| Literature DB >> 20977741 |
Carren A Watsierah1, Walter G Z O Jura, Henry Oyugi, Benard Abong'o, Collins Ouma.
Abstract
BACKGROUND: Interventions to reverse trends in malaria-related morbidity and mortality in Kenya focus on preventive strategies and drug efficacy. However, the pattern of use of anti-malarials in malaria-endemic populations, such as in western Kenya, is still poorly understood. It is critical to understand the patterns of anti-malarial drug use to ascertain that the currently applied new combination therapy to malaria treatment, will achieve sustained cure rates and protection against parasite resistance. Therefore, this cross-sectional study was designed to determine the patterns of use of anti-malarial drugs in households (n = 397) in peri-urban location of Manyatta-B sub-location in Kisumu in western Kenya.Entities:
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Year: 2010 PMID: 20977741 PMCID: PMC2984571 DOI: 10.1186/1475-2875-9-295
Source DB: PubMed Journal: Malar J ISSN: 1475-2875 Impact factor: 2.979
Demographic characteristics and the use of three major anti-malarial drugs
| Demographic factors | ||||||||
|---|---|---|---|---|---|---|---|---|
| Age | < | 98 (37.4%) | 86 (32.8%) | 20 (7.6%) | 159 (60.7%) | 103 (39.3%) | 202 (77.1%) | 60 (22.9%) |
| ≥ | 49 (36.3%) | 41 (30.4%) | 8 (5.9%) | 69 (51.1%) | 51 (48.9%) | 102 (75.6%) | 33 (24.4%) | |
| Gender | 67 (36.6%) | 60 (32.8) | 16 (8.7%) | 104 (56.8%) | 79 (43.2%) | 146 (79.8%) | 37 (20.2%) | |
| 80 (37.4%) | 67 (31.3%) | 12 (5.6%) | 124 (57.9%) | 90 (42.1%) | 158 (73.8%) | 56 (26.2%) | ||
| Marital status | 7 (30.4%) | 6 (26.1%) | 2 (8.7%) | 14 (60.9%) | 9 (39.1%) | 18 (78.3%) | 5 (21.7%) | |
| 31 (31.2%) | 31 (35.2%) | 3 (3.4%) | 50 (56.8%) | 38 (43.2%) | 67 (76.1%) | 21 (23.9%) | ||
| 1 (50.0%) | 0 | 0 | 0 (0.00%) | 2 (100%) | 1 (50.0%) | 1 (50.0%) | ||
| 5 (41.7%) | 0 | 3 (25.0%) | 8 (66.7%) | 4 (33.3%) | 8 (66.7%) | 4 (33.3%) | ||
| 102 (37.6%) | 90 (33.2%) | 20 (7.4%) | 155 (57.3%) | 116 (42.8%) | 209 (77.1%) | 62 (22.9%) | ||
| HH head | 108 (40.4%) | 88 (33.0%) | 13(4.9%) | 161 (60.3%) | 106 (39.7%) | 205 (76.8%) | 62 (23.2%) | |
| 24 (31.6%) | 25 (32.9%) | 9 (11.8%) | 35 (46.1%) | 41 (53.9%) | 58 (76.3%) | 18 (23.7%) | ||
| 14 (27.5%) | 13 (25.5%) | 6 (11.8%) | 30 (58.8%) | 21 (41.2%) | 39 (76.5%) | 12 (23.5%) | ||
| 1 (33.3%) | 1 (33.3%) | 0 | 1 (33.3%) | 2 (66.7%) | 2 (66.7%) | 1 (33.3%) | ||
| HH size | 32 (39.5%) | 27 (33.3%) | 4 (4.9%) | 42 (51.9%) | 39 (48.1%) | 59 (72.8%) | 22 (27.2%) | |
| 83 (38.6%) | 65 (30.2%) | 16 (7.4%) | 127 (59.1%) | 88 (40.9%) | 167 (77.7%) | 48 (22.3%) | ||
| 32 (31.7%) | 35 (34.7%) | 8 (7.9%) | 59 (58.4%) | 42 (41.6%) | 78 (77.2%) | 23 (22.8%) | ||
HH- Household, SP- Sulphadoxine pyremethamine, ACT- Artemether-based combined therapies. Proportionality determined by Chi-square test. From the Table, most respondents used SP and ACT relative to other drugs, with close variations in their use. In addition, more than half of the respondents took the drugs within the correct duration in each case considered, except for households who were headed by wives (46.1%). Furthermore, majority in the different categories used the correct dose as opposed to lower proportions of individuals who used the incorrect dose.
