Timing of first antenatal care contact, its associated factors and state-level analysis in Nigeria: a cross-sectional assessment of compliance with the WHO guidelines

BMJ open, Volume 11, No. 9, Year 2021

Interpretação

OBJECTIVES: To assess the compliance of WHO guidelines on the timeliness of antenatal care (ANC) initiation in Nigeria and its associated factors and to provide subcountry analysis of disparities in the timing of the first ANC in Nigeria. DESIGN: Cross-sectional. SETTING: Nationally representative data of most recent pregnancies between 2013 and 2018 in Nigeria. PARTICIPANTS: Women with pregnancies within 5 years before the study. PRIMARY AND SECONDARY OUTCOME MEASURES: The outcome variable was the trimesters of the first ANC contact. Data were analysed using descriptive statistics, bivariable and multivariable multinomial logistic regression at 5% significance level. RESULTS: Of all the 21 785 respondents, 75% had at least one ANC contact during their most recent pregnancies within the five years preceding the data collection. Among which 24% and 63% started in the first and second trimester, respectively. The proportion who started ANC in the first trimester was highest in Benue (44.5%), Lagos (41.4%) and Nasarawa (39.3%) and lowest in Zamfara (7.6%), Kano (7.4%) and Sokoto (4.8%). Respondents aged 40-49 years were 65% (adjusted relative risk ratio (aRRR: 1.65, 95 % CI: 1.10 to 2.45) more likely to initiate ANC during the first trimester of pregnancy relative to those aged 15-19 years. Although insignificant, women who participate in their healthcare utilisation were 4% (aRRR: 1.04, 95 % CI: 0.90 to 1.20) times more likely to have early initiation of ANC. Other significant factors were respondents’ and spousal educational attainment, household wealth quintiles, region of residence, ethnicity, religion and birth order. CONCLUSIONS: Only a quarter of pregnant women, initiated ANC contact during the first trimester with wider disparities across the states in Nigeria and across the background characteristics of the pregnant women. There are needs to enhance women’s autonomy in healthcare utilisation. Concerted efforts on awareness creation and empowerment for women by all stakeholders in maternal and child healthcare are antidotes for early ANC contact initiation. The study setting is Nigeria. Nigeria is divided into 36 states and the Federal Capital Territory for administration purposes as shown in figure 1. The states are further grouped into six regions. The states are are made up of local government areas (LGAs), and each LGA is divided into local administrative units. The LGAs are subdivided into convenient areas, for election purposes, called census enumeration areas (EAs). Map of Nigeria showing the 36 states, the federal capital territory, by the geopolitical zones. We analysed the data collected among women of reproductive age in Nigeria. We used secondary data from the 2018 Nigeria Demographic Health Survey (NDHS). The NDHS is one in the series of surveys conducted by Inner City Fund (ICF) Macro International, Calverton, Maryland, USA, in conjunction with the Nigeria National Population Commission (NPC).32 The DHS data are cross-sectional in design and nationally representative household surveys. DHSs are conducted every 5 years in low-income and middle-income countries. Two-stage sampling procedures were used for the 2018 NDHS survey. The sampling frame is the Population and Housing Census of the Federal Republic of Nigeria which was conducted in 2006 by the Nigeria NPC. The primary sampling unit referred to as a cluster are the EAs in the census frame. In each state, samples of EAs were selected independently in a two-stage selection. At the first stage, 38 EAs were selected with probability proportional to EA size in each state. At the second stage, 30 households were selected in every selected EAs using equal probability of systematic sampling. All eligible women of reproductive age (15–49 years) in all the selected households were interviewed. Sampling weights were applied in the analyses to account for the differences in response rates and population sizes of the states. A total of 41 821 women aged 15–49 years were interviewed in the 2018 NDHS.32 All eligible respondents were asked if they had any pregnancy or birth within 5 years preceding the survey. Those who answered in affirmative to haven had at least a birth within the preceding 5 years were asked questions on the number of ANC contacts made, the onset of the ANC visits, and the ANC provider etc for the pregnancy starting from the most recent. Our analysis is based on the information on the most recent pregnancies of each of the respondents. A total of 21 785 women provided relevant information. Of these 21 785 women, 16 448 (75.5%) attended ANC and were thus included in the final analysis. The outcome variable is the timing of the first ANC visit among women. The time of the first ANC was reported in months by the mother and was grouped as first trimester (early initiation), second