Knowledge, use (misuse) and perceptions of over-the-counter analgesics in sub-Saharan Africa: a scoping review

Figures & data

Figure 1. Flow diagram of study selection

Table 1. Study characteristics arranged by country and design

First Author and Year	Country	Design	Sample size and population	Methodology
Afolabi 2004 [28]	Nigeria	Cross-sectional study	1943 sick children	Record of patient’s diagnosis
Yusuff 2011 [29]	Nigeria	Cross-sectional study	1650 pregnant women	Structured questionnaire
Adelekan 1989 [30]	Nigeria	Cross-sectional study	1000 secondary school students	Self-administered questionnaire
Abasiubong 2012 [31]	Nigeria	Cross-sectional study	518 pregnant women, aged 18–40 years	Structured questionnaire
Enato 2011 [32]	Nigeria	Cross-sectional study	497 heads of household	Questionnaire
Nwankwo 2010 [33]	Nigeria	Cross-sectional study	495 post-menarcheal school girls aged 10–19 years	Semi-structured questionnaire
Bello 2011 [34]	Nigeria	Cross-sectional study	410 women attending antenatal	Self-administered questionnaire
Oshodi 2010 [35]	Nigeria	Cross-sectional study	402 secondary school students	Self-administered questionnaire
Esan 2018 [36]	Nigeria	Cross-sectional study	384 undergraduate university students	Self-administered questionnaire
Lawan 2013 [14]	Nigeria	Cross-sectional study	380 adults	Structured questionnaire
Babatunde 2016 [15]	Nigeria	Cross-sectional study	291 healthcare workers	Self-administered questionnaire
Obu 2012 [37]	Nigeria	Cross-sectional study	231 caregivers to children aged six weeks to 16 years	Self-administered questionnaire
Omolase 2007 [38]	Nigeria	Cross-sectional study	200 hospital outpatients	Structured questionnaire
Amberbir 2011 [39]	Ethiopia	Population-based prospective birth cohort	1065 pregnant women	Face to face interviews in a longitudinal study
Amberbir 2014 [40]	Ethiopia	Population-based prospective birth cohort	1006 newborn children	Face to face interviews with mothers in a longitudinal study
Birru 2016 [41]	Ethiopia	Cross-sectional study	720 students	Self-administered questionnaire
Beyene 2018 [42]	Ethiopia	Cross-sectional mixed methods study	617 pregnant women 9 key informants	Structured questionnaire and IDI guide
Shafie 2018 [9]	Ethiopia	Cross-sectional study	604 heads of households	Structured questionnaire
Amaha 2019 [12]	Ethiopia	Cross-sectional study	577 adults	Structured questionnaire
Duncan 2006 [43]	Ethiopia	Cross-sectional mixed methods study	204 members of the general population for questionnaires and 8 FGDs (number of participants unspecified)	FGDs and questionnaires
Eticha 2014 [6]	Ethiopia	Cross-sectional study	270 community members	Structured questionnaire
Sado 2017 [44]	Ethiopia	Cross-sectional study	154 health professionals	Self-administered questionnaire
Marwa 2018 [45]	Tanzania	Cross-sectional study	372 pregnant women	Self-administered questionnaire
Chipwaza 2014 [46]	Tanzania	Cross-sectional study	93 community members and 14 healthcare workers	FGDs and IDIs
Mensah 2019 [47]	Ghana	Cross-sectional study	361 community members	Self-administered questionnaire
Badzi 2017 [48]	Ghana	Cross-sectional study	206 construction workers	Structured interviews
Myers 2003 [49]	South Africa	Retrospective study	9063 patients from specialist substance abuse treatment centre	Assessment of patients’ prescription forms
Clarke 2003 [50]	Gambia	Cross-sectional study	917 women	Structured questionnaire
Lucas 2007 [51]	Mozambique	Cross-sectional study	797 university students	Self-administered questionnaire
Tesfamariam 2019 [52]	Eritrea	Cross-sectional study	609 adults	Structured questionnaire
Kasilo 1991 [53]	Zimbabwe	Cross-sectional study	498 household members	Questionnaire
Ndol 2013 [54]	Democratic Republic of Congo (DRC)	Cross-sectional study	391 hospital patients	Questionnaire
Angbo-Effi 2011 [55]	Cote d’Ivoire	Cross-sectional study	300 adult household members	Questionnaire
Penda 2018 [56]	Cameroon	Cross-sectional study	295 hospitalised patients aged 0–18 years	Semi-structured questionnaire
Geissler 2000 [57]	Kenya	Cross-sectional study	57 schoolchildren aged 11–17 years	Face to face interviews

