Journal of

Journal of Public Health International

Current Issue Volume No: 5 Issue No: 4

ISSN: 2641-4538
share this page

Research Article Open Access
  • Available online freely Peer Reviewed
  • Socio-Demographic Factors Responsible for Uptake of Intermittent Preventive Treatment and Health Seeking Behaviours for Malaria in Pregnancy among Women of Reproductive Ages in Nigeria

    Timothy Adejoh Attah 1   Abubakar Adamu 1   John-Paul Ojochenemi Enemali 1   Zainab Ibrahim Suleiman 1   Bashira Isah 1   Jeffree Leleji 1   Nazir Ibrahim Bello 1   Solomon Jonathan Hussaini 1   Amina Emidowojo Umar 1   Ishaya Daniel Bwala 1   Asenetu Iye Ibrahim 1   Zainab Ahmed Al-Gazali 1   Rita Ifeyinwa Hanson-Akpan 1   Joel Oluwamurewa Olayemi 1   Empress Omonigho Okhuelegbe 1   Doris E Isokpan 1   William Nebechukwu Eneh 1   Farida Mohammed Shehu 1   Caleb Polok 1   Hannah Shagaya 1  

    1National Space Research and Development Agency, Obasanjo Space Centre, Umaru Musa Ydar'adua express way, P.M.B. 437, Lugbe, Abuja

    Abstract

    Introduction

    Malaria in pregnancy is a serious public health concern that could result in detrimental health outcomes for pregnant women and their foetuses. In Nigeria, there is still a significant risk of the disease epidemic and adverse effects especially in pregnancy. The aim of this study is to assess the socio-demographic factors associated with Intermittent Preventive Treatment and health seeking behaviours for malaria in pregnancy among women of reproductive ages in Nigeria

    Methods

    Using the National Demographic Survey (NDHS) 2018 data, a cross sectional study was conducted to assess socio-demographic factors associated with Intermittent Preventive Treatment (IPT) for Malaria among Nigerian women of reproductive ages.

    Result

    Majority were between ages 30-39 years (39.5%), married/cohabiting (91.8%), Muslims (59.5%), from the north (68.9%), uneducated ( 49.9%), poor (47.5%), and grand parous (65.7%). 63.4% of the women had taken fansidar for malaria in pregnancy while only 6.1% had received healthcare for malaria from informal sources. Except for marital status, all socio-demographic variables (regions, highest educational level, wealth index, age group, religion and parity) were significantly associated with intake of IPT. Additionally, region, education, wealth index, age group, marital status and religion were associated with health seeking behaviour for malaria in pregnancy (P<.05).

    After control for other variables, wealth index, highest educational level, married/cohabiting marital status and religion was significantly associated with intake of IPT while region, primary and secondary education, poorer and richest wealth index, widowed/separation influenced health seeking for malaria in pregnancy (P<.05).

    Conclusion

    The National Malaria Elimination programme should evaluate existing policies that develop interventions that are centred on high risk population in order to prevent malaria in pregnancy while improving health seeking behaviours of women of reproductive ages.

    Author Contributions
    Received 30 Sep 2022; Accepted 12 Oct 2022; Published 21 Oct 2022;

    Academic Editor: Ian James Martin, Edith Cowan University, USA

    Checked for plagiarism: Yes

    Review by: Single-blind

    Copyright ©  2022 Timothy Adejoh Attah

    License
    Creative Commons License     This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    Competing interests

    The authors have declared that no competing interests exist.

    Citation:

    Timothy Adejoh Attah, Abubakar Adamu, John-Paul Ojochenemi Enemali, Zainab Ibrahim Suleiman, Bashira Isah et al. (2022) Socio-Demographic Factors Responsible for Uptake of Intermittent Preventive Treatment and Health Seeking Behaviours for Malaria in Pregnancy among Women of Reproductive Ages in Nigeria. Journal of Public Health International - 5(4):28-41.

    Download as RIS, BibTeX, Text (Include abstract )

    DOI 10.14302/issn.2641-4538.jphi-22-4329

    Introduction

    Malaria has been identified as one of the most severe public health problems globally 1. It is a threat to about 40% of the world’s population and characterised with recurrent and repeatedly large scale epidemics 2. The economic burden of malaria remains high with an estimated US$ 12 billion spent annually for prevention and treatment and about 25% of household income spent on treatment 3.

    This disease remains one of the main causes of mortality and morbidity especially in Sub –Saharan Africa. Africa accounts for the highest burden of malaria with 95% of all cases and 96% of the deaths occurring globally 3. Additionally, the burden of malaria is greatest in Nigeria and as a result, Nigeria accounted for the largest proportion (25%) of cases which is equivalent to 84 million cases 4. Nigeria also accounted for 24% of the global mortality among the 19 countries with 85% of the global burden 4.

    Although malaria affects all Nigerians, children and pregnant women have been found to be at higher risk of severe malaria infection and mortalities. In 2018, about 39% of the 11 million malaria cases among pregnant women occurred in Nigeria 4. While this disease increases the risk of birth complications and mortalities among pregnant women, there are evidence of malaria in pregnancy leading to low birth weight, anaemia and other health impacts on children born to malaria infected mothers 5.

