Factors associated with anaemia among women of reproductive age in Ethiopia: Multilevel ordinal logistic regression analysis

Maternal and Child Nutrition, Volume 17, No. 1, Year 2021

Interprétation

Anaemia has prevailed as a mild to severe public health problem in Ethiopian women of reproductive age. Many studies carried out on anaemia have been limited to subnational assessments and subgroups of women. The effects of potential factors thought to affect anaemia and severity levels of anaemia have not been well considered. Therefore, this study identifies individual, household and community level factors associated with anaemia among women of reproductive age in Ethiopia applying multilevel ordinal logistic regression models. Proportional odds assumption was tested by likelihood ratio test. About 35.6% of the variation on anaemia was due to between household and community level differences. Pregnancy (adjusted odds ratio [AOR] = 2.30, 95% confidence interval [CI]: 1.82, 2.91), HIV (AOR = 2.40, 95% CI: 1.76, 3.25), giving birth once (AOR = 1.2, 95% CI: 1.05, 1.40), giving birth more than once (AOR = 1.4, 95% CI: 1.19, 1.71), living with five or more family members (AOR = 1.24, 95% CI: 1.05, 1.47), living in poorest households (AOR = 1.34, 95% CI: 1.2, 1.61) and rural area (AOR = 1.57, 95% CI: 1.28, 1.92) were associated with greater odds of more severe anaemia compared with their respective counter parts. Secondary and above education (AOR = 0.83, 95% CI: 0.70, 0.97) and use of pills, implants or injectable (AOR = 0.67, 95% CI: 0.59, 0.77) were associated with lower odds of more severe anaemia. Anaemia prevention and control programmes need to be strengthened for women living with HIV/AIDS and during pregnancy. Household poverty reduction and social protection services need to be strengthened and integrated in anaemia prevention and management activities in women. This study used 2016 Ethiopia Demographic and Health Survey (EDHS) data set collected from the nine regions and two administrative cities of Ethiopia. The 2016 EDHS is the fourth and the most recent nationally representative survey conducted with the main objective of providing timely and reliable data on health and demographic outcomes (Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). Stratified two‐stage sampling technique was used to select enumeration areas (EAs) and households. An EA is a geographic area covering on average 181 households (HHs). The 2007 Ethiopia Population and Housing Census (PHC) was used as a sampling frame to select EAs. In the first stage, 645 EAs (202 in urban and 443 in rural) were selected with probability proportional to EA size. The EA size is the number of residential households in the EA as determined in the 2007 PHC. And those EAs with more households have higher probability of being selected. In the second stage, 18,008 HHs were selected by systematic sampling technique, with average of 28 HHs per EAs. All WRA in the selected HH were eligible for anaemia testing (Figure 1; Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). Sample size and sampling procedure for factors associated with anaemia among women of reproductive age in Ethiopia, 2018: Data from 2016 Ethiopia Demographic and Health Survey (EDHS) After obtaining permission from the Inner City Fund (ICF) International; individual, household and HIV data sets were downloaded from the DHS website (http://dhsprogram.com). Details on sampling technique, sample size, data collection tools, data quality control and ethical concerns are available in 2016 EDHS report (Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). All WRA who had data on anaemia status were included in this study. A total of 14,489 WRA who were tested for anaemia were included in the analysis. Anaemia is an ordered categorical variable categorized as none, mild, moderate and severe anaemia based on Hb level. Blood samples were taken from a finger prick of the voluntarily consented women and collected in a micro cuvette. Hb analysis was carried out on‐site using a battery‐operated portable HemoCue analyser (Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). Hb levels were adjusted for pregnancy because during pregnancy the increase in maternal blood volume and the iron needs of the fetus decrease the blood Hb level (World Health Organization, 1989). It was also adjusted for smoking and altitude. The World Health Organization (WHO) Hb cut off points for diagnosis of anaemia are given in Table 1 (World Health Organization, 2001). The WHO Hb cut off points for diagnosis of anaemia Abbreviations: g/dl, gram/deciliter; Hb, haemoglobin; NP‐NL, neither pregnant nor lactating; WHO, World Health Organization. After reviewing recent literature, potential risk factors of anaemia were extracted from the data set. Due to the hierarchical nature of the 2016 EDHS data, the extracted variables were classified as individual, household and community level variables. Individual level variables were characteristics of the women which were specific to each woman (Table 2). Individual level variables extracted from EDHS 2016 data set for studying factors associated with anaemia 0. 