Background
In Ethiopia,
Plasmodium falciparum (
P. falciparum) and
P. vivax are the predominant causative species of malaria. The two coexist in almost all malarious areas at different levels of co-endemicity. Overall, large proportion of infections reported is due to
P. falciparum (~ 60%) followed by
P. vivax (~ 40%) [
1,
2] with micro-epidemiological and seasonal variation. Such co-endemicity makes malaria control and elimination more complicated in Ethiopia than in most other areas where the later species is absent or very low [
2,
3].
Malaria transmission in Ethiopia is seasonal associated with precipitation and temperature changes; peaking from September to December following the large rainy season from June to August in most parts of the country [
4]. This rainfall pattern doesn’t include the southern and south-eastern parts of the country, which have a bimodal rainfall periods with long rainy season from March to May and short period from September to October [
5]. However, construction of dams and irrigation-based agricultural activities sometimes modify malaria seasonal trend in Ethiopia [
6,
7].
Human mobility/displacement is another contributing factor to the resurgence/distribution of infectious diseases such as malaria [
8]. As some other parts of the country, there are also internal displacements in the study area. Among the possible reasons for displacements in the area include seasonal agricultural work especially for coffee cultivation and inter-communal conflict along the borders. Such human activities might have implication on the malaria distribution or reporting system and influence on services rendered at health facilities (or on the health system) in the area.
The past decades witnessed a sharp decline in morbidity and mortality, putting Ethiopia among the few African countries on track to meet the global 2020 milestone of cutting incidence by 40% or more [
9]. These successes, enabled the Ethiopian national malaria control program (NMCP) to stratify the country’s malaria transmission into four based on annual parasite incidence (API); malaria free (API ~ 0 cases/1000 population/year), low (API > 0 and < 5), moderate (API ≥ 5 and < 100) and high (API ≥ 100) [
4], as a preparation to embark on nationwide malaria elimination. The policy and strategy shift to elimination requires data-driven decision making to tailor interventions [
10,
11]. Thus, policy makers should be provided timely with quality and relevant data to inform national programs.
In Ethiopia, malaria data is captured through the Public Health Emergency Management (PHEM) at different tiers of the healthcare delivery systems. The hierarchy of data flow is from health posts (HPs) and health centers (HCs) to district health offices (HOs) which in turn channels to Zonal Health Departments, then to Regional Health Bureaus and finally to the Federal Ministry of Health [
12]. As malaria in Ethiopia is a weekly reportable disease, HCs are expected to report to the district HO gets registered through the PHEM system. Therefore, HCs data that are organized and archived at respective district HOs are the ones that are analyzed to evaluate the spatial and temporal changes, local malaria dynamics and
Plasmodia species distribution.
Both internal and external data quality control system were there at the HCs level in the study setting. The internal quality control system was done by using different readings and smear preparations. External microscopy quality assessment was employed through sending the randomly selected, both positive and negative, slides to the regional reference laboratory.
Although the overall malaria trend could help to evaluate the progress of elimination activities, under- (over)-reporting of cases could pause a serious repercussion on the country’s elimination efforts. Yet, validation studies comparing data from the different tiers of the health care delivery system hardly exist in most settings and at micro-epidemiological level. Although the six malarious districts of Gedeo Zone are stratified as elimination targeted low transmission districts by NMCP [
4], little information is available to understand the overall trend of malaria and the above issues in the area. Thus, we assessed the overall trend of malaria, species composition, malaria data quality, spatiotemporal distribution and associated socio-demographic and climatic variables. Further, the accuracy of HO malaria records (PHEM data) was checked against the HC data (source document).
