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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Immunological profile in persons under antiretroviral therapy in a rural Nigerian hospital</title><author><name sortKey="Musa, Baba Maiyaki" sort="Musa, Baba Maiyaki" uniqKey="Musa B" first="Baba Maiyaki" last="Musa">Baba Maiyaki Musa</name><affiliation><nlm:aff id="A1">Rasta Nurah General Hospital, Eastern province, Saudi Arabia;</nlm:aff></affiliation></author><author><name sortKey="Gebi, Usman" sort="Gebi, Usman" uniqKey="Gebi U" first="Usman" last="Gebi">Usman Gebi</name><affiliation><nlm:aff id="A2">Institute of Human Virology, Abuja, Nigeria;</nlm:aff></affiliation></author><author><name sortKey="Etiebet, Mary Ann" sort="Etiebet, Mary Ann" uniqKey="Etiebet M" first="Mary-Ann" last="Etiebet">Mary-Ann Etiebet</name><affiliation><nlm:aff id="A2">Institute of Human Virology, Abuja, Nigeria;</nlm:aff></affiliation></author><author><name sortKey="Omuh, Helen" sort="Omuh, Helen" uniqKey="Omuh H" first="Helen" last="Omuh">Helen Omuh</name><affiliation><nlm:aff id="A2">Institute of Human Virology, Abuja, Nigeria;</nlm:aff></affiliation></author><author><name sortKey="Ekedegwa, Patrick" sort="Ekedegwa, Patrick" uniqKey="Ekedegwa P" first="Patrick" last="Ekedegwa">Patrick Ekedegwa</name><affiliation><nlm:aff id="A3">General Hospital Otukpo, Benue State, Nigeria;</nlm:aff></affiliation></author><author><name sortKey="Dakum, Patrick" sort="Dakum, Patrick" uniqKey="Dakum P" first="Patrick" last="Dakum">Patrick Dakum</name><affiliation><nlm:aff id="A2">Institute of Human Virology, Abuja, Nigeria;</nlm:aff></affiliation></author><author><name sortKey="Blattner, William" sort="Blattner, William" uniqKey="Blattner W" first="William" last="Blattner">William Blattner</name><affiliation><nlm:aff id="A4">Institute of Human Virology, University of Maryland School of Medicine, USA</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">28299037</idno><idno type="pmc">5345394</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345394</idno><idno type="RBID">PMC:5345394</idno><idno type="doi">10.4081/jphia.2010.e3</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">002E94</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">002E94</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Immunological profile in persons under antiretroviral therapy in a rural Nigerian hospital</title><author><name sortKey="Musa, Baba Maiyaki" sort="Musa, Baba Maiyaki" uniqKey="Musa B" first="Baba Maiyaki" last="Musa">Baba Maiyaki Musa</name><affiliation><nlm:aff id="A1">Rasta Nurah General Hospital, Eastern province, Saudi Arabia;</nlm:aff></affiliation></author><author><name sortKey="Gebi, Usman" sort="Gebi, Usman" uniqKey="Gebi U" first="Usman" last="Gebi">Usman Gebi</name><affiliation><nlm:aff id="A2">Institute of Human Virology, Abuja, Nigeria;</nlm:aff></affiliation></author><author><name sortKey="Etiebet, Mary Ann" sort="Etiebet, Mary Ann" uniqKey="Etiebet M" first="Mary-Ann" last="Etiebet">Mary-Ann Etiebet</name><affiliation><nlm:aff id="A2">Institute of Human Virology, Abuja, Nigeria;</nlm:aff></affiliation></author><author><name sortKey="Omuh, Helen" sort="Omuh, Helen" uniqKey="Omuh H" first="Helen" last="Omuh">Helen Omuh</name><affiliation><nlm:aff id="A2">Institute of Human Virology, Abuja, Nigeria;</nlm:aff></affiliation></author><author><name sortKey="Ekedegwa, Patrick" sort="Ekedegwa, Patrick" uniqKey="Ekedegwa P" first="Patrick" last="Ekedegwa">Patrick Ekedegwa</name><affiliation><nlm:aff id="A3">General Hospital Otukpo, Benue State, Nigeria;</nlm:aff></affiliation></author><author><name sortKey="Dakum, Patrick" sort="Dakum, Patrick" uniqKey="Dakum P" first="Patrick" last="Dakum">Patrick Dakum</name><affiliation><nlm:aff id="A2">Institute of Human Virology, Abuja, Nigeria;</nlm:aff></affiliation></author><author><name sortKey="Blattner, William" sort="Blattner, William" uniqKey="Blattner W" first="William" last="Blattner">William Blattner</name><affiliation><nlm:aff id="A4">Institute of Human Virology, University of Maryland School of Medicine, USA</nlm:aff></affiliation></author></analytic><series><title level="j">Journal of Public Health in Africa</title><idno type="ISSN">2038-9922</idno><idno type="eISSN">2038-9930</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Human immunodeficiency virus (HIV) contributes significantly to morbidity and mortality in sub-Saharan Africa, with Nigeria having the third highest burden of HIV infection globally; efforts are made to increases access to HIV/AIDS care and treatment. This has currently reached rural areas with limited manpower and laboratory evaluation capacity. This review is necessitated by the paucity of interim report on treatment profile in Nigerian rural areas. We report on the immunological profile of patients on antiretroviral therapy (ART) in Otukpo General Hospital, a rural Nigerian hospital. This is a retrospective cohort study of patients receiving ART treatment and care, on April 2009, when 2347 patients were under ART therapy. Out of these, 96 patients were selected by simple random sampling from hospital register, with their data abstracted from standardized Ministry of Health registers and facility documents kept at the hospital, and analyzed for descriptive and biometric measures. Ninty-six patients (29% males) with a median age of 35 years, median baseline CD4 lymphocyte count 221 cells/mL, median one year CD4 lymphocyte count of 356 cells/mL and median one year CD4 lymphocyte increment of 124 cells/mL were studied. There is no statistically significant difference in baseline CD4 lymphocyte count when data is disaggregated by type of drug regimen (AZT, D4T and TDF). Fourty-four percent, 23% and 33% of patients were on TDF, D4T & AZT based regimen, respectively (P=0.66). Increment of >100 cells/mL was seen in 64.58% of the reviewed patients. There was a higher CD4 lymphocyte count increment in patients on TDF & D4T compared with those in AZT based regimens (ANOVA; P<0.0003). Multivariate linear regression model showed one year CD4 lymphocyte count, one year increment in CD4 lymphocyte count, WBC count, and absolute neutrophil count to be significant correlates of baseline CD4 lymphocyte count (P<0.0001). Equally, multivariate logistic regression found age, platelet count and CD4 lymphocyte count at 12 months showed to be significant predictors of CD4 lymphocyte increment above 100 cells/µL (P<0.0001). Despite advanced disease presentation and a very large-scale program, high quality HIV/AIDS care was achieved as indicated by good short-term, immunologic outcomes, while TDF & D4T induce higher immunological recovery compared with AZT. