Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2024 Nov 14;19(11):e0310923. doi: 10.1371/journal.pone.0310923

Prevalence of hepatitis B virus infection in Kenya: A study nested in the Kenya Population-based HIV Impact Assessment 2018

Raphael O Ondondo 1,*, Jacques Muthusi 1, Violet Oramisi 2, Daniel Kimani 1, Missiani Ochwoto 3, Peter Young 4, Catherine Ngugi 2, Anthony Waruru 1, Jane Mwangi 1, Ann Chao 5, Megan Bronson 6, Trudy Dobbs 6, Lucy Ng’ang’a 1, Nancy Bowen 7, Appolonia Aoko 1, Paige A Armstrong 8, Rashid Aman 9, Marc Bulterys 1
Editor: Jason T Blackard10
PMCID: PMC11563396  PMID: 39541401

Abstract

Background

Sub-Saharan Africa region bears the highest chronic hepatitis B virus (HBV) infection burden worldwide. National estimates of HBV burden are necessary for a viral hepatitis program planning. This study estimated the national prevalence of HBV infection in Kenya among people aged 15–64 years.

Methods

Of 27,745 participants age 15–64 years in the Kenya Population-based HIV Impact Assessment (KENPHIA) 2018 household survey, we analyzed data for all persons living with HIV (PLHIV; n = 1,521) and a random sample of HIV-negative persons (n = 1,551), totaling to 3,072 participants. We tested whole blood samples for hepatitis B surface antigen (HBsAg) using Determine™ HBsAg rapid test and used population projections to estimate national disease burden. Pearson chi square was performed and the weighted prevalence proportions presented.

Findings

Of the 3,072 participants,124 tested HBsAg positive, resulting in a weighted national HBV prevalence of 3.0% (95% CI: 2.2–3.9%). This translated to an HBV infection burden of 810,600 (95% CI: 582,700–1,038,600) persons age 15–64 years in Kenya. Distribution of HBV prevalence varied widely (p<0.001) by geography, ranging from 0.1% in Eastern Kenya regions to over 5% in northern and western Kenya. Prevalence of HBV infection was higher in PLHIV (4.7%; 95% CI: 3.3–6.0%) compared to HIV-negative persons (3.0%; 95% CI: 2.1–3.9%), and was highest among persons: age 45–54 years (6.4%; 95% CI: 3.3–9.5%), those who reported no formal education (10.7%; 95% CI: 5.1–16.4%), in polygamous marriages (6.8%; 95% CI: 1.7–11.8%), and in the lowest wealth quintile (5.3%; 95% CI: 2.8–7.7). When adjusted for covariates, lack of formal education (aOR = 4.2; 95% CI: 1.5–12.6) was significantly associated with HBV infection. In stratified analysis by HIV status, residing in rural areas and history of blood transfusion were independently associated with HBV infection among PLHIV, while lack of formal education and no history of blood transfusion were associated with HBV infection among HIV-negative participants (p<0.05).

Interpretation

HBV prevalence among persons aged 15–64 years in Kenya was 3.0%. Higher prevalence was documented among persons without formal education, in the lowest wealth quintile, and those living in Kenya’s North-Eastern, Rift Valley-North and Nyanza regions. Targeted programmatic measures to strengthen interventions against HBV infections including newborn vaccination and treatment of infected adults to limit mother-to-child transmission, would be helpful in reducing burden of HBV-associated viral hepatitis.

Introduction

Infection with hepatitis B virus (HBV) is a major global health problem, and over 30% of people worldwide have serological evidence of past or current infection [1]. Chronic HBV is one of the top 10 leading causes of death worldwide, accounting for an estimated 900,000 deaths annually due to HBV–associated liver diseases [2]. The World Health Organization (WHO) set a goal of eliminating viral hepatitis by 2030, with targets including: 90% reduction of new HBV infections, 65% reduction in HBV-related mortality, and 80% access to treatment for those eligible [2]. Despite a significant reduction in the global prevalence of HBV infection among children aged <5 years (from 4.9% during the pre-vaccination era to 0.9% in 2019), primarily due to infant HBV vaccination [3], the global burden of chronic HBV remains significant in adults [3]. Globally, approximately 296 million people were living with chronic HBV infection in 2019 [4], and as of 2015, 2.7 million were co-infected with HIV [2]. An additional 1.5 million people were newly infected with HBV in 2019, and 820,000 people died from HBV infection-related causes [4]. The Western Pacific and African regions remain the WHO regions estimated to have the highest HBV prevalence [2, 4]. In 2019, the African region had the highest burden of new HBV infections, accounting for over 65% of the 1.5 million new cases globally [4]. In 2015, an estimated 1.3 million deaths related to viral hepatitis occurred globally, of which 66% were attributed to complications from chronic HBV infection [2]. Fortunately, HBV infection is at least 90% vaccine-preventable [5].

Morbidity and mortality due to chronic HBV infection could be reduced through appropriate early screening for treatment eligibility and initiation of recommended antiviral therapy [6]. In 2019, about 10% of the estimated 296 million people with chronic HBV infection worldwide were aware of their infection status, of whom 22% were on treatment [4]. Therefore, to achieve the 2030 WHO goal of eliminating viral hepatitis [2], a combination of prevention strategies, including antiviral therapy for eligible patients, routine infant HBV immunization with one birth dose within 24 hours of birth followed by two to three additional doses in infancy [7], and catch-up HBV vaccination among adolescents and young adults in high-prevalence settings must be scaled up. However, access to available effective treatment and catch-up vaccination in most low-resource countries remains poor primarily due to limited availability of diagnostics, perceived high cost of medication [2], and complexity of current WHO treatment guidelines [8]. Lack of nationally representative data on HBV burden is a key limitation to clear understanding of where the need for HBV treatment is greatest, and thus hindering political commitment and scale-up of intervention programs. Consequently, many countries in sub-Saharan Africa (SSA), including Kenya, have made limited progress in implementing the WHO integrated strategy of triple elimination of mother-to-child transmission of HIV, HBV and syphilis [9]. This study estimated the national burden of current HBV infection in Kenya among adolescents and adults aged 15–64 years.

Methods

Population-based HIV Impact Assessments (PHIA) are being implemented across many countries supported by the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR). During June 2018–February 2019, Kenya conducted a national survey to estimate the impact of investments in the HIV epidemic response called the Kenya Population-based HIV Impact Assessment (KENPHIA 2018). This sub-study estimating national HBV prevalence used the samples and data collected by KENPHIA survey. KENPHIA was a cross-sectional, household-based survey that used a two-stage, cluster-sampling design based on county HIV prevalence estimates. In 800 selected clusters, the survey targeted residents aged 0–64 years old, who slept in 16,918 sampled households the night before the survey [10]. A total of 27,745 participants aged 15–64 years old consented to survey participation and provided a blood specimen for biomarker testing. A structured social-behavioral questionnaire interview was administered [10]. All participants living with HIV (PLHIV) as identified in the survey, and a random sample of HIV-negative subjects at a rate designed to yield an overall self-weighting sample of 6% as detailed in the KENPHIA technical report [11], were selected for inclusion in the HBV prevalence sub-study (Fig 1). HBV testing in this study excluded children 0–14 years old because of expected low burden due to routine infant HBV immunization rolled out in Kenya in 2003/2004. Ethical approval was granted by the Kenya Medical Research Institute–Science and Ethics Research Unit (KEMRI-SERU-592), United States Centers for Disease Control and Prevention (CDC-IRB-7094) and Columbia University institutional review boards (IRB-AAAR7792 (Y05M01)).

Fig 1. Sampling of participants for the national HBV study nested within the KENPHIA 2018 survey.

Fig 1

Open in a new tab

All participants aged 18–64 years provided written informed consent. Parental/guardian permission was sought before obtaining written assent for adolescents aged 15–17 years. The structured questionnaire was administered by trained study staff using Open Data Kit (ODK) on a tablet. The questionnaire included modules on demographic characteristics, sexual and reproductive health, marital status, male circumcision, sexual activity, HIV/AIDS knowledge and attitudes, HIV testing and treatment history, history of TB and other diseases, and alcohol use among others.

Venous blood samples were collected for evaluation of HIV and current HBV infections. HIV testing was performed as per the Kenya National HIV Testing Services Guidelines [12], using Determine™ HIV-1/2 (Abbott Rapid Diagnostics, Lake Forest, Illinois, United States) and First Response™ HIV 1–2.0 (Premier Medical Corporation, Mumbai, India) HIV rapid test kits (S1 Fig), and results were provided onsite. At a satellite laboratory, HIV rapid positive results were confirmed using Geenius HIV 1/2 Confirmatory Assay (Bio-Rad Laboratories, Marnes-la-Coquette, France). The presence of hepatitis B surface antigen (HBsAg) was established per manufacturer’s instructions using Abbott’s Determine™ HBsAg rapid test kit that has a sensitivity and specificity of 98% and 100% respectively on whole blood samples [13]. Based on WHO’s recommendations for high HBV infection prevalence regions [14], no further confirmatory serological testing for HBV infection was performed in this study.