Table 2. Prevalence of OTC drug use, drugs used, sources of OTC drugs and common ailments treated

Author and Year	Prevalence of OTC drug use	Recall period	Common drugs used	Sources of drugs	Illnesses or conditions treated
Yusuff 2011 [29]	64%	90 days	Paracetamol (31%) Haematinics and vitamins (23%) Promethazine (8%) Piroxican (8%) Diazepam (8) Amoxicillin (5%) Dipyrone (5%) Chloramphenicol (4%) Ampicillin (4%) Panadol extra (3%) Procold (16%)	Patent medicine stores (55%) Pharmacies (31%) Drug hawkers (15%)	Body pains/fever (30%) Joint pains (15%) Cough (10%) General weaknesses (9%) Indigestion (9%) Headache (8%) Insomnia (8%) Nausea (7%) Heartburn (3%) Inflammation (2%)
Adelekan 1989 [30]	58%	NR	Salicylate analgesics (58%) Alcohol (18%) Stimulants (34%) Antibiotics (18%) Other (10%)	NR	NR
Abasiubong 2012 [31]	72%	Current pregnancy	Analgesics 157 (30%) Antibiotics 138 (27%) Mixed herbs & other drugs 47 (9%) Sedatives 15 (3%) Alcohol (3%) Kolanut (1%)	NR	NR
Bello 2011 [34]	78%	NR	Acetaminophen (48%) Antimalarial (6%) Anthelminthic (69%) Calcium supplements (1%)	NR	NR
Esan 2018 [36]	82%	1 month	Paracetamol (75%) Ibuprofen (12%) Aspirin 5%) Other (8%)	Doctor (3%) School clinic (3%) Hostel (1%) Home (3%) Other (1%)	Headache (46%) Stomach ache (7%) Body pain (15%) Muscle pain (4%) Dysmenorrhoea (12%) Fever (3%) Cough/cold (3%) Arthritis pain (1%) Other (4%)
Lawan 2013 [14]	79%	6 months	Antimalarials (42%) Analgesics (41%) Antibiotics (29%) Cough mixtures (13%) Other (6%)	Patent medical stores (63%) Markets (20%) Drug hawkers (12%) Family/friends (6%)	NR
Babatunde 2016 [15]	52%	12 months	Analgesics (38%) Antibiotics (19%) Antimalarials (13%) Other (29%)	NR	Body pains (15%) Catarrh (15%) Headaches (14%) Sore throat (12%) Diarrhoea (11%) Fever (9%) Toothache (6%)
Omolase 2007 [38]	85%	NR	Antimalarials (16%) Antibiotics (3%) Antimalarials and analgesics (22%) Antimalarials, analgesics and antibiotics (15%) Antibiotics and analgesics (10%) Antimalarials and antibiotics (6%) Anti-hypertensive (1%) Hypoglycaemic (1%)	NR	NR
Amberbir 2011 [39]	29%	1 month	Paracetamol (100%)	NR	Asthma (2%) Hay fever (4%) Eczema (1%)
Amberbir 2014 [40]	60% in the first 3 years of life; 35% for current exposure at 5 years old	12 months	Paracetamol (100%)	NR	Fever (31%); Headache(24%) Common cold (7%) Malaria 4%) Wheezing illness (4%) Coughing illness (1%) Allergy (1%)
Birru 2016 [41]	73%	12 months	Paracetamol (64%) Diclofenac (25%) Ibuprofen (8%) Migraine-specific agents (4%)	Previous treatment (33%) Pharmacy (45%) Physician (7%) Non-drug substances (5%) Other (26%)	Headache (100%)
Beyene 2018 [42]	27%	Current pregnancy	Paracetamol (49%) Amoxicilin (23%) Ibuprofen (5%) Panadol (6%) Diclofenac (5%) Aspirin (3%) Other (10%)	Pharmacy/drug stores (77%) Leftovers (19%) Family/friends or neighbours (10%) Health facilities (1%)	NR
Shafie 2018 [9]	76%	2 months	Antacids (5%) Anthelminthic (6%) Antibacterial (15%) Cough syrup (0%) Traditional herbs (16%) Combination drugs (8%) Not specified (1%)	Pharmacies/drug stores (83%) Neighbours (7%) Previous treatment (7%) Other (3%)	Headache (26%) Abdominal pain (13%) Cough (12%) Diarrhoea (9%) Toothache (8%) Combination (6%) Other (26%)
Amaha 2019 [12]	38%	1 month	Antibiotics (41%) Analgesics (27%) Anthelminthic (14%) Antacids (12%)	Pharmacy/drug stores (64%) Friends/relatives and neighbours (24%) Other (11%)	Gastro-intestinal disease (29%) Urinary tract infection (12%) Eye and skin infection (31%) Fever (20%) Other (8%)
Eticha 2014 [6]	88%	NR	Analgesics (21%) Gastro-intestinal drugs (18% Respiratory drugs (15%) Oral rehydration solution (14%) Vitamins (11%) Antimicrobial (8%)	NR	Headache or fever (21%) Gastro-intestinal (17%) Respiratory infections (16%) Eye disease (14%) Skin disease (13%) Dysmenorrhea (11%) Sexually transmitted diseases (5%) Other (3%)
Sado 2017 [44]	68%	2 months	Analgesics (36%) Antibiotics (24%) Oral contraceptives (17%) Antacid (13%) Oral hypoglycemic agents (7%) Other (4%)	NR	NR
