1 | 2 |

Europe - Sweden | 0 | 1 | 1 |

Europe - Switzerland | 0 | 1 | 1 |

New Zealand | 0 | 0 | 5 |

South America - Argentina | 0 | 0 | 1 |

South America - Brazil | 2 | 0 | 0 |

United Kingdom | 0 | 0 | 11 |

United States | 5 | 2 | 4 |

Total cities | 41 | 9 | 51 |

#### Recruitment sites

Recruitment sites | Number of cities without NSP | Number of cities without & with NSP | Number of cities with NSP |
---|---|---|---|

Detoxification/rehabilitation | 12 | 2 | 5 |

Drug treatment agency | 14 | 9 | 10 |

Field & snowball | 6 | 2 | 9 |

HIV testing/Sexual health centre | 4 | 5 | 14 |

Multiple sites | 2 | 5 | 15 |

NSP/pharmacy | 0 | 11 | 37 |

Other | 6 | 11 | 11 |

Total studies | 44 | 45 | 101 |

### Table 3.1.4 Estimation of the effect of NSPs on HCV prevalence per year using random effects regression

Table 3.1.4 is separated into 3 smaller tables in this HTML version for accessibility reasons. It is presented as one table in the PDF version.#### All cities and all data points

logit(HCV) | Coefficient | Std. Error | p value | 95% CI |
---|---|---|---|---|

Calendar year | -0.008 | 0.02 | 0.7 | -0.05, 0.04 |

Years since NSP | -0.079 | 0.03 | 0.003 | -0.13, -0.02 |

Constant | 1.040 | 0.24 | <0.001 | 0.56, 1.52 |

sigma_u | 0.5637 | |||

sigma_e | 0.8082 | |||

rho | 0.3275 | (fraction of variance due to u_i) | (fraction of variance due to u_i) | (fraction of variance due to u_i) |

#### All cities and excluding data points before 1981

logit(HCV) | Coefficient | Std. Error | p value | 95% CI |
---|---|---|---|---|

Calendar year | -0.0460 | 0.03 | 0.1 | -0.10, 0.12 |

Years since NSP | -0.0576 | 0.03 | 0.05 | -0.11, -0.001 |

Constant | 92.775 | 59.3 | 0.1 | -23.5, 209.1 |

sigma_u | 0.5627 | |||

sigma_e | 0.8084 | |||

rho | 0.3264 | (fraction of variance due to u_i) | (fraction of variance due to u_i) | (fraction of variance due to u_i) |

#### All nine cities with data points before and after NSP

logit(HCV) | Coefficient | Std. Error | p value | 95% CI |
---|---|---|---|---|

Calendar year | 0.0446 | 0.04 | 0.2 | -0.03, 0.11 |

Years since NSP | -0.1317 | 0.05 | 0.01 | -0.24, -0.03 |

Constant | -87.17 | 70.8 | 0.2 | -226, 51.6 |

sigma_u | 0.2255 | |||

sigma_e | 0.8245 | |||

rho | 0.0696 | (fraction of variance due to u_i) | (fraction of variance due to u_i) | (fraction of variance due to u_i) |

### Figure 3.2 HCV seroprevalence among injecting drug users according to NSP status of city and year of study

#### Text version of Figure 3.2

Figures in this description are approximate as they have been read from the graph.The data studies prior to 1987 were no NSP only. The HCV seroprevalence for the studies collected were:

- 1973 - 0.45
- 1974 - 0.85
- 1980 - 0.62
- 1985 - 0.53
- 1986 (early) - 0.88
- 1986 (mid) - 0.73
- 1986 (late) - 0.83 and 0.47

- the data studies between 1987 and 1992 are predominately no NSP ranging from about 0.4 to about 0.9 HCV prevalence.
- during this time the scattered with NSP data studies range from about 0.3 to 1.
- between 1992 and 1997 the amount of no NSP studies decrease with HCV seroprevalence ranging from about 0.4 to about 0.95.
- the studies with NSP during this time range from about 0.3 to about 1.
- from 1997 onwards the range of with NSP HCV seroprevalence has top variance has reduced from about 1 to 0.65 in 2000 while the bottom varience has remained steady at about 0.2.

## 3.1.5 HCV seroprevalence among new injectors

There were 48 studies, from 19 cities, with HCV seroprevalence estimated among people reporting less than three years of injecting drug use (Figure 3.3). Most studies were from nine Australian cities (n=35, 73%). There were also two studies from both Baltimore and New York, and one study each from Chicago, Dublin, Lille, Liverpool, Manipur, Padua, New Zealand (four cities combined), and Valencia.Most studies were carried out in cities with NSPs (43 studies from 16 cities). Five studies were carried out in four cities without NSPs. Before and after NSP data were only available from one city. Studies were carried out between 1985 and 2000, with half since 1996 (Figure 3.3). Sample size ranged from 14 to 303, median 53.

