Reported Malaria Cases Research Articles

Assessment of health staff's proficiency and quality of key malaria indicators in rural district of Ghana.

Routine Health Information Systems (RHIS) are important for not just sure enough control of malaria, but its elimination as well. If these systems are working, they can extensively provide accurate data on reported malaria cases instead of presenting modelled approximations of malaria burden. Queries are raised on both the quality and use of generated malaria data. Some issues of concern include inaccurate reporting of malaria cases as well as treatment plans, wrongly categorizing malaria cases in registers used to collate data and misplacing data or registers for reporting. This study analyses data quality concerning health staff's proficiency, timeliness, availability and data accuracy in the Sissala East Municipal Health Directorate (MHD). A cross-sectional design was used to collect data from 15 facilities and 50 health staff members who offered clinical related care for malaria cases in the Sissala East MHD from 24th August 2020 to 17th September 2020. Fifteen health facilities were randomly selected from the 56 health facilities in the municipality that were implementing the malarial control programme, and they were included in the study. On the question of when did staff receive any training on malaria-related health information management in the past six months prior to the survey, as minimal as 13 out of 50(26%) claimed to have been trained, whereas the majority 37 out of 50 (74%) had no training. In terms of proficiency in malaria indicators (MI), the majority (68% - 82%) of the respondents could not demonstrate the correct calculations of the indicators. Nevertheless, the MHD recorded monthly average timeliness of the 5th day [range: 4.7-5.7] within the reporting year. However, the MHD had a worse average performance of 5.4th and 5.7th days in July and September respectively. Furthermore, results indicated that 14 out of 15(93.3%) facilities exceeded the target to accomplish report availability (> = 90%) and data completeness (> = 90%). However, the verification factor (VF) of the overall malaria indicator showed that the MHD neither over-reported nor under-reported actual cases, with the corresponding level of data quality as Good (+/-5%). The Majority of staff had not received any training on malaria-related RHIS. Some staff members did not know the correct definitions of some of MI used in the malaria programme, while the majority of them could not demonstrate the correct calculations of MI. Timeliness of reporting was below the target, nevertheless, copies of data that were submitted were available and completed. There should be training, supervision and monitoring to enhance staff proficiency and improve the quality of MI.

Distribution and Risk Factors of Malaria in the Greater Accra Region in Ghana.

Malaria remains a serious public health challenge in Ghana including the Greater Accra Region. This study aimed to quantify the spatial, temporal and spatio-temporal patterns of malaria in the Greater Accra Region to inform targeted allocation of health resources. Malaria cases data from 2015 to 2019 were obtained from the Ghanaian District Health Information and Management System and aggregated at a district and monthly level. Spatial analysis was conducted using the Global Moran’s I, Getis-Ord Gi*, and local indicators of spatial autocorrelation. Kulldorff’s space–time scan statistics were used to investigate space–time clustering. A negative binomial regression was used to find correlations between climatic factors and sociodemographic characteristics and the incidence of malaria. A total of 1,105,370 malaria cases were reported between 2015 and 2019. Significant seasonal variation was observed, with June and July being the peak months of reported malaria cases. The hotspots districts were Kpone-Katamanso Municipal District, Ashaiman Municipal Districts, Tema Municipal District, and La-Nkwantanang-Madina Municipal District. While La-Nkwantanang-Madina Municipal District was high-high cluster. The Spatio-temporal clusters occurred between February 2015 and July 2017 in the districts of Ningo-Prampram, Shai-Osudoku, Ashaiman Municipal, and Kpone-Katamanso Municipal with a radius of 26.63 km and an relative risk of 4.66 (p < 0.001). Malaria cases were positively associated with monthly rainfall (adjusted odds ratio [AOR] = 1.01; 95% confidence interval [CI] = 1.005, 1.016) and the previous month’s cases (AOR = 1.064; 95% CI 1.062, 1.065) and negatively correlated with minimum temperature (AOR = 0.86, 95% CI = 0.823, 0.899) and population density (AOR = 0.996, 95% CI = 0.994, 0.998). Malaria control and prevention should be strengthened in hotspot districts in the appropriate months to improve program effectiveness.

Malaria Surveillance - United States, 2018.

Malaria Surveillance - United States, 2018.

