Background
Malaria is the leading public health problem in Tanzania. The disease contributes between 39.4% and 48% of all outpatient children under the age of five years and among those aged five years and above, respectively [
1]. Malaria is the major cause of hospital admissions accounting for 33.4% in children under the age of five years and 42.1% in those aged five years and above. The disease is responsible for more than one-third of deaths in children under the age of five years and up to one-fifth of deaths among pregnant women [
1]. Malaria occurs in all parts of Tanzania with varying levels of endemicity ranging from unstable seasonal malaria, stable malaria with seasonal variations to stable perennial malaria [
2,
3]. Unstable seasonal malaria occurs in areas with low malaria transmission of not more than three months a year. Such areas include the northern and southern highlands and arid areas of central Tanzania with altitude up to 2000 m above sea level, temperature between 20°C and mean vapour pressure of 13-15 millibars. In such areas, malaria may occur as epidemics normally associated with increased morbidity and mortality. About 25% of the 38.7 million Tanzanians live in malaria unstable epidemic prone areas [
4].
During the past six decades, malaria epidemics have been reported in some districts of Tanzania. These include the semi-arid areas of Dodoma and the East and West Usambara Mountains of Muheza and Lushoto; Babati, Hanang, Mbulu, Muleba and Loliondo Districts [
2,
5]. Many of the observed malaria epidemics were caused by an increase in immigrants into and from malarious areas, ecological changes, poor surveillance system and degradation of healthcare infrastructures [
2,
6,
7]. A malaria epidemic is defined as an abrupt increase in malaria transmission that exceeds by far the inter-seasonal variation normally experienced in a given area and often associated with increased morbidity and mortality [
8]. This occurs when the equilibrium between the human host population, malaria parasites and the malaria vector population is disturbed [
9]. The conceptual framework for increased malaria transmission in Sub-Saharan Africa was discussed by Robert
et al [
10] and Deressa
et al [
11]. Briefly, malaria epidemics are linked to environmental changes and increased mean rainfall and ambient temperatures, changes in land use patterns, malaria vector dynamics, host immune status and individual or community factors such as socio-economic status, population movement, knowledge on malaria and protective behaviours [
7,
10‐
13]. Malaria epidemics can also be caused by breakdown of malaria intervention programs and drug or insecticide resistance [
12]. Periods of drought are a common characteristic prior to most malaria epidemics [
6,
7]. Such links between climate variability, vector dynamics, droughts, food shortage and epidemics have been reported during the devastating malaria epidemics in Tanzania [
6,
7,
14‐
18].
Muleba district is known to be a malaria epidemic prone area with unstable transmission of varying seasonality. The highest peak of malaria transmission is usually reached between May - July and November-January, which results from proceeding rain seasons. In 1997, a widespread malaria epidemic was reported affecting parts of North-west Tanzania including Kigoma and Kagera regions [
7,
19]. Poor rainfall in the previous wet season, associated with low food production and poor economic situations, were responsible for the epidemic. The 1997/1998 epidemic in Muleba was also associated with unusually heavy El Nino-Southern Oscillation (ENSO) rains, lack of antimalarial drugs and ineffective chloroquine [
20]. An assessment carried out in Muleba by Garay [
7] indicated that malaria admission and malaria specific mortality for January-March 1998 had risen four and 12 fold respectively, compared to the previous year's figure. In May 2006, the Muleba district authority noticed a drastic increase in number of outpatient and inpatient malaria cases accompanied by increased mortality especially of underfive children. There was a two-fold increase in the number of outpatient malaria cases from 2573 in January 2006 to 4388 in May 2006 in underfive children. There was also an increase of inpatient underfive children from 1094 (January 2006) to 1927 (May 2006). The Case Fatality Rate (CFR) for underfives increased from 10/1000 (January 2006) to 29/1000 (May 2006) (Ministry of Health, unpubl).
Although many studies in Tanzania and other African countries have linked socio-economic and behavioural factors, community knowledge, attitudes and practices with malaria [
21‐
27], no studies which have established such a link between these factors and malaria epidemics. An understanding of knowledge, attitudes and practices among communities in epidemic and non-epidemic areas and identification of the main factors that influenced malaria treatment and protective behaviours during epidemics is therefore important in the design and implementation of appropriate malaria epidemic control strategies. The current study investigated household's knowledge, attitudes and practices concerning malaria epidemics in Muleba district and explored determinant factors that might have contributed to malaria epidemics in the district.
Methods
Description of the study area
Muleba District (1°45'N, 31°40'E) is in the North-western part of Tanzania with an area of 10,739 km
2, of which 62.0% consists of Lake Victoria. Most parts of the district lie at 1200-1500 m above sea level. Administratively the district has 5 divisions, 31 wards, and 134 villages. It has a population of 425,172 people [
28] with 85,035 (20%) being children under the age of five years. The district has 36 health facilities, 3 of them being hospitals (Rubya, Kagondo and Ndolage). Others are health centres (4) and dispensaries (29). The district has two rain seasons which occur in March - June and September-December during which malaria transmission peaks.
