National and regional estimates of term and preterm babies born small for gestational age in 138 low-income and middle-income countries in 2010

The Lancet Global Health, Volume 1, No. 1, Year 2013

Interpretation

Background: National estimates for the numbers of babies born small for gestational age and the comorbidity with preterm birth are unavailable. We aimed to estimate the prevalence of term and preterm babies born small for gestational age (term-SGA and preterm-SGA), and the relation to low birthweight (<2500 g), in 138 countries of low and middle income in 2010. Methods: Small for gestational age was defined as lower than the 10th centile for fetal growth from the 1991 US national reference population. Data from 22 birth cohort studies (14 low-income and middle-income countries) and from the WHO Global Survey on Maternal and Perinatal Health (23 countries) were used to model the prevalence of term-SGA births. Prevalence of preterm-SGA infants was calculated from meta-analyses. Findings: In 2010, an estimated 32·4 million infants were born small for gestational age in low-income and middle-income countries (27% of livebirths), of whom 10·6 million infants were born at term and low birthweight. The prevalence of term-SGA babies ranged from 5·3% of livebirths in east Asia to 41·5% in south Asia, and the prevalence of preterm-SGA infants ranged from 1·2% in north Africa to 3·0% in southeast Asia. Of 18 million low-birthweight babies, 59% were term-SGA and 41% were preterm-SGA. Two-thirds of small-for-gestational-age infants were born in Asia (17·4 million in south Asia). Preterm-SGA babies totalled 2·8 million births in low-income and middle-income countries. Most small-for-gestational-age infants were born in India, Pakistan, Nigeria, and Bangladesh. Interpretation: The burden of small-for-gestational-age births is very high in countries of low and middle income and is concentrated in south Asia. Implementation of effective interventions for babies born too small or too soon is an urgent priority to increase survival and reduce disability, stunting, and non-communicable diseases. Funding: Bill & Melinda Gates Foundation by a grant to the US Fund for UNICEF to support the activities of the Child Health Epidemiology Reference Group (CHERG). © 2013 Lee et al. We defined small for gestational age as a birthweight lower than the 10th centile for a specific completed gestational age by gender, using the Alexander reference population8 (US National Center for Health Statistics, 1991; n=3 134 879 livebirths). We defined term-SGA as a baby born small for gestational age at 37 or more completed weeks of gestation, and we classified preterm-SGA as infants born small for gestational age at fewer than 37 weeks of gestation. We defined low birthweight as a baby born weighing less than 2500 g. Finally, we defined appropriate for gestational age as a birthweight on or higher than the 10th centile for gestational age, using the Alexander reference. We obtained data from three sources: (1) systematic literature reviews to identify birth cohorts with information on birthweight and gestational age; (2) research networks of birth cohorts; and (3) the WHO Global Survey on Maternal and Perinatal Health.9 We considered datasets for inclusion if they contained information on: birthweight; gestational age measured by last menstrual period, ultrasound, or clinical assessment; and vital status (required for analyses described elsewhere).10 Our exclusion criteria were: datasets missing more than 25% of data for birthweight or gestational age; inaccurate gestational age ascertained by study investigators (ie, poorly linked gestational age–birthweight data, or gestational age reported in months); and gestational age established by symphysis fundal height. We only included weight in analyses if the measurement was made within 72 h of birth. We initially searched Medline and WHO regional databases (LILACS, AIM, and EMRO) in September, 2009, to identify potential birth cohorts with data required for a larger scope of secondary analyses related to preterm birth and small-for-gestational-age-related mortality (appendix p 1).10 We identified additional datasets within maternal-neonatal research networks (ongoing maternal-newborn health studies, demographic surveillance sites, and WHO UNIMAPP11 studies). We contacted investigators to ascertain whether their studies met our inclusion criteria and, if so, we asked them to join the Child Health Epidemiology Reference Group (CHERG) SGA-Preterm Birth working group and contribute data for secondary analyses (appendix pp 2–4).10 We did another literature review in February, 2012, to identify published studies reporting the prevalence of both small-for-gestational-age births, using the Alexander reference,8 and low birthweight to use for statistical modelling, since low birthweight was the primary independent modelling predictor. We implemented two strategies: (1) a Medline search using terms (“small-for-gestational-age” OR “intrauterine growth restriction”) AND “low birthweight” AND (“incidence” OR “prevalence”) AND “developing country”; and (2) a Scopus search identifying all published articles that have cited small for gestational age using the Alexander reference.8 Further details on our search strategy are in the appendix (p 5). We also analysed data from the WHO Global Survey on Maternal and Perinatal Health (appendix p 6),9 which gathered data between 2004 and 2008 from 373 facilities in 24 countries and included 