trimester (late initiation) and third trimester (very late initiation). We grouped the States in Nigeria into two: below 85% or ‘greater than or equal to’ 85% global ‘no antenatal contact’ prevalence.1 33 The states’ performances regarding the proportion of women who initiated ANC visits in the first trimester during the most recent pregnancy is presented, with the states grouped into having 0%–33%, 34%–67% and >67% early ANC visits. Based on existing literature,23 34–36 the independent variables used in this study are maternal age (15–19, 20–24, 25–29, 30–39, 40–49 years), educational attainment (no education, primary, secondary and higher), spouse educational attainment (no education, primary, secondary and higher), employment status (currently employed vs unemployed), spouse employment status (currently employed vs unemployed), access to media (at least one of radio, television, newspaper or not), household wealth tertile (low, middle and high), women’s autonomy using who decides respondents healthcare utilisation (respondent alone, respondent/spouse and spouse alone). Other included independent variables are birth interval (firstborn, <36 months and ≥36 months), birth order (1, 2, 3, 4 and 5+), number of children ever born (none, 1–2, 3–4, 4+), current marital status (currently married or living together, divorced/separated/widowed, never married), place of residence (rural vs urban), religion (Islam, Christian, others) and ethnicity (Hausa/Fulani, Igbo, Yoruba and others). Family mobility (had stayed less than 5 years at residence or not), wanted child when became pregnant (wanted then, wanted later, or wanted not more), household headship (male vs female), health insurance coverage (yes vs no), acceptance of wife-beating (yes vs no). We also assessed four community-level factors in the descriptive analysis. The communities are synonymus to the EAs. The four factors are the community poverty rate (high or low), community unemployment rate (high vs low), community illiteracy rate (high vs low) and community media access rate (high vs low). We computed the neighbourhood socioeconomic status disadvantage as a composite score using principal component analysis and grouped into lowest, middle and highest categories. It is the proportion of respondents within each community with no media access, who are illiterates, who are poor and who are unemployed. The ‘xtile’ function in Stata V.16 was used to categorise the already provided wealth index scores (V.191) in the DHS data into three tertiles. Descriptive statistics, bivariable and multivariable multinomial logistic regression were used. The ‘SVY’ command for survey data in Stata V.16 was used to adjust for the study design used and the sample weights. Frequency tables showing percentages were used to describe the distribution of study respondents’ characteristics and we cross-classified the outcome variables by the respondents’ characteristics (table 1). Graphs and maps were produced using Microsoft Office 365 Excel and PowerPoint editable maps, respectively. Association between respondents’ characteristics and timing of first ANC visit *Significant χ2 at 0.05. ANC, antenatal care; SES, socioeconomic status. We used the ‘multinomial logistic’ command in Stata to implement bivariable and multivariable regression models. It is a procedure for estimating the risk ratio (RR) of factors associated with the outcome variables. Variables that were significant at p<0.20 were included in the multivariable model.31 The multinomial logistic regression model computes the maximum-likelihood estimates of the probability of success of an event. In the binary logistic regression, the ORs are computed as the ratio of odds of success divided by the odds of failure, the ith coefficient is ϕi=exp(bi) with SE siϕ=ϕisi, where si is the SE of bi estimated using the logit function. Assuming that the predicted index of the jth observation is defined as Xib. The predicted probability of a positive outcome is Pj(yj≠0|Xj)=exp(Xib)1+exp(Xib)(1). Whereas, multinomial logistic regression is used for the categorical dependent variable, with three or more categories. If y has three outcomes 1, 2 and 3 whereby ‘3’ is not necessarily greater than ‘1’ or ‘2’. Then, multinomial logistic regression is useful in modelling the nominal outcome variables.37 The relative probability of any of the levels, say y=2 to the base outcome, say y=1 is p(y=2)p(y=1)=eXβ(2) which is the relative risk ratio (RRR). Assuming that X and βk(2) are vectors equal to (x1+x2+…xi…..+xk) and (β1(2)+β2(2)+…+βi(2)+…βk(2)), the ratio of the relative risk (RR) for a one-unit change in xt is eβ1(2)x1+….+eβi(2)x(i+1)+……+eβk(2)xkeβ1(2)x1+….+eβi(2)xi+……+eβk(2)xk=eXβ(2)(2) Thus, the exponential of the coefficient is the RRR for a one-unit change in the corresponding variable. This is easily interpreted as the ratio of the probability of choosing one outcome category divided by the probability of choosing the baseline category.37 38 Patients and the public will be involved in the dissemination plan.

