<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">AID</journal-id><journal-title-group><journal-title>Advances in Infectious Diseases</journal-title></journal-title-group><issn pub-type="epub">2164-2648</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/aid.2021.111010</article-id><article-id pub-id-type="publisher-id">AID-107923</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Medicine&amp;Healthcare</subject></subj-group></article-categories><title-group><article-title>
 
 
  Plasmodium Parasitaemia among Pregnant Women in the Niger Delta Region of Nigeria
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Ibinabo</surname><given-names>Laura Oboro</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Omosivie</surname><given-names>Maduka</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Terhemen</surname><given-names>Kasso</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Abimbola</surname><given-names>Temitayo Awopeju</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Nsirimobu</surname><given-names>Paul</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Lucy</surname><given-names>Yaguo-Ide</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Ifeyinwa</surname><given-names>Nwogo Chijioke-Nwauche</given-names></name><xref ref-type="aff" rid="aff5"><sup>5</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Mark</surname><given-names>Ogoro</given-names></name><xref ref-type="aff" rid="aff6"><sup>6</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Iyeopu</surname><given-names>Siminialayi</given-names></name><xref ref-type="aff" rid="aff7"><sup>7</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Claribel</surname><given-names>Ifesimama Abam</given-names></name><xref ref-type="aff" rid="aff8"><sup>8</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Alice</surname><given-names>Romakek Nte</given-names></name><xref ref-type="aff" rid="aff8"><sup>8</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Florence</surname><given-names>Onyemachi Nduka</given-names></name><xref ref-type="aff" rid="aff9"><sup>9</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Orikomaba</surname><given-names>Obunge</given-names></name><xref ref-type="aff" rid="aff8"><sup>8</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Chijioke</surname><given-names>Adonye Nwauche</given-names></name><xref ref-type="aff" rid="aff10"><sup>10</sup></xref></contrib></contrib-group><aff id="aff5"><addr-line>Department of Clinical Pharmacy and Management, University of Port Harcourt, Port Harcourt, Nigeria</addr-line></aff><aff id="aff10"><addr-line>Department of Haematology, Blood Transfusion and Immunology and Centre for Malaria Research and Phytomedicine, University of Port Harcourt, Port Harcourt, Nigeria</addr-line></aff><aff id="aff9"><addr-line>Department of Animal and Environmental Biology, University of Port Harcourt, Port Harcourt, Nigeria</addr-line></aff><aff id="aff4"><addr-line>Department of Paediatrics and Child Health, University of Port Harcourt, Port Harcourt, Nigeria</addr-line></aff><aff id="aff8"><addr-line>NDDC Professorial Chair on Malaria Elimination and Phytomedicine Research, Centre for Malaria Research and Phytomedicine, University of Port Harcourt, Port Harcourt, Nigeria</addr-line></aff><aff id="aff7"><addr-line>Department of Pharmacology and Centre for Malaria Research and Phytomedicine, University of Port Harcourt, Port Harcourt, Nigeria</addr-line></aff><aff id="aff1"><addr-line>Department of Medical Microbiology, University of Port Harcourt, Port Harcourt, Nigeria</addr-line></aff><aff id="aff6"><addr-line>Department of Geography and Environmental Management, University of Port Harcourt, Port Harcourt, Nigeria</addr-line></aff><aff id="aff2"><addr-line>Department of Preventive and Social Medicine, University of Port Harcourt, Port Harcourt, Nigeria</addr-line></aff><aff id="aff3"><addr-line>Department of Obstetrics and Gynaecology, University of Port Harcourt, Port Harcourt, Nigeria</addr-line></aff><pub-date pub-type="epub"><day>01</day><month>02</month><year>2021</year></pub-date><volume>11</volume><issue>01</issue><fpage>84</fpage><lpage>94</lpage><history><date date-type="received"><day>26,</day>	<month>January</month>	<year>2021</year></date><date date-type="rev-recd"><day>20,</day>	<month>March</month>	<year>2021</year>	</date><date date-type="accepted"><day>23,</day>	<month>March</month>	<year>2021</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  Introduction: Malaria is a febrile illness caused by the 
  <em>Plasmodium</em> species. The mangrove swamp forest vegetation and high annual rainfall characteristic of the Niger Delta region of Nigeria encourage all year round transmission of malaria. This study aimed to determine the prevalence and speciation of 
  <em>Plasmodium</em> parasitaemia among pregnant women in the Niger Delta region of Nigeria. 
