VIRAL HEPAT IT I S

Psychological, lifestyle and social predictors of hepatitis C treatmentresponse: a systematic reviewVictoria A. Sublette1, Mark W. Douglas2,3, Kirsten McCaffery1, Jacob George2 and Kathryn Nicholson Perry4

1 School of Public Health, University of Sydney, Sydney, NSW, Australia

2 Storr Liver Unit, Westmead Millennium Institute, University of Sydney and Westmead Hospital, Westmead, NSW, Australia

3 Centre for Infectious Diseases and Microbiology, Sydney Emerging Infections and Biosecurity Institute, University of Sydney and Westmead

Hospital, Westmead, NSW, Australia

4 Centre for Health Research & School of Social Sciences and Psychology, University of Western Sydney, Penrith South, NSW, Australia

Keywords

HCV treatment – hepatitis C – hepatitis C

treatment outcomes – hepatitis C treatment

predictors – systematic review

Correspondence

Dr. Mark W. Douglas, Storr Liver Unit,

Westmead Millennium Institute, University of

Sydney and Westmead Hospital, Westmead,

NSW 2145

Tel: +61 2 9845 7705

Fax: +61 2 9635 7582

e-mail: mark.douglas@sydney.edu.au

Received 7 November 2012

Accepted 13 February 2013

DOI:10.1111/liv.12138

Liver Int. 2013: 33: 894–903

AbstractBackground: To increase cure rates for Hepatitis C, barriers to treatmentadherence and completion must be identified and overcome. Aims: Thisstudy systematically reviewed evidence on the psychological, lifestyle andsocial determinants of achieving viral eradication with antiviral ther-apy. Methods: An electronic search strategy was used to identify relevantstudies that examined psychological, lifestyle and social factors related toachieving a sustained virological response (SVR). Results: Thirty-four stud-ies that matched our criteria were identified. Of the factors that predictresponse to treatment, Asian ethnicity was an independent predictor of SVR.We found an indirect relationship between diet and SVR, with non-respond-ers to treatment consuming more polyunsaturated fatty acids, fats and carbo-hydrates than those who attained SVR. The effect of alcohol consumptionrelied on the amount consumed; fewer than 30 grams daily had no effect onSVR, whereas >70 grams daily had an adverse impact on a patient’s ability toachieve SVR, with termination rates up to 44% in those who drank >2 drinksa day. Patients with psychiatric illnesses had comparable SVR rates tocontrols if they continued psychological therapy (average 42%), althoughdiscontinuation rates were high with 11 studies reporting rates from 14 to48%. Conclusions: There are major gaps in current knowledge of theimpact of variables such as diet, exercise, attitudes and coping skills oncure rates in chronic Hepatitis C. Those who drink limited amounts ofalcohol or have psychiatric disorders should be offered treatment for theirdisease, with adjunctive education and support to improve treatmentcompletion.

Study Highlights

What is current knowledge?

• Known host and viral factors can predict up to 50%of the variability in Hepatitis C treatment efficacy

• Patients with psychiatric illness and those who drinkalcohol are often excluded from treatment and clinicaltrials.

What is new here?

• Fewer than three drinks a day of alcohol does notimpact treatment outcome

• Patients with psychiatric illnesses have comparableSVR rates to controls if they complete the therapy

• The impact of diet, exercise and socioeconomic sta-tus on SVR rates is under-researched.

Chronic Hepatitis C (CHC) is a major public healthconcern, with approximately 170 million people, or 3%of the world’s population, infected with the Hepatitis Cvirus (HCV). Untreated, it can lead to cirrhosis, hepato-cellular carcinoma or to liver transplantation (1). CHCcan be cured, with treatment success defined as a Sus-tained Virological Response (SVR), where the virusremains undetectable in the blood 6 months after treat-ment completion (2). The overall SVR rate in majorclinical trials of PEG-interferon and ribavirin is around60%, but can be substantially lower in ‘real world’ set-tings (3). Most research to date has focused on the med-ical and physiological predictors of treatment outcomes,identifying many histological, virological and geneticpredictors of SVR, including HCV genotype and viralload, the extent of liver fibrosis and polymorphisms

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near the host IL28B gene (4–6). However, these factorsalone cannot accurately predict whether a patient withCHC will attain SVR on therapy. Even as more effectivetreatments are developed, reducing the burden of CHCon the individual and community will depend to a greatextent on behavioural, psychosocial and environmentalfactors that influence an increase in uptake and comple-tion of treatment (7).