Study design and sampling procedures
The study was a community based cross-sectional survey conducted between April and June, 2007 in six selected villages. Study villages were selected using a multistage simple random sampling procedure and a cluster sampling procedure as the final stage. Selection was made with the assistance of village and sub village heads. In the first stage, names of villages with the history of been affected by the epidemic of 2006 were listed from records obtained from the district medical officer's office. From this list three villages namely, Ijumbi (31°6'E; 01°7), Nshambya (31°6' E; 01°7' S) and Ikondo (31°6' E; 01°8') were randomly selected. Another three villages namely Bushemba (31°6'; 01°7'), Kibanga (31°6'E; 01°9') and Bunywambele (31°. 6 E'; S. 01°. 6') were selected randomly from a list of villages not affected by the epidemic and were used as control villages. In the second stage, for each selected village, a list of all sub villages was made from which two sub villages were randomly selected making a total of 12 sub villages (six sub villages in the epidemic area and six sub villages in the non-epidemic area). In the third stage, with the assistance of sub village heads, a list of all households with at least 5 household members was made from which 16 to 20 households were randomly selected. It was estimated that 16 to 20 households per sub village each with al least 5 household members would give an overall sample size of 400 people aged 15 years and above which was considered sufficient for the study. In the fourth (final) stage, all household members in the selected households aged 15 years and above were selected and invited for interview. In total 504 participants aged 15 years and above attended the interviews and were included in the study.
Data collection methods
A structured, pre-tested questionnaire focusing on community knowledge, attitudes and practices on malaria and malaria epidemics was administered to the 504 eligible participants. The questionnaire examined knowledge of participants about malaria transmission, signs and symptoms as well as treatment, prevention and control. Household treatment seeking and preventive behaviour, mosquito nets usage and coverage was explored and documented. In addition, socio-economic factors which could predispose communities to malaria epidemics were investigated.
Data management and analysis
In the field, data was collected in standardized questionnaire and data collection forms and checked for errors and completeness. Data was then counterchecked before entry into DbaseV (Borland International, Scotts Valley, California, USA) using the double entry system. Summary statistics was performed using STATA version 10 (STATA Corp., Texas, USA). Comparison of proportions between epidemic and non-epidemic villages was performed by cross-tabulation using the Chi-square test. The fisher's exact test was used where the figures in cells were less than 5. Logistic regression analysis was performed to assess factors which could have predisposed study communities to the malaria epidemic of 2006. Independent variables included in the model were household location, education level of household head, number of people in the household, knowledge of malaria, ownership and use of mosquito nets, knowledge and use of insecticides to protect against malaria, ownership and use of antimalarials for home management of malaria and type of antimalarials used. A p-value of less than 0.05 was considered significant.
Ethical considerations
Ethical clearance certificate (NIMR/HQ/R.8a/Vol. IX/503) to conduct the study was granted by the Medical Research Coordination Committee (MRCC) of the National Institute for Medical Research (NIMR), Tanzania that acts as the national ethics review board in Tanzania. Before commencement of the study, the principal investigator and his research team conducted meetings with local leaders and communities in all selected villages during which the objectives of the study including procedures to be followed were explained. Participants who consented to participate in the study were invited to attend interviews at selected central places in the villages such as schools. Study identification numbers were used instead of participant names and information collected was kept confidential. Feedback to the study population was conducted in the form of dissemination meetings after completion of the study.
Discussion
Most studies which have attempted to investigate determinants of malaria epidemics in East Africa have used quantitative epidemiological methods focusing on biomedical factors. This study is unique in that it has approached the subject by focusing on socio-economic, knowledge, attitudes, practices and other behavioural factors. Findings of this study indicate that malaria is an important public health problem in Muleba and that community knowledge about its transmission, signs and symptoms, treatment and prevention is high. Furthermore, respondents had good knowledge of the cause, impact and prevention strategies against malaria epidemics. Majority of respondents reported health facilities as the right place where to seek health care for malaria treatment. The high level of knowledge, though comparable to what has been reported by other studies could be attributed in part to health education campaigns that followed immediately after the epidemic. As regards health seeking, treatment and prevention behaviour, the observations of this study is consistent with findings of other studies [
31‐
35] that demonstrated that people with good knowledge about malaria cause and transmission do take appropriate treatment and preventive measures. However during the epidemic, majority of respondents used SP and other monotherapy antimalarials for treatment of malaria cases instead of ACTs which was the first line antimalarial drug according to national malaria treatment guideline [
7,
12]. One explanation for this observation could be non-compliance of the general population and service providers with national malaria treatment guidelines but also could reflect non-availability of ACTs during the time of the epidemic.