Marwa 2018 [45]	46%	Current pregnancy	Antimalarial (25%) Antibiotics (10%) Antiemetics (34%) Analgesics (19%) Anti-asthma (2%) Other (11%)	NR	Malaria (33%) Urinary tract infection (9%) Morning sickness (25%) Heartburn (2%) Headache (20%) Other (14%)
Chipwaza 2014 [46]	NR	NR	Antimalarial* Antipyretic* Antimicrobial*	Community Pharmacies Family and friends and neighbours Previous treatment	Fever Diarrhoea Cough Loss of appetite and ‘flu’ Abdominal pain Wound and headache
Mensah 2019 [47]	NR	3 Months	Antibiotics (32%) Analgesics (21%) Antidiabetic (20%) Antimalarial (10%) Antacids (11%) Antihypertensive (5%)	Licenced chemists (32%) Traditional practitioners (26%) Family/friends (21%) Community pharmacy (20%)	NR
Badzi 2017 [48]	97%	NR	Paracetamol (37%) Aspirin* Ibuprofen (10%) Combined analgesics (52%)	Pharmacy stores (58%) Chemical stores (86%) Drug peddlers (4%) Friends (12%)	Muscle and joint pain* Headaches* Stress* Fatigue*
Myers 2003 [49]	45%	NR	Analgesics*	NR	NR
Clarke 2003 [50]	NR	NR	Paracetamol (28%) Chloroquine (8%)	Pharmacy Market traders Private drug outlets Village shops	Fever (94%) Vomiting (79%) Headache (33%) Diarrhoea (27%)
Lucas 2007 [51]	56%	1 month	Analgesics (38%) Anti-infectives (15%) Antimalarials (6%) Vitamins (8%) Oral contraceptives (5%)	NR	NR
Tesfamariam 2019 [52]	94%	1 month	Analgesics (34%) Antipyretics (16%) Cough and cold preparations (14%) Antacid (10%) Antidiarrheal (10%) Vitamins (8%) Anti-allergy (5%) Anti-inflammatory (2%) Other (1%)	NR	Non-chronic disease (72%) Chronic diseases (25%) Central nervous system disorders (1%) Other (1%)
Kasilo 1991 [53]	95%	NR	Analgesics (50%) Respiratory drugs (21%) Dermatological (9%) Gastrointestinal (8%) Antimalarial (7%) Eye ointments (3%) Other (1%)	Chemist/ pharmacist (56%) Shop/ Supermarket (23%) Hospital/clinic (19%) Other (2%)	Sore throat/ cough/colds (16%) Pain (9%) Stomach pain (6%) Skin infection (5%) Fever (5%) Sore eyes (2%) Cramps/ muscle pain (2%) Other (28%)
Ndol 2013 [54]	60%	NR	Analgesics/Antipyretics (44%) NSAIDS (26%) Antimalarials (21%) Antibiotics (12%)	NR	NR
Angbo-Effi 2011 [55]	72%	NR	Analgesics (75%) Antimalarials (72%) Antibiotics (48%)	NR	NR
Penda 2018 [56]	74%		Antipyretics (75%) Antimalarials (72%) Antibiotics (10%) Anthelminthic (6%) Other (23%)	Family pharmacy box (64%) Pharmacy (22%) Street vendors (14%)	Fever (83%) Vomiting (20%) Diarrhoea (20%) Cough (13%) Headache (9%)
Geissler 2000 [57]	NR	1 week	Antimalarials (14%) Antibiotics (4%) Paracetamol (66%) Aspirin (24%)	Family/friends/ Neighbours* Shop keepers*	Headache (30%) Abdominals (12%) Colds (13%) Other (28%)
Afolabi 2004 [28]	89%	NR	Analgesics/Antipyretics (63%) Antimalarial (23%) Antibiotics (21%) Haematinics/vitamins (55%) Cough syrup (12%) Local herbs (9%) Antihistamines (5%) Antidiarrheals (4%) Anthelminthic (2%) No drug (11%)	NR	Fever (61%) Cough (44%) Diarrhoea (21%) Loss of appetite (17%) Vomiting (17%) Catarrh (16%) Other (52%)
Enato 2011 [32]	45%	2 Weeks	Antidiarrheal (2%) Antimalarial (90%) Antibiotic (3%) Analgesics (3%) Antipyretic (<1%) Anticonvulsant (<1%) Antihistamine (<1%) Surgical operation (<1%)	General hospital (9%) Private hospital (9%) Primary health care (14%) Traditional herbal medicine (1%) Maternity home care (16%) Treated home (1%) Patent medicine store (6%) Self-treatment (45%)	Alimentary (10%) Cardiovascular (1%) Dermatological (1%) Nervous system (58%) Infections (10%) Musculoskeletal (5%) Respiratory (15%) Sensory organs (1%)
Nwankwo 2010 [33]	43%	NR	Paracetamol* Aspirin/Piroxican (80%) Antibiotics (19%) Other (1%)	NR	Dysmenorrhea (25%) Premenstrual disorder (19%) Cycle less than 21 days (12%) Cycle greater than 45 days (5%) Scanty menstruation (5%) Prolonged bleeding (4%)
Obu 2012 [37]	76%	NR	Paracetamol (100%)	Patent medical stores/pharmacy (4%) Self (45%)	Fever (63%) Cough (7%) Abdominal pain (4%) Other (14%) Non-response (12%)
Duncan 2006 [43]	NR	1 month	Aspirin (44%) Paracetamol*	NR	Headache* Fever*
Oshodi 2010 [35]	NR	NR	Paracetamol* Aspirin* Antimalarials*	NR	NR

