Mental health symptoms and their relations with dietary diversity and nutritional status among mothers of young children in eastern Democratic Republic of the Congo

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Study Justification:
This study aimed to investigate the association between mental health symptoms and diet and nutritional status among mothers of young children in South Kivu, DR Congo. The study was conducted in a region where maternal mental health problems have been linked to sub-optimal child feeding practices and child underweight and stunting. However, little is known about how maternal mental health is associated with mothers’ own diets and nutritional status. Therefore, this study aimed to fill this knowledge gap and provide insights into the relationship between mental health symptoms and diet/nutritional status among mothers in this context.
Study Highlights:
– The study found that more severe maternal mental health symptoms were associated with higher dietary diversity scores among mothers of young children.
– However, mental health symptoms were not significantly associated with body mass index (BMI) or underweight among the mothers.
– The study highlights the need for further research to identify underlying factors that could influence mental health symptomatology and diet quality among food insecure and resource-limited populations.
Study Recommendations:
Based on the findings of the study, the following recommendations can be made:
1. Mental health support: Interventions should be developed and implemented to address maternal mental health symptoms in this population. This could include providing access to mental health services, counseling, and support groups.
2. Nutrition education: Programs should be designed to improve the dietary diversity and nutritional knowledge of mothers. This could involve providing information on balanced diets, food preparation, and the importance of consuming a variety of food groups.
3. Integrated interventions: Efforts should be made to integrate mental health and nutrition interventions, recognizing the interconnectedness of these factors. This could involve collaboration between mental health professionals and nutritionists to provide comprehensive support to mothers.
Key Role Players:
1. Mental health professionals: Psychiatrists, psychologists, and counselors who can provide mental health support and interventions.
2. Nutritionists: Experts in nutrition who can provide education and guidance on improving dietary diversity and nutritional status.
3. Community health workers: Trained individuals who can deliver mental health and nutrition interventions at the community level.
4. NGOs and government agencies: Organizations and agencies that can support and fund mental health and nutrition programs in the region.
Cost Items for Planning Recommendations:
1. Training and capacity building: Costs associated with training mental health professionals, nutritionists, and community health workers on providing appropriate support and interventions.
2. Program implementation: Costs related to the implementation of mental health and nutrition programs, including staffing, materials, and logistics.
3. Monitoring and evaluation: Costs for monitoring and evaluating the effectiveness of the interventions, including data collection and analysis.
4. Awareness campaigns: Costs for raising awareness about the importance of mental health and nutrition among mothers and the wider community.
5. Infrastructure and equipment: Costs for establishing and maintaining facilities, equipment, and technology required for mental health and nutrition services.
Please note that the cost items provided are general categories and the actual costs would depend on the specific context and implementation plan.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a clear description of the study design, data collection methods, and statistical analyses. However, it does not provide information on the sample size or the effect sizes of the associations found. To improve the evidence, the authors could include these missing details in the abstract.

Background: In developing countries, maternal mental health problems have been linked to sub-optimal child feeding practices and child underweight and stunting, but little is known about how maternal mental health is associated with mothers’ own diets and nutritional status. The objective of the study was to investigate the association between mental health symptoms and diet and nutritional status of mothers of young children in South Kivu, DR Congo. Methods: Participants were 828 mothers of young children enrolled in a larger, quasi-experimental study evaluating a multi-year food security and nutrition project. The present analysis was conducted with cross-sectional data collected from 2015 to 2016. We assessed symptoms of anxiety and depression using the Hopkins Symptom Checklist-25 (HSCL-25) and post-traumatic stress disorder (PTSD) with the Harvard Trauma Questionnaire (HTQ), using a four-point Likert scale. Mean scale scores were calculated ranging from one to four. A variable was created for high distress (participants scoring in the upper quartile of both measures). Dietary diversity scores were calculated from the number of food groups (range zero to ten) consumed the previous day, identified from an open recall. Nutritional status was measured by body mass index (BMI) and underweight (BMI < 18.5 kg/m2, or mid-upper arm circumference < 23 cm for pregnant women). Bivariate and multivariate (adjusting for parent study intervention group, education, age, health, parity, livelihoods zone, and territory of origin) regression analyses were conducted. Results: Maternal mental health measures were positively and statistically significantly associated with higher dietary diversity scores in adjusted analyses (HSCL-25: ß= 0.18, p = 0.002, HTQ: ß= 0.12, p = 0.029, High Distress: ß= 0.47, p < 0.001). Mental health symptoms were not significantly associated with BMI (HSCL-25: ß = – 0.04, p = 0.824; HTQ: ß = 0.02, p = 0.913; High distress: ß= – 0.02, p = 0.938) or underweight (HSCL 25: OR = 0.91, p = 0.640; HTQ: OR = 1.03, p = 0.866; High distress: OR = 0.78, p = 0.489). Conclusions: More severe maternal mental health symptoms were associated with higher dietary diversity but not nutritional status, and the reasons for these findings are not clear from available data. More research is needed to identify underlying factors that could influence mental health symptomatology and diet quality among food insecure and extremely resource-limited populations.

