Prevalence of HIV drug resistance, its correlates and common mutations among people living with HIV failing on ART in northern Uganda. A cross-sectional study

Prevalence of HIV drug resistance, its correlates and common mutations among people living with HIV failing on ART in northern Uganda. A cross-sectional study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Prevalence of HIV drug resistance, its correlates and common mutations among people living with HIV failing on ART in northern Uganda. A cross-sectional study View ORCID Profile Kayinda Francis , Phyllis Awor , Twaha Mahaba , Alex Muganzi , View ORCID Profile Joanita Kigozi , View ORCID Profile Patrick Odong Olwedo , View ORCID Profile Esther M Nasuuna , Robert Mutumba , View ORCID Profile Rhoda Wanyenze doi: https://doi.org/10.1101/2025.03.06.25322912 Kayinda Francis 1 Makerere University School of Public Health , Kampala, Uganda 2 Infectious Diseases Institute, Makerere University College of Health science , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kayinda Francis For correspondence: kaysfrancis{at}gmail.com fkayinda{at}idi.co.ug Phyllis Awor 1 Makerere University School of Public Health , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site Twaha Mahaba 2 Infectious Diseases Institute, Makerere University College of Health science , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alex Muganzi 2 Infectious Diseases Institute, Makerere University College of Health science , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joanita Kigozi 2 Infectious Diseases Institute, Makerere University College of Health science , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joanita Kigozi Patrick Odong Olwedo 3 Medical Department, Yumbe Regional Referral Hospital , Yumbe, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Patrick Odong Olwedo Esther M Nasuuna 2 Infectious Diseases Institute, Makerere University College of Health science , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Esther M Nasuuna Robert Mutumba 4 STD/AIDS control program, Ministry of Health , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rhoda Wanyenze 1 Makerere University School of Public Health , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rhoda Wanyenze Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background HIV drug resistance (HIVDR) poses a challenge to managing people living with HIV (PLHIV), particularly among those experiencing virological failure (VF). The West-Nile region of Uganda faces HIV treatment challenges and has a high virological failure rate. We estimated the prevalence of HIV drug resistance, described the HIV drug resistance mutations and evaluated the factors associated with HIVDR among PLHIV with virological failure in the West-Nile region of Uganda. Methods We conducted a retrospective cross-sectional analysis of HIVDR data in the West-Nile region of Uganda across the 161 health facilities that offer comprehensive Anti-retroviral therapy (ART) services. All PLHIV, regardless of age, who had been on ART for at least one-year, experienced virological failure and underwent an HIVDR test between 1 st January 2021 to 30 th December 2023 were included in the study. Demographic and clinical data were extracted from the National HIVDR database. HIVDR was defined as having at least one mutation with a penalty score of ≥15. PLHIV were characterized based on age, gender and clinical history. Logistic regression models determined factors associated with HIVDR with a p-value of <0.05 considered significant. Results A total 295 records were analyzed. Of these , majority were female (56.6%) and adults aged ≥20 years (49.2%). The median age was 19 (Inter quartile range [IQR]: 13–41) years, and median duration on ART was 8 (IQR: 5–10) years. Overall, 218 (73.9%) had HIVDR with 66% of subjects having Non-nucleoside reverse transcriptase (NNRTI) mutations. M184V/I (50%), K103N (34%) and TAMS (26%) were the commonest mutations. Resistance to Etravirine (27%) was higher than that of Dolutegravir (12%) and Darunavir (5%). After accounting for gender, age, Nucleoside reverse transcriptase inhibitor (NRTI) anchor drug, ART regimen type and World Health Organization (WHO) clinical stage of the participants; long duration on ART (aOR=; 1.15 95%CI 1.05 -1.26 p=0.003), adolescents failing on first line (aOR=; 3.80 95%CI 1.02-14.08 p=0.046) and participants failing on 2nd line (aOR=; 3.64 95%CI 1.18-11.21 p=0.024) as indications for the HIVDR test, and the year of HIVDR sample collection (aOR=; 0.21 95%CI 0.07 - 0.69 p=0.010), were independently associated with HIVDR mutations. Conclusion The study found a high HIVDR prevalence strongly associated