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Missed opportunities for earlier diagnosis of HIV in British Columbia, Canada: A retrospective cohort study

  • Ni Gusti Ayu Nanditha ,

    Contributed equally to this work with: Ni Gusti Ayu Nanditha, Martin St-Jean

    Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

    Affiliations British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada, Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada

  • Martin St-Jean ,

    Contributed equally to this work with: Ni Gusti Ayu Nanditha, Martin St-Jean

    Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

    Affiliation British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada

  • Hiwot Tafessu,

    Roles Formal analysis, Writing – original draft, Writing – review & editing

    Affiliation British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada

  • Silvia A. Guillemi,

    Roles Methodology, Writing – review & editing

    Affiliations British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada, Department of Family Practice, Faculty of Medicine, University of British Columbia, Vancouver, Canada

  • Mark W. Hull,

    Roles Methodology, Writing – review & editing

    Affiliations British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada, Division of AIDS, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada

  • Michelle Lu,

    Roles Formal analysis, Writing – review & editing

    Affiliation British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada

  • Bonnie Henry,

    Roles Writing – review & editing

    Affiliation British Columbia Ministry of Health, Victoria, Canada

  • Rolando Barrios,

    Roles Data curation, Writing – review & editing

    Affiliations British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada, Vancouver Coastal Health, Vancouver, Canada

  • Julio S. G. Montaner,

    Roles Data curation, Funding acquisition, Writing – review & editing

    Affiliations British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada, Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada

  • Viviane D. Lima

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing

    vlima@cfenet.ubc.ca

    Affiliations British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada, Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada

Missed opportunities for earlier diagnosis of HIV in British Columbia, Canada: A retrospective cohort study

  • Ni Gusti Ayu Nanditha, 
  • Martin St-Jean, 
  • Hiwot Tafessu, 
  • Silvia A. Guillemi, 
  • Mark W. Hull, 
  • Michelle Lu, 
  • Bonnie Henry, 
  • Rolando Barrios, 
  • Julio S. G. Montaner, 
  • Viviane D. Lima
PLOS
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Abstract

Background

Late HIV diagnosis is associated with increased AIDS-related morbidity and mortality as well as an increased risk of HIV transmission. In this study, we quantified and characterized missed opportunities for earlier HIV diagnosis in British Columbia (BC), Canada.

Design

Retrospective cohort.

Methods

A missed opportunity was defined as a healthcare encounter due to a clinical manifestation which may be caused by HIV infection, or is frequently present among those with HIV infection, but no HIV diagnosis followed within 30 days. We developed an algorithm to identify missed opportunities within one, three, and five years prior to diagnosis. The algorithm was applied to the BC STOP HIV/AIDS population-based cohort. Eligible individuals were ≥18 years old, and diagnosed from 2001–2014. Multivariable logistic regression identified factors associated with missed opportunities.

Results

Of 2119 individuals, 7%, 12% and 14% had ≥1 missed opportunity during one, three and five years prior to HIV diagnosis, respectively. In all analyses, individuals aged ≥40 years, heterosexuals or people who ever injected drugs, and those residing in Northern health authority had increased odds of experiencing ≥1 missed opportunity. In the three and five-year analysis, individuals with a CD4 count <350 cells/mm3 were at higher odds of experiencing ≥1 missed opportunity. Prominent missed opportunities were related to recurrent pneumonia, herpes zoster/shingles among younger individuals, and anemia related to nutritional deficiencies or unspecified cause.

Conclusions

Based on our newly-developed algorithm, this study demonstrated that HIV-diagnosed individuals in BC have experienced several missed opportunities for earlier diagnosis. Specific clinical indicator conditions and population sub-groups at increased risk of experiencing these missed opportunities were identified. Further work is required in order to validate the utility of this proposed algorithm by establishing the sensitivity, specificity, positive and negative predictive values corresponding to the incidence of the clinical indicator conditions among both HIV-diagnosed and HIV-negative populations.

Introduction

Despite advances in HIV testing programs and improved access to healthcare services, late HIV diagnosis remains a problematic reality in high-resource settings [13]. Late presentation to HIV testing has been associated with pre-diagnosis encounters with healthcare providers where an HIV test was indicated, but not ordered; thereby resulting in missed opportunities for earlier HIV detection [4]. Consequences associated with late HIV diagnosis extend across various dimensions of the HIV epidemic: at the individual level, an increased disease burden (e.g., high mortality, risk of hospitalization, and AIDS-defining illness) [57]; at the population level, an exacerbated HIV transmission risk [8]; and at a structural level, an amplified healthcare resource utilization and related expenditures [9].

British Columbia (BC), Canada, is the first jurisdiction to implement Treatment as Prevention (TasP), expanded under the Seek and Treat for Optimal Prevention of HIV/AIDS (STOP HIV/AIDS) initiative, which encompasses widespread HIV testing and immediate initiation of free antiretroviral therapy (ART) [10]. Since 2014, provincial HIV testing guidelines have recommended routine testing (i.e., every five years) for individuals aged 18–70 years and annual testing for populations with a higher burden of HIV [11]. HIV testing (nominal and non-nominal), is free of charge for all BC residents. Despite widespread access to HIV testing and the existence of provincial HIV testing guidelines, in 2017, nearly a quarter of diagnosed people living with HIV (PLWH) presented to care with a CD4 count <350 cells/mm3 [12]. Taken together, this body of evidence strongly indicates existing opportunities to further optimize the TasP strategy by diagnosing PLWH earlier in the course of HIV infection.