Analisis Faktor Risiko Malaria di Asia Tenggara

Malaria is a disease caused by the parasites of the genus Plasmodium, which are transmitted by the bite of the female Anopheles mosquito. All tropical countries, including Southeast Asia, reported malaria cases. Several factors related to human behavior and the environment in which people live cause the incidence of malaria. The objective of this study was to analyze the risk factors for malaria in Southeast Asia and determine which risk variables had the greatest impact on the prevalence of malaria. This study is a review of the literature with a narrative review model using English and Indonesian journals from 2017 to 2021. The journals were obtained through the health journal provider databases Pubmed, Google Scholar, ScienceDirect, Researchgate, and Academic.edu. Malaria risk factors in Southeast Asia are influenced by behavioral and environmental factors. Behavioral factors include not use of mosquito nets, being active at night, male gender, and risky work. Environmental factors include rural areas, open-access dwellings, housing near cattle pens, and proximity to mosquito breeding sites. Changes in human behavior to avoid mosquito contact and environmental control are required to reduce the incidence of malaria.

Tracking malaria health disbursements by source in Zambia, 2009–2018: an economic modelling study

BackgroundZambia has made profound strides in reducing both the incidence and prevalence of malaria followed by reducing malaria related deaths between 2009 and 2018. The number of partners providing malaria funding has significantly increased in the same period. The increasing number of partners and the subsequent reduction of the number of reported malaria cases in the Ministry of Health main data repository Health Management Information System (HMIS) stimulated this research. The study aimed at (1) identifying major sources of malaria funding in Zambia; (2) describe malaria funding per targeted interventions and (3) relating malaria funding with malaria disease burden.MethodsData was collected using extensive literature review of institutional strategic document between the year 2009 to 2018, assuming one-year time lag between investment and the health outcome across all interventions. The National’s Health Management Information System (HMIS) provided information on annual malaria admission cases and outpatient clinic record. The statistical package for social sciences (SPSS) alongside Microsoft excel was used to analyze data in the year 2019.ResultsThe investigation observed that about 30% of the funding came from PMI/USAID, 26% from the global funds, the government of Zambia contributed 17% and other partners sharing the remaining 27%. Multivariate regression analysis suggests a positive correlation between reducing reported malaria disease burden in HMIS 2009–2018 and concurrent increasing program/intervention funding towards ITNs, IRS, MDA, and Case Management with r2 = 77% (r2 > 0.77; 95% CI: 0.72–0.81). Furthermore, IRS showed a p-value 0.018 while ITNs, Case Management and MDA having 0.029, 0.030 and 0.040 respectively.ConclusionOur findings highlight annual funding towards specific malaria intervention reduced the number of malaria admission cases.

Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention

BackgroundThe spatial and temporal variability inherent in malaria transmission within countries implies that targeted interventions for malaria control in high-burden settings and subnational elimination are a practical necessity. Identifying the spatio-temporal incidence, risk, and trends at different administrative geographies within malaria-endemic countries and monitoring them in near real-time as change occurs is crucial for developing and introducing cost-effective, subnational control and elimination intervention strategies.MethodsThis study developed intelligent data analytics incorporating Bayesian trend and spatio-temporal Integrated Laplace Approximation models to analyse high-burden over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015.ResultsThe results show that at least 5.4 million people live in catchment areas with increasing trends of malaria, covering over 47% of all health facilities, while 5.7 million people live in areas with a declining trend (95% CI), covering 27% of health facilities. A two-scale spatio-temporal trend comparison identified significant differences between health facilities and higher-level districts, and the pattern observed in the southeastern region of Zambia provides the first evidence of the impact of recently implemented localised interventions.ConclusionsThe results support our recommendation for an adaptive scaling approach when implementing national malaria monitoring, control and elimination strategies and a particular need for stratified subnational approaches targeting high-burden regions with increasing disease trends. Strong clusters along borders with highly endemic countries in the north and south of Zambia underscore the need for coordinated cross-border malaria initiatives and strategies.

