| Literature DB >> 35501929 |
Chidinma Ihuoma Amuzie1,2, Kalu Ulu Kalu3, Michael Izuka3, Uche Ngozi Nwamoh3, Uloaku Emma-Ukaegbu3,4, Franklin Odini3, Kingsley Metu3, Chigozie Ozurumba3, Ijeoma Nkem Okedo-Alex5,6.
Abstract
BACKGROUND: COVID-19 has led to restrictions on movements and lockdown measures, which have resulted to higher utilization of over-the-counter drugs compared to prescription-only drugs. This study determined the prevalence, pattern and predictors of self-medication for COVID-19 prevention and treatment.Entities:
Keywords: COVID-19; Pandemic; Pattern; Prevalence; Self-medication
Year: 2022 PMID: 35501929 PMCID: PMC9058746 DOI: 10.1186/s40545-022-00429-9
Source DB: PubMed Journal: J Pharm Policy Pract ISSN: 2052-3211
Socio-demographic characteristics of respondents (N = 469)
| Variables | Frequency | Percentage (%) |
|---|---|---|
| ≤ 30 | 129 | 27.5 |
| 31–40 | 128 | 27.3 |
| 41–50 | 118 | 25.2 |
| 51–60 | 55 | 11.7 |
| > 60 | 39 | 8.3 |
| Mean | 39.9 ± 13.5 years | |
| Male | 202 | 43.1 |
| Female | 267 | 56.9 |
| None/primary | 20 | 4.3 |
| Secondary | 115 | 24.5 |
| Tertiary | 203 | 43.3 |
| Post graduate | 131 | 27.9 |
| Single | 135 | 28.8 |
| Married | 288 | 61.4 |
| Cohabiting | 12 | 2.6 |
| Widowed | 27 | 5.8 |
| Divorced | 7 | 1.5 |
| Christianity | 460 | 98.1 |
| Others | 9 | 1.9 |
| Catholic | 84 | 17.9 |
| Orthodox | 219 | 46.7 |
| Pentecostal | 152 | 32.4 |
| Salary earner | 199 | 42.4 |
| Self employed | 148 | 31.6 |
| Unemployed | 122 | 26.0 |
| No income | 110 | 23.5 |
| < 50,000 | 140 | 29.9 |
| 50,000–100,000 | 79 | 16.8 |
| > 100,000 | 140 | 29.9 |
Fig. 1Drugs and supplements used for self-medication among the respondents (n = 142)
Fig. 2Source of information for self-medication among the respondents (n = 142)
Fig. 3Place of purchase of drugs used for self-medication (n = 142)
Fig. 4Triggers of self-medication among the respondents (N = 469)
Factors associated with self-medication for COVID-19 prevention and treatment (N = 469)
| Variables | Self-medication | cOR | 95% CI | ||
|---|---|---|---|---|---|
| Yes | No | ||||
| < 40 | 58(24.3) | 181(75.7) | 1 | ||
| ≥ 40 | 84(36.5) | 146(63.5) | 1.80 | 1.20–2.68 | |
| None/primary | 12(60.0) | 8(40.0) | 5.51 | 2.05–14.81 | |
| Secondary | 45(39.1) | 70(60.9) | 2.36 | 1.34–4.14 | |
| Tertiary | 57(28.1) | 146(71.9) | 1.43 | 0.85–2.41 | 0.171 |
| Postgraduate | 28(21.4) | 103(78.6) | 1 | ||
| Single | 31(23.0) | 104(77.0) | 1 | ||
| Married/cohabiting | 95(31.7) | 205(68.3) | 1.55 | 0.97–2.49 | 0.065 |
| Widowed/separated | 16(47.1) | 18(52.9) | 2.98 | 1.36–6.53 | |
| Salary earner | 53(26.6) | 146(73.4) | 0.75 | 0.46–1.22 | 0.239 |
| Self-employment | 49(33.1) | 94(66.9) | 1.01 | 0.61–1.69 | 0.955 |
| Unemployed | 40(32.8) | 82(67.2) | 1 | ||
| None | 34(30.9) | 76(69.1) | 1 | ||
| < 50,000 | 48(34.3) | 92(65.7) | 1.17 | 0.68–1.99 | 0.573 |
| 50–100,000 | 24(30.4) | 55(69.6) | 0.98 | 0.52–1.83 | 0.938 |
| > 100,000 | 36(25.7) | 104(74.3) | 0.77 | 0.44–1.35 | 0.364 |
| Yes | 125(33.8) | 245(66.2) | 2.46 | 1.40–4.33 | |
| No | 17(17.2) | 82(82.8) | 1 | ||
| Yes | 125(30.3) | 287(69.7) | 1 | ||
| No | 17(29.8) | 40(70.2) | 1.02 | 0.56–1.88 | 0.937 |
| Yes | 122(30.5) | 278(69.5) | 0.93 | 0.53–1.63 | 0.800 |
| No | 20(29.0) | 49(71.0) | 1 | ||
P value < 0.05 are considered significant, SM self-medication, *binary logistic regression
Predictors of self-medication against COVID-19 prevention among the respondents
| Variable | aOR | 95%CI | |
|---|---|---|---|
| < 40 | 1 | ||
| ≥ 40 | 1.87 | 1.11–3.13 | |
| None/primary | 3.78 | 1.28–11.19 | |
| Secondary | 2.15 | 1.17–3.97 | |
| Tertiary | 1.50 | 0.87–2.59 | 0.146 |
| Postgraduate | 1 | ||
| Single | 1 | ||
| Married/cohabiting | 1.31 | 0.74–2.33 | 0.352 |
| Widow/separated | 1.30 | 0.49–3.45 | 0.595 |
| Yes | 2.29 | 1.24–4.24 | |
| No | 1 | ||
P value < 0.05 are considered significant, SM self-medication, *binary logistic regression