You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#Sec1}
============

Hepatitis C virus (HCV) is the etiology of the most common bloodborne infection in the United States (US) \[[@CR1]\] and can lead to significant co-morbidities such as cirrhosis and hepatocellular carcinoma \[[@CR2]\]. Deaths from HCV between 2003 and 2013 surpassed those from 60 other nationally notifiable infectious conditions, including those from human immunodeficiency virus (HIV) \[[@CR3]\]. HCV burden of cases in the US is estimated at 5 million, many of whom are asymptomatic and unaware they are infected \[[@CR4]\].

It has been identified that baby boomers---those born between 1945 and 1965---constitute approximately three-quarters of all HCV antibody-positive cases. This finding led to the institution of a grade B recommendation by the United States Preventive Services Task Force for a one-time HCV screening of all baby boomers \[[@CR5]\], and the enactment of the Hepatitis Testing Law in New York State \[[@CR6]\]. However, recent reports indicate a rising prevalence of HCV in persons who inject drugs (PWID) among younger, non-urban populations \[[@CR7], [@CR8]\].

Numerous studies in nonurban areas have identified injection drug use as the most common risk factor for HCV transmission \[[@CR9]--[@CR11]\] and that a longer duration of injection drug use is associated with higher infection risk \[[@CR12]\]. In Massachusetts, the distribution of HCV by age was found to have drastically changed from a unimodal model focused on baby boomers to bimodal with an additional peak centered on young persons in their 20s or 30s \[[@CR13]\]. Thus, not only is HCV becoming more common in PWID but also in persons born after 1965.

While several studies have concluded PWID and young adults are associated with a higher risk of HCV infection in nonurban areas, to our knowledge, there are far fewer studies that assess the barriers to linkage to care (LTC) in these particular cohorts. The majority of studies reporting on LTC focus on baby boomers and often conclude risk factors to primarily be lack of insurance and being of certain races or ethnicities \[[@CR14]--[@CR18]\]. As Stony Brook Medicine (SBM) is the only tertiary medical center in suburban Suffolk County, New York (NY) \[[@CR19]\] and serves a population of approximately 1.6 million persons, we aimed to: (1) determine the prevalence of HCV in the SBM cohort; (2) identify risk factors for LTC in both the baby boomer cohort and those born in other years; and (3) identify specific risk factors within the PWID cohort.

Methods {#Sec2}
=======

Study Design {#Sec3}
------------

A retrospective study was designed to identify cases of HCV diagnosed at SBM between January 1, 2016 and December 31, 2018. SBM comprises a 603-bed tertiary care hospital, a 125-bed hospital in Southampton, as well as over 90 community-based healthcare settings throughout Suffolk County, NY. The electronic health system primarily used for both inpatient and outpatient care is the Cerner Electronic Health Record System (EHRS) (Cerner, Kansas City, MO, USA).

Search Criteria {#Sec4}
---------------

The total number of patients tested were extracted from SBM inpatient, emergency department, and outpatient clinic visits with the assistance of Stony Brook Information Technology services. HCV antibody-positives were identified via EHRS search with diagnostic codes ICD-9: 070.54 (Chronic hepatitis C without mention of hepatic coma) and ICD-10: B18.2 (Chronic viral hepatitis C).

Case Definitions {#Sec5}
----------------

Eligible patients for this study included those (1) 18 years and older, and (2) diagnosed with a positive HCV antibody test. Baby boomer was defined as birth year between 1945 and 1965. LTC requires a positive RNA result and was defined as attending an outpatient appointment in which the physician addressed HCV management. Patients incapable of LTC included RNA-positives who declined LTC, were deceased/terminally ill, or were determined to already be in care (a known diagnosis already following with a physician specifically for the disease). The adjusted LTC subtracts those incapable of LTC from the denominator.

Data Collection {#Sec6}
---------------

The following data were obtained from each patient's chart: date of admission or outpatient encounter, age, gender, race, ethnicity, place of birth, zip code of residence, insurance, marital status, occupational status, injection drug use/history of substance use, psychiatric disease, hepatitis B virus and HIV serostatus, number of comorbid medical conditions, liver function, radiologic imaging, stage of fibrosis, and HCV genotype. Race/ethnicity were collected from the EHRS and were self-reported by the patient upon presentation to SBM. Race was divided into the following categories: Native American or Alaska native, Asian, black or African American, native Hawaiian or other Pacific Islander, white, or other/declined to specify. Possible ethnicities were Hispanic and non-Hispanic. Data were collected and stored on a shared network drive with password protection.