with long ART duration which is likely lead to increased ART treatment failure rates. The high Etravirine resistance and increasing Dolutegravir resistance are likely to complicate future treatment options while low Darunavir resistance makes it a future third-line treatment option. Strengthening routine resistance surveillance, timely VL monitoring, and adherence support are critical to mitigating drug resistance and preserving ART effectiveness among PLHIV in the West-Nile region. Introduction HIV drug resistance (HIVDR) is a growing challenge to the long-term success of antiretroviral therapy (ART) and the overall goal of HIV epidemic control. HIVDR arises when genetic mutations in HIV reduce the effectiveness of ART, resulting in virological failure (VF), increased transmission of resistant strains, and limited future treatment options. HIVDR can be transmitted (TDR), occurring before ART initiation, or acquired during treatment ( 1 , 2 ). If unaddressed, HIVDR could lead to millions of deaths, new difficult-to-treat variants, and increased healthcare costs due to switching to more toxic and expensive regimens ( 3 ). HIV remains a major global epidemic with 39.9 million people living with HIV (PLHIV) by the end of 2023, including 1.3 million new infections and over 630,000 deaths ( 4 ). Sub-Saharan Africa (SSA) bears the largest burden, with 25.9 million PLHIV ( 4 , 5 ). Uganda had an estimated 1.5 million PLHIV in 2023 with 38,000 new HIV infections and 20,000 deaths ( 6 ). ART has significantly improved health outcomes for PLHIV who achieve HIV viral suppression through maintaining immune function which leads to decreased mortality and morbidity from opportunistic infections. It also helps achieve epidemic control through the principle of undetectable = untransmissible (U=U) ( 7 ). Uganda has made substantial progress in ART expansion, achieving a treatment coverage rate of 84% ( 7 ). However, the national viral suppression rate remains suboptimal, estimated at 79% overall, 75% among adult males, and 63% among children and adolescents below 15 years, falling short of the Joint United Nations Program for AIDS (UNAIDS) 95-95-95 target ( 4 , 8 ). Despite these achievements, the growing prevalence of HIVDR poses a serious threat to ART effectiveness. A World Health Organization (WHO) study (2004–2021) in 66 countries, including Uganda, found an 83.1% prevalence of acquired resistance among adults on first-line ART with virological failure (>1,000 copies/ml) ( 9 ). In Uganda, a 2018 national survey reported 90.4% HIVDR among non-suppressed PLHIV ( 10 ), with other studies reporting rates ranging from 73.2% to 84.6% ( 11 – 13 ). WHO (2014–2020) also reported the highest global TDR in Non-nucleoside Reverse Transcriptase Inhibitors (NNRTIs) (12.9%), followed by Nucleoside Reverse Transcriptase Inhibitors (NRTIs) (5.4%), Integrase strand transfer Inhibitors (INSTIs) (0.6%), and Protease Inhibitors (PIs) at (0.4%) ( 9 ). A similar pattern was reported in Africa with NNRTIs at 15.4%, NRTIs at 6.1%, PIs at 0.3% and INSTIs at 0.1% ( 9 ). In Uganda, NNRTI resistance was 15.4%, NRTIs at 5.1%, and PIs at 1%, while INSTIs remained largely unassessed due to low uptake. Other Ugandan studies reported TDR rates ranging from 5.9% and 18.2% ( 9 ). The West Nile region of Uganda has a low HIV viral suppression rate (77%), compared to the national average ( 8 ). The region faces unique challenges including a significant refugee population (22%), high levels of migration, bordering conflict-affected countries (South Sudan and Democratic republic of Congo), and high levels of poverty. These factors create social, economic, and health vulnerabilities which may impact ART adherence and the emergence of HIVDR ( 14 – 16 ). Despite these concerns, there is limited data on the prevalence, specific mutations, and associated factors for HIVDR in this region. This study estimated the prevalence of HIVDR, described common mutations, and evaluated factors associated with HIVDR among non-suppressed PLHIV in the West Nile region of Uganda. Materials and Methods Study design and Participants We conducted a retrospective cross-sectional study in the West-Nile region of Uganda across the 161 health facilities that offer comprehensive ART services ( 17 ). The region had a population of 3.6 million people in 2023 and an estimated 49,000 PLHIV ( 15 ). Study participants included all PLHIV, regardless of age, who had been on ART for at least one year, had a non-suppressed HIV viral load (>1000 copies/ml) and underwent an HIVDR test between 1 st January 2021 to 30 th December 2023. HIV drug resistance testing in Uganda All PLHIV on ART in Uganda receive a VL test