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Population Movement as a Risk Factor for Malaria Infection in High-Altitude Villages of Tahtay–Maychew District, Tigray, Northern Ethiopia: A Case–Control Study

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  • 1 School of Water and Public Health, Ethiopian Institute of Water Resource, Addis Ababa University, Ethiopia;
  • | 2 Federal Ministry of Health, Addis Ababa, Ethiopia;
  • | 3 Public Health Department, College of Health Sciences, Mekelle University, Mekelle, Ethiopia;
  • | 4 Department of Medical Microbiology and Immunology, Institute of Biomedical Sciences, College of Health Sciences, Mekelle University, Mekelle, Ethiopia

Key goal and targets of the Ethiopia National Malaria Control Program are to achieve malaria elimination within specific geographical areas with historically low malaria transmission and to reach near-zero malaria transmission in the remaining malarious areas by 2020. However, back and forth population movement between high-transmission and low-transmission area imposes challenge on the success of national malaria control programs. Therefore, examining the effect of human movement and identification of at-risk populations is crucial in an elimination setting. A matched case–control study was conducted among 520 study participants at a community level in low malaria transmission settings in northern Ethiopia. Study participants who received a malaria test were interviewed regarding their recent travel history. Bivariate and multivariate analyses were carried out to determine if the reported travel was related to malaria infection. Younger age (adjusted odds ratio [AOR] = 3.20, 95% confidence interval [CI]: 1.73, 5.89) and travel in the previous month (AOR = 11.40, 95% CI: 6.91, 18.82) were statistically significant risk factors for malaria infection. Other statistically significant factors, including lower educational level (AOR = 2.21, 95% CI: 1.26, 3.86) and nonagricultural in occupation (AOR = 2.0, 95% CI: 1.02, 3.94), were also found as risk factors for malaria infection. Generally, travel history was found to be a strong predictor for malaria acquisition in the high-altitude villages. Therefore, besides the existing efforts in endemic areas, targeting those who frequently travel to malarious areas is crucial to reduce malaria infection risks and possibility of local transmissions in high-altitude areas of northern Ethiopia.

BACKGROUND

Malaria is the most important parasitic disease of human beings.1 Historically, malaria has killed more people than any other infectious disease and still accounts for massive levels of mortality and morbidity in more than 100 countries.1,2

Malaria is a disease affects more than 212 million people and kills more than 429,000 people annually. Approximately 80% of cases and 90% of deaths are estimated to occur in the sub-Saharan African (SSA), where it is the leading cause of morbidity and mortality among children under 5 years.3 Apart from its direct impact on the health of individuals, malaria is also responsible for millions of lost workdays as well as absences from school that have serious repercussions on children’s academic performance.4,5

As any SSA country, malaria is a major public health problem in Ethiopia. An estimated three-fourths of the landmass is potentially malarious and more than two-thirds (68%) of the population is residing in this area.6 Malaria transmission mainly occurs up to the 2,000 m above sea level (masl) but can also occasionally affect areas up to 2,300 m elevation. Malaria transmission pattern is affected by the large diversity in altitude, rainfall, and population movement.7,8

The epidemiology of malaria in Ethiopia is peculiar and different from that of the large parts of SSA. First, the unstable nature of the transmission makes the population nonimmune and prone to focal outbreaks and cyclic epidemics. This unstable nature also accounts to the fact that all age groups are at risk of the disease and all malaria infections, even with low-level parasitemia, are associated with clinical illness. Second, unlike most SSA countries where Plasmodium falciparum accounts for almost all malaria infections, in Ethiopia both P. falciparum and Plasmodium vivax are codominant, where the former accounts for approximately 60% of all cases. In the low-transmission season, P. vivax increases its proportion due to its relapsing nature and the seasonal drop in P. falciparum infection. The majority of malaria admissions and deaths occur due to P. falciparum infections, being also the species triggering the epidemics.8–10

In Ethiopia, to control malaria, interventions including early diagnosis and treatment, the use of vector control methods using insecticide indoor residual spraying (IRS) of households with insecticide and insecticide-treated mosquito nets (ITNs) are applied. These interventions are highly effective in reducing both the transmission and exposure to infectious mosquito bites and also the concomitant burden of malaria disease.11,12 However, conventional vector control interventions such as IRS and ITNs protect the household but are less effective for individuals who are away from their homes during the peak times of transmission, or away from personal control measures.12,13