NR = Variable not recorded; *Percentages not reported

Table 3. Reasons, sources of information and socio-demographic characteristics for OTC drug use

Author and Year	Reasons for OTC use	Sources of knowledge or information OTC use	Socio-demographic characteristics of OTC use
Yusuff 2011 [29]	Accessibility/uncontrolled availability (40%) Long distance to public health facility (30%) Financial difficulty (19%) Perceived poor service delivery at facility (12%)	Mothers-in-law and relatives (41%) Patent medicine vendors (20%) Pharmacist (13%) Nurse (10%) Neighbour (8%) Traditional healer (7%)	NR
Adelekan 1989 [30]	Cost saving (22%) Influence from others (33%) Mild severity of illness (18%) Bureaucracy (23%)	NR	Gender: More OTC use among females than males*
Abasiubong 2012 [31]	NR	NR	Education: More OTC use among participants with more education (25% vs 7%)
Bello 2011 [34]	NR	NR	Education: More OTC drug use among participants with less education* Age: More OTC drug use among older than younger participants*
Esan 2018 [36]	Unfriendly attitude of health care workers (28%) Lack of time to go to school clinic (27%) School clinic is too far from hostel (15%) Don’t trust quality of drugs (15%)	NR	Gender: More OTC use among females than males (88% vs 71%)
Lawan 2013 [14]	Long queues (38%) Doctors not available (25%) Services too expensive (18%) Not necessary (to consult doctors for prescription) (19%)	Drug vendors* Family/friends* Self* Other*	Age: More OTC use among older participants (aged ≥40 years) than among younger participants* Education: More OTC use among participants with below secondary education*
Babatunde 2016 [15]	Financial problems (11%) Mild sickness (11%) Lack of time (13%) Knowledge of diagnosis (6%) Convenience (2%) Non-availability of doctor (3%)	NR	Age: More OTC use among older (49 years above) participants than the younger ones* Education: More OTC use among participants with less education*
Omolase 2007 [38]	Complaint is minor (55%) Financial constraint (22%) Services not readily available (9%) Certainty of efficacy of self-medication 10%) Lack of escort (3%) Ignorance (1%)	NR	NR
Amberbir 2014 [40]	Readily available (77%) Affordable (92%)	NR	NR
Birru 2016 [41]	NR	NR	Gender: More OTC drug use among females than males*
Beyene 2018 [42]	Easy access (71%) Disease not serious (55%) Saves time (27%) Cheaper (18%) Previous experience (21%) Poor health service provision (1%) Long waiting time (20%)	NR	NR
Shafie 2018 [9]	Minor illness (47%) Prior knowledge of the drug (23%) Emergency case (11%) Time constraint (6%) Long queues (2%)	Health professional (45%) Previous treatment (21%) Friends (16%) Self (13%) Reading books/internet (4%)	Age: More OTC drug use for older participants (≥55 years) than younger participants* Income: More OTC drug use for low earners than higher earners* Gender: More OTC use for females than males (54% vs 22%)
Amaha 2019 [12]	Minor illness (48%) Quick relief (79%) Long queues (59%) Attitude of health workers (8%) Health facilities expensive (13%) Long distance (28%)	Friends, relatives or neighbours (59%) Labels, leaflets or promotional materials (8%) TV advertisement (9%) Internet (7%) Past experience (37%)	Education: More OTC use among participants with less education* Income: More OTC use for high earners than low earners*