Statistical Analysis {#Sec7}
--------------------

Univariate and multivariate analyses were used for the RNA-positive population only, with linkage to care as the outcome variable. Additionally, univariate and multivariate analyses were conducted for RNA-positive PWID, with IV drug use as the outcome variable. Odds ratios (OR) and confidence intervals (CI) were calculated for the above analyses. A map of HCV antibody-positive cases was created using Tableau Software (Seattle, WA, USA) to visualize geographic clusters of HCV by zip code, adjusted for population density by zip code according to 2010 national census data. A *p* value of ≤ 0.05 was considered statistically significant for all tests. Data were analyzed with SAS v.9.4 (SAS Institute, Cary, NC, USA).

Compliance with Ethics Guidelines {#Sec8}
---------------------------------

This project was reviewed and approved by the Institutional Review Board (IRB) at SBM, IRB \#1033821, who waived the requirement for informed consent. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. As data were collected utilizing a retrospective chart review and placed into a deidentified patient database, harm was minimized and patient consent for study participation was therefore not obtained.

Results {#Sec9}
=======

Demographics {#Sec10}
------------

The median age of the total population (*n* = 1017) was 59 years, of which 604 (59.4%) were male and 788 (81.5%) were Caucasian (Table [1](#Tab1){ref-type="table"}). Medicaid (350; 35.7%) and Medicare (348; 35.5%) were the two commonest insurance types. Baby boomers composed of 61.8% (628 cases), while 338 persons were born after 1965 (33.2%) and 51 born before 1945 (5.0%). Of the 338 persons born after 1965, 176 (52.1%) were 35 years or younger and 118 (67.0%) of those were PWID. A bimodal distribution with two peaks was created from all antibody-positive cases, one peak centered on baby boomers and the other on young adults (Fig. [1](#Fig1){ref-type="fig"}).Table 1Demographic characteristics of those who were HCV antibody-positive as well as the subgroup of RNA-positives who presented to SBM between 2016 and 2018CharacteristicsCount (%)Count (%)*n* = 1017 (total population)*n* = 437 (RNA + only)Age Median (IQR)59 (22)56 (28)Gender Male604 (59.39)265 (60.64) Female413 (40.61)172 (39.36)Race^a^ White788 (81.49)329 (79.28) Black103 (10.65)56 (13.49) Asian22 (2.28)8 (1.93) Other54 (5.58)22 (5.30)Ethnicity^a^ Non-Hispanic872 (91.79)373 (91.87) Hispanic78 (8.21)33 (8.13)Insurance^a^ Private205 (20.92)73 (17.18) Medicare348 (35.51)131 (30.82) Medicaid350 (35.71)190 (44.71) Self-pay69 (7.04)30 (7.06) V.A8 (0.82)1 (0.24)^a^Sum may not equal total due to missing dataFig. 1Bimodal distribution of HCV antibody-positives presenting to Stony Brook Medicine between 2016 and 2018

Clinical-Epidemiological Features {#Sec11}
---------------------------------

A total of 27,119 patients were tested for HCV between 2016 and 2018. Of these, 1017 had a positive antibody test for HCV for a seroprevalence of 3.8%, 437 (42.9%) tested RNA-positive (HCV RNA prevalence of 1.6%) (60 patients were incapable of LTC for a total of 377 cases included in our analysis), 153 (40.6%) were LTC and 53 (34.6%) were started on direct acting antiretroviral (DAA) therapy (Fig. [2](#Fig2){ref-type="fig"}a). An RNA test was not completed for 88 antibody-positives. The median length of time for LTC was 57 days (IQR: 26--126).Fig. 2**a** HCV Care Cascade at Stony Brook Medicine for all HCV antibody (*Ab*)-positives between 2016 and 2018. **b** HCV Care Cascade at Stony Brook Medicine for all HCV antibody (*Ab*)-positives who identified as PWID between 2016 and 2018

Regarding only the PWID population born after 1965, there were 187 HCV antibody-positives and 126 (75.4%) RNA-positives (18 cases who were incapable of LTC for a total of 108 cases included in our analysis). Of the RNA-positives, 30 (27.8%) were LTC and 12 (40.0%) were started on DAA therapy (Fig. [2](#Fig2){ref-type="fig"}b). Twenty individuals did not have an RNA test conducted. Regarding this cohort, the median length of time for LTC was 40.5 days (IQR 27.25--136.5).