Thus, efforts to understand missed opportunities are crucial to minimize the aforementioned adverse outcomes, and to further optimize TasP in BC and other settings. In this context, insights pertaining to characteristics associated with missed opportunities will be instrumental in achieving the Joint United Nations Programme on HIV/AIDS’ (UNAIDS) target of diagnosing at least 90% and 95% of all PLWH by 2020 and 2030, respectively [13].

In this population-based study, we proposed a case-finding algorithm, based on administrative data, to identify missed opportunities for earlier HIV diagnosis. This algorithm was fundamental in addressing our study objectives, which were: i) to quantify missed opportunities corresponding to a clinical manifestation which may be caused by HIV infection, or is frequently present among those with HIV infection (i.e., clinical indicator conditions) among diagnosed PLWH in BC; and ii) to identify specific clinical indicator conditions and population sub-groups associated with experiencing these missed opportunities. Findings are expected to inform evidence-based recommendations for future interventions, policies, and guidelines surrounding HIV testing.

Methods

Study setting

In BC, ART is provided free of charge (no copayments and deductibles) to all HIV-diagnosed BC residents. ART provision has been under the auspices of the BC Centre for Excellence in HIV/AIDS (BC-CfE) Drug Treatment Program (DTP) since 1992. ART eligibility in BC is based on the BC-CfE’s HIV therapeutic guidelines, which have remained generally consistent with those put forward by the International Antiviral Society-USA since 1996 [14].

HIV testing guidelines and practices have evolved in light of sustained advancements in testing technologies resulting in shortened window periods, the availability of point-of-care testing [15], and the emergence of the compelling evidence supporting TasP [16]. Conventionally, testing guidelines were based on the presence of HIV symptoms and known HIV risk factors [17]. In 2010, the BC-CfE set new guidelines recommending healthcare providers to routinely offer an HIV test to sexually active individuals who present to care without having been tested in the year prior, individuals with a history of sexually transmitted infections (STI), and those tested for Hepatitis C, STI, or tuberculosis [17]. In 2014, the Office of the Provincial Health Officer further expanded guidelines by recommending annual testing for those in populations with high HIV burden and testing every five years for everyone else [11].

Study data

Data were obtained from the STOP HIV/AIDS population-based cohort, which is composed of individual-level longitudinal data on all diagnosed PLWH in the province, by virtue of linkages between various provincial databases and the DTP [10, 1822]. These aforementioned linkages, along with their corresponding data capture are comprehensively detailed in the S1 File.

Study design

The eligibility criteria for this retrospective cohort study were as follows: i) ART-naïve individuals aged ≥18 years, ii) who were diagnosed between 1 January 2001 and 31 March 2014, and iii) had a CD4 count measurement within six months. Individuals diagnosed in the acute stage of HIV infection were not considered for missed opportunities given that these infections were detected in a timely manner.

Due to the population-based aspect of this study, HIV testing data is not the exclusive source utilized to ascertain HIV-diagnosed individuals in BC. A considerable number of those HIV-diagnosed have simply never been formally diagnosed via HIV antigen/antibody screening tests (i.e., a confirmed HIV-positive test [10]. Additional data sources of HIV diagnosis play a critical role in supplementing HIV testing data to construct a comprehensive population-based cohort of all HIV-diagnosed individuals.

Thus, HIV diagnosis was ascertained by one of the following validated criterion [23]: a confirmed HIV-positive test, a detectable plasma viral load >50 copies/mL, an HIV-related hospitalization, three HIV-related outpatient care visits, a reported AIDS-defining illness, or ART dispensation. Date of HIV diagnosis was ascertained by the first instance of one of the above-mentioned criteria.

The restriction to ART-naïve individuals was applied to ensure that individuals were accurately classified as a new positive HIV case. Acute HIV infection was defined based on meeting the laboratory criteria (i.e., detection of HIV DNA or RNA by nucleic acid amplification test [NAT], or detection of p24 antigen in the absence of confirmed detection of HIV antibody), or a previous negative or indeterminate HIV test within 180 days of the first confirmed positive HIV test [24].

Analytical approach and measures

This study consisted of a three-pronged analysis. For all individuals, information pertaining to clinical events identified from healthcare encounters were reviewed for missed opportunities (i.e., the outcome of interest) throughout the following time-frames: i) one year, ii) three years, and iii) five years prior to the HIV diagnosis date. Note that these time-frames were not mutually exclusive, and missed opportunities identified from the five-year analysis also incorporates those identified in the three- and one-year analyses.

This approach strictly served as a sensitivity analysis to account for the considerable individual variability in CD4 counts associated with the natural course of untreated HIV infection. Thus, the inclusion of individuals for each of the analyses was based on the stage of HIV infection at diagnosis. All three analyses comprised of individuals with a CD4 count <500 cells/mm3 at HIV diagnosis. Individuals with a CD4 count ≥500 cells/mm3 at HIV diagnosis were only included in the one-year analysis in an effort to minimize potential overestimation of the outcome, as these infections are generally of shorter duration. This decision was supported by a large study which estimated that CD4 count depletion reaching the 500 cells/mm3 threshold occur on average one year after HIV seroconversion [25]. Other studies opted to examine missed opportunities for HIV diagnosis among individuals diagnosed ≤350 cells/mm3 [26, 27], while others have not adhered to any restrictions on the basis of CD4 counts [4, 28].