Human movement is known to have an impact on infectious disease transmission. It is a key factor in determining vector-borne infectious disease transmission as well. It has a significant effect on the degree to which humans and vectors interact. Even small-scale movements around a neighborhood or town can be very important factors in determining if there will be transmission between a vector and a human.14,15

When populations move from highland and low malaria transmission areas to lowland and wet high-transmission areas, they are more susceptible to malaria infection than the resident population. Back movement from these high-transmission areas to the low-transmission areas can expose previously malaria-free vectors to the infection.10,16,17 Such population movement imposes a challenge on the success of national malaria control programs.12

The key goal and targets of the Ethiopia national malaria control strategies is to achieve malaria elimination within specific geographical areas with historically low malaria transmission (i.e., highlands and arid) and to reach near-zero malaria transmission in the remaining malarious areas of the country by 2020.7 Identification of determinant factors of malaria transmission has a tremendous advantage to achieve these goals and targets.

Although travel to a malaria-endemic area in Ethiopia is recognized as a risk factor for malaria infection, little attention has been given to whether temporarily, seasonal, and routine human movement patterns can lead to higher risks of malaria infection.12 Nationwide population-based surveys such as demographic, health survey, and malaria indicator survey, which are conducted every 5 years, even do not include the effect of short-term and seasonal population movement on malaria control and elimination efforts. Thus, lack of proper understanding of those determinant factors could be a challenge in the overall management of malaria program and especially for the elimination effort.

Therefore, examination of the effect of human movement and identification of at-risk populations, and the most effective methods to target them, is crucial in a malaria control and elimination setting.16 Understanding the risk factors of malaria transmission at high-altitude villages is important to facilitate implementing sustainable malaria control and elimination programs. The aim of this study was to explore the determinant factors that drive malaria transmission in high-altitude villages in northern Ethiopia.

METHODS

Study area and study period.

This study was conducted in seven villages of Tahtay–Maychew District (Figure 1), Central zone, Tigray, northern Ethiopia, from August 1 to December 30, 2014. The total area of the district is 1,954 km2. The district was inhabited by 112,769 population at the time of the study period.18 The district has five health centers and 14 health posts. From Universal transverse Mercator coordinate the study sites are located as follows: Hadisadi (456737, 1557233), Kewanit (460663, 1557826), Maysiye (458442, 1561683), May berazyo (451109, 1561171), Akabseat (448704,1560152), Miryena (461426, 1549107), and Maekel (455880, 1554099). Its altitude ranges from 1,500 meters above sea level (masl) to 2,530 masl. This can be divided into three highland fringe zones: the high transmission and epidemic prone (between 1,500 and 1,750 masl), highland fringes with low transmission and epidemic prone (1,750–2,000 masl), and highlands affected by occasional epidemics (between 2,000 and 2,500 masl), and this study was undertaken in the last zone.19 Agriculture is the main occupation of the residents. The study included the peak of the malaria transmission season (September–November). Malaria transmission is seasonal and P. falciparum is the dominant species.10

Figure 1.
Figure 1.

Location of the study sites/Villages, Tahtay Maychew woreda, Tigray, Ethiopia. This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene 97, 3; 10.4269/ajtmh.17-0129

Study design and study population.

A community-based case–control study matched by sex and village was used to determine factors associated with malaria risk infection, and frequency of exposure to several sociodemographic and behavioral factors were compared with malaria-confirmed participants and participants free from malaria in the high-altitude villages of the district.