15–24 1. 25–34 2. 35–49 0. No formal education 1. Primary 2. Secondary and above 0. Protestant 1. Orthodox 2. Muslim 3. Other 0. Not living with husband 1. Living with husband 0. Yes 1. No 0. None 1. Less than once/week 2. At least once/week 3. More than once/week 0. Yes 1. No 0. Pregnant 1. Lactating 2. Neither pregnant nor lactating 0. Yes 1. No 0. None 1. Pill/injectables/implants 0. IUD 1. Nonhormonal 0. No 1. One child 2. More than one children 0. Yes 1. No 0. Yes 1. No 0. Yes 1. No 0. Negative 1. Positive Abbreviation: EDHS, Ethiopia Demographic and Health Survey; IUD, intrauterine device. Household level variables are household level characteristics which are common for all women living in the same household and include variables described in Table 3. Household level variables extracted from Ethiopia Demographic and Health Survey 2016 data set for studying factors associated with anaemia 0. Poorest 1. Poor 2. Middle 3. Rich 4. Richest 0. ≤2 1. 3 and 4 2. ≥5 persons 0. Cleaner fuel 1. Solid fuel 0. Improved 1. Unimproved 0. Improved sources 1. Unimproved sources Community level variables were characteristics which are common for all women residing in the same community (cluster) and include place of residence, region, community (cluster) women education, community poverty, community women unemployment and community mass media exposure. Variables like community women education, community poverty, community women unemployment and community mass media exposure were generated by aggregating individual characteristics within the cluster. The generated variables were further categorized as low or high based on the national median values of the generated variables. These variables are measured as shown in Table 4. Community level variables extracted from Ethiopia Demographic and Health Survey 2016 data set for studying factors associated with anaemia 0. Urban 1. Rural 0. Tigray 1. Afar 2. Amhara 3. Oromia 4. Somali 5. Benishangul Gumuz 6. SNNPR 7. Gambela 8. Harari 9. Addis Ababa 10. Dire Dawa 0. Low 1. High 0. Low 1. High 0. High 1. Low 0. High 1. Low Due to hierarchical nature of the 2016 EDHS data where individuals are nested within households and households are in turn nested within clusters, multilevel (three‐level) OLR was used. Ignoring hierarchical nature and use of single‐level analysis could result in biased estimation of parameters and standard errors. Furthermore, the assumption of independent observation in ordinary logistic regression does not hold true in hierarchical data. Multilevel analysis handles these limitations by examining simultaneously the effects of explanatory variables at different levels (Diez‐Roux, 2000). OLR is a well‐suited technique to this study because of the ordered nature of outcome variable (none, mild, moderate and severe anaemia; Hedeker, 2015). Stata software version 14 was used for analysis of the data. A P‐value ≤ 0.25 in bivariate analysis was used to consider candidate variables for multivariable analysis (Stoltzfus, 2011). In a multivariable analysis, a P value < 0.05 was used to identify variables significantly associated with anaemia. Adjusted odds ratios with 95% confidence intervals were estimated and interpreted (Raman & Hedeker, 2005). The proportion of variations in odds of anaemia between households and communities was expressed using variance partition coefficients (VPC). The VPC measures the proportion of outcome (anaemia) variation unexplained by the predictor variables that lies at each level of the model hierarchy. It measures the relative importance of clusters, households and individual (women) as sources of variation on anaemia status (Leckie & French, 2013). The mixed‐effects OLR (proportional odds) model can be written in terms of the cumulative logits as below in the box: Log Pijkc1−Pijkc = ᵧc − (x ijk β + u ij + u i) P ijkc—is accumulative probability of being at ‘c’ category of anaemia for kth individual in jth household and ith cluster. ᵧc—is a model threshold or intercept for C‐1 level of anaemia, and it is a fixed parameter. It represents the cumulative logits of being at or below C‐1 level of anaemia when the covariates and random effects equal to zero. It is strictly increasing (i.e., γ1 < γ2 < · · · < γC − 1). C = number of categories