Mind the gap: what explains the rural-nonrural inequality in diarrhoea among under-five children in low and medium-income countries? A decomposition analysis

BMC Public Health, Volume 21, No. 1, Year 2021

Interpretation

Background: Diarrhoea poses serious health problems among under-five children (U5C) in Low-and Medium-Income Countries (LMIC) with a higher prevalence in rural areas. A gap exists in knowledge on factors driving rural-non-rural inequalities in diarrhoea development among U5C in LMIC. This study investigates the magnitude of rural-non-rural inequalities in diarrhoea and the roles of individual-level and neighbourhood-level factors in explaining these inequalities. Methods: Data of 796,150 U5C, from 63,378 neighbourhoods across 57 LMIC from the most recent Demographic and Health Survey (2010–2018) was analysed. The outcome variable was the recent experience of diarrhoea while independent variables consist of the individual- and neighbourhood-level factors. Data were analysed using multivariable Fairlie decomposition at p < 0.05 in Stata Version 16 while visualization was implemented in R Statistical Package. Results: Two-thirds (68.0%) of the children are from rural areas. The overall prevalence of diarrhoea was 14.2, 14.6% vs 13.4% among rural and non-rural children respectively (p < 0.001). From the analysis, the following 20 countries showed a statistically significant pro-rural inequalities with higher odds of diarrhoea in rural areas than in nonrural areas at 5% alpha level: Albania (OR = 1.769; p = 0.001), Benin (OR = 1.209; p = 0.002), Burundi (OR = 1.399; p < 0.001), Cambodia (OR = 1.201; p < 0.031), Cameroon (OR = 1.377; p < 0.001), Comoros (OR = 1.266; p = 0.029), Egypt (OR = 1.331; p < 0.001), Honduras (OR = 1.127; p = 0.027), India (OR = 1.059; p < 0.001), Indonesia (OR = 1.219; p < 0.001), Liberia (OR = 1.158; p = 0.017), Mali (OR = 1.240; p = 0.001), Myanmar (OR = 1.422; p = 0.004), Namibia (OR = 1.451; p < 0.001), Nigeria (OR = 1.492; p < 0.001), Rwanda (OR = 1.261; p = 0.010), South Africa (OR = 1.420; p = 0.002), Togo (OR = 1.729; p < 0.001), Uganda (OR = 1.214; p < 0.001), and Yemen (OR = 1.249; p < 0.001); and pro-non-rural inequalities in 9 countries. Variations exist in factors associated with pro-rural inequalities across the 20 countries. Overall main contributors to pro-rural inequality were neighbourhood socioeconomic status, household wealth status, media access, toilet types, maternal age and education. Conclusions: The gaps in the odds of diarrhoea among rural children than nonrural children were explained by individual-level and neighbourhood-level factors. Sustainable intervention measures that are tailored to country-specific needs could offer a better approach to closing rural-non-rural gaps in having diarrhoea among U5C in LMIC. The data from the Demographic and Health Surveys (DHS) collected periodically across the LMIC was used in this study. The ICF Macro, the USA in conjunction with the ministry of health, the office of statistics, and the population commissions in respective LMIC conduct the periodical cross-sectional nationally representative population-based household DHS. We pooled data from the most recent DHS conducted within the last ten years (2010–2018) and available as of April 2020 and which provided information on diarrhoea among U5C. Only 57 LMIC met these inclusion criteria and were included in this study. We analysed data of 796,150 U5C, from 63,378 neighbourhoods across the 57 LMIC. In each of the countries, DHS used a multi-stage (usually from states/divisions/regions to the district to clusters), stratified sampling design. The households (the sampling units) are selected from the clusters known as the primary sampling units (PSU) [21, 22]. We applied sampling weights provided in the data to all our analysis. This was to adjust for unequal cluster sizes and to ensure that our findings adequately represent the target population. The DHS uses similar surveys and research protocols, standardized questionnaire, similar interviewer training, supervision, and implementation in all the countries. For full details of the sampling methodologies, please visit dhsprogram.com. The outcome variable in this study is the recent experience of diarrhoea. Diarrhoea is defined as “passage of liquid stools three or more times a day” [4, 5] and “recent experience of diarrhoea” as having any symptom of diarrhoea within two weeks before the interview date [23]. The mothers were asked if any of their U5C had diarrhoea within two weeks preceding the survey. The responses were binary: Yes or No. The main determinate variable in this decomposition study is the rural-non-rural differentials in the location of the residence of the mothers. The DHS data have already classified study clusters into either rural or non-rural areas using similar standard classification procedures as of the time of the surveys with minimal differences in what rural areas were across the countries. We named children born to rural and non-rural women as rural and non-rural children respectively. The identified variables in the literature [5, 20, 24–28] and the Moseley’s systematic conceptual framework on study of child survival in developing countries was used to select the explanatory variables in this study [29]. The independent variables used in the study were based on the identified factors associated with diarrhoea among U5C in the literature [5, 20, 24–28]. These are made up of the individual-level and neighbourhood-level factors. The individual-level consists of childs’ characteristics, mothers’ characteristics and the households’ characteristics. Childs’ characteristics: sex (male versus female), age in years (under 1 year and 12–59 months), weight at birth (average+, small and very small), birth interval (firstborn, < 36 months and > 36 months) and birth order (1, 2, 3 and 4+). Mothers’ characteristics: maternal education (none, primary or secondary plus), maternal age (15 to 24, 25 to 34, 35 to 49), marital status (never, currently and formerly married), employment status (working or not working). Households’ characteristics: access to media (at least one of radio, television or newspaper), sources of drinking water (improved or unimproved), toilet type (improved or unimproved), cooking fuel (clean fuel or biomass), housing materials (improved or unimproved) and household wealth index (poorest, poorer, middle, richer and richest). The DHS uses “clusters” as the PSU as people of the same cluster shares similar contextual factors [21, 22]. We used the word “neighbourhood” to describe the clustering of the children within the same cluster and “neighbours” as members of the same cluster. The PSUs were identified using the most recent census in each country where DHS held. In this study, we considered neighbourhood socioeconomic status (SES) as a community-level