The source population was all adult males and females aged between 15 and 50 years (potentially migrants) and living in purposely selected seven villages of the district (of the 16 villages in the district, only 7 fulfilled the criteria, i.e., above 2,000 masl). Study participants were those who sought malaria care in health posts and their neighborhoods from August to December 2014. A “case of malaria” is defined as an individual currently living in the study area and having experience of malaria infection confirmed by laboratory or rapid diagnostic test (RDT) during the study period. A “control” is defined as an individual living in the study area neighboring (an individual randomly selected from the nearby households) to the case and who had not had self-reported and laboratory-confirmed malaria illness within the study period. Study participants who were mentally sick or critically ill during enumeration and data collection were excluded from the study. The main outcome variable was malaria illness in the study participants. Travel taken as variable of exposure was the travel made outside their home village to malaria-endemic areas20 and it was categorized as a yes–no question regarding if the subject traveled or not. Two definitions of travel were used. “T1” is limited to the month prior to visiting the health post and of at least 3 days duration and “T2” referred to lifetime travel to such areas but not limited to survey period of such characteristics.21

Sample size determination.

The sample size was calculated by taking account of the major determinant factors by using Stat Calc module of Epi-Info (Epi Info™ version 3.5.4, U.S. CDC, Atlanta, GA). In this regard, the study was powered to detect a minimum detectable OR (odds ratio) of 2, a 5% level of significance, and a power of 90% on the measure of having traveled and stayed in malaria-endemic areas either temporally or seasonal from the home (outside of their immediate community). A one to three allocation ratio of case group to control group (n1: n3) were assumed. Accordingly, by taking 32% (proportion of nonmalaria study participants who had travel history to the malaria-endemic areas within the period of respondent recruitment) and the above assumptions, a total of 520 study participants, 130 cases (positive by RDT), and 390 controls (had no known malaria infection during the study period) were recruited for this study.

Sampling procedure.

First, seven rural health posts/villages were selected from the woreda based on purposive sampling technique (i.e., altitude above 2,000 masl). Then total enumeration of case population and their controls was conducted in the seven selected villages. The seven villages were Hadisadi (2,088 masl), Kewanit (2,144 masl), Maysiye (2,200 masl), May Berazyo (2,174 masl), Akab Seat (2,220 masl), Miryena (2,100 masl), and Maekel (2,050 masl). Then, cases were proportionally allocated to the total confirmed malaria cases in the selected villages. Next, the sampling frame was prepared to select the study participants randomly. Finally, seven community health promoters were recruited to enumerate the availability of the selected cases for the study. The enumerators were provided 10% contingent cases to substitute for those who were absent during the enumeration period. In this context, 13 cases were replaced. In parallel to this, the enumerator selected three controls for each case. The controls of each case were obtained and selected from the neighboring households.

Data collection.

Data collection was carried out by seven health extension workers, who were given training with practical exercises. Four health professionals were assigned to supervise the data collection process. Collected data were checked by the principal investigator and supervisors on a daily basis for any incompleteness and/or inconsistency. The data were collected using a pretested structured questionnaire covering sociodemographic and individual risk factors for malaria infection including recent travel to an endemic area. It was prepared by reviewing previously done similar studies.12,22 Then, it was translated into the local language (Tigrigna) to be used at the interview time. To minimize recall bias, an Ethiopian calendar was used to help participants recall their travel history and history of travel to malaria-endemic areas. Information from interviews was complemented by self-reported travel history from the health post registries. When cases visit the health post to sought malaria diagnosis, they were asked whether they had traveled or not to a malarious area20 either before 2 weeks or 30 days.

Data processing and analysis.

After being cleared and edited, data were entered into Epi Info 3.5.4 (Centers for Disease Control and Prevention, Atlanta, GA) for Windows and exported to Statistical Package for Social Sciences, version 22.0 (SPSS, Chicago, IL), for Windows for analysis. Then, bivariate analysis using cross-tabulation was performed to determine the distribution of study participants by independent variables of interest. The bivariate logistic regression technique was used to see the crude association between the independent and dependent variables. Finally, variables that were significantly associated with malaria infection in the bivariate analysis were included in the multivariate logistic regression analysis to evaluate the independent effect of independent variables on the outcome variable. A significant level of 0.05 was used as the cut point for significant tests, and P value less than 0.05 with 95% confidence interval [CI] was taken. Moreover, efforts were made to assess whether the necessary assumptions for the application of multiple logistic regression were fulfilled. In this regard, the Hosmer and Lemeshow’s goodness-of-fit test was considered. A good fit, as stated by Hosmer and Lemeshow’s test, will provide a large P value. Case-wise listing of residuals was also checked to identify outliers.22

Ethical issues.