    In Nigeria, malaria accounts for 60 % of all Out-patient visits to hospitals, 11 % maternal mortality and 30 % child mortality 6. As a result, over 200,000 deaths was recorded which resulted in about 31.9% of the 627,000 deaths that occurred due to malaria in 2020 7. Malaria in pregnancy is a serious public health concern that could result in detrimental health outcomes for pregnant women and their foetuses. As a result, strategies have been adopted to prevent the occurrence of malaria in pregnancy. Intermittent Preventive Treatment (IPT) for malaria has been recommended by World Health Organization (WHO) which includes the intake of sulfadoxine-pyrimethamine (SP) at least 3 times during the course of pregnancy 8.

    Despite the numerous programmes that have been implemented to mitigate the risk of malaria in Nigeria, there is still a significant risk of the disease epidemic and adverse effects especially in pregnancy. Considering that the malaria is epidemic and occurs throughout the year, there are limited population based studies assessing certain socio-demographic factors that influences individual as well as community risks for the disease. With pregnant women being at risk of malaria during pregnancy and poor health outcomes, it is important that studies are conducted to assess these risks and improve access to preventive care for this high risk population. The aim of this study is to assess the socio-demographic factors associated with uptake of IPT among pregnant women in Nigeria. This will be achieved through the following objectives;

    To assess the uptake of IPT among pregnant women in Nigeria

    To determine socio-demographic factors associated with uptake of IPT for prevention of malaria in pregnancy

    To determine socio-demographic factors associated with health seeking behaviours for malaria in pregnancy

    Methods

    Study Design

    A cross sectional study was conducted to assess socio-demographic factors associated with the uptake of IPT during malaria in pregnancy among women of reproductive ages in Nigeria.

    Study Setting

    Nigeria is the most densely populated country in Africa. Occupying a land area of about 910,770 km2, the Nigerian population was estimated at 196,000,000 inhabitants in 2018 9, 10. Nigeria is divided into 36 states including the Federal Capital Territory which is divided into 6 geopolitical zones including North-central, North West, North-East, South- West, South-East and South-South Respectively. The Nigerian population is made up of over 250 ethnic groups and 52% of the population occupying the urban region 11. In Nigeria, malaria is the most endemic infectious diseases with 76% of the population at risk of one infection annually 12. The Nigerian climate is tropical with majorly rainy and dry seasonal conditions of which the rainy season gives rise to increase in the density of mosquitoes and the resultant increase in vector borne diseases including malaria. The prevention and control of malaria is primarily coordinated by the Federal Ministry of Health which collaborates with other implementing partners through the National Malaria Elimination Programme 13 This programme ensures malaria prevention measures, diagnosis and treatment, through the provision of infrastructures, technical expertise that are targeted at strengthening of coordination of stakeholders for effective implementation of malaria programmes in Nigeria.

    Data Source and Sampling

    The study utilized 2018 NDHS data of women of reproductive ages in Nigeria. This survey was conducted using a multi-stage random sampling of the 6 geo-political zones in Nigeria. This includes a stratification of households as urban-rural in all states followed by the mapping and documentation of households and cluster random sampling of households. All women aged 15-49 years within the selected clusters were recruited and included into the survey. With 99% response rate, a total of 41,821 women aged 15-49 years from 40,427 households were interviewed in 2018.

    Data Collection and Study Variables

    The 2018 NDHS survey was presented in an excel spreadsheet. From the 2018 NDHS data, information such as socio-demography, socio-economic status, incidence of malaria in pregnancy, and uptake of IPT for malaria in pregnancy were retrieved and transcribed in to statistical software SPSS version 24 for data cleaning and coding. Outcome variables for this study were presented as self-reported maternal malaria in pregnancy and uptake of IPT for malaria in pregnancy. Intake of fansidar for malaria in pregnancy was defined as either yes or no. Health seeking for malaria in pregnancy was also categorized as either formal or informal. Formal health seeking was defined as receiving healthcare from a healthcare treatment facility while informal was defined as receiving healthcare from a place other than a healthcare treatment facility.

    Data Analysis

    Statistical analysis was conducted using statistical software SPSS version 24. Descriptive statistics were presented in frequency and percentages while continuous variables were presented in mean and standard deviation. In this study, Chi-squared analysis was used to determine association between socio-demographic variables and outcome variables (intake of fansidar for malaria in pregnancy, health seeking for malaria in pregnancy) while logistic regression analysis was conducted to determine the strength of association between predictor and outcome variables.