variable. It was computed using principal component factor comprised of the proportion of respondents within the same neighbourhood without education, belonging to a household in poor wealth quintiles and unemployed. We used both descriptive and inferential statistics in this study. Descriptive statistics such as chats, tables, percentages were used to show the distribution of respondents by country, outcome variable and other key variables. Bivariable analysis was conducted to using the Z-test for equality of proportions who had diarrhoea among rural and non-rural children within each country and region (Table 1 (a) and (b)). We also determined the existence of an association between the explanatory variables and the outcome variable among the rural and non-rural groups of children (Table 2(a) and (b)). We carried out country-level comparison of the prevalence of diarrhoea in each of the countries by computing the risk difference (RD) in the development of diarrhoea between U5C from rural and non-rural settings and presented the results in Fig. 1. An RD greater than 0 suggests that diarrhoea is more prevalent among rural children (pro-rural inequality). Whereas, a negative RD indicates that diarrhoea is prevalent among non-rural children (pro-non-rural inequality). We estimated the fixed effects as the weighted country-specific risk differences and the random effect as the overall risk difference irrespective of a child’s country of residence. As shown in Fig. Fig.1,1, forest plot was used to illustrate this distributions. Charts were used to show the distributions of the RDs (Figs. 2 and and3).3). We conducted tests of heterogeneity to ascertain that the 57 countries are different with regards to the odds ratio of having diarrhoea among the rural and non-rural children using adapted z-test in Stata and carried out a test of homogeneity of ORs among the 20 countries with a significant odds ratio of having diarrhoea to determine if the odds of having diarrhoea in those countries are homogenous. Lastly, the adjusted logistic regression method was applied to the pooled cross-sectional data from the 57 LMIC to carry out a Fairlie decomposition analysis (FDA) and the results presented in Fig. 4. Description of demographic and health surveys data by countries, rural percentage and diarrhoea prevalence among under-five children in LMIC, 2010–2018 **significant at 5% Chi-square test, *significant at 5% test of equality of proportions a and b Summary of pooled sample characteristics of the studied children in 57 LMIC *significant at 5% Chi-square test Prevalence and risk difference of diarrhoea between rural and non-rural children by countries Risk difference between rural and non-rural children in the prevalence of diarrhoea by countries Scatter plot of prevalence and risk difference of diarrhoea between rural and non-rural children in LMIC Contributions of differences in the distribution of ‘compositional effect’ of the determinants of having diarrhoea to the total gap between rural and non-rural children by countries We applied Fairlie Multivariable decomposition based on the binary regression model. It belongs to the family of decomposition techniques used to quantify the contributions to differences in the prediction of an outcome of interest between two groups in multivariate models [30]. It is an extension of the Blinder-Oaxaca Decomposition Analysis (BODA) [31–33]. While the BODA works best for continuous outcomes Fairlie is renowned for the logit and probit model [34–38]. Fairlie et al. noted that the nonlinear decomposition techniques helped to overcome the challenges of the BODA when group differences are large for an independent variable [35]. We used the Fairlie methods in this study as it was purposively developed for non-linear regression models including the logit and probit models [38]. The Fairlie works by decomposing the difference in proportions based on either the probit or logit models into the portion of the characteristic [30]. The decomposition analysis was carried out by calculating the difference between the predicted probability for one group (say Group A) using the other group’s (say Group B) regression coefficients and the predicted probability for that group (Group A) using its regression coefficients [35]. The Fairlie decomposition technique works by constraining the predicted probability between 0 and 1. Fairlie et al. showed that the decomposition for a nonlinear equation Y = F(X), can be expressed as: Where NA is the sample size for group J [39]. In eq. (1), Y¯ is not necessarily the same as FX¯β^, unlike in BODA where F(Xiβ) = Xiβ. The 1st term is the part of the gap in the binary outcome variable that is due to group differences in distributions of X, and the 2nd term is the part due to differences in the group processes determining levels of Y . The 2nd term also captures the portion of the binary outcome variable gap due to group differences in unmeasurable or unobserved endowments. The estimation of the total contribution is the difference between the average values of the predicted probabilities. Using coefficient estimates from a logit regression model for a pooled sample, β^∗, the independent contribution of X1 and X2 to the group, gap can be written as and respectively. The contribution of each variable to the gap is thus equal to the change in the average predicted probability from replacing the group B distribution with the group A distribution of that variable while holding the distributions of the other variable constant. To obtain an accurate decomposition estimate, Fairlie et al. recommended the replication of the decomposition from a minimum of 1000 subsamples and finding the mean values of estimates from each separate decomposition [35]. Further numerical details have been reported [35, 36, 38–40]. Respectively, the contribution of each variable to the gap is thus equal to the change in the average predicted probability from replacing the group B distribution with the group A distribution of that variable while holding the distributions of the other variable constant. To obtain an accurate decomposition estimates, Fairlie et al. recommended the replication of the decomposition from a minimum of 1000 subsamples and finding the mean values of estimates from each separate decomposition [35]. Further numerical details have been reported [35, 36, 38–40]. We used the “Fairlie” command in STATA 16 (StataCorp, College Station, Texas, United States of America) to carry out the decomposition analysis to enable the quantification of how much of the gap between the “advantaged” (non-rural) and the “disadvantaged” (rural) groups is attributable to differences in specific measurable characteristics. Using the generalised structure of the model, we fitted a logistic model to determine factors influencing diarrhoea occurrence among rural and non-rural children.

