Prescribing indicators at primary health care centers within the WHO African region: a systematic analysis (1995–2015)
Ofori-Asenso et al. BMC Public Health
Prescribing indicators at primary health care centers within the WHO African region: a systematic analysis (1995-2015)
Richard Ofori-Asenso 0
Petra Brhlikova 1
Allyson M. Pollock 1
0 Research Unit, Health Policy Consult , P. O. Box WJ 537, Weija-Accra , Ghana
1 Centre for Primary Care and Public Health, Barts and the London School of Medicine & Dentistry, Queen Mary, University of London , Yvonne Carter Building, 58 Turner Street, London E1 2AB , UK
Background: Rational medicine use is essential to optimize quality of healthcare delivery and resource utilization. We aim to conduct a systematic review of changes in prescribing patterns in the WHO African region and comparison with WHO indicators in two time periods 1995-2005 and 2006-2015. Methods: Systematic searches were conducted in PubMed, Scopus, Web of science, Africa-Wide Nipad, Africa Journals Online (AJOL), Google scholar and International Network for Rational Use of Drugs (INRUD) Bibliography databases to identify primary studies reporting prescribing indicators at primary healthcare centres (PHCs) in Africa. This was supplemented by a manual search of retrieved references. We assessed the quality of studies using a 14-point scoring system modified from the Downs and Black checklist with inclusions of recommendations in the WHO guidelines. Results: Forty-three studies conducted in 11 African countries were included in the overall analysis. These studies presented prescribing indicators based on a total 141,323 patient encounters across 572 primary care facilities. The results of prescribing indicators were determined as follows; average number of medicines prescribed per patient encounter = 3.1 (IQR 2.3-4.8), percentage of medicines prescribed by generic name =68.0 % (IQR 55.4-80.3), Percentage of encounters with antibiotic prescribed =46.8 % (IQR 33.7-62.8), percentage of encounters with injection prescribed =25.0 % (IQR 18.7-39.5) and the percentage of medicines prescribed from essential medicines list =88.0 % (IQR 76.3-94.1). Prescribing indicators were generally worse in private compared with public facilities. Analysis of prescribing across two time points 1995-2005 and 2006-2015 showed no consistent trends. Conclusions: Prescribing indicators for the African region deviate significantly from the WHO reference targets. Increased collaborative efforts are urgently needed to improve medicine prescribing practices in Africa with the aim of enhancing the optimal utilization of scarce resources and averting negative health consequences.
Prescribing indicators; Drug use indicators; Pharmacoepidemiology; Prescribing evaluation; Medicine utilization studies; Systematic reviews; Africa
According to the World Health Organization (WHO),
more than half of all medicines are inappropriately
prescribed, dispensed or sold with such practices deemed to
be most prevalent in healthcare settings in the developing
world where mechanisms for routine monitoring of
medicines use are still in early stages of development [
developing and low middle income countries,
pharmaceuticals account for a high proportion of household and
overall healthcare expenditure . Improvement in the
way in which medicines are used is important in reducing
morbidity and mortality, building public confidence,
reinforcing health system credibility as well as optimising
the utilisation of scarce resources [
]. The “wise list” in
Stockholm, Sweden for instance, is an example of an
improvement in medicine use with an essential medicines list
(EML) with high adherence to just 200 medicines to
improve physician familiarity with quality medicines and
reduce costs in a high income country that could provide
valuable lessons for developing countries seeking to
optimize resource utilization [
Since the late 80's, the WHO together with the
International Network for Rational Use of Drugs (INRUD)
have been advocating proper documentation of
medicines use and have developed core drug use indicators in
three related areas of prescribing practices, patient care
and facility specific factors [
]. The drug use indicators
are regarded as objective measures that can be extended
to describe patterns of medicines usage in any health
facility, country or an entire region.
The core drug use indicators include five prescribing
indicators which are meant to detail particular
prescribing characteristics related to poly-pharmacy, antibiotic
use, injection use, generic prescribing and adherence to
the essential medicines list (EML) [
]. Even though
an international standard of the prescribing indicators
has not been empirically determined, the WHO has
recommended reference values for each of the indicators
(see Table 1) [
]. In 1993, the WHO published the
guideline “How to investigate drug use at health
facilities: selected drug use indicators” aimed at outlining
methods for the collection and presentation of
information on medicines use in primary health care (PHC)
]. Subsequently, the WHO has been publishing
information on global medicines usage as part of its
World Medicines Situation reports [
]. A more detailed
fact book focusing mainly on medicines use at PHCs in
developing and transitional countries was also published
in 2009 . The broadest review on medicines usage was
published in 2013; this incorporates data from 900 studies
covering facilities at various level of care in 104 countries
between 1990 and 2009 [
]. For the African region, the
review reported the average number of medicines per
patient encounter to be 2.6, percentage of encounters with
antibiotics prescribed as 45.9 %, percentage of encounters
resulting in prescription of injection as 28.4 %, percentage
of medicines prescribed from EML to be 89.0 % and
percentage of medicines prescribed in generic name as 65.1 %
]. Despite not meeting the WHO targets, the estimates
show relatively frequent prescribing from EML and of
generic products. The high percentage of antibiotic and
injection prescriptions has been attributed to disease
burden, weak health systems and patients’ preferences. A
trend analysis showed ‘little progress over time’ [
Percentage of medicines prescribed from an essential 100 %
medicines list or formulary
The WHO African Region is one of the six regions of
the WHO and consists of 47 member states with over 927
million inhabitants in 2013 [
]. The region faces one of
the greatest disease burden compared to all other WHO
regions. In 2013, the life expectancy at birth in Africa was
58 years, the lowest among all the WHO regions and
10 years below that of Southeast Asia (68 years), the region
with the second lowest life expectancy [
]. According to
the 2013 Global burden of disease estimates, while their
relative burdens have seen some decline, communicable,
newborn, nutritional, and maternal causes such as
diarrhoeal diseases, lower respiratory infections, and
proteinenergy malnutrition still remain the top drivers of health
loss in most African countries [
]. Yet, many countries in
the region are also experiencing significant epidemiological
transition characterised by a growing burden of
noncommunicable diseases (NCDs) thereby resulting in a
"double disease burden" [
]. For instance, a recent
systematic review demonstrated consistent increase in
prevalence of hypertension in Africa from 19.7 % in 1990 to
27.4 % in 2000 and 30.8 % in 2010 [
]. While the
emerging double disease burden presents unique public health
challenges and may call for greater intervention measures
resources for improving health delivery in Africa remain
scarce. In 2013, the region’s average total health
expenditure per capita (PPP int. $) was 222, the lowest among all
WHO regions and extremely low when compared to
Europe (2214) and Americas (3873) [
]. Additionally, there
are siginficant gaps in available health system structures
that hinder effective healthcare delivery. For instance, in
the period 2007–2013, the physician to population ratio
(per 10,000 population) in Africa was 2.7; this was far
lower than the global average of 13.9 [
]. According to
], financial and human resource challenges
have hindered many healthcare systems within the African
region from evolving to meet the emerging healthcare
demands. The increasing emergence of non-communicable
diseases is likely to further exacerbate these trends.
Most health systems in Africa do not have established
mechanisms for routine system-wide medicine monitoring
and utilization. Moreover, reviews of specifically designed
studies are deemed to be out of date after 3 to 5 years or
even less (Whitlock et al.) [
]. This paper presents a
systematic review to summarize available information on
prescribing indicators for the WHO African region over the
last two decades (1995–2015). Our aim was to critically
appraise the quality of studies on prescribing practices
in the Africa region and to compare the results of
studies on prescribing indicators at PHCs in the African
region against WHO recommended reference values. We
also wished to understand whether there are observable
differences in prescribing at private and public facilities
in the WHO African region. To this end, we defined
public to represent all fully government owned or
quasi-governmental facilities. Private was defined to
cover for-profit and mission health facilities.