Proposed algorithm to identify a missed opportunity

A missed opportunity was defined as a healthcare encounter due to a clinical manifestation which may be caused by HIV infection, or is frequently present among those with HIV infection (i.e., clinical indicator conditions), but no HIV diagnosis followed within a 30-day period. A healthcare encounter was considered to be due to a clinical indicator condition if at least one of the following criterion were met:

  1. Medical diagnosis of a condition associated with a prevalence of undiagnosed HIV in the European setting (>0.5%) as specified in the Swiss Federal Office of Public Health 2015 HIV Testing Recommendations [2, 29].
  2. Medical diagnosis of an AIDS-defining condition as established by the BCCDC [30];
  3. Medical diagnosis of anemia related to nutritional deficiencies or of unspecified cause, which was not mentioned in i) and ii), and identified by expert opinion of two family physicians with >30 years of experience in HIV care (RB & SAG).

These clinical indicator conditions are comprehensively presented in Table 1 and S1 Table. The presence of a clinical indicator condition was ascertained by means of an algorithm using the International Classification of Disease (Ninth and Tenth Revisions) (ICD 9/10) diagnosis codes. This algorithm was applied to the following STOP HIV/AIDS administrative databases: i) the Discharge Abstract Database, which captures diagnostic information on the circumstances of all in-patients discharges, transfers, and deaths, as well as day surgery patients from acute care hospitals across BC; and ii) the Medical Services Plan billing database, which captures diagnostic information related to inpatient and outpatient services provided by physicians and supplementary healthcare practitioners, as well as diagnostic procedures.

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Table 1. List of clinical indicator conditions utilized in our case-finding algorithm for missed opportunities.

https://doi.org/10.1371/journal.pone.0214012.t001

Given that multiple diagnosis records may correspond to the same unique healthcare encounter, we have employed a set of rules to identify distinct encounters. A healthcare encounter in MSP was considered to be unique if one of the following conditions were fulfilled [31]:

  1. If date in which the service was provided by a practitioner was different; or
  2. If the practitioner’s specialty was different; or
  3. If the location in which the service occurred was different.

An encounter in DAD was considered to be unique from the admission date to the discharge date, including transfers which may have occurred between acute care institutions. In the event that multiple clinical indicator conditions were diagnosed within the same healthcare encounter, only one missed opportunity was counted.

Several data quality control measures have been implemented to ensure coding accuracy [32, 33]. For the DAD, professionally trained coders are responsible for translating the diagnoses from medical charts into ICD-10 codes [34]. The Canadian Institute for Health Information evaluates coding accuracy by conducting reabstraction studies, which involves returning to the original source (e.g., medical charts) and comparing the information with the DAD [32]. These studies have been conducted routinely and yielded favorable results as well as areas for improvement [35]. All acute care facilities in BC are required to report data to the DAD [36]. For MSP, billing records are submitted electronically by a practitioners’ offices to MSP in ICD-9 format. Audits and quality checks for select data fields are subsequently conducted by MSP [18]. It has been demonstrated that these codes are valid at the population level [37]. Nearly 100% of BC residents are covered under MSP [38].

To enhance the rigor of this algorithm, specific restrictions were imposed when constraints were indicated for specific clinical indicator conditions. Recurring conditions required at least two clinical events diagnosed within a 12-month period, while the first clinical event was not considered a missed opportunity. Chronic clinical indicator conditions (i.e., >1 month duration) were considered as a missed opportunity strictly if diagnosed by a specialist. This rule was imposed since administrative data cannot directly capture the duration of a condition; however, conditions diagnosed by a specialist can serve as a proxy measure. For example, conditions with long-standing symptoms may be referred to specialists and are typically associated with potentially lengthy wait times after the initial consultation with a general practitioner [39].

Explanatory variables

The explanatory variables measured at baseline (i.e., date of HIV diagnosis) included: gender (female and male), age (<30, 30–39, 40–49 and ≥50 years), risk for HIV acquisition (gay, bisexual, and other men who have sex with men [gbMSM], people who have ever injected drugs [PWID], gbMSM/PWID, heterosexual/other, and unknown), CD4 count (<200, 200–349, 350–499, ≥500 cells/mm3), calendar year of HIV diagnosis (continuous), BC health authority (Fraser, Interior, Northern, Vancouver Coastal, Vancouver Island, and unknown), and rurality (urban, mixed, rural, and unknown). The health authorities are responsible for the management and delivery of health services in geographically defined areas in BC [40]. Of note, Vancouver Coastal is the largest health authority in BC, caring for >50% of PLWH [41], and where the DTP is located, while Northern is one of our most remote health authorities. Rurality was categorized by classifying local health areas (i.e., 89 provincial health regions aggregated up to the five health authorities) in accordance with their degree of rurality as detailed in S1 File.