290 610 births. We excluded data from Japan (n=3318) because it is a high-income country. Therefore, a total of 23 countries contributed to the analysis. Details of survey methods are reported elsewhere.9 The WHO Global Survey selected countries randomly from every WHO subregion and then picked facilities at random from the capital city and two other randomly selected provinces. For this facility-based survey, trained data collectors abstracted relevant data from medical records into standardised forms from all births in the facility over a specific period. Several facilities had data with improbable values or unrepresentative data. To exclude these poor data-quality facilities, we omitted those with fewer than 500 births (small sample size), preterm birth rates greater than 40% or less than 3% (outside biological plausibility range), or rates of low birthweight less than 1% (implausible). We aggregated data at the country level. Datasets analysed by the original study investigators were approved by existing site institutional review boards. For datasets shared with the CHERG working group, personal identifiers were not included and, therefore, were deemed exempt by the Johns Hopkins Bloomberg School of Public Health institutional review board. In the first step of the estimation process, we developed a model to estimate the national prevalence of term-SGA, based on the included input data. We then estimated the prevalence of preterm-SGA, using meta-analytical methods, and we applied these proportions to recent national preterm birth estimates developed by members of our working group.2 We used Stata 11.0 for all analyses. We did random-effects regression with logit(term-SGA prevalence) as the dependent variable and study region as the clustered unit of analysis (appendix p 7). Variables tested included: biological factors (prevalence of low birthweight, neonatal mortality rate); health-care access (proportion of deliveries in a facility, proportion of births by caesarean section, proportion of mothers with more than four antenatal care visits); and demographic factors (proportion of cohort in highest risk categories of maternal age, parity, and maternal education). We created categorical dummy variables for: degree of selection bias (population-based or community-level recruitment, facility-based or antenatal care recruitment with some or minimum selection bias, tertiary care or referral facility); study decade; and method of assessment of gestational age (last menstrual period, ultrasound, clinical). To examine candidate models, we included the natural log of low-birthweight prevalence as the main predictor, added individual predictors, and assessed for significance (p<0·05), improvement in adjusted R2, and Akaike information criterion. To select the final model, we did cross-validation12 to compare prediction accuracy between potential models (appendix p 8). We undertook sensitivity analyses using two datasets. In the first (modelling dataset A, n=45), we included all birth cohort data. In the second restricted dataset (modelling dataset B, n=20), we included only population-representative studies, excluding facility-based studies that might have selection bias (WHO studies,9 Pakistan Aga Khan University [ZAB], Uganda 200513). Both datasets A and B resulted in similar estimates of variables and model fit; thus, we present results of the larger dataset A, which enabled cross-validation. We also did multiple imputation14 to incorporate infants with missing birthweight back into the individual cohort studies (appendix p 9). For every cohort in dataset A, we calculated the prevalence of small-for-gestational-age babies within two categories of preterm births: moderate-to-late preterm (between 32 weeks and <37 weeks of gestation) and early preterm (<32 weeks of gestation). We used random-effects meta-analyses to estimate the pooled regional and overall prevalence of infants born small for gestational age between 32 weeks and less than 37 weeks of gestation and those born at less than 32 weeks of gestation. We did sensitivity analyses to look at the effect of region, facility-based versus community-based recruitment, and study quality. We judged studies of a high quality to be those with population-based recruitment, adequate data capture (defined as missing <15% of birthweight data and <30% of birthweight data among neonatal deaths, and the proportion of infants born at <32 weeks comprising at least 5% of preterm births), and biological plausibility (prevalence of small for gestational age >1%). We used the prediction model to estimate term-SGA prevalence in countries of low and middle income (UN Millennium Development Goal [MDG] classification) for the year 2010. We took national neonatal mortality rates from the UN Interagency Group for Child Mortality Estimation15 and low-birthweight rates from several sources (appendix p 10). To obtain the number of small-for-gestational-age liveborn infants, we multiplied the prevalence of term and preterm small for gestational age by the estimated number of livebirths for 2010.16 We used a bootstrap approach to estimate ranges of uncertainty (appendix p 11). In every dataset, we calculated the proportion of term-SGA infants who were low birthweight and did meta-analyses, using random effects to pool the estimate at the major regional level. We multiplied this value by term-SGA estimates for every country and summarised them regionally. The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

