Contextual factors associated with health care service utilization for children with acute childhood illnesses in Nigeria

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Study Justification:
This study aimed to examine the factors influencing healthcare service utilization for children with acute childhood illnesses in Nigeria. By analyzing data from the 2013 Nigeria Demographic and Health Survey, the study sought to identify individual, community, and state-level factors that contribute to the use or non-use of healthcare services. Understanding these factors is crucial for developing effective public health strategies and improving healthcare provision in Nigeria.
Highlights:
– The study found that only 30% of children utilized healthcare services when they were sick, indicating a low level of healthcare service utilization for acute childhood illnesses.
– Maternal factors such as higher education attainment, wealth, access to media, and employment were significantly associated with higher healthcare service utilization.
– Community-level factors, including place of residence, distance to health facilities, community socioeconomic status, and ethnicity diversity, also influenced healthcare service utilization.
– State-level socioeconomic disadvantage was found to be a significant factor affecting healthcare service utilization.
Recommendations:
– Increase female school enrollment to improve maternal education attainment, which has been shown to positively impact healthcare service utilization.
– Provide interest-free loans for small and medium-scale enterprises to improve household wealth and increase access to healthcare services.
– Introduce mobile clinics to improve access to healthcare services, particularly in rural areas.
– Establish more primary healthcare centers to ensure the availability of healthcare services in communities.
Key Role Players:
– Ministry of Health: Responsible for implementing public health strategies and policies to improve healthcare service utilization.
– Education Ministry: Involved in increasing female school enrollment and improving maternal education attainment.
– Financial Institutions: Responsible for providing interest-free loans for small and medium-scale enterprises.
– Healthcare Providers: Involved in the establishment and operation of mobile clinics and primary healthcare centers.
Cost Items for Planning Recommendations:
– Education Programs: Budget for initiatives aimed at increasing female school enrollment and improving maternal education attainment.
– Loan Programs: Budget for providing interest-free loans to small and medium-scale enterprises.
– Mobile Clinics: Budget for the procurement and maintenance of mobile clinics, including medical equipment and personnel.
– Primary Healthcare Centers: Budget for the establishment and operation of additional primary healthcare centers, including infrastructure, equipment, and staffing.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a large sample size (6,427 under-five children) and utilizes multilevel logistic regression models. The study also considers individual, community, and state-level factors, providing a comprehensive analysis. To improve the evidence, the abstract could include more information on the methodology, such as the specific variables used and the statistical analysis techniques employed. Additionally, it would be beneficial to mention any limitations of the study and potential implications of the findings for healthcare policy and practice.

Objective To examine the independent contribution of individual, community and state-level factors to health care service utilization for children with acute childhood illnesses in Nigeria. Materials and methods The study was based on secondary analyses of cross-sectional population-based data from the 2013 Nigeria Demographic and Health Survey (DHS). Multilevel logistic regression models were applied to the data on 6,427 under-five children who used or did not use health care service when they were sick (level 1), nested within 896 communities (level 2) from 37 states (level 3). Results About one-quarter of the mothers were between 15 and 24 years old and almost half of them did not have formal education (47%). While only 30% of the children utilized health service when they were sick, close to 67% lived in the rural area. In the fully adjusted model, mothers with higher education attainment (Adjusted odds ratio [aOR] = 1.63; 95% credible interval [CrI] = 1.31-2.03), from rich households (aOR = 1.76; 95% CrI = 1.35-2.25), with access to media (radio, television or magazine) (aOR = 1.18; 95% CrI = 1.08-1.29), and engaging in employment (aOR = 1.18; 95% CrI = 1.02-1.37) were significantly more likely to have used healthcare services for acute childhood illnesses. On the other hand, women who experienced difficulty getting to health facilities (aOR = 0.87; 95% CrI = 0.75-0.99) were less likely to have used health service for their children. Conclusions Our findings highlight that utilization of healthcare service for acute childhood illnesses was influenced by not only maternal factors but also community-level factors, suggesting that public health strategies should recognise this complex web of individual composition and contextual composition factors to guide provision of healthcare services. Such interventions could include: increase in female school enrolment, provision of interest-free loans for small and medium scale enterprises, introduction of mobile clinics and establishment of more primary health care centres.