Eticha 2014 [6]	Disease was not serious (22%) Prior experience of the illness and/or the drug (21%) Less expensive or time-consuming (20%) Emergency care (17%) Prevention of known or unknown illness (17%) Symptoms of illnesses (17%)	Pharmacists (23%) Health workers (21%) Friends, neighbours or relatives (19%) Reading drug-related information (13%) Traditional healers (13%)	NR
Sado 2017 [44]	Familiarity with drug (24%) Mildness of illness (14%) Privacy (17%) Less cost/financial constraint (33%) Lack of time (7%)	NR	NR
Marwa 2018 [45]	NR	NR	Education: More OTC use among pregnant women with secondary education and below* Occupation: More OTC use among unemployed than among employed*
Chipwaza 2014 [46]	Shortage of drugs at health facilities* Long waiting times at health facilities* Long distance to health facility* Unable to pay for health care costs* Freedom to choose drug of choice*	Parents/guardians* Pharmacy/Drug shop/vendors* Health workers*	NR
Mensah 2019 [47]	Cost saving (2%) Influence from others (friends/family) (33%) Mild severity of illness (18%) Bureaucracy of health system (23%) Other (4%)	NR	Education: More OTC use among participants with more education*
Badzi 2017 [48]	Prescribed (7%) Induce sleep (27%) Relieve aches and pains (66%)	Advertisements (73%) Friends (64%) Prescriptions (7%) Other (2%)	NR
Myers 2003 [49]	NR	NR	Gender: More OTC use among females than males)*
Clarke 2003 [50]	Hospital services too expensive* Long distance to health facility*	NR	NR
Lucas 2007 [51]	NR	NR	Gender: More OTC use among females than males (65% vs 42%)
Tesfamariam 2019 [51]	Ease of accessibility (34%) Saving time (24%) Perception of being safe and tolerable (15%) Saving money (6%) Treating minor ailments (4%) Getting quick relief (4%)	Pharmacists (35%) Medical doctors (27%) Friends/family (21%) Internet/mobile applications (3%)	Education: More OTC use among participants with less education*
Kasilo 1991 [53]	Long queues at hospitals* Long waiting time at hospitals*	NR	NR
Ndol 2013 [54]	NR	NR	Age: More OTC use among younger participants than older patients (71% for those aged <30 years; 59% for those 31–50; and 50% for those ≥ 50) Marital status: More OTC use among those who lived as couples self-medicated than singles (66% vs 55%)
Angbo-Effi 2011 [55]	Accessibility (13%) Cheaper than other options (69%) Lack of finances (16%)	Influence from others (54%)	NR
Penda 2018 [56]	Mildness of illness (56%) Disease severity (27%) Persistent disease (52%) Financial problem (7%)	Drug information leaflets (75%) Pharmaceutical advice (65%)	NR
Geissler 2000 [57]	NR	NR	Gender: More OTC drug use among males than females (34% vs 9%)
Afolabi 2004 [28]	Perception of illness (96%)	NR	NR
Enato 2011 [32]	NR	Family members, neighbours and friends (62%) Clinic (1%)	NR
Obu 2012 [37]	NR	Self (45%) Doctor (20%) Nurse (7%) Patent medical dealer/pharmacist (4%)	NR
Oshodi 2010 [35]	NR	NR	Gender: More OTC use among females than males*

NR = Variable not recorded; *Percentages not reported

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