Ethical permission was obtained from the institutional review board of Ethiopian Institute of Water Resource, Addis Ababa University. Permission letter was obtained from administrators of the Regional Health Bureau, Woreda Health Department, and respective health facilities. A verbal consent was obtained from each study participant.

RESULTS

Sociodemographic.

A total of 520 study participants (130 cases and 390 controls) (Table 1) were recruited. Of the 130 cases, 99 (76.2%) were found to be positive for P. falciparum and 31 (23.8%) for P. vivax (Table 1). The study sample contains more male (92.3%) due to the matching effect of the sample. The age of study participants ranged from +15 to 50 years and 45.8% of the study participants were between the ages of +15 and 24 years. Regarding educational level, 42.3% of the study participants had primary educational level. The main occupation of the study participants was agriculture (84.10%). Study participants self-reported that 447 (86%) of them own livestock with 78.7% owning milk cows or oxen and their homes were primarily roofed with corrugated iron sheet. Only 93 (17.9%) of the study participants had microsized rainwater harvesting ponds, locally known as Paska and Horreyo, in their premises.

Table 1

Distribution of study participants by sociodemographic characteristics, Tahtay Maychew woreda, northern Ethiopia, 2014

VariablesCase, N (%)Control, N (%)
Sex
 Male120 (92.3)360 (92.3)
 Female10 (7.7)30 (7.7)
Age group (year)
 +15–2484 (64.6)154 (39.5)
 25–3428 (21.5)107 (27.4)
 35+18 (13.8)129 (33.1)
Marital status
 Single81 (62.3)170 (43.6)
 Married49 (37.7)220 (56.4)
Family size
 ≤ 565 (50.0)206 (52.8)
 > 565 (50.0)184 (47.2)
Educational level
 No education28 (21.5)122 (31.3)
 Primary (2–8)69 (53.1)151 (38.7)
 Above primary33 (25.4)117 (30.0)
Main occupation
 Agriculture100 (76.9)335 (85.9)
 Student26 (20.0)48 (12.3)
 Others4 (3.1)7 (1.8)
Roof type of the main house
 Corrugated iron sheet120 (92.3)360 (92.3)
 Thatched roof10 (7.7)30 (7.7)
Functional radio
 Yes42 (32.3)122 (31.3)
 No88 (67.7)268 (68.7)
Mobile telephone
 Yes77 (59.2)219 (56.2)
 No53 (40.8)171 (43.8)
Own livestock
 Yes104 (80.0)343 (87.9)
 No26 (20.0)47 (12.1)
Wealth quartile
 Most poor8 (6.2)45 (11.5)
 Second54 (41.5)166 (42.6)
 Middle54 (41.5)135 (34.6)
 Least poor14 (10.8)44 (11.3)

Travel history.

Of the total study participants, 220 (52.4%) traveled away from their permanent home or residence to a malarious area during the last month prior to their visit to health post (T1) and the percentage of travel history was reported to be 82.3% among the cases. Among the cases who traveled away, 105 (80.8%) had a history of travel to malarious areas in 30-day recall period before the time of diagnosis (as reviewed from the health post registries), and from those who traveled to the malarious areas, 171 (77.7%) had traveled for the purpose of traditional gold mining and almost all (216, 98.2%) traveled to areas such as Western zone as their main place of destination (e.g., Asgede Tsimbla, Tahtay Adiyabo, and Humara) where malaria transmission is highly endemic.

Four hundred ninety-four (95%) of the study subjects had at least one mosquito net in their home. Only 33.3% of cases with a history of travel reported long-lasting insecticidal net (LLIN) use during their travel. Of these who had traveled outside their village, 151 (68.6%) reported that they had used antimalaria drug such as artemisinin-based combination therapy as a malaria prevention method (Table 2).

