Prevalence of prenatal zinc deficiency and its association with socio-demographic, dietary and health care related factors in Rural Sidama, Southern Ethiopia: A cross-sectional study

BMC Public Health, Volume 11, Year 2011

Interpretation

Background: Several studies witnessed that prenatal zinc deficiency (ZD) predisposes to diverse pregnancy complications. However, scientific evidences on the determinants of prenatal ZD are scanty and inconclusive. The purpose of the present study was to assess the prevalence and determinants of prenatal ZD in Sidama zone, Southern Ethiopia. Methods. A community based, cross-sectional study was conducted in Sidama zone in January and February 2011. Randomly selected 700 pregnant women were included in the study. Data on potential determinants of ZD were gathered using a structured questionnaire. Serum zinc concentration was measured using Atomic Absorption Spectrometry. Statistical analysis was done using logistic regression and linear regression. Results: The mean serum zinc concentration was 52.4 (9.9) g/dl (95% CI: 51.6-53.1 g/dl). About 53.0% (95% CI: 49.3-56.7%) of the subjects were zinc deficient. The majority of the explained variability of serum zinc was due to dietary factors like household food insecurity level, dietary diversity and consumption of animal source foods. The risk of ZD was 1.65 (95% CI: 1.02-2.67) times higher among women from maize staple diet category compared to Enset staple diet category. Compared to pregnant women aged 15-24 years, those aged 25-34 and 35-49 years had 1.57 (95% CI: 1.04-2.34) and 2.18 (95% CI: 1.25-3.63) times higher risk of ZD, respectively. Women devoid of self income had 1.74 (95% CI: 1.11-2.74) time increased risk than their counterparts. Maternal education was positively associated to zinc status. Grand multiparas were 1.74 (95% CI: 1.09-3.23) times more likely to be zinc deficient than nulliparas. Frequency of coffee intake was negatively association to serum zinc level. Positive association was noted between serum zinc and hemoglobin concentrations. Altitude, history of iron supplementation, maternal workload, physical access to health service, antenatal care and nutrition education were not associated to zinc status. Conclusion: ZD is of public health concern in the area. The problem must be combated through a combination of short, medium and long-term strategies. This includes the use of household based phytate reduction food processing techniques, agricultural based approaches and livelihood promotion strategies. © 2011 Gebremedhin et al; licensee BioMed Central Ltd. This is community based, cross-sectional, quantitative study with descriptive and analytic designs. The study was conducted in January and February 2011 in 18 kebeles of Sidama zone, Southern Ethiopia. The period was selected as it was neither food insecured nor harvest season. A kebele is the smallest administrative unit in Ethiopia comprising approximately 1000 households. Sidama zone is one of the 15 zones of Southern Nations Nationalities Peoples Region (SNNPR) [14]. The zone has population of 2,966,652 and population density of 430 people/km2 [14]. It is characterized by three agro-ecological zones. The lowlands (20%), the midlands (50%) and the highlands (30%) [15]. About 85% of the population livelihood depends on subsistent farming [16]. Major crops grown in the area are enset (Enset ventricosum), coffee and maize [15]. The average rural household has 0.3 ha of land [16]. In the SNNPR access to health care is limited [17]. Sample size adequate for estimating the prevalence of ZD was computed using single proportion sample size calculation formula with the inputs of 95% confidence level, 5% of margin of error, design effect of 2, non-response rate of 10% and expected prevalence of ZD of 72% [6]. Accordingly, sample size of 682 was computed. However, in order to maximize the sample size for the analytic study component, 750 pregnant women were included in the study. The adequacy of the sample size for investigating the key determinants of ZD (parity, maternal age, and gestational age) was assessed via double proportion sample size calculation formula using an online application [18]. The calculation was made based on the inputs of 95% confidence level, 80% study power and 1:1 ratio between cases and controls. Expected prevalence figures of the exposure factors in cases and controls were taken from studies conducted elsewhere [10,19]. Ultimately, the available sample size was judged to be adequate to study the aforementioned determinants. Initially all the kebeles in the zone were listed and stratified into the three agro-ecological zones: lowlands, midlands and highlands. The total sample size was divided to the three strata proportionally to their population size. From each