Household food insecurity and mental distress among pregnant women in Southwestern Ethiopia: A cross sectional study design

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Study Justification:
– The study aims to examine the association between household food insecurity and mental distress during pregnancy in Ethiopia.
– This is important because both household food insecurity and mental distress are common problems in the region, and understanding their relationship can help inform interventions and policies to improve maternal and child health.
Study Highlights:
– The study found that pregnant women living in food insecure households were 4 times more likely to have mental distress compared to their counterparts.
– This association remained significant even after controlling for confounding factors.
– The study highlights the need for further investigation to understand the mechanism by which food insecurity is associated with mental distress during pregnancy.
Study Recommendations:
– The study recommends further research to explore how food insecurity during pregnancy leads to mental distress or whether mental distress contributes to the development of food insecurity.
– The findings of this study can inform the development of interventions and policies to address household food insecurity and mental distress among pregnant women.
Key Role Players:
– Researchers and research institutions: Conduct further investigations to understand the relationship between food insecurity and mental distress during pregnancy.
– Health centers and hospitals: Provide support and resources for pregnant women experiencing food insecurity and mental distress.
– Government agencies: Develop policies and programs to address household food insecurity and mental health during pregnancy.
– Non-governmental organizations: Implement interventions to improve food security and mental well-being among pregnant women.
Cost Items for Planning Recommendations:
– Research funding: Allocate resources for further investigations on the association between food insecurity and mental distress during pregnancy.
– Program implementation: Budget for interventions and services targeting pregnant women experiencing food insecurity and mental distress.
– Capacity building: Invest in training and education for healthcare providers to effectively address the needs of pregnant women in food insecure households.
– Monitoring and evaluation: Allocate resources to assess the impact and effectiveness of interventions and policies addressing household food insecurity and mental health during pregnancy.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design is cross-sectional, which limits the ability to establish causality. Additionally, the abstract mentions that the mechanism by which food insecurity is associated with mental distress is not clear, suggesting a need for further investigation. To improve the evidence, future studies could consider using a longitudinal design to establish temporal relationships and explore potential mechanisms linking food insecurity and mental distress. Additionally, qualitative research methods could be employed to gain a deeper understanding of the experiences and perspectives of pregnant women in relation to food insecurity and mental distress.

Background: There are compelling theoretical and empirical reasons that link household food insecurity to mental distress in the setting where both problems are common. However, little is known about their association during pregnancy in Ethiopia. Methods: A cross-sectional study was conducted to examine the association of household food insecurity with mental distress during pregnancy. Six hundred and forty-two pregnant women were recruited from 11 health centers and one hospital. Probability proportional to size (PPS) and consecutive sampling techniques were employed to recruit study subjects until the desired sample size was obtained. The Self Reporting Questionnaire (SRQ-20) was used to measure mental distress and a 9-item Household Food Insecurity Access Scale was used to measure food security status. Descriptive and inferential statistics were computed accordingly. Multivariate logistic regression was used to estimate the effect of food insecurity on mental distress. Results: Fifty eight of the respondents (9 %) were moderately food insecure and 144 of the respondents (22.4 %) had mental distress. Food insecurity was also associated with mental distress. Pregnant women living in food insecure households were 4 times more likely to have mental distress than their counterparts (COR = 3.77, 95 % CI: 2.17, 6.55). After controlling for confounders, a multivariate logistic regression model supported a link between food insecurity and mental distress (AOR = 4.15, 95 % CI: 1.67, 10.32). Conclusion: The study found a significant association between food insecurity and mental distress. However, the mechanism by which food insecurity is associated with mental distress is not clear. Further investigation is therefore needed to understand either how food insecurity during pregnancy leads to mental distress or weather mental distress is a contributing factor in the development of food insecurity.

