The prevalence of malaria’ in this study refers to the number of households who reported malaria attacks suffered by any family member during the three months preceding the interviews. The reported number of cases was divided by the total number of all individuals living in the studied households and then multiplied by one hundred.
4.2.6.1 Findings
A total of 4 662 persons lived in 857 households and 29.6% (n=252) of the respondents said that at least one member of their family had been infected with malaria within three months preceding the interviews. That provided a reported prevalence rate of 5.4% (252 out of 4 662 household members). According to the 252 respondents, malaria occurred frequently among children under five years of age (53.6%; n=135). For 56.3% (n=142) of the households, the reported attack of malaria occurred once whereas it occurred twice in 38.9% (n=98) of the households over the three month period. Repeated attacks exceeding two incidences occurred only in 4.8% (n=12) of the
households. In terms of the number of persons in the households who reportedly suffered malaria attacks, only one person per household had the disease in 60.7% (n=153) of the households while 29.4% (n=74) reported two persons and the figure was three or more for 9.9% (n=25) of the households.
Factors associated with reported malaria cases
This study identified four characteristics that explained the households who reported malaria cases. These included the overall practices of households regarding malaria prevention and control strategies during the time of the survey, the utilisation of ITNs the night preceding the interview, distances of the households from the nearest health post and the application of IRS during the previous six months (see table 4.10). Households that consistently used ITNs were 56.0% less likely to report malaria cases due to the protective effect of the nets. Houses located far away (30 minutes or more walking distance) from health care services were 2.1 times more likely to report malaria cases.
Similarly, households who reported that their houses had been sprayed during the previous six months were 1.9 times more likely to report malaria cases. Families residing in houses sprayed with IRS were expected to be protected from malaria infections but this was not always the case. Out of 201 rural residents who reported malaria attacks, 26.4% (n=53) were from houses that had not been sprayed while 73.6% (n=148) were from houses that had reportedly been sprayed.
The increased number of malaria cases reported by those whose houses had reportedly been sprayed might be attributed to different factors. One is that these respondents might have re-plastered their walls since the IRS, as this was the practice in Ethiopia (FMoH 2012b:21) or it might be associated with resistance of mosquitoes to the insecticides used during the IRS (FMoH 2012b:18). This might also be due to the lack of consistent use of ITNs among those who reported IRS. Regarding the latter, it was observed that the proportion of respondents who reported consistent use of ITNs were 56.0% (79 out of 141) among those whose houses had not been sprayed compared to 48.6% (197 out of 405) among those whose houses had been sprayed.
4.2.6.2 Discussion
Determining the prevalence of malaria was not the main objective of this study. However, it is plausible to discuss the meaning of these figures within the context of the empirical prevalence of malaria in the study population. The reported prevalence of malaria in this study might not necessarily indicate the actual situation of malaria in the study area. The figures were compiled based on what the respondents believed caused the sickness. Whether the illness was indeed malaria was not confirmed either clinically or by laboratory-based diagnostic tests.
Since malaria is endemic in the study areas, people tended to associate any acute febrile presentation with malaria thus, possibly inflating the prevalence of malaria. Most respondents of 97.2% (n=826 of 850) sought modern medical attention at health facilities (see table 4.7) when signs and symptoms of malaria manifested. This observed treatment seeking behaviour of the respondents could be regarded as effective RBM behaviours.
The reported malaria prevalence of 5.4% in the study’s sample is higher than the diagnostically confirmed prevalence of 2.8% (n=425) reported in Ethiopia by Abate et al 2013:315), 95% CI of the difference: 26% (2.2%, 31%) and 4.8% (n=1 429) by Bekele et al (2012:130), 95% CI of the difference: 6.0% (4.5%, 7.5%). In the malaria indicator survey of 2011, the microscopically confirmed national malaria prevalence of 1.3% (n=11 933) was documented (FMoH 2012a:9) which is lower than the findings of this study (95% CI of the difference: 52.7% (52%, 54%). The prevalence rate reported in the health facilities annual report of 2011 (FMoH 2011:29) was 10% (n=919 469) clinical malaria (not laboratory confirmed) among female outpatients. The reported prevalence of malaria attacks in this study area might have been due to an overestimation of fever attacks by the respondents and other fevers might have been reported as malaria. This study’s findings depended on respondents’ answers to questions and no laboratory diagnoses of malaria were available.