AI Digest

Study Justification:

This study aimed to estimate the prevalence of term and preterm babies born small for gestational age (SGA) in low-income and middle-income countries. The study was conducted because national estimates for these numbers were previously unavailable. Understanding the prevalence of SGA babies is important for identifying the burden of this condition and its comorbidity with low birthweight. This information can help inform interventions to improve the survival and health outcomes of these babies.

Highlights:

– In 2010, an estimated 32.4 million infants were born SGA in low-income and middle-income countries, accounting for 27% of livebirths.
– Of these SGA infants, 10.6 million were born at term and low birthweight.
– The prevalence of term-SGA babies ranged from 5.3% in east Asia to 41.5% in south Asia.
– The prevalence of preterm-SGA infants ranged from 1.2% in north Africa to 3.0% in southeast Asia.
– Two-thirds of SGA infants were born in Asia, with 17.4 million in south Asia.
– Most low-birthweight babies were term-SGA (59%) rather than preterm-SGA (41%).
– The burden of SGA births is highest in countries of low and middle income, particularly in south Asia.

Recommendations:

– Implementation of effective interventions for babies born SGA or preterm-SGA is an urgent priority.
– These interventions should aim to increase survival, reduce disability, stunting, and non-communicable diseases.
– Policy makers should prioritize allocating resources to address the high burden of SGA births in low-income and middle-income countries, particularly in south Asia.

Key Role Players:

– Researchers and scientists in the field of maternal and child health
– Government health departments and ministries
– Non-governmental organizations (NGOs) working in maternal and child health
– Healthcare providers, including doctors, nurses, and midwives
– Community health workers and volunteers
– International organizations, such as the World Health Organization (WHO) and United Nations Children’s Fund (UNICEF)

Cost Items for Planning Recommendations:

– Funding for research and data collection on SGA births
– Development and implementation of interventions for SGA and preterm-SGA babies
– Training and capacity building for healthcare providers and community health workers
– Health system strengthening to improve access to quality maternal and child healthcare services
– Monitoring and evaluation of interventions and outcomes
– Advocacy and awareness campaigns to raise awareness about SGA and its impact on health
Please note that the cost items provided are general categories and not actual cost estimates. The specific cost of implementing recommendations will vary depending on the context and resources available in each country.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is based on data from 22 birth cohort studies and the WHO Global Survey on Maternal and Perinatal Health, which provides a solid foundation for the estimates. However, the abstract does not provide specific details about the methodology used in the studies or the statistical analysis performed. To improve the strength of the evidence, the authors could provide more information about the study design, data collection methods, and statistical modeling techniques used. Additionally, including references to the original studies and providing access to the full dataset would allow for further scrutiny and replication of the findings.