annually. Those with a non-suppressed VL undergo intensive adherence counselling (IAC) for three months with a repeat VL at the end. Individuals with a persistent non-suppressed VL are eligible for an HIVDR test. A Plasma or dried blood spot (DBS) sample is collected and referred to the HIVDR testing facility at a central national laboratory. Here, RNA extraction is done using commercial kits followed by reverse transcription and Polymerase Chain Reaction (PCR) amplification. PCR is a molecular technique used to amplify specific DNA sequences by generating millions of copies from a small initial sample. This step is crucial for increasing the quantity of genetic material required for sequencing. Sequencing, where individual mutations are identified using either Sanger sequencing or Next generation sequencing is then carried out. Mutations are identified and interpreted using the Stanford HIV drug resistance database ( 18 – 20 ). The results of the HIVDR test are uploaded onto the national database together with the patients clinical and demographic information. Operational definitions Penalty score: A penalty score is the value assigned to each HIV mutation to estimate the level of resistance it brings to a particular drug. Penalty scores of 0-9, represent no resistance; 10-14 represent potential low-level resistance; 15-29 represent low level resistance; 30-59 represent intermediate resistance and scores of ≥ 60 represent high level resistance ( 20 ). The penalty score of ≥ 15 was used in this study to determine which mutations were drug resistant. HIV Drug resistance: This was defined as the presence of a penalty score ≥15 assigned to an ARV drug using the Stanford HIV dB algorithm ( 20 , 21 ). A participant with atleast one HIV drug resistant mutation to any ART drug was categorized as having HIVDR. Virological failure: Virological failure was defined as two consecutive viral load results ≥ 1000 copies/ml despite undergoing at least three consecutive IAC sessions with good adherence between the tests ( 22 ). VL Non suppression: This was defined as viral load copies ≥ 1000 copies per ml ( 22 ). Age groups: Because of the varying risks of HIV acquisition, transmission, and disease progression, participants’ age was classified as follows: Children (<10 years), Adolescents (10– 19 years) and Adults (≥20 years) ( 7 , 23 , 24 ). Data collection Census sampling was used to include all PLHIV with HIV DR results. The data abstraction tool was pretested on 30 individual participants (10% of the participants’ data) before it was used to collect the demographic and clinical data. Data were accessed from the national database on 16 th May 2024. Demographic data included age and sex. Clinical data included viral load copies preceding the DR test, WHO clinical stage, ART regimen class at the time of sample collection, NRTI anchor drug of the regimen, the full ART regimen, duration on ART, the line of ART treatment, indication for the HIVDR test, year of HIVDR test and actual mutations per participant. Data Analysis We performed statistical analysis using Stata SE version 14.2, developed by Stata Corp LLC in the United States. To assess the distributions of the independent variables (clinical and demographic), we used the Shapiro-Wilk test to assess the normality of continuous variables. Continuous variables were summarized using medians and interquartile ranges while categorical variables were presented as frequencies and proportions. We performed a Chi-square test, a Wilcoxon rank- sum test and bivariable logistic regression analysis to assess association of each of the independent potential correlates with the outcome. Variables that had an odds ratio (OR) with P ≤ 0.2 were included in multivariable logistic regression model together with age and sex as potential confounders. Results were presented as adjusted odds ratios (aORs) with 95% confidence interval (CI). Variables were considered significantly associated with the outcome if the P value for the aOR was less than or equal to 0.05. Ethics approval and consent to participate This study was conducted in accordance with ethical principles of the Helsinki declaration. Institutional ethical approval was obtained from the Makerere University School of Public Health Research and Ethics Committee with reference number 366. Permission to use national data was granted by the Ugandan Ministry of Health through the Director General, Health Services. The study used de-identified secondary data, and the authors did not have access to information that could identify individual participants during or after data collection, hence the need for individual participant consent was waived by the ethics