The present study is a sub-study of a larger, quasi-experimental evaluation of a United States Agency for International Development (USAID) food assistance program called Jenga Jamaa II [30]. Jenga Jamaa II was designed to improve household food security and child nutrition in Uvira and Fizi territories in South Kivu through four distinct nutrition-specific and nutrition-sensitive interventions, and was implemented by the non-governmental organizations (NGOs) Adventist Development and Relief Agency (ADRA) and World Vision International from 2011 to 2016. Enrollment of project beneficiaries and control group participants for the parent study occurred from August to October 2012, with 1820 households enrolled and followed for three and a half years. Data were collected via eight cross-sectional surveys occurring in August/September and February/March of each year to account for seasonal variation in food security. More details related to the parent study can be found elsewhere [30]. The present study is a cross-sectional sub-study utilizing data collected during the last two Jenga Jamaa II surveys conducted in September 2015 and March 2016. Participants from the Jenga Jamaa II parent study who were mothers of children under five also enrolled were eligible for the sub-study. Individuals were excluded from the study if they were not enrolled in the parent study or were not the biological mother of a child also enrolled in the parent study. Maternal mental health was assessed at one time point (September 2015 for the majority of the participants), but maternal diet and weight data were utilized from both the September 2015 and March 2016 surveys (among participants present at both) to account for seasonal variation. Data were collected electronically using the mobile data collection application Magpi and Android tablets provided by ADRA [31]. The Institutional Review Board of the Johns Hopkins Bloomberg School of Public Health approved all data collection instruments and procedures. At every data collection encounter for the parent study, study staff obtained oral consent in Swahili, the predominant local language, and participants were reminded that they could decline to participate at any time. Additional oral consent was requested for participation in the sub-study, in which the mental health questionnaire was described as well as the potential risks and benefits of participation. ADRA field agents who served as study enumerators received training on the questionnaire and on research ethics prior to data collection. Independent variables assessed were depression/anxiety symptoms, post-traumatic stress disorder (PTSD) symptoms, and a variable (high mental distress) constructed to identify participants with high levels of both depression/anxiety and PTSD symptoms, having mean item scale scores in the upper quartile of both measures. The Hopkins Symptom Checklist (HSCL-25) was used to assess depression symptoms (15 items) and anxiety symptoms (10 items), and the Harvard Trauma Questionnaire (HTQ) was used to assess PTSD symptoms (16 items) [32, 33]. One item on suicidality was dropped from the HSCL-25 due to ethical considerations. Participants rated the frequency of each symptom in the prior four weeks on a four-point Likert scale, and mean item scores were calculated for depression/anxiety (HSCL-25) and PTSD (HTQ), with a possible range of one to four. The mental health questionnaire that included the HSCL-25 and HTQ tools was adapted from an earlier study in South Kivu that evaluated the impact of a cognitive behavioral therapy intervention among female survivors of sexual violence [34]. The questionnaire was administered in Swahili, the predominant local language. The HSCL-25 is frequently used in a variety of cultural settings [7, 35–37], and the validity of a Swahili version of the scale has been evaluated in a sample of Tanzanian women using content and construct validation methods [36]. Additionally, it has been used to assess depression symptoms among Congolese refugees in the United States [38]. The validity of the Harvard Trauma Questionnaire has been assessed in multiple settings and is often used among populations who have experienced conflict and displacement, such as refugees [33, 39–41]. After data collection was complete, the internal consistency reliability of mental health measures was assessed using Cronbach’s alpha [42]. The HSCL-25 items had a scale reliability coefficient of 0.92 and the HTQ items had a scale reliability coefficient of 0.91. The anxiety and depression subscales of the HSCL-25 had a correlation coefficient of 0.72 (r(826), p < 0.001). The correlation coefficient