AI Digest

Study Justification:

– The study aims to assess the compliance of WHO guidelines on the timeliness of antenatal care (ANC) initiation in Nigeria and its associated factors.
– It provides subcountry analysis of disparities in the timing of the first ANC in Nigeria.
– The study highlights the need to enhance women’s autonomy in healthcare utilization and the importance of awareness creation and empowerment for women in maternal and child healthcare.

Study Highlights:

– Only a quarter of pregnant women in Nigeria initiated ANC contact during the first trimester.
– There are wide disparities across the states in Nigeria and across the background characteristics of the pregnant women.
– Factors associated with early initiation of ANC include maternal age, educational attainment, household wealth, region of residence, ethnicity, religion, and birth order.

Recommendations:

– Enhance women’s autonomy in healthcare utilization to promote early initiation of ANC.
– Increase awareness and empowerment programs for women in maternal and child healthcare.
– Implement targeted interventions in states with low rates of early ANC initiation.
– Strengthen healthcare systems to ensure access to quality ANC services.

Key Role Players:

– Ministry of Health: Responsible for policy development and implementation of interventions to improve ANC initiation.
– Healthcare Providers: Involved in delivering ANC services and promoting early initiation.
– Non-Governmental Organizations (NGOs): Engaged in awareness creation and empowerment programs for women.
– Community Leaders: Play a role in mobilizing communities and promoting the importance of early ANC initiation.

Cost Items for Planning Recommendations:

– Awareness and Empowerment Programs: Budget for conducting workshops, training sessions, and community outreach activities.
– Healthcare Infrastructure: Allocate funds for improving healthcare facilities and equipment for ANC services.
– Human Resources: Budget for hiring and training additional healthcare providers to meet the increased demand for ANC services.
– Monitoring and Evaluation: Allocate funds for monitoring the implementation of interventions and evaluating their impact.
– Research and Data Collection: Budget for conducting further research and data collection to monitor progress and identify areas for improvement.

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is strong, as it is based on a nationally representative cross-sectional study in Nigeria. The study used a large sample size and employed rigorous statistical analysis. However, to improve the evidence, it would be beneficial to include information on the sampling methodology, response rates, and any potential limitations of the study. Additionally, providing more details on the specific variables used in the analysis and their definitions would enhance the clarity of the findings.