committee. All filled forms were kept safely in a waterproof locked cabinet where they could only be accessed by the principal investigator. Results During the study period , 363 PLHIV had virological failure but only 295 (81.3%) had HIV DR results and were considered for the study ( Fig 1 ). Download figure Open in new tab Figure 1: Enrollment study profile A flow chart that shows the participants who were included in the study. Baseline participant characteristics and HIVDR Of the 295 participants, 167 (56.6%) were female, with a median age of 19 years. The participants had been on ART for a median 8 years and 59.3% were on INSTIs at sample collection. Majority of participants (65.4%) underwent HIVDR testing in 2023 following revised national criteria to test all PLHIV failing on ART regardless of treatment line ( Table 1 ) . HIVDR mutations were present in 218 participants (73.9%: 95% Confidence Interval (CI) 68.9–78.9%), where CI represents the range within which the true population proportion is expected to fall with a 95% certainty. Majority of the participants (66%) had NNRTI mutations while 55% had NRTI mutations ( Table 2 ) . HIVDR mutations were observed across different drug classes in the same individuals where 55% (161) of all participants had resistance to at least two drug classes and 1% (03) of the participants had resistance across all the 4 drug classes ( Table 3 ). View this table: View inline View popup Table 1: Baseline Participant characteristics and HIVDR View this table: View inline View popup Download powerpoint Table 2: HIVDR Mutations according to drug classes View this table: View inline View popup Download powerpoint Table 3: Patterns of resistance across drug classes. HIVDR Mutations among participants The M184V was the commonest mutation observed in NRTI class appearing among 146 (50%) participants. The thymidine analogue mutations (TAMs) were observed among 77 (26%) participants and Type 2 pattern TAMs were more common (56% of all TAMs) than type 1 pattern TAMs. K65R was only observed among only 4 (1%) participants. Among NNRTIs, K103N/S mutations were the commonest appearing among 99 (34%) participants. Overall, 81 (27%) participants had resistance to Etravirine. The darunavir sparing M46V/A mutations were the commonest among Protease Inhibitors (PIs) appearing in 20 (7%). Overall, 14 (5%) participants had resistance to darunavir. The E138K/A and G118R mutations were the commonest among Integrase strand transfer Inhibitors (INSTIs) occurring in 18 (6%) and 12 (4%) participants respectively and in total 35 (12%) participants had resistance to Dolutegravir (DTG). 24 (8%) participants had a mixture of wild type virus with the mutated virus ( Fig 2 ). Download figure Open in new tab Figure 2: Commonest HIVDR mutations amongst participants shows the commonest HIVDR mutations among the 218 participants with atleast one mutation categorized by drug class. Factors associated with HIVDR After accounting for gender, age, NRTI anchor drug, ART regimen type and WHO clinical stage of the participants; long duration on ART, indication for the HIVDR test and the year of HIVDR sample collection were independently associated with HIVDR mutations. The odds of HIVDR significantly increased by 15% for each additional year an individual spent on ART (aOR=; 1.15 95%CI 1.05 -1.26 p=0.003). For the indication for the HIVDR test, adolescents failing on first line (aOR=; 3.80 95%CI 1.02-14.08 p=0.046) and participants failing on 2nd line (aOR=; 3.64 95%CI 1.18-11.21 p=0.024) were found to be significantly associated with HIVDR. The HIVDR tests conducted in 2023 (aOR=; 0.23 95%CI 0.07 - 0.76 p=0.01) were also significantly associated with HIVDR ( Table 4 ) . View this table: View inline View popup Table 4: Correlation of baseline participant characteristics with odds of HIVDR mutations. HIVDR among children and Adolescents Among the 295 participants, 150 (50.8%) were children and adolescents living with HIV (CALHIV) under 20 years. Of these, 37 (24.7%) were children under 10, and 113 (75.3%) were adolescents (10-19 years). Overall, 76% (114/150) of CALHIV had HIVDR, with children having a higher rate of 81.1% compared to 74.3% in adolescents. Although the HIVDR rate was highest amongst children compared to other age groups, the difference in age was not statistically significant (p= 0.455) ( Table 1 and 4). NNRTI resistance was the commonest in both groups, found in 65% of CALHIV. NRTI resistance was present in 54% of CALHIV and 55% of all participants. INSTI and PI resistance rates were 12% and 11% in CALHIV, compared to 14% and 16% in the overall group. The most frequent mutations in CALHIV were comparable to that among