| Literature DB >> 24172163 |
John O Ikwuobe1, Brian E Faragher, Gafar Alawode, David G Lalloo.
Abstract
BACKGROUND: Rapid diagnostics tests for malaria (RDT) have become established as a practical solution to the challenges of parasitological confirmation of malaria before treatment in the public sector. However, little is known of their impact in private health sector facilities, such as pharmacies and drug shops. This study aimed to assess the incidence of malaria among unwell patients seeking anti-malarial treatment in two community pharmacies in Nigeria and measure the impact RDTs have on anti-malarial sales.Entities:
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Year: 2013 PMID: 24172163 PMCID: PMC4228493 DOI: 10.1186/1475-2875-12-380
Source DB: PubMed Journal: Malar J ISSN: 1475-2875 Impact factor: 2.979
Figure 1Schematic summary of the number and disposition of the pharmacies at each stage of selection.
Baseline characteristics of study participants
| Gender | | | | |
| -Female | 328 (54.0%) | 306 (49.4%) | 634 (51.7%) | 0.107 |
| -Male | 279 (46.0%) | 313 (50.6%) | 592 (48.3%) | |
| Age (years) | 30.12 (10.23) | 31.55 (11.46) | 1.42 (0.20-2.64) | 0.022 |
| Age group | | | | |
| -Adolescent | 65 (10.7%) | 73 (11.8%) | 138 (11.3%) | 0.548 |
| -Adult | 542 (89.3%) | 546 (88.2%) | 1088 (88.7%) | |
| Level of education (whole population) | | | | |
| -Primary or less | 61 (10%) | 57 (9.2%) | 118 (9.6%) | 0.618 |
| -At least secondary | 546 (90%) | 562 (90.8%) | 1108 (90.4%) | |
| Level of income | | | | |
| -Less than average income | 372 (61.3%) | 370 (60.5%) | 742 (60.5%) | 0.588 |
| -Average income or more | 235 (38.7%) | 249 (40.2%) | 484 (39.5%) | |
| Reported last treatment for malaria | | | | |
| -Less than 6 months ago | 381 (62.8%) | 386 (62.4%) | 767 (62.6%) | 0.882 |
| -More than 6 months ago | 226 (37.2%) | 233 (37.6%) | 459 (37.4%) | |
| Who recommended anti-malarial | | | | |
| -Self | 255 (42.0%) | 303 (48.9%) | 558 (45.5%) | 0.015 |
| -Health professional | 352 (58.0%) | 316 (51.1%) | 668 (54.5%) | |
| Participants with a doctor’s prescription prior to purchase of anti-malarial | 169 (27.8%) | 52 (8.4%)) | 221 (18.0%) | < 0.001 |
| Reported positive lab test prior to purchase of anti-malarial | 103 (17.0%) | 96 (15.5%) | 199 (16.2%) | 0.488 |
| History of fever in the last 48 hours | 394 (64.9%) | 166 (26.8%) | 560 (45.7%) | < 0.001 |
Incidence of malaria (using RDTs) in different sub-populations of patients in the intervention arm
| Gender | | |
| Female | 11.4 | 306 (49.0%) |
| Male | 15.7 | 313 (51.0%) |
| Age group | | |
| Adolescent | 26.0 | 73 (11.8%) |
| Adult | 11.9 | 546 (88.2%) |
| Level of education (adults) | | |
| Primary education or less | 11.8 | 34 (33.5%) |
| Secondary education or more | 11.9 | 512 (66.5%) |
| Level of income (adults) | | |
| Less than average | 10.8 | 297 (59.8%) |
| Greater than average | 13.3 | 249 (40.2%) |
| Who recommended anti-malarial? | | |
| Self | 12.5 | 303 (49.0%) |
| Health professional | 14.6 | 316 (51.0%) |
Multivariate analysis examining risk factors for RDT confirmed malaria
| Adult age group | 0.384 (0.214-0.688) | 0.514 (0.280-0.946) |
| 0.001 | 0.033 | |
| Reported positive lab test prior to presentation | 2.221 (1.282-3.812) | 2.177 (1.208-3.925) |
| 0.004 | 0.010 | |
| A history of fever in the last 48 hours | 4.441 (2.757-7.151) | 4.027 (2.482-6.532) |
| < 0.001 | < 0.001 | |
| Reported last treatment for malaria (more than 6 months ago) | 1.611 (1.014-2.560) | 1.627 (1.002-2.640) |
| 0.042 | 0.049 |
Figure 2A box plot showing the total mean daily number of anti-malarial sales in the intervention and control pharmacy 35 days prior to the start of the study (Retrospective) and 35 days after the study commenced (Prospective).
Selected risk factors for anti-malarial purchase after an RDT negative test result showing adjusted and unadjusted odds
| Average income or more | 0.658 (0.465-0.931) | 0.687 (0.468-1.007) |
| 0.018 | 0.054 | |
| At least secondary education | 0.438 (0.232-0.824) | 0.504 (0.256-0.993) |
| 0.009 | 0.048 | |
| Reported positive lab test prior to presentation | 2.020 (1.207-3.382) | 1.737 (1.007-2.996) |
| 0.007 | 0.047 | |
| Reported last treatment for malaria (more than 6 months ago) | 0.782 (0.549-1.114) | 0.714 (0.494-1.032) |
| 0.173 | 0.073 | |
| Anti-malarial recommended by a health professional | 1.812 (1.286-2.553) | 1.617 (1.134-2.305) |
| 0.001 | 0.008 | |
| A history of fever in the last 48 hours | 1.592 (1.051-2.410) | 1.440 (0.934-2.218) |
| 0.027 | 0.098 |
Figure 3Schematic summary of the number and disposition of anti-malarials within different sub-groups in the intervention pharmacy.
Anti-malarial prescription practice in both study arms showing the frequency of use of different ACT and non-ACT drugs within each study group
| Artemether-Lumefantrine | 180 | 337 |
| (50.0%) | (55.5%) | |
| Artesunate-Amodiaquine | 42 | 47 |
| (11.7%) | (7.7%) | |
| Dihydroartemisinin-Piperaquine | 30 | 33 |
| (8.3%) | (5.4%) | |
| Artesunate-Sulfadoxine-Pyrimethamine | 8 | 12 |
| (2.2%) | (2.0%) | |
| ACT (total) | 260 | 429 |
| (72.2%) | (70.6%) | |
| Sulfadoxine-Pyrimethamine alone | 39 | 75 |
| (10.8%) | (12.4%) | |
| Artesunate alone | 47 | 58 |
| (13.1%) | (9.6%) | |
| Chloroquine alone | 13 | 30 |
| (3.6%) | (4.9%) | |
| Quinine alone | 1 | 15 |
| (0.3%) | (2.5%) | |
| Non-ACT (total) | 100 | 178 |
| (27.8%) | (29.4%) |