While biomedical (physiological and medical) hostdeterminants explain approximately 50% of the variancein treatment outcomes, psychosocial predictors havenot been well-studied (6, 7). A multifactorial approachis likely to be required to identify the behavioural andpsychosocial factors that affect success of treatment forCHC, either directly or via adherence to treatment.Adherence, or compliance to treatment, is a critical con-tributor to positive treatment outcomes (8). Amongpatients with CHC, those who take at least 80% of theprescribed dose of pegylated interferon and 80% of theribavirin dose for 80% of the time, achieve higher SVRrates than those who do not (9), resulting in the socalled ′80/80/80′ rule. Unfortunately, poor adherence isexpected in 30–50% of all patients regardless of setting,disease or prognosis. (8, 10).

Consistent patient guidelines to improve adherenceand completion of treatment in CHC are lacking (11,12), as current behavioural research primarily focuseson prevention, and not removing the psychosocial andbehavioural barriers to treatment success. Therefore, itis necessary to examine the evidence for factors thatimprove patient adherence and thus positivetreatmentoutcomes (13). In other medical specialties, such asoncology, cardiology and gastroenterology, biobehavio-ural (psychosocial, behavioural and biological) variableshave been found to have a strong influence on the out-comes of interventions for health conditions(8, 14, 15).Socioeconomic status has also been found to impactoverall health outcomes, because of the influence ofeducation, income and ethnicity on a person’s exposureto stressors, health practices and behaviours and theiraccess to medical care (16).

In addition, psychosocial factors, such as depressionor substance abuse, routinely prevent individuals fromaccessing treatment, or participating in clinical trials,with presumed negative impact on health outcomes (17,18). If these assumptions are correct, then exclusion ofthese individuals from clinical registration trials maypartially explain the difference in treatment outcomesbetween clinical trials and ‘real world’ settings (3, 17).However, in many cases these assumptions do notappear to be based on firm evidence, and so require fur-ther investigation.

The effects of some relevant biobehavioural factors,such as age, gender and drug use, have already beenwell-researched (17, 19–21), so they were not examinedin this review. Summarizing previous studies, those whoare younger than 40 years have a better response totreatment (13, 22–24). The effect of gender on SVR is

controversial, with the higher overall SVR rates infemales confounded by oestrogen levels, and meno-pausal status (25). Although injecting drug users (IDUs)are routinely excluded from antiviral therapy, previoussystematic reviews examining treatment and adherenceoutcomes in this population have found little clinicaljustification for their exclusion (20, 26).

The aim of this study was to explore other social, life-style and psychological factors in people with CHC,which may affect their response to treatment. Oncethese predictors are better understood, they can be tar-geted for interventions that improve adherence andtreatment outcomes.

Methods

Data sources and searches

We conducted a systematic search of the literature fromJuly to August 2011 using the following online databas-es: Cinahl (via Ebsco) (1982 to present), CochraneLibrary (via Wiley Interscience), Embase (1966 to pres-ent), Medline (via OvidSP) (1950 to present) and Psy-cINFO (via OvidSP) (1806 to present).

The medical subject headings (MeSH) in Medline,PsychINFO, and Cochrane Library, and Cinahl’s subjectterms were searched for synonyms to our primarysearch categories: ‘Hepatitis C’, ‘exercise,’ ‘diet,’ ‘socio-economic status,’ ‘attitude to health’ and ‘coping,’ inaddition to the terms ‘depression,’ ‘depressive disorder,’‘psychiatric disorder(s),’ ‘psychiatric illness(es),’ ‘alco-hol drinking,’ ‘alcoholism’ and ‘alcohol addiction’(Appendix S1).