Risk Factors {#Sec12}
------------

The HCV antibody- and RNA-positive population was assessed for risk factors to LTC. In a multivariate analysis among RNA-positive cases only, excluding people incapable of LTC, when controlling for polysubstance use, cirrhosis, chronic kidney disease, marital status, primary care physician, and being a baby boomer, those who had Medicare (OR 0.29, CI 0.13--0.63, *p* = 0.002) or Medicaid (0.45, CI 0.22--0.90, *p* = 0.02) had lower odds of LTC than those with private insurance. Conversely, those who had cirrhosis (OR 2.87, CI 1.41--5.84, *p* = 0.004) had higher odds of LTC than those with no cirrhosis (Table [2](#Tab2){ref-type="table"}).Table 2Multivariate analysis of factors associated with linkage to care (LTC) among HCV antibody- and RNA-positive individuals presenting to SBM*n* = 308Probability of linking to care*p* valueOR (CI)Insurance Medicare vs. private0.29 (0.13--0.63)0.002 Medicaid vs. private0.45 (0.22--0.90)0.024 Self-pay vs. private0.70 (0.22--2.29)0.559Polysubstance use Yes vs. no0.60 (0.29--1.23)0.160Cirrhosis Yes vs. no2.87 (1.41--5.84)0.004Chronic kidney disease Yes vs. no1.55 (0.84--2.89)0.163Marital status Single vs. married/domestic partner1.33 (0.70--2.51)0.385 Divorced/separated vs. married/domestic partner0.81 (0.35--1.89)0.629 Widowed vs. married/domestic partner0.67 (0.23--1.95)0.466Primary care physician Yes vs. no1.54 (0.85--2.76)0.152Baby boomer Yes vs. no1.17 (0.65--2.10)0.599

HCV-positive RNA cases were further stratified among those born after 1965. When comparing PWID (187 total HCV antibody cases) and non-PWID (79 total HCV antibody cases), several significant risk factors were found (Table [3](#Tab3){ref-type="table"}). A multivariate analysis of PWID born after 1965, after controlling for number of comorbidities, race, ethnicity, insurance, tobacco use, polysubstance use, marital status, psychiatric disease, and primary care physician found that older age (OR 0.92, CI 0.86--0.98, *p* = 0.01) and being African American (OR 0.09, CI 0.01--0.60, *p* = 0.01) were associated with lower odds of IV drug use (Table [3](#Tab3){ref-type="table"}). Conversely, polysubstance use (OR 73.13, CI 13.09--408.51, *p* \< 0.0001) and psychiatric disease (OR 2.79, CI 1.09--7.15, *p* = 0.03) had higher odds of IV drug use.Table 3Multivariate sub-analysis of factors associated with intravenous drug use among HCV antibody and RNA-positive PWID at Stony Brook Medicine between 2016 and 2018*n* = 202Probability of IV drug use OR (CI)*P* valueAge0.92 (0.86--0.98)0.01Number of comorbidities ≥ 4 vs. \< 42.22 (0.46--10.66)0.32Race Black vs. white0.09 (0.01--0.60)0.01 Asian vs. white\< 0.001 (\< 0.001--\> 999.9)0.99 Other vs. white0.54 (0.02--19.07)0.74Ethnicity Hispanic vs. non-Hispanic0.27 (0.02--4.10)0.35Insurance Medicare vs. private1.21 (0.17--8.48)0.85 Medicaid vs. private1.76 (0.51--6.11)0.37 Self-pay vs. private0.57 (0.06--5.79)0.63Tobacco Yes vs. no0.83 (0.28--2.08)0.68Polysubstance use Yes vs. no73.13 (13.09--408.51)\< 0.0001Marital status Married/domestic partner vs. single0.34 (0.10--1.14)0.08 Divorced/separated vs. single0.53 (0.09--3.13)0.49 Widowed vs. single\> 999.9 (\< 0.001--\> 999.9)0.99Psychiatric disease Yes vs. no2.79 (1.09--7.15)0.03Primary care physician Yes vs. no0.61 (0.25--1.52)0.29

"Hot spots" or High Incidence Areas of HCV Cases {#Sec13}
------------------------------------------------

Zip codes for every HCV antibody-positive case were mapped in order to assess the high incidence areas of HCV on Long Island (Fig. [3](#Fig3){ref-type="fig"}), and were population adjusted according to 2010 national census data. The locations of Stony Brook University Hospital (SBUH) and Southampton Hospital (SHH) are indicated on the map. The most populous zip code for HCV-positive antibody is located approximately 50 miles east of Stony Brook University Hospital. Other populous zip codes are located in central and southern Suffolk County, NY.Fig. 3HCV antibody-positives between 2016 and 2018 living in Suffolk County, NY, mapped by zip code. Stony Brook University Hospital (*SBUH*) and Southampton Hospital (*SHH*) are indicated on the map