Abstract

We defined small for gestational age as a birthweight lower than the 10th centile for a specific completed gestational age by gender, using the Alexander reference population8 (US National Center for Health Statistics, 1991; n=3 134 879 livebirths). We defined term-SGA as a baby born small for gestational age at 37 or more completed weeks of gestation, and we classified preterm-SGA as infants born small for gestational age at fewer than 37 weeks of gestation. We defined low birthweight as a baby born weighing less than 2500 g. Finally, we defined appropriate for gestational age as a birthweight on or higher than the 10th centile for gestational age, using the Alexander reference. We obtained data from three sources: (1) systematic literature reviews to identify birth cohorts with information on birthweight and gestational age; (2) research networks of birth cohorts; and (3) the WHO Global Survey on Maternal and Perinatal Health.9 We considered datasets for inclusion if they contained information on: birthweight; gestational age measured by last menstrual period, ultrasound, or clinical assessment; and vital status (required for analyses described elsewhere).10 Our exclusion criteria were: datasets missing more than 25% of data for birthweight or gestational age; inaccurate gestational age ascertained by study investigators (ie, poorly linked gestational age–birthweight data, or gestational age reported in months); and gestational age established by symphysis fundal height. We only included weight in analyses if the measurement was made within 72 h of birth. We initially searched Medline and WHO regional databases (LILACS, AIM, and EMRO) in September, 2009, to identify potential birth cohorts with data required for a larger scope of secondary analyses related to preterm birth and small-for-gestational-age-related mortality (appendix p 1).10 We identified additional datasets within maternal-neonatal research networks (ongoing maternal-newborn health studies, demographic surveillance sites, and WHO UNIMAPP11 studies). We contacted investigators to ascertain whether their studies met our inclusion criteria and, if so, we asked them to join the Child Health Epidemiology Reference Group (CHERG) SGA-Preterm Birth working group and contribute data for secondary analyses (appendix pp 2–4).10 We did another literature review in February, 2012, to identify published studies reporting the prevalence of both small-for-gestational-age births, using the Alexander reference,8 and low birthweight to use for statistical modelling, since low birthweight was the primary independent modelling predictor. We implemented two strategies: (1) a Medline search using terms (“small-for-gestational-age” OR “intrauterine growth restriction”) AND “low birthweight” AND (“incidence” OR “prevalence”) AND “developing country”; and (2) a Scopus search identifying all published articles that have cited small for gestational age using the Alexander reference.8 Further details on our search strategy are in the appendix (p 5). We also analysed data from the WHO Global Survey on Maternal and Perinatal Health (appendix p 6),9 which gathered data between 2004 and 2008 from 373 facilities in 24 countries and included 290 610 births. We excluded data from Japan (n=3318) because it is a high-income country. Therefore, a total of 23 countries contributed to the analysis. Details of survey methods are reported elsewhere.9 The WHO Global Survey selected countries randomly from every WHO subregion and then picked facilities at random from the capital city and two other randomly selected provinces. For this facility-based survey, trained data collectors abstracted relevant data from medical records into standardised forms from all births in the facility over a specific period. Several facilities had data with improbable values or unrepresentative data. To exclude these poor data-quality facilities, we omitted those with fewer than 500 births (small sample size), preterm birth rates greater than 40% or less than 3% (outside biological plausibility range), or rates of low birthweight less than 1% (implausible). We aggregated data at the country level. Datasets analysed by the original study investigators were approved by existing site institutional review boards. For datasets shared with the CHERG working group, personal identifiers were not included and, therefore, were deemed exempt by the Johns Hopkins Bloomberg School of Public Health institutional review board. In the first step of the estimation process, we developed a model to estimate the national prevalence of term-SGA, based on the included input data. We then estimated the prevalence of preterm-SGA, using meta-analytical methods, and we applied these proportions to recent national preterm birth estimates developed by members of our working group.2 We used Stata 11.0 for all analyses. We did random-effects regression with logit(term-SGA prevalence) as the dependent variable and study region as the clustered unit of analysis (appendix p 7). Variables tested included: biological factors (prevalence of low birthweight, neonatal mortality rate); health-care access (proportion of deliveries in a facility, proportion of births by caesarean section, proportion of mothers with more than four antenatal care visits); and demographic factors (proportion of cohort in highest risk categories of maternal age, parity, and maternal education). We created categorical dummy variables for: degree of selection bias (population-based or community-level recruitment, facility-based or antenatal care recruitment with some or minimum selection bias, tertiary care or referral facility); study decade; and method of assessment of gestational age (last menstrual period, ultrasound, clinical). To examine candidate models, we included the natural log of low-birthweight prevalence as the main predictor, added individual predictors, and assessed for significance (p<0·05), improvement in adjusted R2, and Akaike information criterion. To select the final model, we did cross-validation12 to compare prediction accuracy between potential models (appendix p 8). We undertook sensitivity analyses using two datasets. In the first (modelling dataset A, n=45), we included all birth cohort data. In the second restricted dataset (modelling dataset B, n=20), we included only population-representative studies, excluding facility-based studies that might have selection bias (WHO studies,9 Pakistan Aga Khan University [ZAB], Uganda 200513). Both datasets A and B resulted in similar estimates of variables and model fit; thus, we present results of the larger dataset A, which enabled cross-validation. We also did multiple imputation14 to incorporate infants with missing birthweight back into the individual cohort studies (appendix p 9). For every cohort in dataset A, we calculated the prevalence of small-for-gestational-age babies within two categories of preterm births: moderate-to-late preterm (between 32 weeks and <37 weeks of gestation) and early preterm (<32 weeks of gestation). We used random-effects meta-analyses to estimate the pooled regional and overall prevalence of infants born small for gestational age between 32 weeks and less than 37 weeks of gestation and those born at less than 32 weeks of gestation. We did sensitivity analyses to look at the effect of region, facility-based versus community-based recruitment, and study quality. We judged studies of a high quality to be those with population-based recruitment, adequate data capture (defined as missing <15% of birthweight data and <30% of birthweight data among neonatal deaths, and the proportion of infants born at 1%). We used the prediction model to estimate term-SGA prevalence in countries of low and middle income (UN Millennium Development Goal [MDG] classification) for the year 2010. We took national neonatal mortality rates from the UN Interagency Group for Child Mortality Estimation15 and low-birthweight rates from several sources (appendix p 10). To obtain the number of small-for-gestational-age liveborn infants, we multiplied the prevalence of term and preterm small for gestational age by the estimated number of livebirths for 2010.16 We used a bootstrap approach to estimate ranges of uncertainty (appendix p 11). In every dataset, we calculated the proportion of term-SGA infants who were low birthweight and did meta-analyses, using random effects to pool the estimate at the major regional level. We multiplied this value by term-SGA estimates for every country and summarised them regionally. The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Materials