of anaemia which equals to 4. β—is a coefficient (fixed effect of explanatory variable). X ijk—is a covariate vector for kth individual in jth household and ith cluster. u ij—is level‐2 (household) random effect, and it is assumed to be normally distributed with variance σ2(v2). ui—is level‐3 (cluster) random effect, and it is assumed to be normally distributed with variance σ2(v3). (Raman & Hedeker, 2005). Violation of the proportional odds assumption is common. In such occasions, a model which relaxes the assumption is nonproportional or partial‐proportional odds model in which covariates are allowed to have different effects on the C − 1 cumulative logits. It is given as below in the box: Log Pijkc1−Pijkc = ᵧc − (x ijk β + u ijk α c + u ij + u i) u ijk—is a covariate vector for set of variables for which proportional odds is not assumed. α c—is a vector of regression coefficients associated with these covariates for C‐1 levels of outcome. Because α c carries the c subscript, the effects of these covariates are allowed to vary across the C − 1 cumulative logits. (Raman & Hedeker, 2005) Both household and cluster random effects variance was expressed in terms of VPC. VPC(3) is a proportion of total variation on anaemia attributable to cluster random effect. It is given as in the box: VPC(3) = σ2v3σ2v3+σ2v2+π2/3, where π2/3 is individual level variance which equals to 3.29. σ2(ν3)—is cluster (level‐3) random effect variance. σ2(ν2)—is household (level‐2) random effect variance. VPC for level‐2 and 3 clustering effects (VPC(2 + 3)) is a proportion of total variation on anaemia attributable to both household and cluster level random effect. It is given as below (Leckie & French, 2013). VPC(2 + 3) = σ2v2+σ2v3σ2v3+σ2v2+π2/3.VPC(2) is a proportion of total variation on anaemia attributable to household level random effect. It is given as: VPC(2) = σ2v2σ2v3+σ2v2+π2/3. The explained variances at cluster and household level were quantified by proportional change in variance (PCV; Merlo, Yang, Chaix, Lynch, & RÅstam, 2005). The proportional odds assumption states that the effects of all covariates are constant across categories of outcome variable. After fitting both proportional and nonproportional odds models, the proportional odds assumption was tested using likelihood ratio test. It tests the null hypothesis that there is no difference in the effects of explanatory variables across the levels of anaemia. The P value ≥ 0.05 is desirable to retain null hypothesis (Bauer & Sterba, 2011). The likelihood ratio test supported the nonproportional odds assumption. Furthermore, each variable in the model was tested to identify the variables for which the proportional odds assumption was violated. An Akaike information criterion (AIC) was used to select the final model which fits the data best compared with other fitted models. The AIC of the all models were compared, and the model with the lowest AIC was considered as the best model fits the data (Hox, Moerbeek, & Van De Schoot, 2010; Table 6 ). Random intercept variances and model fit statistics of three‐level mixed effect models Note: σ2(ν3) and σ2(ν2) are community and household random intercept variances, respectively. VPC(3), variance partition coefficient for cluster, VPC(2 + 3), variance partition coefficient for household and cluster, VPC(2), variance partition coefficient for household. PCV3, proportional change in cluster level variance; Model 1, model with no independent variable; Model 2, model adjusted for individual level variables; Model 3, model adjusted for household level variables; Model 4, model adjusted for community level variables; Model 5, model adjusted for individual, household and community level variables simultaneously. Abbreviation: AIC, Akaike information criteria. For a substantial number of clusters and households, the 95% confidence intervals of random intercepts do not overlap zero. This implied that the random effects of the many households and clusters on anaemia were significantly different from zero (above or below zero; Leckie & French, 2013). The normality assumptions were tested graphically using quantile‐quantile plots (Leckie & French, 2013). The result suggested that cluster and household random effects were approximately normally distributed, respectively. This implied that the final model is appropriate for predicting the outcome variable and describing the data at hand (adequate). The permission for access to the data was obtained from ICF International by registering and stating the objective of the study. The data set has no individual names or house hold addresses. The data were used for the registered research topic only and were not shared to another person.