of the HSCL-25 and HTQ was 0.82 (r(826), p < 0.001). Maternal BMI and underweight were used as measures of maternal nutritional status. Maternal height, weight, and mid-upper arm circumference (MUAC) were measured by trained ADRA field agents using standard protocols [43]. BMI was calculated for non-pregnant participants, and MUAC was used to assess nutritional status of pregnant participants [44]. Weight was averaged for the two data collection periods if participants were present at both surveys, or a sole weight measure was used for participants who were only present at one survey. Pregnant mothers with MUAC < 23 cm and non-pregnant mothers with BMI < 18.5 kg/m2 were classified as underweight [44]. Participants were measured using a Model 1582 Tanita Mommy and Baby Infant Scale (Arlington Heights, IL) and a Shorr Productions (Olney, MD) height board. Dietary diversity scores were used to measure diet quality, using the Minimum Dietary Diversity for Women (MDD-W) tool [45]. Enumerators asked participants to list all foods consumed the previous day and night. When composite dishes were mentioned, they asked for a list of ingredients and probed for additional items. All of the food items were recorded in Swahili, and then translated to English and classified into one of ten possible food groups: 1) starchy staples (grains, white roots, tubers, and plantains); 2) pulses (beans, peas, lentils); 3) nuts and seeds; 4) dairy; 5) meat, poultry, and fish; 6) eggs; 7) dark green leafy vegetables; 8) other vitamin A-rich fruits and vegetables; 9) other vegetables; and 10) other fruit. The number of food groups consumed was summed to create a dietary diversity score, with a possible range of zero to ten, with higher scores indicating greater diversity. Maternal dietary data were collected at two time points six months apart, and an average score was calculated, in order to address seasonal variation. The mental health questionnaire included a section on background and demographic characteristics: age, years of education obtained, ethnic group, living in territory of origin, currently pregnant, number of children, and marital status. Education was recoded as a categorical variable with three categories: no education, completing at least some primary school, and completing at least some secondary school or higher education. Participants were also asked if they had a child that died, and to rate their physical health status, using a scale that ranged from excellent to poor. Household-level data, including household size, income in the past month, and food insecurity, were collected as part of the parent study survey questionnaire. Food insecurity was measured using the Household Food Insecurity Access Scale (HFIAS) [46]. Households were classified in categories ranging from food secure to severely food insecure based on HFIAS score. Participants were also asked their income in the past month. Household income was not included in the final analysis because it was not necessarily reflective of socioeconomic status; in this context it may have represented the sale of assets due to hardship or food insecurity. Indicator variables for intervention groups were created for the four parent study interventions and the control group. Indicator variables for geographic region (Uvira or Fizi territory) and livelihoods zone (plains, mountains, or lakeside) were also included. Data were analyzed using Stata 13 [47]. Distributions of continuous variables and frequencies of categorical variables were explored, and outlying values were identified. The three dependent variables assessed were dietary diversity score, BMI, and underweight. Dietary diversity score was a continuous variable. BMI was continuous and limited to non-pregnant participants. Underweight was constructed as a binary variable. Independent variables for maternal mental health were mean item HSCL-25 score (measuring depression and anxiety symptoms), mean item HTQ score (measuring PTSD symptoms), and a binary variable for high psychological distress (upper quartile of both measures). Separate analyses were conducted for each of the three independent variables due to multicollinearity. Bivariate regression analyses were conducted between demographic/socioeconomic measures and independent and dependent variables (Additional file 1: Tables 4 and 5). Potential confounding variables were selected for inclusion in the model if they were associated with both independent and dependent variables, or if they had a conceptual relationship to both. Multivariate linear and logistic regression analyses were conducted, adjusting for potential confounding variables.