Resumo

The study setting is Nigeria. Nigeria is divided into 36 states and the Federal Capital Territory for administration purposes as shown in figure 1. The states are further grouped into six regions. The states are are made up of local government areas (LGAs), and each LGA is divided into local administrative units. The LGAs are subdivided into convenient areas, for election purposes, called census enumeration areas (EAs). Map of Nigeria showing the 36 states, the federal capital territory, by the geopolitical zones. We analysed the data collected among women of reproductive age in Nigeria. We used secondary data from the 2018 Nigeria Demographic Health Survey (NDHS). The NDHS is one in the series of surveys conducted by Inner City Fund (ICF) Macro International, Calverton, Maryland, USA, in conjunction with the Nigeria National Population Commission (NPC).32 The DHS data are cross-sectional in design and nationally representative household surveys. DHSs are conducted every 5 years in low-income and middle-income countries. Two-stage sampling procedures were used for the 2018 NDHS survey. The sampling frame is the Population and Housing Census of the Federal Republic of Nigeria which was conducted in 2006 by the Nigeria NPC. The primary sampling unit referred to as a cluster are the EAs in the census frame. In each state, samples of EAs were selected independently in a two-stage selection. At the first stage, 38 EAs were selected with probability proportional to EA size in each state. At the second stage, 30 households were selected in every selected EAs using equal probability of systematic sampling. All eligible women of reproductive age (15–49 years) in all the selected households were interviewed. Sampling weights were applied in the analyses to account for the differences in response rates and population sizes of the states. A total of 41 821 women aged 15–49 years were interviewed in the 2018 NDHS.32 All eligible respondents were asked if they had any pregnancy or birth within 5 years preceding the survey. Those who answered in affirmative to haven had at least a birth within the preceding 5 years were asked questions on the number of ANC contacts made, the onset of the ANC visits, and the ANC provider etc for the pregnancy starting from the most recent. Our analysis is based on the information on the most recent pregnancies of each of the respondents. A total of 21 785 women provided relevant information. Of these 21 785 women, 16 448 (75.5%) attended ANC and were thus included in the final analysis. The outcome variable is the timing of the first ANC visit among women. The time of the first ANC was reported in months by the mother and was grouped as first trimester (early initiation), second trimester (late initiation) and third trimester (very late initiation). We grouped the States in Nigeria into two: below 85% or ‘greater than or equal to’ 85% global ‘no antenatal contact’ prevalence.1 33 The states’ performances regarding the proportion of women who initiated ANC visits in the first trimester during the most recent pregnancy is presented, with the states grouped into having 0%–33%, 34%–67% and >67% early ANC visits. Based on existing literature,23 34–36 the independent variables used in this study are maternal age (15–19, 20–24, 25–29, 30–39, 40–49 years), educational attainment (no education, primary, secondary and higher), spouse educational attainment (no education, primary, secondary and higher), employment status (currently employed vs unemployed), spouse employment status (currently employed vs unemployed), access to media (at least one of radio, television, newspaper or not), household wealth tertile (low, middle and high), women’s autonomy using who decides respondents healthcare utilisation (respondent alone, respondent/spouse and spouse alone). Other included independent variables are birth interval (firstborn, <36 months and ≥36 months), birth order (1, 2, 3, 4 and 5+), number of children ever born (none, 1–2, 3–4, 4+), current marital status (currently married or living together, divorced/separated/widowed, never married), place of residence (rural vs urban), religion (Islam, Christian, others) and ethnicity (Hausa/Fulani, Igbo, Yoruba and others). Family mobility (had stayed less than 5 years at residence or not), wanted child when became pregnant (wanted then, wanted later, or wanted not more), household headship (male vs female), health insurance coverage (yes vs no), acceptance of wife-beating (yes vs no). We also assessed four community-level factors in the descriptive analysis. The communities are synonymus to the EAs. The four factors are the community poverty rate (high or low), community unemployment rate (high vs low), community illiteracy rate (high vs low) and community media access rate (high vs low). We computed the neighbourhood socioeconomic status disadvantage as a composite score using principal component analysis and grouped into lowest, middle and highest categories. It is the proportion of respondents within each community with no media access, who are illiterates, who are poor and who are unemployed. The ‘xtile’ function in Stata V.16 was used to categorise the already provided wealth index scores (V.191) in the DHS data into three tertiles. Descriptive statistics, bivariable and multivariable multinomial logistic regression were used. The ‘SVY’ command for survey data in Stata V.16 was used to adjust for the study design used and the sample weights. Frequency tables showing percentages were used to describe the distribution of study respondents’ characteristics and we cross-classified the outcome variables by the respondents’ characteristics (table 1). Graphs and maps were produced using Microsoft Office 365 Excel and PowerPoint editable maps, respectively. Association between respondents’ characteristics and timing of first ANC visit *Significant χ2 at 0.05. ANC, antenatal care; SES, socioeconomic status. We used the ‘multinomial logistic’ command in Stata to implement bivariable and multivariable regression models. It is a procedure for estimating the risk ratio (RR) of factors associated with the outcome variables. Variables that were significant at p<0.20 were included in the multivariable model.31 The multinomial logistic regression model computes the maximum-likelihood estimates of the probability of success of an event. In the binary logistic regression, the ORs are computed as the ratio of odds of success divided by the odds of failure, the ith coefficient is ϕi=exp(bi) with SE siϕ=ϕisi, where si is the SE of bi estimated using the logit function. Assuming that the predicted index of the jth observation is defined as Xib. The predicted probability of a positive outcome is Pj(yj≠0|Xj)=exp(Xib)1+exp(Xib)(1). Whereas, multinomial logistic regression is used for the categorical dependent variable, with three or more categories. If y has three outcomes 1, 2 and 3 whereby ‘3’ is not necessarily greater than ‘1’ or ‘2’. Then, multinomial logistic regression is useful in modelling the nominal outcome variables.37 The relative probability of any of the levels, say y=2 to the base outcome, say y=1 is p(y=2)p(y=1)=eXβ(2) which is the relative risk ratio (RRR). Assuming that X and βk(2) are vectors equal to (x1+x2+…xi…..+xk) and (β1(2)+β2(2)+…+βi(2)+…βk(2)), the ratio of the relative risk (RR) for a one-unit change in xt is eβ1(2)x1+….+eβi(2)x(i+1)+……+eβk(2)xkeβ1(2)x1+….+eβi(2)xi+……+eβk(2)xk=eXβ(2)(2) Thus, the exponential of the coefficient is the RRR for a one-unit change in the corresponding variable. This is easily interpreted as the ratio of the probability of choosing one outcome category divided by the probability of choosing the baseline category.37 38 Patients and the public will be involved in the dissemination plan.