stratum, 6 kebeles were selected at random and the sample size for each stratum was distributed to the kebeles proportional to their population size. Ultimately 750 subjects were selected using systematic random sampling technique. The sampling frame for pregnant women was developed by having a house to house enumeration. Presumptive symptoms of pregnancy (ammenoria and/or change in the size of uterus) with subsequent pregnant urine test were used to diagnose pregnancy. A structured and pretested questionnaire used to assess potential determinants of ZD. The parts of the questionnaire on dietary diversity (DD) and household food insecurity level were adopted from Food and Nutrition Technical Assistance (FANTA) indicator guide for Household Dietary Diversity Score (HDDS) [20] and Household Food Insecurity Access Scale (HFIAS) [21], respectively. Other parts of the tool were developed by the principal investigators (PIs). The content validity of the questionnaire was assessed against the conceptual framework of the study. Reliability of the tool was checked using test-retest method. Questions with less than 0.7 kappa or Pearson coefficient values were removed or revised. Three trained and experienced enumerators collected the data. Interviews were made at the nearby health posts. The questionnaire was administered in local language. Height and weight were measured using calibrated Seca® scales and the measurements were registered to the nearest 0.1 cm and 0.1 kg, respectively. Altitude of the kebeles measured using Magilan® GPS system. Venous blood was collected using plain SARSTEDT Monovette® system and stainless steel needles. The blood was allowed to clot for 20 min and consecutively centrifuged at 3000 × g for 10 min. Visibly hemolyzed samples were discarded. Serum was extracted and transferred immediately into screw-top vials. The samples were kept and transported in icebox. The same day the samples were stored frozen at-20°C. Serum zinc concentration was determined at Ethiopian Health and Nutrition Research Institute using Varian SpectrAA® Flame Atomic Absorption Spectrometer. Zinc deficiency was defined as a serum zinc level of less than 56 μg/dl during the first trimester, or less than 50 μg/dl during the second or third trimester [22] Hemoglobin level was determined at the field using HemoCue Hb 301®. Anemia was defined as a hemoglobin level of less than 11.0 g/dl during the first or third trimester or less than 10.5 g/dl during the second trimester [23]. C-Reactive Protein (CRP) determined qualitatively using HumaTex CRP®. Data entry, screening and analysis were carried out by the PIs using SPSS 19.0. Descriptive analysis was done using mean, frequency and percentage. Independent t-test and one-way Analysis of Variance (ANOVA) used to compare serum zinc levels across categories of independent variables. The assumptions of ANOVA (normal distribution and homoscedasticity of the dependent variable across the categories of the independent variables) were checked to be fulfilled. Wealth index quintiles (poorest, poorer, middle, richer, and richest) were computed using Principal Component Analysis (PCA). The index was calculated based on ownership of selected household assets, size of agricultural land, quantity of livestock and materials used for housing construction. PCA was also applied to reduce variables pertaining to maternal workload. Logistic and linear regression analyses were used to control potential confounders. Independent variables which significantly associated (P < 0.05) to the dependent variable in simple regression models were exported to a multiple regression model for adjustment. In addition conceptually important confounders (like CRP status) were also adjusted. The major assumptions of logistic regression analysis (absence of multicollinearity and interaction among independent variables) and linear regression analysis (normally distributed error terms, linear relation between dependent and independent variables, homoscedasticity and absence of multicollinearity) were checked to be satisfied. The fitness of logistic and linear regression models were assessed using Hosmer-Lemeshow statistic and adjusted R squared value, respectively. Hemoglobin values were adjusted for altitude according to the formulae recommended by Center for Disease Prevention and Control (CDC) [24]. The study was conducted in confirmation of national and international ethical guidelines for biomedical research involving human subjects. Ethical clearance was obtained from the institutional review board of Addis Ababa University. Informed written consent was taken from the study subjects. Needle safety procedures were in line with WHO standard. Nutrition education was given to all subjects. Anemic women were given iron-folate supplementation.

