Effect of the second and third COVID-19 pandemic waves on routine outpatient malaria indicators and  case management practices in Uganda; an interrupted time series analysis.

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Pius Mukisa, Freddy Eric Kitutu, Joan Nankabirwa, Arthur Mpimbaza, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5039547/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Oct, 2024 Read the published version in Malaria Journal → Version 1 posted 7 You are reading this latest preprint version Abstract Background: Reports on the impact of COVID-19 pandemic on the quality of malaria care and burden in sub Saharan Africa have provided a mixed picture to date. We assessed the impact of the 2 nd (Delta) and 3 rd (Omicron) COVID-19 waves on outpatient malaria indicators and case management practices at three public health facilities with varying malaria transmission intensities in Uganda. Methods: Individual level data from all patients presenting to the out-patient departments (OPD) of the three facilities (Kasambya, Walukuba and Lumino) between January 2019 and February 2022 were included in the analysis. Outcomes of interest included total number of outpatient (OPD) visits, proportion of patients suspected to have malaria, proportion of suspected malaria cases tested with a malaria diagnostic test, test positivity rates (TPR) and proportion of malaria cases prescribed artemether-lumefantrine (AL). Using the pre-COVID-19 trends between January 2019 and February 2020, interrupted time series analysis was used to predict the expected trends for these study outcomes during the 2 nd wave (May 2021-August 2021) and 3 rd wave (November 2021-February 2022). The observed trends of the study outcomes were compared with the expected trends. Results: There were no significant differences between the observed versus expected overall outpatient visits in the 2 nd wave, however, a significant decline in OPD attendance was observed during the 3 rd wave (15101 vs 31154; incidence rate ratio (IRR)=0.48 [0.41-0.56]). No significant differences in the overall observed versus expected proportions of suspected malaria cases and test positivity rates in both COVID waves. However, a significant decrease in the overall proportion of suspected malaria cases tested with a malaria diagnostic test was observed during the 3 rd wave (99.86% vs 99.99%; relative percent ratio [RPR]=0.99 [0.99-0.99]). Finally, a significant decline in the overall proportion of malaria cases prescribed AL was observed during the 2 nd wave (94.99% vs 99.85%; RPR =0.95 [0.92-0.98]) but not the 3 rd wave. Conclusion: Significant declines in OPD attendance and suspected malaria cases tested with malaria diagnostic test were observed during the 3 rd COVID-19 wave, while AL prescription significantly reduced during the 2 nd COVID-19 wave. These findings add to the body of knowledge highlighting the adverse impact of COVID-19 pandemic on the malaria which could explain the increase in the malaria burden observed during this period. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Since 2000, substantial reductions in malaria disease burden have been realized at global level and in sub Saharan Africa. However, a rise in malaria case incidence was observed from 2020, part of which was attributable to the COVID-19 pandemic and its disruptions to malaria control interventions (W.H.O, 2023). The World Health Organization Africa region accounts for majority of the cases reported worldwide (W.H.O, 2023). In Uganda, malaria is the leading diagnosis at outpatient departments (OPD) accounting for 31.1% of all OPD visits and the commonest reason for inpatient department (IPD) admissions accounting for approximately 25% of all IPD admissions (MoH, 2023). In addition, malaria is the 2 nd leading cause of death after neonatal conditions, accounting for 7.4% of all inpatient death in the country (MoH, 2023). Uganda is a malaria endemic country with 95% of the population at risk of infection. Malaria transmission in Uganda varies geographically, from less than 1% malaria prevalence in southwest Uganda to greater than 20% in Busoga subregion, northwestern Uganda, and northeast Uganda (MoH, 2020). The country has made tremendous progress by reducing the malaria burden with parasite prevalence declining from 42% (based on microscopy in under five children) in 2009 to 9.1% in 2018 (MoH, 2010). However, an increase in the number of cases was observed in areas previously reporting marked declines in burden starting in 2020 (Epstein et al., 2022; Nankabirwa et al., 2022). In 2022, Uganda experienced a rebound epidemic leading with some areas reporting more than a 30 percent increase in the total number of malaria confirmed cases (Epstein et al., 2022). This period corresponds to the time the country was having the COVID-19 epidemic, however, the contribution of the epidemic to this increase in burden has not well documented. Indeed, from the on-set of the COVID-19 pandemic, there were concerns that the documented success in malaria control in Africa may be significantly reversed by the pandemic and modelling studies predicted that malaria cases would double during the pandemic (Weiss et al., 2021). The impact of COVID-19 on malaria burden could be through a number of mechanisms including disruptions in health seeking behaviors, reallocation of resources, misdiagnosis due to overlap of symptoms, and interruptions in malaria preventive services (Caglar et al., 2021; Hussein et al., 2020). In Uganda, the first COVID-19 case was registered on 21 st March 2020 (Kitara & Ikoona, 2020), and three waves were observed through the course of the pandemic. The 1 st COVID-19 was between August 2020 to January 2021, 2 nd wave between May 2021 and August 2021 and 3 rd between November 2021 and February 2022 (Atek Kagirita, 2022; Mahmud & Riley, 2021). A study by Namuganga et al showed no impact of the 1 st COVID-19 wave on malaria burden (Namuganga et al., 2021), however, this study was done when the number of reported COVID-19 cases in Uganda were low and less severe in presentation. The 2 nd COVID-19 wave (Delta) in Uganda had more severe cases (F.Bongomin et al., 2021) and the 3 rd COVID-19 wave (Omicron wave) had more infectious cases. Despite these differences in presentation to the first wave, the impact of the 2 nd and 3 rd wave on malaria burden and case management have not been evaluated. We assessed the effect of the 2 nd and 3 rd COVID-19 waves on routine outpatient malaria indicators and case management practices at three public health facilities located in varying malaria transmission settings in Uganda. Methods Study design and setting. This is a time trend analysis of malaria burden indicators and case management practices using data of patients attending out-patient departments of three public health facilities in Uganda. The health facilities included two level III health centers (Kasambya and Lumino) and one level IV health center (Walukuba). All facilities are part of 77 malaria reference centers (MRCs) in Uganda, where enhance malaria surveillance activities are conducted as part of routine surveillance. The three facilities are supported by the Uganda Malaria Surveillance Project (UMSP) as part of MRC activities to capture accurate, reliable and complete individual patient level data, using the standardized health management information system (HMIS) registers (HMIS 002 outpatient register). Staff capacity building is provided through training, onsite mentorship, support supervision and regular data quality assessments. The facilities attend to between 1000-3000 outpatients monthly. The main malaria control interventions in the districts have been limited to the use of long-lasting insecticidal nets (LLINs) and to date there have been four mass net distribution campaigns (2013, 2017, 2020 and 2023). Kasambya HC III is located in Mubende district, in the Central Region of Uganda. Mubende is one of the largest districts in the country with agriculture being the main economic activity of the population in the district. The entomological inoculation rate (EIR) of Mubende district is estimated at 4 infective bites per person per year (Okello et al., 2006) and malaria parasite prevalence in children under 5 years of age was estimated at of 9% in the last malaria indicator survey (MoH, 2020), and it is considered to be a moderate malaria transmission area. Lumino is located in Busia, eastern Uganda, an area with an EIR of 108.2 infective bites per person per year (Mawejje et al., 2022), and is a high malaria transmission area. Walukuba is located in Jinja, east central Uganda. The EIR of the area is 6 infective bites per person per year (Okello et al., 2006), with a malaria parasite prevalence of 21% (based on microscopy in under 5 children) (MoH, 2020). Lumino HC III is located in Busia district in eastern Uganda. Busia is a rural district, with high malaria transmission and the EIR was estimated at 108.2 infective bites per person per year in 2020 (Mawejje et al., 2022). Walukuba HC IV is located in Jinja district in east central Uganda. The district is semi-urban with varying levels of malaria transmission intensities. The malaria parasite prevalence of the district was estimates at 21% in under 5years in the 2018/19 MIS (MoH, 2020). Study population, sampling and sample size. All records of patients presenting to the outpatients department of the participating facilities between January 1 st 2019 to February 28 th 2022. The routinely collected data in registers including patient demographics, village of residence, history of fever, whether a malaria diagnostic test was performed, type of malaria test done (malaria rapid diagnostic test (mRDTs) vs microscopy), results of laboratory tests, diagnoses given, and treatments prescribed was extracted from the routine HMIS registers. The outcome variables included total OPD visits, suspected malaria cases, TPR, proportions of suspected malaria cases for whom a malaria laboratory test was recommended, and proportion of confirmed malaria cases prescribed AL. The main exposure variable was the time period in which a patient presented to the OPD (before the COVID-19 pandemic, or during the 2 nd or 3 rd COVID-19 wave). The potential confounders controlled for in this study included rainfall distribution and temperature. The data on average monthly temperature and rainfall was extracted from remote sensing sources. Rainfall data was extracted from climate hazards group infrared precipitation with station data (CHRIPS) database which data is recorded in millimeters. Temperature data was extracted from the moderate resolution imaging spectro-radiometer (MODIS). Data analysis. Single group Newey approach interrupted time series analysis (ITSA) with two interruptions (Linden, 2017)was conducted using STATA 14. Monthly time points were considered, utilizing monthly aggregated data collected from January 2019 to February 2022 for each outcome. The two interruption time points included; 1) the month of onset of the 2 nd COVID-19 wave (May 2021) and 2) the time of onset of the 3 rd COVID-19 wave (November 2021) in Uganda. The 1 st interruption (2 nd COVID-19 wave) begun in May 2021 and continued until August 2021 therefore, it had 4 time points in its post interruption duration. There was a wash out period of 2 months (September 2021 to October 2021) before onset of the 2 nd interruption (3 rd COVID-19 wave). The 2 nd interruption had 2 time points in its post interruption period (December 2021 to February 2022). The Single group newey approach ITSA with two interruptions model output is as follows; Y t = β 0 + β 1 T + β 2 f 1 +β 3 f 1 T 1 + β 4 f 2 + β 5 f 2 T 2 + Et, where Et= β 6 Dt + β 7 Tet + β 10 Rt, Yt is outcome Y(e.g., total OPD visits, proportion suspected malaria cases, test positivity rate, proportion malaria cases prescribed AL, proportion of suspected malaria cases tested) at month t, β 0 is the intercept (outcome Y at the beginning of the study), β 1 is the slope of the outcome before arrival of the 1 st interruption (pre-intervention slope), β 2 is the change in level of the outcome immediately on arrival of the first interruption, β 3 is the difference between the pre-intervention slope (pre-COVID-19 slope) and the first interruption outcome slope, β 4 is the change in the level of slope of the outcome on arrival of the second interruption (3 rd COVID-19 wave), β 5 is the difference between the first interruption (2 nd COVID-19 wave) and second interruption slopes (3 rd COVID-19 wave slope) of the outcome. T is a linear term denoting the duration since the start of the study. F 1 t is a linear term denoting the time in month since the start of the 2 nd COVID-19 wave (models the observed change in trend/slope immediately after onset of the 2 nd COVID-19 wave ). F 2 t is a linear term denoting the time in month since the start of the 3 rd COVID-19 wave (models the observed change in trend/slope immediately after onset of the 3 rd COVID-19 wave, f 1, and f 2 are dummy variables depicting the interventions. Dt is a linear term denoting fixed calendar month effects to model seasonality, Tet is a linear term of monthly temperature data averaged across district level to control for confounding effects of temperature, Rt is a linear term of monthly rainfall data(mm) averaged across district level to control for confounding effects of rainfall. To account for serial autocorrelation between time points, an autoregressive order two (Lag 2) was used since autocorrelation was present at lags< 2. Negative binomial was used to model the relationship between the count outcome (OPD visits) and the various independent variables (time, confounders and interruptions indicators). In the same way, fractional regression was used to model the proportional outcomes. Monthly expected values (counterfactual values) of all the outcomes hadn’t the interruptions occurred were predicted based on the fixed model (negative binomial and fractional regression) after adjusting for the calendar month effects, rainfall, temperature and setting the post interruption slopes at zero (to model what would happen if the interruptions hadn’t occurred). For count outcomes, monthly expected values were summed up for the durations of the 2 nd and 3 rd COVID-19 waves and incidence rate ratios calculated comparing the observed versus expected outcome. For proportion outcomes, an average from the monthly expected values was calculated and a relative percent ratio calculated comparing the observed versus expected outcome. Significant change in level of the outcome meant immediate impact of the disruption. Significant difference between the pre-intervention and post intervention outcome slopes meant impact of the intervention/ disruption overtime. Significant differences between the observed and predicted post intervention outcome values also indicated impact on the intervention. Results A total of 180,666 patients were treated at the outpatients department of the three facilities between 1st January 2019 and 28th February 2022. Most were female 118,815 (65.75%) and the median age of the patients was 16 (6–32) years. The average atmospheric temperature across the three study sites was 30.25 (± 5.49)˚C ranging from 30.8˚C to 32.5˚C. The average rainfall distribution at the sites was 140.4 (± 77.21) mm with the lowest 104.81mm and the highest 169.88mm. as shown in Table 1 . Table 1 Patient demographics and environmental characteristics stratified by site from January 2019 to February 2022. Lumino Kasambya Walukuba All sites combined Median Age (IQR) 17(5–31) 16(6–32) 15(7–33) 16(6–32) Female (n%) Male (n%) 42803(67.81%) 20319(32.19%) 31189(64.34%) 17287(35.66%) 44346(65.65%) 23204(34.35%) 118815(65.75%) 61849(34.23%) Average temperature in˚C (SD) 31.88(2.51) 30.83(2.65) 32.53(1.96) 30.25(5.49) Average rainfall in mm (SD) 169.88(101.29) 104.81(52.35) 146.61(89.24) 140.43(77.21) Impact of the COVID-19 waves on outpatient malaria indicators. OPD attendance (Overall impact). There was neither change in level of OPD visits a month immediately on onset of the 2nd COVID-19 wave (β 2 = -626.06, P > 0.05) nor change in trend of OPD visits during the 2nd COVID-19 wave duration (β 3 = -192.89, P > 0.05). However, immediately on onset of the 3rd COVID-19 wave (during its 1st month), there was a significant increase in the OPD visits (β 2= 1532.91, P = 0.03) but overtime there wasn’t significant change in trend of OPD visits during the 3rd COVID-19 wave ( β 3 = -161.39, P > 0.05) as shown in Fig. 1 and Table 2 . Overall (all sites combined), there was no significant difference between the observed versus expected total number of patients seen at the out-patient departments during the 2nd COVID-19 wave (14950 vs 20016; IRR = 0.75[0.29–1.20]) however, there was a 52% decline in the number of observed versus expected total number of patients seen at the out-patient departments during the 3rd COVID-19 wave duration (15101 vs 31154; IRR = 0.48[0.41–0.56]) as shown in Table 3 . Table 2 Change in level of the outcome and change in trend of the outcome on onset of the two interruptions 2nd COVID-19 wave onset (May 2021) 3rd COVID-19 wave onset (November 2021) Change in level/β 2 (P-value). Change in trend/β 3 (P-Value). Change in level/β 2 (P-value). Change in trend/β 3 (P-Value). OPD attendance -626.06(0.22) -192.89(0.13) 1532.91(0.03) -161.39(0.44) Proportion of suspected malaria cases 0.37(0.86) -0.25(0.55) -4.51(0.09) 2.55(0.01) Test positivity rate -0.16(0.00) 0.02(0.08) -0.01(0.88) -0.04(0.15) Proportion of suspected malaria cases tested -3.84e-16(0.51) 7.95e-18(0.96) -4.11e-16(0.43) -2.77e-16(0.41) Proportion of malaria cases treated 3.33e-14(0.34) 7.66e-16(0.95) 5.31e-14(0.24) 1.21e-14(0.60) Table 3 Estimated and observed outcomes (averages for proportion outcomes and totals for the count outcome) during the three interruption durations (ALL SITES COMBINED). 