Study selection

English language quantitative and qualitative studieswere included if the primary focus of research was asocial, psychological or behavioural predictor of SVR.Studies of patients who were co-infected with HIV orhad hepatocellular carcinoma were excluded. See Fig. 1for the model of the inclusion criteria.

Data extraction and quality assessment

Consistent with the Preferred Reporting Items for Sys-tematic Reviews and Meta-Analyses (PRISMA) guide-lines, a thorough search of the relevant databases wasperformed, followed by data extraction and qualityassessment of the chosen studies. Data were extractedfrom the five databases by the primary researcher (VAS)and an independent reviewer (a librarian – RM).A totalof 11,399 research articles were identified, the title ofeach of which was reviewed by the primary researcher toconfirm that they met inclusion criteria. A total of 3445articles were duplicates and 7819 were excluded afterreviewing the title and abstract. The remaining 135 arti-cles were downloaded for a complete text review, and of

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these, 34 were chosen for final analysis as they fulfilledall selection criteria (Fig. 2). Ambiguities regarding theapplication of the inclusion criteria to selected studieswere resolved through discussion between the primaryresearcher and other members of the research team.

Studies were assessed for inclusion using the QualityAssessment Tool for Quantitative Studies, developed bythe Effective Public Health Practice Project (EPHPP),Canada (27). Articles were graded by the primaryresearcher (VAS) and allocated an overall quality scoreof strong, moderate or weak, based on the following:evidence of selection bias, study design, presence of andcontrol for confounding variables, application of blind-ing, data collection methods and number of participantwithdrawals. These ratings were then reviewed by a

researcher independent of the project (SG), and any dis-agreement was resolved through discussion between thetwo researchers. All qualified studies were included inthe review, regardless of quality rating.

Data synthesis and analysis

Data extracted from the selected studies were summa-rized in detailed evidence tables. Across studies, poten-tial predictors of SVR were divided into three categories:psychological (Table S1), lifestyle (Table S2) and socio-economic factors (Table S3). Adherence (taking thetreatment according to plan) and treatment discontinu-ation (ending treatment prematurely) were examinedas secondary outcomes. Adherence is normally defined

Secondary Treatment Outcomes

Primary Treatment Outcomes

Predictors

Social• Socioeconomic Status

Lifestyle• Physical Fitness• Nutri on• Alcohol use

Psychological• Health A tudes• Coping Skills• Psychiatric illness

Virological Response

• SVR (Sustained Virological Response) to treatment

• Treatment Failure (No SVR to treatment a ained)

Treatment Adherence

• Completed full course of treatment

• Treatment discon nua on by health care provider, due to either physical or psychological adverse events

• Treatment discon nua on by pa ent (for any reason)

Note that selected studies must have a primary treatment outcome to be included in the review. Secondary outcomes are op onal.

Fig. 1. Systematic review inclusion criteria.

3445 duplicate ar cles removed

7819 ar cles excluded based on tle and abstract review

101 ar cles excluded based on full text review:

68 Did not include a treatment outcome7 Not empirical research7 Did not include a predictor of interest4 Pa ents were being re-treated4 Other clinical outcome (liver disease, etc.)3 Effect of treatment on QOL only3 HIV co-infec on2 CAM (complementary and alterna ve medicine)

study2 Explora on of a health model only1 Treatment side effect (induced depression, etc)

34 studies included in Systema c Review

11,399 records retrieved from search criteria

Iden

fica

onSc

reen

ing

Elig

ibili

tyIn

clud

ed

7954 ar cles screened

135 full-text ar cles assessed for eligibility

Fig. 2. Systematic review process.

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according to the 80/80/80′ rule (80% of the prescribeddose of pegylated interferon and 80% of the ribavirindose for 80% of the time), although many studies didnot explicitly state their criteria for adherence.