Statistical analysis

Categorical variables were compared using the Fisher’s exact test or the Chi-square test, and continuous variables were compared using the Kruskal-Wallis test [42]. For each of the one-year, three-year, and five-year analyses, an explanatory multivariable logistic regression model was used to model the probability of having 0 versus ≥1 missed opportunity. Model selection was conducted using a backward elimination procedure based on the Akaike Information Criterion (AIC) and Type III p-value [43]. All p‐values are two‐sided, and the level of significance was set at 5%. All analyses were performed SAS version 9.4 (SAS, Cary North CA, USA).

Ethics

Linkage and usage of administrative databases were approved and performed by data stewards in each collaborating agency and facilitated by the BC Ministry of Health. The University of British Columbia Ethics Review Committee at the St. Paul’s Hospital site provided ethics approval for this study (H18-02208). This study was conducted using strictly anonymized laboratory and administrative databases, and thus informed consent was not required. This study complies with the BC’s Freedom of Information and Protection of Privacy Act.

Results

Study population

Of the 2119 individuals in the final analytical sample, the median calendar year of HIV diagnosis was 2007 (25th-75th percentiles [Q1-Q3]: 2003–2011), 82% were male, 53% aged ≥40 years, 75% were either MSM, PWID or both, 56% resided in Vancouver Coastal health authority, 75% resided in an urban area, and 58% were diagnosed with a CD4 count of <350 cells/mm3 (Table 2).

In the five-year analysis, 298 individuals (14%) recorded a total of 649 unique missed opportunities. In the one-year and three-year analysis, 142 (7%) and 247 (12%) individuals contributed 506 and 287 missed opportunities, respectively (Fig 1). For all three analyses, recurrent pneumonia was the most prominent clinical indicator condition identified as a missed opportunity (33%, 31%, and 30%, respectively), followed by anemia related to iron and other vitamin B12 deficiencies or of unspecified cause (21%, 19%, and 18%, respectively), and herpes zoster/shingles among individuals aged <50 years (8%, 13%, and 13%, respectively). Although not preeminent, sexually transmitted infections, lymphatic disorders and mucosal fungal infections (primarily oral candidiasis) also emerged as important clinical indicator conditions. S2 Table presents the distribution of clinical indicator conditions diagnoses categorized by distinct disease groups, corresponding to the missed opportunities identified in all three analyses.

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Fig 1. Participants flow chart.

PLWH: people living with HIV; STI: sexually transmitted infections; MOs: missed opportunities; *: clinical indicator condition identified by the expert opinion of two family physicians with >30 years of experience in HIV care (RB & SAG).

https://doi.org/10.1371/journal.pone.0214012.g001

Characteristics associated with missed opportunities

Bivariable analyses.

Consistent across all analyses, having ≥1 missed opportunity was associated with having a CD4 count <200 cells/mm3, being older than 50, having a heterosexual/other or PWID HIV acquisition risk, residing in the Northern health authority and in rural areas. An in-depth presentation of these associations, along with their corresponding significance, is reported in Table 3.

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Table 3. Study population characteristics by analysis, stratified by missed opportunities, (n = 2119 individuals).

https://doi.org/10.1371/journal.pone.0214012.t003

Adjusted multivariable analyses.

Factors associated with having ≥1 missed opportunity are comprehensively presented in Table 4 and included:

  1. Age: Compared to individuals aged <30 years, those aged ≥50 years had the most elevated odds of having ≥1 missed opportunity for the one-year analysis (adjusted Odds Ratio [aOR]) 2.93; 95% CI 1.52–5.64), three-year analysis (aOR 2.36; 95% CI 1.44–3.89), and the five-year analysis (aOR 2.53; 95% CI 1.58–4.05).
  2. Risk of HIV acquisition: Relative to gbMSM, PWID had the highest odds of having ≥1 missed opportunity for the one-year analysis (aOR 1.77; 95% CI 1.12–2.79), three-year analysis (aOR 1.70; 95% CI 1.19–2.43), and the five-year analysis (aOR 1.80; 95% CI 1.29–2.51). Increased odds were also observed for heterosexuals/other compared to gbMSM in each of the analyses.
  3. CD4 count at diagnosis: In comparison with individuals diagnosed with a CD4 count ≥500 cell/mm3, those diagnosed with a CD4 count <200 cells/mm3 had the greatest odds of having ≥1 missed opportunity for the three-year analysis (aOR 3.18; 95%CI 2.01–5.01) and the five-year analysis (aOR 4.07; 95%CI 2.60–6.37
  4. Health authority: Relative to individuals residing in Vancouver Coastal health authority, those residing in the Northern health authority had the highest odds of having ≥1 missed opportunity for the one-year analysis (aOR 2.41; 95%CI 1.32–4.41), three-year analysis (aOR 1.94; 95%CI 1.16–3.25), and five-year analysis (aOR 1.78; 95%CI 1.08–2.92).
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Table 4. Multivariable logistic regression models for factors associated with ≥1 missed opportunities.

https://doi.org/10.1371/journal.pone.0214012.t004

Discussion

Findings from this population-based cohort study underscored missed opportunities for earlier HIV diagnosis in BC using a newly-developed case-finding algorithm based on ICD 9/10 codes for clinical indicator conditions. This study demonstrated that despite a setting where access to healthcare is unrestricted, opportunities for earlier HIV diagnoses remain; between 7%-14% of individuals experienced ≥1 missed opportunity. The most prominent missed opportunities were related to diagnoses of recurrent pneumonia, anemia related to nutritional deficiencies or of unspecified cause, and herpes zoster/shingles among individuals aged <50 years.