    Results

    Table 1 presents the descriptive statistics of women of reproductive ages (15-49 years) in Nigeria. As indicated in Table 1, majority were between ages 30-39 years (39.5%) and 40-49 years (37.8%) compared to only 0.7% of women that were between ages <18 years. Also, 91.8% of the women were married or cohabiting while only ≤ 5% were either widowed or separated. Islam was the most practiced religion and accounted for 59.5% of the religion practiced by women of reproductive ages while 39.6% were Christians and <1% practicing traditional or other religion. Majority of the study participants were located in the north where 31.3% were from the north-west, 20.6% and 17.0%from the north-east and north-central respectively. The south-east account for 11.0% of the population while <11% were found in the south-south and south-west. About 49.9% of the population were uneducated compared to only 6.1% of the population that had acquired a higher level of education. In addition, there were declining proportions of women from poorest to richest wealth index as majority of the population were either in the poorest (24.4%), poorer (23.1%) and middle (21.3%) while the richest accounted for only 13.0% of the population. Also majority were either grandparous (65.7%) or multiparous (23.2%) compared to only 11.1% that had low parity.

    In this population, 63.4% of the women of reproductive ages had taken fansidar for malaria in pregnancy while only 6.1% had received healthcare for malaria from informal sources.

    Table 2 represents the chi-squared analysis to assess the association between socio-demographic variables and initiation of IPT for malaria in pregnancy. As indicated in table 2, there was a statistically significant association between region of residence and intake of IPT for malaria in pregnancy among women of reproductive ages (X2=452.31, P:.000). Similarly, educational level (X2=1212.76, P:.000), wealth index (X2=1372.83, P:.000) and age group (X2=43.56, P:.000) of women were significantly associated with intake of IPT intake for malaria in pregnancy among women of reproductive ages. Also, religion (X2=269.76, P;.000) and parity (X2=84.20, P:.000) were significantly associated with IPT intake for malaria in pregnancy among women of reproductive ages. However, marital status was no significantly associated with intake of IPT for malaria in pregnancy (X2=5.72, P:.126).

    As represented in (Table 3) below, there was a statistically significant association between region of residence and health seeking behaviour for malaria among women of reproductive ages (X2=63.21, P;.000). Similarly, educational level (X2=144.42, P;.000), wealth index (X2=188.28, P;.000) and age group (X2=17.61, P;.001) of women were significantly associated with health seeking behaviour for malaria in pregnancy among women of reproductive ages. Also, marital status (X2=14.01, P;.003) and religion (X2=501.04, P;.000) were associated with health seeking for malaria in pregnancy among women of reproductive ages. However, parity (X2=3.85, P; .146) was not significantly associated with health seeking for malaria in pregnancy among women of reproductive ages.

    Table 4 represent the summary of statistics assessing the relationship between socio-demographic variables and intake of IPT for malaria in pregnancy among women of reproductive ages. The logistic regression model was statistically significant (P<.001). As represented in (Table 4), rural residents had 97% reduced likelihood of taking IPT for malaria in pregnancy compared to urban women AOR = 0.97 (CI: 0.91. Adjusted odds ratio revealed increased odds of taking IPT for malaria during pregnancy among women of reproductive ages that had primary AOR = 2.43 (CI: 2.07, secondary AOR = 1.50 (CI: 1.30 and higher education AOR = 1.22 (CI: 1.06.

    All wealth index categories were significant associated with increased likelihood of taking IPT for malaria in pregnancy including poorer [AOR = 2.72 (CI: 2.38-3.11), P<.05], middle [AOR = 2.18 (CI: 1.92-2.49), P<.05], richer [AOR = 1.50 (CI: 1.33-1.69), P<.05] and richest [AOR = 1.19 (CI: 1.06-1.35), P<.05]. On the other hand, the odds of taking fansidar for malaria in pregnancy were insignificant among the different age groups (P>.05). Married/cohabiting [AOR = 1.39 (CI: 1.07-1.81), P<.05] women were more likely than women that were never in a union to take IPT for malaria in pregnancy. Similarly, Muslim women [AOR = 0.45 (CI: 0.30-0.67), P<.05], traditional women [AOR = 0.39 (CI: 0.26-0.58), P<.05] and women that practice other religion [AOR = 0.26 (CI: 0.14-0.48), P<.05] had reduced likelihood of taking IPT for malaria in pregnancy compared to Christian women of reproductive ages. However, parity had no influence over intake of IPT for malaria in pregnancy among women of reproductive ages in Nigeria.

    (Table 5) below represents the relationship between socio-demographic variables and health seeking behaviour for malaria in pregnancy. Women living in the rural region were 80% less likely to seek formal healthcare compared to women living in the urban region [AOR = 0.80 (CI: 0.67-0.97), P<.05]. Also, women that had primary [AOR = 2.17 (CI: 1.44-3.27), P<.05] and secondary [AOR = 1.52 (CI: 1.01-2.30), P<.05] education had higher likelihood of seeking formal healthcare for malaria during pregnancy compared to women that had no form of education. However, there was significant relationship between health seeking behaviour for malaria in pregnancy among women with tertiary education compared to women that had not form of education [AOR = 1.34 (CI: 0.92-1.94), P>.05].