HBV and HIV rapid testing were carried out in the household, and results were given directly to participants. Those who tested positive were appropriately referred for further treatment eligibility assessments and care according to national guidelines. Polygamous marital status was defined as a relationship in which the man was married to or cohabiting with more than one woman. Wealth quintiles (lowest, second, third, fourth, and highest) were determined based on the wealth index as described elsewhere [15]. Due to small numbers in the highest wealth quintile, this was combined with the fourth quintile. Therefore, in this analysis, we presented wealth quintiles in four categories: lowest, second, third, and highest.

Data analysis

Survey weights were calculated for probabilities of selection and adjusted for the interview, blood draw, HIV testing non-response, and under-coverage. After post-stratification, the weights were normalized to the survey sample populations. A detailed description of the survey weighting process is discussed in the Sampling and Weighting Technical Report [11]. The estimated HBV burden in the general population was based on projections for the 2018 de facto population by sex and age group [11]. Among PLHIV, HBV burden was based on the 1.3 million adults (15–64 years old) living with HIV in Kenya as estimated in 2018, and 96.0% (95% CI: 94.7%–97.3%) were on antiretroviral therapy (ART) against HIV [10]. Pearson chi-square test was used to test for differences by demographic characteristics and risk exposure categories of enrolled participants by sex. HBV prevalence was measured and presented as weighted proportions of participants who tested positive to hepatitis B surface antigen during the survey period.

To describe the geographical distribution of the HBV burden in Kenya, we used the ten regions of the National AIDS and STI Control Programme (NASCOP) described in the Kenya AIDS Indicator Survey (KAIS) of 2012 [16]. These NASCOP regions were categorized by HIV burden based on administrative regions (former provinces). Logistic regression modeling on weighted data was used to evaluate factors associated with current HBV infection, and multiple logistic regression modeling was performed to obtain estimates of adjusted odds ratios (aOR) and 95% confidence intervals (CI). Stratified logistic regression by HIV status was performed to identify independent factors for HBV infection in each group (PLHIV and HIV-negative separately). Analyses were conducted using Statistical Analysis Software (SAS) version 9.4 (SAS® 9.4 Base SAS. Cary, NC: SAS Institute Inc., 2014). Variables with p-values less than 0.05 or non-overlapping 95% confidence interval (CI) were considered statistically significant. Survey design parameters including survey weights, clustering, stratification, and variance replication methods were used in all analyses.

Results

In this sub-study, a total of 3,072 (1,521 PLHIV and 1,551 HIV-negative) participants drawn from the KENPHIA 2018 survey were evaluated for HBV infection. The median age was 28.8 years, interquartile range (IQR): (20.7–39.3) years, and two-thirds of study participants were female (Table 1). The survey found that 64.0% (95% CI: 61.1–67.0%) of the study population were residents of rural settings, 50.9% reported being married or cohabiting, and only 33.4% (95% CI: 30.9–35.9%) reported having completed primary school education level (Table 1). Distribution of other sociodemographic characteristics among males and females did not differ significantly (based on overlapping CIs) (Table 1).

Table 1. Characteristics of KENPHIA 2018 survey participants sampled for Kenya HBV sub-study.

  Male (n = 1084) Female (n = 1988) Total (N = 3072)
Characteristic Total (n) Weighted (%) 95% CI Total (n) Weighted (%) 95% CI Total (N) Weighted (%) 95% CI
Residence                  
    Urban 361 36.3 (32.1–40.6) 726 35.6 (31.7–39.6) 1087 36.0 (33.0–38.9)
    Rural 723 63.7 (59.4–67.9) 1262 64.4 (60.4–68.3) 1985 64.0 (61.1–67.0)
Ten-year age groups                  
    15–24 260 36.3 (36.3–36.3) 402 34.9 (34.9–34.9) 662 35.6 (35.6–35.6)
    25–34 233 27.1 (27.1–27.1) 614 28.7 (28.7–28.7) 847 27.9 (27.9–27.9)
    35–44 253 18.5 (18.5–18.5) 468 18.8 (18.8–18.8) 721 18.7 (18.7–18.7)
    45–54 207 11.5 (11.5–11.5) 320 11.1 (11.1–11.1) 527 11.3 (11.3–11.3)
    55–64 131 6.6 (6.6–6.6) 184 6.5 (6.5–6.5) 315 6.6 (6.6–6.6)
Marital status (n = 2983)                  
    Never married/ cohabited 313 42.0 (39.4–44.6) 403 29.8 (27.4–32.2) 716 35.8 (34.1–37.6)
    Married/cohabiting—monogamous 591 46.8 (43.6–50.0) 798 46.5 (43.5–49.6) 1389 46.6 (44.4–48.9)
    Married/cohabiting—polygamous 49 2.1 (1.2–3.1) 175 6.5 (5.1–7.8) 224 4.3 (3.5–5.2)
    Divorced or separated or Widowed 120 7.8 (5.7–9.8) 534 14.3 (12.3–16.2) 654 11.1 (9.6–12.5)
Education level (n = 3071)                  
    No Formal Education 60 4.9 (3.1–6.6) 180 8.3 (6.6–10.0) 240 6.6 (5.4–7.9)
    Incomplete Primary 484 37.0 (33.2–40.8) 1106 42.5 (39.4–45.6) 1590 39.8 (37.2–42.4)
    Completed Primary 360 35.4 (31.6–39.3) 504 31.5 (28.6–34.3) 864 33.4 (30.9–35.9)
Wealth quintile (n = 3071)                  
    Highest 318 34.6 (30.2–39.0) 601 36.9 (33.2–40.6) 919 35.8 (32.8–38.8)
    Third 252 21.5 (18.1–24.9) 464 21.4 (18.4–24.4) 716 21.4 (19.0–23.8)
    Second 270 23.2 (19.8–26.5) 466 20.4 (17.8–23.0) 736 21.8 (19.5–24.1)
    Lowest 244 20.8 (16.8–24.7) 456 21.3 (18.5–24.1) 700 21.0 (18.4–23.6)
Religion (n = 3071)                  
    Roman Catholic 241 19.7 (16.4–23.0) 386 18.1 (15.4–20.7) 627 18.9 (16.8–20.9)
    Protestant/Other Christians 712 67.1 (63.0–71.2) 1451 71.5 (68.4–74.7) 2163 69.3 (66.7–72.0)
    Muslim 67 7.8 (4.9–10.8) 107 8.1 (6.3–9.9) 174 8.0 (6.1–9.9)
    No Religion 45 3.6 (2.2–5.0) 23 1.5 (0.5–2.6) 68 2.6 (1.7–3.4)
    Other 18 1.6 (0.5–2.7) 21 0.7 (0.1–1.3) 39 1.2 (0.5–1.8)
Ever had blood transfusion (n = 3068)                  
    Yes 40 2.9 (1.6–4.2) 154 5 (3.7–6.3) 194 4 (3.1–4.9)
    No 1041 97.1 (95.8–98.4) 1833 95 (93.7–96.3) 2874 96 (95.1–96.9)
Pregnancy Status
    Pregnant 78 4.9 (3.4–6.3)
    Non-pregnant 1887 93.4 (91.7–95.1)
HIV Status                  
    Positive 423 3.1 (2.7–3.5) 1098 6.6 (6.0–7.1) 1521 4.9 (4.5–5.3)
    Negative 661 96.9 (96.5–97.3) 890 93.4 (92.9–94.0) 1551 95.1 (94.7–95.5)
Open in a new tab

Note: n = 3072 unless stated. For n<3072, the difference accounts for missing data due to non-response to the specific survey question

Prevalence of hepatitis B infection

Of the 3072 participants, 124 tested positive for HBsAg resulting in a weighted national HBV prevalence of 3.0% (95% CI: 2.2–3.9%) in Kenya among persons aged 15–64 years old (Table 2). HBV prevalence was higher among PLHIV (4.7%; 95% CI: 3.3–6.0%) compared to HIV-negative participants (3.0%; 95% CI: 2.1–3.9%). These translated to an estimated 810,600; (95% CI: 582,700–1,038,600) persons living with HBV in Kenya among 15–64 years old in the general population and 61,000 (95% CI: 42,400–79,600) among PLHIV. The proportion of HBV-coinfected PLHIV on ART was 72.7% (95% CI: 61.4–82.3%). The prevalence of HBV infection was similar in men (3.3%; 95% CI: 2.1–4.6%) and women (2.8%; 95% CI: 1.7–3.8%), while HIV-positive participants residing in rural Kenya had a higher prevalence of HBV infection (5.7%; 95% CI: 3.8–7.7%) compared to 2.8% (95% CI: 1.6–4.1%) among their counterparts residing in urban Kenya (Table 2). The prevalence was lowest at 1.9% (95% CI: 0.6–3.2%) among participants aged 15–24 years and highest at 6.4% (95% CI: 3.3–9.5%) among those aged 45–54 years (Table 2). However, none of these variations in the prevalence of HBV infection by sex or age was statistically significantly different.