WHO prescribing indicators
The prescribing indicators measure the performance of
healthcare providers in five key areas related to the
appropriate use of medicines (Table 1) [
]. The derivation of
these indicators for any health facility (s) is based on an
analysis of patient clinical encounters. A patient encounter
is recognized to represent “the duration of interaction
between patient and health provider. Ideally, this encounter
includes a number of components: history taking,
diagnosis process: selection of non-pharmacological or
pharmacological treatment, prescription (and perhaps dispensing)
of treatment; and explanations about treatment and its
adverse effects, follow-up, and prevention.” [
encounters may be analyzed retrospectively using data from
medical history records or can be analyzed prospectively
as patients arrive during the period of data collection [
It is important to highlight that the determination of the
core prescribing indicators does not require information
on patients’ signs and symptoms as they provide general
prescribing tendencies (non-disease specific). The various
prescribing indicators are meant to elucidate peculiar
prescribing characteristics relating to polypharmacy, level of
antibiotic and injection use and adherence to guidelines
relating to generic and EML prescribing [
Studies retrieval process
We conducted a structured review of the literature in
accordance with the PRISMA (Preferred Reporting Items for
Systematic Reviews and Meta-Analyses) guidelines [
Comprehensive searches were conducted in PubMed,
Scopus, Web of science, Africa-Wide Nipad, Africa Journals
Online (AJOL), Google scholar and INRUD Bibliography
databases. The main key words used were “primary health
care, primary health services, community health centres,
community-based healthcare, health facilities, primary
healthcare settings’ AND “prescribing indicators,
prescribing patterns, drug use indicators, drug utilization patterns,
prescribing evaluation, prescribing statistics, rational
prescribing, rational use of medicines, health facility
indicators” AND “Africa, Sub-Saharan Africa, WHO African
Region”. The main limits used were ‘humans’ and ‘English’.
Additionally, we searched references of published reviews
and selected papers for additional publications.
Inclusion and exclusion of studies
We included only observational studies published in
English in peer-reviewed Journals between 1st January 1995
and 31st December 2015, which reported at least one
WHO/INRUD core prescribing indicator or where these
indicators were derivable from results/data presented. A study
must have specified the total number of patient encounters
involved for it to be accepted into the review. Furthermore,
to minimise potential bias, studies in which the patient
encounters were derived through a random sampling
technique were mainly included [
]. For studies with duplicate
publications, the version published first or one with
complete dataset was selected. In the case of interventional
studies, we included only pre (baseline) values. Although,
most hospital facilities provide secondary level care, in
certain instances, outpatient departments provide primary care
services. Hence, where full description of this has been
provided, studies conducted in such settings were included.
Critical appraisal of studies
Each study’s quality was assessed using a 14-point scoring
system modified from the Downs and Black checklist with
inclusions of recommendations in the WHO guidelines
(Table 2) [
]. We awarded a one point value if study
satisfied each criteria. If study did not meet criteria, it was
awarded a zero. As studies may not assess all the five
indicators (e.g. a study may not measure antibiotic use), the
criteria were applied in relation to the indicator (s)
assessed. In view of this, the quality grading was expressed
as a percentage. Irrespective of the number of criteria
applied, a study is considered as ‘high quality’ if it scores
≥70 % of the total tally scores based on the applicable
criteria. A score of 69–51 % was regarded as ‘moderate
quality’ and a score of ≤50 % was graded as ‘low quality’.
Due to the wide heterogeneity of studies, a formal
metaanalysis was not conducted. We therefore adopted a more
descriptive approach as employed in previous reviews
]. For each WHO/INRUD prescribing indicator,
we determined the median as well as the 25 and 75th
percentiles . Mean values of prescribing indicators
across studies was not used as this would be unduly
influenced by outliers [
]. To minimise the influence of
larger sample-sized studies, prescribing indicator values
were not weighted by sample size [
]. In this case, the
approach we adopted was to treat each study as a
single data point with equal weight, without regard
to sample size and variance. All computations were
done electronically using Microsoft Excel 2015® and
results of prescribing indicators were compared to
the WHO’s recommended reference values and with
previous reported values [
]. Statistical estimates
of the difference between the results of prescribing
indicators obtained for private and public PHCs as
well as between different publication periods was not
conducted since variance would have been greatly
underestimated in such circumstances .
Subanalysis was also conducted across different facility
ownerships (private vs public) as well as across the
studies publication periods 1995–2005 and 2006–
Studies identification and retrieval
Figure 1 outlines the schematic flow of the studies'
identification and inclusion processes. A total of 4208 articles
were identified by literature search. After the exclusion
of duplicates and irrelevant studies based on titles and
abstracts, 45 articles were retrieved for detailed full-text
analysis. Out of the 45 studies, 41 met the inclusion
criteria for addition to the review. Two (2) additional
studies were identified through the reference screening
bringing the total number of studies included in the
review to forty-three (43) [
7, 8, 22, 28–67
]. The 43 studies
included in this review (Table 3) collectively reported
WHO/INRUD prescribing indicators based on overall
analysis of 141,323 patient encounters across 572 PHCs. The
PHCs included 359 (62.8 %) public and 213 (37.2 %)
private facilities. We were unable to separate ‘mission-based’
and ‘business/for-profit’ in the private facilities category as
studies gave a limited description of their activities. About
65.1 % (n = 28) of studies were published in the period
2006–2015 whereas 34.9 % (n = 15) were published in the
years 1995–2005. The 43 studies included in this review
were conducted in 11 countries representing 23.4 %
(11/47) of countries in the region under study. The 11
Table 3 Descriptive characteristics of included studies
No. Author Details Year of Country
1. Abdella and Wabe [
]. 2012 Ethiopia
2. Adisa et al. [
]. 2015 Nigeria
3. Afriyie and Tetteh [
]. 2013 Ghana
4. Afriyie et al. [
]. 2015 Ghana
5. Ahiabu et al. [
]. 2015 Ghana
Angamo et al. [
Babalola et al. [
8. Ball et al. [
9. Bantie [
10. Bexell et al. [
11. Boonstra et al. [
12. Boonstra et al. [
13. Bosu and Ofori-Adjei [
14. Desta et al. [
15. Dippenar et al. [
16. Enato et al. [
17. Enato et al. [
18. Isah. [
19. Kapp et al. [
20. Katende-Kyenda et al. [
21. Krause et al. [
22. Massele and Nsimba. [
23. Massele et al. [
24. Massele et al. [
26. Meyer et al. [
27. Mohlala et al. [
28. Nsimba et al. [
29. Nsimba [
30. Odusanya and Oyediran [
31. Olayemi et al. [
32. Oyeyemi and Ogunleye [
33. Phillips-Howard et al. [
34. Risk et al. [
35. Savadogo et al. [
36. Shiferaw et al. [
37. Sisay and Mekonnen [
38. Suleman et al. [
39. Tamuno [
40. Trap et al. [
41. Truter et al. [
42. Tsega et al. [
43. Uzochukwu et al. [
n.s not specified, m mobile clinic
Table 3 Descriptive characteristics of included studies (Continued)
25. Massele et al. [
] 2012 Tanzania Prospective
countries included Ghana (4), Nigeria (11), Tanzania
(6), Kenya (1), Gambia (1), Zambia (1), Zimbabwe (2),
South Africa (6), Ethiopia (7), Burkina Faso (2) and
Quality of studies
Overall, using the quality assessment criteria outlined,
51 % of studies were graded as of high quality whereas
42 % and 7 % were graded as of medium and low
quality, respectively. The major factors that affected quality
grades of studies included smaller sample size, lack of
adherence to WHO recommendations (especially
counting and classification of medications) and poor
reporting of study information. Around one-third
(32.6 %) of studies included in the review involved patient
encounters <600 and were deemed to be small per
recommendations outlined in the WHO guidelines [
]. This is
an important consideration as studies with larger sample
size are more likely to present representative/generalizable
The studies collected data either prospectively (using
current patients as they present for consultation) or
retrospectively (using past medical records). In 27
studies, data on prescribing were collected retrospectively, in
14 studies this was done prospectively while another two
studies used a mix approach of collecting prescribing
information both prospectively and retrospectively. The
fact that majority of studies adopted a retrospective
approach is quite understandable as such data are easier to
collect. Nonetheless, retrospective analysis introduces
some bias if certain information is excluded owing to
poor record keeping. In the study by Babalola et al. [
in Nigeria for instance, records of 40 patients were
excluded from the analysis because they had incomplete
data while in the case of Massele et al. [
] in Tanzania,
the patient registers of three consecutive years were
abandoned for another register because they had
incomplete data. It is possible that the excluded information
may have presented different prescribing characteristics
than those reported in the studies. Also most
retrospective analyses rely on prescription sheets and hence may
exclude patients who are not prescribed medicines. This
is likely to lead to overestimation of variables such as
average number of medicines per patient, injection
prescribing rate and antibiotic prescribing rate although
EML and generic prescribing rates are unlikely to be
affected. While studies that adopted a prospective
approach may minimize the loss of data and deal with other
limitations of retrospective assessments, they also
introduce an observer bias (Hawthorne effect) as it is difficult
to blind the health facility staff. In the Nsimba et al. [
study in Tanzania for instance, all health staff were briefed
on the study prior to prospective data collection.