Discussion {#Sec14}
==========

The HCV RNA prevalence (1.6%) found in this study is approximately 1.7 times higher than the estimated national prevalence of 0.93%, and 2.1 times higher than the estimated New York State (NYS) prevalence of 0.75% \[[@CR20]\]. HCV was most commonly found among baby boomers; however, among non-boomers, the most common HCV-positives were young (less than 35 years), white, non-Hispanics. These findings mirror a new trend of HCV acquisition among young (adolescents and young adults, aged 15--24), white, non-Hispanics living in urban, suburban and rural locations \[[@CR21]\]. Additionally, the high HCV prevalence in Suffolk County coincides with its high opioid burden (opioid overdose deaths, non-fatal ED visits and hospital discharges involving opioid use disorder), ranking in the top quartile of all counties in NYS \[[@CR22]\].

When HCV cases were graphed by age of diagnosis and gender, we found a bimodal distribution with baby boomers and young adults as the two major cohorts. The baby boomers show a higher amount of male cases, while females and males are in similar numbers in the younger cohort. Although data from this study support HCV affecting equal numbers of males and females, female PWID may be more exposed to HCV due to riskier injection drug practices \[[@CR23]\]. Current and future public health awareness efforts should address female injection drug practices.

Our HCV care cascade mirrors the national HCV care continuum, where the largest gaps in care occur first between HCV RNA confirmatory testing and attendance at first appointment (LTC), then between LTC and initiation of direct acting antiviral (DAA) therapy. LTC was 40.6% in our study, lower than other integrated health systems that utilize best practice interventions and patient navigators \[[@CR14], [@CR15], [@CR17], [@CR18]\]. We found that 34.6% of those LTC began DAA therapy, which is higher than in some studies \[[@CR24]--[@CR26]\] but not others \[[@CR27]\]. Whether this rate was due to our patients linking early or that greater than 90% of our patients had insurance is unclear. However, it must be noted that due to our low LTC, there was not a large cohort to assess for initiation or completion of DAA therapy. Among PWID, 27.8% achieved LTC, lower than reported elsewhere \[[@CR27], [@CR28]\]. However, 40% were started on DAA therapy, which is higher than that found in other reports \[[@CR27]\].

Our length of time from diagnosis to date of LTC was comparatively shorter than LTC timelines reported elsewhere \[[@CR26], [@CR29]\]. Studies indicate that most individuals achieve LTC within the first 6 months of diagnosis \[[@CR30]\], and the majority of our LTC occurred within this time frame. This LTC timeline may simply be transient during the time period observed. Further stratification of the length of time until LTC may explain this occurrence by indicating the LTC time frames for different cohorts, perhaps by age, insurance type, or drug use.

In our multivariate analysis of all HCV and RNA-positives, being a baby boomer and having a diagnosis of cirrhosis is associated with higher odds of LTC. Baby boomers overall have limited knowledge about the necessity of HCV screening \[[@CR31]\]. Thus, like carrying a diagnosis of cirrhosis and chronic kidney disease (multimorbidity domains), once diagnosed with HCV they may be more motivated to attend an appointment with a specialist to seek care due to perception of illness in the setting of other age-related comorbidities. This multimorbidity hypothesis has been stated in a previous study \[[@CR29]\]. In contrast, polysubstance use was not associated with decreased LTC. Previous studies in suburban locations have identified young injection drug users as associated with decreased linkage to care \[[@CR8]\]. However, our multivariate analysis did not substratify injection drug use and its association with LTC. Having Medicaid as an insurance carrier was found to be associated with decreased LTC, a finding reported in multiple studies \[[@CR24], [@CR29], [@CR32], [@CR33]\]. According to NYS Medicaid law in March 2018, in an attempt to increase access to HCV treatment, there is no longer a requirement to be an HCV-experienced provider in order to treat patients \[[@CR34]\]. This law aims to increase the number of programs meant to connect New York residents in high-risk communities with comprehensive HCV prevention, screening, and treatment. Thus, our decreased LTC is a failure of the care cascade, rather than of public policy.