  Methodology: Cross-sectional study carried out in three states of the Niger Delta region; Akwa-Ibom, Delta and Rivers between April and June 2019. Study Sites were chosen by stratified random sampling. Demographic information was collected using pretested interviewer-administered questionnaires via the Open Data Kit application on android mobile phones. Diagnosis was by rapid diagnostic test (RDT) and Microscopy. Ethical approval and informed consent were obtained. Data was analyzed using the SPSS v25 software. Chi-square statistic and Fischer’s exact test were used to compare data, all at a 95% confidence interval and significance level of 0.05. 
  Results: Two thousand, eight hundred and twenty (2820) pregnant women were studied; 948, 992 and 880 from Akwa-Ibom, Delta and Rivers respectively. Overall prevalence of parasitaemia using RDT and Microscopy was 6.8% and 6.7% respectively. All except 1% of malaria was attributed to falciparum species. The other species were plasmodium ovale and plasmodium malariae. 
  Conclusion: The prevalence of 
  <em>Plasmodium</em> parasitaemia among pregnant women in the Niger Delta region of Nigeria has reduced considerably, giving credence to the malaria preventive strategies applied in antenatal care. When properly stored and used as recommended, malaria RDTs compare favorably with microscopy; therefore, no case of malaria should be missed due to a facility’s incapability to carry out microscopic diagnosis.
 
</p></abstract><kwd-group><kwd>Malaria Parasitaemia</kwd><kwd> Pregnant Women</kwd><kwd> Malaria RDT</kwd><kwd> Microscopy</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Background</title><p>Malaria is a febrile illness caused by the protozoan parasites of the genus Plasmodium which until recently was classified into five species: P. falciparum,P. ovale,P. malariae, P. vivax andknowlesi; however, current studies [<xref ref-type="bibr" rid="scirp.107923-ref1">1</xref>] have shown that there are two non-recombining species of ovale (ovale curtisi and ovale wallikeri) which are non-sympatric in nature [<xref ref-type="bibr" rid="scirp.107923-ref2">2</xref>]. Of all these, Plasmodium falciparum is the most common species in virtually all parts of Africa, accounting for up to 98% of confirmed cases in Nigeria. It is the agent of the most malignant form of malaria, usually presenting with greater morbidity and mortality, mostly among children and pregnant women [<xref ref-type="bibr" rid="scirp.107923-ref3">3</xref>]. P. malariae tends to occur as a mixed infection with P. falciparum [<xref ref-type="bibr" rid="scirp.107923-ref4">4</xref>].</p><p>Malaria is essentially a disease of the tropics and subtropics particularly the sub-Saharan African region although it has been reported in temperate areas due to migration from the tropics. It is holo-endemic in Nigeria where there is a year-round transmission. Malaria transmission is the highest in Nigeria during the rainy season which usually spans April to September, with the peak of rains between May and July. Rainfall pattern in Nigeria varies largely, with the South having more rains than the North. Annual rainfall decreases northward; rainfall ranges from about 2000 millimeters in the coastal zone (averaging more than 3550 millimeters in the Niger Delta) to 500 - 750 millimeters in the north. The far south is defined by its tropical rainforest climate, where annual rainfall is 1524 to 2032 mm (60 to 80 inches) per year. The Niger Delta is located on the Atlantic coast of Southern Nigeria encompassing an area of 20,000 km<sup>2</sup> and is the world’s third largest wetland. The mangrove swamp forest vegetation here encourages all year-round transmission of malaria. Malaria prevalence in Nigeria thus varies widely, ranging from 14% in the South East Zone to 37% in the North West Zone [<xref ref-type="bibr" rid="scirp.107923-ref5">5</xref>].</p><p>Malaria remains a major public health challenge for developing countries. Nigeria bears the major portion of this burden as she accounts for 25% of the global malaria burden. Approximately 173 million (97%) of Nigeria’s population is at risk of malaria [<xref ref-type="bibr" rid="scirp.107923-ref6">6</xref>] with greater than half a million new cases recorded in 2017 [<xref ref-type="bibr" rid="scirp.107923-ref7">7</xref>]. Nigeria was one of three (3) countries which had the highest estimated increases in malaria burden in 2017 compared with 2016 [<xref ref-type="bibr" rid="scirp.107923-ref7">7</xref>]. This is worrisome as the Nigeria Malaria Indicator Survey of 2015, had reported a decline in malaria prevalence from 42% in 2010 to 27% in 2015 [<xref ref-type="bibr" rid="scirp.107923-ref8">8</xref>].