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Public Health Africa</journal-id><journal-id journal-id-type="pmc">JPHIA</journal-id><journal-id journal-id-type="publisher-id">JPHIA</journal-id><journal-title-group><journal-title>Journal of Public Health in Africa</journal-title></journal-title-group><issn pub-type="ppub">2038-9922</issn><issn pub-type="epub">2038-9930</issn><publisher><publisher-name>PAGEPress Publications</publisher-name><publisher-loc>Pavia, Italy</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">28299037</article-id><article-id pub-id-type="pmc">5345394</article-id><article-id pub-id-type="publisher-id">jphia.2010.e3</article-id><article-id pub-id-type="doi">10.4081/jphia.2010.e3</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Immunological profile in persons under antiretroviral therapy in a rural Nigerian hospital</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Musa</surname><given-names>Baba Maiyaki</given-names></name><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>Gebi</surname><given-names>Usman</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Etiebet</surname><given-names>Mary-Ann</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Omuh</surname><given-names>Helen</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Ekedegwa</surname><given-names>Patrick</given-names></name><xref ref-type="aff" rid="A3">3</xref></contrib><contrib contrib-type="author"><name><surname>Dakum</surname><given-names>Patrick</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Blattner</surname><given-names>William</given-names></name><xref ref-type="aff" rid="A4">4</xref></contrib></contrib-group><aff id="A1"><label>1</label>Rasta Nurah General Hospital, Eastern province, Saudi Arabia;</aff><aff id="A2"><label>2</label>Institute of Human Virology, Abuja, Nigeria;</aff><aff id="A3"><label>3</label>General Hospital Otukpo, Benue State, Nigeria;</aff><aff id="A4"><label>4</label>Institute of Human Virology, University of Maryland School of Medicine, USA</aff><author-notes><corresp id="FN1">Correspondence: Baba Maiyaki Musa, <addr-line>Rasta Nurah General Hospital, Eastern province, Saudi Arabia.</addr-line> E-mail: <email>babamaiyaki2000@yahoo.co.uk</email></corresp></author-notes><pub-date pub-type="epub"><day>19</day><month>8</month><year>2010</year></pub-date><pub-date pub-type="collection"><day>01</day><month>9</month><year>2010</year></pub-date><volume>1</volume><issue>1</issue><elocation-id>e3</elocation-id><history><date date-type="received"><day>02</day><month>3</month><year>2010</year></date><date date-type="accepted"><day>05</day><month>4</month><year>2010</year></date></history><permissions><copyright-statement>©Copyright B.M. Musa et al., 2010</copyright-statement><copyright-year>2010</copyright-year><license license-type="open-access"><license-p>This work is licensed under a Creative Commons Attribution NonCommercial 4.0 License (CC <uri xlink:type="simple" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/">BY-NC 4.0</uri>).</license-p><license-p>Licensee PAGEPress, Italy</license-p></license></permissions><self-uri xlink:title="pdf" xlink:type="simple" xlink:href="jphia-2010-1-e3.pdf"></self-uri><abstract><p>Human immunodeficiency virus (HIV) contributes significantly to morbidity and mortality in sub-Saharan Africa, with Nigeria having the third highest burden of HIV infection globally; efforts are made to increases access to HIV/AIDS care and treatment. This has currently reached rural areas with limited manpower and laboratory evaluation capacity. This review is necessitated by the paucity of interim report on treatment profile in Nigerian rural areas. We report on the immunological profile of patients on antiretroviral therapy (ART) in Otukpo General Hospital, a rural Nigerian hospital. This is a retrospective cohort study of patients receiving ART treatment and care, on April 2009, when 2347 patients were under ART therapy. Out of these, 96 patients were selected by simple random sampling from hospital register, with their data abstracted from standardized Ministry of Health registers and facility documents kept at the hospital, and analyzed for descriptive and biometric measures. Ninty-six patients (29% males) with a median age of 35 years, median baseline CD4 lymphocyte count 221 cells/mL, median one year CD4 lymphocyte count of 356 cells/mL and median one year CD4 lymphocyte increment of 124 cells/mL were studied. There is no statistically significant difference in baseline CD4 lymphocyte count when data is disaggregated by type of drug regimen (AZT, D4T and TDF). Fourty-four percent, 23% and 33% of patients were on TDF, D4T & AZT based regimen, respectively (P=0.66). Increment of >100 cells/mL was seen in 64.58% of the reviewed patients. There was a higher CD4 lymphocyte count increment in patients on TDF & D4T compared with those in AZT based regimens (ANOVA; P<0.0003). Multivariate linear regression model showed one year CD4 lymphocyte count, one year increment in CD4 lymphocyte count, WBC count, and absolute neutrophil count to be significant correlates of baseline CD4 lymphocyte count (P<0.0001). Equally, multivariate logistic regression found age, platelet count and CD4 lymphocyte count at 12 months showed to be significant predictors of CD4 lymphocyte increment above 100 cells/µL (P<0.0001). Despite advanced disease presentation and a very large-scale program, high quality HIV/AIDS care was achieved as indicated by good short-term, immunologic outcomes, while TDF & D4T induce higher immunological recovery compared with AZT. This report suggests that quality HIV care and treatment can be effective despite the challenges of a resource-limited setting.