MULTIVARIATE TIME SERIES ANALYSIS IN MODELLING MALARIA CASES IN JIMETA METROPOLIS OF ADAMAWA STATE, NIGERIA

Sub-Sahara Africa harbours most of the Malaria burden including Nigeria. There are scanty studies that aim at modelling these cases particularly in the study area. This study therefore, focused on a multivariate time series model for malaria cases among the residents in Jimeta metropolis of Adamawa State. A secondary data on reported malaria cases for adults, pregnant women and children was collected from January 2011 to December 2020 on monthly basis from medical records at the specialist Hospital, Jimeta, Yola, Adamawa State. The vector autoregressive (VAR) model was employed for modelling. A descriptive analysis was performed on the data. The lag order selection for stationary VAR model suggest lag three as the optimal lag for VAR model with malaria cases among children, adult and pregnant women. To assess how well the model fit the data set, AIC of 26.9458 for model with lag (3) was best. The Breusch-Godfrey LM test for residual serial correlation of VAR model suggest no autocorrelation at each lag, there is no problem of autocorrelation, since the associated p-value is greater than the conventional 0.05 level of significance. Jarque-Bera test shows that the residuals are not normally distributed and the forecast made showed that, rates of malaria cases are higher among adult followed by children and then pregnant women.

Malaria Epidemiology and COVID-19 Pandemic: Are They Interrelated?

Coronavirus disease 2019 (COVID-19) is a systemic disease, impacting multiple organs in the human body. But COVID-19 also impacts other diseases of relevance to public and planetary health. To understand and respond to the COVID-19 pandemic, we need an intersectional conceptual lens and systems thinking. For example, the strain on health care systems due to COVID-19 has adversely impacted global malaria elimination programs. With many epidemiological, clinical, and biological parallels documented, we examined in this study the scenario of malaria and COVID-19 syndemic in India. The disruptive influence of COVID-19 on the National Framework for Malaria Elimination (NFME), impact of unintended chemoprophylaxis, population genetic influences, and the shifting patterns of epidemiology are compared. Importantly, a time series analysis forecasted the burden of malaria increasing in the upcoming years. Although reported malaria cases showed a decline in 2020 compared to the previous years, an increase in cases was documented in 2021, with nine states reporting an increase up to July 2021. Pandemics often cause crosscutting disruptions in health care. Reshaping the priorities of the malaria elimination program and a diligent implementation of the priorities in the NFME would, therefore, be well-advised: (1) vector control, (2) antimalarial therapy recommendations, (3) monitoring drug resistance, (4) prevention of the spread of asymptomatic disease-causing low-density transmission, and (5) large-scale testing measures. In conclusion, the findings from the present study inform future comparative studies in other world regions to better understand the broader, systemic, temporal, and spatial impacts of the COVID-19 pandemic on existing and future diseases across public health systems and services.

Spatiotemporal analysis of environmental and physiographic factors related to malaria in Bareilly district, India.

ObjectivesThe aim of this study was to explore the spatiotemporal clustering of reported malaria cases and to study the effects of various environmental and physiographic factors on malaria incidence in Bareilly district, Uttar Pradesh, India.Methods Malaria surveillance data were collected from the state health department and cleaned into an analyzable format. These data were analyzed along with meteorological, physiographic, and 2019 population data, which were obtained from the Indian Meteorological Department, National Aeronautics and Space Administration web portal, the Bhuvan platform of the Indian Space Research Organization, and the 2011 Census of India.ResultsIn total, 46,717 malaria cases were reported in Bareilly district in 2019, of which 25.99% were Plasmodium vivax cases and 74.01% were P. falciparum cases. The reported malaria cases in the district showed clustering, with significant spatial autocorrelation (Moran’s I value=0.63), and space-time clustering (p<0.01). A significant positive correlation was found between monthly malaria incidence and the monthly mean temperature (with a lag of 1−2 months) and rainfall (with a lag of 1 month). A significant negative correlation was detected between the elevation of blocks (i.e., intermediate-level administrative districts) and annual malaria reporting.Conclusion The presence of space-time clustering of malaria cases and its correlation with meteorological and physiographic factors indicate that routine spatial analysis of the surveillance data could help control and manage malaria outbreaks in the district.

