Eight-year Retrospective Analysis of Malaria Trends in Gedeo Zone, South Ethiopia (2012-2019)

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Poor data record system coupled with under- (over)-reporting of malaria cases affects the country’s elimination activities. Thus, malaria data at health facilities and health offices are important particularly to monitor and evaluate malaria elimination progresses. This study was intended to assess overall reported malaria cases, spatiotemporal trends and factors associated in Gedeo zone, South Ethiopia, and compare malaria case reports by the health centers and health offices. Methods: Past eight years retrospective data stored in 17 health centers and 5 district health offices in Gedeo Zone were extracted. Malaria cases data at each health center with sociodemographic information, between 2012 and 2019, were included. Meteorological data were obtained from the national meteorology agency. The data were analyzed using Stata 13. Results: A total of 485,414 clinical suspects were examined for malaria during the previous 8 years at health centers. Of these suspects, 57,228 (11.79%) were confirmed malaria cases. We noted, an overall under reporting of malaria, 3,758 clinical suspects and 467 confirmed malaria cases were not captured at the health offices level. Based on the health centers records, Plasmodium falciparum (49.74%) was slightly higher (p = 0.795) than P. vivax (47.59%). The majority of cases were found in adults (≥15 years of age) that accounted for 11.47% of confirmed malaria cases (p < 0.0001). There was high spatiotemporal variation: highest cases record was during autumn (12.55%) (p < 0.0001) and, the highest (18,150, 13.17%)) and lowest (5,187 (10.44%)) malaria cases were reported from Dilla town and Yirgacheffe rural district, respectively (p = 0.0002). Monthly rainfall and minimum temperature exhibited strong positive associations with the number of confirmed malaria cases. Conclusion: A notable decline in malaria cases was observed over the eight-year period. Both P. falciparum and P. vivax co-exist; hence, control measures should continue targeting both species. The high malaria burden in urban (Dilla town) and suburban (Dilla zuria district) settings and autumn season need spatiotemporal consideration by the elimination program. Infectious Diseases Malaria retrospective spatiotemporal trend meteorological factors Gedeo zone Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 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 very low [ 2 , 3 ]. Malaria transmission in Ethiopia is seasonal associated with precipitation and temperature changes; peaking from September to November following the large rainy season [ 4 ]. However, construction of dams and irrigation-based agricultural activities sometimes modify malaria seasonal trend in Ethiopia [ 5 , 6 ]. 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 [ 7 ]. These successes, encouraged 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/1,000 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 [ 8 , 9 ]. Thus, policy makers should be provided timely with quality and relevant data to inform national programs. In Ethiopia, malaria data is captured through the health management information system (HMIS) at different tiers of the healthcare delivery systems. The hierarchy of data flow is from Health Posts (at kebele level) 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 [ 10 ]. Therefore, health post and HC 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. Although understanding malaria trend could help to recognize the progress of elimination activities, under- (over)-reporting of malaria cases could affect 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 species composition, malaria data quality, spatiotemporal trend and associated socio-demographic and climatic variables. Further, the accuracy of HO malaria records (HMIS data) was checked against the HC data (source document). Methods Study setting Gedeo zone (Fig. 1 ) is 360 kilometers from Addis Ababa, it is one of the 14 zones in the Southern Nations, Nationalities and Peoples' Region. It is located 5°53’N to 6°27’N latitude, and 38°8’ to 38°30’ east longitude. The altitude of the zone ranges from 1,268 to 2,993 meters above sea level. The mean annual temperature is between 12.6 o C and 30 o C and the mean annual rainfall ranges from 1,001–1,800 mm. Based on the Gedeo Zone health department report, 36.31% (423,411/1,166,163) of the population is at risk of malaria. Malaria transmission in the zone is seasonal with peak from September to November. The API of the zone in 2019 was close to 2.0. The zone is sub-divide into six districts and two town administrations [ 11 ]. Districts, also known as “ woredas ” in Ethiopia, are the third level administrative divisions of the country, following regional states and zones and are further sub-divided into “ kebeles ” (the smallest administrative unit with its own jurisdiction). Six elimination-targeted settings, low transmission, by the NMCP; Dilla and Yirgacheffe towns, and Dilla zuria, Wonago, Kochore and Yirgacheffe rural districts were included [ 4 ]. Data collection Eight-year (from 2012 to 2019) data were collected from malaria laboratory registration logbooks in the HCs and HMIS of the district HOs. Six district HOs and seventeen public HCs with at least 8 years of service which report them were covered. HC records with missing information of cases; address ( kebele /district), dates of HC visit, age, sex or results of malaria diagnosis were excluded from the main part analysis. These excluded data were again analyzed separately to address the data quality issues at HCs. Data on malaria diagnosis results (negative, or positive, and infecting Plasmodium species for positives), time of diagnoses (date/month/year) and socio-demographic data ( kebele /district, age and sex) were collected. In addition, meteorological data; station level monthly and annual precipitation, maximum and minimum temperatures and relative humidity was obtained from the national meteorology agency of Ethiopia. Data collectors attended adequate training to assure quality. Further, the consistency and completeness of the extracted data was checked for each HC and district. Data analysis Microsoft office excel worksheet 2019 and the Stata data software 13 (College Station, Texas 77845 USA) were used for data entry and analysis. Descriptive statistics was used to show the distribution of malaria cases with respect to months, years, sex, age, Plasmodia species and district. Pearson’s chi-square ( X 2 ) test was used to determine the association of the different variables with confirmed malaria cases. Logistic regression was also performed to assess the association of malaria cases with socio-demographic variables, seasons and districts. Odds ratio (OR) with the corresponding 95% confidence interval (CI) was used to assess the differences in malaria prevalence with selected predictors. Spearman correlations were used to measure the strength of association of monthly malaria cases with meteorological variables. P-value of less than 0.05 was taken as statistically significant. Results Year-based data with missing variables at HCs In respective order 1852 (0.38%) and 249 (0.43%) of clinical suspects and confirmed malaria cases were excluded from the downstream analysis due to incompleteness. Overall, there was a significant reduction in missing data between 2012 and 2019 (p = 0.001). During the first two years there were more numbers of both suspected and confirmed cases of missing variables. Specifically, the highest data with missing variables (413 clinically suspected and 76 confirmed cases) occurred in 2012. Whereas, the lowest records of missing variables with 107 clinically suspected and 10 confirmed cases were reported in 2019 and 2018 respectively. Generally, the number of clinical suspects with missing variable declined from 413 in 2012 to 107 in 2019 by approximately 4-fold and confirmed cases from 76 in 2012 to 11 by 7-fold in 2019 (Table 1 ). Table 1 Number of missing data on clinical suspects (N) and confirmed malaria cases (n) by year, Gedeo zone, South Ethiopia, 2012–2019 Year Grand total clinical suspects (N) Excluded clinical suspects n (%) Grand total confirmed cases (N) Excluded confirmed cases n (%) P-value 2012 80601 413 (0.51) 11683 76 (0.65) 0.001 2013 89979 320 (0.36) 16075 38 (0.24) 2014 37076 224 (0.60) 4450 40 (0.90) 2015 58788 311 (0.53) 5042 24 (0.48) 2016 74693 182 (0.24) 10384 19 (0.18) 2017 61395 186 (0.30) 4419 31 (0.70) 2018 38006 109 (0.29) 2557 10 (0.39) 2019 46728 107 (0.23) 2867 11 (0.38) Total 487266 1852 (0.38) 57477 249 (0.43) N, n: number of cases; %: percentage (n/N*100); Grand total: quantitatively analyzed + excluded data (missing data that analyzed separately) Annual clinical suspects and confirmed malaria cases based on HC versus HO records There is an evidence of statistically significant inconsistency (p = 0.041) in both clinical and confirmed malaria case reports between HCs and HOs, overall, the 8-years period (2012–2019). Higher clinical suspects (485,414) were recorded at HCs compared to the HOs (481,656). Similarly, the corresponding confirmed malaria cases were 57,228 (11.79%) at the HCs and 56,761 (11.78%) at HOs although establishing which one is more accurate is rather not easy. With this, the number of clinical suspects recorded by the HCs was higher by 3758. The data kept by the HOs was lower on average by about 470 each year (except 2012, 2018). This difference was pronounced in 2016. During 2012 and 2018 the numbers of clinical suspects recorded at HCs were consistent with HO data. In addition, the HMIS captured on average 58 less confirmed malaria cases each year except in 2012 and 2018. In 2012 and 2018 the numbers of confirmed cases were higher at HO records than at HC records (Table 2 ). Table 2 Numbers of clinical suspects (N) and confirmed malaria cases (n) by year; HCs and HOs data, Gedeo zone, South Ethiopia, 2012–2019 Year Data from HCs Data from HOs P-value Clinical suspects (N) Confirmed Clinical suspects (N) Confirmed Pf n (%) Pv n (%) Pf / Pv mixed n (%) P-value Pf n (%) Pv n (%) 2012 80188 5921 (51.01) 5403 (46.55) 283 (2.44) 0.795 80188 5785 (48.40) 6168 (51.60) 2013 89659 7386 (46.06) 8168 (50.93) 483 (3.01) 89298 7876 (49.48) 8041 (50.52) 2014 36765 2110 (47.85) 2199 (49.86) 101 (2.29) 36649 2277 (52.13) 2091 (47.87) 2015 58564 2642 (52.65) 2232 (44.48) 144 (2.87) 58455 2289 (46.52) 2631 (53.48) 2016 74511 5660 (54.61) 4450 (42.93) 255 (2.46) 71502 4937 (49.55) 5027 (50.45) 2017 61209 2014 (45.90) 2242 (51.09) 132 (3.01) 61049 1928 (45.18) 2339 (54.82) 2018 37897 1298 (50.98) 1182 (46.43) 66 (2.59) 37897 1313 (51.47) 1238 (48.53) 2019 46621 1437 (50.30) 1359 (47.57) 61 (2.13) 46618 1442 (51.12) 1379 (48.88) Total 485,414 28,468 (49.74) 27,235 (47.59) 1,525 (2.67) 481,656 27,847 (49.06) 28,914 (50.94) 0.041 N, n: number of cases; %: percentage (n/N*100), Pf: Plasmodium falciparum; Pv: Plasmodium vivax Overall, a notable decline in malaria, HCs (source data), was observed during the eight-year period, except in 2016 and between 2018 and 2019. The number of confirmed malaria cases declined from 11607 in 2012 to 2857 in 2019, an 8.34% reduction from the baseline. Maximum and minimum numbers of confirmed cases were documented during 2013 and 2018 respectively. The case burden due to P. falciparum (49.74%) was comparable to P. vivax (47.59%) (p = 0.795). Yet, P. vivax overtook P. falciparum in cases burden (p = 0.588) during 2013, 2014 and 2017. Mixed species infections, P. vivax and P. falciparum , accounted a low proportion (2.67%) (Table 2 ). Diagnostic performance; data from HCs Regarding the diagnostic performance, the yearly trend of clinical suspects was directly proportional to the confirmed cases in each year. But the proportions of examined clinical suspects against confirmed cases were increased from 6.91 in 2012 to 16.32 in 2019. The highest proportion (16.32) was recorded in 2019, whilst the lowest (5.59) was during 2013. This shows the proportion of 'non-malarial' febrile cases were increasing from 2012 to 2019 except in 2016 (Table 3 ). Table 3 Proportion of clinical suspects against