Table 2

Distribution of Study participants by travel history and malaria prevention practice, Tahtay Maychew woreda, northern Ethiopia, 2014

VariableCase, N (%)Control, N (%)
Travel history (T1)
 Yes107 (82.3)113 (29.0)
 No23 (17.7)277 (71.0)
Duration of stay in weeks (N = 220)
 1–2 weeks17 (15.9)17 (15.9)
 3–4 weeks73 (68.2)65 (57.5)
 Above 4 weeks17 (15.9)31 (27.4)
Purpose of travel
 Gold mine94 (87.9)77 (68.1)
 Agriculture10 (9.3)33 (29.2)
 Other purposes3 (2.8)3 (2.7)
Name of place traveled
 Western zone107 (100)109 (96.5)
 Other places0 (0.0)4 (3.5)
Lifetime travel history to malaria endemic areas
 Yes89 (68.5)187 (47.9)
 No41 (31.5)203 (52.1)
Own mosquito net
 Yes123 (94.4)371 (95.1)
 No7 (5.4)19 (4.9)
Number of mosquito net per household
 Has only one ITN24 (19.5)73 (19.7)
 Has two ITNs62 (50.4)202 (54.4)
 Has more than two ITNs37 (30.1)96 (25.9)
Usage of malaria prevention method during travel
 Yes60 (46.6)165 (42.3)
 No70 (53.4)225 (57.7)
Type of malaria prevention method used
 Mosquito nets20 (33.3)54 (32.7)
 Antimalaria drug/Coartem40 (66.7)111 (67.3)
Microsized RWH ponds in the premises of HHs
 Yes32 (24.6)60 (15.4)
 No98 (75.4)330 (84.6)
Traveled before the onset of their malaria episodes?
 Yes105 (80.8)
 No25 (19.2)

HH = household; ITN = insecticide-treated mosquito net; RWH = rain water harvesting.

Multivariate logistic regression analysis.

All variables that had significant association (at significance level of 0.05) with the outcome variable in the bivariate analysis were entered stepwise to multiple logistic regression models and five variables were found to have a statistically significant independent association with malaria status (Table 3).

Table 3

Multivariate logistic analysis-adjusted for, socio demographic and behavioral variables, Tahtay Maychew woreda, northern Ethiopia, 2014

Explanatory variableMalaria statusOR (adjusted)95% CIP value
CaseControlLowerUpper
Age of the respondent0.001**
 +15–24841543.201.735< 0.001*
 25–34281071.570.783.180.21
 35+181291.00
Educational level0.020**
 No education281221.800.903.610.095
 Primary691512.211.263.860.005*
 Above primary331171.00
Travel history (T1)
 Yes10711311.406.9118.82< 0.001*
 No232771.00
Own livestock
 Yes1043430.500.260.940.032*
 No26471.00
Main occupation
 Agricultural1023411.00
 Nonagricultural28492.001.023.940.044*

CI = confidence interval; OR = odds ratio.

The odds of developing malaria morbidity was 3.20 (adjuster OR [AOR] = 3. 20, 95% CI: 1.73, 5.89) times higher among study participants in the age group of between 15 and 24 years compared with those among study participants in the age group of 35 years and above. The odds of getting malaria was also 2.21 (AOR = 2. 21, 95% CI: 1.26, 3.86) times higher among those who had a primary level of education compared with those who had above-primary education.

The odds of developing malaria morbidity (relative to never during the study period) was 11.40 (AOR = 11. 40, 95% CI: 6.91, 18.82) times higher among study participants who had a history of travel to malaria-endemic areas during the study period than those study participants who had no a history of travel to such areas.

The odds of getting malaria infection were 50% less likely among study participants who had livestock in their home compared with study participants who had no livestock (AOR = 0.50, 95% CI: 0.26, 094). The odds of developing malaria morbidity was 2.0 (95% CI: 1.02, 3.94) times higher among study participants who reported nonagricultural occupation as their main occupation compared with that among study participants who reported agricultural works as their main occupation.

DISCUSSION

Human population movement, specifically in low-transmission and -elimination settings, is important to estimate for the success of control and elimination programs. Thus, accounting human movement needs to be a key aspect of malaria control, elimination, and postelimination, as evidenced by previous elimination attempts that were undermined by the reintroduction of malaria through human population movements.17

Travel to a malaria-endemic area in Ethiopia is recognized as a risk factor for malaria infection and much of the country is endemic for malaria transmission.12 However, little attention has been paid to whether these routine internal or short-term human movement patterns can lead to higher risks of malaria infection, especially in high-altitude (i.e., above 2,000 masl) villages of the country. This study tried to assess the potential amenable risk factors for malaria infection in high-altitude villages in northern Ethiopia.