Prescribers may modify their behaviour if they know they are
been investigated and as such, results derived this way
may also not be representative of typical prescribing
]. In the two studies that adopted dual
prospective and retrospective analysis, no significant
difference in results were observed in the two approaches
thereby affirming to a large extent the validity of their
It is recommended that prescribing indicators are
analysed over an extended period (ideally ≥1 year) to
minimize the impact of seasonal variations in morbidity
patterns, peculiarities in staffing and inconsistencies in
medicines supply which can all impact on the patterns
of medicines prescribing [
]. However, across
studies reviewed, the period over which prescribing data
were collected varied widely from as short as 1 day to as
long as 24 months. Nineteen studies reported data
collection period less than 1 year and these are likely to be
prone to seasonal variations in prescribing and may not
necessarily represent usual trends.
Average number of medicines prescribed per patient encounter
Information on the number of medicines prescribed per
patient encounter was obtained from 40 studies that
included a total 138,671 patient encounters. Among these
studies, the median number of medicines prescribed per
patient encounter was 3.1 (IQR 2.3–4.8) (Table 4). The
average number of medicines prescribed per patient
encounter was higher for public 2.6 (IQR 2.2–4.7) than
private 2.5 (IQR 2.3–3.2) centres. The reported average
medicines prescribed per patient was higher for studies
published in the period 2006–2015 (3.5; IQR 2.2–5.6)
than the period 1995–2005 (2.4; IQR 2.3–4.0).
Percentage of medicines prescribed by generic name
Generic prescribing rate was reported in 33 studies that
involved a total of 121,797 patient encounters. Among
these studies, the generic prescribing rate was 68.0 %
(IQR 55.4–80.3). Public PHCs reported a higher
percentage (68.9 %; IQR 57.6–84.5) of medicines prescribed
generically than private centres (61.3 %; IQR 47.7–75.7).
Generic prescribing rate for studies published in the
period 2006–2015 (70.4 %; IQR 60.7–81.1) was higher
than for studies published in the period 1995–2005
(64.2 %; IQR 51.9–77.9).
Percentage of encounters with antibiotic prescribed
Data on antibiotic prescribing rate was also retrieved
from 34 studies comprising of a total of 120,422 patient
encounters. The overall proportion of encounters
resulting in the use of antibiotics was 46.8 % (IQR 33.7–62.8).
Public PHCs reported lower antibiotic prescribing rate
(45.0 %; IQR 30.13–60.2) compared to private facilities
(51.3 %; IQR 37.5–66.6). Higher antibiotic prescribing
IQR interquartile range, n total number of patient encounters used in analysis, aexcludes Ahiabu et al. [
] which did not provide individual results for public and
rate was recorded among studies published in the period
2006–2015 (49.0 %; IQR 37.8–63.1) than for those
published in the period 1995–2005 (43.1 %, IQR 33.7–61.7).
Percentage of encounters with injection prescribed
Injection prescribing rate was retrieved from 32 studies
consisting of a total 40,096 patient encounters. The
overall proportion of encounters resulting in the
prescription of an injection was 25.0 % (IQR 18.7–39.5).
The proportion of encounters at public PHCs which
resulted in the prescription of an injection was determined
as 25.6 % (IQR 14.1–44.8) while that of private facilities
was 29.0 % (IQR 19.0–39.5). Injection prescribing rate
across studies published in the period 2006–2015
(25.0 %; IQR 17.1–41.4) was similar to studies published
in the period 1995–2005 (24.8 %; IQR 18.7–37.4).
Percentage of medicines prescribed from an essential medicines list or formulary
Adherence to EML was determined using data from 27
studies in which a total of 101,077 medicines were
prescribed. The overall proportion of medicines prescribed
from an EML was estimated as 88.0 % (IQR 76.3–94.1).
Higher proportion of prescriptions from public centres
(89.9 %, IQR 82.9–95.6) adhered to the use of EML than
private centres (84.0 %; IQR 69.8–91.9). EML use rate
was higher among studies published in the period 2006–
2015 (88.9 %; IQR 70.8–94.0) than for the studies
published within 1995–2005 (87.1 %; IQR 84.9–92.0).
Average number of medicines per patient encounter
Our review showed a high number of medicines (3.1)
prescribed per patient encounter. This value is higher than
that reported by the WHO factbook for the African region
(2.6) and that for the European (2.5), Southeast Asia (2.5)
and the Americas (1.8) regions [
]. The WHO analysis
was however based on a larger number of studies as the
review was not limited to studies published in
peerreviewed journals, but included those reported in NGOs
and ministry of health reports as well as from other grey
literature. On the other hand, while the WHO factbook
and other reports have generally reported higher number
of medicines prescribed per patient in private compared
to public facilities we found the reverse with slightly
higher number of medicines per patient in public (2.6)
than private (2.5).
A generally high number of medicines prescribed per
patient exceeding WHO reference value may point to
polypharmacy as an increasing problem in Africa. Many
parts of the region are experiencing a changing
epidemiological transition creating a double disease burden
of both communicable and NCDs [
] and there is
evidence that poly-pharmacy becomes more prominent
when health personnel need to treat multiple diseases
]. Additionally, demographic shifts
in most parts of Africa is resulting in an increasing
elderly population who are likely to suffer significant
comorbidities and need for multiple medications [
In countries like Ghana, Kenya, Nigeria and Tanzania
the population aged 60 and over is projected to increase
by around 147 %, 144 % and 80 %, respectively between
2005 and 2030 [
]. Such patterns may partly account
for the observed higher number of medicines prescribed
per patient in the period 2006–2015 (3.5) as compared
to the period 1995–2005 (2.4). Nonetheless, a number of
studies reviewed reported very high levels of
symptomatic management of cases [
], and this may have
also contributed to the overall high number of medicines
prescribed per patient.