Results

A total of 7954 research articles were identified usingour search strategy, but few studies provided SVR data(Fig. 2). Thirty-four publications qualified for the finalanalysis. The earliest study produced by the search waspublished in 1994, and the latest in 2011. Sixty-five percent of the studies were published in 2006 or later, with18% published in 2010. The number of articles analysedin each category was: psychological factors: 12 (28–39);socioeconomic status: 10 (40–49); and lifestyle factors:19 (alcohol consumption: 10 (11, 40, 50–57) and theimpact of weight, obesity, or BMI and diet: 9 (11, 13,40, 44, 45, 53, 58, 59)). Five studies were included inmore than one category (11, 40, 44, 45, 53).

Psychological factors: Psychiatric illness, attitudes andcoping skills

Psychiatric illness

To determine whether psychiatric illness has animpact on SVR, treatment adherence or discontinua-tion rates, twelve studies that reported on these factorswere analysed (Table S1). Four studies specificallyexamined depressive symptoms(28, 29, 34, 35); fiveresearched general psychiatric issues(30, 33, 37–39);major depressive disorder, schizophrenia and severemental illness were analysed in one study each(31, 32,36). Ten of the studies found no significant relation-ship between depressive symptoms, general mental orpsychiatric illness, schizophrenia or major depressivedisorder and treatment failure, with SVR rates rangingbetween 10 and 58.5%. One study found that thosewho had a history of depression and who were nottaking antidepressants had significantly lower SVRrates, as well as higher rates of treatment discontinua-tion, than those taking antidepressants (28). Anotherstudy found a relationship between higher scores on amajor depression inventory and onset of depressionduring treatment. Although this study was rated asstrong, it was not powered sufficiently to determinewhether the use of antidepressants affected treatmentoutcomes (34).

Surprisingly, one study found that patients withschizophrenia actually had higher rates of SVR thanthose in a control group with no schizophrenia (32).

Treatment discontinuation

Although the presence of psychiatric illness did notaffect SVR rates in patients who completed treatment,discontinuation rates were reasonably high, with 11

studies reporting early termination rates of 14–48% forpatients with a comorbid psychological illness. Early ter-mination rates owing to non-compliance in these stud-ies were as high as 43%, compared to 0–30% for thosewith no psychiatric illness.

Attitudes, coping skills

No published reports meeting our inclusion criteriawere identified that investigated effects of patient atti-tudes, coping skills or any synonyms to these attributes,on SVR rate.

Lifestyle factors: Alcohol consumption, diet and exercise

Alcohol consumption

Ten studies were identified that researched the effects ofalcohol use on treatment outcomes; all assessed self-reported consumption (Table S2). Three studies exam-ined the effect of the amount of alcohol on SVR (11, 40,53), six compared alcohol intake and duration of absti-nence before treatment on SVR rates (50–52, 55–57)and two studies investigated the effect of cumulative, orlifetime alcohol consumption on SVR (54, 57), one ofwhich examined both cumulative consumption andabstinence (57). Of the three studies that examined theeffects of different amounts of alcohol on SVR, twostudies found no relationship between alcohol con-sumption and SVR. Of these, one study, rated strong inevidence, found that an average of 63 grams daily ofalcohol had no effect on SVR rates (53), while anotherstrongly rated study concluded that 30 grams or moreof alcohol daily was not a significant predictor of SVR(40). However, a moderately rated study found thatSVR decreased as alcohol intake increased, with alcoholconsumption of 15 grams daily having no effect onSVR, but 32 grams a day significantly impacting treat-ment failure (11). Six studies that investigated the effectof alcohol consumption and duration of abstinence (orno abstinence) on SVR rates also produced mixed find-ings. Three studies, rated as strong in evidence, failed tofind a significant difference between recent drinkers(those who had consumed alcohol within the past year)and those who consumed alcohol more than 1 yearbefore treatment, or an impact of alcohol consumptionlevels on SVR (40, 50, 51). However, three weak tomoderate studies reported mixed findings regarding theeffects of different periods of abstinence and levels ofalcohol intake. Confounders such as the effect of genderand the presence and severity of comorbidities such asliver fibrosis may partially explain the inconsistencies ofthese findings (55).