    Women categorized into the poorer wealth index were 2.22 times more likely than the poorest women to seek formal healthcare for malaria in pregnancy [AOR = 2.22(1.59-3.09), P<.05]. Similarly, the richest women were 1.36 times more likely than the poorest women to seek formal healthcare for malaria in pregnancy [AOR = 1.36 (1.01-1.83), P<.05]. Other socio-demographic variables were insignificantly related with health seeking for malaria in pregnancy among women of reproductive ages in Nigeria.

    Table 1. Descriptive Statistics of Women of Reproductive Ages in Nigeria
    S/No Variables Frequency Percent Valid Percent
    1. Age Group      
      <18 years 889 0.7 0.7
      19-29 years 28115 22.0 22.0
      30-39 years 50358 39.5 39.5
      40-49 years 3300 37.8 37.8
    2. Marital Status      
      Never in union 1581 1.2 1.2
      Married/Cohabiting 117150 91.8 91.8
      Widowed 5569 4.4 4.4
      Divorced/Separated 3245 2.5 2.5
    3. Religion      
      Christianity 50451 39.6 39.6
      Islam 75942 59.5 59.5
      Traditional 677 0.5 0.5
      Others 475 0.4 0.4
    4. Geo-Political Zone      
      North Central 21656 17.0 17.0
      North East 26293 20.6 20.6
      North West 39928 31.3 31.3
      South East 14072 11.0 11.0
      South South 12436 9.8 9.8
      South West 13169 10.3 10.3
    5. Highest Educational Level      
      None 63699 49.9 49.9
      Primary 25311 19.8 19.8
      Secondary 30756 24.1 24.1
      Higher 7779 6.1 6.1
    6. Wealth Index      
      Poorest 31148 24.4 24.4
      Poorer 29448 23.1 23.1
      Middle 27120 21.3 21.3
      Richer 23210 18.2 18.2
      Richest 16619 13.0 13.0
    7. Parity      
      Low 14167 11.1 11.1
      Multi 29545 23.2 23.2
      Grand 83833 65.7 65.7
    8. Presentation of self-reported symptoms of febrile illness      
      No 13705 10.7 63.4
      Yes 7916 6.2 36.6
      Missing 105924 83.0  
    9. Health Seeking Behaviour for self-reported symptoms of febrile illness      
      Formal 12,874 10.1 93.9
      Informal 831 0.7 6.1
      Missing 113840 89.3  

    Table 2. Chi-Squared Analysis Assessing the Association between Socio-Demographic Variables and Intake of Intermittent Preventive Treatment During Malaria in Pregnancy among Women of Reproductive Ages
    S/No Variable Took Intermittent Preventive Treatment for Malaria During Pregnancy X 2 (df) P-Value
        No Yes Total    
    1. Region of Residence       452.31(1) .000
      Urban 2075(27.2) 5559(72.8) 7634(35.3)    
      Rural 5841(41.8) 8146(58.2) 13987(64.7)    
    2. Highest Educational Level       1212.76(3) .000
      None 4636(48.8) 4862(51.2) 9498(43.9)    
      Primary 1141(33.8) 2232(66.2) 3373(15.6)    
      Secondary 1811(26.0) 5162(74.0) 6973(32.3)    
      Higher 328(18.5) 1449(81.5) 1777(8.2)    
    3. Wealth Index       1372.83(4) .000
      Poorest 2639(52.7) 2368(47.3) 5007(23.2)    
      Poorer 2179(44.8) 2686(55.2) 4865(22.5)    
      Middle 1477(32.5) 3072(67,5) 4549(21.0)    
      Richer 996(25.0) 2990(75.0) 3986(18.4)    
      Richest 625(19.4) 2589(80.6) 3214(14.9)    
    4. Age Group       43.56(3) .000
      <18 years 364(46.7) 415(53.3) 779(3.6)    
      19-29 years 3723(36.7) 6431(63.3) 10154(47.0)    
      30-39 years 2893(35.2) 5330(64.8) 8223(38.0)    
      40-49 years 936(38.0) 1529(62.0) 2465(11.4)    
    5. Marital Status       5.72(3) .126
      Never in union 222(37.0) 378(63.0) 600(2.8)    
      Married/cohabiting 7438(36.7) 12821(63.3) 20259(93.7)    
      Widowed 102(37.4) 171(62.6) 273(1.3)    
      Divorced/Separated 154(31.5) 335(68.5) 489(2.3)    
    6. Religion       269.76(3) .000
      Christianity 2653(30.1) 6148(69.9) 8801(40.7)    
      Islam 5191(41.1) 7454(58.9) 12645(58.5)    
      Traditional 25(34.7) 47(65.3) 72(0.3)    
      Others 47(45.6) 56(54.4) 103(0.5)    
    7. Parity       84.20(2) .000
      Low 2588(34.8) 4840(65.2) 7428(34.4)    
      Multi 2083(33.7) 4095(66.3) 6178(28.6)    
      Grand 3245(40.5) 4770(59.5) 8015(37.1)    