Table 2. Prevalence of hepatitis B surface antigen (HBsAg) positivity among KENPHIA 2018 survey participants sampled for Kenya HBV sub-study, by sex and HIV status (N = 3072).

Characteristic By Sex By HIV Status Total
  Male (n = 1084) Female (n = 1988) Positive (n = 1521) Negative (n = 1551) (N = 3072)
  Unweighted n/N HBV positive Weighted % (95% CI) Unweighted n/N HBV positive Weighted % (95% CI) Unweighted n/N HBV positive Weighted % (95% CI) Unweighted n/N HBV positive Weighted % (95% CI) Unweighted n/N HBV positive Weighted % (95% CI)
Overall 49/1084 3.3 (2.1–4.6) 75/1988 2.8 (1.7–3.8) 77/1521 4.7 (3.3–6.0) 47/1551 3.0 (2.1–3.9) 124/3072 3.0 (2.2–3.9)
Sex                
    Male         26/423 5.1 (2.6–7.6) 23/661 3.3 (2.0–4.6) 49/1084 3.3 (2.1–4.6)
    Female         51/1098 4.5 (3.0–6.0) 24/890 2.6 (1.5–3.8) 75/1988 2.8 (1.7–3.8)
HIV Status                
    Positive 26/423 5.1 (2.6–7.6) 51/1098 4.5 (3.0–6.0)         77/1521 4.7 (3.3–6.0)
    Negative 23/661 3.3 (2.0–4.6) 24/890 2.6 (1.5–3.8)         47/1551 3.0 (2.1–3.9)
Residence                
    Urban 15/361 2.2 (0.4–3.9) 23/726 2.6 (0.6–4.7) 26/604 2.8 (1.6–4.1) 12/483 2.4 (0.8–3.9) 38/1087 2.4 (0.9–3.9)
    Rural 34/723 4.0 (2.2–5.7) 52/1262 2.8 (1.7–3.9) 51/917 5.7 (3.8–7.7) 35/1068 3.3 (2.2–4.4) 86/1985 3.4 (2.3–4.4)
Ten-year age groups                
    15–24 7/260 3.1 (0.7–5.5) 7/402 0.7 (0.0–1.5) 5/139 2.5 (0.0–5.2) 9/523 1.9 (0.6–3.2) 14/662 1.9 (0.6–3.2)
    25–34 9/233 1.8 (0.0–4.0) 29/614 4.2 (1.5–7.0) 26/450 5.3 (2.7–7.8) 12/397 3.0 (1.0–5.0) 38/847 3.1 (1.2–5.0)
    35–44 13/253 3.2 (0.2–6.3) 16/468 3.1 (0.7–5.4) 20/430 4.3 (2.1–6.6) 9/291 3.0 (0.9–5.1) 29/721 3.1 (1.2–5.1)
    45–54 12/207 7.6 (1.9–13.3) 17/320 5.1 (1.9–8.4) 16/331 3.9 (1.8–6.0) 13/196 6.6 (3.2–10.0) 29/527 6.4 (3.3–9.5)
    55–64 8/131 3.5 (0.0–8.1) 6/184 2.7 (0.0–5.8) 10/171 7.9 (1.7–14.0) 4/144 2.7 (0.0–5.7) 14/315 3.1 (0.3–5.8)
Marital status (n = 2983)                
    Never married/ cohabited 7/313 2.6 (0.7–4.5) 9/403 1.8 (0.0–3.8) 5/199 1.4 (0.0–2.7) 11/517 2.3 (0.9–3.7) 16/716 2.3 (0.9–3.6)
    Married/cohabiting—monogamous 30/591 3.5 (1.6–5.4) 29/798 2.8 (1.2–4.3) 35/646 5.4 (3.3–7.5) 24/743 3.0 (1.6–4.4) 59/1389 3.1 (1.8–4.4)
    Married/cohabiting—polygamous 4/49 5.6 (0.0–15.7) 12/175 7.2 (1.4–12.9) 11/145 6.7 (1.7–11.7) 5/79 6.8 (1.2–12.3) 16/224 6.8 (1.7–11.8)
    Divorced or separated or Widowed 7/120 5.0 (0.0–10.8) 21/534 2.2 (0.6–3.7) 23/479 4.3 (2.2–6.3) 5/175 3.0 (0.4–5.6) 28/654 3.2 (0.9–5.4)
Education level (n = 3071)                
    No formal education 9/60 12.2 (0.8–23.5) 12/180 9.9 (3.7–16.1) 9/121 7.7 (1.2–14.3) 12/119 10.9 (4.9–16.9) 21/240 10.7 (5.1–16.4)
    Incomplete Primary 20/484 3.9 (1.7–6.2) 45/1106 2.9 (1.3–4.5) 44/975 4.3 (2.7–6.0) 24/750 3.1 (1.7–4.5) 68/1725 3.2 (1.9–4.5)
    Completed Primary 17/360 2.1 (0.4–3.8) 11/504 1.6 (0.0–3.3) 24/424 4.6 (2.5–6.6) 11/682 1.7 (0.6–2.8) 35/1106 1.8 (0.8–2.8)
Wealth quintile (n = 3071)                
    Highest 10/318 1.2 (0.0–2.7) 17/601 2.0 (0.3–3.8) 20/414 4.4 (2.1–6.7) 7/505 1.5 (0.3–2.7) 27/919 1.6 (0.4–2.8)
    Third 11/252 3.8 (0.6–6.9) 12/464 2.3 (0.2–4.3) 13/370 3.9 (1.6–6.2) 10/346 3.0 (1.0–4.9) 23/716 3.0 (1.2–4.9)
    Second 11/270 4.6 (1.2–8.0) 18/466 1.8 (0.1–3.5) 18/377 4.8 (2.2–7.5) 11/359 3.2 (1.1–5.2) 29/736 3.3 (1.3–5.2)
    Lowest 17/244 5.0 (1.3–8.8) 28/456 5.5 (2.5–8.5) 26/359 5.9 (2.0–9.8) 19/341 5.2 (2.6–7.8) 45/700 5.3 (2.8–7.7)
Religion (n = 3071)                
    Roman Catholic 14/241 3.4 (0.3–6.6) 16/386 2.2 (0.4–4.0) 22/322 5.3 (1.5–9.1) 8/305 2.7 (0.4–4.9) 30/627 2.8 (0.7–5.0)
    Protestant/Other Christian 28/712 2.7 (1.3–4.1) 47/1451 2.5 (1.2–3.7) 48/1092 4.5 (3.0–5.9) 27/1071 2.5 (1.5–3.5) 75/2163 2.6 (1.6–3.5)
    Muslim 2/67 3.8 (0.0–9.8) 8/107 6.4 (1.4–11.5) 3/53 4.5 (0.0–12.2) 7/121 5.2 (1.1–9.3) 10/174 5.2 (1.2–9.2)
    No Religion 3/45 11.1 (0.0–25.1) 2/23 5.0 (0.0–15.1) 1/31 2.7 (0.0–8.1) 4/37 9.5 (0.0–20.3) 5/68 9.2 (0.0–19.5)
    Other 2/18 8.1 (0.0–26.1) 2/21 1.1 (0.0–2.6) 3/23 9.3 (0.0–20.5) 1/16 5.6 (0.0–18.7) 4/39 5.8 (0.0–18.0)
Ever had blood transfusion (n = 3068)        
    Yes 1/40 0.2 (0.0–0.7) 11/154 2.7 (1.7–3.8) 11/129 9.0 (3.3–14.6) 1/65 1.1 (0.8–1.5) 12/194 1.8 (1.1–2.5)
    No 48/1041 3.4 (2.1–4.7) 64/1833 2.8 (1.7–3.9) 66/1390 4.3 (2.9–5.7) 46/1484 3.0 (2.1–4.0) 112/2874 3.1 (2.2–4.0)
Pregnant during study (n = 1965)              
    Yes     3/78 4.2 (0.0–10.0) 1/39 1.4 (0.0–3.6) 2/39 4.3 (0.0–10.5) 3/78 4.2 (0.0–10.0)
    No     71/1887 2.7 (1.6–3.7) 50/1048 4.7 (3.1–6.2) 21/839 2.5 (1.4–3.7) 71/1887 2.7 (1.6–3.7)
Geographic Region
    Nairobi 1/65 0.1 (0.0–0.4) 1/94 1.5 (0.0–4.5) 1/53 1.7 (0.0–5.4) 1/106 0.7 (0.0–2.2) 2/159 0.8 (0.0–2.2)
    Central 7/110 5.4 (0.4–10.4) 2/165 1.2 (0.0–3.5) 3/85 3.2 (0.0–7.1) 6/190 3.3 (0.4–6.1) 9/275 3.3 (0.5–6.0)
    Nyanza 15/260 5.0 (1.5–8.5) 26/575 4.1 (0.1–8.2) 34/631 4.9 (3.1–6.6) 7/204 4.5 (1.4–7.7) 41/835 4.6 (1.8–7.3)
    Rift Valley-North 8/138 2.9 (0.0–6.3) 16/242 6.7 (2.7–10.8) 13/172 10.6 (2.1–19.1) 11/208 4.7 (1.9–7.5) 24/380 5.0 (2.3–7.7)
    Rift Valley-South 4/117 3.7 (0.2–7.1) 4/184 0.7 (0.0–2.1) 3/133 1.2 (0.0–2.5) 5/168 2.4 (0.2–4.6) 8/301 2.4 (0.3–4.5)
    Eastern-North 0/14 . (.—.) 2/22 0.2 (0.0–0.5) 2/16 6.4 (0.0–15.4) 0/20 . (.—.) 2/36 0.1 (0.0–0.2)
    Eastern-South 6/162 3.6 (0.8–6.4) 8/297 0.8 (0.0–1.8) 8/175 5.0 (1.4–8.7) 6/284 2.1 (0.5–3.6) 14/459 2.2 (0.7–3.7)
    Western 5/126 2.2 (0.0–6.1) 6/225 1.9 (0.0–4.3) 8/150 4.7 (1.1–8.2) 3/201 1.9 (0.0–4.9) 11/351 2.0 (0.0–4.9)
    North-Eastern 0/15 . (.—.) 2/16 15.8 (0.0–38.9) 0/2 . (.—.) 2/29 7.6 (0.0–17.8) 2/31 7.6 (0.0–17.8)
    Coast 3/77 3.5 (0.0–9.2) 8/168 3.5 (1.3–5.8) 5/104 3.6 (0.0–8.6) 6/141 3.5 (0.5–6.6) 11/245 3.5 (0.6–6.4)
Open in a new tab

