Genetic Analyses of Response of Local Ghanaian Tanzanian Chicken Ecotypes to a Natural Challenge with Velogenic Newcastle Disease Virus

Animals, Volume 12, No. 20, Year 2022

Interpretation

Newcastle disease is a devastating poultry disease that often causes significant economic losses in poultry in the developing countries of Africa, Asia, as well as South and Central America. Velogenic Newcastle disease virus (NDV) outbreaks are associated with high mortalities, which can threaten household livelihoods, especially in the rural areas, and lead to loss of high-quality proteins in the form of meat and eggs, as well as household purchasing power. In this study, we exposed unvaccinated Ghanaian and Tanzanian chickens of six local ecotypes to velogenic NDV strains, measured NDV response traits, sequenced their DNA on a genotyping-by-sequencing platform, and performed variance component analyses. The collected phenotypes included: growth rates (pre- and post-exposure); lesion scores (gross lesion severity) in the trachea, proventriculus, intestine, and cecal tonsils; natural antibody levels; anti-NDV antibody levels at 7 days post exposure (dpe); tear and cloacal viral load at 2, 4, and 6 dpe; and survival time. Heritability estimates were low to moderate, ranging from 0.11 for average lesion scores to 0.36 for pre-exposure growth rate. Heritability estimates for survival time were 0.23 and 0.27 for the Tanzanian and Ghanaian ecotypes, respectively. Similar heritability estimates were observed when data were analyzed either separately or combined for the two countries. Survival time was genetically negatively correlated with lesion scores and with viral load. Results suggested that response to mesogenic or velogenic NDV of these local chicken ecotypes could be improved by selective breeding. Chickens that are more resilient to velogenic NDV can improve household livelihoods in developing countries. In Tanzania, 62 roosters and 302 hens from the Kuchi, Morogoro Medium (MoroMed), and Ching’wekwe (Ching) ecotypes that also produced experimental chickens for the lentogenic infection study described by [17] were used to breed chickens for the natural velogenic NDV exposure trials. Similarly, in Ghana, 42 roosters and 128 hens from the Coastal Savannah (CS), Forest (FO), and Interior Savannah (IS) ecotypes that also produced experimental chickens for the lentogenic study described by [18] were used. All breeder birds in the two countries were raised in a controlled environment with ad libitum access to both feed and water. Experimental birds were produced in 3 hatches for the Tanzania ecotypes and in 6 hatches for the Ghana ecotypes, with each hatch comprising one replicate of the natural exposure trial. For all replicates, chicks were raised in a single pen under similar conditions, with ad libitum access to both water and feed. For each replicate of the natural challenge, birds that were suspected to be infected with velogenic NDV based on clinical signs were purchased from farmers or at a local market. Oropharyngeal and cloacal swabs samples were collected and RT-qPCR was performed to confirm the birds’ infection with mesogenic or velogenic NDV [29,30]. Sick birds were also screened for absence of infection with avian influenza virus, using a FluDETECT® Avian kit (Zoetis, Parsippany-Troy Hills, NJ, USA), in order to avoid introducing another major respiratory virus into the study. For each replicate, the screened sick birds were mixed with 40 healthy naïve birds in an isolated pen to amplify the infection and to standardize timing. Three days later, upon development of clinical signs, the resulting seeder birds were mixed with healthy experimental birds in a ratio of 25:1 in Ghana and 20:1 in Tanzania. Experimental birds were 28 days of age at the time of exposure for all replicates in Ghana, and 42, 35, and 28 days of age for replicates 1, 2, and 3, respectively, in Tanzania. All trials in both countries were terminated at 21 days post-exposure (dpe) and surviving birds were euthanized at that time. Birds that died before the end of the experiment died before 21 days. Across replicates, the natural exposure trials were conducted on a total of 1365 chickens in Ghana (CS (340), FO (708), and IS (317)), and on 1556 chickens in Tanzania (Kuchi (439), Ching (405), and MoroMed (712)). The natural exposure to velogenic NDV caused severe clinical symptoms and allowed observation of phenotypic responses of birds’ post-exposure. Survival time was recorded as the number of dpe when the bird died; birds that survived until 21 dpe were euthanized. Viral RNA was isolated from lachrymal fluid (tears) and cloacal swabs at 2, 4, 6, and 10 dpe using a MagMAX-96 viral RNA isolation kit (Life Technologies, Carlsbad, CA, USA) and quantified using qRT-PCR, following the procedures described in [31]. Average viral RNA was computed and transformed to log10. Blood samples were collected at 7 dpe to measure anti-NDV antibody levels by ELISA (IDEXX, Westbrook, ME, USA). Blood samples were also collected from all birds before NDV exposure using an enzyme-linked immunosorbent assay to measure the natural anti-KLH antibody in birds (MP Biomedicals Inc., Aurora, OH, USA). Lesions in the trachea, proventriculus, intestine (duodenum, jejunum, and ileum), and cecal tonsils were assessed and scored on the dead and euthanized birds on a scale of 0 to 5, where 0 indicated no visible lesions and 5 indicated severe lesions. Lesions scores were averaged across organs for each bird. Body weights were recorded at hatch and weekly thereafter prior to exposure, and at 0, 2, 4, 6, and 10 dpe. Pre-exposure growth rate (g/d) was calculated based on body weights at hatch and at exposure. Post-exposure growth rate (g/d) was calculated based on body weights at 0 and 4 dpe because few birds survived past 4 dpe. Whole blood was collected from breeders and chicks before exposure and placed on Whatman FTA cards (Sigma-Aldrich, St. Louis, MO, USA) for genomic DNA extraction. For breeders, genotyping was conducted on an Affymetrix Axiom 600K Array (Thermo Fisher Scientific Inc., Carlsbad, CA, USA) at GeneSeek (Lincoln, NE, USA). Genotypes for the Ghana and Tanzania breeders were combined, and genotype data quality control was performed using AxiomTM Analysis Suite 3.1 (Applied Biosystems, Thermo Fisher Scientific Inc., Carlsbad, CA, USA), using similar thresholds as described in [18]. A total of 421,492 SNPs remained after quality control and were utilized for imputation analyses. The experimental birds were genotyped using targeted genotyping by sequencing (GBS) for the 5K SNP low-density panel described by [32], with 100 bp paired ends on 4 lanes of an Illumina NextSeq500 Hiseq at GeneSeek (Lincoln, NE, USA). Raw read sequence data were processed using an in-house shell script that utilized publicly available software tools (BWA (0.7.17), SAMtools (1.9), PICARDS (2.17.0), and BCFtools (1.9)), as described by [32]. Reads were aligned to the Gallus gallus version 5 reference genome and genotypes were called from the vcf using an in-house python script. The low density genotyped experimental birds were imputed to the high-density panel that the breeders were genotyped on using Fimpute [33], separately for each country. Experimental birds were assigned to half and full-sib families based on the distribution of genomic relationships among birds within each country. The three ecotypes within each country were previously found to have partial shared ancestries based on analysis of the high-density SNP genotypes of birds used in the lentogenic NDV challenge studies, as described by [17,18]. These studies used Admixture software [34] to determine the optimal number of subpopulations, which were 3 for the Ghana ecotypes [18] and 2 for the Tanzania ecotypes [17]. This same procedure was applied to the imputed genotypes of the experimental birds for the velogenic NDV exposure trials to quantify the proportional contributions of the 3 and 2 subpopulations for the Ghana and Tanzania experimental birds, respectively. Genetic parameters were estimated both by country and using the combined data. Asreml 4.2 software [35] was used to estimate variance components and heritability for each trait using the following univariate animal model: where Y is the phenotype, i.e., pre- and post-exposure growth rate, trachea, proventriculus, intestinal, cecal tonsil, and average lesion scores, natural antibody level, NDV antibody level at 10 dpe, tear and cloacal viral loads at 2, 4, 6, and 10 dpe, and survival time. Fixed effects included replicate, R (1 to 6 for Ghana and 1 to 3 for Tanzania), and population proportions (P), obtained as described above, fitted as one covariate for Tanzania and as two covariates for Ghana, as described by [17,18]. Random effects included animal genetic effects (A) with a genomic relationship matrix obtained based on the procedures described by [36], maternal environmental effects (M), and residuals (e). The maternal effect was removed from the model for traits for which it was estimated to be zero. For viral load traits and for natural and anti-NDV antibody levels, the effect of assay plate was added to the model as a fixed effect. For lesion scores, the effect of bird survival to the end of the trial (0/1) was added as a fixed effect. For the combined analyses, replicate by country and country-specific population proportion covariates were added. Phenotypic variance was estimated as the sum of the estimates of the animal genetic, maternal (if fitted), and residual variances. Heritability was estimated as the ratio of animal genetic and phenotypic variance estimates. Phenotypic and genetic correlations between traits recorded in the velogenic NDV trials were estimated separately for each country and combined using a bivariate version of the univariate models described above. Genetic correlations of the velogenic NDV phenotypes of the current study with phenotypes recorded in the lentogenic NDV trials [17,18] (pre- and post-infection growth rate, anti-NDV antibody levels at 10 days post-infection (dpi), viral load at 2 and 6 dpi, and viral clearance) were also estimated. Bivariate models were used, with the model as described above, for the velogenic NDV response traits and as described in [17,18] for the lentogenic NDV response traits. Because the lentogenic and velogenic NDV challenges were conducted using different birds but from the same ecotypes, environmental correlations were not estimable and were set equal to zero.