A facility-based cross-sectional study was conducted in Jimma Zone, one of the 20 administrative zones in Oromia Regional State, southwest Ethiopia. According to the Central Statistical Authority [38] 2.7 million people live in Jimma Zone on an area of 15,569 km2 with a population density of 159.69 persons per km2. Of these, 1.23 million are women. An estimated 31,050 women become pregnant every year and antenatal care coverage in the zone is 64.3 percent. There are three hospitals and 84 primary health centers in Jimma zone where pregnant mothers can receive antenatal care services. There are 12 public health facilities affiliated with Jimma university within the radius of 70 km for community based education program, research and services. Between the months of June and August 2013, a total of 2,987 pregnant women were on antenatal care (ANC) follow up at the 12 health facilities selected for this study [39]. A single population proportion formula was used to estimate 660 pregnant women to be included in the sample. Assumptions for calculating the sample size were the degree of confidence interval (95 %; Z1-α/2 = 1.96), the estimated magnitude of mental distress among pregnant women (P = 50 %), a 4 % degree of precision, and a non-response rate of 10 %. A total of 11 health centers and one hospital were selected to sample the study subjects. These facilities were chosen purposefully based on their previous affiliation with the Jimma University Community Based Education Training Program (CBTP). Probability proportional to size sampling (PPS) techniques was employed to assign the number of pregnant women to be interviewed from each selected health center and hospital. All pregnant women coming for ANC services during the data collection period were taken as the source population for the study. Finally, a consecutive sampling technique was used to identify the study subjects from each of the health facilities until the desired sample size was obtained. Seriously ill pregnant women were excluded from the study and referred to the respective hospital. The instruments used for data collection were adapted from earlier studies and WHO guidelines [30, 40–48] and translated from English into the two most commonly spoken languages in the study setting (Afan Oromo and Amharic) by two fluent linguists (from the university Language and Literature Department). Each translation was then translated back by another person (linguists from the English Department) to ensure its consistency. Before the actual survey, the final translated questionnaires were pre tested on 5 % of the sample at two different health institutions (one urban and the other rural) so that coherence, wording, sequencing and consistencies of all questions were amended accordingly. The result of this pretest was not included in the main analyses. Two days of intensive training on how to approaches the clients, interview techniques, ethical consideration and how to refer any mothers that needed help. Finally, exit interviews were conducted by trained data collectors at each of the 12 health facilities immediately after the mothers had received their ANC services. Field supervisors and the research team supervised the data collection process. Supervisors also checked the consistency of data before submission to the data manager. Ethical clearance was obtained from the Jimma University (JU) ethical review board. The participants were asked for their oral consent after the purpose of the study was clearly communicated. Confidentiality was ensured for each study participants. Mental distress was measured using The Self Reporting Questionnaire (SRQ-20). The SRQ-20 is a screening instrument developed by the World Health Organization (WHO) to assess the level of symptoms of overall mental distress one month preceding the survey [42]. Scores range from 0 to 20, with higher scores representing more severe mental distress. The SRQ has been used in several previous studies exploring the relationship between maternal psychological wellbeing and infant health [42, 46]. In developing countries, cutoff scores of ≥6, 7, 8, 4 and 10 have been used for identifying cases of mental distress [40, 42, 46, 48]. However, in this study we report the proportion of women scoring SRQ ≥7 or greater to indicate mental distress. The SRQ-20 has been validated in Ethiopia for measuring mental distress among rural pregnant women and the recommended cutoff points greater or equal to 7 have specificity of 62 % and sensitivity of 68.4 % [40]. If the scoring was <7, we coded “0” for no mental distress and if it was greater or equals to 7, we coded “1” to indicate presence of mental distress. Household food insecurity access was measured using items from the validated Household Food Insecurity Access Scale (HFIAS) that was specifically developed for use in developing countries [41, 43–45]. The HFIAS consists of 9 items specific to an experience of food insecurity occurring within the previous four weeks. Each respondent indicated whether they had encountered the following due to lack of food or money to buy food in the last one month: (1) worried about running out of food, (2) lack of preferred food, (3) the respondent or another adult had limited access to a variety of foods due to a lack of resources (4) forced to eat un preferred food due to lack of resources, (5) eating smaller portions, (6) skipping meals, (7) the household ran out of food, (8) going to sleep hungry, and (9) going 24 hours without food. Endorsed items are then clarified with reported estimates of the frequency of food insecurity (rarely, sometimes, and often). Scores range from 0 to 27 where higher scores reflect more severe food insecurity and lower scores represent less food insecurity. To determine the status of food insecurity the average HFIAS score (dividing the sum of Household score by number of household in the sample) was computed and then household food insecurity access prevalence (HFIAP) categories (food secure, mild, moderately and severely food insecure) was generated. But, since none of the mothers reported mild and severely food insecure households, HFIAP was only categorized in to two conditions [44]. The data were entered, cleaned, and analyzed using STATA version 12 for Microsoft Windows. Descriptive statistics, bivariate and multivariate logistic regression analyses were computed to examine the relationship between the explanatory variables and mental distress. Assuming a linear relationship between independent and dependent variables, the binary form of the dependent variable was coded as “1” for mental distress and “0” for not distressed. First binary logistic regression analyses were conducted between each and separate explanatory variables with the outcome of our interest (mental distress) (Model I) and reported using crude Odds Ratios. Finally, all significant variables(P < 0.05) during the bivariate analyses were chosen for multivariate logistic regression modeling using forward selection method to explore the association of food insecurity with mental distress by controlling for other confounding variables such as age, occupation, monthly income, and ownership of agricultural land (Model II). Adjusted odds ratios (AOR) and their 95 % confidence intervals (CI) were presented as indicators of strength of association. A p-value of 0.05 or less was used to determine the cut-off points for statistical significance.

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 pregnant women with information and resources related to maternal health, including nutrition, mental health, and access to healthcare services. These apps could also include features such as appointment reminders and emergency contact information.