Discussion
There was an overall reduction of malaria case from 2012 to 2019. According to data from HCs (source data), a maximum of 16,037 and a minimum of 2546 of cases were observed during 2013 and 2018, respectively with 8.34% reduction. 2013 and 2016 were the exceptions to the declining trend as there were case increases in these periods in some parts of the Zone. The relatively lower malaria cases for the periods between 2014 and 2015 could be associated with rainfall variations and strong intervention activities in the zone. 2016 was an El Nino year that predominantly affected the normal rainfall frequency and distribution along the southern Ethiopia. Thus, this could be the possible contributing factor for the higher occurrence of malaria in the area during 2016. The overall malaria positivity rate (11.79%) in the current study was comparable with certain studies done in Ethiopia including from Batu town (12.43%) [
15], Halaba special district (9.47%) [
16] and north Shoa (8.39%) [
17]. In contrast, higher overall malaria positivity rates were reported from related studies conducted in south-central Ethiopia [
18], southern Ethiopia [
19] and abroad in Dakar, Senegal [
20] with 33.83, 21.79 and 19.68% respectively. On the other hand, the present figure was higher than records in other local studies [
21,
22]. These differences might be due to the variation in quality of laboratory diagnoses, difference in intervention measures, micro-climatic/altitudinal differences, and presence of constructions responsible for occurrence of temporary and permanent dams and drug and insecticide resistances.
P. falciparum and
P. vivax were detected where equivalent; congruent results were reported in some other parts of Ethiopia [
18,
19]. While other local studies [
15,
17,
23] documented that the dominant species was
P. vivax. The proportion of mixed infection in this study was congruent with other studies [
18,
22], whereas inconsistent with other reports [
19,
23]. The higher number of
P. vivax against the national figures could also be an implication for the ability of repeated relapse cases and early emergence of gametocytes during blood-stage infection. In addition, there could be heterogeneity of the Duffy phenotype and the high number of vulnerable Duffy-positive individuals that associated with population movement [
24] in the study area. Environmental fluctuations that change target mosquito species abundance might have an impact on
Plasmodia species occurrence [
25]. Further research is required to clarify whether the equivalent proportion of the two species is associated with the current practice/uptake of primaquine for
P. vivax in the study area. The Ethiopian Federal Ministry of Health states that radical cure primaquine combined with chloroquine should be used to treat vivax malaria cases without prior G6PD testing [
4]. The possible reason for scarcity of mixed infections in this co-endemic area might be a competitive or an antagonistic effect of one
Plasmodium species over the other within the human host during co-infection [
18,
25]. In addition, since the area is low transmission setting, mixed infections with
P. falciparum and
P. vivax infection are less likely to be detected by microscopy. This is because,
P. vivax has an inherently lower parasitemia than its counterpart species, and thus less likely to be detected in a co-infection with
P. falciparum.
For the 8-years period, there was an overall notable difference in both total number of suspected cases and confirmed malaria cases recorded at HCs (source data) and HOs (PHEM data). During 2012 and 2018 the numbers of suspected cases recorded at HCs were consistent with HOs data. In these years the numbers of confirmed cases were higher at HOs records than HCs records. Yirgacheffe rural district had the highest deviation in terms of confirmed malaria records between HCs and HOs data followed by Dilla town and Dilla zuria district, whereas Wonago district was the least. This inconsistency could affect the quality control system of the facilities in the zone. Similar to our finding, a three-month facility-based study comprising of various settings conducted in southern Ethiopia showed that majority of facilities under-reported total malaria (both confirmed and clinical malaria) cases [
12]. Contrary to the present study, a study employed in three provinces of Mozambique stated that under-reporting of suspected and confirmed cases is a bigger issue than over-reporting in the health facility records as compared to the values reported through the health management information system (HMIS). They reported that values of both suspected and confirmed malaria cases documented at health facility records were lower than those reported through the HMIS [
26]. This deviation in malaria data between the two systems, HC and HO, could be due to errors during entering the data from the sources (HCs) into recording formats of PHEM, lack of cross-checking and proofing habits, training gaps on PHEM data use and unintentional/intentional false reports. In addition, limited computer access and skill, inadequate technical support [
27], poor data management skills and limited functionality of electronic data management systems [
28] might be the likely reasons for PHEM implementation challenges.