AI Digest

Study Justification:

This study aimed to investigate the response of local Ghanaian and Tanzanian chicken ecotypes to velogenic Newcastle disease virus (NDV). Newcastle disease is a highly contagious poultry disease that causes significant economic losses in developing countries. Velogenic NDV outbreaks can lead to high mortality rates, threatening household livelihoods and reducing the availability of high-quality protein from meat and eggs. By understanding the genetic factors that influence the response to NDV, this study aimed to identify potential strategies for selective breeding to improve the resilience of local chicken ecotypes to the disease.

Highlights:

– The study exposed unvaccinated Ghanaian and Tanzanian chickens of six local ecotypes to velogenic NDV strains.
– Various response traits were measured, including growth rates, lesion scores, natural antibody levels, anti-NDV antibody levels, tear and cloacal viral load, and survival time.
– Heritability estimates for these traits ranged from low to moderate, suggesting a genetic component to the response to NDV.
– Survival time was genetically negatively correlated with lesion scores and viral load, indicating that chickens with better survival times had lower lesion scores and viral load.
– The results suggested that selective breeding could improve the response of local chicken ecotypes to velogenic NDV, potentially benefiting household livelihoods in developing countries.

Recommendations:

Based on the findings of this study, the following recommendations can be made:

1. Implement selective breeding programs to improve the resilience of local chicken ecotypes to velogenic NDV.
2. Prioritize the selection of chickens with better survival times, lower lesion scores, and lower viral load.
3. Continue monitoring and surveillance of NDV outbreaks to identify high-risk areas and implement appropriate control measures.
4. Promote vaccination programs to reduce the incidence and severity of NDV outbreaks.
5. Conduct further research to identify specific genetic markers associated with resistance to velogenic NDV and develop genomic selection tools for breeding programs.

Key Role Players:

To address the recommendations, the following key role players are needed:

1. Researchers and scientists specializing in poultry genetics and disease resistance.
2. Poultry breeders and farmers who can implement selective breeding programs.
3. Veterinary professionals who can provide guidance on vaccination programs and disease control measures.
4. Government agencies responsible for agricultural policies and funding.
5. International organizations and NGOs that can provide technical support and resources for implementing breeding programs and vaccination campaigns.