Status of malaria and its implications for elimination in an endemic province of South Africa: retrospective analysis

South Africa set a target to eliminate malaria by 2018. Kwa-Zulu-Natal (KZN) province was earmarked to reach the elimination goal first. However, recent evidence suggests that the province has not yet achieved the elimination goal. This study set out to assess the status of malaria in KZN province of South Africa and its implications for elimination. Using retrospective analysis, the study quantified and described 5 787 cases of malaria for the years 2010-2019 in KZN province of South Africa. Data on reported malaria cases were obtained from the Malaria Information System of the KZN Provincial Department of Health. More than 65% of the cases came from male patients aged 16 years and above. A high number of cases were reported in the years 2014, 2017 and 2018. More than 40% of the cases were imported from countries outside of South Africa. The study confirms an increase in malaria cases, especially during the elimination year. The imported malaria cases and other related factors pose a threat to achieving the elimination goal. The KZN province needs to strengthen elimination efforts, including strategies to prevent imported malaria cases to achieve the new elimination goal.

Epidemiological characteristics of imported Plasmodium ovale malaria in Jiangsu Province from 2012 to 2020

To analyze the epidemiological characteristics of imported cases with Plasmodium ovale infections in Jiangsu Province from 2012 to 2020, so as to provide insights into the development of the imported malaria control strategy in the province. All data pertaining to cases with definitive diagnosis of P. ovale malaria in Jiangsu Province from 2012 to 2020 were captured from the National Notifiable Disease Report System and the Information Management System for Parasitic Disease Control in China, including the date of going abroad and returning to China, time of malaria infections overseas, date of malaria onset, initial diagnosis and definitive diagnosis. All data pertaining to epidemic status were descriptively analyzed. A total of 347 cases of P. ovale malaria were reported in Jiangsu Province from 2012 to 2020, with the highest number seen in 2015 (71 cases). All cases were laboratory-confirmed overseas imported malaria cases, accounting for 14.32% of all reported malaria cases in Jiangsu Province during the period from 2012 to 2020. The 5 cities with the highest number of imported P. ovale malaria cases included Lianyungang City (53 cases, 15.27%), Nantong City (44 cases, 12.68%), Huai'an (44 cases, 12.68%), Taizhou City (44 cases, 12.68%) and Yangzhou City (36 cases, 10.37%). The highest number of imported P. ovale malaria cases was reported in October (39 cases, 11.24%), and the lowest number was seen in December (21 cases, 6.05%). P. ovale infections mainly occurred in were Equatorial Guinea (97 cases, 37.95%), Angola (60 cases, 17.29%) and Nigeria (40 cases, 11.53%). The median duration between returning to China and malaria onset was 64 (144) days, and 7.49% (26/347) of all cases developed malaria one year after returning to China. The initial diagnosis of P. ovale malaria was mainly made at county-level medical institutions (117 cases, 33.72%), and the definitive diagnosis was mainly made at city-level medical institutions (122 cases, 35.16%). The correct rate of initial diagnosis of P. ovale malaria increased from 0 in 2012 to 78.26% in 2020, appearing a tendency towards a rise year by year (χ2 = 50.90, P < 0.01). Imported P. ovale malaria cases were reported in Jiangsu Province each year from 2012 to 2020, and P. ovale infections predominantly occurred in Africa. Initial and definitive diagnoses of P. ovale malaria were mainly made at city- and county-level medical institutions. Training on the detection ability of malaria parasites is recommended among grassroots microscopists to improve the diagnostic ability of P. ovale malaria, and consolidate the achievements of malaria elimination in Jiangsu Province.

Analysis of laboratory re-examination results in Sichuan Provincial Malaria Diagnostic Reference Laboratory from 2014 to 2020

To re-examine the diagnosis results of reported malaria cases in Sichuan Province from 2014 to 2020, so as to assess the malaria diagnostic capability of Sichuan Provincial Malaria Diagnostic Reference Laboratory. The blood and blood smear samples from reported malaria cases were collected by Sichuan Provincial Malaria Diagnostic Reference Laboratory from 2014 to 2020, and subjected to re-examinations using microscopy and nested PCR assay. The re-examination results were compared. A total of 1 710 samples from reported malaria cases were re-examined by Sichuan Provincial Malaria Diagnostic Reference Laboratory from 2014 to 2020, and 1 634 samples were identified positive, with a positive coincidence rate of 95.56% (1 634/1 710) and a 92.29% (1 508/1 634) total coincidence rate of the Plasmodium species. The coincidence rates with P. falciparum, P. vivax, P. malariae and P. ovale were 99.48% (961/966), 97.07% (430/443), 83.05% (98/118) and 67.86% (19/28), respectively, and the coincidence rate was 91.81% (1 513/1 648) between microscopic and nested-PCR results. The capability of microscopists remains weak at grassroot medical institutions in Sichuan Province. Further training is required among microscopists to improve the malaria surveillance capability in Sichuan Province during the post-elimination stage.