2nd COVID-19 wave duration (May-August 2021) 3rd COVID-19 wave duration (November 2021-February 2022) Observed Predicted Ratio(95% CI) Observed Predicted Ratio(95% CI) OPD attendance 14950 20016 0.75(0.29–1.20) 15101 31154 0.48(0.41–0.56) Proportion of suspected malaria cases 36.45% 36.44% 1.00(0.92–1.08) 35.25% 33.69% 1.05(0.95–1.13) Test positivity rate 35.30% 33.89% 1.04(0.74–1.34) 40.16% 36.28% 1.11(0.90–1.31) Proportion of suspected malaria cases tested 99.47% 99.97% 0.99(0.98-1.00) 99.86% 99.99% 0.99(0.99–0.99) Proportion of malaria cases treated 94.99% 99.85% 0.95(0.92–0.98) 96.96% 99.93% 0.97(0.94-1.00) OPD attendance (site specific impact). There wasn’t a significant difference between the observed versus predicted OPD visits at a Lumino HCIII for the 2 durations; 1) 2nd COVID-19 wave (5277 vs 4698; IRR = 1.12[0.86–1.39]) and 2) 3rd COVID-19 wave (5391 vs 5332; IRR = 1.01[0.96–1.06]) as shown in Table 4 . However, there was a 37% decline (7849 vs 13931; IRR = 0.63[0.57–0.70]) and 59% decline (3832 vs 9297; IRR = 0.41[0.37–0.45]) in OPD visits during the 2nd COVID-19 wave duration and 3rd COVID-19 wave duration respectively at Kasambya HCIII as shown in Table 5 . At Walukuba HCIV, there was a 46% decline (4770 vs 8768; IRR = 0.54[0.15–0.94]) in OPD visits during the 2nd COVID-19 wave and a 67% decline (4950 vs 15065; IRR = 0.33[0.23–0.43]) during the 3rd COVID-19 wave as shown in Table 6 . Table 4 ESTIMATED AND OBSERVED OUTCOMES (AVERAGES FOR PROPORTION OUTCOMES AND TOTALS FOR THE COUNT OUTCOME) DURING THE THREE INTERRUPTION DURATIONS ( high malaria transmission intensity site). 2nd COVID-19 wave duration (May-August 2021). 3rd COVID-19 wave duration (November 2021-February 2022). Observed Predicted Ratio(95% CI) Observed Predicted Ratio(95% CI) OPD attendance 5277 4698 1.12(0.86–1.39) 5391 5332 1.01(0.96–1.06) Proportion of suspected malaria cases 78.24% 79.08% 0.99(0.97–1.01) 82.53% 78.45% 1.05(0.99–1.01) Test positivity rate 48.40% 36.71% 1.31(0.70–1.93) 59.91% 32.29% 1.19(1.65–2.06) Proportion of suspected malaria cases tested 99.34% 99.93% 0.99(0.98–1.01) 100.00% 100.00% Proportion of malaria cases treated 90.57% 18.48% 4.92(-6.85-16.69) 95.02% 0.24% 397.28(-796.51-7811.09) Table 5 ESTIMATED AND OBSERVED OUTCOMES (AVERAGES FOR PROPORTION OUTCOMES AND TOTALS FOR THE COUNT OUTCOME) DURING THE THREE INTERRUPTION DURATIONS (MODERATE MALARIA TRANSMISSION INTENSITY SITE). 2nd COVID-19 wave duration (May-August 2021). 3rd COVID-19 wave duration (November 2021-February 2022). Observed Predicted Ratio(95% CI) Observed Predicted Ratio(95% CI) OPD attendance 7849 13931 0.63(0.57–0.70) 3832 9297 0.41(0.37–0.45) Proportion of suspected malaria cases 60.12% 75.87% 0.79(0.32–1.26) 60.79% 76.28% 0.79(0.72–0.87) Test positivity rate 22.19% 34.30% 0.65(0.41–0.88) 29.21% 40.16% 0.73(0.63–0.83) Proportion of suspected malaria cases tested 97.59% 99.72% 0.99(0.61–1.39) 99.79% 99.97% 0.99(0.99–0.99) Proportion of malaria cases treated 90.57% 18.40% 4.92(-10.06-19.90) 95.57% 0.24% 392.29(-5420.98-6215.56) Table 6 ESTIMATED AND OBSERVED OUTCOMES (AVERAGES FOR PROPORTION OUTCOMES AND TOTALS FOR THE COUNT OUTCOME) DURING THE THREE INTERRUPTION DURATIONS (LOW MALARIA TRANSMISSION INTENSITY SITE). 2nd COVID-19 wave duration (May-August 2021). 3rd COVID-19 wave duration (November 2021-February 2022). Observed Predicted Ratio(95% CI) Observed Predicted Ratio(95% CI) OPD attendance 4770 8768 0.54(0.15–0.94) 4950 15065 0.33(0.23–0.43 Proportion of suspected malaria cases 41.12% 34.99% 1.18(0.48–1.87) 30.76% 14.54% 2.12(2.07–2.16) Test positivity rate 49.36% 55.02% 0.89(0.63–1.16) 47.53% 32.92% 1.44(0.74–2.15) Proportion of suspected malaria cases (Overall impact). On onset of the 2nd COVID-19 wave, there was neither an immediate significant change in the level (β 2 = 0.37, P > 0.05) of the proportion of suspected malaria cases nor a significant change in trend (β 3 = -0.25, P > 0.05) of the proportion of suspected malaria cases during the 2nd COVID-19 wave. There wasn’t a significant change in the level (β 2 = -4.51, P > 0.05) of the proportion of suspected malaria cases on onset of the 3rd COVID-19 wave however, there was a significant change in trend (β 3 = 2.55, P = 0.01) with an increment in the proportion of suspected malaria cases during the 3rd COVID-19 wave duration as shown in Fig. 2 and Table 2 . Overall, there was no significant differences between the observed versus predicted mean proportion of suspected malaria cases during the 2nd COVID-19 wave duration (36.45% vs 36.44%; RPR = 1.00[0.92–1.08]). Likewise, there were no significant differences between the observed versus predicted mean proportion of suspected malaria during the 3rd COVID-19 wave (35.25% versus 33.69%; RPR = 1.05[0.95–1.13]) as shown in Table 3 . Proportion of suspected malaria cases (Site specific impact). At Lumino HCIII, there wasn’t a significant difference between the observed versus predicted proportion of suspected malaria cases; 1) during the 2nd COVID-19 wave (78.24% versus 79.08%; RPR = 0.99[0.97–1.01]) and 2) during the 3rd COVID-19 wave (82.53% versus 78.45%; RPR = 1.05[0.99–1.01]) as shown in Table 4 . At Kasambya HCIII, there was a 21% decline in the proportion of suspected malaria cases during the 3rd COVID-19 wave (60.79% versus 76.28%; RPR = 0.79[0.71–0.87]) however, during the 2nd COVID-19 wave duration, there was no significant difference between the observed versus predicted (60.12% versus 75.87%; RPR = 0.79[0.32–1.26]), proportion of suspected malaria cases at the moderate malaria transmission site as depicted in Table 5 . At Walukuba HCIV, there were no significant differences between the observed versus the predicted proportion of suspected malaria cases tested during the 2nd COVID-19 wave duration (49.36% versus 55.02%; RPR = 0.89[0.63–1.16]). However, during the 3rd COVID-19 wave duration, the observed suspected malaria cases were significantly higher than expected (30.76% versus 14.54%; RPR = 2.12[2.07–2.16]). Test Positivity rate (Overall impact). On onset of the 2nd COVID-19 wave, there was an immediate significant decline (β 2 = -0.16, P = 0.00) in the malaria TPR as depicted in Fig. 3 and Table 2 . However, there wasn’t a significant change in trend (β 3 = 0.02, P > 0.05) of the TPR during the entire 2nd COVID-19 wave duration. In the same way, there wasn’t a significant change in level (β 2 = -0.01, P > 0.05) of the malaria TPR immediately on onset of the 3rd COVID-19 wave nor was there a significant change in trend (β 3 = -0.04, P > 0.05) of the malaria TPR during the 3rd COVID-19 wave duration as shown in Table 2 . Overall, there was no significant difference between the observed versus expected malaria TPR (all sites of varying malaria transmission intensities combined) during the 2nd COVID-19 wave (35.30% vs 33.89%; RPR = 1.04[0.74–1.34]) and during the 3rd COVID-19 wave (40.16% vs 36.28%; RPR = 1.11[0.90–1.31]) as shown in Table 3 . Test Positivity rate (Site specific impact). At Lumino HCIII, there was no significant difference between the observed versus the predicted malaria TPR during the 2nd COVID-19 wave duration (48.40% vs 36.71%; RPR = 1.31[0.70–1.93]). However, during the 3rd COVID-19 wave, the observed TPR was significantly higher (59.91% vs 32.29%; RPR = 1.19[1.65–2.06]) than expected as shown in Table 4 . At Kasambya HCIII, there was a 35% decline and 27% decline in the malaria TPR during the 2nd COVID-19 wave and 3rd COVID-19 wave duration respectively at this site as shown in Table 5 . At Walukuba HCIV, there was no significant difference between the observed versus predicted malaria TPR for the three durations; 1) during the 2nd COVID-19 wave duration (49.36% vs 55.02%; RPR = 0.89[0.63–1.16]), 2) and during the 3rd COVID-19 wave duration (47.53% vs 32.92%; RPR = 1.44[0.74–2.15]) as shown in Table 6 . Impact of the COVID-19 waves on the proportion of suspected malaria cases tested. Overall impact On onset of the 2nd COVID-19 wave, there was neither an immediate change in level (β 2 = -3.84 e-16, P > 0.05) of the proportion of suspected malaria cases tested nor a significant change in trend (β 3 = -7.95e-18, P > 0.05) of the proportion of the suspected malaria cases tested during the 2nd COVID-19 wave duration. There was neither a significant change in the level (β 2 = -4.11e-16, P > 0.05) of the proportion of suspected malaria cases tested on onset of the 3rd COVID-19 wave nor a significant change in the trend (β 3 = -2.77e-16, P > 0.05) of the proportion of suspected malaria cases tested during the 3rd COVID-19 wave duration as shown in Fig. 4 and Table 2 . Overall, there was both a 1% decline in the proportion of suspected malaria cases tested during the 3rd COVID-19 wave duration (99.86% vs 99.99%; RPR = 0.99[0.99–0.99]). However, there was no significant difference between the observed versus predicted mean proportion of tested malaria during the 2nd COVID-19 wave (99.47% vs 99.97%; RPR = 0.99[0.98-1.00]) as shown in Table 3 . Site specific impact. At Lumino HCIII, there were no significant differences between the observed versus predicted proportion of suspected malaria cases tested during the all the two interruption durations as shown in Table 4 . Likewise, there wasn’t significant differences between the observed versus predicted proportion of suspected malaria cases tested at Kasambya HCIII during the 2nd COVID-19 wave however, during the 3rd COVID-19 wave duration, there was a 1% decline in the proportion of suspected malaria cases tested (99.79% vs 99.97%; RPR = 0.99[0.99–0.99]) as shown in Table 5 . Impact of the COVID-19 waves the proportion of malaria cases prescribed artemether lumefantrine (AL). Overall impact. On onset of the 2nd COVID-19 wave, there wasn’t an immediate significant change in level (β 2 = 3.33 e-14, P > 0.05) of confirmed cases prescribed AL nor was there a significant change in trend (β 3 = 7.66 e-16, P > 0.05) of the proportion of malaria cases prescribed AL. There wasn’t an immediate significant change in level (β 2 = 5.31 e-14, P > 0.05) of the proportion of malaria cases prescribed AL on onset of the 3rd COVID-19 wave nor a significant change in trend of the proportion of malaria cases prescribed AL during the 3rd COVID-19 wave duration (β 3 = 1.21 e-14, P > 0.05) as shown in Fig. 5 and Table 2 . Overall, there was a 5% decline (94.99% vs 99.85%; RPR = 0.95[0.92–0.98]) in the proportion of malaria cases treated during the 2nd COVID-19 wave and a no significant difference between the observed versus predicted proportion of malaria cases prescribed AL during the 3rd COVID-19 wave (96.96% vs 99.93%; RPR = 0.97[0.94-1.00]). Site specific impact. At Lumino HCIII, during the 2nd (90.57% vs 18.40%; RPR = 4.92[-6.85-16.69]), and 3rd (95.02% vs 0.24%; RPR = 397.28[-796.51-7811.09]) COVID-19 waves, there was no significant differences between the observed versus expected proportion of malaria cases prescribed AL. At Kasambya HCIII, there were no significant differences between the observed versus expected proportion of malaria cases treated for the 2nd (90.57% vs 18.40%; RPR = 4.92[-10.06-19.90]) and 3rd (95.57% vs 0.24%; RPR = 392.29[-5470.98-6215.56]) COVID-19 wave durations. DISCUSSION COVID-19 has been documented to negatively impact health care delivery and affect roll out of control interventions for several diseases including malaria. In this study, we assessed the impact of the 2nd and 3rd wave of COVID-19 on out-patient attendance, suspected malaria cases, test positivity rates and malaria case management. Summary of the results. During the 3rd COVID-19 wave, OPD visits were lower than expected, while no significant differences between the observed versus expected OPD visits were observed during the 2nd COVID-19 wave. However, at the two sites situated within moderate and low malaria transmission intensity settings, there was a significant decline in outpatient attendance during both the 2nd and 3rd COVID-19 waves. The observed proportions of suspected malaria cases were not significantly different from the expected during both the 2nd and 3rd COVID-19 waves, except at a site situated in a moderate malaria transmission setting where a decline was noted during the 3rd COVID-19 wave. Test positivity rates remained consistent (no significant differences between the observed versus expected) overall, with significant increases during the 3rd COVID-19 wave at a site situated in a high malaria transmission setting and declines during the 2nd and 3rd COVID-19 waves at a moderate malaria setting situated site. The proportions of suspected malaria cases tested declined during the 3rd COVID-19 wave with no significant difference during the 2nd COVID-19 wave. The proportion of malaria cases prescribed AL proportions declined during the 2nd COVID-19 wave, with no significant difference during the 3rd COVID-19 wave, and no impact observed at sites situated in moderate and high malaria settings. Impact of the COVID-19 waves on outpatient malaria indicators, diagnostic and treatment practices. The reduction in OPD attendance (during both COVID-19 waves) at the sites situated in moderate and low malaria transmission settings could have resulted from either the instituted restrictions on travel as a measure for COVID-19 transmission reduction for the peri urban setting (low transmission situated site) and/or fear to contract COVID-19 on visiting the health facility (Atek Kagirita, 2022 ; Heuschen et al., 2023 ; Mahmud & Riley, 2021 ). Unfortunately, reductions in the number of patients seeking care at these public health facilities would mean that most people were staying at home even when they are getting ill and only presenting to the facilities when they have severe disease resulting into increases in complicated malaria cases and mortality (although this was not assessed as part of this study). A study conducted in Benin reported minimal effects of COVID-19 on health seeking behavior with some people reporting that they reduced how often they visted health facilities due to the COVID-19 pandemic and others saying that they didn’t change their health seeking behavior (Duguay et al., 2023 ). A retrospective analysis of routine surveillance data conducted in northern Ghana to determine the impact of COVID-19 on malaria reported a reduction in OPD visits during the 1st 6 months when COVID-19 restrictions were put in place and increases thereafter but still remained low relative to the previous years (Heuschen et al., 2022 ). Another similar study conducted in Nigeria to determine the effect of COVID-19 on malaria intervention coverage also reported decline in care seeking practices across all age groups (Ilesanmi et al., 2021 ). For the site located in a high malaria transmission setting were COVID-19 didn’t have effect on OPD attendance, it could be due to the fact that the site is located in a rural setting were people could still walk to the facility or use bicycles being a rural setting. The overall result (all sites combined) of decline in OPD attendance during the third COVID-19 wave would be due to the fact that the 3rd COVID-19 had more infectious cases therefore people would have feared more to visit health facilities (in fear of contracting COVID-19) more in the 3rd COVID-19 wave relative to the other waves. However, overall (all sites combined), there wasn’t impact of COVID-19 on OPD attendance during the 2nd COVID-19 wave duration. This result is similar with that of the study conducted during the 1st COVID-19 wave in facilities located in the rural areas of Uganda, the study reported no impact on OPD attendance (Namuganga et al., 2021 ). However, this disagrees with results from similar studies that reported decline in OPD attendance during COVID-19 (Duguay et al., 2023 ; Heuschen et al., 2022 ). The overall (all sites combined), no effect of COVID-19 on the proportion of suspected malaria cases could be explained by change in the health seeking behavior of individuals within the communities were people feared to visit health