</p></abstract><kwd-group><title>Key words:</title><kwd>acquired immunodeficiency syndrome</kwd><kwd>antiretroviral therapy</kwd><kwd>CD4 lymphocyte count</kwd><kwd>human immunodeficiency virus</kwd><kwd>retrospective study.</kwd></kwd-group></article-meta></front><body><sec sec-type="intro" id="S1"><title>Introduction</title><p>Human immunodeficiency virus (HIV) contributes significantly to morbidity and mortality in sub-Saharan Africa, with Nigeria having one of the highest burdens of HIV infection globally. This has lead to a rapid scale up in the provision of anti retroviral medication to eligible patients throughout the country, which in recent times have reached rural areas.</p><p>Although there is substantial variation in the size and rate of CD4 lymphocyte T cell count recovery among patients receiving antiretroviral therapy (ART), there are only few studies describing the pattern of immunological response to ART in rural settings.</p><p>This study intends to highlight interim immunological outcome in ART treatment program from a Nigerian rural area.</p></sec><sec sec-type="materials|methods" id="S2"><title>Materials and Methods</title><p>This is a retrospective cohort study of adult patients receiving ART and care, in Otukpo General Hospital, Benue State, Nigeria. Eligibility for inclusion in the study was based on being hitherto ART naïve adult patient, enrolled on ART with minimum of two CD4 lymphocyte count results available (at initiation and at 12 months post commencement of therapy); documentation of clinical evaluation on at least two visits and documentation of basal hematological profile. All patients were on first-line ART regimen, which comprised of nucleoside reverse transcriptase inhibitor (NRTI9, zidovudine (AZT), stavudine (D4T), or nucleoside reverse transcriptase inhibitor Tenofovir disoproxil fumarate (DTF) with lamivudine (3TC) or emtricitabine (FTC) plus a non-nucleoside reverse transcriptase inhibitor (efavirenz or nevirapine). All studied patients were on one of AZT/3TC/NVP, D4T/3TC/NVP and TDF/FTC/EFV regimen. Exclusion criteria were age less than 15 years and prior history of ART exposure.</p><p>As of April 2009, 2347 patients were on ART; out of these, a convenient sample of 96 patients was chosen through simple random sampling. Random number table was used with numbers chosen based on serial numbers on Hospital register. Selections were serially made until designated sample size was achieved. Data was abstracted from standardized Ministry of Health registers and facility documents kept at the hospital, and analyzed for descriptive and biometric measures.</p><p>Data were analyzed using Stata version 11.0 (College Station, Texas, USA). Differences in categorical variables were assessed using Pearson's χ<sup>2</sup>-test. Differences in means were assessed with ANOVA analysis, with Bonferroni test used to evaluate single paired comparison. All statistical tests were two-sided at α=0.05. Correlates were determined using multivariate linear regression, while predictors were determined using multivariate logistic regression analysis.</p></sec><sec sec-type="results" id="S3"><title>Results</title><p>Ninety-six patients (29% males) where studied with a median age of 35 years, 95% CI [35–37]. Majority of the patients are in the age range 15–40 years, with 4.2% below 18 years and 33.3% above 40 years. The median baseline CD4 lymphocyte count was 221cells/mL, 95% CI [189.00-238.65] with no gender difference, [Pr (|T| > |t|) = 0.1317]. Median one-year CD4 lymphocyte count was 356 cells/mL, 95% CI [330.99- 453.00, also with no gender difference: [Pr (|T| > |t|) = 0.0591] and median one year CD4 lymphocyte increment of 124 cells/mL, CI [110.00- 181.844] with no gender difference: [Pr (|T| > |t|) = 0.3445] (<xref ref-type="table" rid="T1">Table 1</xref>).</p><p><table-wrap id="T1" orientation="portrait" position="float"><label>Table 1</label><caption><title>Median values.</title></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Measures</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Values</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">Median age</td><td align="center" valign="top" rowspan="1" colspan="1">35 years</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Median baseline CD4 lymphocyte count</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">221 cells/µL</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Median one year CD4 lymphocyte count</td><td align="center" valign="top" rowspan="1" colspan="1">356 cells/µL</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Median one year CD4 lymphocyte count increment</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">124 cells/µL</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Median baseline creatinine</td><td align="center" valign="top" rowspan="1" colspan="1">90.7 µmol/L</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Median baseline hemoglobin</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">6.1 g/dL</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Median baseline white blood cell count</td><td align="center" valign="top" rowspan="1" colspan="1">6.1×10<sup>6</sup> cells/mL</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Median baseline absolute neutrophil count</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">2.15×10<sup>6</sup> cells</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Median baseline platelet</td><td align="center" valign="top" rowspan="1" colspan="1">221×10<sup>3</sup> cells</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Age groups</td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1"> <18 years</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">4.2%</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1"> 15-40 years</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">62.5%</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1"> >40 years</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">33.3%</td></tr></tbody></table></table-wrap></p><p>Median values for hemoglobin [Hb] was 6.1g/dL; median white blood cell [WBC] count was 6.1×10<sup>9</sup>/L; median platelet count was 221×10<sup>3</sup> cells; median creatinine level was 90.7 µmol/L; median absolute neutrophil count was 2.15×10<sup>9</sup> cells/L (<xref ref-type="table" rid="T1">Table 1</xref>).