Excessive use of multiple medicines per patient
(polypharmacy) is likely to result in increased risk of adverse
drug interactions, dispensing errors and decreased
patients’ knowledge of the correct doses of medications. In
the study in Nigeria by Uzochukwu et al. [
percentage of patients remembering their dosing schedules
decreased significantly as the number of medicines
increased whereas Kapp et al. [
], reported a direct
correlation between the number of medicines prescribed
and the occurrence of adverse events in South Africa.
Increased risk of drug adverse effects as a result of
poly-pharmacy could create a cycle of health demands
and costs as new treatments may be required [
Percentage of medicines prescribed by generic name
The generic prescribing rate attained in this study (68.0 %)
was lower than that recommended by the WHO (100 %).
This result however portrays a better generic prescribing
rate than reported by the WHO factbook for the African
region (60 %) albeit based on smaller number of studies.
However, the results appear lower when compared to
values reported for the WHO’s Western Pacific region
(78 %), although higher than generic prescribing rates
reported for the Eastern Mediterranean (27.7 %) and
Southeast Asian regions (48.9 %) [
]. The lower generic
prescribing rate observed in private than public centres is
consistent with trends reported by the factbook as well as
other WHO reports [
1, 14, 15
The overall improved generic prescribing rate as
documented by higher generic prescribing for the period
2006–2015 compared to the period 1995–2005 may be
due to the increasing availability of standard medicines
as generics. For instance, over 45 top brand medications
are expected to have patent expired between 2011 and
2020 and thus likely to make generic versions readily
]. Once patency expires and
availability/accessibility improves, lower cost becomes an incentive
that could drive generic prescription. As an example;
higher rates of generic prescribing [for proton pump
inhibitors (PPIs) and statins] were seen in South Africa in
2010/2011 among patients enrolled into medical aid
schemes receiving discounted medications [
Netherlands, similar trends have been observed where
about threefold increase in statins utilization was
observed between 2000 and 2010 despite a 58 %
decrease in reimbursed expenditure mainly as a result of
multiple supply and demand measures, including a
preferential pricing policy [
]. Furthermore, in recent years,
considerable education and studies demonstrating no
difference in outcomes between originators and generics
across a wide range of products and classes including
antipsychotics, anti-infectives and cardiovascular medicines
have been undertaken and these may have contributed to
the increase in generic prescribing [
The lower generic prescribing rates observed for private
facilities may be due to the fact that prescribers in the
private sector may perceive generic medicines as not
financially rewarding as patients typically purchase medicines
from same facilities and there may be a financial incentive
to prescribe most expensive products [
the more frequent prescribing of innovator (expensive)
brands in the private sector may be due to prescriber’s
quest to satisfy the expectations of their clients (often the
- well -to do) who may falsely perceive the issuance of
expensive (innovator) medicines as constituting ‘quality
care’. Persistent prescription of branded (innovator)
medicines is likely to result in increased treatment costs. In a
study by Nwolisa et al. at outpatient centres in Nigeria,
the difference in cost between same drugs prescribed in
brand names as against generic names were between 41.7
% and 60 % [
]. Nicolosi and Gray investigated the cost
impact of generic and proprietary prescribing among
chronic disease patients in South Africa and their findings
indicated that of “all generic medicines identified 67.5 %
were more than 40 % cheaper, per defined daily dose
(DDD) per month, than the branded version” [
analysis of facility-based medicines price data from 17
countries by Cameron and Laing [
], found that an average of
9–89 % could be saved by switching from originator brands
to lowest-price generic equivalents. To further improve
generic prescribing, diverse approaches may be adopted
including addressing fears related to generics, thorough
education of prescribers (beginning when they are in school
or training) or in some instance through the adoption of a
compulsory INN prescribing policy [
Percentage of encounters with an antibiotic prescribed
The percentage of encounters with antibiotics prescribed
in this review was 46.8 % which exceeds the reference
value of <30 % recommended by the WHO [
antibiotic use rate in this study is similar to that reported by
the WHO (47 %) [
]. However, it is lower when
compared to estimates provided for the Eastern Mediterranean
region (53.2 %) but higher than that of the Americas
(39.3 %) and European (33.5 %) regions [
]. A higher
value for antibiotic prescribing was reported for the
private facilities (51.3 %) than public facilities (45 %) which
does imply that antibiotic prescribing may be more of a
problem in the private than public sector—an observation
consistent with WHO reported trends [
The higher antibiotic prescribing rate reported for the
2006–2015 period than for the 1995–2005 period may
point to a non-improving or potentially worsening
problem of antibiotic use in Africa. The overall high levels of
antibiotic prescribing may partly be accounted for by the
extensively documented high burden of infectious diseases
within the African region. For instance, in the studies by
Massele et al. [
] in Tanzania, Enato et al. [
] in Nigeria
and Bosu and Ofori-Adjei [
] in Ghana, 58 %, 38.3 % and
22 % of conditions presented at the PHCs, respectively
were attributable to infectious diseases (excluding
malaria). These high levels of reported infections are likely
to contribute to more antibiotic presciption.
Additionally, in many parts of Africa, HIV/AIDS remains
endemic which although does not require the use of
antibiotics can increase the prevalence of opportunistic
bacterial infections necessitating the use of antibiotics
Regardless of the seemingly high levels of infection
rates, not all antibiotic prescribing and use reported may
be appropriate. In a number of studies, antibiotics were
reported as been prescribed to treat diseases like malaria,
diarrhoea and RTIs (mostly viral in origin) conditions
which do not usually require antibiotic use [
40, 56, 59
PHCs in many parts of Africa, microbiology laboratory
facilities are often non-existent and as such prescribers may
rely mainly on their clinical judgment. While empirical
use of antibiotics based on clinical judgment other than
laboratory confirmations is permitted in many instances
such as otitis, apparent pneumonia and cellulitis, it is well
recognized that consistent use of antimicrobials when
infection or diagnosis has not been established or fully
confirmed can lead to overprescribing [
]. While it is
recommended good practice that medicines are written
for specified diagnosis, in one study conducted in Nigeria
by Isah [
], over 50 % of the patients’ folders reviewed
had no established diagnosis. In another study conducted
in Ethiopia by Desta et al. [
], the researchers reported
that any compliant presented by a patient was recorded as
final diagnosis. One study investigated prescribing
patterns across different health professionals and found
higher level of antibiotic prescribing more prevalent in
lower cadre staff like community nurses and health
assistants than in medical doctors and pharmacists [
However, across all the studies, higher antibiotic use were
generally reported for mix of health workers (physicians,
nurses, medical assistants etc.). Lack of in-service training
was recognised as contributing to poor prescribing
practices as demonstrated by one study in Ghana, in which the
investigators reported that for the PHCs surveyed, none of
the prescribers had received an in-service training in the
preceding 5 years [
In addition to lack of adequate training, prevailing
socio-cultural factors and demand are known to influence
irrational antibiotics use [
]. These factors were reported
by some studies to have influenced prescribing behaviours
]. In private settings, prescribers are more likely to
adhere to patient demand for antibiotics and injections for
fear of losing out on customers and this may underline
the higher antibiotic prescribing rate observed. Some
studies found a correlation between patient overload and
injection and antibiotic use [
]. In many parts of
Africa there are widespread reports of acute shortage of
health staff, therefore in many instances, health personnel
may play dual role of prescriber and dispenser. Such
occurrences can be a breeding ground for irrational
prescribing as no control mechanisms will be in place to
check wrong, incorrect or poor prescribing. Dispensing
prescribers may be more likely to prescribe irrationally in
the private-for-profit sector where there may be a financial
incentive for over-prescribing. For instance, Trap et al.
 found that PHC dispensing doctors were more likely
to prescribe antibiotics than non-dispensing doctors in
Zimbabwe. High patient load and inadequate prescriber
time can contribute to irrational prescribing as prescribers
may find it more convenient and time-saving to prescribe
an antibiotic rather than educate a patient that his
condition does not require an antibiotic as it will require more
lengthy discussion [
]. In a study in Ghana by Polage et
], 98 % of physicians stated that they rarely order or
never order tests, because of time constraints.