AI Digest

Study Justification:

– The study aimed to assess the prevalence and determinants of prenatal zinc deficiency in Sidama zone, Southern Ethiopia.
– The prevalence of prenatal zinc deficiency is a public health concern in the area.
– The study aimed to provide scientific evidence on the determinants of prenatal zinc deficiency, which are currently scarce and inconclusive.

Study Highlights:

– The mean serum zinc concentration among pregnant women was 52.4 (9.9) g/dl.
– Approximately 53.0% of the pregnant women were zinc deficient.
– Dietary factors such as household food insecurity level, dietary diversity, and consumption of animal source foods were major determinants of serum zinc levels.
– Women from the maize staple diet category had a higher risk of zinc deficiency compared to those from the Enset staple diet category.
– Pregnant women aged 25-34 and 35-49 years had a higher risk of zinc deficiency compared to those aged 15-24 years.
– Women without self-income had an increased risk of zinc deficiency compared to those with self-income.
– Maternal education was positively associated with zinc status.
– Grand multiparas were more likely to be zinc deficient compared to nulliparas.
– Frequency of coffee intake was negatively associated with serum zinc levels.
– Serum zinc levels were positively associated with hemoglobin concentrations.

Recommendations for Lay Reader and Policy Maker:

– Prenatal zinc deficiency is a significant public health concern in the study area.
– Short, medium, and long-term strategies are needed to combat the problem.
– Household-based phytate reduction food processing techniques should be used to improve zinc availability in diets.
– Agricultural-based approaches and livelihood promotion strategies should be implemented to enhance food security and access to diverse food sources.
– Maternal education should be promoted to improve zinc status.
– Nutrition education and iron-folate supplementation should be provided to pregnant women, especially those who are anemic.

Key Role Players:

– Researchers and scientists
– Health professionals
– Government officials
– Non-governmental organizations (NGOs)
– Community leaders and volunteers
– Women’s groups and associations

Cost Items for Planning Recommendations:

– Research and data collection expenses
– Training and capacity building for health professionals and community volunteers
– Development and implementation of nutrition education programs
– Provision of iron-folate supplementation
– Promotion of household-based phytate reduction food processing techniques
– Agricultural development programs to enhance food security
– Livelihood promotion strategies
– Monitoring and evaluation of interventions
– Advocacy and awareness campaigns

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study design is community-based, cross-sectional, and quantitative, which provides a good foundation for gathering data. The sample size calculation was appropriate and the sampling technique was systematic random sampling. The questionnaire used was structured and pretested, and the data collection methods were well-described. The statistical analysis included logistic regression and linear regression, which are appropriate for the study objectives. However, there are a few areas that could be improved. First, the abstract does not mention if the study had a control group, which could affect the strength of the evidence. Second, the abstract does not provide information on the response rate, which could impact the generalizability of the findings. Finally, the abstract does not mention if any measures were taken to minimize bias or confounding, which could affect the validity of the results. To improve the evidence, it would be helpful to include a control group, report the response rate, and describe any measures taken to minimize bias or confounding.