Résumé de l’IA

Study Justification:

– Anaemia is a significant public health problem among women of reproductive age in Ethiopia.
– Previous studies on anaemia have been limited to specific regions or subgroups of women.
– This study aims to identify the factors associated with anaemia among women of reproductive age in Ethiopia using a multilevel ordinal logistic regression analysis.
– The findings of this study will provide valuable insights for the development of effective anaemia prevention and control programs.

Highlights:

– Pregnancy, HIV, giving birth multiple times, living with a large family, living in a rural area, and living in the poorest households were associated with higher odds of more severe anaemia.
– Secondary and above education and the use of pills, implants, or injectables were associated with lower odds of more severe anaemia.
– Anaemia prevention and control programs should focus on women living with HIV/AIDS and during pregnancy.
– Household poverty reduction and social protection services should be integrated into anaemia prevention and management activities.

Recommendations:

– Strengthen anaemia prevention and control programs for women living with HIV/AIDS and during pregnancy.
– Implement household poverty reduction and social protection services to address anaemia among women.
– Integrate anaemia prevention and management activities into existing health programs and services.

Key Role Players:

– Ministry of Health: Responsible for policy development and implementation of anaemia prevention and control programs.
– Health Facilities: Provide healthcare services and implement anaemia prevention and management activities.
– Community Health Workers: Conduct awareness campaigns and provide education on anaemia prevention.
– Non-Governmental Organizations: Support the implementation of anaemia prevention and control programs through funding and technical assistance.

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers and community health workers.
– Procurement of anaemia testing equipment and supplies.
– Development and dissemination of educational materials.
– Monitoring and evaluation activities to assess the effectiveness of anaemia prevention and control programs.
– Integration of anaemia prevention and management activities into existing health programs and services.

Force de la preuve

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 survey and uses multilevel ordinal logistic regression analysis. However, to improve the evidence, the abstract could include more details on the sample size, data collection methods, and statistical analysis techniques used.