</p><p>When disaggregated by type of NRTI drug regimen (AZT, D4T, TDF), there was no statistically significant difference in sex, absolute neutrophil count, hemoglobin platelet count and base line CD4 lymphocyte count. However there was statistically significant difference in age, WBC, creatinine and number of patients with CD4 lymphocytes count >100 cells/mL (P<0.05).</p><p>The sub-analysis also shows 64.58% of patients having CD4 lymphocyte count increment of >100 cells/mL, with 43.75% being on TDF, 22.91% on D4T and 33.33% on AZT based regimen (<xref ref-type="table" rid="T2">Table 2</xref>).</p><p><table-wrap id="T2" orientation="portrait" position="float"><label>Table 2</label><caption><title>NRTI drug regimen variables.</title></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Drug base variable</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">AZT</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">D4T</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">TDF</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">P</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">Sex</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td align="center" valign="top" rowspan="1" colspan="1">χ<sup>2</sup> 0.661</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"> Female</td><td align="center" valign="top" rowspan="1" colspan="1">24</td><td align="center" valign="top" rowspan="1" colspan="1">14</td><td align="center" valign="top" rowspan="1" colspan="1">30</td><td rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"> Male</td><td align="center" valign="top" rowspan="1" colspan="1">8</td><td align="center" valign="top" rowspan="1" colspan="1">8</td><td align="center" valign="top" rowspan="1" colspan="1">12</td><td rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Age, in years</td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1"> Mean & S.D.</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">41.50, 11.39</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">31.54, 5.56</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">36.61, 9.72</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.0013</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">WBC</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"> Mean & S.D</td><td align="center" valign="top" rowspan="1" colspan="1">5.38, 1.80</td><td align="center" valign="top" rowspan="1" colspan="1">7.49, 1.35</td><td align="center" valign="top" rowspan="1" colspan="1">6.21, 1.68</td><td align="center" valign="top" rowspan="1" colspan="1">0.0012</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Absolute neutrophil count</td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1"> Mean & S.D</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.41, 1.52</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">2.22, 2.14</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.80, 1.47</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.2203</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Hemoglobin g/dL</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"> Mean & S.D</td><td align="center" valign="top" rowspan="1" colspan="1">11.63, 1.25</td><td align="center" valign="top" rowspan="1" colspan="1">11.08, 2.09</td><td align="center" valign="top" rowspan="1" colspan="1">11.71, 6.68</td><td align="center" valign="top" rowspan="1" colspan="1">0.9074</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Platelet count</td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1"> Mean & S.D</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">214.67, 62.99</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">267.78, 86.95</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">284.29, 140.46</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.0734</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Creatinine</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"> Mean & S.D</td><td align="center" valign="top" rowspan="1" colspan="1">214.67, 62.99</td><td align="center" valign="top" rowspan="1" colspan="1">267.78, 86.95</td><td align="center" valign="top" rowspan="1" colspan="1">284.28, 140.46</td><td align="center" valign="top" rowspan="1" colspan="1">0.0004</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Baseline CD4 lymphocyte count</td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1"> Mean & S.D</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">296.13 114.48</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">253.27 103.39</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">261.76, 234.01</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.6104</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Difference in CD4 count</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"> <100 cells/mL</td><td align="center" valign="top" rowspan="1" colspan="1">23</td><td align="center" valign="top" rowspan="1" colspan="1">2</td><td align="center" valign="top" rowspan="1" colspan="1">9</td><td align="center" valign="top" rowspan="2" colspan="1">χ<sup>2</sup> 0.0001</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"> >100 cells/mL</td><td align="center" valign="top" rowspan="1" colspan="1">9</td><td align="center" valign="top" rowspan="1" colspan="1">20</td><td align="center" valign="top" rowspan="1" colspan="1">33</td></tr></tbody></table></table-wrap></p><p>All studied patients were on TDF, D4T & AZT based regimen in the proportion (44%, 23% and 33%), respectively (P=0.66), with no statistically significant gender difference in type of drug usage (<xref ref-type="fig" rid="F1">Figure 1</xref> and <xref ref-type="table" rid="T3">Table 3</xref>).</p><p><fig id="F1" orientation="portrait" position="float"><label>Figure 1</label><caption><p>Drug type spreading.</p></caption><graphic xlink:href="jphia-2010-1-e3-g001"></graphic></fig></p><p><table-wrap id="T3" orientation="portrait" position="float"><label>Table 3</label><caption><title>Gender difference in type of drug.