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Background: Uganda accounts for 5% of all malaria cases and deaths reported globally and, in endemic countries, pregnancy is a risk factor for both acquisition of P. falciparum infection and development of severe malaria. In recent years, malaria control has been threatened by COVID-19 pandemic and by the emergence, in Northern Uganda, of both resistance to artemisinin derivatives and to sulfadoxine-pyrimethamine. Methods: In this facility-based, prospective, observational study, pregnant women will be recruited at antenatal-care visits and followed-up until delivery. Collected data will explore the incidence of asymptomatic parasitemia and malaria-related outcomes, as well as the attitudes towards malaria prevention, administration of intermittent preventive treatment, healthcare seeking behavior and use of insecticide-treated nets. A subpopulation of women diagnosed with malaria will be recruited and their blood samples will be analyzed for detection of genetic markers of resistance to artemisinin derivatives and sulfadoxine-pyrimethamine. Also, to investigate the impact of COVID-19 on malaria care among pregnant women, a retrospective, interrupted-time series will be conducted on at the study sites for the period January 2018 to December 2021. Discussion: The present study will explore the impact of COVID-19 pandemic on incidence of malaria and malaria-related adverse outcomes, along with the prevalence of resistance to artemisinin derivatives and to sulfadoxine-pyrimethamine. To our knowledge, this is the first study aiming to explore the combined effect of these factors on a cohort of pregnant women. Trial registration: This study has been registered on the ClinicalTrials.gov public website on 26th April, 2022. ClinicalTrials.gov Identifier: NCT05348746.
This will be a facility-based, prospective observational study, using quantitative methods of data collection. Semi-structured questionnaires will be administered to collect the data. The study will be conducted on three separate populations, that is: “Cohort of pregnant women”, “Antimalarial resistance sub-population”, and “COVID-19 impact population” for which study methods are described separately. The data will be collected following a cohort of pregnant women presenting to antenatal care visits. We shall have both a retrospective cohort for the period January 2018 to December 2021 to determine the Impact of COVID-19 pandemic on malaria control and a prospective cohort for the period July 2022 to June 2024 to determine the incidence of malaria related adverse maternal and foetal outcomes. For the prospective cohort, recruitment will take place at ANC clinic. Collected data will explore the practices towards malaria prevention during the COVID-19 pandemic, malaria and COVID risk-perception and use of insecticide-treated nets, while and follow up will investigate access to antenatal care visits, administration of IPTp, healthcare seeking behaviour in case of fever. Follow-up will end at delivery, when maternal and foetal outcomes will be collected. To estimate the epidemiological burden of resistance to first-line drugs for treatment and prevention of malaria in pregnant women, a separate sub-population of women diagnosed with positive malaria parasitaemia will be recruited. Prevalence of antimalaria resistance will be evaluated with a cross-sectional analysis of genetic polymorphisms in plasmodium parasites isolated from blood samples collected for the period July 2022 to June 2024. Individuals eligible for this subpopulation will be all pregnant women presenting with microscopically confirmed P. falciparum malaria in the study sites. For individuals included in this population, clinical data will be collected, and blood samples to be sent to Italian National Institute of Health, Rome, for detection of genetic markers of resistance to artemisinin derivatives and sulfadoxine-pyrimethamine. To investigate the impact of COVID-19 pandemic on malaria care, we will conduct a retrospective time-interrupted series. Data will be collected at multiple and equally spaced time points (monthly) comparing trends in two different time periods: “pre-pandemic”, from January 2018 to December 2019, and “during pandemic”, from January 2020 to December 2021. Facility-based aggregate data will be extracted about the following indicators: total admissions in maternity ward, total deliveries in maternity unit, women presenting to first ANC contact, women presenting to fourth ANC contact, administration of at least one dose of IPTp, administration of at least three doses of IPTp, number of pregnant women presenting to outpatient visits, number of pregnant women diagnosed of malaria during outpatient clinic, number of pregnant women diagnosed of severe malaria, total number of stillbirths. Inclusion criteria: All pregnant women at any gestational age presenting to the study sites, both at the emergency department, outpatient or ANC clinic will be eligible to participate in this study. Inclusion criteria will be: Exclusion criteria: All pregnant women presenting to Aber Hospital and selected healthcare facilities with microbiologically confirmed malaria will be eligible for recruitment. Inclusion criteria: Exclusion criteria: For the retrospective cohort investigating the impact of COVID-19, we shall use a facility-based census to include all pregnant mothers who sought care at the study sites for the period January 2018 to December 2021. For the prospective cohort, the sample size was estimated using the sample size estimation function in STATA12 for two-sample comparison of proportions. Null hypothesis: p1 = p2, (no difference in preterm birth rates), where p1 is the preterm birth rate among pregnant mothers diagnosed with malaria (exposed group) and p2 is the preterm birth rate among pregnant mothers with no diagnosis of malaria (non-exposed group). Assuming a type I error, alpha, of 0.050 for two-sided hypothesis, power of the study at 0.80, p1 = 0.075 and p2 = 0.039 based on the preterm birth rates reported in the Uganda Birth Cohort Study conducted from 2014–2016 in 12 districts in rural northern and southwestern Uganda [20]. Assuming equal number of participants in both groups, the required sample sizes in each of the two groups is 705. And after factoring in 10% for non-response in both groups, the total minimum required sample size is 1552 pregnant women (776 