ASSESSING RISK FACTORS OF MALARIA IN LAKHIMPUR DISTRICT OF ASSAM

Malaria is a life-threatening parasitic disease transmitted from person to person through the bite of a female Anopheles mosquito. The transmission of malaria can be determined by climatic and host factors. The objective of this paper is to trace the host factors of malaria incidence and also to determine the relationship between climatic factors and malaria incidence in Lakhimpur district of Assam. In order to examine the association between monthly malaria incidence rates and climatic variables, Pearson correlation analysis has been used. Also, Chi-square test for independence of attributes is performed to trace the host factors of malaria incidence. A uctuating trend was observed for reported malaria cases during the years 2000-2011. Both positive and negative correlation have occurred between climatic variable and MIR. Also, we have observed that male is more affected by malaria incidence than female. Among the age groups, the age group 15-39 years was found most affected age group than other age groups. After Chi-square test for independence of attributes we reveal that malaria depends on sex and age. SUMMARY: From our study, we suggest that even if the climatic factors play a primary role for transmission of malaria, sex and age are other important risk factors in characterizing malaria incidence in the district.

Residual malaria of Atlantic Forest systems and the influence of anopheline fauna.

In Brazil, the Amazon region comprises 99.5% of the reported malaria cases. However, another hotspot of the disease is the Atlantic Forest regions, with the sporadic occurrence of autochthonous human cases. In such context, this study sought to investigate the role of anopheline mosquitoes (Diptera: Culicidae) in the residual malaria transmission in Atlantic Forest areas. Two rural areas in the Espírito Santo state were the surveyed sites. Mosquitoes were captured using Shannon trap and CDC light traps and identified into species based on morphological characters. Ecological indexes (Shannon-Wiener diversity, Simpson's dominance, Pielou equability, and Sorensen similarity) were the tools used in the anopheline fauna characterization and comparison along with the two explored areas. The assessment of the sampling adequacy in the studied areas was possible through the generation of a species accumulation curve. A correlation test verified the influence of climatic variables on the anopheline species abundance. A total of 1471 female anopheline mosquitoes were collected from May 2019 to April 2020, representing 13 species. The species richness was higher in Valsugana Velha (hypo-endemic) than in Alto Caparaó (non-endemic). There was a significant variation in the species abundance between Valsugana Velha (n = 1438) and Alto Caparaó (n = 33). The most abundant species was Anopheles (Kerteszia) cruzii complex Dyar and Knab, 1908 representing 87% of the total anophelines collected. These results suggest that the Plasmodium spp. circulation in Brazilian Atlantic Forest areas occurs mainly due to the high frequency of Anopheles (K.) cruzii complex, considered the principal vector of simian and human malaria in the region.

Integrating Parasitological and Entomological Observations to Understand Malaria Transmission in Riverine Villages in the Peruvian Amazon.

BackgroundRemote rural riverine villages account for most of the reported malaria cases in the Peruvian Amazon. As transmission decreases due to intensive standard control efforts, malaria strategies in these villages will need to be more focused and adapted to local epidemiology.MethodsBy integrating parasitological, entomological, and environmental observations between January 2016 and June 2017, we provided an in-depth characterization of malaria transmission dynamics in 4 riverine villages of the Mazan district, Loreto department.ResultsDespite variation across villages, malaria prevalence by polymerase chain reaction in March 2016 was high (>25% in 3 villages), caused by Plasmodium vivax mainly and composed of mostly submicroscopic infections. Housing without complete walls was the main malaria risk factor, while households close to forest edges were more commonly identified as spatial clusters of malaria prevalence. Villages in the basin of the Mazan River had a higher density of adult Anopheles darlingi mosquitoes, and retained higher prevalence and incidence rates compared to villages in the basin of the Napo River despite test-and-treat interventions.ConclusionsHigh heterogeneity in malaria transmission was found across and within riverine villages, resulting from interactions between the microgeographic landscape driving diverse conditions for vector development, housing structure, and human behavior.