In the present study there was a significant reduction in data incompleteness; with missing variables between 2012 and 2019 in terms of both suspected and confirmed cases. Apart from the statistical figures of declining trend of missing data (data incompleteness) from year-to-year, there are substantial proportions of missing data in recent years in the health facilities of the study area. When we ignored the missing data there could be over/under estimates of malaria data affecting the real figure of API [
29] in the area. Since API estimates are vital for malaria stratification and further elimination activities. Thus, the declining trends of data incompleteness noted in this study needs to be sustained as the country’s NMCP has prioritized improving data quality issue in its strategic plan.
Concerning the diagnostic performance, the proportions of suspected cases against confirmed cases were increased from year to year. The decreased proportion of confirmed malaria cases detected could likely be due to decreasing malaria incidence in the study area. But the proportions of ‘non-malarial’ febrile cases were increasing from 2012 to 2019. This can be an indication of declining lab capacity of detecting malaria parasites. This declining lab capacity of detecting malaria might be due to the fact that the increased false negative reports associated with reduced sensitivity of microscopy with decreasing parasite densities [
30], unable to detect sequestered
P. falciparum parasites [
31] and low competency of microscopists [
32]. In the other way, such increased number of non-malarial febrile illnesses might be related to other febrile cases including yellow fever virus [
33,
34] and typhoid fever [
35,
36] infections, as per the studies conducted in southern and south-central Ethiopia. In addition, this high number of non-malarial febrile illness might be due to fevers among positive individuals with malaria where the fever is coexisted with but not caused by the
Plasmodia infection [
37]. Thus, non-malarial febrile illnesses of bacterial and viral (e.g. now covid-19) etiologies could complicate malaria diagnosis. This needs further investigation in the area. If laboratory performance percent confirmed declines it means; laboratory performance was decreasing over the years or something causing febrile illness in the area is increasing. Misdiagnosis and treating malaria clinically based only on fever with antimalarial drugs, which is still in remote settings, may contribute to the rapid emergence of antimalarial drug resistance [
38,
39].
The slightly more infection among males in the present study was paralleled with other studies conducted in different parts of Ethiopia [
15,
18,
19,
23]. However, this finding was not consistent with other reports in southern Ethiopia [
17] and elsewhere in Mozambique [
40] where higher malaria cases in females were documented. Individuals in the age group of 15 and above were also more significantly affected. This was in line with other local studies [
15,
23]. In contrast, a finding in Metema, northwest Ethiopia by Ferede et al. [
41] showed that 5–14 years old were more infected. Possible justifications for the higher occurrence of malaria among males and young adults and above age group could be their engagement in various outdoor activities and staying outdoors during the nights [
42]. Apart from outdoor exposures, differences in treatment-seeking behavior, access to health facilities and travel history [
43] might be the possible contributors for the sex- and age-based variations of malaria cases. In addition, a review report revealed that adult females are better protected from parasitic diseases than males due to genetic and biological (hormonal) factors [
44].
Confirmed malaria cases were significantly influenced by altitude in the present study area. Higher malaria was in areas of low altitude (below 1750 masl) compared to in high altitudes (1750–2500 masl).
Plasmodia spp. distribution also varied with elevation;
P. vivax was relatively higher at higher altitudes while
P. falciparum was higher at the lower altitudes. The likely reason for the slightly higher proportion of
P. falciparum in lower altitudes and higher proportion of
P. vivax in high elevations could be related to temperatures. That is, cooler environments are suitable for
P. vivax while
P. falciparum are adapted to a relatively higher temperature for the growth in human host and mosquito vectors [
45,
46].
The peak number of confirmed malaria cases was recorded during
Belg, following the long rainy season in the area, followed by
Kiremt, short rainy season in the area, and
Bega, the dry season in the area, with a statistically significant variation. The long rain season and peak malaria cases association seen in this finding is consistent with various studies in Ethiopia [
17‐
19,
23] although the long rain season in these areas is from June to September. The high prevalence of malaria cases during the two seasons, following the rainfall pattern corroborates the two-season transmission pattern in the entire country. Following the long rainy season higher proportion of
