Evaluation of the Hepatitis C Cascade of Care Among People Living With HIV in New South Wales, Australia

A Data Linkage Study

Samira Hosseini-Hooshyar; Maryam Alavi; Marianne Martinello; Heather Valerio; Shane Tillakeratne; Gail V. Matthews; Gregory J. Dore

Disclosures

J Viral Hepat. 2022;29(4):271-279. 

In This Article

Methods

Study Setting

Australia is one of the few settings globally with well-established infrastructure for linking positive HCV serology notifications to administrative databases.[16] This study was conducted in NSW; Australia's most populous state with over 7 million inhabitants. In 2017, around 32% of HIV notifications (310/963) and 39% of hepatitis C notifications (4078/10,537) in Australia occurred in NSW.[17] Further, an estimated 2290 individuals (uncertainty range: 1920–2690) were living with HIV/HCV coinfection in Australia in 2016.[18]

Data Sources and Record Linkages

HIV and HCV are both notifiable diseases in NSW, Australia. Records of all individuals with HCV positive serology are held with the NSW Notifiable Conditions Information Management System (NCIMS) since 1993. Further, records of all new HIV diagnoses are maintained with National HIV Registry since 1985. A notification of both positive HCV and HIV serology (i.e. people living with HIV/HCV coinfection) was the study inclusion criteria.

Data linkage occurred in two stages. First, records of all individuals with HCV positive serology from NCIMS were linked to the (1) National HIV Registry, holding data from 1985, (2) Perinatal Data Collection (PDC Mothers) dataset, holding data from 1994, (3) NSW Admitted Patient Data Collection (APDC) database, holding data from 2001, (4) NSW Registry of Births, Deaths, and Marriages (BRDM), for date of death from 1993, (5) NSW Electronic Recording and Reporting of Controlled Drugs system (ERRCD), holding data from 1985 and (6) NSW Bureau of Crime Statistics and Reporting Corrective Services Custody Database (BOCSAR Custody) dataset, holding data from 1994. The NSW Centre for Health Record Linkage (CHeReL) used demographic details (including full name, sex, date of birth and address) to link records probabilistically and deterministically between the above-mentioned datasets. However, linkage between NCIMS and the National HIV Registry dataset occurred using name codes (first two letters of last and first names) of those notified for enhanced privacy protection. CHeReL then provided the above-described set of identifiers for the NCIMS cohort to the Integration Services Centre at the Australian Institute of Health and Welfare (AIHW). AIHW conducted the second round of linkage between NCIMS records and Medicare Benefits Schedule (MBS) dataset holding dates of HCV RNA testing from 2010 and Pharmaceutical Benefits Schedule (PBS) dataset holding HCV therapy dispensing data from 2010.

Study Period

For the study period, data extractions occurred from each database as follows: HCV notifications (1 January 1993–31 December 2017); HIV diagnoses (1 January 1985–31 December 2017); hospitalizations (1 July 2001–30 June 2018); deaths (1 January 1993–30 June 2018); opioid agonist therapy (OAT) dosing (1 January 1985–19 September 2018); incarcerations (1 January 1994–31 December 2017); and HCV RNA testing and treatment (1 April 2010–31 December 2018).

Study Outcomes

The primary outcomes were HCV RNA testing and HCV treatment uptake. People who have received an RNA test included those with RNA testing records in MBS dataset (first time counted), people with genotype testing records in MBS dataset (first time counted), and people who have been prescribed and dispensed HCV treatment in PBS dataset. Subsequent records of diagnostic testing or genotype testing were not included in the analyses.

HCV treatment uptake was estimated among those with an indicator of chronic HCV infection. Individuals with an indicator of chronic HCV infection were defined as those with records of genotype testing in MBS dataset or people who were dispensed HCV treatment in PBS dataset. HCV treatment uptake was defined by the date of first HCV treatment dispensing (interferon or DAA therapy) in the PBS dataset. Subsequent dispensing records were not included in the treatment uptake analyses, unless retreatment occurred in the DAA era.

Study Population and Inclusion Criteria

For analysis of HCV RNA testing and treatment uptake in the pre-DAA era (2010–2012 and 2013–2015) and post-DAA era (2016–2018), all individuals who were alive for at least the first six months of each follow-up period were included. Individuals also needed to be at least 18 years old by the end of each follow-up period (i.e. 18 years old by 31 December 2012, 31 December 2015 and 31 December 2018).

Exclusion Criteria

As the target population of this study were people living with HIV/HCV coinfection, records of HCV mono-infection were excluded. Duplicate records of HCV notifications were also excluded. To allow time for treatment uptake, records were removed if death occurred before or in 2009. Records with no Medicare number were excluded. For analyses of HCV RNA testing and treatment uptake, only HCV notifications occurring during or after 2009 were included.

Exposure Variables

Year of birth, gender (male, female), and local health district (LHD) of residence at time of HCV notification (metropolitan [metro], outer-metro, and rural),[19] were obtained from NCIMS. Identification of Aboriginal and Torres Strait Islander Peoples (hereafter referred to as Aboriginal) was obtained using PDC Mothers dataset. However, since the reporting of Aboriginal identification in administrative data was suboptimal, an algorithm developed by NSW Health[20] was applied across datasets to identify Aboriginal Peoples. History of hospitalizations occurring due to alcohol-use disorder[21] and injecting drug use (IDU) was obtained using APDC database. Alcohol-use disorder is a standard term used to describe continued drinking despite adverse mental and physical consequences.[21] All hospitalization records were coded using the 10th revision of the Classification of Diseases and Related Health Problems (ICD-10). As previously described,[22] a hospital discharge diagnosis code (ICD-10) was used to infer the presence of alcohol-use disorder (Table S1). IDU-related hospitalizations (i.e. hospitalizations occurring due to injectable drugs and/or infections indicative of injection drug use) were also identified using the ICD-10 classifications of disease manual (Table S2). Drug dependence was defined by combining IDU-related hospitalizations from APDC database and/or receipt of OAT dosing from ERRCD, as previously described.[23] IDU-related hospitalizations and/or receipt of OAT occurring between 2016 and 2018 were considered as indicators of recent drug dependence; records with last hospitalization or OAT dosing recorded any time pre-2016 were considered as indicators of distant drug dependence; and records with no hospitalization or OAT dosing were considered as indicators of no evidence of drug dependence. Recent incarceration defined as experiencing any length of imprisonment during the DAA era (2016–2018) was obtained from the BOCSAR Custody dataset.

Statistical Analysis

Characteristics of people with an HCV/HIV coinfection notification were described overall and by outcomes: HCV RNA testing and HCV treatment uptake.

Number and proportion of PLHIV for the following steps of HCV care cascade between 2010 and 2018 were calculated: (a) HCV notification; (b) HCV RNA testing; (c) chronic HCV infection (indication); and (d) HCV treatment uptake. Proportion of people with HCV RNA testing, chronic HCV infection (indication) and HCV treatment uptake were also calculated from the people in each preceding step. Number and proportion of people with HCV RNA testing and treatment uptake were also calculated and compared between pre- (2010–2015) and post-DAA (2016–2018) era. Median time from HCV RNA testing (earliest HCV RNA testing date on record) to first treatment uptake on record were also calculated by each calendar year.

Unadjusted and adjusted logistic regression models were applied to evaluate factors associated with not receiving HCV RNA testing overall (2010–2018), and factors associated with not receiving DAA treatment uptake (2016–2018). All exposures with a p-value less than .2 in the unadjusted model, or those that were known to be associated with each outcome were considered for inclusion in the adjusted model. All analyses were performed in STATA v.14.0 [College Station, TX, USA].

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