</p><p>The bulk (94%) of all malaria deaths in 2018 occurred in the WHO African Region with pregnant women and children bearing the greatest impact. Children aged under 5 years are the most vulnerable group and accounted for 67% (272,000) of all malaria deaths globally. The West African sub-region where Nigeria is located was one of two sub-regions in Africa with the highest prevalence (35%) of exposure to malaria infection in pregnancy. Pregnant women in this region had the highest prevalence of low-birth-weight children (872,000, 16%) due to malaria in pregnancy [<xref ref-type="bibr" rid="scirp.107923-ref7">7</xref>].</p><p>Among the many possible complications of malaria in pregnancy, the most critical are maternal anaemia and delivery of low-birth-weight babies with the associated complications [<xref ref-type="bibr" rid="scirp.107923-ref3">3</xref>]. Beyond financial implications, the indirect costs of malaria cannot be quantified including the long-term effect on cognitive function and educational attainment in children [<xref ref-type="bibr" rid="scirp.107923-ref9">9</xref>] [<xref ref-type="bibr" rid="scirp.107923-ref10">10</xref>]. This situation is worsened by the scourge of multi-drug resistance resulting from improper diagnosis, self-medication, and improper treatment, including use of sub-optimal and incomplete doses of antimalarials.</p><p>Light microscopic examination of Giemsa-stained blood films has been recommended by WHO for use in diagnosis of malaria where adequate support for its use is available. Light microscopy however is quite limited in Nigeria especially in rural, hard to reach or poorer settings, as well as where trained personnel are not available. Rapid diagnostic tests on the other hand, are lateral flow devices designed based on antigen-antibody interactions for the qualitative diagnosis of malaria parasite antigens in blood. The target antigens for which RDTs have been designed include the histidine rich protein 2 (HRP2) expressed by Plasmodium falciparum and/or Plasmodium lactate dehydrogenase (pLDH) expressed by all human Plasmodium species [<xref ref-type="bibr" rid="scirp.107923-ref11">11</xref>].</p><p>This study aimed to determine the prevalence of Plasmodium parasitaemia among pregnant women in three states of the Niger Delta region of Nigeria, the distribution of the Plasmodium species causing infection and determine the diagnostic efficacy of RDT compared with microscopy.</p></sec><sec id="s2"><title>2. Materials and Methods</title><p>This was a cross-sectional study carried out among pregnant women in three states of the Niger Delta region of Nigeria, Akwa-Ibom, Delta, and Rivers states between April and June 2019. Eligible participants were those who were attending antenatal care at public or private health facilities in the state.</p><p>A sample size of 758 per state was calculated using the sample size formula for single proportion with 27% prevalence rate of malaria from the 2015 Nigeria Malaria indicator survey [<xref ref-type="bibr" rid="scirp.107923-ref8">8</xref>], a degree of accuracy of 0.05% and 95% confidence interval, 20% non-response rate and a multiplication factor of two (2) to compensate for design effect. Stratified sampling was employed to select two local government areas (LGA) each from the three senatorial zones, making a total of six local governments. Computer generated table of random numbers was used to select two heath facilities from two zones and four from the largest zone to get eight health facilities. The sample size was then distributed across these facilities in a proportionate manner based on average antenatal clinic attendance. Systematic sampling was used to select pregnant women from each of the selected health facilities based on sampling interval calculated by dividing the average antenatal attendance by the allocated sample size per facility. Data collection spanned two weeks. Research assistants, data collectors and microscopists were trained on the study protocol for