Two weak to moderate quality studies investigatedthe relationship between cumulative alcohol con-sumption and SVR, both of which found thatincreasing lifetime alcohol consumption reduced SVRrates (54, 57).

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Treatment discontinuation

Treatment discontinuation rates were reported in threestudies, two of which measured the impact of absti-nence, or recent drinking, on early withdrawal fromtreatment. Two studies rated strong in evidence investi-gated the independent effect of alcohol consumption ontreatment discontinuation rates for recent (in the past12 months) drinkers, and found that levels and recencyof alcohol consumption were associated with discontin-uation (40, 50).

In summary, the effect of alcohol consumption onSVR depends on the amount consumed; fewer thanthree standard drinks (30 grams) daily does not appearto have an effect on SVR, whereas higher levels of con-sumption may have an adverse impact on SVR. Alcoholconsumption within 1 year of treatment – and possiblythroughout treatment – does not appear to have animpact on SVR for those who consume fewer than threedrinks (< 30 grams) a day(40, 50, 51). However, discon-tinuation rates are higher in those who consume alco-hol, with up to 44% of those consuming more than twodrinks a day terminating treatment early, compared to26% in non-drinkers (P = 0.0001)(50).

Diet

From the synonym search for diet, seven studies exam-ined the relationship between SVR and weight, obesityand/or BMI, and two investigated the role of diet quality(Table S2). In all but one study, which examined thetreatment outcomes of Latinos and Caucasians exclu-sively(40), higher weight and BMI were associated withlower rates of SVR (11, 13, 44, 45, 53, 58) as has beenshown previously (17, 60). Of the two studies on dietquality, one found that non-response to treatment wasassociated with a higher intake of polyunsaturated fattyacids, iron and Vitamin A; and a lower intake of mono-unsaturated fatty acids, zinc and niacin (11). Anotherstudy examining the use of a tomato-based Food forSpecial Medical Purposes found no relationship withSVR (59).

With a paucity of studies on the relationship betweendietary intake and SVR, its impact on treatment out-comes remains indeterminate.

Exercise and SVR

No published reports meeting our inclusion criteriainvestigated the impact of exercise on SVR.

Socioeconomic factors

In our search for socioeconomic factors includingincome, education, social class or poverty (Appendix S1),only 10 studies met our inclusion criteria, and all exam-ined the effects of ethnicity on SVR. Nine of these stud-ies were rated strong in evidence and one was ratedmoderate. Four articles included Hispanics (40, 41, 43,

46), six included African Americans (41–43, 46, 47, 49),and six included Caucasians and Asians (42–46, 48).SVR rates ranged from 30–85% in Asians, 12–52% inCaucasians, 4–28% in African Americans and 10–23%in Hispanics (Table S3).

Adherence to treatment can also be a confoundingfactor, with one study reporting higher SVR rates inCaucasian Americans than African Americans, alongwith significantly higher rates of adherence (at least 80%of the maximum doses of both drugs) in CaucasianAmericans(49).

In summary, we found strong evidence for Asian eth-nicity as an independent predictor of SVR, with non-South Asians in particular having a better response totreatment than Caucasians, African Americans or Latinos(40–48), while African American ethnicity was an inde-pendent predictor of failure to achieve SVR (41–43, 49).

Discussion

Summary of findings

This study investigated the impact of social, lifestyle andpsychological factors in people with CHC on their treat-ment outcomes, including SVR, treatment adherenceand discontinuation.

Previous research has shown that patients withpsychiatric illnesses are often excluded from treatment(3, 17), but the articles identified in our review foundthat this group achieved comparable SVR rates to con-trols if they completed therapy (28–33, 35, 38, 39).Despite significantly higher discontinuation rates in thispopulation, they could be treated successfully if offeredadjunctive education and support to improve treatmentcompletion (28, 31).