Materiais

Inovações

Based on the information provided, here are some potential innovations that could improve access to maternal health in Nigeria:

1. Mobile Health (mHealth) Solutions: Develop mobile applications or SMS-based systems that provide pregnant women with information on antenatal care, including the importance of early initiation, appointment reminders, and access to healthcare providers.

2. Community Health Workers: Train and deploy community health workers to provide education and support to pregnant women in rural areas, where access to healthcare facilities may be limited. These workers can conduct home visits, provide antenatal care services, and refer women to appropriate healthcare facilities.

3. Telemedicine: Establish telemedicine services that allow pregnant women to consult with healthcare providers remotely, reducing the need for travel and increasing access to specialized care.

4. Public Awareness Campaigns: Launch targeted public awareness campaigns to educate women and their families about the importance of early initiation of antenatal care and the benefits of regular check-ups during pregnancy.

5. Financial Incentives: Implement financial incentives, such as conditional cash transfers or vouchers, to encourage pregnant women to seek early and regular antenatal care.

6. Strengthening Healthcare Infrastructure: Invest in improving healthcare infrastructure, including the construction and renovation of healthcare facilities, to ensure that pregnant women have access to quality antenatal care services.

7. Policy and Regulatory Reforms: Advocate for policy and regulatory reforms that prioritize maternal health, including the allocation of resources, training of healthcare providers, and implementation of evidence-based guidelines for antenatal care.

These innovations, when implemented effectively, have the potential to improve access to maternal health services and contribute to better maternal and child health outcomes in Nigeria.

AI Innovations Description

Based on the study’s findings and objectives, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Enhancing Women’s Autonomy in Healthcare Utilization: The study found that women who participate in their healthcare decision-making were more likely to have early initiation of antenatal care (ANC). Therefore, an innovation could focus on empowering women and promoting their autonomy in making decisions about their healthcare. This could be achieved through educational programs, community awareness campaigns, and the provision of resources and support for women to actively engage in their healthcare utilization.