Nigeria is located in the western part of Africa and shares borders with Niger, Chad, Cameroon and Benin. It comprises 36 states, 6 geo-political zones and its population at the last Census exercise was over 140 million [17]. At independence in 1960, the main income for the country was generated from agriculture. However, attention has shifted from agriculture since the discovery of oil. The earnings from the sale of oil contributed immensely to developments in the country. Over the years, the health sector witnessed major developments in terms of establishment of hospitals, provision of equipment and drugs and introduction of programmes to promote the well-being of the citizens. Presently, the country operates a National Health Insurance Scheme which aims at making people have access to health services, relieving family of high medical expenses and ensuring equitable distribution of health care services [18]. The scheme covers both the formal and informal sectors. The formal sector comprises the public sector, organized private sector and the armed forces including the police and uniformed services. In the public sector scheme, 3.25% and 1.75% of the employee’s salary are paid by the employer and employee respectively [18]. In the private sector category, the employee pays 5% while the employer pays 10%. These contributions are meant to cover the health care expenses incurred by the employee, his/her spouse and four children below 18 years of age. The scheme has three categories in the informal sector: the tertiary social health insurance programme which makes provision to cater for the health care expenses of students in higher institutions; community-based social health insurance programme which is a voluntary scheme that enables communities to enjoy health services through the payment of flat rate per household or individual household member and; public primary school social health insurance which is targeted at primary school pupils from middle and lower socioeconomic status. This study was based on analyses of secondary data set from the Nigeria Demographic and Health Survey (DHS) 2013 which is cross-sectional and covers all the geo-political zones in the country. Details of the methods used in the DHS have been published elsewhere [17]. Briefly, the survey involved a three-stage cluster sampling technique. Nigeria was divided into 36 States and the Federal Capital Territory (FCT), Abuja making 37 districts in total. The primary sample unit (PSU) was based on 2006 Nigeria population census enumeration areas (EAs). The first stage involved selecting 896 localities (clusters). In the second stage, one EA was randomly selected from most localities. A total of 904 EAs were selected, with 372 in urban areas and 532 in rural areas. The third stage involved the selection of a fixed number of 45 households in every urban and rural geographical area. The total number of selected households was 40,680, with urban areas accounting for 16,740 and 23,940 from rural areas. The methods for data collection have been published elsewhere [17]. In brief, data were collected through household visitation and interviews with individual participants in the selected localities. Information on socio-demographic characteristics, wealth, reproduction, child health, knowledge of HIV/AIDS, domestic violence, household and environmental characteristics was obtained from the participants. This study was based on secondary analysis of existing survey datasets from the archive of the DHS Program who granted us permission for its usage after all the identifying information have been removed. The instruments and conduct of the 2013 Nigeria DHS was approved by the Institutional Review Board (IRB) of ICF Macro International in the United States and Nigeria Health Research Ethics Committee (NHREC) of the Federal Ministry of Health (FMOH). This research is limited to the use of previously collected anonymised data. Users/non users of health services; children under-five who had episode of diarrhea and/or fever or cough in the preceding 2 weeks before the survey and who sought consultation from a health care provider (either public or private) were defined as ‘users’; not seeking care were categorized as ‘non-users’. We adopted behavioural model developed by Andersen to understand the dynamic inter-relations among people and environmental factors associated with health services use for a sick child (Fig 1) [19]. We subsequently grouped such factors into individual-, community- and state-level factors. Figure adapted from Andersen RM. Revisiting the Behavioral Model and Access to Medical Care: Does it Matter? Journal of Health and Social Behavior. 