In a multivariate analysis of those born after 1965, PWID were significantly more likely to be younger, white, and have concomitant polysubstance and psychiatric disease. Although we did not assess for LTC in this subpopulation, there is reason to believe that this is a vulnerable population with which the healthcare community would have difficulty with connecting to DAA therapy. Previous studies show that barriers to LTC and treatment include lack of trust with healthcare providers \[[@CR35]\], misconceptions regarding treatment adherence \[[@CR36]\] as well as higher rates of depression and psychiatric illness \[[@CR37]\]. Injection drug use is also a common reason for treatment deferral due to factors such as deteriorating financial status, decline in personal health status, less access to healthcare resources, and higher financial burden \[[@CR38]\]. However, a recent investigation in rural Kentucky found that PWID achieved LTC in their community (59%) but were not accessing subsequent treatment \[[@CR39]\]. According to a survey of HCV prescribers at the American Association for the Study of Liver Diseases (AASLD) Liver Meeting in 2014, only 15% of clinicians stated they would treat an active PWID (last injection within 30 days) with DAAs, citing reinfection and cost as the most impactful concerns \[[@CR40]\]. However, AASLD guidelines from 2018 explicitly state "recent or active \[injection drug use\] should not be seen as an absolute contraindication to HCV therapy. \[...\] Scaling up HCV treatment in persons who inject drugs is necessary to positively impact the HCV epidemic in the US \[[@CR41]\]." Further interventions are clearly needed to decrease the stigma surrounding treatment of this population.

Analysis of zip codes of HCV-positive patients shows numerous hot spots of HCV infection, with the most populous zip code located in Eastern Long Island, approximately 50 miles from Stony Brook Hospital. To our knowledge, these are the first data demonstrating the presence of clusters of HCV antibody-positive individuals in Suffolk County, NY. The distant eastern zip code cluster is a compelling finding, given that the other most populous zip codes are in close proximity to SBM. With further expansion of SBM to eastern Long Island through Stony Brook Southampton Hospital, Southampton, NY, the results may vary in location or by cohort as more results from eastern Suffolk County will be collected.

Suffolk County, and possibly the rest of Long Island, is facing an HCV epidemic; detection of HCV RNA prevalence was 1.7 times higher than the national average and 2.1 times higher than the previously reported state average. As the majority of non-boomers (55.3%) were known PWID, it is vital to initiate timely interventions to both decrease the spread of HCV and increase the LTC for those already affected by the disease. It has been shown that harm reduction interventions, early detection and treatment, and special attention to social barriers may all help in drastically reducing the number of HCV infections in this cohort \[[@CR42], [@CR43]\]. We suggest point of care testing for HCV antibody with reflex RNA as well as prompt linkage to care to start DAA with the assistance of patient navigators and concomitant referral to substance use treatment centers.

Our study had a few limitations. First, our study was a retrospective chart review. As a result, we did not have longitudinal information to determine how many patients have achieved a sustained virologic response. Second, although our time to appointment is much less than national data, which may be because the majority of our population was insured, and thus may not be generalizable to urban or rural areas where the proportion of uninsured may be higher.

Conclusion {#Sec15}
==========

In conclusion, a higher prevalence of HCV was found in Suffolk County than other locations in New York State and we have identified a new cohort of young HCV-positives who are less likely to be linked to care compared with their older counterparts. Further interventions are urgently needed to increase the linkage to care in this younger population in order to prevent long-term health sequelae and to reduce the risk of HCV transmission.

**Enhanced Digital Features**

To view enhanced digital features for this article go to 10.6084/m9.figshare.8125025.

We thank the participants of the study.

Funding {#FPar1}
=======

No funding or sponsorship was received for this study or publication of this article. The article processing charges were funded by the authors.

Authorship {#FPar2}
==========

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Disclosures {#FPar3}
===========

Pruthvi Patel and Luis A. Marcos have received a grant from FOCUS (Gilead Sciences, Inc.). Luis A. Marcos has participated as an advisor for Gilead and has received consultation fees from Gilead. Audun J. Lier, Kalie Smith, Kerim Odekon, Silvia Bronson, Erin Taub, Mathew Tharakan and Gerald J. Kelly do not have any conflicts of interest to declare. FOCUS funding supports HIV, HCV, and hepatitis b virus (HBV) screening and linkage to the first medical appointment after diagnosis.

Compliance with Ethics Guidelines {#FPar4}
=================================

This project was reviewed and approved by the Institutional Review Board (IRB) at SBM, IRB \#1033821, who waived the requirement for informed consent. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. As data were collected utilizing a retrospective chart review and placed into a deidentified patient database, harm was minimized and patient consent for study participation was therefore not obtained.

Data Availability {#FPar5}
=================

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Open Access {#d29e1285}
===========

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (<http://creativecommons.org/licenses/by-nc/4.0/>), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.