Twenty (83.3%) people among the 24 with elevated GPT were on ART, 21(70.0%) of 30 people that had elevated GOT were on ART and also 13(81.3%) of 16 that had elevated ALP use ART. This implied that greater percentages of the participants that had elevated levels of the liver enzymes use ART.
This study recorded 1.6% prevalence of HCV among people living with HIV/AIDs, which was exactly the percentage reported in Kano by Hanza et al., (2013) but the 5.2%
prevalence of HBV recorded in this study was lower than 12.3% reported by Hanza et al., (2013) in their own research.
From Table 13, higher percentage of the participants (58.4%) were married, 32.3% were single while 7.7% and 1.6% were widowed and divorced respectively. In educational status, 224(44.6%) of the participants had secondary school certificate, while 196(39.0%) had Tertiary Institution certificate. This really showed high literacy among the participants.
Majority of the participants were students, 157(31.3%) or in business, 146(29.1%) while unemployed group had the least members, 34 (6.8%). About 159 (31.7%) of the participants were exposed to risk factors (NB: 45 were exposed to more than one risk factor). Those that were exposed to blood transfusion were the highest followed by smokers, IV drug users and homosexual/lesbians have the least number.
From Table 14, for HIV/Viral hepatitis, the analyses show that the marital status of the participant does not affect the coinfection of viral hepatitis with HIV (P > 0.05).
For HIV/HBV, The coinfection of HIV with HBsAg is not determined by the marital status of the individual (P > 0.05).
For HIV/HCV, Marital status is not a determinant factor for coinfection with HIV/HCV (P > 0.05).
From Table 15, for HIV/GPT, The analysis showed that marital status affected the level of GPT of an individual living with HIV/AIDS (P < 0.05).
For HIV/GOT, Marital status does not affect the GOT level of a person living with HIV/AIDS (P > 0.05).
For HIV/ALP, The analysis showed that marital status of an individual does not in any way affect the ALP of a person living with HIV/AIDS (P > 0.05).
Table 13: Socio-demographic characteristics of the participants
Characteristics Groups Frequency (%)
Marital status Single 162(32.3)
Married 293(58.4)
Widowed 39(7.7)
Divorced 8(1.6)
Total 502
Educational level Non-formal Edu. 50(10.0)
Primary 32(6.4)
Secondary 224(44.6)
Tertiary Edu. 196(39.0)
Total 502
Occupation Civil servants 63(12.5)
Artisans 102(20.3)
Business 146 (29.1)
Students 157(31.8)
Unemployed 34(6.8)
Total 502
Risk factors IV drug users 11(2.2)
Blood transfusion 78(15.5)
Surgery 32(6.4)
Alcohol abuse 25(5.0)
Smokers 48(9.6)
Homosexual/lesbians 10(2.0)
Total 204
Table 14: Marital status of participants that tested positive to viral hepatitis
Marital status HIV only (%) HIV/HBsAg(%) HIV/HCV (%) Total (%)
Single 150(92.6) 9(5.3) 3(1.9) 162 (32.3)
Married 276 (94.2) 13(4.4) 4(1.2) 293 (58.4)
Widowed 35(89.7) 3(7.7) 1(2.6) 39 (7.7)
Divorced 7(87.5) 1(12.5) 0(0.0) 8 (1.6)
468 26 8 502 (100)
For HIV/HBV; P = 0.626 (P > 0.05) For HIV/HCV; P = 0.912 (P > 0.05)
Table 15: Marital Status of participants with elevated GPT or GOT or ALP Marital
status
HIV only (%) HIV/GPT (%) HIV/GOT (%) HIV/ALP(%)
Single 150(92.6) 8(4.9) 10(6.2) 5(3.1)
Married 276 (94.2) 11(3.8) 19(6.5) 11(3.8)
Widowed 35(89.7) 3(7.7) 1(2.6) 0(0.0)
Divorced 7(87.5) 2(12.5) 0(0.0) 0(0.0)
468 24 30 16
For HIV/GPT; P = 0.035 (P < 0.05) For HIV/GOT; P = 0.691 (P > 0.05) HIV/ALP; P = 0.602 (P > 0.05)
4.10: Marital Status of the Participants
The 502 participants were grouped into 4 groups; single, married, widowed and divorced.
Two hundred and ninety-three representing 58.4% of the participants were married while the divorced group had least participants, 8 representing 1.6%. Among the male participants, 68 representing 44% of them were single while 94 female participants (27%) were single. The female participants had higher percentages of participants that were married and widowed than the male counterparts. From Fig. 6, General Hospital Ekwulobia had highest percentage of married participants while General Hospital Onitsha had highest percentage of single participants.
From Table 16,for HIV/HBV
The above analysis showed that educational level of a person living with HIV/AIDS does not affect coinfection of HIV/HBV (P > 0.05).
For HIV/HCV,
This implied that coinfection of HIV/HCV was not significantly affected by the educational level of the individual (P > 0.05).
From Table 17,for HIV/GPT, This means that the level of education of an individual living with HIV/AIDS does not affect GPT level (P > 0.05).
For HIV/GOT, This showed that the GOT level of a person living with HIV/AIDS is not dependent on his/her level of education (P > 0.05).
For HIV/ALP, From the analysis, ALP level of an individual living with HIV/AIDS is not affected by the educational level of the individual (P > 0.05).