Indiscriminate use of antibiotics backed by no diagnostic
certainty can contribute to the development of drug
]. In one study included in this review, the
researchers carried out further antibiotic sensitivity testing.
Their findings indicated that, vaginal and endocervical
isolates were always resistant to the commonly used
antibiotics such as ampicillin and tetracycline but almost always
sensitive to antibiotics like cefuroxime and gentamicin
which were less frequently prescribed at the facilities [
The development of antibiotic drug resistance can cause
significant morbidity and mortality as infectious disease
rates remain high in the African region. High use of
antibiotics is also costly and the development of resistance
can further aggravate treatment cost by requiring the use
of more powerful and expensive antibiotics which are
likely to be unavailable in many parts of Africa. In the
Bosu and Ofori-Adjei study in Ghana, antibiotics alone
accounted for about 40 % of treatment cost in patients in
whom they were prescribed [
Percentage of encounters with an injection prescribed
The overall injection prescribing rate determined in this
study was 25.0 % which exceeds the reference value
(<20 %) recommended by the WHO [
]. The WHO fact
book reported an injection use rate of 27.5 % which is a
bit higher than that attained in this study [
]. The result
also indicates a higher use of injectable medications when
compared to results reported for Eastern Mediterranean
(20.1 %), European (17.2 %) and West Pacific (23.2 %)
regions. In comparison, the study found higher use of
injections at private facilities (29 %) than at public centres
(25.6 %) which is also in accord with global trends
reported by the WHO [
]. The similar injection
prescribing rate in the periods 2006–2015 and 1995–2005
may highlight a non-changing injection use behaviours
among health personnel in Africa.
Widespread injection prescribing was reported across
all mix of health workers (doctors, nurses, medical
assistants etc.). Patient preference, socio-cultural beliefs have
been also noted to influence prescribing behaviours. In a
study by Massele and Mwaluko in Tanzania, it was
reported that some patients walked into the PHC facility
with their own supply of injectable medicines, syringes
and needles asking for them to be prescribed these
medicines because they believed injections were more
powerful in restoring and maintaining health than other
]. As indicated previously, patient
influences are likely to be felt more in the private sector
where there may be a financial implication if prescribers
do not adhere to client demands. As the administration
of injections often requires supervision by skilled health
care providers, the frequency of prescription of injectable
medications is important [
]. Excessive and indiscriminate
use of injections can increase the risk of spreading
bloodborne diseases such as hepatitis B and even HIV/AIDS
especially in a region where infections rates remain high.
Moreover, overuse of injections sets up a cycle of repeated
visits putting pressure on healthcare staff and driving costs.
Percentage of medicines prescribed from an essential medicines list or formulary
The overall EML prescribing adherence of 88.0 % in this
study is comparable to the 87.8 % reported by the WHO
albeit lower than the optimal recommended value
(100 %) [
]. The EML prescribing rate presented in this
review is higher when compared to estimates reported
for other regions like the European (55.1 %), Americas
(71.4 %) and the South East Asia (81 %) regions [
The results obtained indicate that adherence to EML
when prescribing is better at public (93.5 %) that private
facilities (83.95 %), a pattern consistent with what has
been reported previously by the WHO [
The general high EML prescribing rate may be due to
wider adoption of the use of EML in many countries as
well as expanding number of medicines on various
]. Regardless, the non-optimal use of EML as
reported in this study can be attributed to a myriad of
factors such as ineffective distribution of EML,
inadequate sensitization among health workers and a general
lack of enforcement mechanisms. In separate studies by
Bosu and Ofori-Adjei [
] and Odusanya and Oyediran [
conducted in Ghana and Nigeria, respectively, all the
facilities studied lacked a copy of an EML. Moreover, some
studies reported that the main source of information for
prescribers were drug representatives [
]. Such sources
have been documented to be problematic as drug
companies may over-represent the efficacy of their medicines,
discredit the efficacy of competitor brands and likely to induce
prescribers to prescribe outside established guidelines [
]. The lower EML adherence observed in private practice
may be due to the fact that in many countries in Africa, the
private sector is encouraged but not obliged to prescribe
from EML as may be the case for public centres [
This systematic review has some limitations. Firstly, the
identified studies were concentrated in a few (11 out of
47) countries in the studied region. While a lack of
research into this area in parts of Africa may have
contributed to this; it may have also been due to the inclusion of
only articles published in English and also the exclusion of
grey literature. Around one-third (32.6 %) of studies
included in the review involved patient encounters <600
and were deemed to be small per recommendations
outlined in the WHO guidelines [
]. This review also took
the assumption that the African region is homogenous,
although, in reality, there may be differences in disease
burden, health system challenges, socio-cultural and political
climates across countries which all can affect how
medicines are used. Majority of the studies (74 %) also
collected data retrospectively. We consider that
retrospective analysis may result in the overestimation of
polypharmacy (average number of medicines), antibiotic
utilization and injection use because patients who were
not given a prescription are likely to be excluded [
our analysis we stratified results by key sector (public and
private) but did not control for differences in prescriber
characteristics. Therefore, the apparent differences in the
prescribing indicators between the two sectors may be
due to multiple factors. We assessed prescribing indicators
at two time points and this is unlikely to reveal much
about prescribing trends. Importantly, this review reports
indicator-based studies which are unable to ascertain
whether the reported prescribed medicines were actually
taken by the patients involved. Indicator-based studies
while able to identify medicine use problem areas, do not
answer the question of rationality or appropriateness of
treatment which may require a different methodology and
]. It is also important to reiterate that while the
prescribing indicator reference values are useful, these
have not been empirically determined and the extent to
which different factors influence them have not been
thoroughly investigated [
Our analysis reveals prescribing indicators at PHCs
within the WHO African region which deviate
significantly from proposed reference values. While our review
is based on limited studies, it does highlight that some
improvements in prescribing practices are needed. The
prescribing patterns observed are reflective of population
factors as well as varied health system challenges on the
African continent. Greater commitments from
governments and all stakeholders are required to improve
medicine prescribing practices in the region. This is
necessary not only to avert negative health consequences
but also to afford the optimal utilization of scarce
AIDS, acquired immune deficiency syndrome; EML, essential medicines list;
GDP, gross domestic product; HIV, human immunodeficiency virus; LRI, lower
respiratory infection; NCD, non communicable disease; PHC, primary health
care; PRISMA, preferred reporting items for systematic reviews and
metaanalyses; RTI, respiratory tract infections; WHO, World Health Organization
Thank you to the reviewers for their helpful comments.
Availability of data and materials
We declare that the data supporting the conclusions of this article are fully
described within the article.
RO conceived the study, undertook the literature review and appraisal,
analysis and first draft. PB and AP contributed to the design of the study,
supervised the study analysis and contributed to drafting of the manuscript.