Cost Items for Planning Recommendations:

While the actual costs will vary depending on the specific context and scale of implementation, the following cost items should be considered in planning the recommendations:

1. Research and development costs for identifying genetic markers associated with resistance to velogenic NDV.
2. Costs for establishing and maintaining selective breeding programs, including the acquisition and management of breeding stock.
3. Costs for implementing vaccination programs, including the procurement of vaccines and vaccination equipment.
4. Training and capacity-building costs for poultry breeders, farmers, and veterinary professionals.
5. Monitoring and surveillance costs for NDV outbreaks, including laboratory testing and data collection.
6. Administrative and logistical costs for coordinating and implementing the recommendations at the national or regional level.
Please note that the above cost items are general considerations and a detailed budget would require a comprehensive assessment of the specific context and resources available.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a detailed description of the study design, data collection methods, and statistical analyses. However, it does not mention the sample size or provide specific results or conclusions. To improve the evidence, the abstract could include the sample size, key findings, and implications of the study.

Abstract

In Tanzania, 62 roosters and 302 hens from the Kuchi, Morogoro Medium (MoroMed), and Ching’wekwe (Ching) ecotypes that also produced experimental chickens for the lentogenic infection study described by [17] were used to breed chickens for the natural velogenic NDV exposure trials. Similarly, in Ghana, 42 roosters and 128 hens from the Coastal Savannah (CS), Forest (FO), and Interior Savannah (IS) ecotypes that also produced experimental chickens for the lentogenic study described by [18] were used. All breeder birds in the two countries were raised in a controlled environment with ad libitum access to both feed and water. Experimental birds were produced in 3 hatches for the Tanzania ecotypes and in 6 hatches for the Ghana ecotypes, with each hatch comprising one replicate of the natural exposure trial. For all replicates, chicks were raised in a single pen under similar conditions, with ad libitum access to both water and feed. For each replicate of the natural challenge, birds that were suspected to be infected with velogenic NDV based on clinical signs were purchased from farmers or at a local market. Oropharyngeal and cloacal swabs samples were collected and RT-qPCR was performed to confirm the birds’ infection with mesogenic or velogenic NDV [29,30]. Sick birds were also screened for absence of infection with avian influenza virus, using a FluDETECT® Avian kit (Zoetis, Parsippany-Troy Hills, NJ, USA), in order to avoid introducing another major respiratory virus into the study. For each replicate, the screened sick birds were mixed with 40 healthy naïve birds in an isolated pen to amplify the infection and to standardize timing. Three days later, upon development of clinical signs, the resulting seeder birds were mixed with healthy experimental birds in a ratio of 25:1 in Ghana and 20:1 in Tanzania. Experimental birds were 28 days of age at the time of exposure for all replicates in Ghana, and 42, 35, and 28 days of age for replicates 1, 2, and 3, respectively, in Tanzania. All trials in both countries were terminated at 21 days post-exposure (dpe) and surviving birds were euthanized at that time. Birds that died before the end of the experiment died before 21 days. Across replicates, the natural exposure trials were conducted on a total of 1365 chickens in Ghana (CS (340), FO (708), and IS (317)), and on 1556 chickens in Tanzania (Kuchi (439), Ching (405), and MoroMed (712)). The natural exposure to velogenic NDV caused severe clinical symptoms and allowed observation of phenotypic responses of birds’ post-exposure. Survival time was recorded as the number of dpe when the bird died; birds that survived until 21 dpe were euthanized. Viral RNA was isolated from lachrymal fluid (tears) and cloacal swabs at 2, 4, 6, and 10 dpe using a MagMAX-96 viral RNA isolation kit (Life Technologies, Carlsbad, CA, USA) and quantified using qRT-PCR, following the procedures described in [31]. Average viral RNA was computed and transformed to log10. Blood samples were collected at 7 dpe to measure anti-NDV antibody levels by ELISA (IDEXX, Westbrook, ME, USA). Blood samples were also collected from all birds before NDV exposure using an enzyme-linked immunosorbent assay to measure the natural anti-KLH antibody in birds (MP Biomedicals Inc., Aurora, OH, USA). Lesions in the trachea, proventriculus, intestine (duodenum, jejunum, and ileum), and cecal tonsils were assessed and scored on the dead and euthanized birds on a scale of 0 to 5, where 0 indicated no visible lesions and 5 indicated severe lesions. Lesions scores were averaged across organs