| Literature DB >> 32837218 |
David Onchonga1,2, Joshua Omwoyo2, Duke Nyamamba2.
Abstract
BACKGROUND: Self-medication plays a key role in public health as it influences both negatively and positively on the health of individuals and the existing healthcare systems. This is especially the case during public health emergencies like the 2019 SARS-CoV-2 disease.Entities:
Keywords: 2019 SARS-CoV-2; COVID-19; Healthcare workers; Kenya; Prevalence; Self-medication
Year: 2020 PMID: 32837218 PMCID: PMC7426227 DOI: 10.1016/j.jsps.2020.08.003
Source DB: PubMed Journal: Saudi Pharm J ISSN: 1319-0164 Impact factor: 4.330
Socio-demographic characteristics of respondents before and during COVID-19 Outbreak.
| Socio-demographic variable | N | (%) | |
|---|---|---|---|
| Gender | Female | 195 | 51.5 |
| Male | 184 | 48.5 | |
| Education | College diploma | 153 | 40.4 |
| University Degree | 226 | 59.6 | |
| Age | 18–30 | 128 | 33.8 |
| 31–40 | 159 | 42.0 | |
| 41–50 | 72 | 19.0 | |
| 51–60 | 20 | 5.3 | |
| Religion | Christian | 346 | 91.3 |
| Muslim | 17 | 4.5 | |
| Hindu | 4 | 1.1 | |
| Traditional | 2 | 0.5 | |
| No religion | 10 | 2.6 | |
| Marital status | Single | 131 | 34.6 |
| Married | 248 | 56.4 | |
| Geographical location | Rural | 99 | 21.6 |
| Peri-urban | 112 | 29.6 | |
| Urban | 168 | 44.3 | |
| Place of work | Public Health Facility | 260 | 68.6 |
| Private practice | 75 | 19.8 | |
| NGO | 36 | 9.5 | |
| Pharmaceutical company | 8 | 2.1 | |
| Cadre | Public Health Officers | 86 | 22.7 |
| Nursing Officer | 116 | 30.6 | |
| Clinical Officers | 13 | 3.4 | |
| Pharmacy/Technologists | 67 | 17.7 | |
| Medical Officers | 22 | 5.8 | |
| Community Health Assistants | 16 | 4.2 | |
| Nutritionists | 10 | 2.6 | |
| Laboratory Scientists/Technicians | 14 | 3.7 | |
| Physiotherapist | 5 | 1.3 | |
| Medical Consultants | 8 | 2.1 | |
| Medical Registrars | 9 | 2.4 | |
| Radiologists | 6 | 1.6 | |
| Health Records Information | 2 | 0.5 | |
| Dentist/ Dental Technologists | 5 | 1.3 | |
Fig. 1Conditions for self-medication before and during COVID-19 Outbreak.
Relationship between Demographic Variables and Self-Medication before and during COVID-19 Outbreak.
| Socio-demographic | Self-medication Self-medication during COVID-19 before COVID-19 | χ2 | p value | ||
|---|---|---|---|---|---|
| n (%) | n (%) | ||||
| 1. Gender | Female | 129(56.3) | 66(44.0) | 5.518 | 0.019 |
| Male | 100(43.7) | 84(56.0) | |||
| 2. Education | College Diploma | 83(36.2) | 70(46.7) | 4.090 | 0.043 |
| University | 146(63.8) | 80(53.3) | |||
| 3. Age | 18–30 | 69(30.1) | 59(39.3) | 10.592 | 0.014 |
| 31–40 | 111(48.5) | 48(32.0) | |||
| 41–50 | 37()16.2 | 35(23.3) | |||
| 51–60 | 12(5.2) | 8(5.4) | |||
| 4. Marital status | Single | 119(52.0) | 32(21.3) | 36.606 | 0.000 |
| Married | 110(48.0) | 118(78.7) | |||
| 5. Geographical location | Rural | 57(24.9) | 42(28.0) | 1.353 | 0.509 |
| Peri-urban | 65(28.4) | 47(31.3) | |||
| Urban | 107(46.7) | 61(40.7) | |||
| 6. Physical activity | Yes | 181(79.0) | 48(21.0) | 9.578 | 0.002 |
| No | 97(64.7) | 53(35.3) | |||
| 7. Drug reaction event | Yes | 135(59.0) | 94(41.0) | 14.759 | 0.000 |
| No | 117(78.0) | 33(22.0) | |||
| 8. Health status | Perfect health | 38(16.6) | 37(24.7) | 3.860 | 0.145 |
| Good health | 177(77.3) | 106(70.7) | |||
| Average health | 14(6.1) | 7(4.7) | |||
| 9. Psychiatric condition | Yes | 205(89.5) | 24(10.5) | 0.132 | 0.716 |
| No | 136(90.7) | 14(9.3) | |||
| 10. Sleeping pattern | More than 8 hrs | 28(12.2) | 28(18.7) | 3.523 | 0.172 |
| 5–8 hrs | 187(81.7) | 116(77.3) | |||
| 1–4 hrs | 14(6.1) | 6(4.0) | |||
Multivariable logistic regression analyses on influencing factors of self-medication during the COVID-19 outbreak.
| Independent variables (n = 379) | B | S.E. | Wald X2 | P | Exp(B) | (95% CI) |
|---|---|---|---|---|---|---|
| Physical activity (Yes/No) | −1.160 | 0.356 | 10.615 | 0.001 | 0.313 | (0.156,0.630) |
| Gender (Female/Male) | −0.081 | 0.308 | 0.069 | 0.793 | 0.922 | (0.504,1.687) |
| Drug reaction event (Yes/No) | −0.469 | 0.338 | 1.929 | 0.165 | 0.625 | (0.322,1.213) |
| Work shift (Night/Day) | −4.414 | 1.155 | 14.597 | 0.000 | 0.012 | (0.001,0.117) |
| Reading prescription (All the time/Sometimes/Not at all) | −0.105 | 0.626 | 0.028 | 0.866 | 0.900 | (0.264,3.070) |
| Level of education (Diploma/Degree) | 0.890 | 0.340 | 6.832 | 0.009 | 2.434 | (1.249,4.743) |
| Any known psychiatric condition (Yes/No) | 0.169 | 0.519 | 0.106 | 0.745 | 1.184 | (0.428,3.274) |
| Health status (Perfect /Good/Average) | 0.701 | 0.716 | 0.960 | 0.327 | 2.016 | (0.496,8.201) |
| Feeling unwell in the last 3 weeks (Yes/No) | −2.587 | 0.346 | 55.910 | 0.000 | 0.075 | (0.038,1.48) |
| Constant | −20.099 | 40193.683 | 0.000 | 1.000 | 0.000 |
Action taken following adverse drug reaction event during COVID-19 outbreak.
| Action taken after adverse drug reaction event | N | (%) |
|---|---|---|
| Went to a private doctor | 12 | 11.8 |
| Went to primary health centre | 17 | 16.7 |
| Went to a pharmacist | 11 | 10.8 |
| Stopped taking the medicine | 62 | 60.8 |