Socio-economic factors and the use of three major anti-malarial drugs
| Socio-economic factors | ||||||||
|---|---|---|---|---|---|---|---|---|
| Education level | 19 (35.8%) | 18 (34.0%) | 5 (9.4%) | 28 (52.8%) | 25 (47.2%) | 44 (83.0%) | 9 (17.0%) | |
| 19 (39.6%) | 15 (31.3%) | 1 (2.1%) | 5 (50.0%) | 21 (43.8%) | 36 (75.0%) | 12 (25.0%) | ||
| 5 (50.0%) | 3 (30.0%) | 0 | 27 (56.3%) | 5 (50.0%) | 6 (60%) | 4 (40.0%) | ||
| 17 (30.4%) | 20 (35.7%) | 4 (7.1%) | 37 (66.1%) | 19 (33.9%) | 45 (80.4%) | 11(19.6%) | ||
| 4 (33.3%) | 0 | 1 (8.3%) | 10 (83.3%) | 2 (16.7%) | 8 (66.7%) | 4(33.3%) | ||
| 83 (38.1%) | 71 (32.6%) | 17(7.8%) | 121 (55.5%) | 97 (44.5%) | 165 (75.7%) | 53(24.3%) | ||
| HH breadwinner | 105 (37.9%) | 97 (35.0%) | 16 (5.8%) | 164 (59.2%) | 113 (40.8%) | 219 (79.1%) | 58 (20.9%) | |
| 21 (33.3%) | 18 (28.6%) | 4 (6.3%) | 30 (47.6%) | 33 (52.4%) | 45 (71.4%) | 18 (28.6%) | ||
| 21 (37.3%) | 11 (20.4%) | 8 (14.8%) | 34 (61.1%) | 22 (38.9%) | 39 (68.5%) | 17 (31.5%) | ||
| HH inc. source | 27 (33.8%) | 29 (36.3%) | 6 (7.5%) | 50 (62.5%) | 30 (37.5%) | 65 (81.3%) | 15 (18.8%) | |
| 53 (35.3%) | 47 (31.3%) | 7 (4.7%) | 96 (64.0%) | 54 (36.0%) | 110 (73.3%) | 40 (26.7%) | ||
| 42 (41.6%) | 27(26.7%) | 11 (10.9%) | 51 (50.5%) | 50 (49.5%) | 79 (78.2%) | 22 (21.8%) | ||
| 24 (39.3%) | 23 (37.7%) | 4 (6.6%) | 29 (47.5%) | 32 (52.5%) | 47 (77.0%) | 14 (23.0%) | ||
| 1 (50.0%) | 1 (50.0%) | 0 | 1 (50.0%) | 1 (50.0%) | 1(50.0%) | 1 (50.0%) | ||
| 0 | 0 | 0 | 1(50.0%) | 1 (50.0%) | 1 (50.0%) | 1 (50.0%) | ||
| HH monthly inc. | 56 (34.4%) | 48 (29.4%) | 11 (6.7%) | 85 (52.1%) | 78 (47.9%) | 115 (70.6%) | 48 (29.4%) | |
| 74 (40.2%) | 58 (31.5%) | 12 (6.5%) | 111 (60.3%) | 73(39.7%) | 147 (79.9%) | 37 (20.1%) | ||
| 17 (35.4%) | 21 (43.8%) | 4(8.3%) | 30 (62.5%) | 18 (37.5%) | 40 (83.3%) | 8 (16.7%) | ||
| Ability to read | 121 (38.2%) | 114 (36.0%) | 20 (6.3%) | 181 (57.1%) | 136 (42.9%) | 246 (77.6%) | 71 (22.4%) | |
| 26 (32.9%) | 13 (16.5%) | 7 (8.9%) | 46 (58.2%) | 33 (41.8%) | 57 (72.2%) | 22 (27.8%) | ||
Data are presented as proportions (n,%). HH-Household, SP- Sulfadoxine pyremethamine, ACT- Artemether-based combined therapies, inc.-Income. From the results above the drugs that were taken by majority of the respondents were SP and ACT in all the socio-economic factors considered, with very small differences in proportions in each category. In addition, averagely half of the households had used drugs for correct duration, unlike households whose breadwinners were wives (47.6%) and those who depended on casual work as the main source of income (47.5%). Moreover, most households took correct dosages of drugs with majority scoring more than 70% in each socio-economic factor considered.
Demographic, socio-economic and environmental factors associated with patterns of use of anti-malarial drugs
| Dependent variables | |||
|---|---|---|---|
| Drugs taken | Dosage of use | Duration of use | |
| Age (A) | β = 0.113, | β = 0.006, | β = 0.179, |
| Gender (G) | β = 0.008, | β = 0.061, | β = 0.018, |
| Marital status (MS) | β = 0.101, | β = 0.115, | β = 0.105, |
| Household size (HHS) | β = 0.092, | β = 0.027, | β = 0.008, |
| Household head (HHH) | β = 0.019, | β = 0.011, | β = 0.093, |
| Education level (EL) | β = 0.018, | β = 0.132, | β = 0.057, |
| Household breadwinner (HHB) | β = 0.008, | β = 0.086, | β = 0.071, |
| Household income source (HIS) | β = 0.028, | β = 0.010, | β = 0.097, |
| Household monthly income (HHI) | β = 0.037, | β = 0.113, | β = 0.064, |
| Ability to read (AR) | β = 0.059, | β = 0.005, | β = 0.030, |
| Source of drug (SD) | β = 0.113, | β = 0.013, | β = 0.029, |
| Distance to source (DS) | β = 0.049, | β = 0.021, | β = 0.058, |
| Availability at source (AS) | β = 0.092, | β = 0.002, | β = 0.060, |
Logistic regression analysis between the independent and dependant variables was used to identify variables associated with pattern of use of anti-malarial drugs, including demographic, socio-economic and environmental factors. P-values in bold were statistically significant at P ≤ 0.05. b = standard co-efficient. The result above shows that age, household size, source of drugs and availability of drugs at the source work together to influence types of anti-malarial that are taken in the households. Furthermore, household monthly income and source of drug work together to influence dosage of use; while age, household head and household income source work together to influence duration of use of these drugs.