for each bird. Body weights were recorded at hatch and weekly thereafter prior to exposure, and at 0, 2, 4, 6, and 10 dpe. Pre-exposure growth rate (g/d) was calculated based on body weights at hatch and at exposure. Post-exposure growth rate (g/d) was calculated based on body weights at 0 and 4 dpe because few birds survived past 4 dpe. Whole blood was collected from breeders and chicks before exposure and placed on Whatman FTA cards (Sigma-Aldrich, St. Louis, MO, USA) for genomic DNA extraction. For breeders, genotyping was conducted on an Affymetrix Axiom 600K Array (Thermo Fisher Scientific Inc., Carlsbad, CA, USA) at GeneSeek (Lincoln, NE, USA). Genotypes for the Ghana and Tanzania breeders were combined, and genotype data quality control was performed using AxiomTM Analysis Suite 3.1 (Applied Biosystems, Thermo Fisher Scientific Inc., Carlsbad, CA, USA), using similar thresholds as described in [18]. A total of 421,492 SNPs remained after quality control and were utilized for imputation analyses. The experimental birds were genotyped using targeted genotyping by sequencing (GBS) for the 5K SNP low-density panel described by [32], with 100 bp paired ends on 4 lanes of an Illumina NextSeq500 Hiseq at GeneSeek (Lincoln, NE, USA). Raw read sequence data were processed using an in-house shell script that utilized publicly available software tools (BWA (0.7.17), SAMtools (1.9), PICARDS (2.17.0), and BCFtools (1.9)), as described by [32]. Reads were aligned to the Gallus gallus version 5 reference genome and genotypes were called from the vcf using an in-house python script. The low density genotyped experimental birds were imputed to the high-density panel that the breeders were genotyped on using Fimpute [33], separately for each country. Experimental birds were assigned to half and full-sib families based on the distribution of genomic relationships among birds within each country. The three ecotypes within each country were previously found to have partial shared ancestries based on analysis of the high-density SNP genotypes of birds used in the lentogenic NDV challenge studies, as described by [17,18]. These studies used Admixture software [34] to determine the optimal number of subpopulations, which were 3 for the Ghana ecotypes [18] and 2 for the Tanzania ecotypes [17]. This same procedure was applied to the imputed genotypes of the experimental birds for the velogenic NDV exposure trials to quantify the proportional contributions of the 3 and 2 subpopulations for the Ghana and Tanzania experimental birds, respectively. Genetic parameters were estimated both by country and using the combined data. Asreml 4.2 software [35] was used to estimate variance components and heritability for each trait using the following univariate animal model: where Y is the phenotype, i.e., pre- and post-exposure growth rate, trachea, proventriculus, intestinal, cecal tonsil, and average lesion scores, natural antibody level, NDV antibody level at 10 dpe, tear and cloacal viral loads at 2, 4, 6, and 10 dpe, and survival time. Fixed effects included replicate, R (1 to 6 for Ghana and 1 to 3 for Tanzania), and population proportions (P), obtained as described above, fitted as one covariate for Tanzania and as two covariates for Ghana, as described by [17,18]. Random effects included animal genetic effects (A) with a genomic relationship matrix obtained based on the procedures described by [36], maternal environmental effects (M), and residuals (e). The maternal effect was removed from the model for traits for which it was estimated to be zero. For viral load traits and for natural and anti-NDV antibody levels, the effect of assay plate was added to the model as a fixed effect. For lesion scores, the effect of bird survival to the end of the trial (0/1) was added as a fixed effect. For the combined analyses, replicate by country and country-specific population proportion covariates were added. Phenotypic variance was estimated as the sum of the estimates of the animal genetic, maternal (if fitted), and residual variances. Heritability was estimated as the ratio of animal genetic and phenotypic variance estimates. Phenotypic and genetic correlations between traits recorded in the velogenic NDV trials were estimated separately for each country and combined using a bivariate version of the univariate models described above. Genetic correlations of the velogenic NDV phenotypes of the current study with phenotypes recorded in the lentogenic NDV trials [17,18] (pre- and post-infection growth rate, anti-NDV antibody levels at 10 days post-infection (dpi), viral load at 2 and 6 dpi, and viral clearance) were also estimated. Bivariate models were used, with the model as described above, for the velogenic NDV response traits and as described in [17,18] for the lentogenic NDV response traits. Because the lentogenic and velogenic NDV challenges were conducted using different birds but from the same ecotypes, environmental correlations were not estimable and were set equal to zero.