AI Digest

Study Justification:

This study aims to investigate the rural-nonrural inequality in diarrhoea among under-five children in low and medium-income countries (LMIC). Diarrhoea is a significant health problem among children in LMIC, with higher prevalence in rural areas. However, there is a lack of knowledge on the factors driving these rural-nonrural inequalities. Understanding the magnitude of these inequalities and the role of individual-level and neighbourhood-level factors can help inform targeted interventions to reduce diarrhoea prevalence among rural children.

Highlights:

– The study analyzed data from 796,150 under-five children in 57 LMIC.
– Two-thirds (68.0%) of the children were from rural areas.
– The overall prevalence of diarrhoea was 14.2% among rural children and 13.4% among non-rural children.
– The study identified 20 countries with statistically significant pro-rural inequalities in diarrhoea prevalence.
– Factors associated with pro-rural inequalities varied across the 20 countries.
– Neighbourhood socioeconomic status, household wealth status, media access, toilet types, maternal age, and education were identified as main contributors to pro-rural inequality.

Recommendations:

– Implement sustainable intervention measures tailored to country-specific needs to address rural-nonrural gaps in diarrhoea prevalence among under-five children in LMIC.
– Focus on improving neighbourhood socioeconomic status, household wealth status, media access, toilet facilities, and maternal education to reduce rural-nonrural inequalities in diarrhoea.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating intervention measures.
– Local Health Departments: Involved in implementing interventions at the community level.
– Non-Governmental Organizations (NGOs): Provide support and resources for intervention programs.
– Community Health Workers: Engage with communities to promote health education and behavior change.
– Researchers and Academics: Conduct further studies and provide evidence-based recommendations.

Cost Items for Planning Recommendations:

– Health Education and Awareness Campaigns: Budget for materials, training, and dissemination.
– Infrastructure Improvement: Allocate funds for improving toilet facilities and access to clean water.
– Healthcare Services: Ensure adequate funding for healthcare facilities and staff to provide treatment and preventive services.
– Research and Evaluation: Allocate resources for conducting further studies and monitoring the effectiveness of interventions.
– Capacity Building: Invest in training programs for healthcare workers and community health workers.
Please note that the provided cost items are general categories and the actual cost will vary depending on the specific context and requirements of each country.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study used a large dataset from 57 LMIC and conducted a decomposition analysis to investigate rural-nonrural inequalities in diarrhea among under-five children. The analysis showed significant pro-rural inequalities in 20 countries and pro-non-rural inequalities in 9 countries. The study identified several factors associated with these inequalities, including neighborhood socioeconomic status, household wealth status, media access, toilet types, maternal age, and education. The study provides valuable insights into the factors driving rural-nonrural inequalities in diarrhea and suggests that tailored intervention measures could help close these gaps. However, the abstract could be improved by providing more details on the methodology used, such as the specific statistical techniques and models employed. Additionally, it would be helpful to include information on the limitations of the study and potential implications for policy and practice.