confirmed malaria cases, Gedeo zone, South Ethiopia, 2012–2019 Year Clinical suspects (N) Confirmed (n) Proportion (N/n) 2012 80188 11607 6.91 2013 89659 16037 5.59 2014 36765 4410 8.34 2015 58564 5018 11.67 2016 74511 10365 7.19 2017 61209 4388 13.95 2018 37897 2546 14.88 2019 46621 2857 16.32 Total 485,414 57,228 8.48 Malaria cases number by sex and age; data from HCs Slightly more males (29,480 (11.34%)) were malaria positive (p = 0.236) than females (27,748 (12.30%)) (Fig. 2 ). The above 15 years age group was the most affected (30,406, 11.47%) than the other age groups, followed by under 5 children which accounted for 15116 (13.83%) of the cases. The above 15 years age group was twice more likely (OR = 2.00, 95% CI: 1.90, 2.11, p < 0.0001) to have malaria compared to the under 5 (Table 4 ). Table 4 Logistic regression analysis of factors associated with malaria at HCs in Gedeo zone, South Ethiopia, 2012–2019 Variable Clinical suspects N (%) Confirmed n (%) OR (95% CI) P-value Sex Male 259906 (53.54) 29480 (11.34) 1.0 Female 225508 (46.46) 27748 (12.30) 1.02 (0.97, 1.08) 0.236 Age category <5 109302 (22.52) 15116 (13.83) 1.0 5–14 111028 (22.87) 11706 (10.54) 1.33 (1.29, 1.37) < 0.0001 15+ 265084 (54.61) 30406 (11.47) 2.00 (1.90, 2.11) < 0.0001 Season Winter 97227 (20.03) 11010 (11.32) 1.0 Autumn 134054 (27.62) 16820 (12.55) 1.53 (1.37, 1.70) < 0.0001 Summer 123200 (25.38) 13618 (11.05) 1.24 (1.19, 1.29) < 0.0001 Spring 130933 (26.97) 15780 (12.05) 1.42 (1.34, 1.51) < 0.0001 District/urban center Yirgacheffe rural 49684 5187 (10.44) 1 Dilla town 137860 18150 (13.17) 3.16 (2.11, 4.22) 0.0002 Dilla zuria 119207 12588 (10.56) 2.30 (1.82, 2.78) < 0.0001 Wonago 71689 7427 (10.36) 1.40 (1.21, 1.60) < 0.0001 Yirgacheffe town 60531 6271 (10.36) 0.24 (0.07, 1.42) 0.780 Kochore 46443 7605 (16.37) 1.59 (1.20, 1.98) < 0.0001 %: percentage (n/N*100) Spatiotemporal distribution of malaria cases The peak confirmed case load (12.55%) was during autumn followed by spring (12.05%), summer (11.05%) and winter (11.32%) (Fig. 3 ). In autumn the likelihood of having malaria is 1.53 times more than winter (OR = 1.53, 95% CI: 1.37, 1.70, p < 0.0001) (Table 4 ). Similarly, the highest proportions of both P. falciparum (8445 (50.21%)) and P. vivax (7911 (47.03%)) were noted during autumn particularly in April. On the other hand, in winter the proportion of infection due to the two species, P. falciparum (5451 (49.51%)) and P. vivax (5312 (48.25%)) was relatively lowest (Fig. 3 ). Although P. falciparum was higher than P. vivax in all seasons, the difference was the smallest (1.26%) during winter, while in autumn it is 3.17%. The number of mixed infections also had same pattern throughout the four seasons. Overall, based on the HC records, the highest malaria case burden reported was from Dilla town (18,150 (13.17%)) from a total of 137,860 clinical suspects followed by the adjacent district, Dilla zuria (12,588 (10.56%)) from 119,207 tested suspects. The lowest was from Yirgacheffe rural district (5,187 (10.44%)). Dilla town annual malaria cases remained the highest throughout the 8-year period except in 2013 and 2019 (OR = 3.16, 95% CI: 2.11, 4.22, p = 0.0002) (Table 4 ). The highest confirmed cases during these two years were reported by Kochore (4390 (19.78%)) and Dilla zuria districts (1178 (8.04%)) from 22197 and 14648 clinical suspects respectively. Although there was an overall declining trend of confirmed malaria cases from 2012 to 2019, there were peaks in Kochore during 2013, Dilla town and Dilla zuria in 2016. Yirgacheffe rural district had the highest variation (376 (80.51%)) in terms of confirmed malaria records between HC and HO data (HC-HO) followed by Dilla town (164 (35.12%)) and Dilla zuria district (-138 (29.55%)), data obtained by HC-HO. Whereas, Wonago district had the lowest (-6 (1.28%)) deviation of malaria data recording between HC and HO (Fig. 4 ). The monthly total confirmed malaria cases showed a strong and positive association with monthly rainfall (Spearman's rho = 0.895, p < 0.0001) and monthly minimum temperature (Spearman's rho = 0.671, p = 0.017). Nevertheless, the maximum monthly temperature was negatively and weakly associated with the monthly confirmed malaria cases. Relative humidity seems not to have an effect on malaria transmission in the study area (Fig. 5 ). Discussion In the present study there was a significant reduction in data incompleteness; with missing variables between 2012 and 2019. Specifically, the number of clinical suspects with missing data variables declined between 2012 and 2019 by approximately 4-fold and confirmed cases with about 7-fold. Decreasing trend in data incompleteness overtime, is plausible indication of huge improvement in data quality which thereby an implication for improving programmatic performance over the years. There was an overall notable difference in both total number of clinical suspects (3758) and confirmed malaria cases (467) recorded at HCs (source data) and HOs (HMIS data). For the 8-years period, discrepancies in clinically suspected and confirmed cases were observed between the HOs compared to the HCs. During 2012 and 2018 the numbers of clinical suspects recorded at HCs were consistent with HO data. In these years the numbers of confirmed cases were higher at HO records than HC records. Yirgacheffe rural district had the highest deviation in terms of confirmed malaria records between HC and HO data followed by Dilla town and Dilla zuria district, whereas Wonago district was the least. 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 [ 10 ]. 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 HMIS, lack of cross-checking and proofing habits, training gaps on HMIS data use and unintentional/intentional false reports. In addition, limited computer access and skill, inadequate technical support [ 12 ], poor data management skills and limited functionality of electronic data management systems [ 13 ] might be the likely reasons for HMIS implementation challenges. Concerning the diagnostic performance, the proportions of clinically suspected cases against confirmed cases were increased from 6.91 in 2012 to 16.32 in 2019. The highest proportion was recorded in 2019, whilst the lowest was during 2013. This revealed that the 'non-malarial' febrile cases were increasing from 2012 to 2019, which 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 [ 14 ], unable to detect sequestered P. falciparum parasites [ 15 ] and low competency of microscopists [ 16 ]. In the other way, such increased number of non-malarial febrile illnesses might be related to other febrile cases including yellow fever virus [ 17 , 18 ] and typhoid fever [ 19 ] infections, as per the studies conducted in southern 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 [ 20 ]. 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 incorrect treatment of such non-malarial febrile illnesses with antimalarial drugs is possibly to contribute to rapid emergence of antimalarial drug resistance [ 21 , 22 ] in the study area. 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 2,546 of cases were observed during 2013 and 2018, respectively with 8.34 percent reduction. 2013 and 2016 were the exceptions to the declining trend as there were small epidemics in these periods in some parts of the Zone. Over the eight years period, overall, there was malaria positivity rate of 11.79%, data from the HCs. This positivity rate was comparable with certain studies done in Ethiopia including from Batu town (12.43%) [ 23 ], Arsi Negelle (11.40%) [ 24 ] and Halaba special district (9.47%) [ 25 ]. In contrast, higher overall malaria positivity rates were reported from related studies conducted in south-central Ethiopia [ 26 ], southern Ethiopia [ 27 ] and abroad in Dakar, Senegal [ 28 ] with 33.83%, 21.79% and 19.68% respectively. On the other hand, the present figure was higher than records in other local studies [ 29 , 30 ]. 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. The possible contributing factors for the peaks/epidemics of malaria cases in 2013 and 2016 could be associated with feeble intervention activities in certain areas of the Zone. P. falciparum and P. vivax were detected where equivalent; congruent results were reported in some other parts of Ethiopia [ 26 , 27 ]. While other local studies [ 23 , 25 , 31 ] documented that the dominant species was P. vivax . The proportion of mixed infection in this study was congruent with other studies [ 26 , 30 ], whereas inconsistent with other reports [ 27 , 31 ]. The likely reason for the slightly higher proportion of P. falciparum over P. vivax could be related to temperature; that is temperatures more than 18 °C for P. falciparum and more than 15 °C for P. vivax is suitable for the growth of these two species in human host and mosquito vectors [ 32 , 33 ]. Apparently, in the current study area the average mean temperature during the eight years period found to be above 18 °C. The higher proportion 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 [ 34 ] in the study area. Environmental fluctuations that change target mosquito species abundance might have an impact on Plasmodia species occurrence [ 35 ]. The issue demands additional study. 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 [ 26 , 35 ]. Males were slightly more infected (51.51%) by malaria than females (48.49%) over the eight-year period. This was paralleled with other studies conducted in different parts of Ethiopia [ 23 , 26 , 27 , 31 ]. However, this finding was not consistent with other reports in southern Ethiopia [ 25 ] and elsewhere in Mozambique [ 36 ] 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 [ 23 , 31 ]. Inconsistent result was observed in southern Ethiopia [ 24 ] arguing that malaria cases cluster among the under-5. And a finding in Metema, northwest Ethiopia by Ferede et al. [ 37 ] showed that 5–14 years old were more infected. Possible justifications for the higher occurrence of malaria among males and older age group could be their engagement in various outdoor activities and staying outdoors during the nights [ 38 ]. Apart from outdoor exposures, differences in treatment-seeking behavior, access to health facilities and travel history [ 39 ] 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 [ 40 ]. The peak number of confirmed malaria cases was recorded during autumn followed by spring, summer and winter with a statistically significant variation. This seasonal peak in malaria cases in autumn deviates from various studies in Ethiopia which is during spring after the main rain season [ 24 , 26 , 27 , 31 ]. In addition, nationally the main malaria transmission season is from September to December following the peak rain season [ 1 , 4 ]. This trade-off in seasonal peak of malaria cases might be as a result of varying climatological conditions (rainfall pattern and temperature changes) in the area against other settings. Despite the high prevalence of malaria cases during the three seasons, there were a substantial number of confirmed malaria cases in the dry (winter) season in this study. These, absence of significant variation in the proportion of the P. vivax and P. falciparum burden and almost, year-round presence of malaria might suggest the presence of suitable local environments for mosquitoes. The comparable proportion of P. vivax against P. falciparum in winter 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 clinical suspects and confirmed malaria cases were documented in Dilla town (urban) and Dilla zuria district (sub-urban) as compared to other districts. 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 two 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, peaks 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 [ 39 ], 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 [ 35 ] and changes in intervention (like IRS and LLINs) periods might have effect for these spatial differences of malaria cases. Monthly rainfall and minimum temperature demonstrated statistically significant positive correlation with malaria cases. Previous studies in Ethiopia [ 41 , 42 ] and elsewhere [ 43 , 44 ] documented similar findings. However, the result of the current study on the association of rainfall and malaria cases was deviating from previous findings, stating higher rainfall does not necessarily influence the malaria changes [ 45 , 46 ]. In contrast to our finding, minimum temperature was weakly correlated with malaria cases in southeast Ethiopia [ 42 ]. 