Materials

https://efashare.b-cdn.net/materials/5237cd1d87889658d15c6dd8c4fafb7df01be5fd1a842ccfc5e8d3a37087cbc8/animals-12-02755-s001.zip

Innovations

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

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals for pregnant women in rural areas. This allows for virtual consultations, monitoring, and guidance throughout the pregnancy.

2. Mobile Health (mHealth) Applications: Developing mobile applications that provide educational resources, appointment reminders, and personalized health information can empower pregnant women to take control of their health and make informed decisions.

3. Community Health Workers: Training and deploying community health workers who can provide basic prenatal care, education, and support to pregnant women in underserved areas. These workers can also help identify high-risk pregnancies and refer women to appropriate healthcare facilities.

4. Mobile Clinics: Establishing mobile clinics that travel to remote areas to provide prenatal care, vaccinations, and health screenings for pregnant women who may not have access to nearby healthcare facilities.

5. Health Information Systems: Implementing electronic health records and data management systems to improve the tracking and monitoring of maternal health outcomes. This can help identify trends, gaps in care, and areas for improvement.

6. Maternal Health Vouchers: Introducing voucher programs that provide financial assistance for pregnant women to access prenatal care, delivery services, and postnatal care. This can help reduce financial barriers and increase access to quality maternal healthcare.

7. Maternal Health Education Campaigns: Conducting targeted educational campaigns to raise awareness about the importance of prenatal care, nutrition, and healthy behaviors during pregnancy. This can help empower women to seek timely and appropriate care.

8. Public-Private Partnerships: Collaborating with private healthcare providers and organizations to expand access to maternal health services in underserved areas. This can involve subsidizing costs, improving infrastructure, and training healthcare professionals.

9. Maternal Transport Systems: Establishing transportation systems or networks to ensure that pregnant women have access to timely and safe transportation to healthcare facilities for prenatal visits, delivery, and emergency care.

10. Maternal Health Financing: Exploring innovative financing models, such as microinsurance or community-based health financing, to make maternal healthcare more affordable and accessible for low-income women.

These innovations can help address the challenges of improving access to maternal health in underserved areas and contribute to reducing maternal mortality and improving maternal and child health outcomes.

AI Innovations Description

The study described focuses on the genetic analyses of the response of local Ghanaian and Tanzanian chicken ecotypes to a natural challenge with velogenic Newcastle disease virus (NDV). The goal of the study is to understand the genetic factors that influence the response to NDV and to identify potential ways to improve the resilience of these chicken ecotypes to the virus.

The study collected various phenotypic data, including growth rates, lesion scores, natural antibody levels, anti-NDV antibody levels, tear and cloacal viral load, and survival time. These data were used to estimate heritability and genetic correlations for the different traits.

The results of the study suggest that the response to velogenic NDV in these local chicken ecotypes could be improved through selective breeding. Chickens that are more resilient to the virus could help improve household livelihoods in developing countries.