The effect of alcohol consumption on treatment out-comes was an important finding in our review. Theeffect of alcohol was dependent on the amount con-sumed: fewer than 30 grams daily (three standarddrinks) had no effect on SVR, whereas >70 grams dailymay have an adverse impact on a patient’s ability toachieve SVR. Therefore, to increase treatment uptakerates, we recommend treating patients who limit theamount of alcohol they consume, rather than excludingmoderate drinkers from treatment. The high treatmenttermination rates in those who drink alcohol suggestthat providing extra psychological support during treat-ment may be helpful, and warrants further study.

Finally, major knowledge gaps were identified in theimpact of psychological and behavioural factors such asdiet, exercise, attitudes and coping skills on cure rates inCHC. These factors have been shown to have predictivevalue in other areas of medicine (61, 62).

Overall, the number of studies examining the effectsof psychological, lifestyle and social factors on CHCtreatment response was low, with no studies that metinclusion criteria looking at exercise, education,socioeconomic status or coping skills. Furthermore,

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the quality of the studies was mixed, with the highestquality seen among those on ethnicity and the lowest onalcohol consumption. No qualitative studies were foundthat met our criteria.

Psychological factors

There were no studies examining the relationshipbetween attitudes or coping skills and SVR. However,there were a number of studies that examined the rela-tionship between people having a psychiatric illness andSVR. People with psychiatric illness are often considereddifficult to treat (28), are excluded from registration tri-als and are regularly denied medical therapy (50). Suchrestrictive patient selection for clinical trials excludesthose who may have lower treatment success rates, as aresult of higher rates of physical and psychologicalcomorbidities, contributing to the gap between pub-lished response rates and ‘real life’ patients. SVR rates of56–63% are reported in registration trials, (17, 63) incontrast to rates of 10–57% in the studies we identifiedthat include patients with psychiatric illness (32, 37).Cure rates for patients with psychiatric illness werereported to be equal or even higher than controls insome of the studies we analysed (28, 31–33). However,caution must be applied to the results, as only patientswho completed treatment were included, and high dis-continuation rates would result in poorer outcomes ifintention-to-treat analysis had been performed, as inmost clinical trials.

Surprisingly, in one small study, researchers foundhigher SVR rates in patients with schizophrenia byintention-to-treat analysis, despite slightly higher dis-continuation rates. The reason for this result was notclear, but one potential confounding factor is that thepatients with schizophrenia were more likely to havebeen prescribed psychotropic medications and to havereceived additional support from mental health provid-ers during treatment (32). This intriguing finding war-rants further research and should be followed up infuture studies.

Despite psychiatric illness having an adverse impacton treatment adherence and discontinuation, severalstudies have confirmed that this population can be suc-cessfully treated. Adherence and treatment-completionrates are improved when additional psychiatric or clini-cal management is provided (29, 32), or antidepressantuse is continued or initiated during treatment for thosewho experience depression (28). In addition, educatingpatients on their treatment course and outcomes, andinforming them of the side effects before the start oftreatment, has been recommended to improve adher-ence (32, 64).

Lifestyle factors

We found that the effect of alcohol consumptiondepends on the amount consumed, with an adverse

impact reported among those consuming >30 grams perday (52, 55–57). However, those consuming two orfewer drinks daily attain SVR at similar rates to non-drinkers (50, 51). Recent alcohol consumption (drink-ing within 1 year of treatment, or throughouttreatment) does not appear to have an impact on SVRfor those who consume fewer than three drinks a day(40, 50, 51, 53). However, there is some evidence thatrecent drinking can affect early treatment discontinua-tion rates (40, 50). A major weakness in all of thesestudies was that alcohol consumption was self-reported,using questionnaires and interviews. Therefore, actualconsumption could be underestimated, because of inac-curacies in recollection or social desirability bias.