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 relative humidity were weakly correlated with malaria cases. In disagreement to our finding, studies conducted in Jimma, Ethiopia by Alemu and others [ 41 ] and Sena and colleagues [ 42 ] in Gilgel-Gibe, southwest Ethiopia reported that inter-monthly relative humidity was significantly associated with monthly malaria cases. 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. Missing of asymptomatic cases and poor competence of the laboratory personnel at HCs could be the other limitations. 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. Conclusion Over the entire period, 2012 to 2019, the program data quality improved over the years; underreporting and proportion of data incompleteness declined significantly, and the burden dropped continuously except in 2016. Yet, equivalent P. falciparum and P. vivax malaria existed, thus intervention actions should target both species. The high malaria prevalence in urban setting (Dilla town) and its vicinity and in autumn season necessitates spatiotemporal consideration by the control campaign. The intervention strategies need also consider older age groups. In general, malaria still remains a public health problem in the area, which demands strengthening of interventions and short-term forecasting based on local meteorological factors to achieve elimination goals in the area. In addition, the data recorded at the HCs and district HOs should be monitored for consistency. Abbreviations Pf Plasmodium falciparum ; Pv : Plasmodium vivax , NMCP:national malaria control program; API:annual parasite incidence; HMIS:health management information system; HC:health center; HO:health office; OR:odds ratio; CI:confidence interval; IRS:indoor residual spraying; LLINs:long-lasting insecticide-treated nets Declarations Ethics approval and consent to participate Ethical approval was obtained from Department of Microbial, Cellular and Molecular Biology and College of Natural and Computational Sciences Ethical Review Committee, Addis Ababa University (CNSDO/318/11/2019). Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this manuscript. Competing interest statement The authors declare that they have no competing interests. Funding This study was partially funded by Dilla and Addis Ababa Universities. But the funders have no role in the study design, data collection, analysis, interpretation of data and in writing the manuscript. Authors’ contributions EM designed the study, involved in data collection, analyzed and interpreted the data, and drafted the manuscript. SWB contributed in data analysis works. FGT initiated the idea, involved in the data entry template development and gave feedback on the data collection protocol. SD was responsible for revising the draft manuscript. EG initiated the idea, guided the process of data collection and substantively revised the manuscript. HM participated in guidance of the data collection progresses and critically commented the whole section of the paper. All authors read and approved the final manuscript. Acknowledgments would like to thank heads of HOs and HCs for their willingness to provide the retrospective malaria data and the national meteorology agency of Ethiopia for providing meteorological data of the study area. We are also grateful to Mr Eyuel Asemahegn for constructing the study area map. References Ethiopian Public Health Institute. Ethiopia National Malaria Indicator Survey 2015. Addis Ababa, 2016. Taffese HS, Hemming-Schroeder E, Koepfli C, Tesfaye G, Lee M, Kazura J, et al. Malaria epidemiology and interventions in Ethiopia from 2001 to 2016. Infect Dis Poverty. 2018;7:103. Gari T, Lindtjørn B. Reshaping the vector control strategy for malaria elimination in Ethiopia in the context of current evidence and new tools: opportunities and challenges. Malar J. 2018;17:454. President’s Malaria Initiative Ethiopia. Malaria Operational Plan FY 2019; 2019. Kibret S, Wilson GG, Ryder D, Tekie H, Petros B. Malaria impact of large dams at different eco-epidemiological settings in Ethiopia. Trop Med Int Health. 2017;45:4. Chung B. Impact of irrigation extension on malaria transmission in Simret, Tigray, Ethiopia. Korean J Parasitol. 2016;54(4):399-405. World malaria report 2019. Geneva: World Health Organization; 2019. Moonen B, Cohen JM, Tatem AJ, Cohen J, Hay SI, Sabot O, et al. A framework for assessing the feasibility of malaria elimination. Malar J. 2010;9:322. From malaria control to malaria elimination: a manual for elimination scenario planning. Geneva: World Health Organization; 2014. Endriyas M, Alano A, Mekonnen E, Ayele S, Kelaye T, Shiferaw M, et al. Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia. BMC Health Serv Res. 2019;19:175. Gedeo zone health department. Gedeo zone health department annual report 2019. Dilla, 2019. Kiberu VM, Matovu JK, Makumbi F, Kyozira C, Mukooyo E, Wanyenze RK. Strengthening district-based health reporting through the district health management information software system: the Ugandan experience. BMC Med Inform Decis Mak. 2014;14:40. Ledikwe JH, Grignon J, Lebelonyane R, Ludick S, Matshediso E, Sento BW, et al. Improving the quality of health information: a qualitative assessment of data management and reporting systems in Botswana. Health Res Policy Syst. 2014;12:7. Wongsrichanalai C, Barcus MJ, Muth S, Sutamihardja A, Wernsdorfer WH. A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT). Am J Trop Med Hyg. 2007;77 Suppl 6:119-127. Leke RF, Djokam RR, Mbu R, Leke RJ, Fogako J, Megnekou R, et al. Detection of the Plasmodium falciparum antigen histidinerich protein 2 in blood of pregnant women: implications for diagnosing placental malaria. J Clin Microbiol. 1999;37:2992-2996. Nega D, Abebe A, Abera A, Gidey B, G/ Tsadik A, Tasew G. Comprehensive competency assessment of malaria microscopists and laboratory diagnostic service capacity in districts stratified for malaria elimination in Ethiopia. PLoS ONE. 2020;15(6):e0235151. Lilay A, Asamene N, Bekele A, Mengesha M, Wendabeku M, Tareke I, et al. Reemergence of yellow fever in Ethiopia after 50 years, 2013: epidemiological and entomological investigations. BMC Infect Dis. 2017;17:343. Nigussie E, Shimelis T, Eshetu D, Shumie G, Chali W, Aseffa A, et al. Seropositivity of yellow fever virus among acute febrile patients attending selected health facilities in Borena district, southern Ethiopia. Ethiop Med J. 2020;58:57-62. Habte L, Tadesse E, Ferede G, Amsalu Typhoid fever: clinical presentation and associated factors in febrile patients visiting Shashemene Referral Hospital, southern Ethiopia. BMC Res Notes. 2018;11:605. Dalrymple U, Cameron E, Arambepola R, Battle KE, Chestnutt EG, Keddie SH, et al. The contribution of non‑malarial febrile illness co‑infections to Plasmodium falciparum case counts in health facilities in sub‑Saharan Africa. Malar J. 2019;18:195. Onchiri FM, Pavlinac PB, Singa BO, Naulikha JM, Odundo EA, Farquhar C, et al. Frequency and correlates of malaria over-treatment in areas of differing malaria transmission: a cross-sectional study in rural Western Kenya. Malar J. 2015;14: 97. Nyaoke BA, Mureithi MW, Beynon C. Factors associated with treatment type of non-malarial febrile illnesses in under-fives at Kenyatta National Hospital in Nairobi, Kenya. PLoS ONE. 2019;14(6):e0217980. Hassen J, Dinka H. Retrospective analysis of urban malaria cases due to Plasmodium falciparum and Plasmodium vivax : the case of Batu town, Oromia, Ethiopia. Heliyon 6. 2020;e03616. Hailemariam M, Gebre S. Trend analysis of malaria prevalence in Arsi Negelle health center southern Ethiopia. J Infect Dis Immun. 2015;7(1):1-6. Shamebo T, Petros B. Trend analysis of malaria prevalence in Halaba special district, Southern Ethiopia. BMC Res Notes. 2019;12(1):190. Yimer F, Animut A, Erko B, Mamo H. Past five-year trend, current prevalence and household knowledge, attitude and practice of malaria in Abeshge, south-central Ethiopia. Malar J. 2015;14:230. Dabaro D, Birhanu Z, Yewhalaw D. Analysis of trends of malaria from 2010 to 2017 in Boricha district, southern Ethiopia. Malar J. 2020;19:88. Diallo MA, Badiane AS, Diongue K, Sakande´ L, Ndiaye M, Seck MC, et al. A twenty-eight-year laboratory-based retrospective trend analysis of malaria in Dakar, Senegal. PLoS ONE. 2020;15(5):e0231587. Derbie A, Alemu M. Five years malaria trend analysis in Woreta health center, Northwest Ethiopia. J Health Sci. 2017;27(5):465. Yimer M, Hailu T, Mulu W, Abera B, Ayalew W. A 5 year trend analysis of malaria prevalence with in the catchment areas of Felegehiwot referral hospital, Bahir Dar city, northwest‑Ethiopia: a retrospective study. BMC Res Notes. 2017;10:239. Solomon A, Kahase D, Alemayehu M. Trend of malaria prevalence in Wolkite health center: an implication towards the elimination of malaria in Ethiopia by 2030. Malar J. 2020;19:112. Bodker E. Relationship between altitude and intensity of malaria transmission in the Usambara Mountains, Tanzania. Med Entomol. 2003;40:706-17. BradfieldLyon E. Temperature suitability for malaria climbing the Ethiopian highlands. Environ Res Lett. 2017;12:064015. Howes RE, Battle KE, Mendis KN, Smith DL, Cibulskis RE, Baird JK, et al. Global epidemiology of Plasmodium vivax . Am J Trop Med Hyg. 2016;95 Suppl 6:15-34. Phimpraphi W, Paul RE, Yimsamran S, Puangsa-art S, Thanyavanich N, Maneeboonyang W, et al. Longitudinal study of Plasmodium falciparum and Plasmodium vivax in a Karen population in Thailand. Malar J. 2008; 7 :99. Temu A, Coleman M, Abilio P, Kleinschmidt I. High prevalence of malaria in Zambezia, Mozambique: the protective effect of IRS versus increased risks due to pig-keeping and house construction. PLoS ONE. 2012;7:e31409. Ferede G, Worku A, Getaneh A, Ahmed A, Haile T, Abdu Y, et al. Prevalence of malaria from blood smears examination: a seven-year retrospective study from Metema hospital, northwest Ethiopia. Malar Res Treat. 2013;2013:704-30:5. Kigozi SP, Kigozi RN, Epstein A, Mpimbaza A, Sserwanga A, Yeka A, et al. Rapid shifts in the age‑specific burden of malaria following successful control interventions in four regions of Uganda. Malar J. 2020;19:128. Mathanga DP, Tembo AK, Mzilahowa T, Bauleni A, Mtimaukenena K, Taylor TE, et al. Patterns and determinants of malaria risk in urban and peri‑urban areas of Blantyre, Malawi. Malar J. 2016;15:590. Krogstad DJ. Malaria as a re-emerging disease. Epidemiol Rev. 1996;18:77-89. Alemu A, Abebe G, Tsegaye W, Golassa L. Climatic variables and malaria transmission dynamics in Jimma town, Southwest Ethiopia. Parasit Vectors. 2011;4:30. Sena L, Deressa W, Ali A. Correlation of climate variability and malaria: a retrospective comparative study, southwest Ethiopia. Ethiop J Health Sci. 2015;25:2. Gunda R, Chimbari MJ, Shamu S, Sartorius B, Mukaratirwa Malaria incidence trends and their association with climatic variables in rural Gwanda, Zimbabwe, 2005-2015. Malar J. 2017;16:393. Nanvyat N, Mulambalah CS, Barshep Y, Dakul DA, Tsingalia HM. Retrospective analysis of malaria transmission patterns and its association with meteorological variables in lowland areas of Plateau state, Nigeria. Int J Mosq Res. 2017;4(4):101-106. Haque U, Hashizume M, Glass GE, Dewan AM, Overgaard HJ, Yamamoto T. The role of climate variability in the spread of malaria in Bangladeshi highlands. PLoS ONE. 2010;5(12):1-9:e14341. Singh N, Sharma VP. Patterns of rainfall and malaria in Madhya Pradesh, central India, Ann Trop Med Parasitol. 2002;96(4):349-359. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-72130","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":2501955,"identity":"a9e1a357-a2a7-42c6-9450-a29fc5295d22","order_by":0,"name":"Eshetu Molla Belete","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie2PsQrCMBCGA4V0Ocx6QukzRApVUPBVBKFT6i6Io6OuPoZTZkvRqcU1e8FBHCqCOIlpF51i3QTzDXfL/3H3E2Kx/CRQT69FnK3e6DVTdBYooaNKgW8U4K+jJhjLk+IiBkDd7HpSsx4QN91tTEp7PRnzREZAIZZ9sdePQRQpk8IVhJjIVD8Wy0BQrSCERmV4yLr3WmHnYyAeDRROREhqBYVTxIsGCioRYF51wWPoxEvUpT50YausU07lwGdsXFzFbe4zN90blXco1rNpvMIpv0lbLBbL//AEDURB+VkwQuQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8262-0622","institution":"Department of Medical Laboratory Sciences, Dilla University, Dilla, Ethiopia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Eshetu","middleName":"Molla","lastName":"Belete","suffix":""},{"id":2501956,"identity":"f0561391-e0f8-4be5-988d-fba3f1a7d247","order_by":1,"name":"Sinknesh Wolde Behaksra","email":"","orcid":"","institution":"Armauer Hansen Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sinknesh","middleName":"Wolde","lastName":"Behaksra","suffix":""},{"id":2501957,"identity":"9d9e7696-c960-4ae8-9448-f83148712b1a","order_by":2,"name":"Fitsum G Tadesse","email":"","orcid":"","institution":"Armauer Hansen Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fitsum","middleName":"G","lastName":"Tadesse","suffix":""},{"id":2501958,"identity":"5b4a50e2-ae9c-4b37-a51d-9c58ee56a070","order_by":3,"name":"Sisay Dugassa A","email":"","orcid":"","institution":"Aklilu Lemma Institute of Pathobiology, Addis Ababa University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sisay","middleName":"Dugassa","lastName":"A","suffix":""},{"id":2501959,"identity":"c10e4ea6-4947-44c5-8996-55bfa5e9222e","order_by":4,"name":"Endalamaw Gadisa Belachew","email":"","orcid":"","institution":"Armauer Hansen Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Endalamaw","middleName":"Gadisa","lastName":"Belachew","suffix":""},{"id":2501960,"identity":"d7b0996b-c991-460d-819a-f23e71c18566","order_by":5,"name":"Hassen Mamo Idrees","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hassen","middleName":"Mamo","lastName":"Idrees","suffix":""}],"badges":[],"createdAt":"2020-09-04 10:26:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-72130/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-72130/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-021-05783-8","type":"published","date":"2021-01-21T15:00:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":2578635,"identity":"064f6aeb-4af9-4b63-be59-f15f2a6d6972","added_by":"auto","created_at":"2020-09-24 15:02:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93112,"visible":true,"origin":"","legend":"Map of the study districts; Gedeo Zone Southern Nationals Nationalities and Peoples Regional state, Ethiopia","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-72130/v1/Fig1.jpg"},{"id":2578636,"identity":"5a52eedf-80cd-4136-b9d9-df31c7b67ebc","added_by":"auto","created_at":"2020-09-24 15:02:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40245,"visible":true,"origin":"","legend":"Annual clinical and confirmed malaria trend by sex and age in Gedeo zone, South Ethiopia from 2012-2019","description":"","filename":"Fig2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-72130/v1/Fig2.JPG"},{"id":2578637,"identity":"cf6354a3-253a-46df-a0eb-0594032642e5","added_by":"auto","created_at":"2020-09-24 15:02:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56133,"visible":true,"origin":"","legend":"Seasonal trend of clinical and confirmed malaria cases based on HC data, Gedeo zone, South Ethiopia, 2012-2019","description":"","filename":"Fig3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-72130/v1/Fig3.JPG"},{"id":2578638,"identity":"de914c78-17f4-478f-878f-5f89b1f01938","added_by":"auto","created_at":"2020-09-24 15:02:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":102637,"visible":true,"origin":"","legend":"Spatial distribution of confirmed malaria cases in HCs and HOs, Gedeo zone, South Ethiopia, 2012-2019","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-72130/v1/Fig4.jpg"},{"id":2578639,"identity":"30f43fec-4854-48ce-b9bf-3613b835aba0","added_by":"auto","created_at":"2020-09-24 15:02:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":58507,"visible":true,"origin":"","legend":"Monthly HC-level confirmed malaria cases and meteorological data in Gedeo zone, South Ethiopia from 2012-2019","description":"","filename":"Fig5.JPG","url":"https://assets-eu.researchsquare.com/files/rs-72130/v1/Fig5.JPG"},{"id":13596106,"identity":"111f80d9-cd92-41a4-a80c-d49c4bd51f83","added_by":"auto","created_at":"2021-09-17 05:27:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":753527,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-72130/v1/3d9eaa69-da06-46ec-acc2-e7b2662250b5.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEight-year Retrospective Analysis of Malaria Trends in Gedeo Zone, South Ethiopia (2012-2019)\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eIn Ethiopia, \u003cem\u003ePlasmodium falciparum\u003c/em\u003e (\u003cem\u003eP. falciparum\u003c/em\u003e) and \u003cem\u003eP. vivax\u003c/em\u003e 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 \u003cem\u003eP. falciparum\u003c/em\u003e (~\u0026thinsp;60%) followed by \u003cem\u003eP. vivax\u003c/em\u003e (~\u0026thinsp;40%) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] 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 very low [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMalaria transmission in Ethiopia is seasonal associated with precipitation and temperature changes; peaking from September to November following the large rainy season [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, construction of dams and irrigation-based agricultural activities sometimes modify malaria seasonal trend in Ethiopia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These successes, encouraged the Ethiopian national malaria control program (NMCP) to stratify the country\u0026rsquo;s malaria transmission into four based on annual parasite incidence (API); malaria free (API\u0026thinsp;~\u0026thinsp;0 cases/1,000 population/year), low (API\u0026thinsp;\u0026gt;\u0026thinsp;0 and \u0026lt;\u0026thinsp;5), moderate (API\u0026thinsp;\u0026ge;\u0026thinsp;5 and \u0026lt;\u0026thinsp;100) and high (API\u0026thinsp;\u0026ge;\u0026thinsp;100) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], as a preparation to embark on nationwide malaria elimination. The policy and strategy shift to elimination requires data-driven decision making to tailor interventions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Thus, policy makers should be provided timely with quality and relevant data to inform national programs.\u003c/p\u003e \u003cp\u003eIn Ethiopia, malaria data is captured through the health management information system (HMIS) at different tiers of the healthcare delivery systems. The hierarchy of data flow is from Health Posts (at \u003cem\u003ekebele\u003c/em\u003e level) 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 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, health post and HC 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 \u003cem\u003ePlasmodia\u003c/em\u003e species distribution.\u003c/p\u003e \u003cp\u003eAlthough understanding malaria trend could help to recognize the progress of elimination activities, under- (over)-reporting of malaria cases could affect the country\u0026rsquo;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 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], little information is available to understand the overall trend of malaria and the above issues in the area. Thus, we assessed the species composition, malaria data quality, spatiotemporal trend and associated socio-demographic and climatic variables. Further, the accuracy of HO malaria records (HMIS data) was checked against the HC data (source document).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eMethods\u003c/h2\u003e \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eGedeo zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is 360 kilometers from Addis Ababa, it is one of the 14 zones in the Southern Nations, Nationalities and Peoples' Region. It is located 5\u0026deg;53\u0026rsquo;N to 6\u0026deg;27\u0026rsquo;N latitude, and 38\u0026deg;8\u0026rsquo; to 38\u0026deg;30\u0026rsquo; east longitude. The altitude of the zone ranges from 1,268 to 2,993 meters above sea level. The mean annual temperature is between 12.6\u003csup\u003eo\u003c/sup\u003eC and 30\u003csup\u003eo\u003c/sup\u003eC and the mean annual rainfall ranges from 1,001\u0026ndash;1,800\u0026nbsp;mm.\u003c/p\u003e \u003cp\u003eBased on the Gedeo Zone health department report, 36.31% (423,411/1,166,163) of the population is at risk of malaria. Malaria transmission in the zone is seasonal with peak from September to November. The API of the zone in 2019 was close to 2.0. The zone is sub-divide into six districts and two town administrations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Districts, also known as \u0026ldquo;\u003cem\u003eworedas\u003c/em\u003e\u0026rdquo; in Ethiopia, are the third level administrative divisions of the country, following regional states and zones and are further sub-divided into \u0026ldquo;\u003cem\u003ekebeles\u003c/em\u003e\u0026rdquo; (the smallest administrative unit with its own jurisdiction). Six elimination-targeted settings, low transmission, by the NMCP; Dilla and Yirgacheffe towns, and Dilla zuria, Wonago, Kochore and Yirgacheffe rural districts were included [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eEight-year (from 2012 to 2019) data were collected from malaria laboratory registration logbooks in the HCs and HMIS of the district HOs. Six district HOs and seventeen public HCs with at least 8\u0026nbsp;years of service which report them were covered. HC records with missing information of cases; address (\u003cem\u003ekebele\u003c/em\u003e/district), dates of HC visit, age, sex or results of malaria diagnosis were excluded from the main part analysis. These excluded data were again analyzed separately to address the data quality issues at HCs. Data on malaria diagnosis results (negative, or positive, and infecting \u003cem\u003ePlasmodium\u003c/em\u003e species for positives), time of diagnoses (date/month/year) and socio-demographic data (\u003cem\u003ekebele\u003c/em\u003e/district, age and sex) were collected. In addition, meteorological data; station level monthly and annual precipitation, maximum and minimum temperatures and relative humidity was obtained from the national meteorology agency of Ethiopia. Data collectors attended adequate training to assure quality. Further, the consistency and completeness of the extracted data was checked for each HC and district.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eMicrosoft office excel worksheet 2019 and the Stata data software 13 (College Station, Texas 77845 USA) were used for data entry and analysis. Descriptive statistics was used to show the distribution of malaria cases with respect to months, years, sex, age, \u003cem\u003ePlasmodia\u003c/em\u003e species and district. Pearson\u0026rsquo;s chi-square (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) test was used to determine the association of the different variables with confirmed malaria cases. Logistic regression was also performed to assess the association of malaria cases with socio-demographic variables, seasons and districts. Odds ratio (OR) with the corresponding 95% confidence interval (CI) was used to assess the differences in malaria prevalence with selected predictors. Spearman correlations were used to measure the strength of association of monthly malaria cases with meteorological variables. P-value of less than 0.05 was taken as statistically significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cp\u003e \u003cb\u003eYear-based data with missing variables at HCs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn respective order 1852 (0.38%) and 249 (0.43%) of clinical suspects and confirmed malaria cases were excluded from the downstream analysis due to incompleteness. Overall, there was a significant reduction in missing data between 2012 and 2019 (p\u0026thinsp;=\u0026thinsp;0.001). During the first two years there were more numbers of both suspected and confirmed cases of missing variables. Specifically, the highest data with missing variables (413 clinically suspected and 76 confirmed cases) occurred in 2012. Whereas, the lowest records of missing variables with 107 clinically suspected and 10 confirmed cases were reported in 2019 and 2018 respectively. Generally, the number of clinical suspects with missing variable declined from 413 in 2012 to 107 in 2019 by approximately 4-fold and confirmed cases from 76 in 2012 to 11 by 7-fold in 2019 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of missing data on clinical suspects (N) and confirmed malaria cases (n) by year, Gedeo zone, South Ethiopia, 2012\u0026ndash;2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrand total clinical suspects (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcluded clinical suspects n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrand total confirmed cases (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcluded confirmed cases n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e413 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e320 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38 (0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24 (0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (0.