facilities in fear of contracting COVID-19 or being classified as COVID-19 patients on presenting with fever (Guerra et al., 2020 ; Hakizimana et al., 2022 ). The suspected malaria cases/fever rates were expected to be high in all waves because both COVID-19 and malaria present with fever however, this wasn’t the case, attributable to changes in health seeking behaviors among the communities (Guerra et al., 2020 ). Also, during COVID-19, the prescription algorithm for COVID-19 was known in the communities, so even in cases where individuals got fever, they just bought the recommended drugs to handle COVID-19 or used lemons, oranges, ginger among others instead of visiting the health facilities. The fever cases could have been increasing within the communities but these were not being documented in the public health facilities because people were not visiting the health facilities as before out of the COVID-19 stigma. This result is similar to the result documented in the study conducted during the first wave (Namuganga et al., 2021 ). The overall (all sites combined) no effect of COVID-19 on the malaria test positivity rates at these facilities during the 2nd and 3rd COVID-19 waves could be explained by the fact that people with malaria fevers were not reporting to the health facilities out of fear of being taken to be COVID-19 suspects therefore could remain home and self-medicate or could seek malaria care from the village health workers (Hakizimana et al., 2022 ). It could also have been due to the fact that people were entering their house early enough avoiding exposure to mosquitoes. It should also be noted that despite the delay in the third mass distribution campaign of mosquito nets that had to start in February 2020 but started later in June 2020 due to COVID-19 interruptions, the campaign was successful and ended in June 2021 ensuring continued protection from exposure to mosquito bites among communities explaining the no difference between the observed versus expected malaria test positivity rates (NMCP, 2021 ). This result is also similar to the results of a study done during the 1st COVID-19 wave in rural areas of Uganda which also reported no significant differences in the observed and expected TPR (Namuganga et al., 2021 ). A similar study conducted in northern Ghana to determine the impact of COVID-19 on malaria reported that OPD and IPD malaria cases remained below during the pandemic relative to the previous years (Heuschen et al., 2022 ). Another study conducted in in three malaria endemic districts of Rwanda to determine the effect of COVID-19 on malaria reported no change in the overall presentation rate of uncomplicated malaria and a reduction in the proportion of severe malaria (Hakizimana et al., 2022 ). However, at the site located in a high malaria transmission setting, the significant increment in the malaria test positivity rate during the 3rd COVID-19 wave could be associated with the fact that the duration of the 3rd COVID-19 wave coincides and/ or follows a rainy and malaria season in Uganda. However, it could also be due to a cumulative community buildup of malaria from the previous waves were people with suspected malaria couldn’t visit facilities out of fear of being classified as COVID-19 suspects. A study conducted in Indochina an area that had a co-endemicity of COVID-19 and malaria showed an increment in malaria cases after removal of the lockdown and concluded that though lockdowns were effective in reducing COVID-19 transmissions, there removal was followed by increment in malaria cases (Mungmunpuntipantip & Wiwanitkit, 2023 ). When people don’t access the services, malaria transmission in the communities increases. It is no wander that post the third COVID-19 wave many malaria outbreaks have been noted in many parts of Uganda causing many malaria morbidities and mortalities (M.O.H, 2022). This may be an impact of COVID-19 where the disease burden appeared to decrease as per health facility data due yet it was increasing in the communities. The result of increment in the malaria test positivity rate during the 3rd COVID-19 wave in a high malaria transmission setting agrees with results of studies done in Zimbabwe and Central African Republic (CAR) which reported increment in malaria cases after onset of COVID-19. It should however be noted that the study done in Zimbabwe which reported an excessive increment of malaria cases (Gavi et al., 2021 ) didn’t control for environmental factors specifically rainfall and temperature that are known covariates of malaria in that country. The reported increase in malaria morbidity and mortality also coincided with the malaria peak season in that country. The study done in CAR reported increment in the prevalence of asymptomatic malaria from August to September 2021 compared to a similar study done before COVID-19 (Bylicka-Szczepanowska & Korzeniewski, 2022 ). The result on overall (all sites combined) decline in the proportion of suspected malaria cases tested (using both mRDTs and microscopy) reported in this study agrees with a result of a similar study conducted in Senegal that aimed to determine the impact of COVID-19 on biological diagnosis of malaria which reported a decline in the malaria tests done (both mRDTs and microscopy) in 2020 COVID-19 year relative to the prior years (Manga et al., 2023 ). Another study conducted in Mozambique to determine the impact of COVID-19 on malaria surveillance with a specific focus on diagnosis and treatment reported a decline in the number of people tested for malaria in the health facilities and an increase in the number tested for malaria in the communities (Afai et al., 2021 ). This result on overall (all sites combined) decline in the proportion of suspected malaria cases tested (using both mRDTs and microscopy) during the 3rd COVID-19 wave duration could have been due to shortage on malaria rapid diagnostic test kits since most biomedical firms shifted focus to producing COVID-19 rapid diagnostic test kits (Aborode et al., 2021 ). The decline could also be due to absentia of health workers at the facilities due to either COVID-19 stigma or lack of transport however much the government waived their movement despite the travel restrictions. A similar study conducted in 3 malaria endemic districts in Rwanda to determine the impact of COVID-19 on malaria services reported a decline in malaria testing at the health facilities and an increment in malaria testing at community level which they attributed to COVID-19 mitigation measures such as travel restrictions but also highlighted people’s fear to contract COVID-19 on visiting the health facilities (Hakizimana et al., 2022 ). During the 2nd COVID-19 wave duration, there wasn’t significant difference (all sites combined) between the observed versus expected proportion of suspected malaria cases tested (which is also true for the site located in the high malaria transmission setting throughout both the 2nd and 3rd COVID-19 waves) which could be attributed to campaigns such as “Why survive COVID-19 and die of malaria?” which were put in place ensuring the malaria testing of all fever cases at the health centers through (WHO, 2020). Health workers were re-trained, redistributed and reassigned to ensure adherence to malaria testing of all fever cases in an attempt to prevent the disease from re-emerging due to focus shift to COVID-19. The significant decline in the proportion of malaria cases treated with Artemether lumefantrine (first line antimalarial for treatment of uncomplicated malaria in Uganda) during the 2nd wave duration would have been due to a run out on supply of AL due to the shift of the resources to fight COVID-19. The 1st COVID-19 study also reported similar findings (Namuganga et al., 2021 ). Evidence has shown that access to antimalarials was disrupted in sub-Saharan Africa during COVID-19 (Dzianach et al., 2023 ). Strength of the study. This study had strength when compared to most studies done in malaria endemic countries to assess the impact of COVID-19 on malaria in that seasonality and the most important covariates of malaria predominantly rainfall and temperature were adjusted for when predicting the study outcomes hadn’t the second and third COVID-19 waves occurred. If not controlled for, these could confound the study results. Interrupted time series analysis used to assess the impact of COVID-19 on malaria in this study has beauty of taking the pre-COVID-19 malaria trends into consideration and also producing counterfactual trends if at all COVID-19 hadn’t occurred. Most of the studies done to assess the impact of COVID-19 were done in the first wave when COVID-19 cases were still few but this study covered both the second and third COVID-19 waves were COVID-19 cases were at peak and more infectious. The sample size used was 18 times the estimated therefore the study had a final power of more than 99%. Limitations. However, there were still limitations, only three study sites were purposively selected to represent low, moderate and high malaria transmission settings of Uganda but more sites would have been chosen to represent each malaria transmission setting. On calculating outcome estimates hadn’t COVID-19 occurred since only rainfall, temperature and calendar month effects were adjusted for leaving out other environmental covariates of malaria including humidity, vegetation index among others. Disparities in IRS status and LLIN distribution status across the study sites was not taken into account. There remains a question on the completeness of the data even in the case where surveillance data was used in this study. Absence of health workers and data officers during COVID-19 due to either COVID-19 stigma and/or difficulty in movement could also have impacted the quality of the data. Single group ITSA also has a limitation of lack of controls. Conclusions The 3rd COVID-19 wave was associated with a significant reduction in outpatient department attendance. Subgroup analysis however showed consistent negative impact across both the 2nd and 3rd COVID-19 waves at a low and moderate malaria transmission situated sites. While there were no significant changes in the proportion of suspected malaria cases and test-positivity rates overall, subgroup analysis showed varying effects, including a significant increase in test-positivity rates during the 3rd COVID-19 wave at a high malaria transmission situated site and declines in both test-positivity rates and proportion of suspected malaria cases during the 2nd and 3rd COVID-19 waves at a moderate malaria transmission situated site. Additionally, there were notable impacts on malaria diagnostic practices during the 3rd COVID-19 wave unlike the 2nd COVID-19 wave and impacts on the antimalarial (artemether lumefantrine) prescription practices during the 2nd COVID-19 wave unlike the 3rd COVID-19 wave. This means there is need to bring the malaria services near to the communities during out-breaks like COVID-19 so that care is not disrupted. If this intervention is not done, there would be more severe disease and even increased malaria mortalities in the communities. Recommendations. We recommend that in case of any other outbreak, all efforts should be made to ensure continuous delivery of malaria services. This can be done through strengthening and extending the integrated case community management for malaria in all districts such that in circumstances where people fear to visit health facilities in fear of contracting an emergent disease such as COVID-19, they can still access malaria care at community level. The MoH should sensitize the public about the changes in HSB that happened during the lockdowns to encourage people seek medical care again. There is need for a more extensive study with data from more health facilities within each malaria transmission setting and covering the entire COVID-19 duration to either refute or to agree with the results of this study. Other studies should be also be conducted to determine the impact of COVID-19 on malaria at community level. Studies should also look at the burden of severe malaria that happened during COVID-19 comparing them with the pre- COVID-19 period. Abbreviations AL Artemether Lumefantrine Aug August CAR Central African Republic Dec December DRC Democratic Republic of Congo Feb February HCs Health Centers HIV Human Immunodeficiency virus HSB Health seeking behavior IPTp Intermittent Preventive Treatment for Pregnancy IRS Indoor Residual Spraying ITN Insecticide treated bed net ITSA Interrupted time series analysis Jan January LLINS Long lasting insecticide treated bed nets MaK Makerere University MCM Malaria case management MoH Ministry of Health Oct October OCT October OLS Ordinary least squares OPD Outpatient department RDTS Rapid diagnostic tests SEP September SOMREC School of medicine research ethics committee TB Tuberculosis TPR Test Positivity rate UMIS Uganda Malaria Indicator Survey WHO World Health Organization Declarations Ethics approval and consent to participate. Ethic review and approval was obtained from the Makerere University School of Medicine Research Ethics Committee reference number - Mak-SOMREC-2022-364). The study was given a waiver for informed consent by MaK-SOMREC. The study was registered and cleared by the Uganda National Council of Science and Technology (UNCST). Consent for publication. Not applicable. Availability of data and materials. The datasets used/analyzed in the study are available on request from the corresponding author. Competing interests. The authors declare that they have no competing interests. Funding. This study was funded by the Consolidating Early Career Academics Programme (CECAP) of the Makerere University Directorate of Research and Graduate Training with support from Carnegie Corporation. Authors contributions. PM, JN and JNK designed and conceptualized the study. PM did the data abstraction, data cleaning, data management and preliminary analysis of the data. PM, AM, JNK and AM contributed to the data collection, data analysis and report writing. JO and FEK interpreted data and contributed to data analysis. 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A double challenge: Tackling COVID-19 and malaria in Uganda. https://www.afro.who.int/news/double-challenge-tackling-covid-19-and-malaria-uganda Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Oct, 2024 Read the published version in Malaria Journal → Version 1 posted Editorial decision: Revision requested 25 Sep, 2024 Reviews received at journal 25 Sep, 2024 Reviewers agreed at journal 18 Sep, 2024 Reviewers invited by journal 17 Sep, 2024 Editor assigned by journal 06 Sep, 2024 Submission checks completed at journal 06 Sep, 2024 First submitted to journal 05 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5039547","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359066555,"identity":"5974f6b0-66aa-43c4-852b-a4a84bd8831d","order_by":0,"name":"Pius Mukisa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACZjCZwANkHTjwAchkYydeC1viwxkgLczE2ZUAxDzKxjwIQ3ADc3Ye408329Jk5NvPsEnb/Nomz8fMwPjhYw5uLZbNPGbSuW05PAZnco9J5/bdNmxjZmCWnLkNtxaDwzxmzLltFTwGDHlp0rk9txmBWtiYefFrMf4M0iLf/8ZM2rLntj0xWgzADmO4kWNszPDjdiJBLZbNbGXSOefSeAxuPEt82NtwO7mNmbEZr1/M+Q9v/pxTlmwv35984MCPP7dt57c3H/zwEZ/DUHiMbWCyAbd6DC0Mf/AqHgWjYBSMghEKANJnS+4RPAAFAAAAAElFTkSuQmCC","orcid":"","institution":"Clinical Epidemiology Unit, Makerere university College of Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Pius","middleName":"","lastName":"Mukisa","suffix":""},{"id":359066556,"identity":"b356bc7d-f0c2-4504-b190-5cb4c799b0e9","order_by":1,"name":"Freddy Eric Kitutu","email":"","orcid":"","institution":"Department of Pharmacy, Makerere University School of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Freddy","middleName":"Eric","lastName":"Kitutu","suffix":""},{"id":359066557,"identity":"83baa671-b944-4ca5-b516-c990fcf5bb4e","order_by":2,"name":"Joan Nankabirwa","email":"","orcid":"","institution":"Clinical Epidemiology Unit, Makerere university College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Joan","middleName":"","lastName":"Nankabirwa","suffix":""},{"id":359066558,"identity":"acd1fb7c-8eb5-48fa-a4a0-951a78a7687b","order_by":3,"name":"Arthur Mpimbaza","email":"","orcid":"","institution":"Clinical Epidemiology Unit, Makerere university College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Arthur","middleName":"","lastName":"Mpimbaza","suffix":""},{"id":359066560,"identity":"b5d3b118-e13f-4009-987c-84926d6b41d0","order_by":4,"name":"Jaffer Okiring","email":"","orcid":"","institution":"Clinical Epidemiology Unit, Makerere university College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jaffer","middleName":"","lastName":"Okiring","suffix":""},{"id":359066562,"identity":"e8a96508-fc51-4b14-8551-d0ac88f5d7a1","order_by":5,"name":"Joan N Kalyango","email":"","orcid":"","institution":"Clinical Epidemiology Unit, Makerere university College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Joan","middleName":"N","lastName":"Kalyango","suffix":""}],"badges":[],"createdAt":"2024-09-05 16:11:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5039547/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5039547/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12936-024-05153-0","type":"published","date":"2024-10-29T16:20:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66326716,"identity":"f42c43ca-e60b-4bbd-9c32-92d981b925c3","added_by":"auto","created_at":"2024-10-10 13:00:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52589,"visible":true,"origin":"","legend":"\u003cp\u003eACTUAL AND PREDICTED OPD VISITS DURING THE STUDY DURATION.