    Note: X2: Chi squared value, P value: level of significance, df: degree of freedom, S/No: Serial number
    Table 3. Chi-Squared Analysis Assessing the Association between Socio-Demographic Variables and Health Seeking Behaviours of Women Diagnosed with Malaria in Pregnancy
    S/No Variable Health Seeking Behaviour of Women of Reproductive Ages Diagnosed with Malaria in Pregnancy X 2 (df) P-Value
        Formal Informal Total    
    1. Region of Residence       63.21(1) .000
      Urban 5331(95.9) 228(4.1) 5559(40.6)    
      Rural 7543(92.6) 603(7.4) 8146(59.4)    
    2. Highest Educational Level       144.42(3) .000
      None 4414(90.8) 448(9.2) 4862(35.5)    
      Primary 2110(94.5) 122(5.5) 2232(16.3)    
      Secondary 4938(95.7) 224(4.3) 5162(37.7)    
      Higher 1412(97.4) 37(2.6) 1449(10.6)    
    3. Wealth Index       188.28(4) .000
      Poorest 2092(88.3) 276(11.7) 2368(17.3)    
      Poorer 2509(93.4) 177(6.6) 2686(19.6)    
      Middle 2917(95.0) 155(5.0) 3072(22.4)    
      Richer 2843(95.1) 147(4.9) 2990(21.8)    
      Richest 2513(97.1) 76(2.9) 2589(18.9)    
    4. Age Group       17.61(3) .001
      <18 years 380(91.6) 35(8.4) 415(3.0)    
      19-29 years 6008(93.4) 423(6.6) 6432(46.9)    
      30-39 years 5060(94.9) 270(5.1) 5330(38.9)    
      40-49 years 1426(93.3) 103(6.7) 1529(11.2)    
    5. Marital Status       14.01(3) .003
      Never in union 342(90.5) 36(9.5) 378(2.8)    
      Married/cohabiting 12064(94.1) 757(5.9) 12821(93.5)    
      Widowed 163(95.3) 8(4.7) 171(1.2)    
      Divorced/Separated 305(91.0) 30(9.0) 335(2.4)    
    6. Religion       50l.04(3) .000
      Christianity 5868(95.4) 280(4.6) 6148(44.9)    
      Islam 6910(92.7) 544(7.3) 7454(54.4)    
      Traditional 41(87.2) 6912.8) 47(0.3)    
      Others 55(98.2) 1(1.8) 56(0.4)    
    7. Parity       3.85(2) .146
      Low 4564(94.3) 276(5.7) 4840(35.3)    
      Multi 3855(94.1) 240(5.9) 4095(29.9)    
      Grand 4455(93.4) 315(6.6) 4770(34.8)    

    Note: X2: Chi squared value, P value: level of significance, df: degree of freedom, S/No: Serial number
    Table 4. Relationship between Socio-Demographic Variables and Intake of Intermittent Preventive Treatment for Malaria in Pregnancy among Women of Reproductive Ages
    Variable Took Intermittent Preventive Treatment for Malaria During Pregnancy
    Socio-demography OR (95% CI) P-Value AOR (95% CI) P-Value
    Region of Residence        
    Urban Ref Ref Ref Ref
    Rural 0.52(0.49-0.55) .000 0.97(0.91-1.05) .487
    Highest Educational Level        
    None Ref Ref Ref Ref
    Primary 4.21(3.71-4.78) .000 2.43(2.07-2.84) .000
    Secondary 2.26(1.96-2.60) .000 1.52(1.30-1.7) .000
    Higher 1.55(1.40-1.77) .000 1.22(1.06-1.41) .005
    Wealth Index        
    Poorest Ref Ref Ref Ref
    Poorer 4.62(4.16-5.12) .000 2.72(2.38-3.11) .000
    Middle 3.36(3.03-3.73) .000 2.18 (1.92-2.49) .000
    Richer 1.99(1.79-2.22) .000 1.50(1.33-1.69) .000
    Richest 1.38(1.23-1.55) .000 1.19(1.06-1.35) .004
    Age Group        
    <18 years Ref Ref Ref Ref
    19-29 years 1.43(1.22-1.69) .000 1.12(0.93-1.36) .232
    30-39 years 0.95(0.86-1.04) .228 1.01(0.90-1.13) .836
    40-49 years 0.89(0.81-0.97) .011 1.04(0.94-1.15) .459
    Marital Status        
    Never in union Ref Ref Ref Ref
    Married/cohabiting 1.28(0.99-1.65) .057 1.39(1.07-1.81) .014
    Widowed 1.26(1.04-1.53) .018 1.11(0.91-1.36) .307
    Divorced/Separated 1.30(0.95-1.77) .100 1.23(0.89-1.70) .207
    Religion        
    Christianity Ref Ref Ref Ref
    Islam 0.51(0.35-0.76) .001 0.45(0.30-0.67) .000
    Traditional 0.83(0.56-1.23) .347 0.39(0.26-0.58) .000
    Others 0.63(0.34-1.18) .150 0.26(0.14-0.48) .000
    Parity        
    Low Ref   Ref Ref
    Multi 0.79(0.74-0.84) .000 1.05(0.96-1.16) .278
    Grand 0.75(0.70-0.80) .000 0.96(0.88-1.04) .286