</title></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Type of regimen</th><th align="center" valign="top" colspan="2" content-type="background-color:#B2B3B6" rowspan="1">Sex</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Total</th></tr><tr><th content-type="background-color:#B2B3B6" rowspan="1" colspan="1"></th><td align="left" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">F</td><td align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">M</td><th content-type="background-color:#B2B3B6" rowspan="1" colspan="1"></th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">AZT</td><td align="center" valign="top" rowspan="1" colspan="1">24</td><td align="center" valign="top" rowspan="1" colspan="1">8</td><td align="center" valign="top" rowspan="1" colspan="1">32</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">D4T</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">14</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">8</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">22</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">TDF</td><td align="center" valign="top" rowspan="1" colspan="1">30</td><td align="center" valign="top" rowspan="1" colspan="1">12</td><td align="center" valign="top" rowspan="1" colspan="1">42</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Total</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">68</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">28</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">96</td></tr></tbody></table><table-wrap-foot><fn fn-type="abbr"><p>Person χ<sup>2</sup>(2)=0.8277; Pr=0.661.</p></fn></table-wrap-foot></table-wrap></p><p>Whereas there was no difference in one year CD4 lymphocyte count among the regimen types [Prob>χ<sup>2</sup>=0.192], there was a higher CD4 lymphocyte count increment in patients on TDF & D4T compared with those in AZT (ANOVA; P<0.0003) (<xref ref-type="fig" rid="F2">Figure 2</xref> and <xref ref-type="table" rid="T4">Table 4</xref>).</p><p><fig id="F2" orientation="portrait" position="float"><label>Figure 2</label><caption><p>CD4 lymphocyte count analysis among regimen types.</p></caption><graphic xlink:href="jphia-2010-1-e3-g002"></graphic></fig></p><p><table-wrap id="T4" orientation="portrait" position="float"><label>Table 4</label><caption><title>Bonferroni test analysis.</title></caption><table frame="hsides" rules="groups"><thead><tr><th content-type="background-color:#B2B3B6" rowspan="1" colspan="1"></th><th content-type="background-color:#B2B3B6" rowspan="1" colspan="1"></th><th align="center" valign="top" colspan="2" content-type="background-color:#B2B3B6" rowspan="1">Analysis of variance</th><th content-type="background-color:#B2B3B6" rowspan="1" colspan="1"></th><th content-type="background-color:#B2B3B6" rowspan="1" colspan="1"></th></tr><tr><th align="left" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Source</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">SS</th><th align="left" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">df</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">MS</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">F</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Prob>F</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">Between groups</td><td align="char" char="." valign="top" rowspan="1" colspan="1">521518.442</td><td align="center" valign="top" rowspan="1" colspan="1">2</td><td align="char" char="." valign="top" rowspan="1" colspan="1">260759.221</td><td align="center" valign="top" rowspan="1" colspan="1">8.75</td><td align="center" valign="top" rowspan="1" colspan="1">0.003</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Within groups</td><td align="char" char="." valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">2770552.46</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">93</td><td align="char" char="." valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">29790.8867</td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Total</td><td align="char" char="." valign="top" rowspan="1" colspan="1">3292070.91</td><td align="center" valign="top" rowspan="1" colspan="1">95</td><td align="char" char="." valign="top" rowspan="1" colspan="1">34653.378</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr></tbody></table><table-wrap-foot><fn fn-type="other"><p>Barlett's test for equal variances χ<sup>2</sup>(2)=3.6530 Prob>χ<sup>2</sup>=0.161.</p></fn></table-wrap-foot></table-wrap></p><p><table-wrap orientation="portrait" id="d35e720" position="float"><caption><title>Comparison of diff in CD4 by type of regimen (Bonferroni).</title></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Row mean-col mean</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">AZT</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">D4T</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">D4T</td><td align="char" char="." valign="top" rowspan="1" colspan="1">167.645</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"></td><td align="char" char="." valign="top" rowspan="1" colspan="1">0.002</td><td rowspan="1" colspan="1"></td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">TDF</td><td align="char" char="." valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">149.329</td><td align="char" char="." valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">-18.316</td></tr><tr><td content-type="background-color:#DCDDDF" rowspan="1" colspan="1"></td><td align="char" char="." valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.001</td><td align="char" char="." valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.000</td></tr></tbody></table></table-wrap></p><p>Multivariate linear regression model shows correlation among baseline CD 4 count and one year CD4 lymphocyte count, one year increment in CD4 lymphocyte count, WBC count, and absolute neutrophil count (P< 0.0001) (<xref ref-type="table" rid="T5">Table 5</xref>).</p><p><table-wrap id="T5" orientation="portrait" position="float"><label>Table 5</label><caption><title>Multivariare linear regression.