In the present study, malaria infection was found to vary among different age categories of the study participants. Malaria risk was found to increase by a factor of 3.20 (95% CI: 1.73, 5.89) among study participants aged between 15 and 24 years compared to that among participants aged 35 years and above. This variation could be due to the increasing importance of occupational and behavioral factors outside of their home village that put these groups in contact with infective vectors. Study participants in the former age group, especially adult men, are more likely to have travel history to peripheral malaria-endemic areas and to engage in traditional gold mining, seasonal agricultural harvesting, and other social and economic affairs. As a result, they might be at great risk of acquiring malaria infection due to either lack of previous exposure or relaxed use of malaria prevention interventions. In low-transmission areas in Latin America, such as Peru and Suriname, malaria risk increased substantially for men aged 15 years and older and was occupationally related to charcoal producers, gold miners, and loggers.13 This finding is also consistent with those of other studies conducted in the East African highlands.23–25

Furthermore, study participants in the latter age groups are more likely to stay at home and probably travel less as they might be overburdened by home affairs (cultivation, harvesting, and caring for their family) and also may use mosquito netting and other precautions against exposure.

In this study, study participants who traveled to peripheral malaria-endemic areas within the study period were 11.40 times more likely to be under the risk of getting malaria infection compared with those who did not travel. This finding is compatible with the result of many other similar studies done in Ethiopia, Kenya, and Peru.10,12,20,25–28 This relationship might be due to a combination of different factors including the movement of nonimmune people from these relatively malaria-free high-altitude villages to areas such as Asgede Tsimbla, Tahtay Adiyabo, and Humara, which are low lands with intensive malaria-endemic areas. When populations move from low-transmission areas to high-transmission areas, they are more susceptible to malaria than the resident population if exposed to an infective mosquito.10

Movements to high-transmission areas in search of alternative income generation activities such as gold mining, seasonal agricultural job seeking, and even other social affairs are mainly seasonal and take place during the risk period for mosquito biting, in this manner they may be exposed to an infective vector foci in these areas. This correlates with malaria transmission season in Ethiopia.12 Relaxed use of preventive measures when individuals are away from their home village and possible behavioral differences by age during travel could also be other contributing factor for the difference observed among study participants. Similar findings were documented from prior studies elsewhere.10,11,26 Thus, travel away from home is a significant predictor of malaria infection in high-altitude villages in northern Ethiopia.

Nevertheless, that notable number of cases (19.2% from the 130 cases) did not travel outside their home village to malaria-risk areas may indicate some level of transmission in low-malaria transmission settings (i.e., high-altitude villages) as the vectors of malaria are identified at 2,200 masl in some highlands of Ethiopia29 or many of the P. vivax infection reported in this study may have originated not from the initial sporozoite inoculations, but rather as relapses.

In this study, it was found that livestock ownership has a negative effect on the risk of malaria. This was true even when the data were analyzed by adjusting for many other sociodemographic and behavioral characteristics. Those study participants who had livestock regardless of their number (relative to none) showed a half reduction in acquiring malaria infection compared with those who had no livestock in their home (AOR = 0.50, 95% CI: 0.26, 0.94). But this finding is not consistent with the result of many other similar studies.12,30 This variation might be explained by two things. The first explanation might be methodological differences among studies because the study conducted in Bulbulla woreda, Ethiopia,12 was a facility-based case–control study, and the study conducted in Adama26 was a multilevel analysis. The second explanation might be socioeconomic variations among study participants. In this regard, livestock ownership may indicate greater socioeconomic status in agrarian societies or those who had no livestock might travel more and be exposed to malaria infection.