To develop this recommendation into an innovation to improve access to maternal health, the following steps can be taken:

1. Conduct further research: Conduct additional research to understand the specific genetic factors that contribute to the resilience of chickens to velogenic NDV. This will help in developing targeted breeding programs to improve the resistance of local chicken ecotypes to the virus.

2. Establish breeding programs: Establish breeding programs that focus on selecting and breeding chickens with higher resilience to velogenic NDV. This can be done by identifying and selecting chickens with desirable genetic traits related to resistance to the virus.

3. Collaborate with local communities: Collaborate with local communities, farmers, and poultry producers to implement the breeding programs. This will help ensure that the improved chicken ecotypes are accessible and available to those who need them the most.

4. Provide training and support: Provide training and support to farmers and poultry producers on the management and care of the improved chicken ecotypes. This will help ensure that the chickens are raised in a way that maximizes their resilience to velogenic NDV.

5. Monitor and evaluate: Continuously monitor and evaluate the impact of the breeding programs on access to maternal health. This can be done by tracking the reduction in NDV outbreaks, improvements in chicken health and productivity, and the overall impact on household livelihoods.

By implementing these steps, the innovation of selectively breeding local chicken ecotypes with improved resilience to velogenic NDV can contribute to improving access to maternal health in developing countries.

AI Innovations Methodology

To improve access to maternal health, here are some potential recommendations:

1. Mobile Health (mHealth) Solutions: Develop and implement mobile health applications that provide pregnant women with access to information, resources, and support. These apps can provide prenatal care reminders, nutrition advice, appointment scheduling, and emergency contact information.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women in remote areas to consult with healthcare professionals through video calls. This can help address the shortage of healthcare providers in rural areas and provide timely advice and guidance.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal care, education, and support to pregnant women in underserved communities. These workers can conduct home visits, monitor pregnancies, and refer women to healthcare facilities when necessary.

4. Transportation Support: Develop transportation programs or partnerships to ensure that pregnant women have access to reliable and affordable transportation to healthcare facilities. This can include providing vouchers for public transportation or organizing community-based transportation services.

5. Health Education Programs: Implement comprehensive health education programs that focus on maternal health, including prenatal care, nutrition, hygiene, and childbirth preparation. These programs can be conducted in schools, community centers, and through community outreach initiatives.

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

1. Define the target population: Identify the specific population that will benefit from the recommendations, such as pregnant women in rural areas or underserved communities.

2. Collect baseline data: Gather data on the current state of maternal health access in the target population. This can include information on healthcare facilities, transportation availability, healthcare provider-to-patient ratios, and maternal health outcomes.

3. Define indicators: Determine key indicators that will be used to measure the impact of the recommendations. This can include metrics such as the number of prenatal care visits, maternal mortality rates, and access to emergency obstetric care.

4. Develop a simulation model: Create a simulation model that incorporates the recommendations and their potential impact on the defined indicators. This model can be based on existing data, expert opinions, and assumptions about the effectiveness of the recommendations.

5. Run simulations: Use the simulation model to run various scenarios and assess the potential impact of the recommendations on improving access to maternal health. This can involve adjusting parameters such as the coverage of mobile health apps, the number of community health workers deployed, or the availability of transportation services.

6. Analyze results: Analyze the simulation results to evaluate the potential impact of the recommendations. This can include comparing different scenarios, identifying bottlenecks or barriers to implementation, and assessing the cost-effectiveness of the recommendations.

7. Refine and iterate: Based on the analysis of the simulation results, refine the recommendations and the simulation model as needed. Iterate the process to further optimize the impact on improving access to maternal health.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential benefits and challenges of implementing innovations to improve access to maternal health.

Author

Dima Health

Identifiers:

DOI: 10.3390/ani12202755

Research Areas:

Environmental, Genetics and Genomics, Health System and Policy, Maternal and Child Health, Social Determinants

Study Countries:

Ghana, Multi-Countries, Tanzania

Study Design:

Cross Sectional Study

Study Approach:

Quantitative