standardization of data collection and laboratory processes. Information was collected using pretested interviewer administered questionnaires using a mobile data collection tool, the Open Data Kit (ODK) on android devices</p><p>One millilitre of venous blood was collected from each woman. A rapid diagnostic test was immediately performed using the SD Bioline Malaria Ag P.f Kit (Standard Diagnostics Inc., USA) according to manufacturers’ instruction and thereafter, two thin and thick blood smears were made on two clean glass slides per woman. Smears were stained using freshly prepared 3% Giemsa stain and examined for the Plasmodium species, stage, and density according to World Health Organization’s recommendations. Each one of a participant’s blood films was read by two independent microscopists. Each microscopist attached to a specimen was blind to the result of the other microscopist. A patient was reported as positive if either one or both tests were interpreted as positive. All patients with infection were managed according to standard of care. Ethical approval was obtained from the ethical boards of the states and facilities and informed consent was obtained from participants before each interview.</p><p>Data was presented using summary statistics (frequency and percentages) and analyzed using the SPSS version 25 software. The Chi-square statistic and Fischer’s exact test were used for inferential analysis. Decisional analysis using a two-by-two table was done with sensitivity, specificity, positive and negative predictive values, and test accuracy, using microscopy as the gold standard. All analyses were done at a 95% confidence interval and a significance level of 0.05.</p></sec><sec id="s3"><title>3. Results</title><p>A total of two thousand, eight hundred and twenty (2820) pregnant women were studied; nine hundred and forty-eight (948) from Akwa-Ibom state, nine hundred and ninety-two (992) from Delta state and eight hundred and eighty (880) from Rivers state.</p><sec id="s3_1"><title>3.1. Sociodemographics</title><p>Study participant’s sociodemographic information is shown on <xref ref-type="table" rid="table1">Table 1</xref>. Across</p><table-wrap id="table1" ><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> Socio-demographic characteristics of study participants</title></caption><table><tbody><thead><tr><th align="center" valign="middle" ></th><th align="center" valign="middle" >Akwa Ibom (n = 948), %</th><th align="center" valign="middle" >Delta (n = 992), %</th><th align="center" valign="middle" >Rivers (n = 880), %</th><th align="center" valign="middle" >Total (n = 2820) %</th></tr></thead><tr><td align="center" valign="middle" >Age groups</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >19 - 29 years</td><td align="center" valign="middle" >551 (58.12)</td><td align="center" valign="middle" >486 (48.99)</td><td align="center" valign="middle" >372 (42.27)</td><td align="center" valign="middle" >1409 (50.0)</td></tr><tr><td align="center" valign="middle" >30 - 39 years</td><td align="center" valign="middle" >384 (40.51)</td><td align="center" valign="middle" >472 (47.58)</td><td align="center" valign="middle" >483 (54.89)</td><td align="center" valign="middle" >1339 (47.4)</td></tr><tr><td align="center" valign="middle" >40 - 49 years</td><td align="center" valign="middle" >13 (1.37)</td><td align="center" valign="middle" >34 (3.43)</td><td align="center" valign="middle" >25 (2.84)</td><td align="center" valign="middle" >72 (2.6)</td></tr><tr><td align="center" valign="middle" >Marital status</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Married</td><td align="center" valign="middle" >891 (93.99)</td><td align="center" valign="middle" >954 (96.17)</td><td align="center" valign="middle" >860 (97.73)</td><td align="center" valign="middle" >2705 (95.9)</td></tr><tr><td align="center" valign="middle" >Divorced</td><td align="center" valign="middle" >1 (0.11)</td><td align="center" valign="middle" >2 (0.20)</td><td align="center" valign="middle" >1 (0.11)</td><td align="center" valign="middle" >4 (0.14)</td></tr><tr><td align="center" valign="middle" >Single</td><td align="center" valign="middle" >54 (5.70)</td><td align="center" valign="middle" >35(3.53)</td><td align="center" valign="middle" >17 (1.93)</td><td align="center" valign="middle" >106 (3.8)</td></tr><tr><td