Abstract

The data from the Demographic and Health Surveys (DHS) collected periodically across the LMIC was used in this study. The ICF Macro, the USA in conjunction with the ministry of health, the office of statistics, and the population commissions in respective LMIC conduct the periodical cross-sectional nationally representative population-based household DHS. We pooled data from the most recent DHS conducted within the last ten years (2010–2018) and available as of April 2020 and which provided information on diarrhoea among U5C. Only 57 LMIC met these inclusion criteria and were included in this study. We analysed data of 796,150 U5C, from 63,378 neighbourhoods across the 57 LMIC. In each of the countries, DHS used a multi-stage (usually from states/divisions/regions to the district to clusters), stratified sampling design. The households (the sampling units) are selected from the clusters known as the primary sampling units (PSU) [21, 22]. We applied sampling weights provided in the data to all our analysis. This was to adjust for unequal cluster sizes and to ensure that our findings adequately represent the target population. The DHS uses similar surveys and research protocols, standardized questionnaire, similar interviewer training, supervision, and implementation in all the countries. For full details of the sampling methodologies, please visit dhsprogram.com. The outcome variable in this study is the recent experience of diarrhoea. Diarrhoea is defined as “passage of liquid stools three or more times a day” [4, 5] and “recent experience of diarrhoea” as having any symptom of diarrhoea within two weeks before the interview date [23]. The mothers were asked if any of their U5C had diarrhoea within two weeks preceding the survey. The responses were binary: Yes or No. The main determinate variable in this decomposition study is the rural-non-rural differentials in the location of the residence of the mothers. The DHS data have already classified study clusters into either rural or non-rural areas using similar standard classification procedures as of the time of the surveys with minimal differences in what rural areas were across the countries. We named children born to rural and non-rural women as rural and non-rural children respectively. The identified variables in the literature [5, 20, 24–28] and the Moseley’s systematic conceptual framework on study of child survival in developing countries was used to select the explanatory variables in this study [29]. The independent variables used in the study were based on the identified factors associated with diarrhoea among U5C in the literature [5, 20, 24–28]. These are made up of the individual-level and neighbourhood-level factors. The individual-level consists of childs’ characteristics, mothers’ characteristics and the households’ characteristics. Childs’ characteristics: sex (male versus female), age in years (under 1 year and 12–59 months), weight at birth (average+, small and very small), birth interval (firstborn, 36 months) and birth order (1, 2, 3 and 4+). Mothers’ characteristics: maternal education (none, primary or secondary plus), maternal age (15 to 24, 25 to 34, 35 to 49), marital status (never, currently and formerly married), employment status (working or not working). Households’ characteristics: access to media (at least one of radio, television or newspaper), sources of drinking water (improved or unimproved), toilet type (improved or unimproved), cooking fuel (clean fuel or biomass), housing materials (improved or unimproved) and household wealth index (poorest, poorer, middle, richer and richest). The DHS uses “clusters” as the PSU as people of the same cluster shares similar contextual factors [21, 22]. We used the word “neighbourhood” to describe the clustering of the children within the same cluster and “neighbours” as members of the same cluster. The PSUs were identified using the most recent census in each country where DHS held. In this study, we considered neighbourhood socioeconomic status (SES) as a community-level variable. It was computed using principal component factor comprised of the proportion of respondents within the same neighbourhood without education, belonging to a household in poor wealth quintiles and unemployed. We used both descriptive and inferential statistics in this study. Descriptive statistics such as chats, tables, percentages were used to show the distribution of respondents by country, outcome variable and other key variables. Bivariable analysis was conducted to using the Z-test for equality of proportions who had diarrhoea among rural and non-rural children within each country and region (Table 1 (a) and (b)). We also determined the existence of an association between the explanatory variables and the outcome variable among the rural and non-rural groups of children (Table 2(a) and (b)). We carried out country-level comparison of the prevalence of diarrhoea in each of the countries by computing the risk difference (RD) in the development of diarrhoea between U5C from rural and non-rural settings and presented the results in Fig. 1. An RD greater than 0 suggests that diarrhoea is more prevalent among rural children (pro-rural inequality). Whereas, a negative RD indicates that diarrhoea is prevalent among non-rural children (pro-non-rural inequality). We estimated the fixed effects as the weighted country-specific risk differences and the random effect as the overall risk difference