| Literature DB >> 29743062 |
Jacinter A Amadi1,2, Daniel O Olago3, George O Ong'amo4, Silas O Oriaso3, Isaac K Nyamongo5, Benson B A Estambale6.
Abstract
BACKGROUND: The decline in global malaria cases is attributed to intensified utilization of primary vector control interventions and artemisinin-based combination therapies (ACTs). These strategies are inadequate in many rural areas, thus adopting locally appropriate integrated malaria control strategies is imperative in these heterogeneous settings. This study aimed at investigating trends and local knowledge on malaria and to develop a framework for malaria control for communities in Baringo, Kenya.Entities:
Keywords: Community-based strategies; Framework; Local knowledge; Malaria trends
Mesh:
Year: 2018 PMID: 29743062 PMCID: PMC5944038 DOI: 10.1186/s12889-018-5513-7
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Fig. 1Map of Baringo County (Kenya) showing selected ecological zones, health facilities and focus group discussions (FGD) sites
Fig. 2Trends in malaria cases in the riverine and lowland zones during 2005–2014 period
Fig. 3Total annual malaria cases per location for the year 2014
Trend in monthly malaria cases during 2005–2014 period
| Lowland zone | Riverine zone | |||
|---|---|---|---|---|
| Months | Tau |
| Tau |
|
| January | 0.02 | 1.00 | 0.42 | 0.11 |
| February | −0.11 | 0.72 | 0.29 | 0.29 |
| March | 0.02 | 1.00 | 0.67 | 0.0092 |
| April | 0.02 | 1.00 | 0.69 | 0.0073 |
| May | 0.02 | 1.00 | 0.73 | 0.0042 |
| June | 0.02 | 1.00 | 0.56 | 0.032 |
| July | −0.20 | 0.47 | −0.07 | 0.866 |
| August | −0.38 | 0.15 | 0.09 | 0.79 |
| September | −0.07 | 0.86 | 0.20 | 0.47 |
| October | −0.20 | 0.47 | 0.56 | 0.032 |
| November | −0.11 | 0.72 | 0.33 | 0.21 |
| December | −0.02 | 1.00 | 0.49 | 0.059 |
Notes: p is the significance level: ‘+’ 0.1, ‘*’ 0.05, ‘**’ 0.01; tau is the Mann Kendall’s statistic for trend analysis. A negative symbol (−) denotes a decreasing while a positive symbol denotes an increasing (+) trend
Demographic information of survey respondents (n = 300)
| Gender | Male | Female | |
| Lowland | 66 (44.0%) | 84 (56.0%) | |
| Riverine | 73 (49.0%) | 77 (51.0%) | |
| Total | 139 (46.3%) | 161 (53.7) | |
| Level of Education | Total frequency (%) | Gender | |
| Male (%) | Female (%) | ||
| None | 65 (21.7) | 21 (32.3) | 44 (67.7) |
| Primary | 159 (53) | 70 (44.0) | 89 (56.0) |
| Secondary | 53 (17.7) | 31 (58.5) | 22 (41.5) |
| Middle-level College | 20 (6.7) | 15 (75.0) | 5 (25.0) |
| University | 3 (1) | 2 (66.7) | 1 (33.3) |
Fig. 4Causes of malaria
Sources of malaria information from survey respondents (n = 136)
| Respondents | ||
|---|---|---|
| Source | n | (%) |
| Family | 46 | 33.8 |
| Other | 7 | 5.1 |
| Friends | 12 | 8.8 |
| Radio (national) | 22 | 16.2 |
| Television | 6 | 4.4 |
| Posters/pamphlets | 6 | 4.4 |
| School text books | 2 | 1.5 |
| Health facility | 22 | 16.0 |
| Community health worker/public health official | 13 | 9.6 |
| Total | 136 | 100 |
Fig. 5Malaria risk reduction framework developed by communities in Baringo County