\u003c/p\u003e\n\u003cp\u003eRed line denotes March 2020, the time when the country had its first COVID-19 cases and institution of restrictive measures on transport and lockdowns.\u0026nbsp; Purple block depicts the duration when the country had the 2\u003csup\u003end\u003c/sup\u003e COVID-19 wave (May 2021 to August 2021). Green block denotes the duration covered by the 3\u003csup\u003erd\u003c/sup\u003e COVID-19 wave (November 2021to February 2021).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5039547/v1/e9815cff251f3073fd9d3c22.png"},{"id":66325544,"identity":"0f27da07-d511-4dce-ab0e-53c8eb76bc3b","added_by":"auto","created_at":"2024-10-10 12:52:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59913,"visible":true,"origin":"","legend":"\u003cp\u003eActual and predicted proportion of suspected malaria cases\u003c/p\u003e\n\u003cp\u003eRed line denotes March 2020, the time when the country had its first COVID-19 cases and institution of restrictive measures on transport and lockdowns. Yellow block denotes the duration covered by the 1\u003csup\u003est\u003c/sup\u003e\u0026nbsp; COVID-19 wave ( August 2020 to January 2021), Purple block depicts the duration when the country had the 2\u003csup\u003end\u003c/sup\u003e COVID-19 wave (May 2021 to August 2021). Green block denotes the duration covered by the 3\u003csup\u003erd\u003c/sup\u003e COVID-19 wave (November 2021to February 2021).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5039547/v1/412668e012d402c8be142b74.png"},{"id":66326715,"identity":"632f1b17-ab78-4661-b1c7-43464e5ff410","added_by":"auto","created_at":"2024-10-10 13:00:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51009,"visible":true,"origin":"","legend":"\u003cp\u003eACTUAL AND PREDICTED MALARIA TEST POSITIVITY RATE\u003c/p\u003e\n\u003cp\u003eRed line denotes March 2020, the time when the country had its first COVID-19 cases and institution of restrictive measures on transport and lockdowns. Purple block depicts the duration when the country had the 2\u003csup\u003end\u003c/sup\u003e COVID-19 wave (May 2021 to August 2021). Green block denotes the duration covered by the 3\u003csup\u003erd\u003c/sup\u003e COVID-19 wave (November 2021to February 2021).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5039547/v1/85042ed4c5b9f0c558041f7a.png"},{"id":66325546,"identity":"14501931-1af5-48d8-907e-001ba1eddb0e","added_by":"auto","created_at":"2024-10-10 12:52:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49836,"visible":true,"origin":"","legend":"\u003cp\u003eActual and predicted proportion of suspected malaria cases tested.\u003c/p\u003e\n\u003cp\u003eRed line denotes March 2020, the time when the country had its first COVID-19 cases and institution of restrictive measures on transport and lockdowns. Purple block depicts the duration when the country had the 2\u003csup\u003end\u003c/sup\u003e COVID-19 wave (May 2021 to August 2021). Green block denotes the duration covered by the 3\u003csup\u003erd\u003c/sup\u003e COVID-19 wave (November 2021to February 2021).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5039547/v1/4142b0b3bc7748c543f406cc.png"},{"id":66326717,"identity":"2d981e3e-1ec6-4b47-98dc-0be88ce1f9e5","added_by":"auto","created_at":"2024-10-10 13:00:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48672,"visible":true,"origin":"","legend":"\u003cp\u003eActual and predicted proportion of confirmed malaria cases prescribed artemether lumefantrine.\u003c/p\u003e\n\u003cp\u003eRed line denotes March 2020, the time when the country had its first COVID-19 cases and institution of restrictive measures on transport and lockdowns. Purple block depicts the duration when the country had the 2\u003csup\u003end\u003c/sup\u003e COVID-19 wave (May 2021 to August 2021). Green block denotes the duration covered by the 3\u003csup\u003erd\u003c/sup\u003e COVID-19 wave (November 2021to February 2021).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5039547/v1/9d312b0e769f6a264d6e6177.png"},{"id":68207266,"identity":"586dd497-1104-4a04-ad66-a3392c89349a","added_by":"auto","created_at":"2024-11-04 16:36:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1321286,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5039547/v1/a88c7874-c67a-4d17-8cd1-8db1438ad50d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of the second and third COVID-19 pandemic waves on routine outpatient malaria indicators and case management practices in Uganda; an interrupted time series analysis.","fulltext":[{"header":"Background","content":"\u003c!DOCTYPE html\u003e\n\u003chtml\u003e\n\n\u003cbody autocomplete=\"off\" spellcheck=\"false\"\u003e\n \u003cp\u003eSince 2000, substantial reductions in malaria disease burden have been realized at global level and in sub Saharan Africa. However, a rise in malaria case incidence was observed from 2020, part of which was attributable to the\u0026nbsp;COVID-19 pandemic and its \u0026nbsp; disruptions to malaria control interventions\u0026nbsp;(W.H.O, 2023).\u0026nbsp;The World Health Organization Africa region accounts for majority of the cases reported worldwide\u0026nbsp;(W.H.O, 2023).\u003c/p\u003e\n \u003cp\u003eIn Uganda, malaria is the leading diagnosis at outpatient departments (OPD) \u0026nbsp;accounting for 31.1% of all OPD visits and the commonest reason for inpatient department (IPD) admissions accounting for approximately 25% of all IPD admissions\u0026nbsp;(MoH, 2023). In addition, malaria is the 2\u003csup\u003end\u003c/sup\u003e leading cause of death after neonatal conditions, accounting for 7.4% of all inpatient death in the country\u0026nbsp;(MoH, 2023).\u0026nbsp;Uganda is a malaria endemic country with 95% of the population at risk of infection. Malaria transmission in Uganda varies geographically, from less than 1% malaria prevalence in southwest Uganda to greater than 20% in Busoga subregion, northwestern Uganda, and northeast Uganda\u0026nbsp;(MoH, 2020). The country has made tremendous progress by reducing the malaria burden with parasite prevalence declining from 42% (based on microscopy in under five children) in 2009 to 9.1% in 2018\u0026nbsp;(MoH, 2010). However, an increase in the number of cases was observed in areas previously reporting marked declines in burden starting in 2020\u0026nbsp;(Epstein et al., 2022; Nankabirwa et al., 2022).\u0026nbsp;In 2022, Uganda experienced a rebound epidemic leading with some areas reporting more than a 30 percent increase in the total number of malaria confirmed cases\u0026nbsp;(Epstein et al., 2022). This period corresponds to the time the country was having the COVID-19 epidemic, however, the contribution of the epidemic to this increase in burden has not well documented.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIndeed, from the on-set of \u0026nbsp;the COVID-19 pandemic, there were concerns that the documented success in malaria control in Africa may be significantly reversed by the pandemic and modelling studies predicted that malaria cases would double during the pandemic\u0026nbsp;(Weiss et al., 2021). The impact of COVID-19 on malaria burden could be through a number of mechanisms including disruptions in health seeking behaviors, reallocation of resources, misdiagnosis due to overlap of symptoms, and\u0026nbsp;interruptions \u0026nbsp;in malaria preventive services\u0026nbsp;(Caglar et al., 2021; Hussein et al., 2020). In Uganda, the \u0026nbsp;first COVID-19 case was registered on 21\u003csup\u003est\u003c/sup\u003e March 2020\u0026nbsp;(Kitara \u0026amp; Ikoona, 2020), and three waves were observed through the course of the pandemic. The 1\u003csup\u003est\u003c/sup\u003e COVID-19 was between August 2020 to January 2021, 2\u003csup\u003end\u003c/sup\u003e wave between May 2021 and August 2021 and 3\u003csup\u003erd\u003c/sup\u003e between November 2021 and February 2022\u0026nbsp;(Atek Kagirita, 2022; Mahmud \u0026amp; Riley, 2021). A study by Namuganga et al showed no\u0026nbsp;impact of the 1\u003csup\u003est\u003c/sup\u003e\u0026nbsp; COVID-19 wave on malaria burden\u0026nbsp;(Namuganga et al., 2021), however, this study was done when the number of reported COVID-19 cases in Uganda were low and \u0026nbsp;less severe in presentation. The 2\u003csup\u003end\u003c/sup\u003e COVID-19 wave (Delta) in Uganda had more severe cases\u0026nbsp;(F.Bongomin et al., 2021)\u0026nbsp;and the 3\u003csup\u003erd\u003c/sup\u003e COVID-19 wave (Omicron wave) had more infectious cases. Despite these differences in presentation to the first wave, the impact of the 2\u003csup\u003end\u003c/sup\u003e and 3\u003csup\u003erd\u003c/sup\u003e wave on malaria burden and case management have not been evaluated. We assessed the effect of the \u0026nbsp;2\u003csup\u003end\u003c/sup\u003e and 3\u003csup\u003erd\u003c/sup\u003e COVID-19 waves on routine outpatient malaria indicators and case management practices at three public health facilities located in varying malaria transmission settings in Uganda.\u003c/p\u003e\n\u003c/body\u003e\n\u003c/html\u003e"},{"header":"Methods","content":"\u003c!DOCTYPE html\u003e\n\u003chtml\u003e\n\n\u003cbody autocomplete=\"off\" spellcheck=\"false\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy design and setting. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis is a time trend analysis of malaria burden indicators and case management practices using data of patients attending out-patient departments of three public health facilities in Uganda. The health facilities included two level III health centers \u0026nbsp;(Kasambya and Lumino) and one level IV health center (Walukuba). All facilities are part of 77 malaria reference centers (MRCs) in Uganda, where enhance malaria surveillance activities are conducted as part of routine surveillance. The three facilities are supported by the Uganda Malaria Surveillance Project (UMSP) as part of MRC activities to capture accurate, reliable and complete individual patient level data, using the standardized health management information system (HMIS) registers (HMIS 002 outpatient register). Staff capacity building is provided through training, onsite mentorship, support supervision and regular data quality assessments. The facilities attend to between 1000-3000 outpatients monthly. The main malaria control interventions in the districts have been limited to the use of long-lasting insecticidal nets (LLINs) and to date there have been four mass net distribution campaigns (2013, 2017, 2020 and 2023).\u0026nbsp;Kasambya HC III is located in Mubende district, in the Central Region of Uganda. Mubende is one of the largest districts in the country with agriculture being the main economic activity of the population in the district. The entomological inoculation rate (EIR) of Mubende district is estimated at 4 infective bites per person per year\u0026nbsp;(Okello et al., 2006)\u0026nbsp;and malaria parasite prevalence in children under 5 years of age was estimated at of 9% in the last malaria indicator survey\u0026nbsp;(MoH, 2020), and it is considered to be a moderate malaria transmission area. Lumino is located in Busia, eastern Uganda, an area with an EIR of 108.2 infective bites per person per year\u0026nbsp;(Mawejje et al., 2022), and is a high malaria transmission area. Walukuba is located in Jinja, east central Uganda. The EIR of the area is 6 infective bites per person per year\u0026nbsp;(Okello et al., 2006), with a malaria parasite prevalence of 21% (based on microscopy in under 5 children)\u0026nbsp;(MoH, 2020). Lumino HC III is located in Busia district in eastern Uganda. Busia is a rural district, with high malaria transmission and the EIR was estimated at 108.2 infective bites per person per year in 2020\u0026nbsp;(Mawejje et al., 2022). Walukuba HC IV is located in Jinja district in east central Uganda. The district is semi-urban with varying levels of malaria transmission intensities. The malaria parasite prevalence of the district was estimates at 21% in under 5years in the 2018/19 MIS\u0026nbsp;(MoH, 2020). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStudy population, sampling and sample size.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAll records of patients presenting to the outpatients department of the participating facilities between January 1\u003csup\u003est\u003c/sup\u003e 2019 to February 28\u003csup\u003eth\u003c/sup\u003e 2022.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe routinely collected data in registers including patient demographics, village of residence, history of fever, whether a malaria diagnostic test was performed, type of malaria test done (malaria rapid diagnostic test (mRDTs) vs microscopy), results of laboratory tests, diagnoses given, and treatments prescribed was extracted from the routine HMIS registers.\u0026nbsp;The outcome variables included total OPD visits, suspected malaria cases, TPR, proportions of suspected malaria cases for whom a malaria laboratory test was recommended, and proportion of confirmed malaria cases prescribed AL. The main exposure variable was the time period in which a patient presented to the OPD (before the COVID-19 pandemic, or during the 2\u003csup\u003end\u003c/sup\u003e or 3\u003csup\u003erd\u003c/sup\u003e COVID-19 wave).\u0026nbsp;The potential confounders controlled for in this study included rainfall distribution and temperature. The data on average monthly temperature and rainfall was extracted from remote sensing sources. Rainfall data was extracted from climate hazards group infrared precipitation with\u0026nbsp;\u003c/p\u003e\n \u003cp\u003estation data (CHRIPS) database which data is recorded in millimeters. Temperature data was extracted from the moderate resolution imaging spectro-radiometer (MODIS).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eData analysis.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSingle group Newey approach interrupted time series analysis (ITSA) with two interruptions\u0026nbsp;(Linden, 2017)was conducted using\u0026nbsp;STATA 14. Monthly time points were considered, utilizing monthly aggregated data collected from January 2019 to February 2022 for each outcome. The two interruption time points included; 1) the month of onset of the 2\u003csup\u003end\u003c/sup\u003e COVID-19 wave (May 2021) and 2) the time of onset of the 3\u003csup\u003erd\u003c/sup\u003e COVID-19 wave (November 2021) in Uganda. The 1\u003csup\u003est\u003c/sup\u003e\u0026nbsp; interruption (2\u003csup\u003end\u003c/sup\u003e COVID-19 wave) \u0026nbsp;begun in May 2021 and continued until August 2021 therefore, it \u0026nbsp;had 4 time points in its post interruption duration. There was a wash out period of 2 months (September 2021 to October 2021) before onset of the 2\u003csup\u003end\u003c/sup\u003e\u0026nbsp; interruption (3\u003csup\u003erd\u003c/sup\u003e COVID-19 wave). The 2\u003csup\u003end\u003c/sup\u003e\u0026nbsp; \u0026nbsp;interruption \u0026nbsp;had 2 time points in its post interruption period (December 2021 to February 2022).