irrespective of a child’s country of residence. As shown in Fig. Fig.1,1, forest plot was used to illustrate this distributions. Charts were used to show the distributions of the RDs (Figs. 2 and and3).3). We conducted tests of heterogeneity to ascertain that the 57 countries are different with regards to the odds ratio of having diarrhoea among the rural and non-rural children using adapted z-test in Stata and carried out a test of homogeneity of ORs among the 20 countries with a significant odds ratio of having diarrhoea to determine if the odds of having diarrhoea in those countries are homogenous. Lastly, the adjusted logistic regression method was applied to the pooled cross-sectional data from the 57 LMIC to carry out a Fairlie decomposition analysis (FDA) and the results presented in Fig. 4. Description of demographic and health surveys data by countries, rural percentage and diarrhoea prevalence among under-five children in LMIC, 2010–2018 **significant at 5% Chi-square test, *significant at 5% test of equality of proportions a and b Summary of pooled sample characteristics of the studied children in 57 LMIC *significant at 5% Chi-square test Prevalence and risk difference of diarrhoea between rural and non-rural children by countries Risk difference between rural and non-rural children in the prevalence of diarrhoea by countries Scatter plot of prevalence and risk difference of diarrhoea between rural and non-rural children in LMIC Contributions of differences in the distribution of ‘compositional effect’ of the determinants of having diarrhoea to the total gap between rural and non-rural children by countries We applied Fairlie Multivariable decomposition based on the binary regression model. It belongs to the family of decomposition techniques used to quantify the contributions to differences in the prediction of an outcome of interest between two groups in multivariate models [30]. It is an extension of the Blinder-Oaxaca Decomposition Analysis (BODA) [31–33]. While the BODA works best for continuous outcomes Fairlie is renowned for the logit and probit model [34–38]. Fairlie et al. noted that the nonlinear decomposition techniques helped to overcome the challenges of the BODA when group differences are large for an independent variable [35]. We used the Fairlie methods in this study as it was purposively developed for non-linear regression models including the logit and probit models [38]. The Fairlie works by decomposing the difference in proportions based on either the probit or logit models into the portion of the characteristic [30]. The decomposition analysis was carried out by calculating the difference between the predicted probability for one group (say Group A) using the other group’s (say Group B) regression coefficients and the predicted probability for that group (Group A) using its regression coefficients [35]. The Fairlie decomposition technique works by constraining the predicted probability between 0 and 1. Fairlie et al. showed that the decomposition for a nonlinear equation Y = F(X), can be expressed as: Where NA is the sample size for group J [39]. In eq. (1), Y¯ is not necessarily the same as FX¯β^, unlike in BODA where F(Xiβ) = Xiβ. The 1st term is the part of the gap in the binary outcome variable that is due to group differences in distributions of X, and the 2nd term is the part due to differences in the group processes determining levels of Y . The 2nd term also captures the portion of the binary outcome variable gap due to group differences in unmeasurable or unobserved endowments. The estimation of the total contribution is the difference between the average values of the predicted probabilities. Using coefficient estimates from a logit regression model for a pooled sample, β^∗, the independent contribution of X1 and X2 to the group, gap can be written as and respectively. The contribution of each variable to the gap is thus equal to the change in the average predicted probability from replacing the group B distribution with the group A distribution of that variable while holding the distributions of the other variable constant. To obtain an accurate decomposition estimate, Fairlie et al. recommended the replication of the decomposition from a minimum of 1000 subsamples and finding the mean values of estimates from each separate decomposition [35]. Further numerical details have been reported [35, 36, 38–40]. Respectively, the contribution of each variable to the gap is thus equal to the change in the average predicted probability from replacing the group B distribution with the group A distribution of that variable while holding the distributions of the other variable constant. To obtain an accurate decomposition estimates, Fairlie et al. recommended the replication of the decomposition from a minimum of 1000 subsamples and finding the mean values of estimates from each separate decomposition [35]. Further numerical details have been reported [35, 36, 38–40]. We used the “Fairlie” command in STATA 16 (StataCorp, College Station, Texas, United States of America) to carry out the decomposition analysis to enable the quantification of how much of the gap between the “advantaged” (non-rural) and the “disadvantaged” (rural) groups is attributable to differences in specific measurable characteristics. Using the generalised structure of the model, we fitted a logistic model to determine factors influencing diarrhoea occurrence among rural and non-rural children.