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31 (0.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (0.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11 (0.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e487266\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1852 (0.38)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e57477\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e249 (0.43)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eN, n: number of cases; %: percentage (n/N*100); Grand total: quantitatively analyzed\u0026thinsp;+\u0026thinsp;excluded data (missing data that analyzed separately)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAnnual clinical suspects and confirmed malaria cases based on HC versus HO records\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere is an evidence of statistically significant inconsistency (p\u0026thinsp;=\u0026thinsp;0.041) in both clinical and confirmed malaria case reports between HCs and HOs, overall, the 8-years period (2012\u0026ndash;2019). Higher clinical suspects (485,414) were recorded at HCs compared to the HOs (481,656). Similarly, the corresponding confirmed malaria cases were 57,228 (11.79%) at the HCs and 56,761 (11.78%) at HOs although establishing which one is more accurate is rather not easy. With this, the number of clinical suspects recorded by the HCs was higher by 3758. The data kept by the HOs was lower on average by about 470 each year (except 2012, 2018). This difference was pronounced in 2016. During 2012 and 2018 the numbers of clinical suspects recorded at HCs were consistent with HO data. In addition, the HMIS captured on average 58 less confirmed malaria cases each year except in 2012 and 2018. In 2012 and 2018 the numbers of confirmed cases were higher at HO records than at HC records (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumbers of clinical suspects (N) and confirmed malaria cases (n) by year; HCs and HOs data, Gedeo zone, South Ethiopia, 2012\u0026ndash;2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eData from HCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eData from HOs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eClinical suspects (N)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eConfirmed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eClinical suspects (N)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eConfirmed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePf\u003c/span\u003e \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePv\u003c/span\u003e \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePf\u003c/span\u003e\u003cb\u003e/\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePv\u003c/span\u003e \u003cb\u003emixed n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePf\u003c/span\u003e \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePv\u003c/span\u003e \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5921 (51.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5403 (46.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283 (2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5785 (48.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6168 (51.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7386 (46.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8168 (50.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e483 (3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7876 (49.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8041 (50.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2110 (47.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2199 (49.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101 (2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2277 (52.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2091 (47.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2642 (52.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2232 (44.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144 (2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2289 (46.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2631 (53.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5660 (54.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4450 (42.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e255 (2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4937 (49.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5027 (50.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 (45.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2242 (51.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132 (3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1928 (45.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2339 (54.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1298 (50.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1182 (46.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1313 (51.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1238 (48.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1437 (50.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1359 (47.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1442 (51.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1379 (48.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e485,414\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e28,468 (49.74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e27,235 (47.59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,525 (2.67)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e481,656\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e27,847 (49.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e28,914 (50.94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eN, n: number of cases; %: percentage (n/N*100), Pf: Plasmodium falciparum; Pv: Plasmodium vivax\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, a notable decline in malaria, HCs (source data), was observed during the eight-year period, except in 2016 and between 2018 and 2019. The number of confirmed malaria cases declined from 11607 in 2012 to 2857 in 2019, an 8.34% reduction from the baseline. Maximum and minimum numbers of confirmed cases were documented during 2013 and 2018 respectively. The case burden due to \u003cem\u003eP. falciparum\u003c/em\u003e (49.74%) was comparable to \u003cem\u003eP. vivax\u003c/em\u003e (47.59%) (p\u0026thinsp;=\u0026thinsp;0.795). Yet, \u003cem\u003eP. vivax\u003c/em\u003e overtook \u003cem\u003eP. falciparum\u003c/em\u003e in cases burden (p\u0026thinsp;=\u0026thinsp;0.588) during 2013, 2014 and 2017. Mixed species infections, \u003cem\u003eP. vivax\u003c/em\u003e and \u003cem\u003eP. falciparum\u003c/em\u003e, accounted a low proportion (2.67%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiagnostic performance; data from HCs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRegarding the diagnostic performance, the yearly trend of clinical suspects was directly proportional to the confirmed cases in each year. But the proportions of examined clinical suspects against confirmed cases were increased from 6.91 in 2012 to 16.32 in 2019. The highest proportion (16.32) was recorded in 2019, whilst the lowest (5.59) was during 2013. This shows the proportion of 'non-malarial' febrile cases were increasing from 2012 to 2019 except in 2016 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProportion of clinical suspects against confirmed malaria cases, Gedeo zone, South Ethiopia, 2012\u0026ndash;2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical suspects (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConfirmed (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion (N/n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e485,414\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e57,228\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMalaria cases number by sex and age; data from HCs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSlightly more males (29,480 (11.34%)) were malaria positive (p\u0026thinsp;=\u0026thinsp;0.236) than females (27,748 (12.30%)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The above 15\u0026nbsp;years age group was the most affected (30,406, 11.47%) than the other age groups, followed by under 5 children which accounted for 15116 (13.83%) of the cases. The above 15\u0026nbsp;years age group was twice more likely (OR\u0026thinsp;=\u0026thinsp;2.00, 95% CI: 1.90, 2.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) to have malaria compared to the under 5 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of factors associated with malaria at HCs in Gedeo zone, South Ethiopia, 2012\u0026ndash;2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical suspects N (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConfirmed n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259906 (53.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29480 (11.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225508 (46.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27748 (12.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.97, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109302 (22.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15116 (13.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111028 (22.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11706 (10.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33 (1.29, 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265084 (54.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30406 (11.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 (1.90, 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeason\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97227 (20.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11010 (11.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134054 (27.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16820 (12.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53 (1.37, 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123200 (25.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13618 (11.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24 (1.19, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130933 (26.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15780 (12.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42 (1.34, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistrict/urban center\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYirgacheffe rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5187 (10.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDilla town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18150 (13.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.16 (2.11, 4.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDilla zuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12588 (10.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.30 (1.82, 2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWonago\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7427 (10.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40 (1.21, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYirgacheffe town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6271 (10.