P. falciparum was recorded than
P. vivax, while the difference was smallest during the dry season. The comparable proportion of
P. vivax against
P. falciparum in
Bega might be explained by the fact that
P. vivax has ability to relapse rather than new infections. Since such traits could affect the temporal patterns of
P. vivax infections.
Overall, high number of suspected cases and confirmed malaria cases were documented in Dilla town (urban) and Dilla zuria district (sub-urban) which are found at lower elevations. Except in 2013 and 2019, Dilla town annual malaria cases remained the highest all over the 8-year period. The highest confirmed cases during these 2 years were overtaken by Kochore in 2013 and Dilla zuria districts in 2019. While the lowest was from Yirgacheffe rural district. In general, though an overall declining trend of confirmed malaria cases from 2012 to 2019, increments were recorded in Kochore during 2013 and Dilla town in 2016. Although there is expectation of a better documentation, treatment-seeking behavior, access to health facilities, community knowledge and coverage of intervention activities in urban settings, the current data pointed to the contrary. Thus, in this study, high burden of urban and suburban malaria was noted. This could be because of massive construction activities (like road, house and small dams) and presence of coffee processing sites in Dilla town and its vicinity that could create suitable habitat for mosquito breeding. Travel history [
43], differences in the competence and skills of the laboratory personnel and relatively good reporting system might also be the main responsible factors influencing the prevalence of malaria in Dilla town compared to rural districts. There have been healthy ongoing malaria control activities incorporating environmental management, indoor residual spraying (IRS), long-lasting insecticide-treated nets (LLINs) and artemisinin-based combination therapy in the area. These intervention activities could be attributed for the decreasing trends of malaria in other sites of the Zone. In addition, micro-environmental variations, micro-climatic situations [
25] and changes in intervention (like IRS and LLINs) periods might have effect for these spatial differences of malaria cases. The higher prevalence of malaria in districts with low altitudes would indicate the association of malaria and elevation.
Monthly rainfall and minimum temperature demonstrated statistically significant correlation with malaria cases. Previous studies in Ethiopia [
47,
48] and elsewhere [
49,
50] documented similar findings. This association suggests an early warning system can be developed to forecast the start of the malaria season using rainfall and temperature forecasts in the area. However, the result of the current study on the association of rainfall and malaria cases was deviating from previous finding, stating higher rainfall does not necessarily influence the malaria changes [
51]. This deviation may be due to high rainfall affected the breeding sites of mosquito vectors in other places apart from the current study area [
52]. In addition, there could be prevention practice variations between the population of the current study area and other sites in taking preventive measures during and after the high rain seasons. In contrast to our finding, minimum temperature was weakly correlated with malaria cases in southwest Ethiopia [
48]. Ideally, rainfall and minimum temperature play a vital role in breeding and survival of malaria vectors and the respective parasites. Moreover, average monthly maximum temperature and RH were weakly correlated with malaria cases. In disagreement to our finding, studies conducted in Jimma, Ethiopia by Alemu and others [
47] and Sena and colleagues [
48] in Gilgel-Gibe, southwest Ethiopia reported that inter-monthly RH was significantly associated with monthly malaria cases. Publications of Akinbobola and colleague showed humidity levels between 60 and 90% were favorable for breeding and multiplication of
Plasmodia parasites [
53]. Although RH in our study was occurred between these risky ranges, no strong correlation with malaria cases was seen. This could be explained by the fact that RH is affected by rainfall and temperature, these might confound the relationship. Yet, additional research is required to explain the relationship between malaria transmission and RH in the area.
The limitation of this study was incompleteness of patient data in the register with missed variables and only 8-year data were available during the data collection time at the HCs. This study was limited to collecting secondary data only at health facility level and district level PHEM data. This prevented the readers for getting a clear situation how there is mismanagement of data at regional and federal levels in the country. Since the age classification on the laboratory registration books of HCs were only < 1, 1–4, 5–14, 15+, it’s difficult to further subdividing of 15+ age groups. Malaria diagnosis in the urban areas (particularly in Dilla and Yirgacheffe towns) could also be done in other public health facilities and private clinics. But this study did not include data collected from these facilities and this might underestimate the actual trend of malaria in the area. Furthermore, clinically treated patients’ (without laboratory confirmation) data and malaria mortality data were not recorded in the laboratory registration logbooks. Hence, interpretation of the finding should be with caution.