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;The Single group newey approach ITSA with two interruptions\u0026nbsp; model output is as follows;\u0026nbsp;Y\u003csub\u003et\u0026nbsp;\u003c/sub\u003e= β\u003csub\u003e0\u003c/sub\u003e + β\u003csub\u003e1\u003c/sub\u003eT + β\u003csub\u003e2\u003c/sub\u003ef\u003csub\u003e1\u003c/sub\u003e +β\u003csub\u003e3\u003c/sub\u003ef\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003e + β\u003csub\u003e4\u003c/sub\u003ef\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e+ β\u003csub\u003e5\u003c/sub\u003ef\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003e + Et, \u0026nbsp;where\u0026nbsp;Et= β\u003csub\u003e6\u003c/sub\u003eDt + β\u003csub\u003e7\u003c/sub\u003eTet + β\u003csub\u003e10\u003c/sub\u003eRt, Yt is outcome Y(e.g., total OPD visits, proportion suspected malaria cases, test positivity rate, proportion malaria cases prescribed AL, proportion of suspected malaria cases tested) at month t, β\u003csub\u003e0\u003c/sub\u003e is the intercept (outcome Y at the beginning of the study), β\u003csub\u003e1\u0026nbsp;\u003c/sub\u003eis the slope of the outcome before arrival of the 1\u003csup\u003est\u003c/sup\u003e interruption (pre-intervention slope), β\u003csub\u003e2\u003c/sub\u003e is the change in level of the outcome immediately on arrival of the first interruption, β\u003csub\u003e3\u003c/sub\u003e is the difference between the pre-intervention slope (pre-COVID-19 slope) and the first interruption outcome slope, β\u003csub\u003e4\u003c/sub\u003e is the change in the level of slope of the outcome on arrival of the second interruption (3\u003csup\u003erd\u003c/sup\u003e\u0026nbsp; COVID-19 wave), β\u003csub\u003e5\u003c/sub\u003e is the difference between the first interruption (2\u003csup\u003end\u003c/sup\u003e COVID-19 wave) and second interruption slopes (3\u003csup\u003erd\u003c/sup\u003e\u0026nbsp; COVID-19 wave slope) of the outcome. \u0026nbsp;T is a linear term denoting the duration since the start of the study. F\u003csub\u003e1\u003c/sub\u003et is a linear term denoting the time in month since the start of the 2\u003csup\u003end\u003c/sup\u003e\u0026nbsp; COVID-19 wave (models the observed change in trend/slope immediately after onset of the 2\u003csup\u003end\u003c/sup\u003e\u0026nbsp; COVID-19 wave ). F\u003csub\u003e2\u003c/sub\u003et is a linear term denoting the time in month since the start of the 3\u003csup\u003erd\u003c/sup\u003e\u0026nbsp; COVID-19 wave (models the observed change in trend/slope immediately after onset of the 3\u003csup\u003erd\u003c/sup\u003e\u0026nbsp; COVID-19 wave, f\u003csub\u003e1,\u003c/sub\u003e and f\u003csub\u003e2\u003c/sub\u003e\u0026nbsp; are dummy variables depicting the interventions.\u003c/p\u003e\n \u003cp\u003eDt is a linear term denoting fixed calendar month effects to model seasonality, Tet is a linear term of monthly temperature data averaged across district level to control for confounding effects of temperature, Rt is a linear term of monthly rainfall data(mm) averaged across district level to control for confounding effects of rainfall. To account for serial autocorrelation between time points, an autoregressive order two (Lag 2) was used since autocorrelation was present at lags\u0026lt; 2.\u003c/p\u003e\n \u003cp\u003eNegative binomial was used to model the relationship between the count outcome (OPD visits) and the various independent variables (time, confounders and interruptions indicators). In the same way, \u0026nbsp;fractional regression was used to model the proportional outcomes. Monthly expected values (counterfactual values) \u0026nbsp;of all the outcomes hadn’t the interruptions occurred were predicted based on the fixed model (negative binomial and fractional regression) after adjusting for the calendar month effects, rainfall, temperature and setting the post interruption slopes at zero (to model what would happen if the interruptions hadn’t occurred). For count outcomes, monthly expected values were summed up for the durations of the 2\u003csup\u003end\u003c/sup\u003e and 3\u003csup\u003erd\u003c/sup\u003e\u0026nbsp; COVID-19 waves \u0026nbsp;and incidence rate ratios calculated comparing the observed versus expected outcome. For proportion outcomes, an average from the monthly expected values was calculated and a relative percent ratio calculated comparing the observed versus expected outcome.\u003c/p\u003e\n \u003cp\u003eSignificant change in level of the outcome meant immediate impact of the disruption. Significant difference between the pre-intervention and post intervention outcome slopes meant impact of the intervention/ disruption overtime. Significant differences between the observed and predicted post intervention outcome values also indicated \u0026nbsp;impact on the intervention. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003c/body\u003e\n\u003c/html\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 180,666 patients were treated at the outpatients department of the three facilities between 1st January 2019 and 28th February 2022. Most were female 118,815 (65.75%) and the median age of the patients was 16 (6\u0026ndash;32) years. The average atmospheric temperature across the three study sites was 30.25 (\u0026plusmn;\u0026thinsp;5.49)˚C ranging from 30.8˚C to 32.5˚C. The average rainfall distribution at the sites was 140.4 (\u0026plusmn;\u0026thinsp;77.21) mm with the lowest 104.81mm and the highest 169.88mm. as shown in Table \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\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePatient demographics and environmental characteristics stratified by site from January 2019 to February 2022.\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumino\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKasambya\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWalukuba\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll sites combined\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Age (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(5\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(6\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(7\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16(6\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (n%)\u003c/p\u003e \u003cp\u003eMale (n%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42803(67.81%)\u003c/p\u003e \u003cp\u003e20319(32.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31189(64.34%)\u003c/p\u003e \u003cp\u003e17287(35.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44346(65.65%)\u003c/p\u003e \u003cp\u003e23204(34.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118815(65.75%)\u003c/p\u003e \u003cp\u003e61849(34.23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage temperature in˚C (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.88(2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.83(2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.53(1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.25(5.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage rainfall in mm (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.88(101.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.81(52.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146.61(89.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140.43(77.21)\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\u003eImpact of the COVID-19 waves on outpatient malaria indicators.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOPD attendance (Overall impact).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere was neither change in level of OPD visits a month immediately on onset of the 2nd COVID-19 wave (β\u003csub\u003e2\u003c/sub\u003e = -626.06, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) nor change in trend of OPD visits during the 2nd COVID-19 wave duration (β\u003csub\u003e3\u003c/sub\u003e = -192.89, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, immediately on onset of the 3rd COVID-19 wave (during its 1st month), there was a significant increase in the OPD visits (β\u003csub\u003e2=\u003c/sub\u003e 1532.91, P\u0026thinsp;=\u0026thinsp;0.03) but overtime there wasn\u0026rsquo;t significant change in trend of OPD visits during the 3rd COVID-19 wave ( β\u003csub\u003e3\u003c/sub\u003e = -161.39, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall (all sites combined), there was no significant difference between the observed versus expected total number of patients seen at the out-patient departments during the 2nd COVID-19 wave (14950 vs 20016; IRR\u0026thinsp;=\u0026thinsp;0.75[0.29\u0026ndash;1.20]) however, there was a 52% decline in the number of observed versus expected total number of patients seen at the out-patient departments during the 3rd COVID-19 wave duration (15101 vs 31154; IRR\u0026thinsp;=\u0026thinsp;0.48[0.41\u0026ndash;0.56]) as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\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\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eChange in level of the outcome and change in trend of the outcome on onset of the two interruptions\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2nd COVID-19 wave onset\u003c/p\u003e \u003cp\u003e(May 2021)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3rd COVID-19 wave onset\u003c/p\u003e \u003cp\u003e(November 2021)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange in level/β\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(P-value).\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChange in trend/β\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(P-Value).\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange in level/β\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(P-value).\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange in trend/β\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(P-Value).\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPD attendance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-626.06(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-192.89(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1532.91(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-161.39(0.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of suspected malaria cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25(0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.51(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.55(0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest positivity rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.16(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01(0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04(0.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of suspected malaria cases tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.84e-16(0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.95e-18(0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.11e-16(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.77e-16(0.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of malaria cases treated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33e-14(0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.66e-16(0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.31e-14(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21e-14(0.60)\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 \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\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eEstimated and observed outcomes (averages for proportion outcomes and totals for the count outcome) during the three interruption durations (ALL SITES COMBINED).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2nd COVID-19 wave duration\u003c/p\u003e \u003cp\u003e(May-August 2021)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3rd COVID-19 wave duration\u003c/p\u003e \u003cp\u003e(November 2021-February 2022)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRatio(95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPD attendance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75(0.29\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48(0.41\u0026ndash;0.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of suspected malaria cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00(0.92\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05(0.95\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest positivity rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04(0.74\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.11(0.90\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of suspected malaria cases tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99(0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99(0.99\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of malaria cases treated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95(0.92\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97(0.94-1.00)\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\u003eOPD attendance (site specific impact).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere wasn\u0026rsquo;t a significant difference between the observed versus predicted OPD visits at a Lumino HCIII for the 2 durations; 1) 2nd COVID-19 wave (5277 vs 4698; IRR\u0026thinsp;=\u0026thinsp;1.12[0.86\u0026ndash;1.39]) and 2) 3rd COVID-19 wave (5391 vs 5332; IRR\u0026thinsp;=\u0026thinsp;1.01[0.96\u0026ndash;1.06]) as shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. However, there was a 37% decline (7849 vs 13931; IRR\u0026thinsp;=\u0026thinsp;0.63[0.57\u0026ndash;0.70]) and 59% decline (3832 vs 9297; IRR\u0026thinsp;=\u0026thinsp;0.41[0.37\u0026ndash;0.45]) in OPD visits during the 2nd COVID-19 wave duration and 3rd COVID-19 wave duration respectively at Kasambya HCIII as shown in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. At Walukuba HCIV, there was a 46% decline (4770 vs 8768; IRR\u0026thinsp;=\u0026thinsp;0.54[0.15\u0026ndash;0.94]) in OPD visits during the 2nd COVID-19 wave and a 67% decline (4950 vs 15065; IRR\u0026thinsp;=\u0026thinsp;0.33[0.23\u0026ndash;0.43]) during the 3rd COVID-19 wave as shown in Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\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\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eESTIMATED AND OBSERVED OUTCOMES (AVERAGES FOR PROPORTION OUTCOMES AND TOTALS FOR THE COUNT OUTCOME) DURING THE THREE INTERRUPTION DURATIONS ( high malaria transmission intensity site).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2nd COVID-19 wave duration\u003c/p\u003e \u003cp\u003e(May-August 2021).\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3rd COVID-19 wave duration\u003c/p\u003e \u003cp\u003e(November 2021-February 2022).\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRatio(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPD attendance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12(0.86\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.01(0.96\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of suspected malaria cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99(0.97\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.05(0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest positivity rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31(0.70\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.19(1.65\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of suspected malaria cases tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99(0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of malaria cases treated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.92(-6.85-16.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e397.28(-796.51-7811.09)\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eESTIMATED AND OBSERVED OUTCOMES (AVERAGES FOR PROPORTION OUTCOMES AND TOTALS FOR THE COUNT OUTCOME) DURING THE THREE INTERRUPTION DURATIONS (MODERATE MALARIA TRANSMISSION INTENSITY SITE).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2nd COVID-19 wave duration\u003c/p\u003e \u003cp\u003e(May-August 2021).\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3rd COVID-19 wave duration\u003c/p\u003e \u003cp\u003e(November 2021-February 2022).\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRatio(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPD attendance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63(0.57\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41(0.37\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of suspected malaria cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79(0.32\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.79(0.72\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest positivity rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65(0.41\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73(0.63\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of suspected malaria cases tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99(0.61\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99(0.99\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of malaria cases treated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.92(-10.06-19.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e392.29(-5420.98-6215.56)\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eESTIMATED AND OBSERVED OUTCOMES (AVERAGES FOR PROPORTION OUTCOMES AND TOTALS FOR THE COUNT OUTCOME) DURING THE THREE INTERRUPTION DURATIONS (LOW MALARIA TRANSMISSION INTENSITY SITE).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2nd COVID-19 wave duration\u003c/p\u003e \u003cp\u003e(May-August 2021).\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3rd COVID-19 wave duration\u003c/p\u003e \u003cp\u003e(November 2021-February 2022).\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRatio(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPD attendance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54(0.15\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33(0.23\u0026ndash;0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of suspected malaria cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18(0.48\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.12(2.07\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest positivity rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89(0.63\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.44(0.74\u0026ndash;2.15)\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\u003eProportion of suspected malaria cases (Overall impact).