Our results complement the growing body of evidence indicating that individuals in high-resource settings continue to be susceptible to missed opportunities [4, 28, 44, 45]. In West Scotland and Italy, 26% and 29% of individuals had ≥1 clinical indicator condition prior to their HIV diagnosis ever, respectively [4, 28]. In the United States and Switzerland, 22% and 44% of individuals were found to have ≥1 HIV indicator condition within five years prior to HIV diagnosis, correspondingly [44, 45]. The relatively low proportion of missed opportunities in BC may be reflective of rigorous HIV prevention, testing and educational outreach programs in this province [46]. However, readers should be aware that differences exist in the definitions of missed opportunities among studies.

Current HIV testing guidelines acknowledge that HIV can have a range of non-specific presentations and recommend that healthcare providers include HIV in the differential diagnosis, irrespective of identified risk for HIV acquisition. This includes when an individual exhibits symptoms that warrant laboratory investigation or symptoms associated with HIV infection or immune compromise. Our findings complement the aforementioned recommendation by underscoring specific indicator conditions in which missed opportunities were most prevalent (i.e., recurrent pneumonia, anemia related to nutritional deficiencies or of unspecified cause, and herpes zoster/shingles among individuals aged <50 years). These conditions constituted 59%-69% of the missed opportunities, thus should particularly be on the forefront of clinical investigation when considering HIV in the differential diagnosis.

Findings from this study also inform healthcare providers and policy makers with important evidence necessary to implement targeted interventions aimed at improving HIV screening among the identified population sub-groups at increased risk of experiencing missed opportunities. The overarching theme appears to put forward a two-tiered issue: i) need to further emphasize underserved sub-populations, which are disproportionately impacted by stigma and geographical barriers to healthcare access [47, 48]; and ii) need to target individuals from population sub-groups not traditionally associated high HIV burden, which may be inaccurately presumed to be at low risk for HIV by healthcare providers [49, 50]. From a population perspective, these findings illustrate the need to provide further education for health care providers regarding universal testing.

While addressing missed opportunities for earlier diagnosis through increased testing in healthcare settings is a valuable strategy in reducing late diagnosis, individuals who do not present to care (e.g., asymptomatic individuals, those experiencing barriers to care) continue to be overlooked and contribute to late diagnosis in the absence of complementary interventions [50].

There are some limitations to consider. First, although our case-finding algorithm for identifying missed opportunities using ICD 9/10 codes has not yet been validated, thus potentially limiting the extrapolation of the results, it was developed on the basis of established clinical indicator conditions from the literature and informed by experts in the fields of HIV epidemiology and medical care. Second, although a number of clinical indicator conditions were selected based on having a prevalence of undiagnosed HIV >0.5% in the European context, the overall trends in HIV prevalence are largely homogeneous among high-income countries of North America and Western Europe [51]. Third, due to the requirement of an HIV diagnosis for entry into the STOP HIV/AIDS cohort, we were unable to assess the incidence of these clinical indicator conditions among those not HIV-diagnosed. However, case-control studies demonstrated that several of these clinical indicator conditions had a higher incidence among HIV cases compared to matched HIV-negative controls [5254]. Fourth, HIV negative testing data did not include screening tests from Vancouver Island health authority (<5% of all screening tests), or those performed on a non-nominal basis [55]. Point-of-care testing data was also not available, though it only consisted of a small proportion of all tests performed in BC (<5%) [56]. Fifth, the STOP HIV/AIDS cohort does include data from the National Ambulatory Care Reporting System, rendering us unable to identify missed opportunities occurring during emergency room visits. Sixth, the STOP HIV/AIDS cohort data capture is restricted to March 2014; future analyses will assess missed opportunities after the revision of the provincial HIV testing guidelines in 2014. Finally, although healthcare administrative data are not collected for the purposes of research and are susceptible to misclassification and coding errors, they represent an important source of information for evidence-based research.

In conclusion, based on our newly-developed algorithm, this study demonstrated that HIV-diagnosed individuals in BC have experienced several missed opportunities for earlier diagnosis. Specific clinical indicator conditions and population sub-groups at increased risk of experiencing these missed opportunities were identified. Further work is required in order to validate the utility of this proposed algorithm by establishing the sensitivity, specificity, positive and negative predictive values corresponding to the incidence of the clinical indicator conditions among both HIV-diagnosed and HIV-negative populations.

Supporting information

S1 File. Methodological supporting information.

Additional methodological information pertaining to the data utilized in this study.

https://doi.org/10.1371/journal.pone.0214012.s001

(DOCX)

S1 Table. List of the International Classification of Diseases (Ninth and Tenth Revisions) diagnosis codes selected for the case-finding algorithm for clinical indicator conditions.

Note: ICD 9: International Classification of Diseases (Ninth Revisions); ICD 10: International Classification of Diseases (Tenth Revisions).

https://doi.org/10.1371/journal.pone.0214012.s002

(DOCX)

S2 Table. Diagnoses of clinical indicator conditions categorized by distinct disease groups for all three analyses. (A) five-year analysis, (B) three-year analysis, and (C) one-year analysis.