Abstract

This is community based, cross-sectional, quantitative study with descriptive and analytic designs. The study was conducted in January and February 2011 in 18 kebeles of Sidama zone, Southern Ethiopia. The period was selected as it was neither food insecured nor harvest season. A kebele is the smallest administrative unit in Ethiopia comprising approximately 1000 households. Sidama zone is one of the 15 zones of Southern Nations Nationalities Peoples Region (SNNPR) [14]. The zone has population of 2,966,652 and population density of 430 people/km2 [14]. It is characterized by three agro-ecological zones. The lowlands (20%), the midlands (50%) and the highlands (30%) [15]. About 85% of the population livelihood depends on subsistent farming [16]. Major crops grown in the area are enset (Enset ventricosum), coffee and maize [15]. The average rural household has 0.3 ha of land [16]. In the SNNPR access to health care is limited [17]. Sample size adequate for estimating the prevalence of ZD was computed using single proportion sample size calculation formula with the inputs of 95% confidence level, 5% of margin of error, design effect of 2, non-response rate of 10% and expected prevalence of ZD of 72% [6]. Accordingly, sample size of 682 was computed. However, in order to maximize the sample size for the analytic study component, 750 pregnant women were included in the study. The adequacy of the sample size for investigating the key determinants of ZD (parity, maternal age, and gestational age) was assessed via double proportion sample size calculation formula using an online application [18]. The calculation was made based on the inputs of 95% confidence level, 80% study power and 1:1 ratio between cases and controls. Expected prevalence figures of the exposure factors in cases and controls were taken from studies conducted elsewhere [10,19]. Ultimately, the available sample size was judged to be adequate to study the aforementioned determinants. Initially all the kebeles in the zone were listed and stratified into the three agro-ecological zones: lowlands, midlands and highlands. The total sample size was divided to the three strata proportionally to their population size. From each stratum, 6 kebeles were selected at random and the sample size for each stratum was distributed to the kebeles proportional to their population size. Ultimately 750 subjects were selected using systematic random sampling technique. The sampling frame for pregnant women was developed by having a house to house enumeration. Presumptive symptoms of pregnancy (ammenoria and/or change in the size of uterus) with subsequent pregnant urine test were used to diagnose pregnancy. A structured and pretested questionnaire used to assess potential determinants of ZD. The parts of the questionnaire on dietary diversity (DD) and household food insecurity level were adopted from Food and Nutrition Technical Assistance (FANTA) indicator guide for Household Dietary Diversity Score (HDDS) [20] and Household Food Insecurity Access Scale (HFIAS) [21], respectively. Other parts of the tool were developed by the principal investigators (PIs). The content validity of the questionnaire was assessed against the conceptual framework of the study. Reliability of the tool was checked using test-retest method. Questions with less than 0.7 kappa or Pearson coefficient values were removed or revised. Three trained and experienced enumerators collected the data. Interviews were made at the nearby health posts. The questionnaire was administered in local language. Height and weight were measured using calibrated Seca® scales and the measurements were registered to the nearest 0.1 cm and 0.1 kg, respectively. Altitude of the kebeles measured using Magilan® GPS system. Venous blood was collected using plain SARSTEDT Monovette® system and stainless steel needles. The blood was allowed to clot for 20 min and consecutively centrifuged at 3000 × g for 10 min. Visibly hemolyzed samples were discarded. Serum was extracted and transferred immediately into screw-top vials. The samples were kept and transported in icebox. The same day the samples were stored frozen at-20°C. Serum zinc concentration was determined at Ethiopian Health and Nutrition Research Institute using Varian SpectrAA® Flame Atomic Absorption Spectrometer. Zinc deficiency was defined as a serum zinc level of less than 56 μg/dl during the first trimester, or less than 50 μg/dl during the second or third trimester [22] Hemoglobin level was determined at the field using HemoCue Hb 301®. Anemia was defined as a hemoglobin level of less than 11.0 g/dl during the first or third trimester or less than 10.5 g/dl during the second trimester [23]. C-Reactive Protein (CRP) determined qualitatively using HumaTex CRP®. Data entry, screening and analysis were carried out by the PIs using SPSS 19.0. Descriptive analysis was done using mean, frequency and percentage. Independent t-test and one-way Analysis of Variance (ANOVA) used to compare serum zinc levels across categories of independent variables. The assumptions of ANOVA (normal distribution and homoscedasticity of the dependent variable across the categories of the independent variables) were checked to be fulfilled. Wealth index quintiles (poorest, poorer, middle, richer, and richest) were computed using Principal Component Analysis (PCA). The index was calculated based on ownership of selected household assets, size of agricultural land, quantity of livestock and materials used for housing construction. PCA was also applied to reduce variables pertaining to maternal workload. Logistic and linear regression analyses were used to control potential confounders. Independent variables which significantly associated (P < 0.05) to the dependent variable in simple regression models were exported to a multiple regression model for adjustment. In addition conceptually important confounders (like CRP status) were also adjusted. The major assumptions of logistic regression analysis (absence of multicollinearity and interaction among independent variables) and linear regression analysis (normally distributed error terms, linear relation between dependent and independent variables, homoscedasticity and absence of multicollinearity) were checked to be satisfied. The fitness of logistic and linear regression models were assessed using Hosmer-Lemeshow statistic and adjusted R squared value, respectively. Hemoglobin values were adjusted for altitude according to the formulae recommended by Center for Disease Prevention and Control (CDC) [24]. The study was conducted in confirmation of national and international ethical guidelines for biomedical research involving human subjects. Ethical clearance was obtained from the institutional review board of Addis Ababa University. Informed written consent was taken from the study subjects. Needle safety procedures were in line with WHO standard. Nutrition education was given to all subjects. Anemic women were given iron-folate supplementation.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women with information on prenatal nutrition, healthcare services, and access to resources. These apps could also include reminders for prenatal appointments and medication adherence.