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24 (0.07, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKochore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7605 (16.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59 (1.20, 1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e%: percentage (n/N*100)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSpatiotemporal distribution of malaria cases\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe peak confirmed case load (12.55%) was during autumn followed by spring (12.05%), summer (11.05%) and winter (11.32%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In autumn the likelihood of having malaria is 1.53 times more than winter (OR\u0026thinsp;=\u0026thinsp;1.53, 95% CI: 1.37, 1.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, the highest proportions of both \u003cem\u003eP. falciparum\u003c/em\u003e (8445 (50.21%)) and \u003cem\u003eP. vivax\u003c/em\u003e (7911 (47.03%)) were noted during autumn particularly in April. On the other hand, in winter the proportion of infection due to the two species, \u003cem\u003eP. falciparum\u003c/em\u003e (5451 (49.51%)) and \u003cem\u003eP. vivax\u003c/em\u003e (5312 (48.25%)) was relatively lowest (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although \u003cem\u003eP. falciparum\u003c/em\u003e was higher than \u003cem\u003eP. vivax\u003c/em\u003e in all seasons, the difference was the smallest (1.26%) during winter, while in autumn it is 3.17%. The number of mixed infections also had same pattern throughout the four seasons.\u003c/p\u003e \u003cp\u003eOverall, based on the HC records, the highest malaria case burden reported was from Dilla town (18,150 (13.17%)) from a total of 137,860 clinical suspects followed by the adjacent district, Dilla zuria (12,588 (10.56%)) from 119,207 tested suspects. The lowest was from Yirgacheffe rural district (5,187 (10.44%)). Dilla town annual malaria cases remained the highest throughout the 8-year period except in 2013 and 2019 (OR\u0026thinsp;=\u0026thinsp;3.16, 95% CI: 2.11, 4.22, p\u0026thinsp;=\u0026thinsp;0.0002) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The highest confirmed cases during these two years were reported by Kochore (4390 (19.78%)) and Dilla zuria districts (1178 (8.04%)) from 22197 and 14648 clinical suspects respectively. Although there was an overall declining trend of confirmed malaria cases from 2012 to 2019, there were peaks in Kochore during 2013, Dilla town and Dilla zuria in 2016.\u003c/p\u003e \u003cp\u003eYirgacheffe rural district had the highest variation (376 (80.51%)) in terms of confirmed malaria records between HC and HO data (HC-HO) followed by Dilla town (164 (35.12%)) and Dilla zuria district (-138 (29.55%)), data obtained by HC-HO. Whereas, Wonago district had the lowest (-6 (1.28%)) deviation of malaria data recording between HC and HO (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe monthly total confirmed malaria cases showed a strong and positive association with monthly rainfall (Spearman's rho\u0026thinsp;=\u0026thinsp;0.895, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and monthly minimum temperature (Spearman's rho\u0026thinsp;=\u0026thinsp;0.671, p\u0026thinsp;=\u0026thinsp;0.017). Nevertheless, the maximum monthly temperature was negatively and weakly associated with the monthly confirmed malaria cases. Relative humidity seems not to have an effect on malaria transmission in the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eIn the present study there was a significant reduction in data incompleteness; with missing variables between 2012 and 2019. Specifically, the number of clinical suspects with missing data variables declined between 2012 and 2019 by approximately 4-fold and confirmed cases with about 7-fold. Decreasing trend in data incompleteness overtime, is plausible indication of huge improvement in data quality which thereby an implication for improving programmatic performance over the years.\u003c/p\u003e \u003cp\u003eThere was an overall notable difference in both total number of clinical suspects (3758) and confirmed malaria cases (467) recorded at HCs (source data) and HOs (HMIS data). For the 8-years period, discrepancies in clinically suspected and confirmed cases were observed between the HOs compared to the HCs. During 2012 and 2018 the numbers of clinical suspects recorded at HCs were consistent with HO data. In these years the numbers of confirmed cases were higher at HO records than HC records. Yirgacheffe rural district had the highest deviation in terms of confirmed malaria records between HC and HO data followed by Dilla town and Dilla zuria district, whereas Wonago district was the least. 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 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. 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 HMIS, lack of cross-checking and proofing habits, training gaps on HMIS data use and unintentional/intentional false reports. In addition, limited computer access and skill, inadequate technical support [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], poor data management skills and limited functionality of electronic data management systems [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] might be the likely reasons for HMIS implementation challenges.\u003c/p\u003e \u003cp\u003eConcerning the diagnostic performance, the proportions of clinically suspected cases against confirmed cases were increased from 6.91 in 2012 to 16.32 in 2019. The highest proportion was recorded in 2019, whilst the lowest was during 2013. This revealed that the 'non-malarial' febrile cases were increasing from 2012 to 2019, which 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 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], unable to detect sequestered \u003cem\u003eP. falciparum\u003c/em\u003e parasites [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and low competency of microscopists [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the other way, such increased number of non-malarial febrile illnesses might be related to other febrile cases including yellow fever virus [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and typhoid fever [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] infections, as per the studies conducted in southern 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 \u003cem\u003ePlasmodia\u003c/em\u003e infection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. 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 incorrect treatment of such non-malarial febrile illnesses with antimalarial drugs is possibly to contribute to rapid emergence of antimalarial drug resistance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] in the study area.\u003c/p\u003e \u003cp\u003eThere 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 2,546 of cases were observed during 2013 and 2018, respectively with 8.34 percent reduction. 2013 and 2016 were the exceptions to the declining trend as there were small epidemics in these periods in some parts of the Zone. Over the eight years period, overall, there was malaria positivity rate of 11.79%, data from the HCs. This positivity rate was comparable with certain studies done in Ethiopia including from Batu town (12.43%) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Arsi Negelle (11.40%) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and Halaba special district (9.47%) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, higher overall malaria positivity rates were reported from related studies conducted in south-central Ethiopia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], southern Ethiopia [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and abroad in Dakar, Senegal [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] with 33.83%, 21.79% and 19.68% respectively. On the other hand, the present figure was higher than records in other local studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. 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. The possible contributing factors for the peaks/epidemics of malaria cases in 2013 and 2016 could be associated with feeble intervention activities in certain areas of the Zone.\u003c/p\u003e \u003cp\u003e \u003cem\u003eP. falciparum\u003c/em\u003e and \u003cem\u003eP. vivax\u003c/em\u003e were detected where equivalent; congruent results were reported in some other parts of Ethiopia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. While other local studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] documented that the dominant species was \u003cem\u003eP. vivax\u003c/em\u003e. The proportion of mixed infection in this study was congruent with other studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], whereas inconsistent with other reports [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The likely reason for the slightly higher proportion of \u003cem\u003eP. falciparum\u003c/em\u003e over \u003cem\u003eP. vivax\u003c/em\u003e could be related to temperature; that is temperatures more than 18\u0026nbsp;\u0026deg;C for \u003cem\u003eP. falciparum\u003c/em\u003e and more than 15\u0026nbsp;\u0026deg;C for \u003cem\u003eP. vivax\u003c/em\u003e is suitable for the growth of these two species in human host and mosquito vectors [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Apparently, in the current study area the average mean temperature during the eight years period found to be above 18\u0026nbsp;\u0026deg;C. The higher proportion of \u003cem\u003eP. vivax\u003c/em\u003e 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 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] in the study area. Environmental fluctuations that change target mosquito species abundance might have an impact on \u003cem\u003ePlasmodia\u003c/em\u003e species occurrence [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The issue demands additional study. The possible reason for scarcity of mixed infections in this co-endemic area might be a competitive or an antagonistic effect of one \u003cem\u003ePlasmodium\u003c/em\u003e species over the other within the human host during co-infection [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMales were slightly more infected (51.51%) by malaria than females (48.49%) over the eight-year period. This was paralleled with other studies conducted in different parts of Ethiopia [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, this finding was not consistent with other reports in southern Ethiopia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and elsewhere in Mozambique [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] 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 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Inconsistent result was observed in southern Ethiopia [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] arguing that malaria cases cluster among the under-5. And a finding in Metema, northwest Ethiopia by Ferede et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] showed that 5\u0026ndash;14\u0026nbsp;years old were more infected. Possible justifications for the higher occurrence of malaria among males and older age group could be their engagement in various outdoor activities and staying outdoors during the nights [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Apart from outdoor exposures, differences in treatment-seeking behavior, access to health facilities and travel history [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] 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 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe peak number of confirmed malaria cases was recorded during autumn followed by spring, summer and winter with a statistically significant variation. This seasonal peak in malaria cases in autumn deviates from various studies in Ethiopia which is during spring after the main rain season [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, nationally the main malaria transmission season is from September to December following the peak rain season [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This trade-off in seasonal peak of malaria cases might be as a result of varying climatological conditions (rainfall pattern and temperature changes) in the area against other settings. Despite the high prevalence of malaria cases during the three seasons, there were a substantial number of confirmed malaria cases in the dry (winter) season in this study. These, absence of significant variation in the proportion of the \u003cem\u003eP. vivax\u003c/em\u003e and \u003cem\u003eP. falciparum\u003c/em\u003e burden and almost, year-round presence of malaria might suggest the presence of suitable local environments for mosquitoes. The comparable proportion of \u003cem\u003eP. vivax\u003c/em\u003e against \u003cem\u003eP. falciparum\u003c/em\u003e in winter might be explained by the fact that \u003cem\u003eP. vivax\u003c/em\u003e has ability to relapse rather than new infections. Since such traits could affect the temporal patterns of \u003cem\u003eP. vivax\u003c/em\u003e infections.