all participants with M184V (46%), K103N (25%), and TAMs (24%) being the commonest. Resistance to Etravirine was 26% in CALHIV, compared to 27% overall, Dolutegravir resistance was 7% in CALHIV and 12% overall, and Darunavir resistance was 4% in CALHIV and 5% overall. Discussion We set out to estimate the prevalence of HIVDR, describe common mutations, and associated factors among non-suppressed PLHIV in the West Nile region of Uganda. We found that 73.9% had at least one mutation. The commonest mutations were the NNRTI mutations mostly K103N/S and G190A/S/E. The associated factors were indication for the HIVDR test, duration on ART and the year of HIVDR sample collection. The observed prevalence (73.9%) is lower than the global and African estimates of 83.5% as reported by WHO in eight countries that included adult PLHIV on ART for four or more years ( 9 ). It is also below what was found in a Ugandan study among 1,064 adult participants on first line for ≥ 48 months that found a prevalence of 90.4% ( 10 ). Estimates from neighboring countries of Rwanda and Kenya with 92.7% and 82.3% respectively were found in a study that included 500 PLHIV that were failing on first line ( 25 , 26 ). We postulate that the lower prevalence we found can be explained by the suboptimal adherence associated with intermittent drug use within the region which could lead to viral failure without sufficient selective pressure to drive resistance mutations. This means continued nonadherence will lead to mutations among the PLHIV and improved adherence is likely to prevent development of mutations ( 2 ). The commonest NNRTI mutations (K103N/S and G190A/S/E) found in 66% of the participants confer resistance to efavirenz and nevirapine with a single mutation conferring resistance to all first-generation drugs ( 27 – 29 ). These two drugs were part of the recommended first line drugs in Uganda until 2018 ( 22 ). The high rates of NNRTI resistance is consistent with other studies conducted in similar settings such as Uganda (84.6%), Rwanda (90.4%), Kenya (81%) among PLHIV failing on treatment ( 9 , 10 , 25 , 26 ). The high rate of etravirine resistance (27%) conferred by mutations such as G190A/S and Y181C is likely to limit third line treatment options ( 23 ). The high rates of M184V/I mutations (50%) from NRTIs is attributed to continued usage of lamivudine as a backbone in all regimens in SSA since the presence of this mutation reduces the viral replicative fitness and increases susceptibility to Tenofovir and Zidovudine (AZT) ( 30 ). The prolonged use of AZT included currently as part of the recommended second line of treatment in Uganda explains the high prevalence of TAMs (26%) among participants ( 23 ). The lower mutation rates in PIs (16%) and INSTI (14%) is due the higher genetic barrier to resistance of boosted PI- based ART and INSTIs ( 31 ) and the fact that INSTIs have just recently been introduced into the HIV program as first line drugs and participants have been exposed to them for a shorter time ( 22 ). Other studies reported similar low prevalences: a review of 20 studies in 11 SSA countries (PI resistance 17%) ( 32 ) and a review of 88 publications worldwide (INSTI resistance 1.5%) among ART-experienced PLHIV ( 33 ). The mutations mainly spared darunavir (5%) the most commonly used drug in third line of treatment, however, the dolutegravir resistance of 12% is high for a drug with a high genetic barrier to resistance and that only been recently scaled up ( 22 ). It is an issue of concern to be noted by the HIV program since it is likely to limit HIV treatment options ( 23 ). This increasing resistance could be due to unintended DTG dual or monotherapy due to failure of other low genetic barrier NRTIs within the treatment regimen which greatly increases the risk of resistance ( 34 , 35 ). The association between duration on ART and HIVDR is supported by the notion that prolonged ART exposure increases the risk of emergence of acquired drug resistant mutations ( 36 ) and could also be a reflection of the time an individual has spent on a suboptimal regimen or with adherence challenges which are associated with insufficient drug pressure in the body which leads to HIVDR ( 37 ). A systematic review of seven studies conducted in Africa, Asia and south America found a similar association ( 38 ) while other similar studies in Uganda and Rwanda didn’t find any association ( 12 , 26 ). In case of the HIVDR test indication, factors such as peer pressure, stigma and discrimination, drug and alcohol abuse and non-disclosure stand out among adolescents leading to poor adherence ( 23 ) and hence development of HIVDR while participants on second-line