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOn onset of the 2nd COVID-19 wave, there was neither an immediate significant change in the level (β\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.37, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the proportion of suspected malaria cases nor a significant change in trend (β\u003csub\u003e3\u003c/sub\u003e = -0.25, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the proportion of suspected malaria cases during the 2nd COVID-19 wave. There wasn\u0026rsquo;t a significant change in the level (β\u003csub\u003e2\u003c/sub\u003e = -4.51, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the proportion of suspected malaria cases on onset of the 3rd COVID-19 wave however, there was a significant change in trend (β\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.55, P\u0026thinsp;=\u0026thinsp;0.01) with an increment in the proportion of suspected malaria cases during the 3rd COVID-19 wave duration as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, there was no significant differences between the observed versus predicted mean proportion of suspected malaria cases during the 2nd COVID-19 wave duration (36.45% vs 36.44%; RPR\u0026thinsp;=\u0026thinsp;1.00[0.92\u0026ndash;1.08]). Likewise, there were no significant differences between the observed versus predicted mean proportion of suspected malaria during the 3rd COVID-19 wave (35.25% versus 33.69%; RPR\u0026thinsp;=\u0026thinsp;1.05[0.95\u0026ndash;1.13]) as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eProportion of suspected malaria cases (Site specific impact).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAt Lumino HCIII, there wasn\u0026rsquo;t a significant difference between the observed versus predicted proportion of suspected malaria cases; 1) during the 2nd COVID-19 wave (78.24% versus 79.08%; RPR\u0026thinsp;=\u0026thinsp;0.99[0.97\u0026ndash;1.01]) and 2) during the 3rd COVID-19 wave (82.53% versus 78.45%; RPR\u0026thinsp;=\u0026thinsp;1.05[0.99\u0026ndash;1.01]) as shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. At Kasambya HCIII, there was a 21% decline in the proportion of suspected malaria cases during the 3rd COVID-19 wave (60.79% versus 76.28%; RPR\u0026thinsp;=\u0026thinsp;0.79[0.71\u0026ndash;0.87]) however, during the 2nd COVID-19 wave duration, there was no significant difference between the observed versus predicted (60.12% versus 75.87%; RPR\u0026thinsp;=\u0026thinsp;0.79[0.32\u0026ndash;1.26]), proportion of suspected malaria cases at the moderate malaria transmission site as depicted in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. At Walukuba HCIV, there were no significant differences between the observed versus the predicted proportion of suspected malaria cases tested during the 2nd COVID-19 wave duration (49.36% versus 55.02%; RPR\u0026thinsp;=\u0026thinsp;0.89[0.63\u0026ndash;1.16]). However, during the 3rd COVID-19 wave duration, the observed suspected malaria cases were significantly higher than expected (30.76% versus 14.54%; RPR\u0026thinsp;=\u0026thinsp;2.12[2.07\u0026ndash;2.16]).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTest Positivity rate (Overall impact).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOn onset of the 2nd COVID-19 wave, there was an immediate significant decline (β\u003csub\u003e2\u003c/sub\u003e = -0.16, P\u0026thinsp;=\u0026thinsp;0.00) in the malaria TPR as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. However, there wasn\u0026rsquo;t a significant change in trend (β\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.02, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the TPR during the entire 2nd COVID-19 wave duration. In the same way, there wasn\u0026rsquo;t a significant change in level (β\u003csub\u003e2\u003c/sub\u003e = -0.01, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the malaria TPR immediately on onset of the 3rd COVID-19 wave nor was there a significant change in trend (β\u003csub\u003e3\u003c/sub\u003e = -0.04, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the malaria TPR during the 3rd COVID-19 wave duration as shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, there was no significant difference between the observed versus expected malaria TPR (all sites of varying malaria transmission intensities combined) during the 2nd COVID-19 wave (35.30% vs 33.89%; RPR\u0026thinsp;=\u0026thinsp;1.04[0.74\u0026ndash;1.34]) and during the 3rd COVID-19 wave (40.16% vs 36.28%; RPR\u0026thinsp;=\u0026thinsp;1.11[0.90\u0026ndash;1.31]) as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTest Positivity rate (Site specific impact).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAt Lumino HCIII, there was no significant difference between the observed versus the predicted malaria TPR during the 2nd COVID-19 wave duration (48.40% vs 36.71%; RPR\u0026thinsp;=\u0026thinsp;1.31[0.70\u0026ndash;1.93]). However, during the 3rd COVID-19 wave, the observed TPR was significantly higher (59.91% vs 32.29%; RPR\u0026thinsp;=\u0026thinsp;1.19[1.65\u0026ndash;2.06]) than expected as shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. At Kasambya HCIII, there was a 35% decline and 27% decline in the malaria TPR during the 2nd COVID-19 wave and 3rd COVID-19 wave duration respectively at this site as shown in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. At Walukuba HCIV, there was no significant difference between the observed versus predicted malaria TPR for the three durations; 1) during the 2nd COVID-19 wave duration (49.36% vs 55.02%; RPR\u0026thinsp;=\u0026thinsp;0.89[0.63\u0026ndash;1.16]), 2) and during the 3rd COVID-19 wave duration (47.53% vs 32.92%; RPR\u0026thinsp;=\u0026thinsp;1.44[0.74\u0026ndash;2.15]) as shown in Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImpact of the COVID-19 waves on the proportion of suspected malaria cases tested.\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOverall impact\u003c/h2\u003e \u003cp\u003eOn onset of the 2nd COVID-19 wave, there was neither an immediate change in level (β\u003csub\u003e2\u003c/sub\u003e = -3.84 e-16, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the proportion of suspected malaria cases tested nor a significant change in trend (β\u003csub\u003e3\u003c/sub\u003e = -7.95e-18, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the proportion of the suspected malaria cases tested during the 2nd COVID-19 wave duration. There was neither a significant change in the level (β\u003csub\u003e2\u003c/sub\u003e = -4.11e-16, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the proportion of suspected malaria cases tested on onset of the 3rd COVID-19 wave nor a significant change in the trend (β\u003csub\u003e3\u003c/sub\u003e = -2.77e-16, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the proportion of suspected malaria cases tested during the 3rd COVID-19 wave duration as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, there was both a 1% decline in the proportion of suspected malaria cases tested during the 3rd COVID-19 wave duration (99.86% vs 99.99%; RPR\u0026thinsp;=\u0026thinsp;0.99[0.99\u0026ndash;0.99]). However, there was no significant difference between the observed versus predicted mean proportion of tested malaria during the 2nd COVID-19 wave (99.47% vs 99.97%; RPR\u0026thinsp;=\u0026thinsp;0.99[0.98-1.00]) as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSite specific impact.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAt Lumino HCIII, there were no significant differences between the observed versus predicted proportion of suspected malaria cases tested during the all the two interruption durations as shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Likewise, there wasn\u0026rsquo;t significant differences between the observed versus predicted proportion of suspected malaria cases tested at Kasambya HCIII during the 2nd COVID-19 wave however, during the 3rd COVID-19 wave duration, there was a 1% decline in the proportion of suspected malaria cases tested (99.79% vs 99.97%; RPR\u0026thinsp;=\u0026thinsp;0.99[0.99\u0026ndash;0.99]) as shown in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImpact of the COVID-19 waves the proportion of malaria cases prescribed artemether lumefantrine (AL).\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOverall impact.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOn onset of the 2nd COVID-19 wave, there wasn\u0026rsquo;t an immediate significant change in level (β\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.33 e-14, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of confirmed cases prescribed AL nor was there a significant change in trend (β\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;7.66 e-16, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the proportion of malaria cases prescribed AL. There wasn\u0026rsquo;t an immediate significant change in level (β\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.31 e-14, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the proportion of malaria cases prescribed AL on onset of the 3rd COVID-19 wave nor a significant change in trend of the proportion of malaria cases prescribed AL during the 3rd COVID-19 wave duration (β\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.21 e-14, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, there was a 5% decline (94.99% vs 99.85%; RPR\u0026thinsp;=\u0026thinsp;0.95[0.92\u0026ndash;0.98]) in the proportion of malaria cases treated during the 2nd COVID-19 wave and a no significant difference between the observed versus predicted proportion of malaria cases prescribed AL during the 3rd COVID-19 wave (96.96% vs 99.93%; RPR\u0026thinsp;=\u0026thinsp;0.97[0.94-1.00]).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSite specific impact.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAt Lumino HCIII, during the 2nd (90.57% vs 18.40%; RPR\u0026thinsp;=\u0026thinsp;4.92[-6.85-16.69]), and 3rd (95.02% vs 0.24%; RPR\u0026thinsp;=\u0026thinsp;397.28[-796.51-7811.09]) COVID-19 waves, there was no significant differences between the observed versus expected proportion of malaria cases prescribed AL. At Kasambya HCIII, there were no significant differences between the observed versus expected proportion of malaria cases treated for the 2nd (90.57% vs 18.40%; RPR\u0026thinsp;=\u0026thinsp;4.92[-10.06-19.90]) and 3rd (95.57% vs 0.24%; RPR\u0026thinsp;=\u0026thinsp;392.29[-5470.98-6215.56]) COVID-19 wave durations.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCOVID-19 has been documented to negatively impact health care delivery and affect roll out of control interventions for several diseases including malaria. In this study, we assessed the impact of the 2nd and 3rd wave of COVID-19 on out-patient attendance, suspected malaria cases, test positivity rates and malaria case management.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSummary of the results.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDuring the 3rd COVID-19 wave, OPD visits were lower than expected, while no significant differences between the observed versus expected OPD visits were observed during the 2nd COVID-19 wave. However, at the two sites situated within moderate and low malaria transmission intensity settings, there was a significant decline in outpatient attendance during both the 2nd and 3rd COVID-19 waves. The observed proportions of suspected malaria cases were not significantly different from the expected during both the 2nd and 3rd COVID-19 waves, except at a site situated in a moderate malaria transmission setting where a decline was noted during the 3rd COVID-19 wave. Test positivity rates remained consistent (no significant differences between the observed versus expected) overall, with significant increases during the 3rd COVID-19 wave at a site situated in a high malaria transmission setting and declines during the 2nd and 3rd COVID-19 waves at a moderate malaria setting situated site. The proportions of suspected malaria cases tested declined during the 3rd COVID-19 wave with no significant difference during the 2nd COVID-19 wave. The proportion of malaria cases prescribed AL proportions declined during the 2nd COVID-19 wave, with no significant difference during the 3rd COVID-19 wave, and no impact observed at sites situated in moderate and high malaria settings.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImpact of the COVID-19 waves on outpatient malaria indicators, diagnostic and treatment practices.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe reduction in OPD attendance (during both COVID-19 waves) at the sites situated in moderate and low malaria transmission settings could have resulted from either the instituted restrictions on travel as a measure for COVID-19 transmission reduction for the peri urban setting (low transmission situated site) and/or fear to contract COVID-19 on visiting the health facility (Atek Kagirita, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Heuschen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mahmud \u0026amp; Riley, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Unfortunately, reductions in the number of patients seeking care at these public health facilities would mean that most people were staying at home even when they are getting ill and only presenting to the facilities when they have severe disease resulting into increases in complicated malaria cases and mortality (although this was not assessed as part of this study). A study conducted in Benin reported minimal effects of COVID-19 on health seeking behavior with some people reporting that they reduced how often they visted health facilities due to the COVID-19 pandemic and others saying that they didn\u0026rsquo;t change their health seeking behavior (Duguay et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A retrospective analysis of routine surveillance data conducted in northern Ghana to determine the impact of COVID-19 on malaria reported a reduction in OPD visits during the 1st 6 months when COVID-19 restrictions were put in place and increases thereafter but still remained low relative to the previous years (Heuschen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another similar study conducted in Nigeria to determine the effect of COVID-19 on malaria intervention coverage also reported decline in care seeking practices across all age groups (Ilesanmi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For the site located in a high malaria transmission setting were COVID-19 didn\u0026rsquo;t have effect on OPD attendance, it could be due to the fact that the site is located in a rural setting were people could still walk to the facility or use bicycles being a rural setting. The overall result (all sites combined) of decline in OPD attendance during the third COVID-19 wave would be due to the fact that the 3rd COVID-19 had more infectious cases therefore people would have feared more to visit health facilities (in fear of contracting COVID-19) more in the 3rd COVID-19 wave relative to the other waves. However, overall (all sites combined), there wasn\u0026rsquo;t impact of COVID-19 on OPD attendance during the 2nd COVID-19 wave duration. This result is similar with that of the study conducted during the 1st COVID-19 wave in facilities located in the rural areas of Uganda, the study reported no impact on OPD attendance (Namuganga et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, this disagrees with results from similar studies that reported decline in OPD attendance during COVID-19 (Duguay et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Heuschen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe overall (all sites combined), no effect of COVID-19 on the proportion of suspected malaria cases could be explained by change in the health seeking behavior of individuals within the communities were people feared to visit health facilities in fear of contracting COVID-19 or being classified as COVID-19 patients on presenting with fever (Guerra et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hakizimana et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The suspected malaria cases/fever rates were expected to be high in all waves because both COVID-19 and malaria present with fever however, this wasn\u0026rsquo;t the case, attributable to changes in health seeking behaviors among the communities (Guerra et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Also, during COVID-19, the prescription algorithm for COVID-19 was known in the