All authors read and approved the final content of this manuscript.
The authors declare that they have no competing interests.
Consent for publish
Ethics approval and consent to participate
An ethical approval was not required for this study as it did not report any
individual data but relied on aggregate data from published studies already
available in the public domain.
Submit your next manuscript to BioMed Central
and we will help you at every step:
1. World Health Organization. The world medicines situation . Geneva: World Health Organization; 2004 .
2. Laing R . Rational drug use; an unsolved problem . Trop Dr . 1990 ; 20 : 101 - 3 .
3. Massele A , Burger J , Katende-Kyenda NL , Kalemeera F , Kenaope T , Kibuule D , Mbachu O , Mubita M , Oluka M , Olusanya A , et al. Outcome of the first Medicines Utilization Research in Africa group meeting to promote sustainable and rational medicine use in Africa . Expert Rev Pharmacoecon Outcomes Res . 2015 ; 15 ( 6 ): 885 - 8 .
4. World Health Organization. World medicines situation report 2011 . Geneva: World Health Organization; 2011 .
5. Cameron A , Ewen M , Ross-Degnan D , Ball D , Laing R . Medicine prices, availability, and affordability in 36 developing and middle-income countries: a secondary analysis . Lancet . 2009 ; 373 ( 9659 ): 240 - 9 .
6. Fraser S. Rational use of essential medicines . World Health Forum . 1985 ; 6 : 36 - 66 .
7. Odusanya O , Oyediran M. Rational drug use at primary health care centres in Lagos, Nigeria . Nig Q J Hosp Med . 2000 ; 10 ( 1 ): 4 - 7 .
8. Babalola C , Awoleye S , Akinyemi J , Kotila O . Evaluation of prescription pattern in Osun State (Southwest) Nigeria . J Public Health Epidemiol. 2011 ; 3 ( 3 ): 94 - 8 .
9. Gustafsson LL , Wettermark B , Godman B , Andersen-Karlsson E , Bergman U , Hasselstrom J , Hensjo LO , Hjemdahl P , Jagre I , Julander M , et al. The 'wise list'- a comprehensive concept to select, communicate and achieve adherence to recommendations of essential drugs in ambulatory care in Stockholm . Basic Clin Pharmacol Toxicol . 2011 ; 108 ( 4 ): 224 - 33 .
10. World Health Organization. How to investigate drug use in health facilities: selected drug use indicators - EDM research series No. 007 . WHO/DAP/93.1. Geneva: World Health Organization; 1993 .
11. Hogerzeil HV , Bimo, Ross-Degnan D , Laing RO , Ofori-Adjei D , Santoso B , Azad Chowdhury AK , Das AM , Kafle KK , Mabadeje AF , et al. Field tests for rational drug use in twelve developing countries . Lancet . 1993 ; 342 ( 8884 ): 1408 - 10 .
12. Harvard Medical School and Harvard Pilgrim Health, World Health Organization. Using indicators to measure country pharmaceutical situations Fact Book on WHO Level I and Level II monitoring indicators, edn . Geneva: World Health Organization; 2006 .
13. Dumoulin J , Kaddar M , Velásquez G . Guide to drug financing mechanisms . Geneva: World Health Organization; 1998 .
14. World Health Organization, Harvard Medical School and Harvard Pilgrim Health . Medicines use in primary care in developing and transitional countries Fact Book summarizing results from studies reported between 1990 and 2006 . Geneva: World Health Organization; 2009 .
15. Holloway KA , Ivanovska V , Wagner AK , Vialle-Valentin C , Ross-Degnan D . Have we improved use of medicines in developing and transitional countries and do we know how to? Two decades of evidence . Tropical Med Int Health . 2013 ; 18 ( 6 ): 656 - 64 .
16. World Health Organization Regional Office for Africa. Atlas of African Health Statistics 2016 - Health situation analysis of the African Region . Brazzaville: Republic of Congo World Health Organization; 2016 .
17. Global Burden of Disease Study C. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990 - 2013 : a systematic analysis for the Global Burden of Disease Study 2013 . Lancet. 2015 ; 386 ( 9995 ): 743 - 800 .
18. Maher D , Smeeth L , Sekajugo J . Health transition in Africa: practical policy proposals for primary care . Bull World Health Organ . 2010 ; 88 ( 12 ): 943 - 8 .
19. Adeloye D , Basquill C . Estimating the prevalence and awareness rates of hypertension in Africa: a systematic analysis . PLoS One . 2014 ; 9 ( 8 ): e104300 .
20. Moeti M. Fighting non-communicable diseases: an overview of Africa's New Silent Killers . In: African Health Monitor . vol. 8 , January- June 2008 edn. Brazzaville: World Health Organization Regional Office for Africa: 2 - 5 .
21. Whitlock EP , Lin JS , Chou R , Shekelle P , Robinson KA . Using existing systematic reviews in complex systematic reviews . Ann Intern Med . 2008 ; 148 ( 10 ): 776 - 82 .
22. Trap B , Hansen EH , Hogerzeil HV . Prescription habits of dispensing and nondispensing doctors in Zimbabwe . Health Policy Plan . 2002 ; 17 ( 3 ): 288 - 95 .
23. Ofori-Asenso R . A closer look at the World Health Organization's prescribing indicators . J Pharmacol Pharmacother . 2016 ; 7 ( 1 ): 51 - 4 .
24. Moher D , Liberati A , Tetzlaff J , Altman DG , Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement . J Clin Epidemiol . 2009 ; 62 ( 10 ): 1006 - 12 .
25. Yin X , Song F , Gong Y , Tu X , Wang Y , Cao S , Liu J , Lu Z. A systematic review of antibiotic utilization in China . J Antimicrob Chemother . 2013 ; 68 ( 11 ): 2445 - 52 .
26. Downs SH , Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions . J Epidemiol Community Health . 1998 ; 52 ( 6 ): 377 - 84 .
27. Cousineau D , Chartier S. Outliers detection and treatment: a review . Int J Psychol Res . 2010 ; 3 ( 1 ): 58 - 67 .
28. Abdella S , Wade N. Prescribers adherence to the basic principles of prescrption orders writing in South West Ethiopia . Natl J Physiol Pharm Pharmacol . 2012 ; 2 ( 1 ): 66 - 70 .
29. Adisa R , Fakeye T , Aindero V . Evaluation of prescription pattern and patients' opinion on healthcare practices in selected primary healthcare facilities in Ibadan, South-Western Nigeria . Afr Health Sci . 2015 ; 15 ( 4 ): 1318 - 29 .
30. Afriyie D , Tetteh R. A description of the pattern of rational drug use in Ghana Police Hospital . Int J Pharm Pharmacol . 2014 ; 3 ( 1 ): 143 - 8 .
31. Afriyie DK , Amponsah SK , Antwi R , Nyoagbe SY , Bugyei KA . Prescribing trend of antimalarial drugs at the Ghana Police Hospital . J Infect Dev Ctries . 2015 ; 9 ( 4 ): 409 - 15 .
32. Ahiabu MA , Tersbol BP , Biritwum R , Bygbjerg IC , Magnussen P. A retrospective audit of antibiotic prescriptions in primary health-care facilities in Eastern Region, Ghana . Health Policy Plan . 2016 ; 31 ( 2 ): 250 - 8 .
33. Angamo M , Wabe N , Raju N. Assessment of Patterns of Drug use by using World Health Organization's Prescribing, Patient Care and Health facility indicators in Selected Health Facilities in Southwest Ethiopia . J Appl Pharm Sci . 2011 ; 1 ( 7 ): 62 - 6 .