Materials

N/A

Innovations

Based on the information provided, it appears that the study focuses on understanding the rural-nonrural inequality in diarrhea among under-five children in low and medium-income countries (LMIC). The study analyzes data from the most recent Demographic and Health Surveys (DHS) conducted in 57 LMIC from 2010 to 2018. The study aims to identify the factors contributing to the rural-nonrural inequalities in diarrhea prevalence among under-five children.

Some potential innovations or recommendations to improve access to maternal health based on the study findings could include:

1. Strengthening healthcare infrastructure in rural areas: Investing in healthcare facilities, clinics, and hospitals in rural areas can improve access to maternal health services. This includes ensuring the availability of skilled healthcare professionals, essential medical equipment, and supplies.

2. Mobile health (mHealth) interventions: Utilizing mobile technology to provide maternal health information, reminders, and access to healthcare services can bridge the gap between rural communities and healthcare providers. Mobile apps, SMS messaging, and telemedicine can be used to provide remote consultations, prenatal care, and postnatal support.

3. Community-based interventions: Implementing community-based programs that focus on maternal health education, awareness, and support can help improve access to care in rural areas. This can involve training community health workers, organizing health camps, and conducting outreach programs to reach pregnant women and new mothers.

4. Transportation and logistics support: Improving transportation infrastructure and providing transportation services for pregnant women in rural areas can help overcome geographical barriers to accessing maternal health services. This can include initiatives such as ambulances, community transport systems, or partnerships with local transportation providers.

5. Financial incentives and subsidies: Introducing financial incentives or subsidies for maternal health services in rural areas can help reduce the financial burden on families and encourage utilization of services. This can include subsidies for transportation, antenatal care, delivery services, and postnatal care.