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

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals, allowing pregnant women in remote or underserved areas to receive prenatal care and mental health support without having to travel long distances.

2. Mobile health (mHealth) applications: Developing mobile applications that provide educational resources, reminders for prenatal appointments and medication, and access to mental health support can help improve maternal health outcomes by increasing knowledge and adherence to recommended practices.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, mental health screenings, and referrals to pregnant women in their communities can help bridge the gap in access to healthcare services, particularly in rural areas.

4. Integrated care models: Implementing integrated care models that combine maternal health services with mental health services can ensure that pregnant women receive comprehensive care that addresses both their physical and mental well-being.

5. Public-private partnerships: Collaborating with private sector organizations, such as technology companies or pharmaceutical companies, can help leverage resources and expertise to develop innovative solutions for improving access to maternal health services.

It’s important to note that these recommendations are general and may need to be tailored to the specific context and needs of the population in South Kivu, DR Congo.
AI Innovations Description
The study mentioned focuses on the association between maternal mental health symptoms and diet and nutritional status among mothers of young children in South Kivu, DR Congo. The study found that more severe maternal mental health symptoms were associated with higher dietary diversity scores but not with nutritional status.

Based on this study, a recommendation to improve access to maternal health could be to integrate mental health support into existing maternal health programs. This could involve training healthcare providers to identify and address mental health symptoms in pregnant and postpartum women, as well as providing counseling and support services. By addressing mental health issues, it may be possible to improve diet and nutritional status among mothers, ultimately leading to better maternal and child health outcomes.
AI Innovations Methodology
Based on the information provided, here are some potential recommendations for improving access to maternal health:

1. Increase awareness and education: Implement programs that focus on raising awareness about maternal mental health and its impact on diet and nutritional status. Provide education and information to mothers and their families about the importance of mental health and its connection to overall well-being.

2. Integrated approach: Develop integrated interventions that address both mental health and nutrition. This could involve incorporating mental health screenings and support into existing maternal health programs, as well as providing nutrition education and support for mothers.

3. Community-based support: Establish community-based support groups or networks where mothers can come together to share their experiences, receive emotional support, and access resources related to mental health and nutrition. These groups can also serve as a platform for education and awareness campaigns.

4. Training for healthcare providers: Provide training for healthcare providers, including doctors, nurses, and midwives, on identifying and addressing maternal mental health issues. This can help ensure that healthcare providers are equipped to provide appropriate support and referrals for mothers in need.

5. Strengthen healthcare systems: Invest in strengthening healthcare systems, particularly in resource-limited settings, to ensure that maternal mental health services are accessible and available. This may involve improving infrastructure, increasing the number of trained healthcare providers, and integrating mental health services into primary healthcare settings.

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

1. Define the indicators: Identify specific indicators that can be used to measure the impact of the recommendations, such as the number of women accessing mental health services, changes in dietary diversity scores, improvements in maternal nutritional status, and changes in awareness and knowledge about maternal mental health.

2. Collect baseline data: Gather baseline data on the selected indicators before implementing the recommendations. This can be done through surveys, interviews, or data collection from existing sources.

3. Implement the recommendations: Put the recommendations into action, ensuring that they are implemented effectively and consistently.

4. Monitor and evaluate: Continuously monitor and evaluate the progress and impact of the recommendations. This can involve collecting data at regular intervals to track changes in the selected indicators.

5. Analyze the data: Analyze the collected data to assess the impact of the recommendations on improving access to maternal health. This can be done through statistical analysis, comparing the baseline data with the data collected after implementing the recommendations.

6. Draw conclusions and make adjustments: Based on the analysis of the data, draw conclusions about the effectiveness of the recommendations in improving access to maternal health. If necessary, make adjustments to the recommendations or implementation strategies to further enhance their impact.

7. Disseminate findings: Share the findings of the impact assessment with relevant stakeholders, including policymakers, healthcare providers, and community members. This can help inform future decision-making and guide efforts to improve access to maternal health.

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