Median HCV prevalence was substantially lower in cities with than without NSPs (19% vs 71%; Table 3.1.5). On average, HCV prevalence in cities with NSPs was 37% lower than in cities without NSPs using random effects regression modelling (mean (sd): 25% (+18%) vs. 66% (+15%), p<0.001; Table 3.1.6).

### Table 3.1.5 Summary of HCV prevalence rates among people reporting less than three years of drug injection according to availability of NSPs

NSP | Number of studies | Mean HCV prevalence | Standard deviation | Median HCV prevalence | Inter-quartile range |
---|---|---|---|---|---|

No NSP | 5 | 66% | 15% | 71% | 5% |

With NSP | 43 | 25% | 18% | 19% | 21% |

### Table 3.1.6 Estimation of the effect of NSPs on HCV prevalence among people reporting less than three years of drug injection using random effects regression

HCV prevalence | Coefficient | Std. Error | p value | 95% CI |
---|---|---|---|---|

NSP | -37.06 | 7.75 | <0.001 | -52.25, -21.86 |

Constant | 64.50 | 8.41 | <0.001 | 48.01, 80.98 |

sigma_u | 22.74 | |||

sigma_e | 8.70 | |||

rho | 0.87 | (fraction of variance due to u_i) | (fraction of variance due to u_i) | (fraction of variance due to u_i) |

### Figure 3.3 HCV seroprevalence among people reporting less than three years of drug injection according to NSP status of city and year of study

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#### Text version of Figure 3.3

Figures in this description are approximate as they have been read from the graph.HCV seroprevalence among people reporting less than three years of drug injection with no NSP:

- 1988 - 0.73
- 1991 - 0.8
- 1992 - 0.4

- Studies began in 1990 until 1998.
- HCV prevalence linearly decreases over time from about 0.6 in 1991 to about 0.2 in 1998.
- Two studies occured which all outside this pattern; about 0.05 in 1994 and almost 1 in 1996.
- Majority of studies were conducted in 1997 to 1998

## 3.1.6 HCV incidence

HCV incidence was reported for 27 time periods from nine countries. All three studies in cities without NSP were from Italy (Naples, Padua and Rome) in early 1990. HCV incidence studies in cities with NSPs were from six Australian cities (nine data points), Amsterdam (four data points), Baltimore (three data points), Berlin (one data point), Czechoslovakia (one data point), Geneva (two data points), Malmo (one data point), New Zealand (one data point), and Seattle (one data point).On average, HCV incidence was 25 per 100 person years in studies from cities without NSPs compared with 16 per 100 person years in studies from cities with NSPs (Table 3.1.7). Similar rates were obtained when HCV incidence was aggregated for each city (Table 3.1.9). Analyses consistently indicated a non-statistically significant protective effect for HCV incidence in cities with NSPs using random effects and GEE negativebinomial regression models for all data points and straightforward negative-binomial regression modelling for data aggregated by city (Table 3.1.8).

### Table 3.1.7 HCV incidence rates per 100 person years for cohorts according to availability of NSPs

NSP | Number of studies | Mean HCV prevalence | Standard deviation | Median HCV prevalence | Inter-quartile range |
---|---|---|---|---|---|

No NSP | 3 | 24.7/100py | 16.9 | 28.6/100py | 33.1 |

With NSP | 24 | 16.4/100py | 9.9 | 15.0/100py | 10.6 |

### Table 3.1.8 Comparison of HCV incidence in cohorts with and without NSP using negative binomial regression modeling

Table 3.1.8 is separated into 2 smaller tables in this HTML version for accessibility reasons. It is presented as one table in the PDF version. Top of page#### Random effects negative binomial model

Type of model/scnumber | IRR | Std. Error | p value | 95% CI |
---|---|---|---|---|

NSP | 0.55 | 0.25 | 0.18 | 0.23, 1.32 |

Total pyrs | (exposure) | |||

/ln_r | 2.38 | 0.95 | 0.52, 4.24 | |

/ln_s | 3.45 | 1.17 | 1.16, 5.75 | |

r | 10.81 | 10.27 | 1.68, 69.53 | |

s | 31.62 | 37.01 | 3.19, 313.5 |

#### GEE negative binomial model

Type of model/scnumber | IRR | Std. Error | p value | 95% CI |
---|---|---|---|---|