6. Telemedicine and teleconsultation: Expanding telemedicine services to rural areas can enable pregnant women to access specialized care and consultations remotely. This can help address the shortage of healthcare professionals in rural areas and provide timely advice and support to pregnant women.

7. Maternal health awareness campaigns: Conducting targeted awareness campaigns to educate rural communities about the importance of maternal health, early detection of complications, and the availability of services can help increase utilization of maternal health services.

It is important to note that these recommendations are based on the general context of improving access to maternal health and may not directly address the specific findings of the study on rural-nonrural inequalities in diarrhea prevalence among under-five children. Further analysis and research would be needed to develop more specific interventions based on the study findings.

AI Innovations Description

Based on the description provided, the study aims to investigate the rural-nonrural inequality in diarrhoea among under-five children in low and medium-income countries (LMIC) and identify the factors contributing to these inequalities. The study analyzed data from the most recent Demographic and Health Surveys (DHS) conducted between 2010 and 2018 in 57 LMIC.

The study found that the prevalence of diarrhoea was higher among rural children compared to non-rural children in most of the countries analyzed. The study identified several factors associated with the rural-nonrural inequalities in diarrhoea, including neighborhood socioeconomic status, household wealth status, media access, toilet types, maternal age, and education.

The study recommends the implementation of sustainable intervention measures tailored to the specific needs of each country to address the rural-nonrural gaps in diarrhoea among under-five children in LMIC. These interventions should focus on improving access to healthcare services, promoting hygiene practices, and addressing socioeconomic disparities. By addressing these factors, it is possible to improve access to maternal health and reduce the prevalence of diarrhoea among under-five children in rural areas.

AI Innovations Methodology

Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Strengthening healthcare infrastructure: Investing in the development and improvement of healthcare facilities, particularly in rural areas, can enhance access to maternal health services. This includes building and equipping healthcare centers, ensuring availability of skilled healthcare professionals, and improving transportation networks for easier access to healthcare facilities.

2. Mobile health (mHealth) interventions: Utilizing mobile technology to provide maternal health information, reminders, and support can help bridge the gap in access to healthcare services. Mobile apps, text messaging, and telemedicine can be used to provide prenatal care, postnatal care, and emergency assistance to pregnant women in remote areas.

3. Community-based interventions: Implementing community-based programs that involve trained community health workers can improve access to maternal health services. These workers can provide education, counseling, and basic healthcare services to pregnant women in their own communities, reducing the need for long-distance travel.

4. Financial incentives and subsidies: Providing financial incentives, such as cash transfers or subsidies, can help overcome financial barriers to accessing maternal health services. This can include covering the costs of transportation, healthcare fees, and essential medications for pregnant women.

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 specific indicators that measure access to maternal health, such as the number of prenatal visits, institutional delivery rates, or maternal mortality rates.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population. This can be done through surveys, interviews, or existing data sources.

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

4. Input intervention parameters: Specify the parameters of each intervention, such as the number of healthcare facilities to be built, the coverage of mobile health interventions, or the amount of financial incentives to be provided. These parameters should be based on evidence-based estimates or expert opinions.

5. Run simulations: Use the simulation model to project the potential impact of the interventions on the selected indicators. This can be done by running multiple scenarios with different combinations of intervention parameters.

6. Analyze results: Analyze the simulation results to assess the potential improvements in access to maternal health services. This can include comparing the projected indicators between different scenarios or estimating the overall impact of the interventions on the target population.

7. Validate and refine the model: Validate the simulation results by comparing them with real-world data or expert opinions. Refine the model as needed to improve its accuracy and reliability.

By following these steps, policymakers and healthcare providers can gain insights into the potential impact of different interventions on improving access to maternal health services and make informed decisions on resource allocation and implementation strategies.

Author

Dima Health

Statistics:

Citations: 1

Identifiers:

DOI: 10.1186/s12889-021-10615-0

Research Areas:

Community Interventions, Disparities, Environmental, Genetics and Genomics, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Social Determinants

Study Countries:

Benin, Burundi, Cameroon, Comoros, Egypt, Liberia, Mali, Multi-Countries, Namibia, Nigeria, Rwanda, South Africa, Togo, Uganda

Study Design:

Case-Control Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female