2. Awareness Creation and Empowerment: The study highlighted the need for concerted efforts on awareness creation and empowerment for women by all stakeholders in maternal and child healthcare. An innovation could involve the development of targeted awareness campaigns that provide information about the importance of early ANC initiation, the benefits of regular prenatal care, and the available healthcare services. Additionally, empowerment programs could be implemented to provide women with the knowledge and skills to navigate the healthcare system and advocate for their own healthcare needs.

3. State-Level Analysis and Disparities: The study identified disparities in the timing of the first ANC contact across different states in Nigeria. An innovation could involve conducting further state-level analyses to identify specific barriers and challenges faced by women in each state. This information can then be used to develop tailored interventions and strategies to address the unique needs of each state and improve access to maternal health services.

4. Collaboration and Stakeholder Engagement: To effectively improve access to maternal health, it is essential to involve all relevant stakeholders, including healthcare providers, policymakers, community leaders, and women themselves. An innovation could focus on fostering collaboration and engagement among these stakeholders to develop comprehensive and sustainable solutions. This could involve establishing partnerships, creating platforms for dialogue and knowledge sharing, and involving women in the decision-making processes related to maternal health.

Overall, the recommendation is to develop innovative approaches that empower women, raise awareness, address disparities, and promote collaboration among stakeholders to improve access to maternal health services in Nigeria.

AI Innovations Methodology

Based on the provided study and data, here are some potential recommendations to improve access to maternal health in Nigeria:

1. Increase awareness and education: Implement comprehensive public health campaigns to raise awareness about the importance of early initiation of antenatal care (ANC) and the benefits it provides for both the mother and the baby. This can be done through various channels such as radio, television, newspapers, and community outreach programs.

2. Strengthen healthcare infrastructure: Invest in improving healthcare facilities and infrastructure, particularly in rural areas where access to quality maternal healthcare services is limited. This includes ensuring the availability of skilled healthcare professionals, necessary medical equipment, and adequate facilities for ANC.

3. Enhance women’s autonomy in healthcare decision-making: Empower women to make informed decisions about their healthcare by providing them with the necessary information and support. This can be achieved through educational programs that promote women’s rights, autonomy, and involvement in their own healthcare decision-making process.

4. Address socioeconomic disparities: Implement targeted interventions to address socioeconomic disparities that contribute to delayed initiation of ANC. This can include providing financial assistance or subsidies for ANC services, particularly for women from low-income households, and improving access to healthcare for marginalized populations.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the percentage of women initiating ANC in the first trimester, the number of ANC visits, and the availability of healthcare facilities in different regions.

2. Collect baseline data: Gather baseline data on the selected indicators from various sources, including surveys, health records, and government reports. This data should cover a representative sample of the population, including different regions and socioeconomic groups.

3. Develop a simulation model: Create a simulation model that incorporates the baseline data and the potential impact of the recommendations. This model should consider factors such as population demographics, healthcare infrastructure, and the effectiveness of the proposed interventions.

4. Run simulations: Use the simulation model to run different scenarios based on the proposed recommendations. This can involve adjusting variables such as the coverage and effectiveness of public health campaigns, the allocation of resources for healthcare infrastructure improvement, and the level of empowerment programs for women.

5. Analyze results: Analyze the simulation results to assess the potential impact of the recommendations on improving access to maternal health. This can include evaluating changes in the percentage of women initiating ANC in the first trimester, the number of ANC visits, and the reduction in disparities across different regions and socioeconomic groups.

6. Refine and validate the model: Continuously refine and validate the simulation model based on real-world data and feedback from experts in the field. This will ensure that the model accurately reflects the complex dynamics of improving access to maternal health in Nigeria.

7. Communicate findings: Present the findings of the simulation study to relevant stakeholders, including policymakers, healthcare providers, and community organizations. This can help inform decision-making and resource allocation to effectively improve access to maternal health in Nigeria.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data. Therefore, it is recommended to consult with experts in the field of maternal health and simulation modeling to develop a robust and context-specific methodology.

Autores & Coautores

Statistics:

Citations: 5

Authors: 3

Identifiers:

DOI: 10.1136/bmjopen-2020-047835

Research Areas:

Community Interventions, Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Nigeria

Study Design:

Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female

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