https://efashare.b-cdn.net/materials/317564bcd24b6d61af87fbeedde1976afa29d899fa921631122f566906fa90fd/mmc1.pdf

Innovations

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

1. Telemedicine: Implementing telemedicine programs that allow pregnant women in remote or underserved areas to access prenatal care and consultations with healthcare providers remotely through video conferencing or phone calls.

2. Mobile health (mHealth) applications: Developing mobile applications that provide pregnant women with information about prenatal care, nutrition, and warning signs during pregnancy. These apps can also send reminders for appointments and medication schedules.

3. Community health workers: Training and deploying community health workers to provide education, support, and basic prenatal care to pregnant women in rural or marginalized communities where access to healthcare facilities is limited.

4. Maternal health clinics: Establishing dedicated maternal health clinics in areas with high maternal mortality rates. These clinics would provide comprehensive prenatal care, including regular check-ups, screenings, and access to essential medications.

5. Transportation services: Improving transportation infrastructure and providing transportation services to ensure that pregnant women can easily access healthcare facilities for prenatal care and emergency obstetric services.

6. Maternity waiting homes: Setting up maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away. These homes provide a safe place for women to stay during the last weeks of pregnancy, ensuring they are close to the facility when labor begins.

7. Mobile clinics: Deploying mobile clinics equipped with medical staff and necessary equipment to reach remote areas and provide prenatal care, screenings, and emergency obstetric services.

8. Health education campaigns: Conducting targeted health education campaigns to raise awareness about the importance of prenatal care, nutrition, and early detection of complications during pregnancy. These campaigns can be conducted through various channels, including radio, television, and community outreach programs.

9. Task-shifting: Training and empowering non-physician healthcare providers, such as nurses and midwives, to perform certain tasks traditionally done by doctors. This can help alleviate the shortage of healthcare professionals and improve access to prenatal care.

10. Public-private partnerships: Collaborating with private healthcare providers and organizations to expand access to maternal health services, especially in underserved areas. This can involve subsidizing services, providing training, and improving infrastructure.