34. Ball DE , Maidza J , Rusike T , Sharief K , Taderera T , Tangawarima T. Drug use indicators at St Mary's Clinic . Cent Afr J Med . 2000 ; 46 ( 2 ): 54 - 5 .
35. Bantie L. Assessment of prescribing practice pattern in Governmental Health Centers of Bahir Dar Town , Ethiopia. World J Pharm Sci . 2014 ; 2 ( 10 ): 1184 - 90 .
36. Bexell A , Lwando E , von Hofsten B , Tembo S , Eriksson B , Diwan VK . Improving drug use through continuing education: a randomized controlled trial in Zambia . J Clin Epidemiol . 1996 ; 49 ( 3 ): 355 - 7 .
37. Boonstra E , Lindbaek M , Khulumani P , Ngome E , Fugelli P . Adherence to treatment guidelines in primary health care facilities in Botswana . Tropical Med Int Health . 2002 ; 7 ( 2 ): 178 - 86 .
38. Boonstra E , Lindbaek M , Ngome E . Adherence to management guidelines in acute respiratory infections and diarrhoea in children under 5 years old in primary health care in Botswana . Int J Qual Health Care . 2005 ; 17 ( 3 ): 221 - 7 .
39. Bosu WK , Ofori-Adjei D. An audit of prescribing practices in health care facilities of the Wassa West district of Ghana . West Afr J Med . 2000 ; 19 ( 4 ): 298 - 303 .
40. Desta Z , Abula T , Beyene L , Fantahun M , Yohannes AG , Ayalew S. Assessment of rational drug use and prescribing in primary health care facilities in north west Ethiopia . East Afr Med J. 1997 ; 74 ( 12 ): 758 - 63 .
41. Dippenaar H , Joubert G , Van Rooyen C. How cheap is primary health care? Cost per script at the Heidedal Community Health Centre and National District Hospital in Bloemfontein . S Afr Fam Pract . 2005 ; 47 ( 7 ): 37 - 40 .
42. Enato E , Sounyo A , Madadi P . Assessment of disease profiles and drug prescribing patterns of health care facilities in Edo state , Nigeria. J Public Health Africa . 2012 ; 3 ( e25 ): 101 - 6 .
43. Enato E , Mohammed A , Dayom D , Ekpe P . Medication prescribing practices of healthcare professionals in primary health centres in Niger State, Nigeria . J Pharm Bioresources . 2013 ; 10 ( 1 ): 1 - 7 .
44. Isah H . Prescription pattern among care providers in Catholic-church-owned primary health care facilities in the Northern Ecclesiastical provinces of Abuja, Jos and Kaduna,Nigeria: Preliminary findings . J Pharm Allied Sci . 2008 ; 5 ( 2 ).
45. Kapp P , Klop A , Jenkins L . Drug interactions in primary health care in the George subdistrict, South Africa: a cross-sectional study . S Afr Fam Pract . 2013 ; 55 ( 1 ): 78 - 84 .
46. Katende-Kyenda N , Lubbe M , Serfontein J , Truter I. Antimicrobial prescribing patterns in a group of private primary health care clinics in South Africa . Health SA Gesondheid . 2007 ; 12 ( 1 ): 21 - 9 .
47. Krause G , Borchert M , Benzler J , Heinmuller R , Kaba I , Savadogo M , Siho N , Diesfeld HJ . Rationality of drug prescriptions in rural health centres in Burkina Faso . Health Policy Plan . 1999 ; 14 ( 3 ): 291 - 8 .
48. Massele AY , Nsimba SE . Comparison of drug utilisation in public and private primary health care clinics in Tanzania . East Afr Med J. 1997 ; 74 ( 7 ): 420 - 2 .
49. Massele AY , Nsimba SE , Rimoy G . Prescribing habits in church-owned primary health care facilities in Dar Es Salaam and other Tanzanian coast regions . East Afr Med J. 2001 ; 78 ( 10 ): 510 - 4 .
50. Massele A , Nsimba S , Fulgence J. A survey of Prescribing practices of Health care workers in Kibaha District in Tanzania . Tanzan Med J. 2007 ; 22 ( 1 ): 31 - 3 .
51. Massele A , Mashalla Y , Mwamba N. The impact of patient-prescriber interaction group discussions on injection prescribing in public and private primary health care facilities in Kinondoni district . J Phys Pharm Adv . 2012 ; 2 ( 9 ): 295 - 300 .
52. Meyer JC , Summers RS , Moller H . Randomized, controlled trial of prescribing training in a South African province . Med Educ . 2001 ; 35 ( 9 ): 833 - 40 .
53. Mohlala G , Peltzer K , Phaswana-Mafuya N , Ramlagan S. Drug prescription habits in public and private health facilities in 2 provinces in South Africa . East Mediterr Health J . 2010 ; 16 ( 3 ): 324 - 8 .
54. Nsimba SE , Massele AY , Makonomalonja J . Assessing prescribing practice in church-owned primary healthcare (PHC) institutions in Tanzania: a pilot study . Trop Doct . 2004 ; 34 ( 4 ): 236 - 8 .
55. Nsimba S. Assessing prescribing and patient care indicators for children under five years old with malaria and other disease conditions in public primary health care facilities . Southeast Asian J Trop Med Public Health . 2006 ; 37 ( 1 ): 206 - 14 .
56. Olayemi SO , Akinyede AA , Oreagba AI . Prescription pattern at primary health care centres in Lagos State . Niger Postgrad Med J. 2006 ; 13 ( 3 ): 220 - 4 .
57. Oyeyemi AS , Ogunleye OA . Rational use of medicines: assessing progress using primary health centres in Shomolu local government area of Lagos, Nigeria . West Afr J Med . 2013 ; 32 ( 2 ): 121 - 5 .
58. Phillips-Howard PA , Wannemuehler KA , ter Kuile FO , Hawley WA , Kolczak MS , Odhacha A , Vulule JM , Nahlen BL . Diagnostic and prescribing practices in peripheral health facilities in rural western Kenya . AmJTrop Med Hyg. 2003 ; 68 ( 4 Suppl) : 44 - 9 .
59. Risk R , Naismith H , Burnett A , Moore SE , Cham M , Unger S . Rational prescribing in paediatrics in a resource-limited setting . Arch Dis Child . 2013 ; 98 ( 7 ): 503 - 9 .
60. Savadogo L , Ilboudo B , Kinda M , Boubacar N , Hennart P , Dramaix M , Donnen P . Antibiotics prescribed to febrile under-five children outpatients in urban public health services in Burkina Faso . Health Policy Plan . 2014 ; 6 ( 2 ): 165 - 70 .
61. Shiferaw G , Gedif T , Gebre-Mariam T . Drug utilization pattern in selected Health Facilities in Bahir Dar and West Gojam Zones, Northwest Ethiopia . Ethiop Pharm J . 2010 ; 28 : 55 - 62 .
62. Sisay A , Mekonnen H . Assessment of prescribers adherence to the basic standards of prescription order writing in serbo and assendabo health centres, Jimma Zone, South West Ethiopia . Int J Pharm Sci Res . 2012 ; 3 ( 10 ): 3806 - 13 .
63. Suleman S , Sabitu K , Idris S . Elimination of unnecessary injection through health education on injection safety and rational prescription among primary health care workers in Katsina State . Niger Jos J Med . 2013 ; 7 ( 1 ): 15 - 20 .
64. Tamuno I. Traditional medicine for HIV infected patients in antiretroviral therapy in a tertiary hospital in Kano, Northwest Nigeria . Asian Pac J Trop Med . 2011 ; 4 ( 2 ): 152 - 5 .