Note: Given that multiple clinical indicator conditions could be diagnosed within the same healthcare encounter, the number of diagnoses of clinical indicator conditions exceed the number of missed opportunities. MSP (Medical Services Plan); DAD (Discharge Abstract Database). Due to privacy concerns, cells with less than 5 counts cannot be further specified.

https://doi.org/10.1371/journal.pone.0214012.s003

(DOCX)

Acknowledgments

Disclaimer: All inferences, opinions, and conclusions drawn in this manuscript are those of the authors, and do not reflect the opinions or policies of the data stewards.

We would like to thank our patients, and the physicians, nurses, social workers and volunteers who support them.

Meeting: Preliminary results were presented at the 27th Annual Canadian Conference on HIV/AIDS Research, Vancouver, Canada, on April 27th-29th, 2018 (abstract EPHP2.01).

References

  1. 1. Hachfeld A, Ledergerber B, Darling K, Weber R, Calmy A, Battegay M, et al. Reasons for late presentation to HIV care in Switzerland. Journal of the International AIDS Society. 2015;18(1):1–8. pmid:26584954
  2. 2. Darling KE, Hachfeld A, Cavassini M, Kirk O, Furrer H, Wandeler G. Late presentation to HIV care despite good access to health services: current epidemiological trends and how to do better. Swiss medical weekly. 2016;146(August):w14348–w. pmid:27544642
  3. 3. Girardi E, Sabin CA, Monforte AdA. Late Diagnosis of HIV Infection: Epidemiological Features, Consequences and Strategies to Encourage Earlier Testing. JAIDS Journal of Acquired Immune Deficiency Syndromes. 2007;46(Suppl 1):S3–S8. pmid:17713423
  4. 4. Brawley D, MacConnachie A, Nandwani R, Bell DJ, Fargie F, Fox R, et al. Missed opportunities for HIV diagnosis: a three-year audit in the West of Scotland. Scott Med J. 2013;58(3):173–7. Epub 2013/08/21. pmid:23960057.
  5. 5. Waters L, Fisher M, Anderson J, Wood C, Delpech V, Hill T, et al. Responses to highly active antiretroviral therapy and clinical events in patients with a low CD4 cell count: late presenters vs. late starters. HIV Medicine. 2011;12(5):289–98. pmid:21054749
  6. 6. Sabin CA, Smith CJ, Gumley H, Murphy G, Lampe FC, Phillips AN, et al. Late presenters in the era of highly active antiretroviral therapy. Aids. 2004;18(16):2145–51. pmid:15577647
  7. 7. Braitstein P, Brinkhof MW, Dabis F, Schechter M, Boulle A, Miotti P, et al. Mortality of HIV-1-infected patients in the first year of antiretroviral therapy: comparison between low-income and high-income countries. Lancet. 2006;367(9513):817–24. pmid:16530575.
  8. 8. Marks G, Crepaz N, Janssen RS. Estimating sexual transmission of HIV from persons aware and unaware that they are infected with the virus in the USA. AIDS. 2006;20(10):1447–50. pmid:16791020
  9. 9. Krentz HB, Gill MJ. The Direct Medical Costs of Late Presentation. AIDS research and treatment. 2012;2012:757135-. pmid:21904673
  10. 10. Heath K, Samji H, Nosyk B, Colley G, Gilbert M, Hogg RS, et al. Cohort profile: Seek and treat for the optimal prevention of HIV/AIDS in British Columbia (STOP HIV/AIDS BC). International journal of epidemiology. 2014;43(4):1073–81. pmid:24695113
  11. 11. Office of the Provincial Health Officer. HIV testing Guidelines for the Province of British Columbia. 2014.
  12. 12. British Columbia Centre for Excellence in HIV/AIDS. HIV Monitoring Quarterly Report for British Columbia: First Quarter 2018 2018 [cited 24 July 2018]. http://stophivaids.ca/qmr/2018-Q1/#/bc.
  13. 13. United Nations General Assembly. Resolution No A/RES/70/266, Political Declaration on HIV and AIDS: On the Fast-Track to Accelerate the Fight against HIV and to End the AIDS Epidemic by 2030 2016 [4 July 2018]. http://www.unaids.org/sites/default/files/media_asset/2016-political-declaration-HIV-AIDS_en.pdf.
  14. 14. British Columbia Centre for Excellence in HIV/AIDS. Therapeutic Guidlines Antiretroviral (ARV) Treatment of Adult HIV Infection http://www.cfenet.ubc.ca/sites/default/files/uploads/Therapeutic%20Guidelines%202013-Feb-final.pdf: 2013.
  15. 15. Gustafson R, Steinberg M. Expanding Provider-Initiated HIV Testing. BC Medical Journal 2011;52(1):13.
  16. 16. Gustafson R, Ogilvie G, Moore D, Kendall P. New HIV Testing Guidelines in BC. BC Medical Journal. 2014;56(4):172–3.
  17. 17. British Columbia Centre for Excellence in HIV/AIDS. Expanded HIV testing guidelines will improve early diagnosis 2010 [11 Feburary 2019]. http://cfenet.ubc.ca/news/releases/expanded-hiv-testing-guidelines-will-improve-early-diagnosis.