Note: n = 3072 unless stated. For n<3072, the difference accounts for missing data due to non-response to the specific survey question

The prevalence of HBV infection was 6-times higher among participants who reported no formal education (10.7%; 95% CI:5.1–16.4%) than those who reported completing primary education level (1.8%; 95% CI: 0.8–2.8%). Higher prevalence of HBV infection was found among participants who reported being in polygamous married/cohabiting relationships (6.8%; 95% CI: 1.7–11.8%) than those who reported single and never married (2.3%; 95% CI: 0.9–3.6%) and those in the lowest wealth quintile (5.3%; 95% CI: 2.8–7.7%) than those in the highest wealth quintile (1.6%; 95% CI: 0.4–2.8%), although with overlapping CIs (Table 2). The prevalence of HBV infection was higher among participants without history of blood transfusion at 3.1% (2.2–4.0%), compared to 1.8% (1.1–2.5%) among those who reported ever having a blood transfution prior to study participation. However, HIV-positive participants with a history of blood transfusion had a higher prevalence of HBV infection at 9.0% (3.3–14.6%), compared to 1.1% (0.8–1.5) found in their HIV-negative counterparts (Table 2).

The prevalence of HBV infection in Kenya varied widely by geographic region (Fig 2), ranging from 0.1% (95% CI: 0.0–0.2%) in some Eastern regions of Kenya to 7.6% (95% CI: 0.0–17.8%) in North-Eastern regions (Table 2). The next top two HBV high-prevalence regions after North-Eastern were Rift Valley North (5.0%; 95% CI: 2.3–7.7%) and Nyanza (4.6%; 95% CI: 1.8–7.3%) in western Kenya (Table 2). Higher HBV prevalence among men was observed in Central (5.4%; 95% CI: 0.4–10.4%) and Nyanza (5.0%; 95% CI: 1.5–8.5%), and among women in Rift Valley-North (6.7%; 95% CI: 2.7–10.8%) and Nyanza (4.1%; 95% CI: 0.1–8.2%) regions, compared to other regions in Kenya (Table 2).

Fig 2. HBV prevalence by NASCOP regions, KENPHIA 2018 survey.

Fig 2

Open in a new tab

Factors associated with hepatitis B infection

Table 3 presents both bivariate and multivariate analysis of factors associated with HBV infection. In multivariate logistic regression analysis, lack of formal education (aOR = 4.2; 95% CI: 1.5–12.6) remained the only independent factor found to be associated with HBV infection (Table 3). HIV infection (aOR = 1.5; 95% CI: 0.9–2.5), age 45–54 years (aOR = 2.6; 95% CI: 0.9–8.4), married/cohabiting polygamous marital status (aOR = 1.2; 95% CI: 0.4–4.3), lowest wealth quintile (aOR = 2.6; 95% CI: 1.0–6.6) and history of blood transfusion (aOR = 1.9; 95% CI: 1.0–3.7) were no longer associated with HBV infection as had been observed in bivariate analysis (Table 3). Additionally, none of the other socio-demographic characteristics was associated with HBV infection (Table 3).

Table 3. Factors associated with hepatitis B prevalence among participants in the KENPHIA 2018 survey HBV sub-study, N = 3072.

Characteristic Total HBV positive, n (Weighted %) OR (95% CI) P-value Global P-Value aOR (95% CI) P-value Global P-value
HIV Status        
    Negative 1551 47 (3.0) ref      
    Positive 1521 77 (4.7) 1.6 (1.0–2.5) 0.024 0.024 1.5 (0.9–2.5) 0.076 0.076
Residence      
    Urban 1087 38 (2.4) ref
    Rural 1985 86 (3.4) 1.4 (0.7–2.8) 0.292 0.292 0.9 (0.4–1.8) 0.707 0.707
Sex  
    Female 1988 75 (2.8) ref
    Male 1084 49 (3.3) 1.2 (0.7–2.1) 0.453 0.453 1.4 (0.7–2.5) 0.294 0.294
Ten-year age groups  
    15–24 662 14 (1.9) ref
    25–34 847 38 (3.1) 1.7 (0.6–4.4) 0.276 0.042 1.6 (0.5–5.3) 0.413 0.286
    35–44 721 29 (3.1) 1.7 (0.6–4.4) 0.26 1.7 (0.5–6.0) 0.349
    45–54 527 29 (6.4) 3.6 (1.5–8.7) 0.003 2.6 (0.9–8.4) 0.077
    55–64 315 14 (3.1) 1.7 (0.5–5.8) 0.41 1.2 (0.2–6.4) 0.811
Religion (n = 3071)  
    Protestant/Other Christians 2163 75 (2.6) ref
    Muslim 174 10 (5.2) 2.1 (0.8–5.3) 0.113 0.255 1.2 (0.4–4.0) 0.733 0.808
    No Religion 68 5 (9.2) 3.8 (0.9–16.8) 0.06 2.5 (0.6–11.4) 0.210
    Other 39 4 (5.8) 2.3 (0.0–280.9) 0.716 2.1 (0.0–295.8) 0.753
    Roman Catholic 627 30 (2.8) 1.1 (0.5–2.5) 0.805 1.1 (0.5–2.5) 0.817
Marital status (n = 2983)  
    Never married/ cohabited 716 16 (2.3) ref
    Divorced or separated or widowed 654 28 (3.2) 1.4 (0.5–3.6) 0.469 0.134 0.7 (0.2–2.3) 0.502 0.607
    Married/cohabiting—monogamous 1389 59 (3.1) 1.4 (0.7–3.0) 0.375 0.8 (0.3–2.0) 0.575
    Married/cohabiting—polygamous 224 16 (6.8) 3.1 (1.1–8.5) 0.019 1.2 (0.4–4.3) 0.734
Education level (n = 3071)  
    Complete Primary 1106 35 (1.8) ref
    Incomplete Primary 1725 68 (3.2) 1.8 (0.8–3.8) 0.107 < .001 1.2 (0.5–2.7) 0.620 0.016
    No formal education 240 21 (10.7) 6.6 (2.9–15.0) < .001 4.2 (1.5–12.6) 0.006
Wealth quintile (n = 3071)        
    Highest 919 27 (1.6) ref 0.035     0.134
    Third 716 23 (3.0) 1.9 (0.8–4.6) 0.135 2.2 (0.9–5.0) 0.055
    Second 736 29 (3.3) 2.1 (0.8–5.3) 0.115 2.1 (0.8–5.4) 0.102
    Lowest 700 45 (5.3) 3.4 (1.4–8.0) 0.003 2.6 (1.0–6.6) 0.037
Ever had blood transfusion (n = 3068)        
    Yes 194 12 (1.8) ref      
    No 2874 112 (3.1) 1.7 (1.0–2.8) 0.025 0.025 1.9 (1.0–3.7) 0.054 0.054
Open in a new tab

Note

a) n = 3072 unless stated. For n<3072, the difference accounts for missing data due to non-response to the specific survey question

b) Table shows both bivariate (odds ratio—OR) and multivariate (adjusted odds ratio–aOR) analysis

In stratified analysis by HIV status (Table 4), residing in rural Kenya (aOR = 2.8; 95% CI: 1.2–6.5) and being married/cohabiting in polygamous relationships (aOR = 3.7; 95% CI: 1.0–13.7) were associated with increased likelihood of HBV infection among PLHIV. Additionally, PLHIV reporting no history of blood transfusion had decreased odds of HBV infection (aOR = 0.4; 95% CI: 0.2–0.9). On the contrary, neither residence nor marital status was associated with HBV infection among HIV-negative participants (Table 4). In this subpopulation, lowest wealth quintile (aOR = 2.5; 95% CI: 1.0–6.1), lack of formal education (aOR = 4.8; 95% CI: 1.5–15.7) and no blood transfusion history (aOR = 3.2; 95% CI: 1.6–6.6) were associated with significantly higher odds of HBV infection (Table 4).