</title></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Source</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">SS</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">df</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1"><sub>MS</sub></th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">Model</td><td align="center" valign="top" rowspan="1" colspan="1">1207152.4</td><td align="center" valign="top" rowspan="1" colspan="1">6</td><td align="center" valign="top" rowspan="1" colspan="1">201192.067</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Residual</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">172069.542</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">66</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">2607.11427</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Total</td><td align="center" valign="top" rowspan="1" colspan="1">1379221.95</td><td align="center" valign="top" rowspan="1" colspan="1">72</td><td align="center" valign="top" rowspan="1" colspan="1">19155.8604</td></tr></tbody></table><table-wrap-foot><fn fn-type="other"><p>Number of obs=73; F(6,66)=77.17; Prob>F=0.0000; R<sup>2</sup>=0.8752; Adj R<sup>2</sup>=0.8639; Root MSE= 51.06.</p></fn></table-wrap-foot></table-wrap></p><p><table-wrap id="ufig" orientation="portrait" position="float"><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Baseline CD4</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Coeff.</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Std. Err.</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">t</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">P>|t|</th><th align="center" valign="top" colspan="2" content-type="background-color:#B2B3B6" rowspan="1">[95% Cl]</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">Diff. in CD4</td><td align="center" valign="top" rowspan="1" colspan="1">-0.6826205</td><td align="center" valign="top" rowspan="1" colspan="1">.0503586</td><td align="center" valign="top" rowspan="1" colspan="1">−13.56</td><td align="center" valign="top" rowspan="1" colspan="1">0.000</td><td align="center" valign="top" rowspan="1" colspan="1">−0.7831646</td><td align="center" valign="top" rowspan="1" colspan="1">−0.5820763</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">WBC</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">−0.13.9389</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">4.098695</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">−3.40</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.001</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">−22.12221</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">−5.755589</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Absol. neutr∼t</td><td align="center" valign="top" rowspan="1" colspan="1">18.12517</td><td align="center" valign="top" rowspan="1" colspan="1">4.975141</td><td align="center" valign="top" rowspan="1" colspan="1">3.64</td><td align="center" valign="top" rowspan="1" colspan="1">0.001</td><td align="center" valign="top" rowspan="1" colspan="1">8.191983</td><td align="center" valign="top" rowspan="1" colspan="1">28.05836</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Oneyr CD4</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.7754799</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">.0436665</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">17.76</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.000</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">.6882969</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.8626629</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Age</td><td align="center" valign="top" rowspan="1" colspan="1">0.4064612</td><td align="center" valign="top" rowspan="1" colspan="1">.6574477</td><td align="center" valign="top" rowspan="1" colspan="1">0.62</td><td align="center" valign="top" rowspan="1" colspan="1">0.539</td><td align="center" valign="top" rowspan="1" colspan="1">−0.9061755</td><td align="center" valign="top" rowspan="1" colspan="1">1.719098</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">HB</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.70998</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.427488</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.20</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.235</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">−1.140092</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">4.560052</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">_Cons</td><td align="center" valign="top" rowspan="1" colspan="1">44.05268</td><td align="center" valign="top" rowspan="1" colspan="1">37.91478</td><td align="center" valign="top" rowspan="1" colspan="1">1.16</td><td align="center" valign="top" rowspan="1" colspan="1">0.249</td><td align="center" valign="top" rowspan="1" colspan="1">-31.64661</td><td align="center" valign="top" rowspan="1" colspan="1">119.752</td></tr></tbody></table></table-wrap></p><p>Multivariate logistic regression model shows age, platelet count and CD4 lymphocyte count at 12 months to be significant predictors of CD4 lymphocyte increment above 100 cells/µL (P=0.03, 0.005 and 0.006, respectively) (<xref ref-type="table" rid="T6">Table 6</xref>).</p><p><table-wrap id="T6" orientation="portrait" position="float"><label>Table 6</label><caption><title>Multivariate logistic regression.</title></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">CD4 diff. cat.</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Odd ratio</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">Std. Err.</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">|z|</th><th align="center" valign="top" content-type="background-color:#B2B3B6" rowspan="1" colspan="1">P>|z|</th><th align="center" valign="top" colspan="2" content-type="background-color:#B2B3B6" rowspan="1">[95% CI]</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">WBC</td><td align="center" valign="top" rowspan="1" colspan="1">0.9705083</td><td align="center" valign="top" rowspan="1" colspan="1">0.3269259</td><td align="center" valign="top" rowspan="1" colspan="1">−0.09</td><td align="center" valign="top" rowspan="1" colspan="1">0.929</td><td align="center" valign="top" rowspan="1" colspan="1">0.5014909</td><td align="center" valign="top" rowspan="1" colspan="1">1.878172</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">Absol. neutr~t</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.4761149</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.2706727</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">−1.31</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.192</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.1562433</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.450849</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Platelet</td><td