  18. 18. British Columbia Ministry of Health (2016). Medical Services Plan (MSP) Payment Information File; Consolidation File (MSP Registration & Premium Billing); Home & Community Care (Continuing Care); Mental Health; PharmaNet. British Columbia Ministry of Health. Data Extract. MOH (2016). http://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central.
  19. 19. Canadian Institute of Health Information (2016). Discharge Abstract Database (Hospital Separations). British Columbia Ministry of Health. Data Extract. MOH (2016). http://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central.
  20. 20. Patterson S, Cescon A, Samji H, Cui Z, Yip B, Lepik KJ, et al. Cohort Profile: HAART Observational Medical Evaluation and Research (HOMER) cohort. Int J Epidemiol. 2015;44(1):58–67. pmid:24639444
  21. 21. British Columbia Vital Statistics Agency (2016). Vital Statistics Deaths. British Columbia Ministry of Health. Data Extract. MOH (2016). http://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central.
  22. 22. British Columbia Centre for Disease Control Public Health Laboratory (2016). HIV laboratory testing datasets (tests: ELISA, Western blot, NAAT, p24, culture). Clinical Prevention Services, British Columbia Centre for Disease Control. MOH (2016) 2016 [12 July 2018]. http://www.bccdc.ca/about/accountability/data-access-requests/public-health-data.
  23. 23. Nosyk B, Colley G, Yip B, Chan K, Heath K, Lima VD, et al. Application and validation of case-finding algorithms for identifying individuals with human immunodeficiency virus from administrative data in British Columbia, Canada. PLoS One. 2013;8(1):e54416. pmid:23382898.
  24. 24. British Columbia Centre for Disease Control. Communicable Disease Control Manual, Chapter 5 –Sexually Transmitted Infections: Guidelines for testing, Follow up, and Prevention of HIV 2016. http://www.bccdc.ca/resource-gallery/Documents/Communicable-Disease-Manual/Chapter5-STI/HIV_Guidelines_Testing_FollowUp_Prevention.pdf.
  25. 25. Lodi S, Phillips A, Touloumi G, Geskus R, Meyer L, Thiebaut R, et al. Time from human immunodeficiency virus seroconversion to reaching CD4+ cell count thresholds <200, <350, and <500 Cells/mm(3): assessment of need following changes in treatment guidelines. Clin Infect Dis. 2011;53(8):817–25. pmid:21921225.
  26. 26. Lin YD, Garner SE, Lau JSY, Korman TM, Woolley IJ. Prevalence of HIV indicator conditions in late presenting patients with HIV: a missed opportunity for diagnosis? QJM. 2019;112(1):17–21. pmid:30295832.
  27. 27. Tominski D, Katchanov J, Driesch D, Daley MB, Liedtke A, Schneider A, et al. The late-presenting HIV-infected patient 30 years after the introduction of HIV testing: spectrum of opportunistic diseases and missed opportunities for early diagnosis. HIV Med. 2017;18(2):125–32. pmid:27478058.
  28. 28. Scognamiglio P, Chiaradia G, De Carli G, Giuliani M, Mastroianni CM, Barbacci SA, et al. The potential impact of routine testing of individuals with HIV indicator diseases in order to prevent late HIV diagnosis. BMC Infectious Diseases. 2013. pmid:24112129
  29. 29. Office fédérale de la santé publique de la Suisse. Dépistage du VIH effectué sur l’initiative des médecins 2015. Dépistage du VIH effectué sur l’initiative des médecins
  30. 30. British Columbia Centre for Disease Control. Acquired Immune Defiency Syndrome (AIDS) Case Report Form 2013 [12 September 2018]. http://www.bccdc.ca/resource-gallery/Documents/Communicable-Disease-Manual/Chapter%205%20-%20STI/AIDSsurveillanceCRF_2013_Final.pdf.
  31. 31. Ma H, Villalobos CF, St-Jean M, Eyawo O, Lavergne MR, Ti L, et al. The impact of HCV co-infection status on healthcare-related utilization among people living with HIV in British Columbia, Canada: a retrospective cohort study. BMC Health Serv Res. 2018;18(1):319. pmid:29720155.
  32. 32. Canadian Institute of Health Information. Data Quality Documentation, Discharge Abstract Database—Multi-Year Information 2012 [6 Febuary 2019]. https://www.cihi.ca/en/dad_multi-year_en.pdf.
  33. 33. Population Data BC. Medical Services Plan (MSP) Payment Information File 2018 [6 February 2019]. https://www.popdata.bc.ca/data/health/msp.
  34. 34. Jiang J, Southern D, Beck CA, James M, Lu M, Quan H. Validity of Canadian discharge abstract data for hypertension and diabetes from 2002 to 2013. CMAJ Open. 2016;4(4):E646–E53. pmid:28018877.
  35. 35. Canadian Institute of Health Information. Discharge Abstract Database (DAD) Re-abstraction Studies 2019 [6 February 2019]. https://secure.cihi.ca/estore/productSeries.htm?pc=PCC228.
  36. 36. Canadian Institute of Health Information. Discharge Abstract Database Metadata (DAD) 2019 [6 February 2019]. https://www.cihi.ca/en/discharge-abstract-database-metadata.