1995; 36: 1–10 In this study, we considered the following variables: age of mother, educational attainment and marital status, mother’s occupation, sex of the child, wealth status and media access. Age of mother (the respondent) was categorised as 15–24, 25–34 and 35–49. The level of education attained by mother was defined as no education, primary, and secondary or higher education. Respondents’ current occupation was categorised into unemployed and employed (professional, technical and managerial, services, agricultural, skilled and unskilled manual and others). Marital status was dichotomized as ever married (i.e. currently married, living with partner, widowed, divorced, separated) and never married. Sex of the child was categorised into Male/Female. Wealth index is measured in the DHS surveys in terms of assets, rather than income. Ownership of consumer items such as a radio or car as well as dwelling characteristics such as floor or roof type, place of cooking, cooking fuel, electricity, toilet facilities and water source were the items that constituted the concept of poverty. This concept has been used by the World Bank to categorise households and their members into different wealth quintiles, through the use of principal components analysis (PCA) [20, 21]. For easy analysis, we re-categorized the weighted scores of five quintiles to three tertiles to allow for nonlinear effects and provide results that would be more readily interpretable in the policy domain. The resultant three tertiles expressed as categorical variables include poor, middle and rich. Access to media was measured as a set of additive scale (from 0 to 3) that counted the number of domains in which each of the respondents was regarding having access to various types of media (radio, television and magazine). This resulted into the following categories: no access, have access to 1 outlet, have access to 2 outlets and have access to all outlets. At community level, we included place of residence, distance to health facility, community socioeconomic status and ethnicity diversity index. Place of residence was categorized into urban and rural. Distance to health facility was grouped into two: those who experienced difficulty in reaching health facility were categorised as ‘a problem’ and those who did not experience difficulty were categorised as ‘not a problem’. Community socioeconomic disadvantage was an index created from the compositional education, wealth and occupation of people within the same PSU. Community socioeconomic disadvantage was obtained through a principal component that consisted of the proportion of respondents with: no education (illiterate), unemployed, and living below the poverty level (asset index below 20% poorest quintile) in the same PSU. This resulted in the generation of a standardized score with mean 0 and standard deviation 1; with higher scores indicative of lower socioeconomic position. We divided the resultant scores into three equal tertiles. The ethnicity of the children was computed by using ethnicity diversity index. We obtained the index by using a formula derived by Vyas and Kumaranayake [22] that captures both the number of different ethnic groups in an area and the relative representation of each group as follows Where: xi = population of ethnic group i of the area, y = total population of the area, n = number of ethnic groups in the area. Scores can range from 0 to approximately 1. For easy interpretation, we multiplied each diversity index by 100; the higher the index score, the greater the diversity in the area. An area with zero diversity indicates that all the people in the area belong to one ethnic group. As the index moves close to 100, the population becomes more evenly distributed into ethnic groups. State socioeconomic disadvantage was an index created from the compositional education, wealth and occupation. State socioeconomic disadvantage was operationalized with a principal component comprised of the proportion of respondents with: no education (illiterate), unemployed, and living below the poverty level (asset index below 20% poorest quintile) within the state. A standardized score with mean 0 and standard deviation 1 was generated from this index; with higher scores indicative of lower socioeconomic position. We divided the resultant scores into three tertiles; tertile 1(least economically disadvantaged), tertile 2 and tertile 3(most economically disadvantaged) In the descriptive statistics, the respondents’ characteristics at different levels were expressed as numbers and percentages. We specified a three-level model with individuals clustered within the communities and the communities clustered within the states in order to estimate the effects of variables at all three levels on utilization of health services. We constructed four models. The first model is a univariable model of the individual-level factors. The second model contained community-level variables while the third model contained the state-level variable at the univariable level. The fourth model adjusted for the individual-level, community-level and state-level variables respectively. P value of < 0.05 was used to define statistical significance. We presented the results of fixed effects as odds ratios (OR) with their corresponding 95% credible intervals (CrI). Random effects were measured through intra-cluster correlation (ICC), variance partition coefficient (VPC) and median odds ratio (MOR). Median odds ratio, which reflects the unexplained cluster heterogeneity, measures the area variance as odds ratios. Details of the procedure used for calculating MOR have been published elsewhere [23, 24]. While we applied Bayesian Deviance Information Criterion (DIC) to assess the goodness-of-fit of the model, Variance Inflation Factor (VIF) was used to check for multicollinearity among the independent variables. All multilevel modelling operations were performed using MLwiN 2.36 [25] calling Stata statistical software for windows version 14 [26]. The Bayesian approach with Markov Chain Monte Carlo (MCMC) estimation was used [27] for the multilevel logistic regression models.