align="center" valign="top" rowspan="1" colspan="1">1.030436</td><td align="center" valign="top" rowspan="1" colspan="1">0.0108929</td><td align="center" valign="top" rowspan="1" colspan="1">2.84</td><td align="center" valign="top" rowspan="1" colspan="1">0.005</td><td align="center" valign="top" rowspan="1" colspan="1">1.009306</td><td align="center" valign="top" rowspan="1" colspan="1">1.052008</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">One yr CD4</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.022787</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.0083326</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">2.77</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.006</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.006585</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.03925</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Age</td><td align="center" valign="top" rowspan="1" colspan="1">0.7489997</td><td align="center" valign="top" rowspan="1" colspan="1">0.1017784</td><td align="center" valign="top" rowspan="1" colspan="1">−2.13</td><td align="center" valign="top" rowspan="1" colspan="1">0.033</td><td align="center" valign="top" rowspan="1" colspan="1">0.5738726</td><td align="center" valign="top" rowspan="1" colspan="1">0.9775698</td></tr><tr><td align="left" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">HB</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.309255</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.2922285</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">1.21</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">0.227</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">.8453454</td><td align="center" valign="top" content-type="background-color:#DCDDDF" rowspan="1" colspan="1">2.027749</td></tr></tbody></table><table-wrap-foot><fn fn-type="other"><p>Logistic regression; Log likelihood = 16.490353; Number of bos =61; LR χ<sup>2</sup>(6)= 48.79; Prob>χ<sup>2</sup>=0.0000; Pseudo R<sup>2</sup>=0.5967.</p></fn></table-wrap-foot></table-wrap></p></sec><sec sec-type="discussion" id="S4"><title>Discussion</title><p>The HIV/AIDS epidemic predominantly affects people in the reproductive age group, with far reaching disastrous impact on socioeconomic structures of societies.<sup><xref rid="R1" ref-type="bibr">1</xref></sup> This study has similarly found 64% of studied patient to be in the age group 15–40 years, in the locality of Otukpo, Benue State, Nigeria, a strategic agricultural area in Nigeria; the cost of HIV/AIDS will be significant in economic, social and human terms. This could have an effect on the standard of living in the area as has been witnessed in other areas, with similar epidemic patterns.<sup><xref rid="R2" ref-type="bibr">2</xref>–<xref rid="R4" ref-type="bibr">4</xref></sup></p><p>In parts of Africa young women ages 15–24 are up to six times more likely to be HIV-positive than young men of the same age, with these proportion growing slowly.<sup><xref rid="R5" ref-type="bibr">5</xref>–<xref rid="R7" ref-type="bibr">7</xref></sup> This study has similarly found the affected patients to be predominantly women. This obviously will have an effect on the stability of homes, rearing of children and often on economy, where women are active members of the economic circuit. Several young women become sexually involved with numerous male associates or clients in return for financial support.<sup><xref rid="R8" ref-type="bibr">8</xref>,<xref rid="R9" ref-type="bibr">9</xref></sup> Some African societies encourage marriage after demise of spouses; if this happens without proper precautions, it will have the effect of propagating the spread of HIV in a geometric manner.<sup><xref rid="R10" ref-type="bibr">10</xref>–<xref rid="R12" ref-type="bibr">12</xref></sup></p><p>Several studies, from developing countries have found low base line CD4 lymphocyte count in HIV positive, ART naïve patients who are at the verge of commencing treatment and care.<sup><xref rid="R13" ref-type="bibr">13</xref>,<xref rid="R14" ref-type="bibr">14</xref></sup> These has been attributed to late presentation to point of care and is often associated with advance co-morbidities like tuberculosis and diarrheal diseases.<sup><xref rid="R15" ref-type="bibr">15</xref></sup> This study found the median baseline CD4 lymphocyte count to be 221 cells/mL, with no gender difference. Several studies have found CD4 lymphocyte count, especially counts of <200/mm<sup>3</sup>, to be a major risk factor for both disease progression and failure to respond to antiretroviral therapy.<sup><xref rid="R16" ref-type="bibr">16</xref></sup></p><p>As a result of the scale-up of antiretroviral treatment (ART) programs and substantial financial support worldwide, an increasing number of HIV-infected individuals in low-income and middle-income countries (LIMCs) now have access to ART. Despite this progress, important questions remain on the best use of ART and how patients should be maintained on a successful regimen. Physicians often choose a particular regimen base on perceived ease of administration, potential for good adherence, and relativity in frequency of adverse effect among others. Another factor is the proportionate availability of different types of ART within various financed treatment programs.<sup><xref rid="R17" ref-type="bibr">17</xref>,<xref rid="R18" ref-type="bibr">18</xref></sup> Drugs are frequently procured on a large scale in order to reduce cost, with the choice of agents largely driven by affordability rather than toxicity. In this study, TDF based regimen appears to be the most frequently prescribed nucleoside reverse transcriptase inhibitor (NRTI). TDF based regimen have the advantage of dosing simplicity, with concomitant effect on adherence. Its safety profile is also encouraging, considering the almost universal occurrence of major side effect, anemia and peripheral neuropathy among users of AZT and D4T, respectively.