Self medication was common among respondents. This involved taking antimalarials from drug shops, use of local herbs or related practices. Self treatment using drugs sourced from drug shops and general stores have been reported by many studies in malaria endemic countries. In Uganda, Ndyomugenyi [
36] reported that 24.7% (n = 1627) of people who visited health facilities had practiced some form of self medication before visiting health facilities and consequently about two thirds of them reported to health facilities late (more than 24 hours after onset of illness). A similar finding in Uganda was reported by Nuwaha [
37]. In a study in Southern Sudan, about half of the people were found to practice self medication before visiting a health facility three days later [
38]. Self medication before seeking appropriate healthcare from health facilities have also been reported in Kenya [
39] and in Tanzania [
31,
40]. More interestingly, a study in Nigeria reported that self medication in the form of herbal preparations is considered the first line of treatment against malaria [
26]. It is evident therefore that self medication contributes to delays in seeking appropriate health care which in turn could exacerbate malaria disease in epidemic situation. Self medication can also explain the observed failure to comply with national malaria treatment guidelines which in turn affect treatment outcome and contribute to development of drug resistance.
More than half (58.7%) of respondents in Muleba district reported that their households owned at least one ITN with significantly higher ITN coverage in the non-epidemic area compared to epidemic area (66.4% vs 55.3%, p = 0.019) (Table
6). Further, in the multivariate logistic regression analysis, not using a mosquito net throughout the year was associated with an increased risk of a household being affected by malaria epidemic (Table
7). These observations points out to the possibility that low ITN coverage in the epidemic area might have contributed to increase in malaria cases during the epidemic. Apart from ITN ownership and use, knowledge of cause of malaria and household location were predictors of a household being affected by the epidemic (Table
7). The observed association between malaria epidemic and household location suggests involvement of climatic and/or environmental factors as the cause of the epidemic. This finding is supported by previous studies in Muleba [
41] and other parts of East Africa [
8,
42,
43] which observed associations between
P. falciparum malaria epidemics and climatic factors such as increased rainfall, temperature and humidity. Likewise, the association between knowledge on malaria and malaria disease was reported by Mboera
et al [
44] in Mvomero district, Tanzania. The study observed that individuals with low knowledge on malaria experienced 2.3 times more malaria cases in their households compared to individuals with higher knowledge. The study further observed that individuals with higher knowledge on malaria were more likely to own an ITN than those with low knowledge. There were concerns by respondents about higher prices of mosquito nets to the extent of not being affordable by some community members. Higher prices of mosquito nets might have contributed to the low mosquito net coverage during the epidemic and hence less protection against malaria during the epidemic. This observation is not uncommon in Tanzania as other studies [
22,
45] also observed that cost in terms of money and time and any form of user fees were limiting factors for accessibility of malaria chemoprophylaxis and other health services by women and other community members. However, it was encouraging to note that majority of those who owned mosquito nets purchased the nets using their own money which indicates that communities in the study area are willing to invest in malaria control consistent with what is expected for a community with higher literacy rate and knowledge on malaria.
Despite good knowledge about malaria transmission, signs and symptoms, treatment and control, this study also revealed evidence of knowledge gaps about malaria by some respondents. Some reported that malaria is transmitted through drinking contaminated/unboiled water, staying in the sun and working in rain. In total there were 21.2% incorrect responses on malaria transmission. It is very surprising that in this study and others in malaria endemic countries, a significant proportion of respondents associated malaria with drinking contaminated water or other incorrect causes. An even higher percentage of respondents gave the same responses in a study conducted in Uganda [
46] and in another similar study in Zimbabwe [
32]. Similar responses were also reported in rural areas of West Africa [
26,
47,
48]. Further, in line with two studies in West Africa [
47,
48], there was also a failure by most respondents in Muleba district to associate anaemia and jaundice with malaria which in turn could lead to failure to recognize malaria cases and hence failure to seek appropriate health care.
With regards to measures to prevent malaria, there were perceptions that ITNs are harmful to the health of users and more particularly to pregnant mothers. Evidence of knowledge gaps on malaria has been reported by other studies. Winch and his colleagues [
49] found that people in Bagamoyo district in Tanzania failed to associate severe malaria (convulsions) in children, severe anaemia and malaria in pregnancy with malaria which in turn lead to people's failure to acknowledge the full burden and hence public health importance of the disease in the area. The knowledge gaps revealed in this study therefore indicates that some people might have opted for unsound measures of malaria control and protection and hence contributed to the increased number of malaria cases observed during the epidemic. These findings show that in order to achieve the required levels of adoption of malaria control measures, more emphasis should be placed on designing and implementation of effective health education interventions that will address knowledge gaps on malaria among communities.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
SMK, FM, SN, GK, JM, SM, RM and LEGM designed the study. SMK and LEGM contributed to analysis and interpretation of results. SMK, FM, GK and JM supervised field data collection, SMK and CK coordinated data entry and performed data analysis, SMK drafted the manuscript, LEGM and JM revised the draft manuscript. All authors read and approved the final version of the manuscript.