Table 4.9: Predictors of reporting malaria cases by household, Sidama Zone Predictors Reported malaria
Cases COR (95%CI) AOR (95%CI) No Yes Residence (n=852) Urban 142 51 1.0 1.0 Rural 458 201 1.22(0.85, 1.75) 1.16(0.72, 1.86) Household size (n=806) <5 persons 293 138 1.0 1.0 => 5 persons 274 101 0.78(0.57, 1.06) 0.66(0.42, 1.04) Knowledge level(n=852) Low 297 157 1.0 1.0 Medium 141 50 0.67(0.46, 0.98)* 0.93(0.52, 1.60) High 162 45 0.53(0.39, 0.77)* 1.03(0.55, 1.92) Wealth index (n=852) Poorest 103 77 1.0 1.0 Very poor 145 77 0.71(0.47, 1.06) 0.82(0.47,1.42) Poor 91 34 0.50(0.31, 0.82)* 0.69(0.36,1.31) Less poor 136 32 0.32(0.19,0.51)* 0.57(0.30, 1.06) Least poor 125 32 0.34(0.21, 0.56)* 0.73(0.36, 1.48) Practice(n=852) Poor 172 157 1.0 1.0 Good 428 95 0.24(0.19, 0.33)* 0.34(0.21, 0.56)* ITNs utilisation Experience (years)(n=711)
< 1 year 28 5 1.0 1.0
1-2 260 80 1.72(0.64, 4.61) 1.65(0.53, 5.14) > 2 201 62 1.73(0.64, 4.66) 1.55(0.49, 4.94) Never used 53 22 2.33(0.79, 6.80) 1.25(0.36, 4.32) ITNs utilisation status (n=711)
Inconsistent 229 115 1.0 1.0
Consistent 314 53 0.32(0.23, 044)* 0.52(0.33, 0.82)* Distance of house from HP(n=849)
< 30 minutes walk 505 183 1.0 1.0 30+ minutes’ walk 93 68 2.02(1.41, 2.88) 2.09(1.29, 3.37)* IRS (n=659)†
Not sprayed 122 53 1.0 1.0
Sprayed 336 148 0.84 (0.62, 1.14) 1.92(1.11, 3.30)* * The test was significant at α=0.05; †: case from urban (n=51) were not included as IRS does not apply to urban respondents.
Households with good practices (routine implementation of malaria preventive methods) were 72.0% less likely to report malaria cases (see table 4.10) compared to those with poor practices. This implies that families adhering to good practices were better protected against malaria than families who did not implement such actions. This finding was consistent with results reported from the central part of Ethiopia (Abate et al 2013:316). These results signify the importance of consistently implementing all the recommended malaria prevention and control methods by households in malaria endemic settings. Given the sporadic nature of malaria, there is always a possibility that inconsistent malaria prevention activities could lead to epidemic situations, particularly where the migration of people from non-immune areas is prevalent.
Contrary to the current study’s finding, Koenker et al (2013) found that households with good levels of knowledge were 62% less likely to be affected by a malaria epidemic, than other families, compared to the 72% reported by this study. Findings of this study, however, might not be comparable with those of Koenker et al’s report since the current study measured endemic cases, rather than epidemic cases of malaria studied by Koenker et al.
Wealth was found in Tanzania and Uganda (Njau et al 2013:245) to be an influential factor in malaria prevention, treatment and control. Children from the wealthiest households were less likely to become parasitaemic than those from the poorest households. The present study provided similar results. As indicated in table 4.8, respondents with better economic status (least poor) were 5.7 times more likely to implement malaria prevention and control strategies compared to the poorest. And those who had demonstrated good overall practices were 66.0% less likely to report malaria cases compared to those who poorly implemented malaria prevention and control measures (see table 4.10).