65. Truter I. The Phelophepa Health Care Train: a pharmacoepidemiological overview of the Western Cape in 2009. S Afr Fam Pract . 2010 ; 52 ( 5 ): 463 - 6 .
66. Tsega B , Hailu W , Ergetie Z. Measuring quality of drug use in primary health care facilities: a year long assessment of WHO prescribing indicators, Wolkite Town South West Ethiopia . Int J Pharm Ind Res . 2012 ; 2 ( 4 ): 485 - 91 .
67. Uzochukwu BS , Onwujekwe OE , Akpala CO . Effect of the Bamako-Initiative drug revolving fund on availability and rational use of essential drugs in primary health care facilities in south-east Nigeria . Health Policy Plan . 2002 ; 17 ( 4 ): 378 - 83 .
68. de-Graft Aikins A , Unwin N , Agyemang C , Allotey P , Campbell C , Arhinful D. Tackling Africa's chronic disease burden: from the local to the global . Glob Health . 2010 ; 6 : 5 .
69. von Lueder TG , Atar D . Comorbidities and polypharmacy . Heart Fail Clin . 2014 ; 10 ( 2 ): 367 - 72 .
70. Fitzgerald SP , Bean NG . An analysis of the interactions between individual comorbidities and their treatments-implications for guidelines and polypharmacy . J Am Med Dir Assoc . 2010 ; 11 ( 7 ): 475 - 84 .
71. Dagli RJ , Sharma A . Polypharmacy: a global risk factor for elderly people . J Int Oral Health . 2014 ; 6(6):i-ii.
72. National Research Council. ( 2006 ). Aging in Sub-Saharan Africa: Recommendations for Furthering Research. Panel on Policy Research and Data Needs to Meet the Challenge of Aging in Africa . Barney Cohen and Jane Menken, Eds. Committee on Population, Division of Behavioral and Social Sciences and Education . Washington, DC: The National Academies Press.
73. Velkoff V , Kowal P. Aging in Sub-Saharan Africa: the changing demography of the region . In: Cohen B, Menken J , editors. Aging in Sub-Saharan Africa: recommendation for furthering research . edn. Washington: National Academies Press (US); 2006 .
74. Patterson SM , Cadogan CA , Kerse N , Cardwell CR , Bradley MC , Ryan C , Hughes C. Interventions to improve the appropriate use of polypharmacy for older people . Cochrane Database Syst Rev . 2014 ; 10 : CD008165 .
75. Schacht W. Drug patent expirations: potential effects on pharmaceutical innovation . Congressional Research Service; 2012 .
76. Godman B , Bishop I , Campbell SM , Malmstrom RE , Truter I . Quality and efficiency of statin prescribing across countries with a special focus on South Africa: findings and future implications . Expert Rev Pharmacoecon Outcomes Res . 2015 ; 15 ( 2 ): 323 - 30 .
77. Woerkom M , Piepenbrink H , Godman B , Metz J , Campbell S , Bennie M , Eimers M , Gustafsson LL . Ongoing measures to enhance the efficiency of prescribing of proton pump inhibitors and statins in The Netherlands: influence and future implications . J Comp Eff Res . 2012 ; 1 ( 6 ): 527 - 38 .
78. Kesselheim AS , Misono AS , Lee JL , Stedman MR , Brookhart MA , Choudhry NK , Shrank WH. Clinical equivalence of generic and brand-name drugs used in cardiovascular disease: a systematic review and meta-analysis . JAMA . 2008 ; 300 ( 21 ): 2514 - 26 .
79. Gagne JJ , Choudhry NK , Kesselheim AS , Polinski JM , Hutchins D , Matlin OS , Brennan TA , Avorn J , Shrank WH . Comparative effectiveness of generic and brand-name statins on patient outcomes: a cohort study . Ann Intern Med . 2014 ; 161 ( 6 ): 400 - 7 .
80. Veronin M. Should we have concerns with generic versus brand antimicrobial drugs? A review of issues . J Pharm Health Serv Res . 2011 ; 2 : 135 - 50 .
81. Paton C . Generic clozapine: outcomes after switching formulations . Br J Psychiatry . 2006 ; 189 : 184 - 5 .
82. Nwolisa CE , Erinaugha EU , Ofoleta SI . Prescribing practices of doctors attending to under fives in a children's outpatient clinic in Owerri, Nigeria . J Trop Pediatr . 2006 ; 52 ( 3 ): 197 - 200 .
83. Nicolosi E , Gray A . Potential cost savings from generic medicines-protecting the prescribed minimum benefits . South Africa Family Practice . 2009 ; 51 ( 1 ): 60 - 63 .
84. Cameron A , Laing R . Cost savings of switching private sector consumption from originator brand medicines to generic equivalents; World Health Report (2010) Background Paper , 35 . Geneva: World Health Organization; 2010 .
85. Godman B , Bishop I , Finlayson AE , Campbell S , Kwon HY , Bennie M. Reforms and initiatives in Scotland in recent years to encourage the prescribing of generic drugs, their influence and implications for other countries . Expert Rev Pharmacoecon Outcomes Res . 2013 ; 13 ( 4 ): 469 - 82 .
86. Abuelkhair M , Abdu S , Godman B , Fahmy S , Malmstrom RE , Gustafsson LL . Imperative to consider multiple initiatives to maximize prescribing efficiency from generic availability: case history from Abu Dhabi . Expert Rev Pharmacoecon Outcomes Res . 2012 ; 12 ( 1 ): 115 - 24 .
87. Llor C , Bjerrum L. Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem . Ther Adv Drug Saf . 2014 ; 5 ( 6 ): 229 - 41 .
88. Md Rezal RS , Hassali MA , Alrasheedy AA , Saleem F , Md Yusof FA , Godman B. Physicians ' knowledge, perceptions and behaviour towards antibiotic prescribing: a systematic review of the literature . Expert Rev Anti-Infect Ther . 2015 ; 13 ( 5 ): 665 - 80 .
89. Kotwani A , Wattal C , Katewa S , Joshi PC , Holloway K. Factors influencing primary care physicians to prescribe antibiotics in Delhi India . Fam Pract . 2010 ; 27 ( 6 ): 684 - 90 .
90. Polage CR , Bedu-Addo G , Owusu-Ofori A , Frimpong E , Lloyd W , Zurcher E , Hale D , Petti CA. Laboratory use in Ghana: physician perception and practice . AmJTrop Med Hyg. 2006 ; 75 ( 3 ): 526 - 31 .
91. Massele AY , Mwaluko GM . A study of prescribing patterns at different health care facilities in Dar es Salaam, Tanzania . East Afr Med J. 1994 ; 71 ( 5 ): 314 - 6 .
92. Wang H , Li N , Zhu H , Xu S , Lu H , Feng Z. Prescription pattern and its influencing factors in Chinese county hospitals: a retrospective crosssectional study . PLoS One . 2013 ; 8 ( 5 ): e63225 .
93. Laing R , Waning B , Gray A , Ford N , t Hoen E. 25 years of the WHO essential medicines lists: progress and challenges . Lancet . 2003 ; 361 ( 9370 ): 1723 - 9 .
94. Narendran R , Narendranathan M. Influence of pharmaceutical marketing on prescription practices of physicians . J Indian Med Assoc . 2013 ; 111 ( 1 ): 47 - 50 .
95. Fugh-Berman A , Ahari S. Following the script: how drug reps make friends and influence doctors . PLoS Med . 2007 ; 4 ( 4 ): e150 .