align="center" valign="middle" >Widowed</td><td align="center" valign="middle" >2 (0.21)</td><td align="center" valign="middle" >1 (0.10)</td><td align="center" valign="middle" >2 (0.23)</td><td align="center" valign="middle" >5 (0.18)</td></tr><tr><td align="center" valign="middle" >Education</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >No formal education</td><td align="center" valign="middle" >3 (0.32)</td><td align="center" valign="middle" >4 (0.40)</td><td align="center" valign="middle" >0 (0.0)</td><td align="center" valign="middle" >7 (0.25)</td></tr><tr><td align="center" valign="middle" >Primary</td><td align="center" valign="middle" >42 (4.43)</td><td align="center" valign="middle" >43 (4.33)</td><td align="center" valign="middle" >3 (0.34)</td><td align="center" valign="middle" >88 (3.1)</td></tr><tr><td align="center" valign="middle" >Secondary</td><td align="center" valign="middle" >459 (48.42)</td><td align="center" valign="middle" >484 (48.79)</td><td align="center" valign="middle" >293 (33.30)</td><td align="center" valign="middle" >1236 (43.8)</td></tr><tr><td align="center" valign="middle" >Tertiary</td><td align="center" valign="middle" >444 (46.84)</td><td align="center" valign="middle" >461 (46.47)</td><td align="center" valign="middle" >584 (66.36)</td><td align="center" valign="middle" >1489 (52.8)</td></tr><tr><td align="center" valign="middle" >Occupation</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Civil servant</td><td align="center" valign="middle" >80 (8.44)</td><td align="center" valign="middle" >40 (4.03)</td><td align="center" valign="middle" >80 (9.09)</td><td align="center" valign="middle" >200 (7.1)</td></tr><tr><td align="center" valign="middle" >Farmer</td><td align="center" valign="middle" >12 (1.27)</td><td align="center" valign="middle" >9 (0.91)</td><td align="center" valign="middle" >8 (0.91)</td><td align="center" valign="middle" >29 (1.0)</td></tr><tr><td align="center" valign="middle" >Public servant</td><td align="center" valign="middle" >71 (7.49)</td><td align="center" valign="middle" >49 (4.94)</td><td align="center" valign="middle" >72 (8.18)</td><td align="center" valign="middle" >192 (6.8)</td></tr><tr><td align="center" valign="middle" >Self-employed</td><td align="center" valign="middle" >228 (24.05)</td><td align="center" valign="middle" >185 (18.65)</td><td align="center" valign="middle" >159 (18.07)</td><td align="center" valign="middle" >572 (20.3)</td></tr><tr><td align="center" valign="middle" >Teacher</td><td align="center" valign="middle" >111 (11.71)</td><td align="center" valign="middle" >109 (10.99)</td><td align="center" valign="middle" >117 (13.30)</td><td align="center" valign="middle" >337 (12.0)</td></tr><tr><td align="center" valign="middle" >Trader</td><td align="center" valign="middle" >321 (33.86)</td><td align="center" valign="middle" >432 (43.55)</td><td align="center" valign="middle" >252 (28.64)</td><td align="center" valign="middle" >1005 (35.6)</td></tr><tr><td align="center" valign="middle" >Others</td><td align="center" valign="middle" >125 (13.19)</td><td align="center" valign="middle" >168 (16.94)</td><td align="center" valign="middle" >192 (21.82)</td><td align="center" valign="middle" >485 (17.2)</td></tr></tbody></table></table-wrap><p>the three states, half of the study population was aged 19 - 29 years (1409; 50.0%), had tertiary education 1489 (52.8) and were either traders 1005 (35.6) or self-employed 572 (20.3).</p></sec><sec id="s3_2"><title>3.2. Prevalence of Plasmodium Parasitaemia</title><p>The detection rate of Plasmodium parasitaemia by RDT and Microscopy is shown in <xref ref-type="fig" rid="fig1">Figure 1</xref>.</p><p>A total of 191 persons (6.8%) tested positive for malaria using RDT compared with 188 (6.7%) who tested positive using microscopy <xref ref-type="fig" rid="fig1">Figure 1</xref>.</p><p>The highest prevalence of malaria using RDT was found in Akwa-Ibom (8.3%) state while the lowest prevalence was from Delta state (5.6%). Microscopy revealed the highest prevalence of malaria from Rivers State (8.4%) while Akwa-Ibom had the lowest prevalence (3.5%).