    Note: OR: Odds ratio, AOR: Adjusted odds ratio, P value: level of significance, CI: confidence interval, S/No: Serial number
    Table 5. Relationship between Socio-demographic Variables and Health Seeking Behaviour of Women of Reproductive Ages Diagnosed with Malaria in Pregnancy
    Variable Health Seeking Behaviour of Women of Reproductive Ages Diagnosed with Malaria in Pregnancy
    Socio-demography OR (95% CI) P-Value AOR (95% CI) P-Value
    Region of Residence        
    Urban Ref Ref Ref Ref
    Rural 0.54(0.46-0.63) .000 0.80(0.67-0.97) .019
    Highest Educational Level        
    None Ref Ref Ref Ref
    Primary 3.87(2.76-5.45) .000 2.17(1.44-3.27) .000
    Secondary 2.21(1.52-3.21) .000 1.52(1.01-2.30) .045
    Higher 1.73(1.22-2.46) .002 1.34(0.92-1.94) .125
    Wealth Index        
    Poorest Ref Ref Ref Ref
    Poorer 4.36(3.36-5.66) .000 2.22(1.59-3.09) .000
    Middle 2.33(1.77-3.07) .000 1.31(0.94-1.82) .114
    Richer 1.76(1.33-2.32) .000 1.18(0.86-1.61) .312
    Richest 1.71(1.29-2.27) .000 1.36(1.01-1.83) .041
    Age Group        
    <18 years Ref Ref Ref Ref
    19-29 years 1.28(0.86-1.90) .233 1.03(0.65-1.64) .889
    30-39 years 0.98(0.78-1.22) .822 1.00(0.76-1.31) .971
    40-49 years 0.74(0.58-0.93) .011 0.82(0.64-1.04) .106
    Marital Status        
    Never in union Ref Ref Ref Ref
    Married/cohabiting 1.07(0.64-1.78) .794 1.31(0.77-2.20) .318
    Widowed 0.64(0.44-0.94) .021 0.57(0.39-0.84) .004
    Divorced/Separated 0.50(0.22-1.11) .090 0.51(0.23-1.15) .102
    Religion        
    Christianity Ref Ref Ref Ref
    Islam 2.62(0.36-19.03) .340 2.72(0.37-19.91) .324
    Traditional 4.33(0.60-31.35) .147 2.98(0.41-21.90) .283
    Others 8.05(0.93-69.46) .058 4.96(0.56-43.57) .149
    Parity        
    Low Ref   Ref Ref
    Multi 0.86(0.72-1.01) .066 0.95(0.75-1.20) .660
    Grand 0.88(0.74-1.05) .150 1.05(0.85-1.28) .676

    Note: OR: Odds ratio, AOR: Adjusted odds ratio, P value: level of significance, CI: confidence interval, S/No: Serial number

    Discussion

    This study was conducted to assess how socio-demographic factors affect the occurrences and health seeking for malaria in pregnancy among Nigeria women of reproductive ages. The socio-demographic factors assessed in this study includes region of residence, highest educational level, wealth index, age group, marital status, religion and parity. After controlling for other variables, some socio-demographic variables were found to be significantly associated with intake of IPT and health seeking for malaria in pregnancy.

    It is also important to note that recommendation has been made by the World Health Organization that countries should adopt intermittent preventive treatment (IPT) for malaria in pregnancy 14. In this study, region of residence was not associated with intake of IPT for malaria in pregnancy. However, health seeking for malaria in pregnancy was significantly influenced by region as women from rural region were less likely to access conventional source for malaria in pregnancy. Some of the challenges associated with healthcare seeking in rural regions compared to urban include lower socio-economic status as represented in this study and other studies that have been conducted 15, 16, 17, 18, 19, 20. Also, distance to health facility and poor perception of healthcare practitioners have been found to influence health seeking for women seeking healthcare 21, 22, 23, 24.

    Despite numerous programs and policies developed and implemented to prevent disease and improve access to healthcare services in African countries, socio-economic status significantly impacts the occurrence and health seeking behaviours for infectious disease 23. In this study, increasing wealth index was found to increase the likelihood of women utilizing preventive treatment for malaria in pregnancy. Health seeking behaviour for malaria in pregnancy was also significant in the poorer and richest group compared to the poorest. A previous study conducted in Nigeria provides an evidence of the prevailing challenges of socio-economic status of pregnant women and its negative impact on the likelihood to seek conventional source for treatment of malaria in pregnancy 17. Other findings have revealed increased likelihood of pregnant women with higher socio-economic status to seek IPT 24, 25.

    Education plays a critical role on health awareness and literacy especially in populations where there are limitations in the health systems and health care service delivery 26. In turn, health literacy also influences actions and decisions to prevent disease occurrences. In this study increase in the level of education was associated with increased likelihood of taking IPT for malaria in pregnancy among women of reproductive ages while increasing education was significantly associated with increased likelihood to seek healthcare from formal sources.