It is important to note that the implementation of these innovations should be context-specific and tailored to the unique needs and challenges of each community or region.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health and address the issue of small-for-gestational-age births in low-income and middle-income countries is to implement effective interventions for babies born too small or too soon. These interventions should focus on increasing survival rates, reducing disability, stunting, and non-communicable diseases associated with small-for-gestational-age births.

Some potential interventions that can be considered include:

1. Improving access to quality antenatal care: Ensuring that pregnant women have access to regular check-ups, screenings, and appropriate medical interventions can help identify and manage conditions that may contribute to small-for-gestational-age births.

2. Promoting proper nutrition during pregnancy: Providing education and support to pregnant women on the importance of a balanced diet and adequate nutrition can help improve fetal growth and reduce the risk of small-for-gestational-age births.

3. Enhancing maternal healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals in low-income and middle-income countries can help improve the quality and accessibility of maternal healthcare services.

4. Strengthening health information systems: Developing robust data collection and reporting systems can help monitor and track the prevalence of small-for-gestational-age births, identify high-risk populations, and evaluate the effectiveness of interventions.

5. Implementing community-based interventions: Engaging local communities and community health workers in promoting maternal health and providing education and support to pregnant women can help increase awareness and access to appropriate care.

It is important to note that these recommendations should be tailored to the specific context and needs of each country and should be implemented in collaboration with relevant stakeholders, including governments, healthcare providers, non-governmental organizations, and international partners.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals in low-income and middle-income countries can improve access to maternal health services. This includes establishing well-equipped maternity clinics, increasing the number of skilled birth attendants, and ensuring the availability of essential medical supplies.

2. Promoting community-based interventions: Implementing community-based interventions, such as mobile clinics and community health workers, can help reach remote areas where access to healthcare facilities is limited. These interventions can provide prenatal care, education on maternal health, and facilitate referrals for high-risk pregnancies.

3. Improving transportation and logistics: Enhancing transportation systems and logistics can address the geographical barriers that hinder pregnant women from accessing healthcare facilities. This can involve providing ambulances or transportation vouchers to pregnant women in need, especially in rural areas.

4. Increasing awareness and education: Conducting awareness campaigns and educational programs can empower women and their families with knowledge about the importance of maternal health and the available services. This can help reduce cultural and social barriers that prevent women from seeking timely and appropriate care.

5. Strengthening health information systems: Developing robust health information systems can help track maternal health indicators, identify gaps in service delivery, and monitor progress towards improving access to maternal health. This can inform evidence-based decision-making and resource allocation.

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

1. Baseline data collection: Gather data on the current state of maternal health access, including indicators such as the number of maternal deaths, percentage of births attended by skilled birth attendants, and availability of healthcare facilities in different regions.

2. Define simulation parameters: Determine the specific variables and parameters that will be used to simulate the impact of the recommendations. This could include factors such as the number of healthcare facilities to be established, the number of community health workers to be trained, and the expected increase in transportation availability.

3. Model development: Develop a simulation model that incorporates the baseline data and the defined parameters. This model should simulate the impact of the recommendations on the selected indicators of maternal health access.

4. Data analysis: Run the simulation model using different scenarios to assess the potential impact of each recommendation. Analyze the results to determine the projected changes in maternal health access indicators.

5. Sensitivity analysis: Conduct sensitivity analysis to test the robustness of the simulation model by varying the input parameters and assessing the impact on the results. This helps identify the most influential factors and potential limitations of the recommendations.

6. Interpretation and reporting: Interpret the simulation results and provide clear and concise summaries of the projected impact of the recommendations on improving access to maternal health. Present the findings in a report or presentation format to stakeholders and policymakers for informed decision-making.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data.

Author

Dima Health

Statistics:

Citations: 496

Identifiers:

DOI: 10.1016/S2214-109X(13)70006-8

Research Areas:

Community Interventions, Disability, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Sexual and Reproductive Health, Social Determinants

Study Countries:

Multi-Countries, Nigeria, Uganda

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Mixed-methods, Quantitative, Systematic Review