Table 4. Factors associated with hepatitis B surface antigen (HBsAg) positivity among participants in the KENPHIA 2018 survey HBV sub-study, stratified by HIV status (N = 3072).

HIV-positive participants—KENPHIA 2018, N = 1521 HIV-negative participants—KENPHIA 2018, N = 1551
Characteristic Total HBV positive, n (Weighted %) OR (95% CI) P-value Global P-value aOR (95% CI) P-value Global P-value Total HBV positive, n (Weighted %) OR (95% CI) P-value Global P-value aOR (95% CI) P-value Global P-value
Residence                        
    Urban 604 26 (2.8) ref       483 12 (2.4) ref      
    Rural 917 51 (5.7) 2.1 (1.2–3.7) 0.008 0.008 2.8 (1.2–6.5) 0.012 0.012 1068 35 (3.3) 1.4 (0.7–2.9) 0.359 0.359 0.8 (0.4–1.7) 0.544 0.544
Sex            
    Female 1098 51 (4.5) ref 890 24 (2.6) ref
    Male 423 26 (5.1) 1.1 (0.6–2.0) 0.655 0.655 1.1 (0.6–2.3) 0.698 0.698 661 23 (3.3) 1.2 (0.7–2.2) 0.423 0.423 1.4 (0.7–2.7) 0.307 0.307
Ten-year age groups          
    15–24 139 5 (2.5) ref 523 9 (1.9) ref
    25–34 450 26 (5.3) 2.1 (0.7–6.2) 0.147 0.353 2.8 (0.7–11.5) 0.127 0.344 397 12 (3.0) 1.6 (0.6–4.4) 0.337 0.038 1.6 (0.5–5.7) 0.431 0.234
    35–44 430 20 (4.3) 1.7 (0.5–5.5) 0.325 1.9 (0.4–9.0) 0.376 291 9 (3.0) 1.6 (0.6–4.5) 0.312 1.7 (0.5–6.9) 0.312
    45–54 331 16 (3.9) 1.6 (0.5–4.9) 0.424 2.0 (0.4–9.1) 0.347 196 13 (6.6) 3.7 (1.5–9.4) 0.003 2.8 (0.9–9.5) 0.070
    55–64 171 10 (7.9) 3.3 (0.8–12.8) 0.075 3.8 (0.6–216) 0.145 144 4 (2.7) 1.5 (0.3–6.3) 0.589 1.1 (0.2–7.5) 0.916
Religion (n = 3071)
    Protestant/Other Christian 1092 48 (4.5) ref 1071 27 (2.5) ref
    Muslim 53 3 (4.5) 1.0 (0.0–30.1) 0.997 0.791 1.2 (0.0–31.9) 0.923 0.714 121 7 (5.2) 2.2 (0.8–5.7) 0.103 0.144 1.2 (0.3–4.2) 0.773 0.689
    No Religion 31 1 (2.7) 0.6 (0.1–2.8) 0.482 0.7 (0.2–2.8) 0.564 37 4 (9.5) 4.2 (0.9–18.9) 0.053 2.6 (0.5–12.2) 0.217
    Other 23 3 (9.3) 2.2 (0.4–11.4) 0.331 3.2 (0.5–15.8) 0.218 16 1 (5.6) 2.3 (0.7–7.4) 0.136 2.1 (0.6–7.9) 0.254
    Roman Catholic 322 22 (5.3) 1.2 (0.6–2.6) 0.63 1.1 (0.6–2.3) 0.707 305 8 (2.7) 1.1 (0.4–2.7) 0.844 1.1 (0.4–2.7) 0.849
Marital status (n = 2983)                  
    Never married/ cohabited 199 5 (1.4) ref       517 11 (2.3) ref
    Divorced or separated or Widowed 479 23 (4.3) 3.2 (0.9–11.4) 0.056 0.065 2.4 (0.6–9.4) 0.174 0.172 175 5 (3.0) 1.3 (0.4–3.9) 0.609 0.187 0.6 (0.2–2.6) 0.497 0.679
    Married/ cohabiting—monogamous 646 35 (5.4) 4.1 (1.3–13.7) 0.014 3.0 (0.9–9.8) 0.061 743 24 (3.0) 1.3 (0.6–2.9) 0.458 0.7 (0.3–2.0) 0.519
    Married/cohabiting—polygamous 145 11 (6.7) 5.2 (1.3–20.2) 0.012 3.7 (1.0–13.7) 0.037 79 5 (6.8) 3.1 (1.1–9.1) 0.03 1.1 (0.3–4.5) 0.848
Wealth quintile (n = 3071)
    Highest 414 20 (4.4) ref 505 7 (1.5) ref
    Third 370 13 (3.9) 0.9 (0.4–2.0) 0.738 0.5 (0.2–1.4) 0.182 346 10 (3.0) 2.0 (0.8–5.3) 0.133 2.9 (1.0–8.0) 0.057
    Second 377 18 (4.8) 1.1 (0.5–2.5) 0.805 0.6 (0.2–1.8) 0.309 359 11 (3.2) 2.2 (0.8–6.1) 0.124 2.4 (0.8–6.7) 0.084
    Lowest 359 26 (5.9) 1.4 (0.6–3.2) 0.467 0.775 0.7 (0.3–2.2) 0.55 0.537 341 19 (5.2) 3.6 (1.4–9.3) 0.005 0.044 2.5 (1.0–6.1) 0.038 0.125
Education level (n = 3071)                        
    Complete Primary 379 20 (4.4) ref       485 8 (1.7) ref      
    Incomplete Primary 950 43 (4.4) 1.0 (0.5–2.0) 0.874 0.414 1.0 (0.5–2.1) 0.961 0.393 640 22 (3.3) 1.9 (0.8–4.9) 0.122 < .001 1.3 (0.5–3.0) 0.588 0.018
    No Formal Education 121 9 (7.7) 1.8 (0.6–5.2) 0.255 1.7 (0.7–4.4) 0.229 119 12 (10.9) 7.0 (2.7–18.4) < .001 4.8 (1.5–15.7) 0.006
Ever had blood transfusion (n = 3068)
    Yes 129 11 (9.0) ref 65 1 (1.1) ref
    No 1390 66 (4.3) 0.5 (0.2–1.0) 0.042 0.042 0.4 (0.2–0.9) 0.027 0.027 1484 46 (3.0) 2.8 (1.8–4.3) < .001 < .001 3.2 (1.6–6.6) 0.001 0.001
Open in a new tab

Note: n = 3072 unless stated. For n<3072, the difference accounts for missing data due to non-response to the specific survey question

Discussion

KENPHIA 2018 was the first nationally representative survey of HBV infection in Kenya that provided immediate test results to participants at the household. Our analysis reports an overall HBV prevalence of 3.0% (95% CI: 2.2–3.9%) among people aged 15–64 years. As expected, this HBV prevalence was somewhat higher than those estimated for Kenya in 2015 WHO estimate of 2.2% (95% CI: 1.6–2.9%) and the 2019 WHO estimate of 1.7% (95% CI: 1.4–1.9%) for the general Kenyan population, which included children <15 years old [2, 3]. These results suggest that HBV prevalence among children is lower than among adults and, when combined, it masks the higher prevalence in the adult population. In the same WHO estimates, Kenya was at a prevalence of 2.1% pre-HBV vaccination era, 0.9% in 2015, and 0.4% in 2019 among children <5 years of age [3].

We estimated the overall national burden of HBV infection as 800,100; (95% CI: 560,100–1,040,200), consistent with the 2019 WHO estimate of 892,500 (95% CI: 772,700–1,023,700) people [3]. Given that approximately 10–30% of people with HBV infection require treatment [4], we estimated that 80,000–240,000 people in Kenya, required HBV treatment in 2018. Overall, lack of formal education was significantly associated with HBV infection in our study. Additionally, being in the lowest wealth quintile was particularly associated with HBV infection among HIV uninfected persons, while rural residence and being married were independently associated with HBV infection among PLHIV. These findings highlight HBV infection in Kenya as a disease of the socioeconomically disadvantaged in society. This underscores the importance of implementing interventions against HBV infection under the umbrella of universal health care (UHC) and the ongoing free primary education programs rolled out by the Kenyan government. These will in part alleviate these factors associated with increased HBV morbidity in Kenya.