2. Community Health Workers: Train and deploy community health workers to provide education and support to pregnant women in rural areas. These workers can conduct home visits, provide counseling on nutrition and healthcare, and connect women to local health services.

3. Telemedicine: Establish telemedicine services to enable pregnant women in remote areas to consult with healthcare professionals via video calls. This would allow for remote monitoring of maternal health and early detection of complications.

4. Nutritional Interventions: Implement programs that promote dietary diversity and increase access to nutrient-rich foods, such as fruits, vegetables, and animal source foods. This could involve community gardens, nutrition education, and support for income-generating activities.

5. Improved Antenatal Care: Strengthen antenatal care services by ensuring availability of essential supplies and equipment, training healthcare providers on best practices, and improving the physical access to health facilities.

6. Transportation Support: Address transportation barriers by providing transportation vouchers or subsidies for pregnant women to access healthcare facilities for prenatal care and delivery.

7. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve access to maternal health services. This could involve leveraging private sector resources and expertise to expand healthcare infrastructure and services in rural areas.

8. Health Information Systems: Develop and implement electronic health records and information systems to improve data collection, monitoring, and evaluation of maternal health services. This would enable better tracking of maternal health indicators and identification of areas for improvement.

9. Maternal Health Financing: Explore innovative financing mechanisms, such as health insurance schemes or microfinance programs, to reduce financial barriers and increase affordability of maternal health services.

10. Health Education and Awareness Campaigns: Conduct targeted health education campaigns to raise awareness about the importance of maternal health, prenatal care, and nutrition. These campaigns could use various media channels, including radio, television, and community gatherings.

It is important to note that the specific context and needs of the community should be taken into consideration when implementing these innovations.

AI Innovations Description

The study mentioned is titled “Prevalence of prenatal zinc deficiency and its association with socio-demographic, dietary and health care related factors in Rural Sidama, Southern Ethiopia: A cross-sectional study.” The study aimed to assess the prevalence and determinants of prenatal zinc deficiency in Sidama zone, Southern Ethiopia.

Based on the findings of the study, the following recommendations can be made to improve access to maternal health:

1. Increase awareness: Implement educational programs to raise awareness about the importance of prenatal nutrition, including the role of zinc in maternal health. This can be done through community health workers, antenatal care clinics, and other healthcare facilities.