    Although there are limited studies assessing relationship between socio-demographic variables and intake of preventive treatment for malaria in pregnancy, finding from this study is consistent with a previous study that revealed the influence of educational level in increasing the likelihood to utilize preventive method for malaria in pregnancy 16, 17, 24, 27, 28, 29, 30, 31. As a result, less educated women in Nigeria may lack adequate information about the prevention of adverse effects of malaria in pregnancy and this in turn influence their decisions to take preventive measures. Also, women that are less educated may be less likely to seek conventional healthcare for malaria in pregnancy. While socio-economic status influencing health seeking behaviours of women, education also influences wealth index of women and therefore influence health seeking for malaria in pregnancy.

    In this study, there was a significant increase in the likelihood of women to take IPT during pregnancy among married/cohabiting women compared to single women. On the other hand, there was a significantly increased likelihood of widowed women to seek formal healthcare for malaria in pregnancy compared to single women. It is expected that married women due to their experiences in pregnancies and increased knowledge of malaria in prevention, should seek formal healthcare for malaria in pregnancy However, another Nigerian study found the divorced/separated were less likely than single women to seek formal healthcare for malaria in pregnancy 24. Additionally, this study showed that Muslim, traditional and women of other religions were less likely to initiate IPT for malaria in pregnancy compared to their Christian counterpart. It could be suggested that as a result of certain religious beliefs among the other religions, choice to initiate IPT may be prevented due to women‘s dependence on their religious beliefs.

    This study is however not without limitations. Due to the use of NDHS, there were many missing variables which may have affected the statistical strength of our findings. Also, due to incomplete data, it was impossible to assess the influence of other socio-demography on other key healthcare indices relevant to this study. Also, the use of survey data increases the risk of measurement errors, and information bias as some of the variables assessed were dependent on the participant’s ability to recall previous experiences.

    However, this research has provided evidence of the existing limitations in strategies adopted in the prevention of malaria in pregnant women in Nigeria. It also provides adequate data to compare findings from previous studies and track progress in the awareness of IPT and utilization for appropriate healthcare for maternal illnesses. By using NDHS data, this study provides a more representative finding that can be generalized on the Nigerian population. With findings from this study, strategies can be adopted to improve prevention of malaria in pregnancy while improving health seeking behaviour of women of reproductive ages in Nigeria. Also, using NDHS data, the potential of selection bias has been reduced.

    Conclusion

    This study has revealed that socio-demographic factors predispose women of reproductive ages to the risk of malaria in pregnancy. Also, health seeking for malaria in pregnancy has been shown to be affected by socio-demographic factors.

    It is therefore important that strategies are adopted to reduce missing survey data. In order to improve the volume of missing data, the NDHS should adopt a semi-structure survey questionnaire that allows surveyors to provide adequate direction towards completion of the questionnaires without influencing participant’s decisions and choices. Also, knowledge about the prevention of malaria in pregnancy should be improved through awareness and campaigns programs that are targeted at high risk populations for malaria occurrence in pregnancy as indicated in this study. Although this study does not address other limitations to health seeking behaviour among women of reproductive ages, it is recommended that strategies be adopted to eliminate the barriers to access to healthcare service delivery among this population such as cost, distance and quality of healthcare service delivery. While the choices of women to seek healthcare can also influence household health seeking, more resource and public health actions must be targeted at this population in order to achieve universal healthcare coverage especially for high risk populations. This can be achieved to collaborations with both private and public stakeholders and program implementers involved in malaria prevention and care in Nigeria.

    Funding

    The authors received no funding for the conduct, authorship or publication of this research.

    Authors Contribution

    TAA designed the study. AA, TAA, IE and JL supervised the conduct of the research. TAA carried out data analysis. JE, BI, ZS performed literature review. TAA drafted the first version of the manuscript. All authors (TAA, AA, JL, BI, JE, ZS, IE) reviewed and approved the final version of the manuscript.

    Acknowledgements

    The authors gratefully acknowledge the Nigerian Demographic Health Survey for providing data to conduct this study.