communities, so even in cases where individuals got fever, they just bought the recommended drugs to handle COVID-19 or used lemons, oranges, ginger among others instead of visiting the health facilities. The fever cases could have been increasing within the communities but these were not being documented in the public health facilities because people were not visiting the health facilities as before out of the COVID-19 stigma. This result is similar to the result documented in the study conducted during the first wave (Namuganga et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The overall (all sites combined) no effect of COVID-19 on the malaria test positivity rates at these facilities during the 2nd and 3rd COVID-19 waves could be explained by the fact that people with malaria fevers were not reporting to the health facilities out of fear of being taken to be COVID-19 suspects therefore could remain home and self-medicate or could seek malaria care from the village health workers (Hakizimana et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It could also have been due to the fact that people were entering their house early enough avoiding exposure to mosquitoes. It should also be noted that despite the delay in the third mass distribution campaign of mosquito nets that had to start in February 2020 but started later in June 2020 due to COVID-19 interruptions, the campaign was successful and ended in June 2021 ensuring continued protection from exposure to mosquito bites among communities explaining the no difference between the observed versus expected malaria test positivity rates (NMCP, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This result is also similar to the results of a study done during the 1st COVID-19 wave in rural areas of Uganda which also reported no significant differences in the observed and expected TPR (Namuganga et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A similar study conducted in northern Ghana to determine the impact of COVID-19 on malaria reported that OPD and IPD malaria cases remained below during the pandemic relative to the previous years (Heuschen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another study conducted in in three malaria endemic districts of Rwanda to determine the effect of COVID-19 on malaria reported no change in the overall presentation rate of uncomplicated malaria and a reduction in the proportion of severe malaria (Hakizimana et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, at the site located in a high malaria transmission setting, the significant increment in the malaria test positivity rate during the 3rd COVID-19 wave could be associated with the fact that the duration of the 3rd COVID-19 wave coincides and/ or follows a rainy and malaria season in Uganda. However, it could also be due to a cumulative community buildup of malaria from the previous waves were people with suspected malaria couldn\u0026rsquo;t visit facilities out of fear of being classified as COVID-19 suspects. A study conducted in Indochina an area that had a co-endemicity of COVID-19 and malaria showed an increment in malaria cases after removal of the lockdown and concluded that though lockdowns were effective in reducing COVID-19 transmissions, there removal was followed by increment in malaria cases (Mungmunpuntipantip \u0026amp; Wiwanitkit, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When people don\u0026rsquo;t access the services, malaria transmission in the communities increases. It is no wander that post the third COVID-19 wave many malaria outbreaks have been noted in many parts of Uganda causing many malaria morbidities and mortalities (M.O.H, 2022). This may be an impact of COVID-19 where the disease burden appeared to decrease as per health facility data due yet it was increasing in the communities. The result of increment in the malaria test positivity rate during the 3rd COVID-19 wave in a high malaria transmission setting agrees with results of studies done in Zimbabwe and Central African Republic (CAR) which reported increment in malaria cases after onset of COVID-19. It should however be noted that the study done in Zimbabwe which reported an excessive increment of malaria cases (Gavi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) didn\u0026rsquo;t control for environmental factors specifically rainfall and temperature that are known covariates of malaria in that country. The reported increase in malaria morbidity and mortality also coincided with the malaria peak season in that country. The study done in CAR reported increment in the prevalence of asymptomatic malaria from August to September 2021 compared to a similar study done before COVID-19 (Bylicka-Szczepanowska \u0026amp; Korzeniewski, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The result on overall (all sites combined) decline in the proportion of suspected malaria cases tested (using both mRDTs and microscopy) reported in this study agrees with a result of a similar study conducted in Senegal that aimed to determine the impact of COVID-19 on biological diagnosis of malaria which reported a decline in the malaria tests done (both mRDTs and microscopy) in 2020 COVID-19 year relative to the prior years (Manga et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another study conducted in Mozambique to determine the impact of COVID-19 on malaria surveillance with a specific focus on diagnosis and treatment reported a decline in the number of people tested for malaria in the health facilities and an increase in the number tested for malaria in the communities (Afai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This result on overall (all sites combined) decline in the proportion of suspected malaria cases tested (using both mRDTs and microscopy) during the 3rd COVID-19 wave duration could have been due to shortage on malaria rapid diagnostic test kits since most biomedical firms shifted focus to producing COVID-19 rapid diagnostic test kits (Aborode et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The decline could also be due to absentia of health workers at the facilities due to either COVID-19 stigma or lack of transport however much the government waived their movement despite the travel restrictions. A similar study conducted in 3 malaria endemic districts in Rwanda to determine the impact of COVID-19 on malaria services reported a decline in malaria testing at the health facilities and an increment in malaria testing at community level which they attributed to COVID-19 mitigation measures such as travel restrictions but also highlighted people\u0026rsquo;s fear to contract COVID-19 on visiting the health facilities (Hakizimana et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). During the 2nd COVID-19 wave duration, there wasn\u0026rsquo;t significant difference (all sites combined) between the observed versus expected proportion of suspected malaria cases tested (which is also true for the site located in the high malaria transmission setting throughout both the 2nd and 3rd COVID-19 waves) which could be attributed to campaigns such as \u0026ldquo;Why survive COVID-19 and die of malaria?\u0026rdquo; which were put in place ensuring the malaria testing of all fever cases at the health centers through (WHO, 2020). Health workers were re-trained, redistributed and reassigned to ensure adherence to malaria testing of all fever cases in an attempt to prevent the disease from re-emerging due to focus shift to COVID-19. The significant decline in the proportion of malaria cases treated with Artemether lumefantrine (first line antimalarial for treatment of uncomplicated malaria in Uganda) during the 2nd wave duration would have been due to a run out on supply of AL due to the shift of the resources to fight COVID-19. The 1st COVID-19 study also reported similar findings (Namuganga et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Evidence has shown that access to antimalarials was disrupted in sub-Saharan Africa during COVID-19 (Dzianach et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrength of the study.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study had strength when compared to most studies done in malaria endemic countries to assess the impact of COVID-19 on malaria in that seasonality and the most important covariates of malaria predominantly rainfall and temperature were adjusted for when predicting the study outcomes hadn\u0026rsquo;t the second and third COVID-19 waves occurred. If not controlled for, these could confound the study results. Interrupted time series analysis used to assess the impact of COVID-19 on malaria in this study has beauty of taking the pre-COVID-19 malaria trends into consideration and also producing counterfactual trends if at all COVID-19 hadn\u0026rsquo;t occurred. Most of the studies done to assess the impact of COVID-19 were done in the first wave when COVID-19 cases were still few but this study covered both the second and third COVID-19 waves were COVID-19 cases were at peak and more infectious. The sample size used was 18 times the estimated therefore the study had a final power of more than 99%.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHowever, there were still limitations, only three study sites were purposively selected to represent low, moderate and high malaria transmission settings of Uganda but more sites would have been chosen to represent each malaria transmission setting. On calculating outcome estimates hadn\u0026rsquo;t COVID-19 occurred since only rainfall, temperature and calendar month effects were adjusted for leaving out other environmental covariates of malaria including humidity, vegetation index among others. Disparities in IRS status and LLIN distribution status across the study sites was not taken into account. There remains a question on the completeness of the data even in the case where surveillance data was used in this study. Absence of health workers and data officers during COVID-19 due to either COVID-19 stigma and/or difficulty in movement could also have impacted the quality of the data. Single group ITSA also has a limitation of lack of controls.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe 3rd COVID-19 wave was associated with a significant reduction in outpatient department attendance. Subgroup analysis however showed consistent negative impact across both the 2nd and 3rd COVID-19 waves at a low and moderate malaria transmission situated sites. While there were no significant changes in the proportion of suspected malaria cases and test-positivity rates overall, subgroup analysis showed varying effects, including a significant increase in test-positivity rates during the 3rd COVID-19 wave at a high malaria transmission situated site and declines in both test-positivity rates and proportion of suspected malaria cases during the 2nd and 3rd COVID-19 waves at a moderate malaria transmission situated site. Additionally, there were notable impacts on malaria diagnostic practices during the 3rd COVID-19 wave unlike the 2nd COVID-19 wave and impacts on the antimalarial (artemether lumefantrine) prescription practices during the 2nd COVID-19 wave unlike the 3rd COVID-19 wave. This means there is need to bring the malaria services near to the communities during out-breaks like COVID-19 so that care is not disrupted. If this intervention is not done, there would be more severe disease and even increased malaria mortalities in the communities.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRecommendations.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe recommend that in case of any other outbreak, all efforts should be made to ensure continuous delivery of malaria services. This can be done through strengthening and extending the integrated case community management for malaria in all districts such that in circumstances where people fear to visit health facilities in fear of contracting an emergent disease such as COVID-19, they can still access malaria care at community level. The MoH should sensitize the public about the changes in HSB that happened during the lockdowns to encourage people seek medical care again. There is need for a more extensive study with data from more health facilities within each malaria transmission setting and covering the entire COVID-19 duration to either refute or to agree with the results of this study. Other studies should be also be conducted to determine the impact of COVID-19 on malaria at community level. Studies should also look at the burden of severe malaria that happened during COVID-19 comparing them with the pre- COVID-19 period.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtemether Lumefantrine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAug\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCAR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral African Republic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDec\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDRC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDemocratic Republic of Congo\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFeb\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHCs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Centers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHIV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Immunodeficiency virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHSB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth seeking behavior\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIPTp\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntermittent Preventive Treatment for Pregnancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIRS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndoor Residual Spraying\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eITN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsecticide treated bed net\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eITSA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterrupted time series analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eJan\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLLINS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong lasting insecticide treated bed nets\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMaK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMakerere University\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMCM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMalaria case management\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMoH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinistry of Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOct\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOCT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOLS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrdinary least squares\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOPD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOutpatient department\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRDTS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRapid diagnostic tests\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSEP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSOMREC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSchool of medicine research ethics committee\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTPR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTest Positivity rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUMIS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUganda Malaria Indicator Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\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\u003eEthic review and approval was obtained from the Makerere University School of Medicine Research Ethics Committee\u0026nbsp;reference number - Mak-SOMREC-2022-364). The study was given a waiver for informed consent by MaK-SOMREC. The study was registered and cleared by the Uganda National Council of Science and Technology (UNCST).