From Table 18, For HIV/HBsAg, This showed that the occupation of a person living with HIV/AIDS does not determine coinfection of HIV/HBsAg (P > 0.05).
For HIV/HCV, This implies that coinfection of HIV/HCV is not affected in any way by the occupation of the individual (P > 0.05).
From Table 19, for HIV/GPT, The above analysis showed that the GPT level of an individual living with HIV/AIDS is independent on the occupation of the individual (P > 0.05).
For HIV/GOT, This showed that the occupation of a person living with HIV/AIDS does not affect his/her GOT level (P > 0.05).
For HIV/ALP, This implies that the ALP level is not determined by the occupation of a person living with HIV/AIDS (P > 0.05).
Table 16: Educational status of participants that tested positive to viral hepatitis Educational level HIV only (%) HIV/HBsAg(%) HIV/HCV (%) Total
Non-formal Edu. 45(9.0) 4(8.0) 1(0.2) 50(1.0)
Primary 30(6.0) 2(0.4) 0(0.0) 32(6.4)
Secondary School 208 (41.4) 11(2.2) 5(1.0) 224(44.6)
Tertiary Edu. 185(36.9) 9(1.8) 2(0.4) 196(39.0)
468 (93.2) 26 (5.2) 8 (1.6) 502(100)
For HIV/HBsAg; P = 0.788 (P > 0.05) For HIV/HCV; P = 0.668 (P > 0.05)
Table 17: Educational status of participants with elevated GPT or GOT or ALP
Educational level HIV only (%) HIV/GPT(%) HIV/GOT(%) HIV/ALP(%)
Non- formal Edu. 45(9.0) 3(0.6) 1(0.2) 0(0.0)
Primary 30(6.0) 3(0.6) 2(0.4) 1(0.2)
Secondary School 208 (41.4) 12(2.4) 16(3.2) 10(2.0)
Tertiary Edu. 185(36.9) 6(1.2) 11(2.2) 5(1.0)
468 (93.2) 24(4.8) 30 (6.0) 16(3.2)
For HIV/GPT; P = 0.379 (P > 0.05) For HIV/GOT; P = 0. 572 (P > 0.05) For HIV/ALP; P = 0.378 (P > 0.05)
Table 18: Occupation of participants that tested positive to viral hepatitis
Occupation HIV only (%) HIV/HBsAg(%) HIV/HCV(%) Total
Civil servant 57(11.4) 4(0.8) 2(0.4) 63 (12.5)
Artisans 96(19.1) 5(1.0) 1(0.2) 102(20.3)
Traders 136(27.1) 8(1.6) 2(0.4) 146(29.1)
Students 148(29.5) 7(1.4) 2(0.4) 157(31.3)
Unemployed 31(6.2) 2(0.4) 1(0.2) 34(6.8)
468 (93.2) 26 (5.2) 8(1.6) 502(100)
For HIV/HBsAg; P = 0.981 (P > 0.05) For HIV/HCV; P = 0.774 (P > 0.05)
Table 19: Occupation of participants with elevated GPT, or GOT or ALP
Occupation HIV only (%) HIV/GPT(%) HIV/GOT(%) HIV/ALP(%)
Civil servant 57(11.4) 3(0.6) 3(0.6) 2(0.4)
Artisans 96(19.1) 4(0.8) 7(1.4) 3(0.6)
Traders 136(27.1) 6(1.2) 11(2.2) 5(1.0)
Students 148(29.5) 8(1.6) 8(1.6) 6(1.2)
Unemployed 31(6.2) 3(0.6) 1(0.2) 0(0.0)
468 (93.2) 24(4.8) 30(6.0) 16(3.2)
For HIV/GPT; P = 0.774 (P > 0.05) For HIV/GOT; P= 0.788 (P > 0.05) For HIV/ALP; P = 0.849 (P > 0.05)
From Table 20, for HIV/HBsAg, This showed that coinfetcion of HIV/HBsAg is significantly affected by exposure to risk factors for people living with HIV/AIDS
(P < 0.05).
For HIV/HCV, This implies that exposure to risk factors does not necessarily determine coinfection of HIV/HCV (P > 0.05).
From Table 21,for HIV/GPT, The above showed that GPT level of a person living with HIV/AIDS is not affected by exposure to any risk factor (P > 0.05).
For HIV/GOT, This implies that GOT level of an individual living with HIV/AIDS is not affected by the individual being exposed to any risk factor (P > 0.05).
For HIV/ALP, This means that the ALP level is not dependent on exposure to risk factors in people living with HIV/AIDS (P > 0.05).
From Table 22, for ART, From the above table, participant being on ART or Non-ART is not determined by the marital status of the participants. Just as greater percentage of the whole participants were on ART, greater percentage of each marital group were on ART (P > 0.05).
From Table 22 , for CD4 From the table above, marital status significantly affected the CD4 level of a person living with HIV/AIDS. All marital groups have greater percentages of their members with CD4 count 200cells/𝜇l and above (P < 0.05).
From Table 23, for Gender, Gender does no significantly affect the educational level of the participant (P > 0.05).
From this table, a person living with HIV/AIDS being on ART is not dependent on the educational level of the individual. All the educational levels have greater percentages of their members on ART.
From the Table 23, educational level significantly affected the CD4 level of a person living with HIV/AIDS, also the illiterate and elementary groups have higher percentages of those with CD4 less than 200cells/𝜇l of blood (P < 0.05).