NSP | 0.69 | 0.47 | 0.58 | 0.19, 2.54 |

Total pyrs | (exposure) |

### Table 3.1.9 HCV incidence rates per 100 person years for each city overall according to availability of NSPs

NSP | Number of studies | Mean HCV prevalence | Standard deviation | Median HCV prevalence | Inter-quartile range |
---|---|---|---|---|---|

No NSP | 3 | 24.7/100py | 16.9 | 28.6/100py | 33.1 |

With NSP | 14 | 18.5/100py | 11.4 | 15.9/100py | 16.2 |

### Table 3.1.10 Comparison of HCV incidence for each city with and without NSP using negative binomial regression (random effects negative binomial model)

Type of model/scnumber | IRR | Std. Error | p value | 95% CI |
---|---|---|---|---|

NSP | 0.73 | 0.30 | 0.44 | 0.32, 1.64 |

Total pyrs | (exposure) | |||

/lnalpha | -1.28 | 0.45 | -2.1, -0.40 | |

alpha | 0.28 | 0.13 | 0.12, 0.67 |

## 3.1.7 Discussion

On average, HIV seroprevalence decreased in studies of injecting drug users in cities with NSPs whereas in studies from cities without NSPs, HIV seroprevalence increased. Seroprevalence of HCV also decreased annually in studies carried out after NSPs were introduced. HCV prevalence was substantially lower among people reporting less than three years of drug injection in cities with NSPs compared to cities without NSPs. There was also a non-statistically significant protective effect for HCV incidence in cities with NSPs when compared to those without NSPs.There are several limitations associated with the ecological study design that should be considered when interpreting the findings from these studies. Seroprevalence data used in the analyses were collected according to different protocols and in diverse populations. It is unlikely that estimates of HIV and HCV seroprevalence in cities with NSPs would differ systematically from those in cities without NSPs, so any such sampling bias would underestimate the effectiveness of NSPs. Because cities were selected for analysis by the existence of published HIV and HCV serological surveys, bias may have been introduced by the decision to do a survey in a particular city at a particular time.

Data on NSPs used in the analyses were based on presence or absence of NSPs rather than on the extent and uptake of these services. Given the positive findings, however, it is likely that inclusion of these parameters would result in a dose response effect on HIV and HCV seroprevalence from NSPs. In addition, it is not possible to separate the effects of implementation of NSPs from the other HIV prevention strategies (Benedikt et al. 2000). In most settings, introduction of NSPs is one component of a broader harm reduction package to reduce the risk of transmission of blood-borne viruses and other harm associated with injecting drug use. Other components include education and counselling, drug dependency treatment strategies such as methadone maintenance therapy, and provision of clean injecting equipment through other outlets in particular pharmacies. Adequate data was not available on individual components of harm reduction strategies to allow an evaluation of the impact of components other than provision of clean injecting equipment (NSPs). Sensitivity analysis has been conducted to determine the outcome of lower rates of NSP effect on HIV (See Section 4.8).

The excess risk of HIV in people who inject drugs is not due solely to sharing needles, other injecting practices and sexual behaviour patterns increase HIV risk. In contrast to HIV, HCV infection is rarely spread through sexual transmission (MacDonald et al. 1996).

It is also possible that HIV seroprevalence may have remained low in some of the cities with NSPs, irrespective of their introduction. HCV infection, however, is universally high among drug injectors. In most countries HCV infection became endemic among this population before there was widespread publicity about transmission of blood borne viruses through injecting practices. Because HCV infection remains asymptomatic for longer than HIV infection, it is also possible that people with HCV infection remain in the population of injectors for longer than those with HIV infection, therefore increasing the prevalence of HCV infection in seroprevalence surveys of injectors.

If NSPs decrease the incidence of HIV and HCV, the rate of increase in seroprevalence should decrease, although the seroprevalence itself may not decrease, at least initially. It is likely that the lower effect of NSP on HCV than HIV seroprevalence can be attributed to the generally higher prevalence of HCV compared to HIV before the introduction of NSPs.

NSPs influence HIV and HCV transmission by increasing use of sterile syringes for injection and lowering the rate of syringe sharing thereby reducing contact with each virus. Some NSPs also provide referrals to drug treatment centres, condoms and education about minimising risk. The difference in rate of change of HIV seroprevalence between cities with and without NSPs and the decrease in HCV prevalence in cities after the introduction of NSPs may not be due solely to NSPs. Nonetheless, the study provides evidence that NSPs reduce the spread of HIV and HCV infection.

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