Abstrait

This study used 2016 Ethiopia Demographic and Health Survey (EDHS) data set collected from the nine regions and two administrative cities of Ethiopia. The 2016 EDHS is the fourth and the most recent nationally representative survey conducted with the main objective of providing timely and reliable data on health and demographic outcomes (Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). Stratified two‐stage sampling technique was used to select enumeration areas (EAs) and households. An EA is a geographic area covering on average 181 households (HHs). The 2007 Ethiopia Population and Housing Census (PHC) was used as a sampling frame to select EAs. In the first stage, 645 EAs (202 in urban and 443 in rural) were selected with probability proportional to EA size. The EA size is the number of residential households in the EA as determined in the 2007 PHC. And those EAs with more households have higher probability of being selected. In the second stage, 18,008 HHs were selected by systematic sampling technique, with average of 28 HHs per EAs. All WRA in the selected HH were eligible for anaemia testing (Figure 1; Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). Sample size and sampling procedure for factors associated with anaemia among women of reproductive age in Ethiopia, 2018: Data from 2016 Ethiopia Demographic and Health Survey (EDHS) After obtaining permission from the Inner City Fund (ICF) International; individual, household and HIV data sets were downloaded from the DHS website (http://dhsprogram.com). Details on sampling technique, sample size, data collection tools, data quality control and ethical concerns are available in 2016 EDHS report (Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). All WRA who had data on anaemia status were included in this study. A total of 14,489 WRA who were tested for anaemia were included in the analysis. Anaemia is an ordered categorical variable categorized as none, mild, moderate and severe anaemia based on Hb level. Blood samples were taken from a finger prick of the voluntarily consented women and collected in a micro cuvette. Hb analysis was carried out on‐site using a battery‐operated portable HemoCue analyser (Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). Hb levels were adjusted for pregnancy because during pregnancy the increase in maternal blood volume and the iron needs of the fetus decrease the blood Hb level (World Health Organization, 1989). It was also adjusted for smoking and altitude. The World Health Organization (WHO) Hb cut off points for diagnosis of anaemia are given in Table 1 (World Health Organization, 2001). The WHO Hb cut off points for diagnosis of anaemia Abbreviations: g/dl, gram/deciliter; Hb, haemoglobin; NP‐NL, neither pregnant nor lactating; WHO, World Health Organization. After reviewing recent literature, potential risk factors of anaemia were extracted from the data set. Due to the hierarchical nature of the 2016 EDHS data, the extracted variables were classified as individual, household and community level variables. Individual level variables were characteristics of the women which were specific to each woman (Table 2). Individual level variables extracted from EDHS 2016 data set for studying factors associated with anaemia 0. 15–24 1. 25–34 2. 35–49 0. No formal education 1. Primary 2. Secondary and above 0. Protestant 1. Orthodox 2. Muslim 3. Other 0. Not living with husband 1. Living with husband 0. Yes 1. No 0. None 1. Less than once/week 2. At least once/week 3. More than once/week 0. Yes 1. No 0. Pregnant 1. Lactating 2. Neither pregnant nor lactating 0. Yes 1. No 0. None 1. Pill/injectables/implants 0. IUD 1. Nonhormonal 0. No 1. One child 2. More than one children 0. Yes 1. No 0. Yes 1. No 0. Yes 1. No 0. Negative 1. Positive Abbreviation: EDHS, Ethiopia Demographic and Health Survey; IUD, intrauterine device. Household level variables are household level characteristics which are common for all women living in the same household and include variables described in Table 3. Household level variables extracted from Ethiopia Demographic and Health Survey 2016 data set for studying factors associated with anaemia 0. Poorest 1. Poor 2. Middle 3. Rich 4. Richest 0. ≤2 1. 3 and 4 2. ≥5 persons 0. Cleaner fuel 1. Solid fuel 0. Improved 1. Unimproved 0. Improved sources 1. Unimproved sources Community level variables were characteristics which are common for all women residing in the same community (cluster) and include place of residence, region, community (cluster) women education, community poverty, community women unemployment and community mass media exposure. Variables like community women education, community poverty, community women unemployment and community mass media exposure were generated by aggregating individual characteristics within the cluster. The generated variables were further categorized as low or high based on the national median values of the generated variables. These variables are measured as shown in Table 4. Community level variables extracted from Ethiopia Demographic and Health Survey 2016 data set for studying factors associated with anaemia 0. Urban 1. Rural 0. Tigray 1. Afar 2. Amhara 3. Oromia 4. Somali 5. Benishangul Gumuz 6. SNNPR 7. Gambela 8. Harari 9. Addis Ababa 10. Dire Dawa 0. Low 1. High 0. Low 1. High 0. High 1. Low 0. High 1. Low Due to hierarchical nature of the 2016 EDHS data where individuals are nested within households and households are in turn nested within clusters, multilevel (three‐level) OLR was used. Ignoring hierarchical nature and use of single‐level analysis could result in biased estimation of parameters and standard errors. Furthermore, the assumption of independent observation in ordinary logistic regression does not hold true in hierarchical data. Multilevel analysis handles these limitations by examining simultaneously the effects of explanatory variables at different levels (Diez‐Roux, 2000). OLR is a well‐suited technique to this study because of the ordered nature of outcome variable (none, mild, moderate and severe anaemia; Hedeker, 2015). Stata software version 