</p></sec><sec id="s3_3"><title>3.3. Distribution of Malaria Parasite Species</title><p><xref ref-type="fig" rid="fig2">Figure 2</xref> shows that majority (99%) of infected women had P. falciparum</p><table-wrap id="table2" ><label><xref ref-type="table" rid="table2">Table 2</xref></label><caption><title> State-based distribution of diagnostic outcome</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  colspan="2"  >Results</th><th align="center" valign="middle" >Akwa Ibom n = 948, (%)</th><th align="center" valign="middle" >Delta n = 992, (%)</th><th align="center" valign="middle" >Rivers n = 880, (%)</th><th align="center" valign="middle" >Total n = 2820, (%)</th></tr></thead><tr><td align="center" valign="middle"  rowspan="2"  >RDT</td><td align="center" valign="middle" >Positive</td><td align="center" valign="middle" >79 (8.3)</td><td align="center" valign="middle" >56 (5.6)</td><td align="center" valign="middle" >56 (6.4)</td><td align="center" valign="middle" >191 (6.8)</td></tr><tr><td align="center" valign="middle" >Negative</td><td align="center" valign="middle" >869 (91.7)</td><td align="center" valign="middle" >936 (94.4)</td><td align="center" valign="middle" >824 (93.6)</td><td align="center" valign="middle" >2629 (93.2)</td></tr><tr><td align="center" valign="middle"  rowspan="2"  >Microscopy</td><td align="center" valign="middle" >Positive</td><td align="center" valign="middle" >33 (3.5)</td><td align="center" valign="middle" >81 (8.2)</td><td align="center" valign="middle" >74 (8.4)</td><td align="center" valign="middle" >188 (6.7)</td></tr><tr><td align="center" valign="middle" >Negative</td><td align="center" valign="middle" >915 (96.5)</td><td align="center" valign="middle" >911 (91.8)</td><td align="center" valign="middle" >806 (91.6)</td><td align="center" valign="middle" >2632 (93.3)</td></tr></tbody></table></table-wrap><table-wrap id="table3" ><label><xref ref-type="table" rid="table3">Table 3</xref></label><caption><title> Distribution of Species detected in the different States</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Specie</th><th align="center" valign="middle" >Akwa Ibom</th><th align="center" valign="middle" >Delta</th><th align="center" valign="middle" >Rivers</th><th align="center" valign="middle" >Fischer’s Exact</th></tr></thead><tr><td align="center" valign="middle" >P. falciparum</td><td align="center" valign="middle" >32 (96.9)</td><td align="center" valign="middle" >81 (100.0)</td><td align="center" valign="middle" >73 (98.7)</td><td align="center" valign="middle"  rowspan="4"  >6.26 (p = 0.1.80)</td></tr><tr><td align="center" valign="middle" >P. malariae</td><td align="center" valign="middle" >1 (3.1)</td><td align="center" valign="middle" >0 (0.0)</td><td align="center" valign="middle" >0 (0.0)</td></tr><tr><td align="center" valign="middle" >P. ovale</td><td align="center" valign="middle" >0 (0.0)</td><td align="center" valign="middle" >0 (0.0)</td><td align="center" valign="middle" >1 (1.35)</td></tr><tr><td align="center" valign="middle" >Total</td><td align="center" valign="middle" >33 (100.0)</td><td align="center" valign="middle" >81 (100.0)</td><td align="center" valign="middle" >74 (100.0)</td></tr></tbody></table></table-wrap><table-wrap id="table4" ><label><xref ref-type="table" rid="table4">Table 4</xref></label><caption><title> Comparison of diagnostic accuracy of mRDT compared to Microscopy</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  >Test (RDT)</th><th align="center" valign="middle"  colspan="2"  >Disease (Microscopy)</th><th align="center" valign="middle"  rowspan="2"  >Total</th></tr></thead><tr><td align="center" valign="middle" >Yes</td><td align="center" valign="middle" >No</td></tr><tr><td align="center" valign="middle" >Yes</td><td align="center" valign="middle" >188 (a)</td><td align="center" valign="middle" >3 (b)</td><td align="center" valign="middle" >191 (a + b)</td></tr><tr><td align="center" valign="middle" >No</td><td align="center" valign="middle" >0 (c)</td><td align="center" valign="middle" >2629 (d)</td><td align="center" valign="middle" >2629 (c + d)</td></tr><tr><td align="center" valign="middle" >Total</td><td align="center" valign="middle" >188 (a + c)</td><td align="center" valign="middle" >2632 (b + d)</td><td align="center" valign="middle" >2820 (a + b + c + d)</td></tr><tr><td align="center" valign="middle"  colspan="2"  >Sensitivity</td><td align="center" valign="middle" >a/(a + c)</td><td align="center" valign="middle" >100.0%</td></tr><tr><td align="center" valign="middle"  colspan="2"  >Specificity</td><td