A meta-analysis of eight earlier studies (published before 23rd October 2013) among different subpopulations estimated a 5.2% prevalence of HBV infection in Kenya [17], higher than the estimated prevalence in our analysis. Although the eight studies were not nationally representative, findings from our study suggest that HBV prevalence is likely decreasing, as suggested elsewhere in systematic reviews and meta-analysis [18, 19]. This highlightes the critical value of direct measures of national burden through nationally representative serosurveys to produce accurate estimates of HBV infection burden to inform programming towards elimination. The lower prevalence can also be attributed to a higher burden among older people with higher mortality, as well as declining rates [7] but also much lower infection rates among children and adolescents due to successful infant immunization. The overall HBV prevalence reported in this survey was somewhat lower than those documented among adults in several other national surveys in Africa: 5.6% in Zambia [20], 4.1% in Uganda [21], 3.9% in Rwanda [22], and 3.5% in Tanzania [23]. Overall prevalence observed in our study was about half the 2015 African region prevalence of 6.2% estimated by WHO [2], and lower than the West and Central African countries that had the highest estimated HBV prevalence in Africa [2], likely skewing the African region HBV infection estimates.

A trend analysis of WHO estimates suggests a significant reduction in the global prevalence of HBV infection (from 4.9% in the pre-vaccination era to 1.3% in 2015 and 0.9% in 2019) among children <5 years old [3]. These data suggest successful infant immunization programs that many countries including Kenya implemented in early 2000’s. However, estimates of the global HBV prevalence in the general population remained high; 5.0% in the pre-vaccination era, 3.5% in 2015, and 3.9% in 2019 [3]. Similar trends were observed in the Africa region; 8.3% in the pre-vaccination era, 3.0% in 2015, and 2.5% in 2019 among under five-year-old children. In contrast, among the general population; 9.0% in the pre-vaccination era, 6.1% in 2015, and 6.8% in 2019 [3]. Therefore, WHO HBV infection estimates for 2015 and 2019, compared to the pre-vaccine era [2–4], together with data from this study and other recent national surveys [20, 21, 23], further suggest that overall HBV prevalence is declining over time globally.

Our analysis documented regional variance in prevalence of HBV infections across Kenya. In 2018, the coverage of infant immunization in Kenya for the pentavalent vaccine (that includes HBV) was reported to average at 81% [24], but also varied widely geographically [24, 25]: among the poor, less educated, those residing in rural areas with limitted access to health facilities and those from urban informal settlements [24–27]. These could provide partial explanation to the observed varied geographical distribution of HBV infection in Kenya. Similar observations were made in the neighboring Uganda, where the estimated national HBV prevalence of 4.1% also varied geographically at subnational level, from 0.8% in the South-West to 4.6% in the Mid-North regions of Uganda [21].

This study reported lack of formal education as a factor significantly associated with higher risk of HBV infection. Higher prevalence of HBV infection among participants with low level of education was also reported by other PHIAs [21, 23]. Similar to our study, the high prevalence of HBV infection was observed among married/cohabiting participants in the Tanzania PHIA survey [23]. These findings suggest increased risk of HBV transmission to family members within most socioeconomically disadvantaged households, higher risk for horizontal transmission to sexual partners and potential risk for vertical transmission from mother-to-child within these families. This underscores the need for a combination of interventions to prevent both vertical and horizontal HBV transmission through increased community awareness, health education, early detection, treatment and vaccination. Robust prenatal screening programs and antiviral treatment of pregnant women with HBV infection is critical to reducing vertical HBV transmission. In this effort, WHO released Interim Guidance for Country Validation of Hepatitis Elimination in June 2021, which highlighted program target of ≥90% coverage for maternal antenatal HBsAg testing [28]. Additionally, preventative measures such as catch up vaccination for household contacts can reduce the likelihood of horizontal transmission. Education and access to these services among socially disadvantaged population as seen in this study is particularly important, as age of acquisition of HBV infection is inversely related to likelihood of developing chronic HBV infection.

We found higher HBV prevalence among PLHIV compared to their HIV-negative counterparts. Other national surveys in sub-Saharan Africa have documented similar findings among HIV infected persons: a prevalence of 4.7% in Uganda [21], 5.2% in Tanzania [23], and 7.0% in Zambia [20] in PLHIV, compared to a prevalence of 2.4% in Uganda, 3.4% in Tanzania, and 3.3.% in Zambia among HIV uninfected persons. These reports confirm the high HBV-HIV coinfection rates previously observed in meta-analyses: 9.9% in Africa [29], and globally at 8.5% [29], and 7.6% [30]. The prevalence of HBV infection found in this survey among HIV-negative participants was similar to 2.1% observed in 2007 among HIV-negative persons in Kenya [31]. The higher prevalence of HBV infection found among PLHIV with history of blood transfusion underscore the imprortance of proper screening of transfusable blood and blood products to guarantee safety against transfusible transmittable infections. However, higher prevalence of HBV infection observed in HIV-negative particpants without history of blood transfusion was rather unexpected and requires further study. The high HBV prevalence among PLHIV underscores the need for excellent synergy in implementing WHO HBV elimination goals [2] and UNAIDS’ HIV epidemic control targets for 2025 [32]. Exploring opportunities for integrated service provision sharing HIV program infrastructure to include HBV services would increase access to appropriate HBV care and treatment. For example, using laboratory diagnostic networks, clinical management clinics, human resources for health, commodity supply chain, health information systems, and community support groups set up for HIV, to provide HBV diagnostic and treatment services.

The 2017 Global Hepatitis Report placed Africa among the highest in viral hepatitis associated mortality in the world [2], yet knowledge of HBV status remains extremely low in most African countries. For instance, almost 90% of the 296 million people estimated to be infected with HBV in 2019 were unaware of their HBV infection status, and less than a quarter (22%) of the 30 million who knew their HBV infection status were on appropriate HBV treatment [4]. In a systematic review on pooled estimates for treatment eligibility per WHO and other guidelines, Tan and colleagues reported that one in every five HBV cases required treatment: 10% based on high HBV DNA level (>20 000 IU/mL), and 31% based on elevated abnormal alanine aminotransferase [33]. Although the median price of generic tenofovir disoproxil fumarate (TDF), a treatment for HBV, reduced by >85% from $208 per year in 2004 to $32 per year in 2016, treatment coverage remained less than 20% globally [6]. Therefore, the key to realizing WHO HBV elimination targets [2] in Africa, beyond availability of accurate diagnostics and affordable effective antiviral therapy, is an enabling policy environment at national levels ensuring increased equitable access to education, healthcare, diagnostic and treatment services.

Due to the high HBV prevalence among PLHIV in Kenya, patients receiving HIV treatment are put on TDF-based ART regimens—which have dual activity against both HIV and HBV [34]. The national ART guidelines further recommend maintaining TDF on any subsequent regimen revisions for HBV-coinfected PLHIV whenever HIV treatment regimen is switched due to virologic HIV treatment failure [34]. Therefore, through this programmatic intervention the 72.7% of HBV-coinfected PLHIV on ART found in this study were already on HBV treatment as part of their routine HIV therapy. However, the need for HBV treatment in Kenya remains unaddressed in the general population. The high absolute burden of HBV infection in the general HIV-uninfected population observed in this study, clearly suggests a need for increased access to TDF and inclusion of HBV treatment in UHC.

Although the implementation of WHO HBV treatment guidelines for limited-resource settings [8] seems plausible, the algorithm informing treatment decisions may prohibit access to HBV treatment in rural settings without robust networks of laboratory infrastructure and therefore require further simplification. Importantly, adapting simple HBV infection screening and treatment strategies would increase treatment coverage in sub-Saharan Africa, including Kenya, where we report high-risk geographic regions and risk groups [35]. When planning and rolling out HBV management programs among African countries lagging on implementation of the HBV elimination agenda, guidelines for scaling up HBV treatment should also emphasize prevention of cirrhosis by expanding treatment coverage to patients without advanced liver disease, as a key step towards reaching the 2030 WHO target on HBV-related mortality. Therefore, careful considerations integrating WHO recommendations and lessons learned from low-resourced countries [36–38], are needed in designing and implementing HBV elimination programs in Kenya and other resource-limited settings. Additionally, embracing the triple elimination agenda [9] to create synergy, would facilitate rapid scale up of HBV vertical transmission prevention efforts.