14 was used for analysis of the data. A P‐value ≤ 0.25 in bivariate analysis was used to consider candidate variables for multivariable analysis (Stoltzfus, 2011). In a multivariable analysis, a P value < 0.05 was used to identify variables significantly associated with anaemia. Adjusted odds ratios with 95% confidence intervals were estimated and interpreted (Raman & Hedeker, 2005). The proportion of variations in odds of anaemia between households and communities was expressed using variance partition coefficients (VPC). The VPC measures the proportion of outcome (anaemia) variation unexplained by the predictor variables that lies at each level of the model hierarchy. It measures the relative importance of clusters, households and individual (women) as sources of variation on anaemia status (Leckie & French, 2013). The mixed‐effects OLR (proportional odds) model can be written in terms of the cumulative logits as below in the box: Log Pijkc1−Pijkc = ᵧc − (x ijk β + u ij + u i) P ijkc—is accumulative probability of being at ‘c’ category of anaemia for kth individual in jth household and ith cluster. ᵧc—is a model threshold or intercept for C‐1 level of anaemia, and it is a fixed parameter. It represents the cumulative logits of being at or below C‐1 level of anaemia when the covariates and random effects equal to zero. It is strictly increasing (i.e., γ1 < γ2 < · · · < γC − 1). C = number of categories of anaemia which equals to 4. β—is a coefficient (fixed effect of explanatory variable). X ijk—is a covariate vector for kth individual in jth household and ith cluster. u ij—is level‐2 (household) random effect, and it is assumed to be normally distributed with variance σ2(v2). ui—is level‐3 (cluster) random effect, and it is assumed to be normally distributed with variance σ2(v3). (Raman & Hedeker, 2005). Violation of the proportional odds assumption is common. In such occasions, a model which relaxes the assumption is nonproportional or partial‐proportional odds model in which covariates are allowed to have different effects on the C − 1 cumulative logits. It is given as below in the box: Log Pijkc1−Pijkc = ᵧc − (x ijk β + u ijk α c + u ij + u i) u ijk—is a covariate vector for set of variables for which proportional odds is not assumed. α c—is a vector of regression coefficients associated with these covariates for C‐1 levels of outcome. Because α c carries the c subscript, the effects of these covariates are allowed to vary across the C − 1 cumulative logits. (Raman & Hedeker, 2005) Both household and cluster random effects variance was expressed in terms of VPC. VPC(3) is a proportion of total variation on anaemia attributable to cluster random effect. It is given as in the box: VPC(3) = σ2v3σ2v3+σ2v2+π2/3, where π2/3 is individual level variance which equals to 3.29. σ2(ν3)—is cluster (level‐3) random effect variance. σ2(ν2)—is household (level‐2) random effect variance. VPC for level‐2 and 3 clustering effects (VPC(2 + 3)) is a proportion of total variation on anaemia attributable to both household and cluster level random effect. It is given as below (Leckie & French, 2013). VPC(2 + 3) = σ2v2+σ2v3σ2v3+σ2v2+π2/3.VPC(2) is a proportion of total variation on anaemia attributable to household level random effect. It is given as: VPC(2) = σ2v2σ2v3+σ2v2+π2/3. The explained variances at cluster and household level were quantified by proportional change in variance (PCV; Merlo, Yang, Chaix, Lynch, & RÅstam, 2005). The proportional odds assumption states that the effects of all covariates are constant across categories of outcome variable. After fitting both proportional and nonproportional odds models, the proportional odds assumption was tested using likelihood ratio test. It tests the null hypothesis that there is no difference in the effects of explanatory variables across the levels of anaemia. The P value ≥ 0.05 is desirable to retain null hypothesis (Bauer & Sterba, 2011). The likelihood ratio test supported the nonproportional odds assumption. Furthermore, each variable in the model was tested to identify the variables for which the proportional odds assumption was violated. An Akaike information criterion (AIC) was used to select the final model which fits the data best compared with other fitted models. The AIC of the all models were compared, and the model with the lowest AIC was considered as the best model fits the data (Hox, Moerbeek, & Van De Schoot, 2010; Table 6 ). Random intercept variances and model fit statistics of three‐level mixed effect models Note: σ2(ν3) and σ2(ν2) are community and household random intercept variances, respectively. VPC(3), variance partition coefficient for cluster, VPC(2 + 3), variance partition coefficient for household and cluster, VPC(2), variance partition coefficient for household. PCV3, proportional change in cluster level variance; Model 1, model with no independent variable; Model 2, model adjusted for individual level variables; Model 3, model adjusted for household level variables; Model 4, model adjusted for community level variables; Model 5, model adjusted for individual, household and community level variables simultaneously. Abbreviation: AIC, Akaike information criteria. For a substantial number of clusters and households, the 95% confidence intervals of random intercepts do not overlap zero. This implied that the random effects of the many households and clusters on anaemia were significantly different from zero (above or below zero; Leckie & French, 2013). The normality assumptions were tested graphically using quantile‐quantile plots (Leckie & French, 2013). The result suggested that cluster and household random effects were approximately normally distributed, respectively. This implied that the final model is appropriate for predicting the outcome variable and describing the data at hand (adequate). The permission for access to the data was obtained from ICF International by registering and stating the objective of the study. The data set has no individual names or house hold addresses. The data were used for the registered research topic only and were not shared to another person.