    References

    1.Dawaki S, Al-Mekhlafi H M, Ithoi I, Ibrahim J, Atroosh W M et al. (2016) Is Nigeria winning the battle against malaria? Prevalence, risk factors and KAP assessment among Hausa communities in Kano State. , Malar 15(1).
    2.Kiszewski A E, Teklehaimanot A. (2004) A review of the clinical and epidemiologic burdens of epidemic malaria. , Am J Trop Med Hyg 71(2), 128-35.
    3. (2021) . WHO. Malaria .Who.int
    4. (2019) WHO. The “World malaria report at a glance Who.int.
    5. (2019) CDC Malaria - Malaria Worldwide - How Can Malaria Cases and Deaths Be Reduced?- Intermittent Preventive Treatment of Malaria for Pregnant Women (IPTp). www.cdc.gov. Published
    6.. (2017).WDI Malaria in Nigeria–Westfield Development Initiative. westfielddevelopment.org/. Published .
    7.. (2021).WHO. Malaria. Who.int. Published
    8. (2018) WHO Intermittent Preventive Treatment of Malaria in Pregnancy with Sulphadoxine/Pyrimethamine. who.int. Published
    9. (2021) World Bank. Land area (sq. km) - Nigeria | Data. data.worldbank.org. Published
    10. (2020) WorldBank. Nigeria Demographics - Place Explorer - Data Commons. datacommons.org. Published
    11. (2019) Worldometer. Nigeria Populatio Worldometers.
    12. (2021) Eliminating Malaria in Nigeria: Five ways the USAID’s “President’s Malaria Initiative for States” is Making a Difference. Usaid.gov.
    13. (2022) FMoH. National Malaria Elimination Programme (NMEP). www.health.gov.ng. Published
    14. (2013) WHO.WHO Policy Brief for the Implementation of Intermittent Preventive Treatment of Malaria in Pregnancy Using Sulfadoxine-Pyrimethamine(IPTp-SP). who.int;.
    15.Olugbade O T, Ilesanmi O S, Gubio A B, Ajayi I, Nguku P M et al. (2019) Socio-demographic and regional disparities in utilization of intermittent preventive treatment for malaria in pregnancy - Nigeria demographic health survey 2013.Pan African Medical Journal.;32.
    16.Zhang Q, Lauderdale D, Mou S, Parish W I, Laumann E O et al. (2009) . Socioeconomic Disparity in Healthcare-Seeking Behavior among Chinese Women with Genitourinary Symptoms.Journal of Women’s Health.;18(11): 1833.
    17. (2019) Livinus Udenweze I. (2022).ScholarWorksImpact of Socioeconomic Status and Health- Seeking Behavior on Malaria in Pregnancy.;.
    18.Hossain D. (2022) Socioeconomic Status and Maternal Health-seeking Behavior: A Comparative Study between a Rural Site and an Urban Community. in Bangladesh.Journal of International Women’s Studies , Accessed 21(6), 122-134.
    19.Islam M S, Begum P. (2020) The Impact of Socio-Economic and Demographic Factors on Health Seeking Behavior of Urban Households, Bangladesh.Researchsquare. Published online
    20.Musoke D, Boynton P, Butler C, Musoke M. (2015) Health seeking behaviour and challenges in utilising health facilities in Wakiso district. , Uganda.African Health 14(4), 1046.
    21.Afolabi M O, Daropale V O, Irinoye A I, Adegoke A A. (2022) Health-seeking behaviour and student perception of health care services in a university community. in Nigeria - Health - SCIRP.Scirporg , Accessed 5(5), 817-824.
    22.Mannava P, Durrant K, Fisher J, Chersich M, Luchters S. (2015) Attitudes and behaviours of maternal health care providers in interactions with clients: a systematic review.Globalization and Health.;11(1).
    23.Nyaruaba R, Okoye C O, Akan O D. (2022) Socio-economic impacts of emerging infectious diseases in Africa.Infectious Diseases. Published online 10, 1-10.
    24.Okiring J, Olwoch P, Kakuru A. (2019) Household and maternal risk factors for malaria in pregnancy in a highly endemic area of Uganda: a prospective cohort study.Malaria Journal.;18(1).
    25.Muhammad F M, Majdzadeh R, Nedjat S, Sajadi H S, Parsaeian M. (2020) Socioeconomic inequality in intermittent preventive treatment using Sulphadoxine pyrimethamine among pregnant women in Nigeria.BMC Public Health.;20(1).
    26.Raghupathi V, Raghupathi W. (2020) The influence of education on health: An empirical assessment of OECD countries for the period1995–2015.Archives of Public Health.;78(1).
    27.Jothula K, PmC Reddy, Rineetha T, Sreeharshika D. (2020) Health care seeking behaviour among rural women in Telangana: A cross sectional study.Journal of Family Medicine and Primary Care.;9(9):. 4778.
    28.Kalidas P, Sujatha K, Senthilkumar S K, Sreesupria P. (2017) A Study on Health Seeking Behaviour among Married Reproductive Age Group Women in a Rural Area.IOSR. , Journal of Dental and Medical 16(03), 08-11.
    29.Chakrabarti I, Chakraborty M, Biswas R, Ghosh N. (2013) Factors affecting the healthcare-seeking behavior of mothers regarding their children in a rural community of Darjeeling district. , West Bengal.International Journal of Medicine and Public 3(1), 12.
    30.Abuduxike G, Aşut Ö, Vaizoğlu S A, Cali S. (2020) Health-Seeking Behaviors and its Determinants: A Facility-Based Cross-Sectional Study in the Turkish Republic of. , Northern Cyprus.International Journal of Health Policy and 9(6), 240-249.
    31.Kim S I. (2022) The Impact of mothers’ education on maternal health seeking practices in Uganda. archives.kdischool.ac.kr. Published , Accessed