align="center" valign="middle" >d/(b + d)</td><td align="center" valign="middle" >99.9%</td></tr><tr><td align="center" valign="middle"  colspan="2"  >Positive predictive value</td><td align="center" valign="middle" >a/(a + b)</td><td align="center" valign="middle" >94.8%</td></tr><tr><td align="center" valign="middle"  colspan="2"  >Negative predictive value</td><td align="center" valign="middle" >d/(c + d)</td><td align="center" valign="middle" >100.0%</td></tr><tr><td align="center" valign="middle"  colspan="2"  >Prevalence of the disease</td><td align="center" valign="middle" >(a + b)/(a + b + c + d)</td><td align="center" valign="middle" >6.8%</td></tr><tr><td align="center" valign="middle"  colspan="2"  >Accuracy* (extent of correct classification)</td><td align="center" valign="middle" >(a + d)/(a + b + c + d)</td><td align="center" valign="middle" >99.9%</td></tr></tbody></table></table-wrap><p>parasitaemia while <xref ref-type="table" rid="table3">Table 3</xref> shows the species distribution according to states.</p></sec><sec id="s3_4"><title>3.4. Diagnostic Accuracy</title><p>An analysis of the diagnostic accuracy of the RDT test compared to microscopy showed that RDT used in this survey has high diagnostic accuracy (99%) compared to microscopy <xref ref-type="table" rid="table4">Table 4</xref>.</p></sec></sec><sec id="s4"><title>4. Discussion</title><p>The Demographics show that many women in the Niger Delta region get married at earlier ages though well educated. This is important because enlightenment and education of individuals are proven means of primary prevention of infectious diseases including malaria [<xref ref-type="bibr" rid="scirp.107923-ref12">12</xref>].</p><p>The prevalence of Plasmodium parasitaemia observed in the pregnant women we studied is much lower than that of other studies done previously in Rivers State and other states in the Niger Delta region of Nigeria. Which reported much higher prevalence in years past [<xref ref-type="bibr" rid="scirp.107923-ref12">12</xref>] [<xref ref-type="bibr" rid="scirp.107923-ref13">13</xref>] [<xref ref-type="bibr" rid="scirp.107923-ref14">14</xref>] [<xref ref-type="bibr" rid="scirp.107923-ref15">15</xref>]. This is very exciting as it reveals a downward trend in prevalence, an indication of the success of the malaria prevention strategies implemented among pregnant women; including intermittent preventive treatment of malaria in pregnancy (IPTp) administered during antenatal care and use of insecticide-treated bed nets as recommended by WHO and the Nigeria Malaria Elimination Program. Despite the increased prevalence observed between 2016 and 2017 in the WHO African Region, case incidence levels were reported to have declined from 294 in 2010 to 229 in 2018, representing a 22% reduction [<xref ref-type="bibr" rid="scirp.107923-ref7">7</xref>]. The implication is that we have obviously made remarkable progress towards the WHO Global technical strategy for malaria 2016-2030 that among other targets aims to reduce malaria case incidence by at least 90% by the year 2030. Pregnant women are an important group to focus on in this regard.</p><p>Plasmodium falciparum is the most prevalent malaria parasite in the WHO African Region, having accounted for 99.7% of estimated malaria cases in 2018. Our study also reflects the same, with P. falciparum accounting for 99% of all infections in our study cohort [<xref ref-type="bibr" rid="scirp.107923-ref7">7</xref>].</p><p>The ease of use of RDTs and high diagnostic accuracy in comparison with Light microscopy has made their development a major contributor to malaria control globally. RDTs unlike microscopic diagnosis are quite easy to perform, do not require complex equipment, electricity supply and highly skilled personnel while providing rapid and reliable results as has been reported severally [<xref ref-type="bibr" rid="scirp.107923-ref16">16</xref>] [<xref ref-type="bibr" rid="scirp.107923-ref17">17</xref>].