Matériaux

N/A

Innovations

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide information and resources related to maternal health, including anaemia prevention and management. These apps can be easily accessible to women in rural areas who may have limited access to healthcare facilities.

2. Telemedicine: Implement telemedicine services that allow pregnant women to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide timely medical advice and support.

3. Community Health Workers: Train and deploy community health workers who can provide education, counseling, and basic healthcare services to pregnant women in remote areas. These workers can also help identify and refer cases of anaemia for further medical intervention.

4. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to pregnant women for accessing maternal health services, including anaemia testing and treatment. This can help reduce the financial burden and increase utilization of healthcare services.

5. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare facilities and resources to provide affordable and quality care to pregnant women.

6. Health Education Campaigns: Conduct targeted health education campaigns to raise awareness about anaemia prevention and management among women of reproductive age. These campaigns can be conducted through various channels, such as radio, television, and community outreach programs.

7. Integration of Services: Integrate anaemia prevention and management services into existing maternal health programs and initiatives. This can ensure that pregnant women receive comprehensive care that addresses their specific needs, including anaemia prevention and treatment.

8. Supply Chain Management: Strengthen the supply chain management system for essential maternal health commodities, such as iron supplements and diagnostic tools for anaemia. This can help ensure the availability and accessibility of these resources in healthcare facilities.

9. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to enhance the delivery of maternal health services. This can involve training healthcare providers on best practices for anaemia prevention and management and improving the overall quality of care.

10. Research and Data Collection: Conduct further research and data collection to better understand the factors contributing to anaemia among women of reproductive age. This can help inform targeted interventions and policies to improve access to maternal health services.

AI Innovations Description

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

1. Strengthen Anaemia Prevention and Control Programs: The study found that pregnancy and HIV were associated with greater odds of more severe anaemia among women of reproductive age in Ethiopia. Therefore, it is recommended to strengthen anaemia prevention and control programs specifically targeting pregnant women and those living with HIV/AIDS. This can include providing regular screening and monitoring for anaemia, as well as ensuring access to appropriate treatment and nutritional support.

2. Enhance Household Poverty Reduction and Social Protection Services: The study also identified living in the poorest households as a factor associated with greater odds of more severe anaemia. To address this, it is important to strengthen household poverty reduction initiatives and social protection services. This can involve providing financial assistance, livelihood support, and access to basic amenities for households living in poverty, which can ultimately improve maternal health outcomes.