\u003c/p\u003e \u003cp\u003eOverall, high number of clinical suspects and confirmed malaria cases were documented in Dilla town (urban) and Dilla zuria district (sub-urban) as compared to other districts. 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 two 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, peaks 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 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], 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 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and changes in intervention (like IRS and LLINs) periods might have effect for these spatial differences of malaria cases.\u003c/p\u003e \u003cp\u003eMonthly rainfall and minimum temperature demonstrated statistically significant positive correlation with malaria cases. Previous studies in Ethiopia [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and elsewhere [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] documented similar findings. However, the result of the current study on the association of rainfall and malaria cases was deviating from previous findings, stating higher rainfall does not necessarily influence the malaria changes [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In contrast to our finding, minimum temperature was weakly correlated with malaria cases in southeast Ethiopia [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. 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 relative humidity were weakly correlated with malaria cases. In disagreement to our finding, studies conducted in Jimma, Ethiopia by Alemu and others [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and Sena and colleagues [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] in Gilgel-Gibe, southwest Ethiopia reported that inter-monthly relative humidity was significantly associated with monthly malaria cases.\u003c/p\u003e \u003cp\u003eThe 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. Missing of asymptomatic cases and poor competence of the laboratory personnel at HCs could be the other limitations. Furthermore, clinically treated patients\u0026rsquo; (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.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eOver the entire period, 2012 to 2019, the program data quality improved over the years; underreporting and proportion of data incompleteness declined significantly, and the burden dropped continuously except in 2016. Yet, equivalent \u003cem\u003eP. falciparum\u003c/em\u003e and \u003cem\u003eP. vivax\u003c/em\u003e malaria existed, thus intervention actions should target both species. The high malaria prevalence in urban setting (Dilla town) and its vicinity and in autumn season necessitates spatiotemporal consideration by the control campaign. The intervention strategies need also consider older age groups. In general, malaria still remains a public health problem in the area, which demands strengthening of interventions and short-term forecasting based on local meteorological factors to achieve elimination goals in the area. In addition, the data recorded at the HCs and district HOs should be monitored for consistency.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003ePf\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003ePlasmodium falciparum\u003c/em\u003e; \u003cem\u003ePv\u003c/em\u003e:\u003cem\u003ePlasmodium vivax\u003c/em\u003e, NMCP:national malaria control program; API:annual parasite incidence; HMIS:health management information system; HC:health center; HO:health office; OR:odds ratio; CI:confidence interval; IRS:indoor residual spraying; LLINs:long-lasting insecticide-treated nets\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from Department of Microbial, Cellular and Molecular Biology and College of Natural and Computational Sciences Ethical Review Committee, Addis Ababa University (CNSDO/318/11/2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch4\u003eFunding\u003c/h4\u003e\n\u003cp\u003eThis study was partially funded by Dilla and Addis Ababa Universities. But the funders have no role in the study design, data collection, analysis, interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEM designed the study, involved in data collection, analyzed and interpreted the data, and drafted the manuscript. SWB contributed in data analysis works. FGT initiated the idea, involved in the data entry template development and gave feedback on the data collection protocol. SD was responsible for revising the draft manuscript. EG initiated the idea, guided the process of data collection and substantively revised the manuscript. HM participated in guidance of the data collection progresses and critically commented the whole section of the paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch1\u003eAcknowledgments\u003c/h1\u003e\n\u003cp\u003ewould like to thank heads of HOs and HCs for their willingness to provide the retrospective malaria data and the national meteorology agency of Ethiopia for providing meteorological data of the study area. We are also grateful to Mr Eyuel Asemahegn for constructing the study area map.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEthiopian Public Health Institute. Ethiopia National Malaria Indicator Survey 2015. Addis Ababa, 2016.\u003c/li\u003e\n\u003cli\u003eTaffese HS, Hemming-Schroeder E, Koepfli C, Tesfaye G, Lee M, Kazura J, et al. Malaria epidemiology and interventions in Ethiopia from 2001 to 2016. Infect Dis Poverty. 2018;7:103.\u003c/li\u003e\n\u003cli\u003eGari T, Lindtj\u0026oslash;rn B. 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Heliyon 6. 2020;e03616.\u003c/li\u003e\n\u003cli\u003eHailemariam M, Gebre S. Trend analysis of malaria prevalence in Arsi Negelle health center southern Ethiopia. J Infect Dis Immun. 2015;7(1):1-6.\u003c/li\u003e\n\u003cli\u003eShamebo T, Petros B. Trend analysis of malaria prevalence in Halaba special district, Southern Ethiopia. BMC Res Notes. 2019;12(1):190.\u003c/li\u003e\n\u003cli\u003eYimer F, Animut A, Erko B, Mamo H. Past five-year trend, current prevalence and household knowledge, attitude and practice of malaria in Abeshge, south-central Ethiopia. Malar J. 2015;14:230.\u003c/li\u003e\n\u003cli\u003eDabaro D, Birhanu Z, Yewhalaw D. Analysis of trends of malaria from 2010 to 2017 in Boricha district, southern Ethiopia. Malar J. 2020;19:88.\u003c/li\u003e\n\u003cli\u003eDiallo MA, Badiane AS, Diongue K, Sakande\u0026acute; L, Ndiaye M, Seck MC, et al. A twenty-eight-year laboratory-based retrospective trend analysis of malaria in Dakar, Senegal. PLoS ONE. 2020;15(5):e0231587.\u003c/li\u003e\n\u003cli\u003eDerbie A, Alemu M. Five years malaria trend analysis in Woreta health center, Northwest Ethiopia. J Health Sci. 2017;27(5):465.\u003c/li\u003e\n\u003cli\u003eYimer M, Hailu T, Mulu W, Abera B, Ayalew W. A 5 year trend analysis of malaria prevalence with in the catchment areas of Felegehiwot referral hospital, Bahir Dar city, northwest‑Ethiopia: a retrospective study. BMC Res Notes. 2017;10:239.\u003c/li\u003e\n\u003cli\u003eSolomon A, Kahase D, Alemayehu M. Trend of malaria prevalence in Wolkite health center: an implication towards the elimination of malaria in Ethiopia by 2030. Malar J. 2020;19:112.\u003c/li\u003e\n\u003cli\u003eBodker E. Relationship between altitude and intensity of malaria transmission in the Usambara Mountains, Tanzania. Med Entomol. 2003;40:706-17.\u003c/li\u003e\n\u003cli\u003eBradfieldLyon E. Temperature suitability for malaria climbing the Ethiopian highlands. Environ Res Lett. 2017;12:064015.\u003c/li\u003e\n\u003cli\u003eHowes RE, Battle KE, Mendis KN, Smith DL, Cibulskis RE, Baird JK, et al. Global epidemiology of \u003cem\u003ePlasmodium vivax\u003c/em\u003e. Am J Trop Med Hyg. 2016;95 Suppl 6:15-34.\u003c/li\u003e\n\u003cli\u003ePhimpraphi W, Paul RE, Yimsamran S, Puangsa-art S, Thanyavanich N, Maneeboonyang W, et al. Longitudinal study of \u003cem\u003ePlasmodium falciparum \u003c/em\u003eand \u003cem\u003ePlasmodium vivax \u003c/em\u003ein a Karen population in Thailand. Malar J. 2008;\u003cstrong\u003e7\u003c/strong\u003e:99.\u003c/li\u003e\n\u003cli\u003eTemu A, Coleman M, Abilio P, Kleinschmidt I. High prevalence of malaria in Zambezia, Mozambique: the protective effect of IRS versus increased risks due to pig-keeping and house construction. PLoS ONE. 2012;7:e31409.\u003c/li\u003e\n\u003cli\u003eFerede G, Worku A, Getaneh A, Ahmed A, Haile T, Abdu Y, et al. Prevalence of malaria from blood smears examination: a seven-year retrospective study from Metema hospital, northwest Ethiopia. Malar Res Treat. 2013;2013:704-30:5.\u003c/li\u003e\n\u003cli\u003eKigozi SP, Kigozi RN, Epstein A, Mpimbaza A, Sserwanga A, Yeka A, et al. Rapid shifts in the age‑specific burden of malaria following successful control interventions in four regions of Uganda. Malar J. 2020;19:128.\u003c/li\u003e\n\u003cli\u003eMathanga DP, Tembo AK, Mzilahowa T, Bauleni A, Mtimaukenena K, Taylor TE, et al. Patterns and determinants of malaria risk in urban and peri‑urban areas of Blantyre, Malawi. Malar J. 2016;15:590.\u003c/li\u003e\n\u003cli\u003eKrogstad DJ. Malaria as a re-emerging disease. Epidemiol Rev. 1996;18:77-89.\u003c/li\u003e\n\u003cli\u003eAlemu A, Abebe G, Tsegaye W, Golassa L. Climatic variables and malaria transmission dynamics in Jimma town, Southwest Ethiopia. Parasit Vectors. 2011;4:30.\u003c/li\u003e\n\u003cli\u003eSena L, Deressa W, Ali A. Correlation of climate variability and malaria: a retrospective comparative study, southwest Ethiopia. Ethiop J Health Sci. 2015;25:2.\u003c/li\u003e\n\u003cli\u003eGunda R, Chimbari MJ, Shamu S, Sartorius B, Mukaratirwa Malaria incidence trends and their association with climatic variables in rural Gwanda, Zimbabwe, 2005-2015. Malar J. 2017;16:393.\u003c/li\u003e\n\u003cli\u003eNanvyat N, Mulambalah CS, Barshep Y, Dakul DA, Tsingalia HM. Retrospective analysis of malaria transmission patterns and its association with meteorological variables in lowland areas of Plateau state, Nigeria. Int J Mosq Res. 2017;4(4):101-106.\u003c/li\u003e\n\u003cli\u003eHaque U, Hashizume M, Glass GE, Dewan AM, Overgaard HJ, Yamamoto T. The role of climate variability in the spread of malaria in Bangladeshi highlands. PLoS ONE. 2010;5(12):1-9:e14341.\u003c/li\u003e\n\u003cli\u003eSingh N, Sharma VP. Patterns of rainfall and malaria in Madhya Pradesh, central India, Ann Trop Med Parasitol. 2002;96(4):349-359.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria, retrospective, spatiotemporal trend, meteorological factors, Gedeo zone","lastPublishedDoi":"10.21203/rs.3.rs-72130/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-72130/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eInformed decision making is underlined by all level of the tiers in the health system. Poor data record system coupled with\u003cem\u003e \u003c/em\u003eunder- (over)-reporting of malaria cases affects the country’s elimination activities. Thus, malaria data at health facilities and health offices are important particularly to monitor and evaluate malaria elimination progresses. This study was intended to assess overall reported malaria cases, spatiotemporal trends and factors associated in Gedeo zone, South Ethiopia, and compare malaria case reports by the health centers and health offices.