2. Telemedicine: Implement telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone consultations. This would improve access to prenatal care and allow for early detection and management of mental distress.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and referrals to pregnant women in their communities. These workers can help identify and address food insecurity and mental distress among pregnant women, and connect them with appropriate resources and services.

4. Integrated Care Models: Implement integrated care models that bring together maternal health services, mental health services, and nutrition support. This would ensure that pregnant women receive comprehensive care that addresses their physical, mental, and nutritional needs.

5. Public-Private Partnerships: Foster collaborations between government agencies, non-profit organizations, and private sector entities to improve access to maternal health services. This could involve leveraging private sector resources and expertise to expand healthcare infrastructure, improve supply chains for essential maternal health products, and implement innovative financing models.

These are just a few examples of potential innovations that could be explored to improve access to maternal health based on the study’s findings. It is important to conduct further research and pilot programs to assess the feasibility, effectiveness, and scalability of these innovations in the specific context of Southwestern Ethiopia.
AI Innovations Description
The study conducted in Southwestern Ethiopia found a significant association between household food insecurity and mental distress among pregnant women. Pregnant women living in food insecure households were four times more likely to experience mental distress compared to those in food secure households. The study recommends further investigation to understand the mechanism by which food insecurity during pregnancy leads to mental distress or whether mental distress contributes to the development of food insecurity.

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

1. Conduct a comprehensive needs assessment: Identify the specific barriers and challenges pregnant women face in accessing maternal health services in the study area. This can include factors such as financial constraints, lack of transportation, limited availability of healthcare facilities, and cultural beliefs.

2. Design targeted interventions: Based on the needs assessment, develop innovative interventions that address the identified barriers. For example, if financial constraints are a major issue, explore options such as providing financial assistance or insurance coverage for maternal health services.

3. Collaborate with stakeholders: Engage with local communities, healthcare providers, government agencies, and non-governmental organizations to gain support and collaboration for implementing the interventions. This can involve forming partnerships, conducting awareness campaigns, and advocating for policy changes to improve access to maternal health services.

4. Implement and evaluate the interventions: Roll out the interventions and closely monitor their implementation. Collect data on the impact of the interventions, including indicators such as increased utilization of maternal health services, improved health outcomes for pregnant women, and reduced levels of food insecurity and mental distress.

5. Scale up successful interventions: Identify interventions that have proven to be effective and scalable. Work towards expanding their implementation to reach a larger population and replicate the positive outcomes in other areas with similar challenges.

By following these steps, the recommendation from the study can be transformed into an innovative approach to improve access to maternal health services and address the underlying issues of food insecurity and mental distress among pregnant women.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Increase availability of antenatal care services: Ensure that there are enough health facilities, hospitals, and primary health centers in the area to provide antenatal care services to pregnant women. This can help improve access to essential prenatal care.

2. Improve transportation infrastructure: Enhance transportation infrastructure to ensure that pregnant women can easily travel to health facilities for antenatal care. This can include improving roads, providing public transportation options, or implementing mobile health clinics.

3. Strengthen community-based education programs: Collaborate with local communities and organizations to establish community-based education programs that provide information and support to pregnant women. These programs can help raise awareness about the importance of antenatal care and provide guidance on accessing healthcare services.

4. Address household food insecurity: Develop interventions to address household food insecurity, as it has been found to be associated with mental distress during pregnancy. This can include implementing food assistance programs, promoting sustainable agriculture practices, and providing nutritional education to pregnant women.

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

1. Define indicators: Identify key indicators that can measure the impact of the recommendations on improving access to maternal health. These indicators can include the number of pregnant women receiving antenatal care, the reduction in travel time to health facilities, the increase in community awareness about antenatal care, and the improvement in household food security.

2. Collect baseline data: Gather baseline data on the current status of access to maternal health in the target area. This can include data on the number of pregnant women receiving antenatal care, the average travel time to health facilities, the level of community awareness about antenatal care, and the prevalence of household food insecurity.

3. Implement interventions: Implement the recommended interventions in the target area. This can involve establishing new health facilities, improving transportation infrastructure, conducting community-based education programs, and implementing initiatives to address household food insecurity.

4. Monitor and evaluate: Continuously monitor and evaluate the implementation of the interventions. Collect data on the indicators identified in step 1 to assess the impact of the interventions on improving access to maternal health. This can involve conducting surveys, interviews, and data analysis to measure changes in the indicators over time.

5. Analyze and interpret results: Analyze the collected data to determine the impact of the interventions on improving access to maternal health. Compare the baseline data with the data collected after the implementation of the interventions to identify any changes or improvements. Interpret the results to understand the effectiveness of the recommendations in improving access to maternal health.

6. Adjust and refine: Based on the results and findings, make any necessary adjustments or refinements to the interventions. This can involve scaling up successful interventions, modifying strategies that did not yield the desired results, or implementing additional measures to further improve access to maternal health.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and make informed decisions on how to effectively address the challenges in the given context.

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