3. Integrate Anaemia Prevention and Management Activities: The study highlights the need to integrate anaemia prevention and management activities into existing maternal health programs. This can be achieved by incorporating anaemia screening, education, and treatment services into antenatal care, postnatal care, and family planning services. By integrating these activities, women can receive comprehensive care that addresses their anaemia risk and promotes overall maternal health.

4. Improve Health Education and Awareness: The study found that women with secondary and above education had lower odds of more severe anaemia. Therefore, it is crucial to improve health education and awareness among women of reproductive age, particularly those with lower levels of education. This can be done through community-based health education programs, targeted messaging campaigns, and the provision of accurate and accessible information on anaemia prevention and management.

By implementing these recommendations, it is possible to develop innovative approaches that improve access to maternal health and reduce the prevalence and severity of anaemia among women of reproductive age in Ethiopia.

AI Innovations Methodology

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

1. Strengthen anaemia prevention and control programs: Given the high prevalence of anaemia among women of reproductive age in Ethiopia, it is important to enhance existing programs that focus on anaemia prevention and control. This can include increasing awareness about anaemia, promoting iron-rich diets, and providing iron supplementation to pregnant women.

2. Improve access to antenatal care: Antenatal care plays a crucial role in monitoring the health of pregnant women and identifying any potential complications, including anaemia. Efforts should be made to improve access to antenatal care services, particularly in rural areas where access may be limited. This can involve increasing the number of healthcare facilities, training healthcare providers, and implementing mobile health initiatives.

3. Integrate anaemia prevention into HIV/AIDS programs: The study found a significant association between HIV and anaemia among women of reproductive age. Therefore, it is important to integrate anaemia prevention and management activities into existing HIV/AIDS programs. This can include routine screening for anaemia among HIV-positive women and providing appropriate treatment and support.

4. Address socioeconomic factors: The study identified household poverty as a risk factor for more severe anaemia. To improve access to maternal health, efforts should be made to reduce poverty and provide social protection services to vulnerable households. This can include income-generation programs, access to microfinance, and targeted interventions for the poorest households.

Methodology to simulate the impact of these recommendations on improving access to maternal health:

To simulate the impact of the recommendations on improving access to maternal health, a multi-dimensional approach can be used. Here is a brief methodology:

1. Data collection: Collect data on the current status of maternal health access, including indicators such as antenatal care coverage, anaemia prevalence, and healthcare facility availability. This data can be obtained from national surveys, health facility records, and other relevant sources.

2. Define indicators: Identify specific indicators that reflect the impact of the recommendations. For example, indicators can include the percentage increase in antenatal care coverage, reduction in anaemia prevalence, and increase in the number of healthcare facilities in underserved areas.

3. Develop a simulation model: Use a simulation model, such as a mathematical or statistical model, to estimate the potential impact of the recommendations on the defined indicators. The model should take into account the current baseline data, the proposed interventions, and any relevant assumptions.

4. Input data and parameters: Input the collected data and parameters into the simulation model. This can include information on the target population, geographical distribution, and the expected effectiveness of the interventions.

5. Run simulations: Run multiple simulations using different scenarios and assumptions to assess the potential impact of the recommendations. This can involve varying parameters such as the coverage of interventions, the time frame for implementation, and the allocation of resources.

6. Analyze results: Analyze the simulation results to determine the projected impact of the recommendations on improving access to maternal health. This can involve comparing the indicators between different scenarios and identifying the most effective interventions.

7. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the results. This can involve varying key parameters and assumptions to understand the potential uncertainties and limitations of the simulation model.

8. Interpret and communicate findings: Interpret the simulation results and communicate the findings to relevant stakeholders, such as policymakers, healthcare providers, and community members. This can help inform decision-making and prioritize interventions to improve access to maternal health.

It is important to note that the methodology described above is a general framework and can be adapted based on the specific context and available data.

Auteur

Dima Health

Statistics:

Citations: 6

Identifiers:

DOI: 10.1111/mcn.13063

ISSN: 17408695

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

Partagez ceci :