\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\u003eThe datasets used/analyzed in the study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Consolidating Early Career Academics Programme (CECAP) of the Makerere University Directorate of Research and Graduate Training with support from Carnegie Corporation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePM, JN and JNK designed and conceptualized the study. PM did the data abstraction, data cleaning, data management and preliminary analysis of the data. \u0026nbsp;PM, AM, JNK and AM contributed to the data collection, data analysis and report writing. JO and FEK interpreted data and contributed to data analysis. \u0026nbsp;All authors contributed to interpretation of findings. PM wrote the first draft of the paper. PM, AM, JNK, JO, FEK and JN reviewed, revised and contributed to writing to the paper. All authors read and approved the final manuscript. PM, AM, JNK, JO, FEK and JN read and met the ICMJE criteria for authorship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the study health facility management, Uganda Malaria Surveillance Project, Uganda National Malaria Control Division, Uganda Ministry of Health and Makerere University for supporting the implementation of the study. The authors also thank Ms. Christine Kusasira and the entire staff of the Clinical Epidemiology Unit, Makerere University School of Medicine for their \u0026nbsp;support during the conduct of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAborode, A. T., David, K. B., Uwishema, O., Nathaniel, A. L., Imisioluwa, J. O., Onigbinde, S. B., \u0026amp; Farooq, F. (2021). Fighting COVID-19 at the Expense of Malaria in Africa: The Consequences and Policy Options. \u003cem\u003eAm J Trop Med Hyg\u003c/em\u003e,\u003cem\u003e\u0026nbsp;104\u003c/em\u003e(1), 26-29. https://doi.org/10.4269/ajtmh.20-1181\u003c/li\u003e\n \u003cli\u003eAfai, G., Banze, A. R., Candrinho, B., Baltazar, C. S., \u0026amp; Rossetto, E. V. (2021). Challenges for malaria surveillance during the COVID-19 emergency response in Nampula, Mozambique, January - May 2020. \u003cem\u003ePan Afr Med J\u003c/em\u003e,\u003cem\u003e\u0026nbsp;38\u003c/em\u003e, 254. https://doi.org/10.11604/pamj.2021.38.254.27481\u003c/li\u003e\n \u003cli\u003eAtek Kagirita, C.-D. I. C. (2022). National Inter Action Review. In.\u003c/li\u003e\n \u003cli\u003eBylicka-Szczepanowska, E., \u0026amp; Korzeniewski, K. (2022). Asymptomatic Malaria Infections in the Time of COVID-19 Pandemic: Experience from the Central African Republic. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e,\u003cem\u003e\u0026nbsp;19\u003c/em\u003e(6). https://doi.org/10.3390/ijerph19063544\u003c/li\u003e\n \u003cli\u003eCaglar, B., Karaali, R., Balkan, II, Mete, B., \u0026amp; Aygun, G. (2021). COVID-19 and Plasmodium ovale Malaria: A Rare Case of Co-Infection. \u003cem\u003eKorean J Parasitol\u003c/em\u003e,\u003cem\u003e\u0026nbsp;59\u003c/em\u003e(4), 399-402. https://doi.org/10.3347/kjp.2021.59.4.399\u003c/li\u003e\n \u003cli\u003eDuguay, C., Accrombessi, M., N\u0026rsquo;Tcha, L. K., Akinro, B. A., Dangbenon, E., Assongba, L., Yee, S. C., Feng, C., Labonte, R., \u0026amp; Krentel, A. (2023). Community-level impacts of the coronavirus pandemic on malaria prevention and health-seeking behaviours in rural Benin: A mixed methods study. \u003cem\u003ePLOS global public health\u003c/em\u003e,\u003cem\u003e\u0026nbsp;3\u003c/em\u003e(5), e0001881.\u003c/li\u003e\n \u003cli\u003eDzianach, P. A., Rumisha, S. F., Lubinda, J., Saddler, A., van den Berg, M., Gelaw, Y. A., Harris, J. R., Browne, A. J., Sanna, F., Rozier, J. A., Galatas, B., Anderson, L. F., Vargas-Ruiz, C. A., Cameron, E., Gething, P. W., \u0026amp; Weiss, D. J. (2023). Evaluating COVID-19-Related Disruptions to Effective Malaria Case Management in 2020-2021 and Its Potential Effects on Malaria Burden in Sub-Saharan Africa. \u003cem\u003eTrop Med Infect Dis\u003c/em\u003e,\u003cem\u003e\u0026nbsp;8\u003c/em\u003e(4). https://doi.org/10.3390/tropicalmed8040216\u003c/li\u003e\n \u003cli\u003eEpstein, A., Maiteki-Sebuguzi, C., Namuganga, J. F., Nankabirwa, J. I., Gonahasa, S., Opigo, J., Staedke, S. G., Rutazaana, D., Arinaitwe, E., Kamya, M. R., Bhatt, S., Rodr\u0026iacute;guez-Barraquer, I., Greenhouse, B., Donnelly, M. J., \u0026amp; Dorsey, G. (2022). Resurgence of malaria in Uganda despite sustained indoor residual spraying and repeated long lasting insecticidal net distributions. \u003cem\u003ePLOS Glob Public Health\u003c/em\u003e,\u003cem\u003e\u0026nbsp;2\u003c/em\u003e(9), e0000676. https://doi.org/10.1371/journal.pgph.0000676\u003c/li\u003e\n \u003cli\u003eF.Bongomin, F., Fleischer, B., Olum, R., Natukunda, B., Kiguli, S., Byakika-Kibwika, P., Baluku, J. B., \u0026amp; Nakwagala, F. N. (2021). High mortality during the second wave of the coronavirus disease 2019 (COVID-19) pandemic in Uganda: experience from a national referral COVID-19 treatment unit. Open Forum Infectious Diseases,\u003c/li\u003e\n \u003cli\u003eGavi, S., Tapera, O., Mberikunashe, J., \u0026amp; Kanyangarara, M. (2021). Malaria incidence and mortality in Zimbabwe during the COVID-19 pandemic: analysis of routine surveillance data. \u003cem\u003eMalaria journal\u003c/em\u003e,\u003cem\u003e\u0026nbsp;20\u003c/em\u003e(1), 233. https://doi.org/10.1186/s12936-021-03770-7\u003c/li\u003e\n \u003cli\u003eGuerra, C. A., Tresor Donfack, O., Motobe Vaz, L., Mba Nlang, J. A., Nze Nchama, L. O., Mba Eyono, J. N., Riloha Rivas, M., Phiri, W. P., Schwabe, C., Aldrich, E., Ratsirarson, J., Fuseini, G., \u0026amp; Garc\u0026iacute;a, G. A. (2020). Malaria vector control in sub-Saharan Africa in the time of COVID-19: no room for complacency. \u003cem\u003eBMJ Glob Health\u003c/em\u003e,\u003cem\u003e\u0026nbsp;5\u003c/em\u003e(9). https://doi.org/10.1136/bmjgh-2020-003880\u003c/li\u003e\n \u003cli\u003eHakizimana, D., Ntizimira, C., Mbituyumuremyi, A., Hakizimana, E., Mahmoud, H., Birindabagabo, P., Musanabaganwa, C., \u0026amp; Gashumba, D. (2022). The impact of Covid-19 on malaria services in three high endemic districts in Rwanda: a mixed-method study. \u003cem\u003eMalar J\u003c/em\u003e,\u003cem\u003e\u0026nbsp;21\u003c/em\u003e(1), 48. https://doi.org/10.1186/s12936-022-04071-3\u003c/li\u003e\n \u003cli\u003eHeuschen, A.-K., Abdul-Mumin, A., Abubakari, A., Agbozo, F., Lu, G., Jahn, A., \u0026amp; M\u0026uuml;ller, O. (2023). Effects of the COVID-19 pandemic on general health and malaria control in Ghana: a qualitative study with mothers and health care professionals. \u003cem\u003eMalaria journal\u003c/em\u003e,\u003cem\u003e\u0026nbsp;22\u003c/em\u003e(1), 1-11.\u003c/li\u003e\n \u003cli\u003eHeuschen, A.-K., Abdul-Mumin, A., Adokiya, M., Lu, G., Jahn, A., Razum, O., Winkler, V., \u0026amp; M\u0026uuml;ller, O. (2022). Impact of the COVID-19 pandemic on malaria cases in health facilities in northern Ghana: a retrospective analysis of routine surveillance data. \u003cem\u003eMalaria journal\u003c/em\u003e,\u003cem\u003e\u0026nbsp;21\u003c/em\u003e(1), 1-8.\u003c/li\u003e\n \u003cli\u003eHussein, M. I. H., Albashir, A. A. D., Elawad, O. A. M. A., \u0026amp; Homeida, A. (2020). Malaria and COVID-19: unmasking their ties. \u003cem\u003eMalaria journal\u003c/em\u003e,\u003cem\u003e\u0026nbsp;19\u003c/em\u003e, 1-10.\u003c/li\u003e\n \u003cli\u003eIlesanmi, O. S., Afolabi, A. A., \u0026amp; Iyiola, O. P. (2021). Effect of the COVID-19 pandemic on malaria intervention coverage in Nigeria: Analysis of the Premise Malaria COVID-19 Health Services Disruption Survey 2020. \u003cem\u003ePopulation Medicine\u003c/em\u003e,\u003cem\u003e\u0026nbsp;3\u003c/em\u003e(September), 1-10.\u003c/li\u003e\n \u003cli\u003eKitara, D. L., \u0026amp; Ikoona, E. N. (2020). COVID-19 pandemic, Uganda\u0026apos;s story. \u003cem\u003ePan Afr Med J\u003c/em\u003e,\u003cem\u003e\u0026nbsp;35\u003c/em\u003e(Suppl 2), 51. https://doi.org/10.11604/pamj.supp.2020.35.2.23433\u003c/li\u003e\n \u003cli\u003eLinden, A. (2017). A Comprehensive set of Postestimation Measures to Enrich Interrupted Time-series Analysis. \u003cem\u003eThe Stata Journal\u003c/em\u003e,\u003cem\u003e\u0026nbsp;17\u003c/em\u003e(1), 73-88. https://doi.org/10.1177/1536867X1701700105\u003c/li\u003e\n \u003cli\u003eM.O.H. (2022). \u003cem\u003eThe malaria weekly bulletin for Epi week 31 (1st August - 7th August 2022.\u003c/em\u003e [periodical report]. M. o. health. http://library.health.go.ug/publications/malaria/malaria-bulletin-week-31\u003c/li\u003e\n \u003cli\u003eMahmud, M., \u0026amp; Riley, E. (2021). Household response to an extreme shock: Evidence on the immediate impact of the Covid-19 lockdown on economic outcomes and well-being in rural Uganda. \u003cem\u003eWorld Development\u003c/em\u003e,\u003cem\u003e\u0026nbsp;140\u003c/em\u003e, 105318.\u003c/li\u003e\n \u003cli\u003eManga, I. A., Seye, C., Dram\u0026eacute;, A., Fall, C. B., L\u0026eacute;lo, S., Minlekib, C. P., Diouf, M. P., Ndiaye, J. L. A., Sylla, K., \u0026amp; Faye, B. (2023). Impact of COVID-19 on Biological Diagnosis of Malaria: Case of the Thierno Mouhamadoul Mansour Barro Hospital in Mbour, Senegal. \u003cem\u003eAdvances in Infectious Diseases\u003c/em\u003e,\u003cem\u003e\u0026nbsp;13\u003c/em\u003e(1), 31-40.\u003c/li\u003e\n \u003cli\u003eMawejje, H. D., Asiimwe, J. R., Kyagamba, P., Kamya, M. R., Rosenthal, P. J., Lines, J., Dorsey, G., \u0026amp; Staedke, S. G. (2022). Impact of different mosquito collection methods on indicators of Anopheles malaria vectors in Uganda. \u003cem\u003eMalaria journal\u003c/em\u003e,\u003cem\u003e\u0026nbsp;21\u003c/em\u003e(1), 388. https://doi.org/10.1186/s12936-022-04413-1\u003c/li\u003e\n \u003cli\u003eMoH. (2010). Uganda Malaria Indicator Survey 2009 http://library.health.go.ug/index.php/communicable-disease/malaria/uganda-malaria-indicator-survey-2009\u003c/li\u003e\n \u003cli\u003eMoH. (2020). Uganda Malaria Indicator Survey 2018-19. https://dhsprogram.com/pubs/pdf/MIS34/MIS34.pdf\u003c/li\u003e\n \u003cli\u003eMoH. (2023). Annual Health SectorPerformance Report FINANCIAL YEAR 2022/23. https://www.kamuli.go.ug/publications/annual-health-sector-performance-report-fy-20222023\u003c/li\u003e\n \u003cli\u003eMungmunpuntipantip, R., \u0026amp; Wiwanitkit, V. (2023). COVID lockdown and malaria incidence: A note from the tropical endemic area. \u003cem\u003eAPIK Journal of Internal Medicine\u003c/em\u003e,\u003cem\u003e\u0026nbsp;11\u003c/em\u003e(2), 137-138.\u003c/li\u003e\n \u003cli\u003eNamuganga, J. F., Briggs, J., Roh, M. E., Okiring, J., Kisambira, Y., Sserwanga, A., Kapisi, J. A., Arinaitwe, E., Ebong, C., Ssewanyana, I., Maiteki-Ssebuguzi, C., Kamya, M. R., Staedke, S. G., Dorsey, G., \u0026amp; Nankabirwa, J. I. (2021). Impact of COVID-19 on routine malaria indicators in rural Uganda: an interrupted time series analysis. \u003cem\u003eMalar J\u003c/em\u003e,\u003cem\u003e\u0026nbsp;20\u003c/em\u003e(1), 475. https://doi.org/10.1186/s12936-021-04018-0\u003c/li\u003e\n \u003cli\u003eNankabirwa, J. I., Bousema, T., Blanken, S. L., Rek, J., Arinaitwe, E., Greenhouse, B., Rosenthal, P. J., Kamya, M. R., Staedke, S. G., \u0026amp; Dorsey, G. (2022). Measures of malaria transmission, infection, and disease in an area bordering two districts with and without sustained indoor residual spraying of insecticide in Uganda. \u003cem\u003ePlos one\u003c/em\u003e,\u003cem\u003e\u0026nbsp;17\u003c/em\u003e(12), e0279464. https://doi.org/10.1371/journal.pone.0279464\u003c/li\u003e\n \u003cli\u003eNMCP, M. (2021). \u003cem\u003eTHIRD NATIONAL MASS CAMPAIGN FOR UNIVERSAL ACCESS TO LONG-LASTING INSECTICIDE-TREATED MOSQUITO NETS (LLINs) FOR MALARIA PREVENTION IN UGANDA\u003c/em\u003e file:///C:/Users/user/Downloads/Final-LLINs-UCC-report-2020%20(1).pdf\u003c/li\u003e\n \u003cli\u003eOkello, P. E., Van Bortel, W., Byaruhanga, A. M., Correwyn, A., Roelants, P., Talisuna, A., d\u0026rsquo;Alessandro, U., \u0026amp; Coosemans, M. (2006). Variation in malaria transmission intensity in seven sites throughout Uganda. \u003cem\u003eThe American journal of tropical medicine and hygiene\u003c/em\u003e,\u003cem\u003e\u0026nbsp;75\u003c/em\u003e(2), 219-225.\u003c/li\u003e\n \u003cli\u003eW.H.O. (2023). World malaria report 2023. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2023\u003c/li\u003e\n \u003cli\u003eWeiss, D. J., Bertozzi-Villa, A., Rumisha, S. F., Amratia, P., Arambepola, R., Battle, K. E., Cameron, E., Chestnutt, E., Gibson, H. S., Harris, J., Keddie, S., Millar, J. J., Rozier, J., Symons, T. L., Vargas-Ruiz, C., Hay, S. I., Smith, D. L., Alonso, P. L., Noor, A. M., . . . Gething, P. W. (2021). Indirect effects of the COVID-19 pandemic on malaria intervention coverage, morbidity, and mortality in Africa: a geospatial modelling analysis. \u003cem\u003eLancet Infect Dis\u003c/em\u003e,\u003cem\u003e\u0026nbsp;21\u003c/em\u003e(1), 59-69. https://doi.org/10.1016/s1473-3099(20)30700-3\u003c/li\u003e\n \u003cli\u003eWHO, U. (2020). A double challenge: Tackling COVID-19 and malaria in Uganda. https://www.afro.who.int/news/double-challenge-tackling-covid-19-and-malaria-uganda\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5039547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5039547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eReports on the impact of COVID-19 pandemic on \u0026nbsp;the quality of malaria care and burden in sub Saharan Africa have provided a mixed picture to date. We assessed the impact of the 2\u003csup\u003end\u003c/sup\u003e (Delta) and 3\u003csup\u003erd\u003c/sup\u003e (Omicron) COVID-19 waves on outpatient malaria indicators and case management practices at three public health facilities with varying malaria transmission intensities in Uganda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Individual level data from all patients presenting to the out-patient departments (OPD) of the three facilities (Kasambya, Walukuba and Lumino) between January 2019 and February 2022 were included in the analysis. Outcomes of interest included total number of outpatient (OPD) visits, proportion of patients suspected to have malaria, proportion of suspected malaria cases tested with a malaria diagnostic test, test positivity rates (TPR) and proportion of malaria cases prescribed artemether-lumefantrine (AL). Using the pre-COVID-19 trends between January 2019 and February 2020, interrupted time series analysis was used to predict the expected trends for these study outcomes during the 2\u003csup\u003end\u003c/sup\u003e wave (May 2021-August 2021) and 3\u003csup\u003erd\u003c/sup\u003e wave (November 2021-February 2022). The observed trends of the study outcomes were compared with the expected trends.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e There were no significant differences between the observed versus expected overall outpatient visits in the 2\u003csup\u003end\u003c/sup\u003e wave, \u0026nbsp;however, a significant\u0026nbsp; decline in OPD attendance was observed during the 3\u003csup\u003erd\u003c/sup\u003e wave (15101 vs 31154; incidence rate ratio (IRR)=0.48 [0.41-0.56]). No significant differences in the overall observed versus expected proportions of suspected malaria cases and test positivity rates in both COVID waves. However, a significant decrease in the overall proportion of suspected malaria cases tested with a malaria diagnostic test was observed during the 3\u003csup\u003erd\u003c/sup\u003e wave (99.86% vs 99.99%; relative percent ratio [RPR]=0.99 [0.99-0.99]). Finally, a significant decline in the overall proportion of malaria cases prescribed AL was observed during the 2\u003csup\u003end\u003c/sup\u003e wave (94.99% vs 99.85%; RPR =0.95 [0.92-0.98]) but not the 3\u003csup\u003erd\u003c/sup\u003e wave.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eSignificant declines in OPD attendance and suspected malaria cases tested with malaria diagnostic test were observed during the 3\u003csup\u003erd\u003c/sup\u003e COVID-19 wave, while AL prescription significantly reduced during the 2\u003csup\u003end\u003c/sup\u003e COVID-19 wave. These findings add to the body of knowledge highlighting the adverse impact of COVID-19 pandemic on the malaria which could explain the increase in the malaria burden observed during this period.\u003c/p\u003e","manuscriptTitle":"Effect of the second and third COVID-19 pandemic waves on routine outpatient malaria indicators and case management practices in Uganda; an interrupted time series analysis.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-10 12:52:42","doi":"10.21203/rs.3.rs-5039547/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-25T19:16:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-25T18:37:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118305983268488420970765908319186297511","date":"2024-09-18T13:31:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-17T10:37:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-06T16:27:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-06T16:25:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2024-09-05T16:10:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13a8f259-e4e9-4397-9b7a-4f5b336d561c","owner":[],"postedDate":"October 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:27:20+00:00","versionOfRecord":{"articleIdentity":"rs-5039547","link":"https://doi.org/10.1186/s12936-024-05153-0","journal":{"identity":"malaria-journal","isVorOnly":false,"title":"Malaria Journal"},"publishedOn":"2024-10-29 16:20:21","publishedOnDateReadable":"October 29th, 2024"},"versionCreatedAt":"2024-10-10 12:52:42","video":"","vorDoi":"10.1186/s12936-024-05153-0","vorDoiUrl":"https://doi.org/10.1186/s12936-024-05153-0","workflowStages":[]},"version":"v1","identity":"rs-5039547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5039547","identity":"rs-5039547","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}