  37. 37. Hu W. Diagnostic Codes in MSP Claim Data 1996 [6 February 2019]. https://www.popdata.bc.ca/sites/default/files/documents/data/MSP%20Diagnostic%20Codes%20paper.pdf.
  38. 38. Rekart ML, Gilbert M, Meza R, Kim PH, Chang M, Money DM, et al. Chlamydia public health programs and the epidemiology of pelvic inflammatory disease and ectopic pregnancy. J Infect Dis. 2013;207(1):30–8. pmid:23100568.
  39. 39. Armstrong D, Barkun AN, Chen Y, Daniels S, Hollingworth R, Hunt RH, et al. Access to specialist gastroenterology care in Canada: the Practice Audit in Gastroenterology (PAGE) Wait Times Program. Can J Gastroenterol. 2008;22(2):155–60. pmid:18299734.
  40. 40. British Columbia Ministry of Health. Health Boundaries 2018 [18 July 2018]. https://www2.gov.bc.ca/gov/content/data/geographic-data-services/land-use/administrative-boundaries/health-boundaries.
  41. 41. British Columbia Centre for Excellence in HIV/AIDS. British Columbia HIV/AIDS Drug Treatment Program Monthly Report: May 2018 2018 [24 July 2018]. http://www.cfenet.ubc.ca/sites/default/files/uploads/publications/centredocs/cfe-status-report_may2018.pdf.
  42. 42. McDonald JH. Handbook of Biological Statistics. 2009.
  43. 43. Lima VD, Bangsberg DR, Harrigan PR, Deeks SG, Yip B, Hogg RS, et al. Risk of viral failure declines with duration of suppression on highly active antiretroviral therapy irrespective of adherence level. J Acquir Immune Defic Syndr. 2010;55(4):460–5. pmid:20838225.
  44. 44. Klein D, Hurley LB, Merrill D, Quesenberry CP, Consortium for HIVAIR. Review of medical encounters in the 5 years before a diagnosis of HIV-1 infection: implications for early detection. Journal of acquired immune deficiency syndromes (1999). 2003;32(2):143–52.
  45. 45. Lhopitallier L, Moulin E, Hugli O, Cavassini M, Darling KEA. Missed opportunities for HIV testing among patients newly presenting for HIV care at a Swiss university hospital: a retrospective analysis. BMJ Open. 2018;8(6):e019806. pmid:29895647.
  46. 46. Olding M, Enns B, Panagiotoglou D, Shoveller J, Harrigan PR, Barrios R, et al. A historical review of HIV prevention and care initiatives in British Columbia, Canada: 1996–2015. J Int AIDS Soc. 2017;20(1):21941. pmid:28953322.
  47. 47. University of Northen British Columbia. The Determinants of Health in Northern, Remote and Rural British Columbia 2018 [18 July 2018]. https://www.unbc.ca/northern-fire/womens-health-in-a-northern-context.
  48. 48. Chesney M, Smith A. Critical Delays in HIV Testing and Care:The Potential Role of Stigma. American Behavioral Scientist. 1999;42(7):1162–74.
  49. 49. Youssef E, Cooper V, Delpech V, Davies K, Wright J. Barriers and facilitators to HIV testing in people age 50 and above: a systematic review. Clin Med (Lond). 2017;17(6):508–20. pmid:29196351.
  50. 50. Traversy G, Austin T, Ha S, Timmerman K, Gale-Rowe M. An overview of recent evidence on barriers and facilitators to HIV testing. Canada Communicable Disease Report Monthly. 2015;41(12).
  51. 51. Fettig J, Swaminathan M, Murrill CS, Kaplan JE. Global epidemiology of HIV. Infect Dis Clin North Am. 2014;28(3):323–37. pmid:25151559.
  52. 52. Joore IK, Twisk DE, Vanrolleghem AM, de Ridder M, Geerlings SE, van Bergen JE, et al. The need to scale up HIV indicator condition-guided testing for early case-finding: a case-control study in primary care. BMC Fam Pract. 2016;17(1):161. pmid:27855639.
  53. 53. Damery S, Nichols L, Holder R, Ryan R, Wilson S, Warmington S, et al. Assessing the predictive value of HIV indicator conditions in general practice: a case-control study using the THIN database. Br J Gen Pract. 2013;63(611):e370–7. pmid:23735407.
  54. 54. Joore IK, Arts DL, Kruijer MJ, Moll van Charante EP, Geerlings SE, Prins JM, et al. HIV indicator condition-guided testing to reduce the number of undiagnosed patients and prevent late presentation in a high-prevalence area: a case-control study in primary care. Sex Transm Infect. 2015;91(7):467–72. pmid:26126531.
  55. 55. British Columbia Centre for Disease Control (BCCDC). HIV in British Columbia: Annual Surveillance Report 2015 Vancouver, Canada: BCCDC; 2017 [18 July 2018]. http://www.bccdc.ca/resource-gallery/Documents/Statistics%20and%20Research/Statistics%20and%20Reports/STI/HIV_Annual_Report_2015-FINAL.pdf.
  56. 56. Gilbert M, Hottes TS, Lester R, Gustafson R, Krajden M, Ogilvie G. Time since last negative HIV test among men who have sex with men and people who use injection drugs in British Columbia, 2006–2011. Can J Public Health. 2014;105(1):e63–8. pmid:24735699.