Allelic diversity of merozoite surface protein genes (msp1 and msp2) and clinical manifestations of Plasmodium falciparum malaria cases in Aceh, Indonesia

BackgroundThe malaria control programme in Indonesia has successfully brought down malaria incidence in many parts in Indonesia, including Aceh Province. Clinical manifestation of reported malaria cases in Aceh varied widely from asymptomatic, mild uncomplicated to severe and fatal complications. The present study aims to explore the allelic diversity of merozoite surface protein 1 gene (msp1) and msp2 among the Plasmodium falciparum isolates in Aceh Province and to determine their potential correlation with the severity of malaria clinical manifestation.MethodsScreening of over 500 malaria cases admitted to the hospitals in 11 districts hospital within Aceh Province during 2013–2015, identified 90 cases of P. falciparum mono-infection without any co-morbidity. The subjects were clinically phenotyped and parasite DNA was extracted and polymerase chain reaction (PCR) amplified for the msp1 and msp2 allelic subfamilies.ResultsAnalysis of clinical manifestation revealed that fever-chill is the most frequent symptom. Based on WHO criteria showed 19 cases were classified as severe and 71 as mild malaria. Analysis of msp1 gene revealed the presence of K1 allele subfamily in 34 subjects, MAD20 in 42 subjects, RO33 in 1 subject, and mixed allelic of K1 + MAD20 in 5 subjects, K1 + RO33 in 4 subjects, and MAD20 + RO33 in 4 subjects. Analysis of msp2 gene revealed 34 subjects carried the FC27 allelic subfamily, 37 subjects carried the 3D7 and 19 subjects carried the mixed FC27 + 3D7. Analysis of multiplicity of infection revealed that msp1 alleles is slightly higher than msp2 with the mean of MOI were 2.69 and 2.27, respectively. Statistical analysis to determine the association between each clinical manifestation and msp1 and msp2 alleles revealed that liver function abnormal value was associated with the msp2 mixed alleles (odds ratio (OR):0.13; 95%CI: 0.03–0.53). Mixed msp1 of K1 + RO33 was associated with severe malaria (OR: 28.50; 95%CI: 1.59–1532.30).ConclusionThis study found a strong association between severe malaria in Aceh with subjects carrying the msp1 mixed alleles of K1 and RO33. The liver function abnormal value associated with the msp2 mixed allelic subfamilies. Further study in different geographic areas is recommended.

Malaria Surveillance - United States, 2017.

Malaria Surveillance - United States, 2017.

Cross-border malaria in Northern Brazil

BackgroundCross-border malaria is a major barrier to elimination efforts. Along the Venezuela-Brazil-Guyana border, intense human mobility fueled primarily by a humanitarian crisis and illegal gold mining activities has increased the occurrence of cross-border cases in Brazil. Roraima, a Brazilian state situated between Venezuela and Guyana, bears the greatest burden. This study analyses the current cross-border malaria epidemiology in Northern Brazil between the years 2007 and 2018.MethodsDe-identified data on reported malaria cases in Brazil were obtained from the Malaria Epidemiological Surveillance Information System for the years 2007 to 2018. Pearson’s Chi-Square test of differences was utilized to assess differences between characteristics of cross-border cases originating from Venezuela and Guyana, and between border and transnational cases. A logistic regression model was used to predict imported status of cases.ResultsCross-border cases from Venezuela and Guyana made up the majority of border and transnational cases since 2012, and Roraima remained the largest receiving state for cross-border cases over this period. There were significant differences in the profiles of border and transnational cases originating from Venezuela and Guyana, including type of movement and nationality of patients. Logistic regression results demonstrated Venezuelan and Guyanese nationals, Brazilian miners, males, and individuals of working age had heightened odds of being an imported case. Furthermore, Venezuelan citizens had heightened odds of seeking care in municipalities adjacent Venezuela, rather than transnational municipalities.ConclusionsCross-border malaria contributes to the malaria burden at the Venezuela-Guyana-Brazil border. The identification of distinct profiles of case importation provides evidence on the need to strengthen surveillance at border areas, and to deploy tailored strategies that recognize different mobility routes, such as the movement of refuge-seeking individuals and of Brazilians working in mining.