</p><p>Our findings also agree with reports of their comparability when used in diagnosis of acute malaria with only 0.1% difference in detection observed between both methods; RDT being higher. This difference could possibly be due to false positive result with the RDT since RDTs have been known to remain positive for a time frame of about 10 - 14 days following successful treatment of malaria. Dalrymple et al. showed that half of RDTs that detect the antigen histidine-rich protein II (HRP2) as was done in our study, are still positive 15 days post-treatment, with about 5% remaining positive 36 days following anti-malarial treatment [<xref ref-type="bibr" rid="scirp.107923-ref18">18</xref>]. An important limitation of RDTs includes not being able to detect some infections with lower parasite density as their sensitivity reduces with reducing parasite density [<xref ref-type="bibr" rid="scirp.107923-ref19">19</xref>] [<xref ref-type="bibr" rid="scirp.107923-ref20">20</xref>] [<xref ref-type="bibr" rid="scirp.107923-ref11">11</xref>]. Microscopy is able to overcome this as well as detect less common species such as P. ovaleand P. malariae which RDTs may not, as seen in our study.</p><p>These findings imply that malaria prevalence in the Niger Delta is on the decline and the goal for of malaria elimination is in sight. However concerted efforts in prevention and prompt diagnosis and treatment are imperative to push the elimination efforts to its destination. In addition, RDTs were shown to be quite useful for case detection implying that its use is quite advantageous in resource limited settings and should be implemented as point of care testing at every health care service delivery setting in the country.</p><p>The strengths of this study are that it employed a large sample size across three states representing the Niger Delta region of Nigeria, and that it used both RDT and microscopy to diagnose malaria cases. The limitation of the study is its cross-sectional design and as such treatment and pregnancy outcomes were beyond the scope of the study. However cross section studies have been showed to be adequate for prevalence studies.</p></sec><sec id="s5"><title>5. Conclusion</title><p>The prevalence of Plasmodium parasitaemia among pregnant women in the Niger Delta region of Nigeria has reduced considerably. This gives credence to the malaria preventive strategies applied in pregnancy. We therefore encourage the promotion of these measures as they have shown effectiveness. When properly stored and used as recommended, RDTs compare favorably with microscopy in diagnosis of malaria; therefore, no case of malaria should go undiagnosed due to a facility’s incapability to carry out microscopic diagnosis.</p></sec><sec id="s6"><title>Acknowledgements</title><p>The study is part of a composite study coordinated by the Niger Delta Development Company (NDDC) Professorial Chair on Malaria Elimination and Phytomedicine Research, Centre for Malaria Research and Phytomedicine, University of Port Harcourt, Rivers State, Nigeria.</p></sec><sec id="s7"><title>Authors’ Contributions</title><p>This work was carried out in collaboration among all authors. Author CAN wrote the protocol. Authors IMS, CIA, ARN, FON, OKO and CAN managed the research design, processes and co-ordination. Author OIL wrote the drafts of the manuscript and managed the literature searches. Author OM performed the statistical analysis. All authors read and approved the drafts and final manuscript.</p></sec><sec id="s8"><title>Conflicts of Interest</title><p>The authors declare no conflicts of interest regarding the publication of this paper.</p></sec><sec id="s9"><title>Cite this paper</title><p>Oboro, I.L., Maduka, O., Kasso, T., Awopeju, A.T., Paul, N., Yaguo-Ide, L., Chijioke-Nwauche, I.N., Ogoro, M., Siminialayi, I., Abam, C.I., Nte, A.R., Nduka, F.O., Obunge, O. and Nwauche, C.A. (2021) Plasmodium Parasitaemia among Pregnant Women in the Niger Delta Region of Nigeria. Advances in Infectious Diseases, 11, 84-94. https://doi.org/10.4236/aid.2021.111010</p></sec></body><back><ref-list><title>References</title><ref id="scirp.107923-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">Sutherland, C.J., Tanomsing, N., Nolder, D., Oguike, M., Jennison, C., Pukrittayakamee, S., Dolecek, C., Hien, T.T., Do Rosario, V.E., Arez, A.P., Pinto, J., Michon, P., Escalante, A.A., Nosten, F., Burke, M., Lee, R., Blaze, M., Otto, T.D., Barnwell, J.W., Pain, A., Williams, J., White, N.J., Day, N.P., Snounou, G., Lockhart, P.J., Chiodini, P.L., Imwong, M. and Polley, S.D. (2010) Two Nonrecombining Sympatric Forms of the Human Malaria Parasite Plasmodium ovale Occur Globally. 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