in each of the 2 groups). Given the total population of 779,600 in Oyam and Kole districts. Given that, based on Uganda bureau of statistics (UBOS), the total number of pregnant women in the two districts is expected to be 5% of the total population (n = 38,980); the expected rate of parasitemia among pregnant mother is expected to be 27% (n = 10,525) [21]; the expected resistance to sulfadoxine-pirimethamine and artemisinin-derivatives are, respectively, 16% and 20% [16, 18]; using the sample size calculation formula developed by Daniel and colleagues [22] and a margin of error, alpha = 5% the minimum required number of pregnant women diagnosed with malaria is 203. Applying 10% correction factor and assuming an increasing trend the required minimum sample size for this population is 224 cases of microscopically confirmed malaria among pregnant women. Sampling of health Units: The study will use purposive sampling to include Aber Hospital, Aboke HCIV and Atipe HCIII as the site for enrollment. This is based on the following criteria: Participants coming to the sampled in healthcare facilities that will meet the eligibility criteria will be included in the study. Consecutive enrollment of participants will be undertaken up to when the minimum sample size required for the study will be met. Thick and thin blood smears will be stained with 2% Giemsa and read by experienced laboratory technologists. Parasite densities will be calculated by counting the number of asexual parasites per 200 leukocytes (or per 500 leukocytes, if the count is < 10 asexual parasites/200 leukocytes), assuming a leukocyte count of 8000/µl. A blood smear will be considered negative when the examination of 100 high power fields does not reveal asexual parasites. Gametocytemia will also be determined from thick smears. Thin smears will be used for parasite species identification. At the time of delivery, recruited women will be screened for parasitemia on placental blood. This will be done by microscopy, with the same methods described above. The blood samples of the patients will be collected using filter paper (Whatman 3 MM) during admission to the healthcare facility. The dried blood spots (DBSs) will be collected through a finger prick (three drops of blood per participant) on filter papers which will be dried and kept in plastic bags with desiccant and stored in boxes in a cool dry place at room temperature before being transferred at the ISS for molecular diagnosis and drug resistance analysis. The collected blood samples will be shipped to Italy for advanced polymorphism analysis. During shipment, all the samples will be stored in a dry, cool place at room temperature to the Italian National Institute of Health (Istituto Superiore di Sanità, ISS). To enhance local capacity building, one laboratory person from Aber hospital will attend a two weeks exposure at the reference laboratory in Italy. Total genomic DNA will be extracted from filter blots (3MM Whatman) using the PureLink Genomic DNA Kits-Invitrogen, according to the manufacturer’s recommendation. Parasite identification is based on nested PCR assay targeting the 18S rRNA gene [23]. The 18S rRNA gene is used as a target since it contains both highly conserved and variable regions for each Plasmodium species. The genus-specific PCR will be followed by Plasmodium species-specific PCR amplification. Amplicons from the second PCR will be separated by electrophoresis on a 2% agarose gel and stained with ethidium bromide for visualization using ultraviolet trans-illumination. The presence of parasitaemia will be confirmed when the expected band size corresponding to P. falciparum, P. vivax, P. malariae and or P. ovale will be identified. Target P. falciparum drug resistance genes: Pfk13 propeller, Pfdhfr and Pfdhps. The polymorphisms analysis of the propeller domain of the Pfk13 gene will be performed by PCR amplifications and subsequent sequencing. Analysis of Pfdhfr gene at codons 51, 59, 108 and Pfdhps gene at codon positions 436, 437, 540, 581, 613 will be done by means of amplifications and subsequent Sanger sequencing. Commercial oligonucleotide primer pairs for Pfk13 will be obtained based on the published article by Taylor et al. [24], whereas for the analysis of dhfr and dhps genes primer pairs will be obtained based on the published article by Menegon et al. [25]. The obtained sequences will be compiled and analyzed by Accelrys DS Gene software. PlasmoDB gene identification no. PF3D7_1343700 (P. falciparum 3D7 strain) will be used as reference in the numbering of nucleotide and amino acid positions. Molecular studies will be performed only for research purposes and will have no impact on the clinical management of study patients. HIV and Syphilis will be measured according to the Uganda National Guidelines [19] Blood glucose will be measured by Glucometer “Accu-Chek Active”, Narang Medical LTD. Blood haemoglobin levels will be measured by Hemoglobin Testing System “Mission Ultra Hb”, Narang Medical LTD. For descriptive purposes continuous and ordinal variables data will be expressed as median with interquartile range. For categorical variables, percentages are calculated. Student’s t-test or analysis of variance (ANOVA) will be used to compare normally distributed numerical variables. Mann Whitney U-tests and Kruskal–Wallis tests will be used to compare numerical variables when normality cannot be assumed, while chi-squared tests will be used to compare categorical variables. Association analysis will be carried out to identify risk factors for Plasmodium infection and adverse maternal or foetal outcomes. We will compare behavioural factors and adherence to IPTp (and type of IPTp regimen) to the incidence of symptomatic/severe malaria and adverse neonatal or foetal outcomes (miscarriage, stillbirth, low birthweight). Multivariable logistic regression models will be used to identify independent risk factors for the same clinical outcomes. A forward and backwards stepwise approach will used to include variables into the models, with a limit of P < 0.2. A P-value of < 0.05 will be considered statistically significant. Final analyses will be conducted after the end of patient recruitment while interim analyses are planned at half 7 months from the incipit. Statistical analysis will be performed with R-software (R Foundation for Statistical Computing, Vienna, Austria).
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