of treatment have spent a longer time on treatment and have also undergone prior regimen changes due to probable or confirmed HIVDR ( 36 ). Addressing adherence challenges among adolescents mainly through peer support and adolescent friendly services will be key in prevention of HIVDR ( 38 ). Prior to 2023, the eligibility criteria for a HIVDR test in Uganda included only PLHIV failing on either second or third line of treatment, however, in 2023 the criteria was changed to include all PLHIV with treatment failure irrespective of the treatment line. This meant that tests conducted in 2023 following the change in eligibility criteria were less likely to have developed HIVDR since they had been on a failing regimen for a short duration ( 38 ). The national viral suppression rates among children were estimated to be as low as 63% compared to the national average of 79% underscoring the heightened vulnerability within the HIV treatment cascade. In this study, age was not statistically associated with HIVDR despite children under 10 years showing the higher proportion of HIVDR at 81.1% compared to other age groups ( Table 1 ) . This apparent discrepancy may be explained by suboptimal ART adherence that is more pronounced among children leading to viral failure without exerting sufficient selective pressure to drive resistance mutations in short term, although persistent poor adherence may eventually result in the development of HIVDR ( 2 , 4 , 8 ). The study has a larger sample size of participants compared to previous studies conducted within Uganda and has resistance data against INSTIs which were not present in the country. Our study has several limitations. Firstly, the HIVDR data prior to current ART initiation was not available so we could not rule out the possibility of transmitted drug resistance. Secondly we were limited to the data available from medical records and patient histories. This means that not all factors potentially associated with HIV drug resistance, were assessed. Furthermore, as our study is a retrospective study using secondary data, it is subject to biases related to omissions and commissions during initial data collection and entry into the HIVDR database and these may impact the accuracy and reliability of our study. Conclusion The study found a high prevalence of drug-resistant mutations among PLHIV failing on ART, strongly associated with long ART duration although INSTI and PI drug classes were mainly spared. This resistance is likely lead to increased ART treatment failure rates and limited regimen options. The high Etravirine resistance and increasing Dolutegravir resistance are likely to complicate future treatment options while low Darunavir resistance makes it a future third-line treatment option. Strengthening routine resistance surveillance, timely VL monitoring, and adherence support are critical to mitigating drug resistance and preserving ART effectiveness. Additional research assessing the prevalence of transmitted drug resistance, or association between other factors like socio-cultural, viral and structural and HIVDR are recommended. Data Availability The data used in this study is available on the Ugandan Ministry of Health HIV Drug Resistance Database but is not publicly accessible due to patient confidentiality policies. Access may be granted upon request to the Ugandan Ministry of Health through the Director General, Health Services. Acknowledgement The authors extend their gratitude to the Ugandan Ministry of Health, the Makerere University School of Public Health, and the Infectious Diseases Institute for supporting this research. Special thanks to the Central Public Health Laboratories for their role in HIV drug resistance testing. Footnotes This version of the manuscript updated the Abstract to clarify the study group; included operational definitions; includes chi square and wilcoxon rank sum test in table one; includes a separate analysis of HIVDR among children and adolescents and has a re-written conclusion to summarize the study findings. References 1. ↵ WHO . Global action plan on HIV drug resistance 2017-2021 . Geneva : World Health Organization ; 2017 November 2021 . 2. ↵ Clutter DS , Jordan MR , Bertagnolio S , Shafer RW . HIV-1 drug resistance and resistance testing . Infect Genet Evol . 2016 ; 46 : 292 – 307 . OpenUrl CrossRef PubMed 3. ↵ WHO . Global Price Reporting Mechanism . WHO: World Health Organization ; 2017 7th July 2017. 4. ↵ UNAIDS . 2024 global AIDS report — The Urgency of Now: AIDS at a Crossroads . Geneva : UNAIDS ; 2024 22 JULY 2024 . 5. ↵ WHO GLOBAL HEALTH OBSERVATORY [Internet] . WHO . 2024 . 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