Findings from this study are critical in setting enabling policies for Kenya to achieve the WHO global hepatitis elimination goals by 2030 through accelerated implementation of targeted HBV diagnostic, treatment, and prevention services. These services include: routine screening of HBV infection in HIV programs to ensure those with HBV/HIV coinfection are initiated on appropriate treatment [39]; routine screening of pregnant women during antenatal care and timely initiation of antiviral therapy for those eligible; routine implementation of the birth-dose HBV vaccine in newborns, catch-up vaccination among young women and PLHIV, and completion of the vaccine series for all persons in a routine HBV vaccination program; and implemention of the triple elimination agenda (of HBV, HIV, and syphilis) to help overcome cost constraint of eradicating each infection separately [9, 40].

This study has several limitations. The study did not include virologic (nuclei acid amplification) confirmatory testing and therefore we could not generate estimates on HBV treatment gaps. HBV infection prevalence and burden estimates provided in this study were limited to adults aged 15–64 years in Kenya and cannot be extrapolated to the younger subpopulation aged 0–14 years old. Additionally, this survey did not collect data on HBV vaccination history. Although, Kenya introduced routine infant HBV immunization in 2004 as part of the expanded program on immunization, this program by design excluded catch-up vaccination for the older eligible population. Therefore, by excluding children (0–14 years) who are beneficiaries to the expanded infant HBV immunization, and by not collecting HBV vaccination data in this survey, we could not assess the impact of HBV immunization program in Kenya. While this study had a nationally representative sample size to estimate the national HBV prevalence, the study was underpowered to estimate HBV prevalence at the sub-national levels or within smaller strata. Furthermore, some regions had low HIV prevalence in the main survey, so the sampling of HIV-negative participants for the HBV sub-study likely yielded lower precision and wider confidence intervals for regional HBV estimates. Therefore, the regional estimates should be interpreted with caution, and the current 47 Counties (administrative regions) remain in need of such data for County level public health planning and programming. This study was not well-powered to estimate HBV prevalence among pregnant women. Such data is needed to inform policy formulation for interventions against HBV infection in this subpopulation to prevent vertical HBV transmission. The study weights were however explicitly designed to account for the study sampling design, allowing for the presentation of nationally representative results despite the over-sampling of HIV-positive respondents. Therefore, in spite of these limitations, the KENPHIA survey provided an important opportunity to report Kenya’s national population-based HBV prevalence estimates.

Conclusions

We report data from the first nationally representative household survey in Kenya that estimated prevalence of HBV infection and HBV-HIV coinfection. A prevalence of 3% for HBV infection documented by this study in Kenya supports adoption and implementation of targeted national HBV screening, vaccination and care/treatment programs as recommended in WHO guidelines [4]. Implementation of the WHO strategy of triple elimination of HIV, HBV, and syphilis, while ensuring availability of the birth-dose of the infant HBV vaccine, screening for HBV infection among pregnant women and all HIV infected persons for treatment eligibility and provision of antiviral therapy for all infected persons, would significantly reduce HBV burden in Kenya and contribute towards the 2030 goal of HBV elimination. Kenya has an uneven geographic distribution of HBV infections. Therefore, targeting HBV treatment and prevention programs to areas with the highest-burden would achieve greater efficiency with limited resources.

Supporting information

S1 Fig. HIV testing algorithm KENPHIA 2018.

(TIF)

pone.0310923.s001.tif (103.5KB, tif)

Acknowledgments

We acknowledge the Kenya Ministry of Health, the Ministry of Planning and Devolution, and all partners for the scientific, strategic, and technical leadership through the KENPHIA Protocol leadership team. The various planning organs of the KENPHIA through the National Executive Steering Committee the Kenya Director General of Health, the KENPHIA Secretariat, the Data Analysis and Advisory Committee and the KENPHIA Technical Working Group and Technical Sub-Committees drawn from relevant survey partner institutions. The operational support from the Council of Governors (CoG) and the 47 County Governments through the County Executive Committee Members for Health, County Directors for Health, County AIDS and STI Coordination Officers and County Medical Laboratory Coordinators. The KENPHIA study team that worked tirelessly to collect high quality data. Kenyans across the country who participated in this survey. Lastly, the unequivocal technical support from CDC and ICAP at Columbia University that made this survey possible.

Disclaimer: The findings and conclusions in this publication are those of the authors and do not necessarily represent the official position of the funding agencies.

Data Availability

The KENPHIA 2018 survey data underlying the results presented in the study are available from the Kenya Ministry of Health upon request to head of the National AIDS & STI Control Programme (NASCOP), Afya Annex, Kenyatta National Hospital, Ministry of Health, P.O. Box 19361 00202 Nairobi. Phone: +254 20 263 0867; Email: kenphia.nascop@gmail.com.