2. Improve dietary diversity: Promote a diverse and balanced diet for pregnant women, including the consumption of animal source foods. This can be achieved through nutrition education programs and initiatives that focus on improving access to nutritious foods.

3. Address household food insecurity: Develop strategies to reduce household food insecurity, as it was found to be a significant factor associated with prenatal zinc deficiency. This can include interventions such as income-generating activities, agricultural support, and social safety nets.

4. Enhance antenatal care services: Strengthen antenatal care services by ensuring regular check-ups, providing iron and zinc supplementation, and offering nutrition education to pregnant women. This can be achieved through training healthcare providers, improving infrastructure, and increasing the availability of essential medications and supplements.

5. Implement phytate reduction food processing techniques: Explore household-based phytate reduction food processing techniques to enhance the bioavailability of zinc in staple foods. This can include methods such as soaking, fermentation, and germination, which can help improve the absorption of zinc from plant-based sources.

6. Promote livelihood promotion strategies: Implement livelihood promotion strategies to improve the economic status of pregnant women, as women devoid of self-income were found to have a higher risk of zinc deficiency. This can include vocational training, microfinance initiatives, and support for income-generating activities.

By implementing these recommendations, it is possible to improve access to maternal health and reduce the prevalence of prenatal zinc deficiency in Sidama zone, Southern Ethiopia.

AI Innovations Methodology

Based on the provided study, here are some potential recommendations for improving access to maternal health:

1. Increase awareness and education: Implement community-based education programs to raise awareness about the importance of maternal health and the risks associated with prenatal zinc deficiency. This can be done through health campaigns, workshops, and outreach programs.

2. Improve access to healthcare services: Enhance the availability and accessibility of healthcare services, particularly in rural areas. This can include establishing more health clinics, mobile health units, and telemedicine services to reach pregnant women in remote areas.

3. Strengthen antenatal care: Enhance the quality and coverage of antenatal care services by training healthcare providers on the importance of prenatal zinc supplementation and screening for zinc deficiency. This can also involve integrating nutritional counseling and support into antenatal care visits.

4. Promote dietary diversity: Encourage pregnant women to consume a diverse and balanced diet that includes foods rich in zinc, such as meat, fish, legumes, and nuts. This can be achieved through nutrition education programs and initiatives that promote local food production and availability.

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 key indicators that reflect access to maternal health, such as the number of pregnant women receiving antenatal care, the percentage of women with adequate zinc intake, and the prevalence of prenatal zinc deficiency.

2. Collect baseline data: Gather data on the current status of access to maternal health services and the prevalence of prenatal zinc deficiency in the target population. This can be done through surveys, interviews, and medical records review.

3. Develop a simulation model: Create a mathematical model that incorporates the identified indicators and their relationships. This model should consider factors such as population size, healthcare infrastructure, education levels, and dietary patterns.

4. Input intervention scenarios: Input the recommended interventions into the simulation model and assess their potential impact on the indicators. This can be done by adjusting relevant parameters, such as the coverage of antenatal care services or the percentage of pregnant women with adequate zinc intake.

5. Run simulations: Run multiple simulations using different intervention scenarios to evaluate their potential effects on improving access to maternal health. This can involve varying the intensity, duration, and coverage of the interventions to assess their effectiveness.

6. Analyze results: Analyze the simulation results to determine the potential impact of the interventions on the identified indicators. This can include comparing the outcomes of different intervention scenarios and identifying the most effective strategies for improving access to maternal health.

7. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data and expert input. This can help ensure the accuracy and reliability of the model for future use.

8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community leaders. This can help inform decision-making and guide the implementation of interventions to improve access to maternal health.

Author

Dima Health

Statistics:

Citations: 44

Identifiers:

DOI: 10.1186/1471-2458-11-898

Research Areas:

Community Interventions, Disparities, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Ethiopia

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female