</p><p>A wide-range of clinical trial have not identified short-term differences (i.e. at week 48 or 96) in CD4 lymphocyte count between different antiretroviral regimens in patients starting AZT or D4T.<sup><xref rid="R19" ref-type="bibr">19</xref>–<xref rid="R21" ref-type="bibr">21</xref></sup> One exception to these treatments was a study that demonstrated a significantly higher increase in CD4 lymphocyte cell count in patients taking a stavudine/lamivudine nucleoside backbone with indinavir compared with zidovudine/lamivudine with indinavir.<sup><xref rid="R22" ref-type="bibr">22</xref></sup> Our review shows higher CD4 lymphocyte count increment in group of patients on D4T compared with those on AZT. This may potentially be due to confounders like differences in adherence, which was not accessed in this study. Another issue is that our study is observational and not subjected to randomization; in this perspective, it has inherent limitations.</p><p>Prospective clinical trials have demonstrated the improvement in lipoatrophy when zidovudine or stavudine are substituted by tenofovir, as such this strategy has often been strongly recommended in antiretroviral therapy.<sup><xref rid="R23" ref-type="bibr">23</xref></sup> Other studies showed comparatively better immunological recovery when TDF is used compared with AZT or D4T.<sup><xref rid="R24" ref-type="bibr">24</xref>,<xref rid="R25" ref-type="bibr">25</xref></sup> Our study has shown better immunological recovery among those on TDF, compared with those on AZT. This result can partly be explained by possibility of a low pill regimen, motivating better compliance, preferential selection of patients to start TDF base regimen; and presumably, patients on AZT and D4T might have been <italic>ab initio</italic> infected with HIV strains carrying genomic resistance to these drugs. In the past, some patients in Nigeria had been on regimens based on D4T and AZT with less rigorous tracking of adherence. Furthermore, with TDF requiring 3 point mutation for complete resistance, it is reputed to be a more ‘forgiving drug’ compared to the other two drugs, thus the potential to better fair. TDF is a widely used drug in clinical practice due to its excellent combination of effectiveness, durability and tolerability, in addition to its ease of administration in a single daily dose; int his study, we had not assessed pattern of adherence, and however it is likely that it may play a key role in the outcome. We have equally not looked at incidence of renal impairment among the studied population.</p><p>As more patients start ART, in locations with limited resources, caregivers face the emerging challenge of achieving and maintaining immunological recovery with often less than ideal means of assessing progress of therapy. This has lead to the search for correlates of immunological status. Our study has shown that, one year CD4 lymphocyte count, one year increment in CD4 lymphocyte count, baseline WBC count, and absolute neutrophil count are significant correlates of baseline CD4 lymphocyte count. Some studies have not consistently associated baseline CD4 lymphocyte count with immunological recovery; however, most studies do find this association.<sup><xref rid="R26" ref-type="bibr">26</xref>–<xref rid="R28" ref-type="bibr">28</xref></sup></p><p>Whereas, we have found age, platelet count, and CD4 lymphocyte count at 12 months to be significant predictors of CD4 lymphocyte increment above 100 cells/µL; different studies have described varying permutations of predictors. These factors include but are not restricted to level of adherence, baseline CD4 lymphocyte count, baseline viral load, hemoglobin, total lymphocyte count, WHO HIV disease stage, and Alanine transaminase (ALT) among others.<sup><xref rid="R30" ref-type="bibr">30</xref>–<xref rid="R32" ref-type="bibr">32</xref></sup></p><p>There are several important limitations to note in this study. First of all, our study is observational, and patients were not randomly allocated to different ART regimens; as such, any findings should be interpreted from this perspective. The differences in CD4 lymphocyte cell count increment could reflect other differences among patients other than the antiretroviral drugs they were taking, and which we either do not know or cannot account for. Such differences include the selection of patients chosen to start different ART regimens and changes in the population over time. Differences in adherence are unlikely to explain these findings as our basic assumption, is varying level of adherence is likely uniformly distributed among treatment groups. We have also demonstrated that there are no differences in baseline CD4 lymphocyte count among groups.</p><p>Overall, we have come to agree that there are different levels of immunological recovery among various treatment regimens; as such, guidance should be sought when initiating therapy based on evidence and best practices. This consideration has become more urgent considering challenges in the options of antiretroviral choices, in LIMCs, with attendant implication on cross-resistance, leading to increased morbidity and mortality. We also have to contend with the fact that second-line and salvage regimens have higher pill burden, complex nutritional requirements, more side effects and potential for spread of resistant virus.</p><p>This study paves the way for conducting a well planned prospective cohort study to address both immunological responses, adherence and adverse effect of the myriad of ART used in the Nigerian landscape.</p></sec><sec sec-type="conclusions" id="S5"><title>Conclusions</title><p>Despite advanced disease presentation and a very large-scale program, high quality HIV/AIDS care was achieved as indicated by good short-term and immunologic outcomes. Furthermore, it is noted that TDF & D4T induce higher immunological recovery compared with AZT. This report suggests that quality HIV care and treatment can be effective, despite the challenges of a resource-limited setting.</p></sec></body><back><ack><title>Acknowledgment:</title><p>PEPFAR - CDC/Nigeria, IHV-UMD Nigeria and Baltimore. FMOH-NACA-NASCP. Forgaty International Center (funded our training in statistic methods in Epidemiology). 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