Funding Statement

This survey was supported by the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) through the United States Centers for Disease Control and Prevention (CDC) under the terms of Cooperative Agreement #U2GGH001226. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Trepo C, Chan HL, Lok A. Hepatitis B virus infection. Lancet. 2014;384(9959):2053–63. doi: 10.1016/S0140-6736(14)60220-8 [DOI] [PubMed] [Google Scholar]
  • 2.World Health Organization (WHO). Global hepatitis report, 2017. Geneva, Switzerland: World Health Organization.2017. [Available from: http://apps.who.int/iris/bitstream/handle/10665/255016/9789241565455-eng.pdf?sequence=1. [Google Scholar]
  • 3.World Health Organization (WHO) Dashboard. Global and Country Estimates of immunization coverage and chronic HBV infection: Hepatitis B HBsAg estimates, a baseline towards the elimination targets. 2021 [Available from: https://whohbsagdashboard.surge.sh/#global-strategies
  • 4.World Health Organization (WHO). Global progress report on HIV, viral hepatitis and sexually transmitted infections, 2021. Accountability for the global health sector strategies 2016–2021: actions for impact. Geneva: World Health Organization; 2021. Licence: CC BY-NC-SA 3.0 IGO.: Geneva: World Health Organization; 2021; 2021 [Available from: https://apps.who.int/iris/rest/bitstreams/1348210/retrieve.
  • 5.Bulterys M, Brotherton J, Chen DS. Prevention of infection-related cancers. In: Thun ML MJ, Cerhan JR, Haiman CA, Schottenfeld D, editor. Cancer Epidemiology and Prevention. Fourth Edition Textbook, ed: Oxford University Press.; 2018. p. 1217–20. [Google Scholar]
  • 6.Hutin Y, Nasrullah M, Easterbrook P, Nguimfack BD, Burrone E, Averhoff F, et al. Access to Treatment for Hepatitis B Virus Infection—Worldwide, 2016. MMWR Morb Mortal Wkly Rep. 2018;67(28):773–7. doi: 10.15585/mmwr.mm6728a2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Hutin Y, Desai S, Bulterys M. Preventing hepatitis B virus infection: milestones and targets. Bull World Health Organ. 2018;96(7):443-A. doi: 10.2471/BLT.18.215210 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.World Health Organization (WHO). Guidelines for the Prevention, Care and Treatment of Persons with chronic Hepatitis B infection 2015 [Available from: https://apps.who.int/iris/bitstream/handle/10665/154590/9789241549059_eng.pdf;jsessionid=277A79582D564508EE613876F386D2A7?sequence=1. [PubMed]
  • 9.Cohn J, Owiredu MN, Taylor MM, Easterbrook P, Lesi O, Francoise B, et al. Eliminating mother-to-child transmission of human immunodeficiency virus, syphilis and hepatitis B in sub-Saharan Africa. Bull World Health Organ. 2021;99(4):287–95. doi: 10.2471/BLT.20.272559 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.National AIDS and STI Control Programme (NASCOP). Kenya Population-based HIV Impact Assessment (KENPHIA) 2018: Final Report. Nairobi: NASCOP; August 2022 2022 [Available from: https://phia.icap.columbia.edu/kenya-final-report-2018/.
  • 11.ICAP. Kenya Population-based HIV Impact Assessment (KENPHIA)Technical Report 2019, 2019 [Available from: https://phia-data.icap.columbia.edu/datasets?country_id=14.
  • 12.National AIDS and STI Control Programme (NASCOP). Guidelines for HIV Testing Services in Kenya. Nairobi: NASCOP. 2015. [Available from: https://www.fast-trackcities.org/sites/default/files/Kenya%20HIV%20Testing%20Services%20Guidelines%20%282015%29.pdf.
  • 13.Abbott. Abbott Determine HBsAg Test Bronchure 2020 [Available from: https://kimhung.vn/wp-content/uploads/2020/03/Test-nhanh-Alere-Determine-HBsAg-1.pdf.
  • 14.WHO. WHO Guidelines on Hepatitis B and C Testing. Geneva: World Health Organization. 2017. Feb. [Google Scholar]
  • 15.Rutstein S O., Kiersten J. The DHS Wealth Index. DHS Comparative Reports No. 6. Calverton, Maryland: ORC Macro; 2004. [Available from: https://dhsprogram.com/pubs/pdf/CR6/CR6.pdf. [Google Scholar]
  • 16.National AIDS and STI Control Programme (NASCOP). Kenya AIDS Indicator Survey 2012: Final Report. Nairobi, NASCOP. June 2014. 2014 [Available from: https://nacc.or.ke/wp-content/uploads/2015/10/KAIS-2012.pdf.
  • 17.Schweitzer A, Horn J, Mikolajczyk RT, Krause G, Ott JJ. Estimations of worldwide prevalence of chronic hepatitis B virus infection: a systematic review of data published between 1965 and 2013. Lancet. 2015;386(10003):1546–55. doi: 10.1016/S0140-6736(15)61412-X [DOI] [PubMed] [Google Scholar]
  • 18.Makokha GN, Zhang P, Hayes CN, Songok E, Chayama K. The burden of Hepatitis B virus infection in Kenya: A systematic review and meta-analysis. Front Public Health. 2023;11:986020. doi: 10.3389/fpubh.2023.986020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Kafeero HM, Ndagire D, Ocama P, Kudamba A, Walusansa A, Sendagire H. Prevalence and predictors of hepatitis B virus (HBV) infection in east Africa: evidence from a systematic review and meta-analysis of epidemiological studies published from 2005 to 2020. Arch Public Health. 2021;79(1):167. doi: 10.1186/s13690-021-00686-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Zambia Ministry of Health. Zambia Population-based HIV Impact Assessment (ZAMPHIA) 2016: Zambia Ministry of Health; ZAMPHIA Final Report. Lusaka, Ministry of Health. February 2019. 2019 [Available from: https://phia.icap.columbia.edu/wp-content/uploads/2019/02/ZAMPHIA-Final-Report__2.22.19.pdf.
  • 21.Uganda Ministry of Health. Uganda Population-based HIV Impact Assessment (UPHIA) 2016–2017: UPHIA Final Report 2019. Kampala: Ministry of Health; July, 2019 2019 [Available from: https://phia.icap.columbia.edu/wp-content/uploads/2019/07/UPHIA_Final_Report_Revise_07.11.2019_Final_for-web.pdf.
  • 22.Makuza JD, Rwema JOT, Ntihabose CK, Dushimiyimana D, Umutesi J, Nisingizwe MP, et al. Prevalence of hepatitis B surface antigen (HBsAg) positivity and its associated factors in Rwanda. BMC Infect Dis. 2019;19(1):381. doi: 10.1186/s12879-019-4013-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Tanzania Commission for AIDS (TACAIDS). Tanzania HIV Impact Survey (THIS) 2016–2017: Tanzania Commission for AIDS (TACAIDS), Zanzibar AIDS Commission (ZAC). THIS Final Report 2018. Dar es Salaam, Tanzania. December 2018. 2018 [Available from: https://phia.icap.columbia.edu/wp-content/uploads/2019/06/FINAL_THIS-2016-2017_Final-Report__06.21.19_for-web_TS.pdf.
  • 24.Global Alliance for Vaccines and Immunizations(GAVI). Programmes & Impact. 2020. Available from: https://www.gavi.org/programmes-impact/country-hub/africa/kenya.
  • 25.Ogero M, Orwa J, Odhiambo R, Agoi F, Lusambili A, Obure J, et al. Pentavalent vaccination in Kenya: coverage and geographical accessibility to health facilities using data from a community demographic and health surveillance system in Kilifi County. BMC Public Health. 2022;22(1):826. doi: 10.1186/s12889-022-12570-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Rainey JJ, Watkins M, Ryman TK, Sandhu P, Bo A, Banerjee K. Reasons related to non-vaccination and under-vaccination of children in low and middle income countries: Findings from a systematic review of the published literature, 1999–2009. Vaccine. 2011;29(46):8215–21. doi: 10.1016/j.vaccine.2011.08.096 [DOI] [PubMed] [Google Scholar]
  • 27.Devkota S, Panda B. Childhood Immunization and Access to Health Care: Evidence From Nepal. Asia Pacific Journal of Public Health. 2016;28(2):167–77. doi: 10.1177/1010539515626268 [DOI] [PubMed] [Google Scholar]
  • 28.World Health Organization (WHO). Interim guidance for country validation of viral hepatitis elimination. Geneva, June 2021. [Available from: https://www.who.int/publications/i/item/9789240028395.
  • 29.Leumi S, Bigna JJ, Amougou MA, Ngouo A, Nyaga UF, Noubiap JJ. Global Burden of Hepatitis B Infection in People Living With Human Immunodeficiency Virus: A Systematic Review and Meta-analysis. Clin Infect Dis. 2020;71(11):2799–806. doi: 10.1093/cid/ciz1170 [DOI] [PubMed] [Google Scholar]
  • 30.Platt L, French CE, McGowan CR, Sabin K, Gower E, Trickey A, et al. Prevalence and burden of HBV co-infection among people living with HIV: A global systematic review and meta-analysis. Journal of viral hepatitis. 2020;27(3):294–315. doi: 10.1111/jvh.13217 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Ly KN, Kim AA, Umuro M, Drobenuic J, Williamson JM, Montgomery JM, et al. Prevalence of Hepatitis B Virus Infection in Kenya, 2007. Am J Trop Med Hyg. 2016;95(2):348–53. doi: 10.4269/ajtmh.16-0059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.The Joint United Nations Programme on HIV/AIDS (UNAIDS). Prevailing against pandemics by putting people at the Centre. 2020 [Available from: https://aidstargets2025.unaids.org/assets/images/prevailing-against-pandemics_en.pdf
  • 33.Tan M, Bhadoria AS, Cui F, Tan A, Van Holten J, Easterbrook P, et al. Estimating the proportion of people with chronic hepatitis B virus infection eligible for hepatitis B antiviral treatment worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2021;6(2):106–19. doi: 10.1016/S2468-1253(20)30307-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.National AIDS & STI Control Program (NASCOP). Guidelines on Use of Antiretroviral Drugs for Treating and Preventing HIV Infection in Kenya 2018 Edition. Nairobi, Kenya: NASCOP, August 2018. Print. 2018 [Available from: http://cquin.icap.columbia.edu/wp-content/uploads/2017/04/ICAP_CQUIN_Kenya-ARV-Guidelines-2018-Final_20thAug2018.pdf.
  • 35.Dusheiko G, Lemoine M. An appraisal of the WHO hepatitis B treatment guidelines applicability to Africans. J Hepatol. 2019;70(6):1046–8. doi: 10.1016/j.jhep.2019.03.009 [DOI] [PubMed] [Google Scholar]
  • 36.Lemoine M, Shimakawa Y, Njie R, Taal M, Ndow G, Chemin I, et al. Acceptability and feasibility of a screen-and-treat programme for hepatitis B virus infection in The Gambia: the Prevention of Liver Fibrosis and Cancer in Africa (PROLIFICA) study. Lancet Glob Health. 2016;4(8):e559–67. doi: 10.1016/S2214-109X(16)30130-9 [DOI] [PubMed] [Google Scholar]
  • 37.Shimakawa Y, Njie R, Ndow G, Vray M, Mbaye PS, Bonnard P, et al. Development of a simple score based on HBeAg and ALT for selecting patients for HBV treatment in Africa. J Hepatol. 2018;69(4):776–84. doi: 10.1016/j.jhep.2018.05.024 [DOI] [PubMed] [Google Scholar]
  • 38.Aberra H, Desalegn H, Berhe N, Mekasha B, Medhin G, Gundersen SG, et al. The WHO guidelines for chronic hepatitis B fail to detect half of the patients in need of treatment in Ethiopia. J Hepatol. 2019;70(6):1065–71. doi: 10.1016/j.jhep.2019.01.037 [DOI] [PubMed] [Google Scholar]
  • 39.Kourtis AP, Bulterys M, Hu DJ, Jamieson DJ. HIV-HBV coinfection—a global challenge. N Engl J Med. 2012;366(19):1749–52. doi: 10.1056/NEJMp1201796 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Tordrup D, Hutin Y, Stenberg K, Lauer JA, Hutton DW, Toy M, et al. Additional resource needs for viral hepatitis elimination through universal health coverage: projections in 67 low-income and middle-income countries, 2016–30. Lancet Glob Health. 2019;7(9):e1180–e8. doi: 10.1016/S2214-109X(19)30272-4 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. HIV testing algorithm KENPHIA 2018.

(TIF)

pone.0310923.s001.tif (103.5KB, tif)

Data Availability Statement

The KENPHIA 2018 survey data underlying the results presented in the study are available from the Kenya Ministry of Health upon request to head of the National AIDS & STI Control Programme (NASCOP), Afya Annex, Kenyatta National Hospital, Ministry of Health, P.O. Box 19361 00202 Nairobi. Phone: +254 20 263 0867; Email: kenphia.nascop@gmail.com.

Articles from PLOS ONE are provided here courtesy of PLOS

RESOURCES