Our findings that BMI and weight affect SVR in CHCare consistent with previous reports. In large, random-ized, clinical trials of interferon-based treatments, BMI>30 kg/m2 and weight above 75 kg were associated withlower SVR (17, 65). Concerning the effects of diet qual-ity on CHC, a high intake of polyunsaturated fatty acidswas found to be associated with non-response to antivi-ral therapy. However, there are potential confounders inthis study, as a high intake of polyunsaturated fatty acidpredicted steatosis, and a high intake of fats and carbo-hydrates was associated with fibrosis (11), which is anindependent predictor of treatment response (66).In one study, a diet high in antioxidants was found notto affect SVR (59). Despite these findings, the paucity ofstudies looking at the effects of diet on treatmentresponse in CHC and confounding factors relating toBMI suggest the need for further research.

Physical activity was included in our expansive search(Appendix S1), but no results were available on theeffect of exercise on SVR. In other contexts, exercise hasbeen shown to improve musculoskeletal symptoms,pain, fatigue and psychological symptoms such asdepression and anxiety (67, 68). These effects may beimportant in the management of patients with CHC(69). In this regard, exercise has been demonstrated toimprove the well-being and quality of life of patientsundergoing antiviral therapy for CHC (70). Further-more, insulin resistance is increased in people withCHC, and is an adverse prognostic factor for responseto peginterferon plus ribavirin (71, 72). Exercise gener-ally improves insulin sensitivity (73, 74), so given thepaucity of existing data, the impact of exercise on CHCtreatment outcomes, including compliance, adherenceand SVR rates, is an important and potentially fruitfularea for future research.

Socioeconomic factors

Our search for ‘socioeconomic status’ and all of its syn-onyms only identified studies that examined the rela-tionship between ethnicity and SVR, and none on theeffects of income or education on SVR. We found Asianethnicity to be an independent predictor of SVR, com-pared with Caucasians and African Americans; and

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African American ethnicity to be an independent pre-dictor of treatment non-response. However, the rela-tionship between ethnicity and SVR is confounded byboth viral factors and host genetics (41, 45). For exam-ple, recent research has found that differences inresponse are associated with IL28B polymorphisms, andthat the IL28B genotype that predicts a more favourableresponse is found more frequently in East Asians, fol-lowed by European Americans, Hispanics and AfricanAmericans (6). Indeed, the IL28B genetic polymorphismexplains approximately 50% of the difference inresponse rates between African and European Ameri-cans (6). Host genetic influences, including IL28B geno-type, were not examined in the studies included in thisreview. In addition to host factors, the geographic distri-bution of HCV genotypes varies, with the more easilytreated genotypes (2 and 3) more prevalent in Asia (75).Finally, before the availability of pegylated interferon,interferon alpha was often administered daily to patientsin Japan (76), compared with three times weekly inmost Western countries, further confounding results.

Limitations

Although there have been many recent advances in CHCtherapy, there was limited data available to draw robustconclusions on the relationship between most social andlifestyle factors and SVR, including the impact of exer-cise, diet and coping skills. In addition, the studies thatwere found – particularly those on alcohol intake – werenot consistent in how they operationalized alcohol use,making it difficult to compare studies. Reasons for treat-ment discontinuation varied, including intolerance toside effects, adverse events and general ‘loss to follow-up’, although most studies did not report the cause ofpatient drop-out. Treatments differing in efficacy (inter-feron alpha or PEG-interferon, with or without ribavi-rin) varied by year and country where the study wasconducted, which could account for some of the vari-ability in SVR rates among studies. There is some evi-dence that PEG-IFN alfa-2a and PEG-IFN alfa-2b maydiffer in clinical efficacy (77–80), but in the largest headto head study (IDEAL) there was no significant differ-ence seen, either in efficacy or side effects (81, 82). Datawere initially extracted by a single researcher (VAS). Anyuncertainty regarding a study’s eligibility was resolved bya second researcher who reviewed each abstract and title.In cases where there was doubt, further discussionoccurred with the wider research team. We restrictedour review to English language and published research,so the possibility of unidentified papers in other lan-guages exists. However, the extensive and broad natureof our search makes it unlikely that any substantial stud-ies or those with significant impact on the field havebeen missed. Finally, the methodological variability ofthe studies means that only cautious conclusions can bedrawn about the factors of interest, even where data wereavailable for analysis.

