6-year pattern evaluation of malaria prevalence on the College of Gondar Specialised Referral Hospital, Northwest Ethiopia, from 2014 to 2019
The present study found that the mean annual malaria prevalence was 7.7% (95% CI 7.3-8.1). This result was significantly lower than the study reported elsewhere in Kola Diba, North Gondar, NW Ethiopia (39.6%)4, Adi Arkay, North Gondar, NW Ethiopia (36.1%)14, Abeshge, South-Central Ethiopia (33.8%)5 , Woreta Health Center, Northwest Ethiopia (32.6%)14, Dembecha Health Center, West Gojjam Zone, Northwest Ethiopia (16.34%)8 and Halaba Special District, South Ethiopia (9.5% )15. However, the current malaria prevalence was higher than other study results conducted in the catchment area of Felegehiwot Referral Hospital, Bahir Dar, NW Ethiopia, Ethiopia (5%)7. The difference could be due to variations in the quality of malaria diagnosis and the abilities of laboratory personnel to detect and identify malaria parasites. In addition, the implementation of malaria prevention and control measures may differ from one area to another. In addition, there may be differences in demographic characteristics (sex, age), geographic location (altitude, temperature, rainfall), and differences in economic activities that also affected malaria prevalence. The population’s awareness of the use of malaria bed nets, their transmission and health-conscious behavior can also vary.
The mean annual trend in malaria prevalence showed that malaria prevalence increased slightly in the first two years of the study (2015 and 2016) compared to 2014, but this was not statistically significant. However, in the last three study years (2017, 2018 and 2019), the trend showed a significant reduction in malaria prevalence. The likelihood of malaria prevalence was reduced by 68%, 60% and 69% in 2017, 2018 and 2019, respectively. The possible reasons for the reduction in malaria during these study periods (2017–2019) could be due to increased attention to malaria control and prevention measures by various responsible agencies, increased community awareness of the use of ITNs, IRS, and the drainage system of mosquito hatcheries, and Climate change at national and international level. Integrated control strategies are being implemented in the Region as part of national malaria control efforts16. The result was similar to the 5-year trend analysis of malaria prevalence at Dembecha Health Center, West Gojjam Zone, NW Ethiopia, which reported a variable decrease in malaria prevalence8. However, the observed prevalence in this study was still significant and a public health concern.
This study showed that P. vivax was the predominant species on average over the six-year study periods, although there was some species variation from year to year. The proportion of P. vivax, P. falciparum and mixed infections was 47.2%, 45.6% and 7.2%, respectively. This finding was consistent with the study conducted in Adama City, East Shoah Zone, Oromia, Ethiopia16, Halaba Health Center South Ethiopia15 and Southwest Ethiopia, around Gilgel Gibe Dam and 10 Kilo Metter away from Gilgel Gibe Dam3 was carried out. The prevalence of P. vivax could be due to a relapse into quiescent states of the liver or increased treatment pressure against P. falciparum17. However, this finding was inconsistent with the study conducted at two health centers Gorgora and Chuahit in Dembia district18, Felegehiwot Referral Hospital catchments7 and Kola Diba, North Gondar, NW Ethiopia4 and reported that P. falciparum was the predominant species. In addition, the trend of P. vivax showed a reduction while P. falciparum showed an increasing trend. In the last three years of the study periods, P. falciparum had become the predominant Plasmodium species. The fluctuating proportion of Plasmodium species could be attributed to heterogeneous parasite species, and disease distribution includes differences in genetic polymorphisms underlying parasite drug resistance and host susceptibility, mosquito vector ecology, and seasonality of transmission. Plasmodium species may interact differently geographically and these interactions may even change from year to year in a given location19. The finding also showed that the proportion of mixed infections varied.
The prevalence of malaria varied between different seasons, ranging from 6.6 to 8.8%, and these variations were statistically significant. The highest peak was observed in autumn (8.8%) and the lowest peak in the winter season (6.6%). Malaria prevalence was reduced by 16% in winter. However, when sex and age were adjusted, peak prevalence was observed in summer rather than autumn, when prevalence was increased by 32%. The reason could be climate change from year to year. In Ethiopia, summer is the season when heavy rainfall is observed and it is not a favorable season for vector spread16. However, there are fluctuations in rainfall from year to year20. It is estimated that changes in temperature, precipitation and relative humidity due to climate change directly affect malaria by altering the behavior and geographic distribution of malaria vectors and changing the length of the parasite’s life cycle. Climate change is also expected to indirectly affect malaria by altering the ecological relationships important to the organisms (vector, parasite and host) involved in malaria transmission21.
The current study found that men were more likely to be infected with malaria than women. Males were 1.41 times more likely to be malaria positive than females. Similar studies showed that men were more affected than women22,23,24,25. The reason for the high incidence of malaria in men could be that men engage in outdoor activities. A study conducted in the Dembia district of north-western Ethiopia found that people who participate in outdoor activities are at a higher risk of malaria25. Another possible reason could be that men travel to malaria-endemic areas to seek temporary employment, while women do not engage in field activities but cook and stay at home, which could reduce the risk of infection.
Age also contributed to the prevalence of malaria. It was higher in the younger age groups than in the older age groups. The likelihood of a malaria positivity rate in children under five years of age and children between the ages of 5 and 14 was 1.60 and 1.64 times higher, respectively, than in the age group over 55 years of age. The reason for this could be that these age groups may be less immune to commuting than the older age groups (> 55 years). This was supported by the World Health Organization report26. The study also showed that the odds of malaria positivity in the early workgroups (15-24) and primary workgroups (25-54) were 2.45- and 1.82-fold higher, respectively, than the over-55 age group. year olds. Another study conducted on pregnant women in Sherkole district of Benshangul Gumuz regional state in western Ethiopia also found that the older age groups were less likely to contract malaria infections27. The reason for the high malaria cases in the mentioned age group of 15-24 and 25-54 years could be the fact that this age group may engage in outdoor activities and be mobile in malaria-endemic areas to seek temporary employment, while the older age groups do not carry out field activities, but stay at home, which could reduce the risk of infection. In addition, the older age groups are often exposed to malaria earlier, which can develop immunity to malaria infection. Natural infection was known to elicit a robust immune response against the blood stage of the parasite that is protective against malaria28. However, according to studies conducted in the rural settings of Arba Minch Town, southern Ethiopia29 and Sudan30, age was not significantly associated with malaria infection. In fact, these studies focused on a specific study population; Children under the age of five or pregnant women.
The result of the current study had its strengths; once it had a sufficient sample size, which increased the power of the study; secondly, it included all age groups of the population (from children to old age groups). However, this study could suffer from the fact that it deals with secondary data; the reliability of the recorded data could not be determined. In addition, the collected data was transferred to the laboratory logbook, which lacks participants’ body temperature, clinical presentations, and place of residence. Information about the weather conditions of the month, seasons and years is also missing.