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

1. Mobile Clinics: Introduce mobile clinics that can travel to rural areas, making it easier for pregnant women to access prenatal care and other maternal health services.

2. Primary Health Care Centers: Establish more primary health care centers in underserved areas, providing comprehensive maternal health services closer to where women live.

3. Female School Enrolment: Increase female school enrolment rates to empower women with education and knowledge about maternal health, enabling them to make informed decisions about their own health and the health of their children.

4. Interest-Free Loans: Provide interest-free loans for small and medium-scale enterprises, particularly targeting women entrepreneurs in the healthcare sector. This can help improve the availability and affordability of maternal health services.

These innovations aim to address the barriers identified in the study, such as limited education, lack of access to healthcare facilities, and socioeconomic factors. By implementing these recommendations, it is hoped that access to maternal health services in Nigeria will be improved, leading to better health outcomes for mothers and their children.
AI Innovations Description
The recommendation to improve access to maternal health based on the provided information is to implement a combination of interventions. These interventions could include:

1. Increase in female school enrollment: By promoting and supporting girls’ education, more women will have the opportunity to gain knowledge and skills that can contribute to better maternal health outcomes. Education can empower women to make informed decisions about their health and seek appropriate healthcare services.

2. Provision of interest-free loans for small and medium-scale enterprises: Economic empowerment of women can have a positive impact on maternal health. By providing access to financial resources, women can start or expand their businesses, generate income, and have the means to afford quality healthcare services during pregnancy and childbirth.

3. Introduction of mobile clinics: Mobile clinics can help overcome geographical barriers and improve access to maternal health services, especially in rural areas where healthcare facilities may be limited. These clinics can provide essential prenatal care, antenatal check-ups, and postnatal care to pregnant women, ensuring that they receive the necessary healthcare services closer to their homes.

4. Establishment of more primary health care centers: Increasing the number of primary health care centers, particularly in underserved areas, can improve access to maternal health services. These centers can offer a range of services, including prenatal care, skilled birth attendance, and postnatal care, making it easier for women to access essential maternal healthcare closer to their communities.

By implementing these interventions, it is expected that access to maternal health services will improve, leading to better maternal and child health outcomes in Nigeria.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Increase female school enrollment: Promoting education for girls and women can have a positive impact on maternal health. Educated women are more likely to have better knowledge about pregnancy and childbirth, access healthcare services, and make informed decisions about their health.

2. Provide interest-free loans for small and medium-scale enterprises: Economic empowerment of women can contribute to improved access to maternal health. By providing interest-free loans, women can start or expand their businesses, generate income, and have the financial means to seek healthcare services during pregnancy and childbirth.

3. Introduce mobile clinics: Mobile clinics can bring healthcare services closer to rural and remote areas where access to maternal health services is limited. These clinics can provide prenatal care, antenatal check-ups, and delivery services, ensuring that pregnant women receive the necessary care regardless of their location.

4. Establish more primary health care centers: Increasing the number of primary health care centers can improve access to maternal health services, especially in underserved areas. These centers can offer a range of services, including prenatal care, skilled birth attendance, postnatal care, and family planning.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the indicators: Identify key indicators that measure access to maternal health, such as the percentage of pregnant women receiving prenatal care, the percentage of births attended by skilled health personnel, and the percentage of women receiving postnatal care.

2. Collect baseline data: Gather data on the current status of access to maternal health services in the target population. This can be done through surveys, interviews, or existing data sources.

3. Develop a simulation model: Create a model that incorporates the recommended innovations and their potential impact on the selected indicators. This model should consider factors such as population size, geographical distribution, and existing healthcare infrastructure.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to estimate the potential impact of the recommendations. Adjust the parameters of the model based on the expected effects of each innovation.

5. Analyze results: Analyze the simulation results to determine the projected changes in access to maternal health services. Assess the effectiveness of each recommendation and identify any potential challenges or limitations.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data or expert input. Ensure that the model accurately reflects the real-world context and provides reliable predictions.

7. Communicate findings and make recommendations: Present the findings of the simulation study, including the projected impact of the recommendations on improving access to maternal health. Use this information to inform policy decisions, resource allocation, and implementation strategies.

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