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Past eight years retrospective data stored in 17 health centers and 5 district health offices in Gedeo Zone were extracted. Malaria cases data at each health center with sociodemographic information, between 2012 and 2019, were included. Meteorological data were obtained from the national meteorology agency. The data were analyzed using Stata 13.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 485,414 clinical suspects were examined for malaria during the previous 8 years at health centers. Of these suspects, 57,228 (11.79%) were confirmed malaria cases. We noted, an overall under reporting of malaria, 3,758 clinical suspects and 467 confirmed malaria cases were not captured at the health offices level. Based on the health centers records, \u003cem\u003ePlasmodium falciparum \u003c/em\u003e(49.74%) was slightly higher (p = 0.795) than \u003cem\u003eP. vivax \u003c/em\u003e(47.59%). The majority of cases were found in adults (≥15 years of age) that accounted for 11.47% of confirmed malaria cases (p \u0026lt; 0.0001). There was high spatiotemporal variation: highest cases record was during autumn (12.55%) (p \u0026lt; 0.0001) and, the highest (18,150, 13.17%)) and lowest (5,187 (10.44%)) malaria cases were reported from Dilla town and Yirgacheffe rural district, respectively (p = 0.0002). Monthly rainfall and minimum temperature exhibited strong positive associations with the number of confirmed malaria cases.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e A notable decline in malaria cases was observed over the eight-year period. Both \u003cem\u003eP. falciparum \u003c/em\u003eand \u003cem\u003eP. vivax \u003c/em\u003eco-exist; hence, control measures should continue targeting both species. The high malaria burden in urban (Dilla town) and suburban (Dilla zuria district) settings and autumn season need spatiotemporal consideration by the elimination program.\u003c/p\u003e","manuscriptTitle":"Eight-year Retrospective Analysis of Malaria Trends in Gedeo Zone, South Ethiopia (2012-2019)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-09-24 15:02:01","doi":"10.21203/rs.3.rs-72130/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2020-10-21T12:00:00+00:00","index":2,"fulltext":"Recommendation: Major revisions required\nForm responses:\n---\n\nComments to Author:\n---\nThe authors present retrospective analysis of malaria data extracted from health centers and health offices in Gedeo Zone, Ethiopia (2012 to 2019) with the overall purpose being to inform malaria elimination strategies in the country. The analysis shows that confirmed malaria cases have decreased over time, the region remains co-endemic with P. falciparum and P. vivax and transmission occurs in rural and urban areas.\n\nMAJOR\n1. Consider professional copy editor review for spelling, grammar revisions and phrasing revision, i.e. 'suspected cases' would be a more common way of expressing 'clinical suspects'.\n\n2. Statistical review recommended, i.e. the appropriateness the statistical methods. It is indicated from the methods that Pearson's chi-squared was used however the comparisons made in Tables 2 and 3 do not fill criteria of independence between variables. The logistic regression used in Table 4 should be reconsidered, particularly the seasonal data which may be best analysed using generalized linear models. Spearman's correlations rather than Pearson's correlations were used.\n\n3. Discussion was logical however there was a tendency to repeat much of the results section. As the paper's overarching aim is to provide data that will inform malaria elimination programs, it would be of value and great interest to read how these results may impact these programs and how reporting systems might be improved. In particular, the proportions of P. falciparum and P. vivax in the country are nearly equal; therefore, it would be interesting to discuss the current practice/uptake of primaquine prescribing for radical therapy of P. vivax, the prevalence and types of G6PD deficiency in the region, what G6PD diagnostics are available in the region.\n\nDiscussion, page 8: The decreased proportion of malaria cases detected could also very likely be due to decreasing malaria incidence in the region, as opposed to lab error.\n\nDiscussion, page 9: The discussion about potential reasons for equal proportions of P. falciparum and P. vivax could be further explored with the data. It would be interesting to see Figure 1 include pie graphs of PF/PV for each study site to see if there is much variation in terms of location in the region. Mixed infections with PF and PV infection are less likely to be detected by microscopy as PV has an inherently lower parasitemia, thus less likely to be detected in a co-infection with PF since PF will inherently have a higher parasitemia.\n\n\nMINOR:\n1. Abstract, line 56: Rephrase; i.e. the proportions of P. falciparum and P. vivax infection were nearly equivalent.\n\n2. Abstract, line 58: the denominator used for the proportion of adults accounting for confirmed malaria cases should be 57,228 (53.1%) rather than 11.47%.\n\n3. Abstract, conclusion, line 13: the abstract conclusion indicates that there was a decline in malaria cases over the 8 year period; however this was not presented in the abstract results.\n\n4. Methods- Study setting: Include (SNNPR) at the end of Southern, Nations, Nationalities, and Peoples' Region such that Figure 1 can be more clearly understood. Definition of seasons would be helpful here.\n\n5. Methods- Data collection: Include in first sentence what kind of study is being presented, i.e. Retrospective analysis of extracted data from HC and HO records etc..Include the specific time frame of data extracted, including month/year; rather than only year such that it can be clearly shown that an 8-year period of data was assessed. Include detail specifying what type of analyses were conducted to address 'data quality issues'; from the results it appears as though an assessment of incompleteness and internal consistency were analysed in terms of a data quality assessment. Include how malaria was diagnosed among confirmed cases, i.e. peripheral blood smears versus rapid diagnostic tests.\n\n6. Results- Annual clinical suspects and confirmed malaria cases based on HO versus HC section. The first sentence of this paragraph is not clear in terms what inconsistencies were analysed.\n\n7. Tables- consider which tables of the 4 tables might be sufficient to show in a supplementary file, i.e. Tables 1, 3 might be suitable for a supplementary file; Table 4 revised based on statistical revision.\n\n8. Figures- consider which tables of the 5 figure might be sufficient to show in a supplementary file, i.e. Figure 2 and 4 might be suitable for a supplementary file;\nFigure 1: Add A, B, C to each panel of figure including description of each; include footnote with abbreviations, i.e. SNNPR. Consider revising Figure 1 as detailed above by including pie graphs of PF/PV overlying each study site. For all figures label and described each panel, i.e. A, B.\n\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **No**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **No**\n* Is the use of statistics and treatment of uncertainties appropriate?: **No**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"decision","content":"Major revision","date":"2020-10-21T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-10-14T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nDear Editor\n\nI have reviewed the manuscript with the title of \" Eight year retrospective analysis of Malaria trends in Gedeo Zone, South Ethiopia\" by Eshetu Molla and team which was submitted for consideration for publication in your respected Journal.\nAs Ethiopia is among the few African countries which are progressing towards reducing the incidence of malaria by 40% or more by 2020, it is important to look into factors which can affect achieving this goal. Under reporting of malaria cases, incompleteness of data can be two important factors which need to look into.\nOverall, the structure and the language of the manuscript is satisfactory. However, some points require further clarification.\n1. Seventeen public HCs were selected for the study. It is not clear whether all the public HCs included or only some selected. If only some selected, selection was based on what? was it based on providing at least 8 years' service? This need to clarified clearly in the data collection section.\n2. Last sentence of Data collection section- \"consistency and completeness of the extracted data was checked for each HC and district\"-How? Need to be mentioned.\n3. What is the basis of dividing age category as \u003c5, 5-14, \u003e15? It is concluded that above 15 age category as most affected. It need to be further subdivided. It is important to know whether most affected people were elderly/middle age/young etc. as above 15 include children, young, middle age and elderly all.\n4. In the discussion, it is mentioned that there is an increase of non-malarial febrile cases. And also mentioned that this can be an indication of declining capacity of detecting malarial parasites. These two statements are contradicting each other. Possibility of false negative results need to be ruled out prior to concluding that there is an increase of non-malarial febrile cases.\n5. It is mentioned that \"the comparable proportion of P vivax against p. falciparum in winter might be explained by the fact that P vivax has ability to relapse rather than new infections\". This statement need to be supported- whether patients with P vivax had history of previous episodes of malaria?\nOverall some conclusions drawn in the discussion are not based on facts.\n6. What could be the reason \"result of current study on association of rainfall and malaria was deviating from previous findings\"\nTable legends are confusing to me as \"N\" and \"n\" used for both suspected and confirmed cases..\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **No**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please publish my name with my report.**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **No**\n* Are the methods sufficiently described to allow the study to be repeated?: **No**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **No**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"editorInvitedReview","content":"","date":"2020-10-11T12:00:00+00:00","index":4,"fulltext":"Recommendation: Reviewer's comments unavailable pending editorial decision\n"},{"type":"editorInvitedReview","content":"","date":"2020-10-11T12:00:00+00:00","index":3,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please publish my name with my report.**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2020-10-02T12:00:00+00:00","index":4,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-09-29T12:00:00+00:00","index":3,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-09-26T12:00:00+00:00","index":2,"fulltext":""},{"type":"editorAssigned","content":"","date":"2020-09-24T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-09-24T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-09-24T12:00:00+00:00","index":1,"fulltext":""},{"type":"checksComplete","content":"","date":"2020-09-18T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-09-16T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-09-10T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d4a8892-c24c-476b-ba01-6bb2d83d6dea","owner":[],"postedDate":"September 24th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":601324,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2021-01-26T15:03:52+00:00","versionOfRecord":{"articleIdentity":"rs-72130","link":"https://doi.org/10.1186/s12879-021-05783-8","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2021-01-21 15:00:47","publishedOnDateReadable":"January 21st, 2021"},"versionCreatedAt":"2020-09-24 15:02:01","video":"","vorDoi":"10.1186/s12879-021-05783-8","vorDoiUrl":"https://doi.org/10.1186/s12879-021-05783-8","workflowStages":[]},"version":"v1","identity":"rs-72130","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-72130","identity":"rs-72130","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}