Educational level (at least primary versus above-primary) had an impact on malaria morbidity according to this study. Study participants with at least primary-level education were 2.21 times more likely to be at risk of contracting malaria compared with those with above-primary level. This sounds true in such study participants where those with better educational level might have good knowledge on the prevention methods of this disease and they might also use these methods accordingly or they might stay in school during the major malaria transmission season. Other studies have shown that use of malaria prevention measures has been directly related with higher educational level.31 However, this finding is not compatible with the result of other similar studies.20,29 The variation observed may be explained by the differences in methodological approach among studies and socioeconomic variation of study participants. The study conducted in Maynas, Peru, was a retrospective cohort study and that conducted in Adama, Ethiopia, was a cross-sectional one.

Unlike the findings of studies conducted in northwest Ethiopia10 and Peru,20 engaging mainly in agricultural activities was inversely associated with being at risk of malaria in the present study. This is generally true that study participants who had reported nonagricultural activities (e.g., students, traders, and others) as their main occupation are more likely to make travel outside of their home village as they might have not a plot of land for agricultural activities during the cultivation and harvesting season. As a result, they often travel to the western zone of Tigray, seeking temporary jobs and gold-mining (mostly true in the central highlands of Tigray). The observed variation among these studies could be arising due to methodological approach and sample size.

In this study, malaria prevention measures including mosquito net ownership and number of bed net available in the household showed no statistically significant protective effect against malaria morbidity, though it is generally taken for granted that mosquito nets reduce malaria infection and mortality. Although people might express interest in mosquito net, anecdotal evidence suggests that even those who owned mosquito net did not use them.25 This finding is compatible with similar studies conducted in Ethiopia and elsewhere.10,12,25,32 In this regard, these findings could arise from a combination of factors that might include primarily vector density and the associated perception that risk might be low, the belief that only those who travel outside of their home village were at risk of getting malaria, belief that who use a bed net at home might fail to do so when they travel, or simply due to limited sample size. In this study, the participants’ age between 15 and 24 years, history of travel in the month before the onset of the malaria episodes (within the study period), lower educational level, and nonagricultural occupation were found to be statistically significant risk factors for malaria infection. There are two limitations of this study: first, it was unable to remove recall bias entirely, and second, it was difficult to avoid overmatching.

In conclusion, this study indicates short-term or return travel history to be a main contributing factor for increased malaria infection and morbidity among the study participants in the highland areas of northern Ethiopia. Thus, the return of infected travelers to highland areas may also play an important role in spreading malaria infection in the highland areas, which actually depends on the distribution of the vectors. Therefore, to achieve the key goal and targets of the Ethiopia National Malaria Control Program, which is to eliminate malaria within specific geographical areas with historically low malaria transmission (i.e., highlands and arid), intervention approaches should be strengthen particularly for short-term or return travelers who reside in high-altitude villages of northern Ethiopia. Further investigation is also recommended to assess the density of malaria vectors and the potential role of returning infected travelers in spreading malaria infection in the highland areas of northern Ethiopia. These collectively may have a potential to influence the national policy to design more appropriate interventions in the highland areas besides the existing tremendous efforts in the malaria-endemic areas.

Acknowledgments:

We acknowledge the financial assistance provided by University of Connecticut, USAID, and Addis Ababa University/Ethiopian Institute of Water Resource. We thank all study participants. We also acknowledge the assistance and permission given by Tigray Region Health Bureau and Tahtay-Maychew district Health Offices to undertake the research. The American Society of Tropical Medicine and Hygiene (ASTMH) assisted with publication expenses.

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Author Notes

Address correspondence Yemane Weldu, Department of Medical Microbiology and Immunology, Institute of Biomedical Sciences, College of Health Sciences, Mekelle University, Mekelle, Ethiopia. E-mail: yemaneweldu@gmail.com

Financial support: University of Connecticut (USA), USAID, and Addis Ababa University/Ethiopian Institute of Water Resource: GRANT NUMBER: RES/ EIWR/009/2014.

Authors’ addresses: Mebrahtom Haile and Hailemariam Lemma, National Malaria Program at Federal Ministry of Health, Addis Ababa, Ethiopia, E-mails: mebrahtom2007@gmail.com and hailelm@gmail.com. Yemane Weldu, Mekelle University College of Health Sciences, Mekelle, Ethiopia, E-mail: yemaneweldu@gmail.com.

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