| Literature DB >> 31406819 |
John Kipsang1, Jia Chen2, Chulei Tang1, Xianhong Li2, Honghong Wang2.
Abstract
AIMS: This study aimed to describe the adherence level to antiretroviral therapy and its associated factors among people living with HIV in Hunan province, China.Entities:
Keywords: Acquired immunodeficiency syndrome; Antiretroviral treatment; China; Medication adherence
Year: 2018 PMID: 31406819 PMCID: PMC6626245 DOI: 10.1016/j.ijnss.2018.04.008
Source DB: PubMed Journal: Int J Nurs Sci ISSN: 2352-0132
Sample Characteristics (N = 418).
| Characteristics | Categories | No. | % |
|---|---|---|---|
| Gender | Male | 293 | 70.1 |
| Female | 125 | 29.9 | |
| Age (yr) | 18–30 | 112 | 26.8 |
| 31–50 | 265 | 63.4 | |
| 51–70 | 41 | 9.8 | |
| Marital status | Single | 204 | 49.0 |
| Married/have stable partner | 214 | 51.0 | |
| Employment | Unemployed | 199 | 47.6 |
| Employed occasionally | 82 | 19.6 | |
| Residence | Rural | 113 | 27.0 |
| Town/city | 305 | 73.0 | |
| Education level | Primary education or less | 65 | 15.6 |
| Middle school | 179 | 42.8 | |
| High school or above | 174 | 41.6 | |
| Annual income RMB | 0–4999 | 154 | 36.8 |
| 5000–19999 | 147 | 35.2 | |
| ≧ 20000 | 116 | 27.8 | |
| Drug use | No | 301 | 72.0 |
| Yes | 117 | 28.0 | |
| Time on ART | ≦ 1yr or less | 232 | 55.5 |
| 1 yr up to 2yrs | 64 | 15.3 | |
| >2yrs | 122 | 29.2 | |
Note: ART = Antiretroviral therapy; RMB = Renminbi; (yr) = age or time in years.
Factors Associated with Adherence to ART.
| Variables | Optimal adherence (≥90%, | Suboptimal adherence (<90%, | ||
|---|---|---|---|---|
| Gender | ||||
| Male | 211 | 82 | ||
| Female | 90 | 35 | 0.991 | 1.00 (0.6–1.6) |
| Age (yr) | ||||
| 18–30 | 76 | 36 | ||
| 31–50 | 192 | 73 | ||
| 51–70 | 33 | 8 | 0.313 | |
| Marital status | ||||
| Single | 139 | 65 | ||
| Married/have stable partner | 162 | 52 | 0.092 | 0.70 (0.4–1.1) |
| Employment | ||||
| Unemployed | 218 | 90 | ||
| Employed | 83 | 27 | 0.346 | 0.80 (0.5–1.3) |
| Residence | ||||
| Rural | 82 | 31 | ||
| Town/city | 219 | 86 | 0.877 | 1.00 (0.6–1.7) |
| Education | ||||
| Primary school or less | 44 | 21 | ||
| Middle school | 123 | 56 | ||
| High school or above | 134 | 40 | 0.203 | |
| Income per year (RMB) 0–4999 | 107 | 47 | ||
| 5000–19999 | 102 | 45 | ||
| ≧ 20000 | 92 | 25 | 0.214 | |
| Drug use | 229 | 72 | ||
| No | ||||
| Yes | 72 | 45 | 0.003** | 2.00 (1.3–3.1) |
| ART time | 156 | 76 | ||
| ≦ 1yr | ||||
| >1 yr to 2yrs | 51 | 13 | ||
| >2 yrs | 94 | 28 | 0.043* | |
| Perceived side effect | 231 | 76 | ||
| No | ||||
| Yes | 70 | 41 | 0.011* | 1.80 (1.1–2.8) |
| CD4+T cell counts | ||||
| <200 cells/mm3 | 116 | 72 | ||
| 200-500 cells/mm3 | 110 | 90 | ||
| >500 cells/mm3 | 14 | 16 | 0.076 | |
Note: ART = Antiretroviral therapy; RMB = Renminbi; (yr) = age or time in years; *P < 0.05, **P < 0.001.
Reasons for missing doses (n = 90).
| Reasons | No. | % |
|---|---|---|
| Forgot to take drugs | 38 | 42.2 |
| Was away from home | 26 | 28.9 |
| Too busy | 15 | 16.7 |
| Felt worse after taking drugs | 11 | 12.2 |
| Didn't want others to notice | 11 | 12.2 |
| Difficulty with times | 5 | 5.6 |
| Confused about directions | 4 | 4.4 |
| Don't think I need the drugs | 2 | 2.2 |
| Too many drugs to take | 1 | 1.1 |
Percentage of Doses Taken Within 2 h as Required (N = 418).
| Doses taken | No. | |
|---|---|---|
| Nearly all | 243 | 58.1 |
| More than half | 145 | 34.7 |
| About half | 9 | 2.2 |
| Less than half | 2 | 0.5 |
| Very little | 5 | 1.2 |
| None | 14 | 3.4 |
Logistic regression analysis of adherence to antiretroviral therapy (N = 418).
| Independent. Variables | Wald | ||||
|---|---|---|---|---|---|
| Drug use | 0.68 | 0.20 | 8.77 | 2.11 (1.3 - 3.3) | 0.003** |
| Perceived side effects | 0.64 | 0.18 | 5.61 | 1.82 (1.1 - 2.8) | 0.009** |
| Time on ART | - 0.31 | 0.13 | 6.72 | 0.72 (0.6 - 0.9) | 0.011* |
Note: ART = Antiretroviral therapy; B: unstandardized regression coefficients; SE: Standardized error; Wald χ2 = Wald Chi-square test; *P < 0.05, **P < 0.001.
This table includes only the terminal model as determined by logistic regression analysis to build multivariate model.