Past eight-year malaria data in Gedeo zone, southern Ethiopia: trend, reporting-quality, spatiotemporal distribution, and association with socio-demographic and meteorological variables

BackgroundInformed decision making is underlined by all tiers in the health system. Poor data record system coupled with under- (over)-reporting of malaria cases affects the country’s malaria elimination activities. Thus, malaria data at health facilities and health offices are important particularly to monitor and evaluate the elimination progresses. This study was intended to assess overall reported malaria cases, reporting quality, spatiotemporal trends and factors associated in Gedeo zone, South Ethiopia.MethodsPast 8 years retrospective data stored in 17 health centers and 5 district health offices in Gedeo Zone, South Ethiopia were extracted. Malaria cases data at each health center with sociodemographic information, between January 2012 and December 2019, were included. Meteorological data were obtained from the national meteorology agency of Ethiopia. The data were analyzed using Stata 13.ResultsA total of 485,414 suspected cases were examined for malaria during the previous 8 years at health centers. Of these suspects, 57,228 (11.79%) were confirmed malaria cases with an overall decline during the 8-year period. We noted that 3758 suspected cases and 467 confirmed malaria cases were not captured at the health offices. Based on the health centers records, the proportions of Plasmodium falciparum (49.74%) and P. vivax (47.59%) infection were nearly equivalent (p = 0.795). The former was higher at low altitudes while the latter was higher at higher altitudes. The over 15 years of age group accounted for 11.47% of confirmed malaria cases (p < 0.001). There was high spatiotemporal variation: the highest case record was during Belg (12.52%) and in Dilla town (18,150, 13.17%, p < 0.001) which is located at low altitude. Monthly rainfall and minimum temperature exhibited strong associations with confirmed malaria cases.ConclusionA notable overall decline in malaria cases was observed during the eight-year period. Both P. falciparum and P. vivax were found at equivalent endemicity level; hence control measures should continue targeting both species. The noticed under reporting, the high malaria burden in urban settings, low altitudes and Belg season need spatiotemporal consideration by the elimination program.

Epidemiological profile of Plasmodium ovale spp. imported from Africa to Anhui Province, China, 2012\u20132019

BackgroundAlthough autochthonous malaria cases are no longer reported in Anhui Province, China, imported malaria has become a major health concern. The proportion of reported malaria cases caused by Plasmodium ovale spp. increased to levels higher than expected during 2012 to 2019, and showed two peaks, 19.69% in 2015 and 19.35% in 2018.MethodsA case-based retrospective study was performed using data collected from the China Information System for Disease Control and Prevention (CISDCP) and Information System for Parasitic Disease Control and Prevention (ISPDCP) from 2012 to 2019 to assess the trends and differences between Plasmodium ovale curtisi (P. o. curtisi) and Plasmodium ovale wallikeri (P. o. wallikeri). Epidemiological characteristics were analyzed using descriptive statistics.ResultsPlasmodium o. curtisi and P. o. wallikeri were found to simultaneously circulate in 14 African countries. Among 128 patients infected with P. ovale spp., the proportion of co-infection cases was 10.16%. Six cases of co-infection with P. ovale spp. and P. falciparum were noted, each presenting with two clinical attacks (the first attack was due to P. falciparum and the second was due to P. ovale spp.) at different intervals. Accurate identification of the infecting species was achieved among only 20.00% of cases of P. ovale spp. infection. At the reporting units, 32.17% and 6.96% of cases of P. ovale spp. infection were misdiagnosed as P. vivax and P. falciparum infections, respectively.ConclusionThe present results indicate that the potential of P. ovale spp. to co-infect with other Plasmodium species has been previously underestimated, as is the incidence of P. ovale spp. in countries where malaria is endemic. P. o. curtisi may have a long latency period of > 3 years and potentially cause residual foci, thus posing challenges to the elimination of malaria in P. ovale spp.-endemic areas. Considering the low rate of species identification, more sensitive point-of-care detection methods need to be developed for P. ovale spp. and introduced in non-endemic areas.