Recommendations for further research

Although some host and viral factors can predict up to50% of the variability in CHC treatment success, thereis an opportunity for further research into other vari-ables that influence treatment adherence and outcomes.Recent research into psychological, social and behavio-ural aspects of CHC treatment suggests possible effectson health outcomes, but quality data are extremely lim-ited. This supports further examination of psychosocialand behavioural predictors of treatment response in evi-dence-based studies, such as systematic reviews andmeta-analysis, to better support patients experiencingside effects, stress, mental health issues and lifestylechanges(7). In addition, as treatments become morepotent and efficacious, treatment adherence will remaina critical factor in determining outcomes, so identifyingfactors which impact upon adherence will continue tobe of importance.

As this systematic review reveals, there is a paucity ofdata about psychosocial and behavioural factors thataffect CHC treatment and adherence, and many of thestudies that are available examine health variables thatare self-reported, such as alcohol consumption, dietquality and exercise intake. Self-reported measures havebeen found to be susceptible to numerous biases (83),and the lack of consistency in assessing the data acrossthe studies highlights the need for the operationalizationof variables using consistent measurement standards,such as SVR. Therefore, future research should stan-dardize the variables being examined, to allow validcomparison of outcomes across studies.

Conclusion

A rise in the morbidity and mortality from chronicHepatitis C is expected over the next decade, as compli-cations typically have a latency of 20–30 years from dis-ease onset (84, 85). Therefore, there is an urgent need totreat as many individuals as possible, to ease the futureburden of disease on the individual and society (85).Even with improved pharmacology and shorter treat-ment durations, predictors of treatment adherence andcompletion that are amenable to intervention must beidentified. The future availability of interferon-free regi-mens would make treatment available to a wider rangeof people. This would further increase the need forresearch into social, lifestyle and psychological predic-tors of treatment adherence, to allow targeted supportto those needing it most.

Author contributions

Victoria Anne Sublette, who made substantial contribu-tions to conception, design, extraction and analysis ofdata, drafting and revision of the manuscript and tablesand gives her final approval of this version of the manu-script for publication. Mark W. Douglas, who made

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substantial contributions to conception, design, analysisof data, drafting and revision of the manuscript andtables, and gives his final approval of this version of themanuscript for publication. Jacob George, who madesubstantial contributions to the conception, content,analysis of data and revision of the manuscript, andgives his final approval of this version of the manuscriptfor publication, Kathryn Nicholson Perry, who madesubstantial contributions to the analysis and inter-pretation of data, drafting and revision of the manu-script and tables and gives her final approval of thisversion of the manuscript for publication, and KirstenMcCaffery, who made substantial contributions to thecontent and design of this manuscript, acquisition ofdata and the drafting of the manuscript and gives herfinal approval of this version of the manuscript forpublication.

Acknowledgements

The authors thank Ms Ruth Mitchell for her assistancewith database extraction, Ms Michelle Harrison foradvice on database coding and Ms Subha Ganewatta forassistance with quality rating.

Financial support: Victoria Anne Sublette is fundedby a University of Sydney UPA scholarship. Mark Doug-las and Jacob George are supported by grants from theNHMRC and the Robert W. Storr bequest to the SydneyMedical Foundation.

Potential competing interests: None.

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Supporting information

Additional Supporting Information may be found in theonline version of this article:

Table S1. Psychological Factors and Sustained Virolo-gical Response (SVR).

Table S2. Lifestyle factors and Sustained VirologicalResponse (SVR).

Table S3. Socioeconomic Factors and Sustained Viro-logical Response (SVR).

Appendix S1. Search Strategy for Predictors of Hepati-tis C Treatment Response.

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