Burden of disease among the world’s poorest billion people: An expert-informed secondary analysis of Global Burden of Disease estimates

PLoS ONE, Volume 16, No. 8 August, Year 2021

Interpretação

Background The health of populations living in extreme poverty has been a long-standing focus of global development efforts, and continues to be a priority during the Sustainable Development Goal era. However, there has not been a systematic attempt to quantify the magnitude and causes of the burden in this specific population for almost two decades. We estimated disease rates by cause for the world’s poorest billion and compared these rates to those in high-income populations. Methods We defined the population in extreme poverty using a multidimensional poverty index. We used national-level disease burden estimates from the 2017 Global Burden of Disease Study and adjusted these to account for within-country variation in rates. To adjust for within-country variation, we looked to the relationship between rates of extreme poverty and disease rates across countries. In our main modeling approach, we used these relationships when there was consistency with expert opinion from a survey we conducted of disease experts regarding the associations between household poverty and the incidence and fatality of conditions. Otherwise, no within-country variation was assumed. We compared results across multiple approaches for estimating the burden in the poorest billion, including aggregating national-level burden from the countries with the highest poverty rates. We examined the composition of the estimated disease burden among the poorest billion and made comparisons with estimates for high-income countries. Results The composition of disease burden among the poorest billion, as measured by disability-adjusted life years (DALYs), was 65% communicable, maternal, neonatal, and nutritional (CMNN) diseases, 29% non-communicable diseases (NCDs), and 6% injuries. Age-standardized DALY rates from NCDs were 44% higher in the poorest billion (23,583 DALYs per 100,000) compared to high-income regions (16,344 DALYs per 100,000). Age-standardized DALY rates were 2,147% higher for CMNN conditions (32,334 DALYs per 100,000) and 86% higher for injuries (4,182 DALYs per 100,000) in the poorest billion, compared to high-income regions. Conclusion The disease burden among the poorest people globally compared to that in high income countries is highly influenced by demographics as well as large disparities in burden from many conditions. The comparisons show that the largest disparities remain in communicable, maternal, neonatal, and nutritional diseases, though NCDs and injuries are an important part of the “unfinished agenda” of poor health among those living in extreme poverty. We downloaded publicly available data from the GBD Study 2017. The available data contain population, incidence, prevalence, deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) by causes of morbidity and mortality for 23 age groups, by sex, and in 195 countries and territories [1, 28, 29]. We used the estimates for the year 2017. For poverty estimates, we used a set of eight household-level indicators from the Multidimensional Poverty Index (MPI) created by the Oxford Poverty and Human Development Initiative (OPHI). Typically, the OPHI MPI is estimated by aggregating ten indicators in three dimensions including education, living standards, and health [30]. We excluded the health indicators because our analysis examined health as an outcome. We defined the poorest billion as those living in households deprived in five or more of the eight categories in our poverty index, which include child school attendance, highest educational attainment, electricity, sanitation, safe water, floor material, cooking fuel, and a set of assets (details in S1 Appendix, p 3). The data to classify populations according to these indicators came from representative country surveys (S1 Appendix, pp 4–11), and more detail on this multidimensional definition of poverty can be found elsewhere [31]. The prevalence of people in the poorest billion was estimated by sex and five-year age groups, ending with and including a group for those 80 years and older. This non-monetary approach had advantages in terms of capturing multiple dimensions of poverty, being derived from microdata that allowed for age- and sex-specific estimates, and being available across a broad set of countries [20]. The poverty indicators have clear theoretical links by which to influence disease—use of biomass fuels is associated with household air pollution, lack of sanitation and safe drinking water is linked to diarrhea and malnutrition, dirt floors provide environments for particular pathogens, maternal and childhood education have well established links to mortality, and certain household assets are linked with general resources and wealth, access to information, and mobility to access health care [20]. Prevalence of people in the poorest billion could be estimated in 105 countries from surveys since 2005. Ninety-one of these surveys (87%) were from 2010 or after. For countries with surveys prior to 2010, only Madagascar, Somalia, and Bolivia had more than one million people estimated in the poorest billion, based on 2017 populations. In addition, there were two low-income, seven lower-middle-income countries, and 22 upper-middle-income countries, according to the World Bank list of economies (calendar year 2017), in which surveys to create the poverty index were not available and in which disease burden data existed (S1 Appendix, pp 4–11) [32]. From these countries without surveys, we assumed that the prevalence of people with five or more deprivations was equal to the age- and sex-specific average by country income group among countries with surveys. This constituted less than 5% of the total population in the poorest billion. We defined the populations in high-income countries to be entirely outside the poorest billion. To define populations consistent with the GBD demographic and disease burden estimates, we applied the proportions of people in the poorest billion from the survey data in each age, sex, and location to the corresponding age-, sex-, and location-specific populations from the GBD study for the year 2017. We used the proportion of under-5-year-olds in the poorest billion as the proportion for the early neonatal (0–6 days), late neonatal (7–27 days), post neonatal (28 days to under 1 year), and 1–4 year old age groups from GBD, as well as the proportion of 80 year-olds and over in the poorest billion for the 80–84, 85–89, 90–94, 95 and older age groups. We found 838 million people with 5 or more deprivations on our poverty index across these countries. Including the low- and middle-income countries without surveys, we added 34 million additional people. In total, our “poorest billion” population contained 873 million people. Though this population was not precisely one billion, we refer to this population of interest as the poorest billion to remain consistent with literature describing this population. To create disease burden estimates for the poorest billion, we utilized GBD 2017 estimates. We employed five strategies to aggregate the burden for the world’s poorest populations. We report results from one method and the range across methods in the main text, and we show comparisons in the S1 Appendix (pp 37–119). We called the main approach presented in the results the Selective Ecological approach. The population in the poorest billion was defined as described using the household survey poverty data. Rather than using country-level burden estimates for both the poorest and non-poorest within each country (an approach we also took, S1 Appendix pp 12–13), we sought to inform estimates using ecological relationships between disease burden and poverty across countries. By age, sex, and cause, we conducted mixed-effects linear regressions predicting rates of death and YLDs across countries, using the prevalence of poorest billion population as a covariate and including a random effect for region to better isolate associations with poverty prevalence from other regional differences. We then made predictions from these models for hypothetical groups in which either 100% of the population was in the poorest billion or 0% was in the poorest billion. We scaled these estimates to the national-level GBD estimates in each country, such that the population-weighted average rate for the poorest and non-poorest in each country was consistent with the national-level estimate. We called this the Full Ecological approach (presented as one of the five approaches, S1 Appendix pp 12–15). Cross-country associations between disease rates and poverty are not necessarily consistent with the within-country associations. To understand how rates of disease may vary by socioeconomic status within LMICs to the best of our knowledge, we conducted a survey of perceived relationships between poverty and disease in LMICs among 97 health practitioners and researchers with a broad range of disease expertise and experience working in or researching health in LMICs. The S1 Appendix (pp 16–17) characterizes the participants in greater detail. Participants answered questions about their perception of relationships between rates of diseases and poverty within LMICs, indicating whether they thought occurrence (defined in survey instructions as incidence) rates and fatality (defined as case fatality) rates were (1) much higher in the non-poorest, (2) higher in the non-poorest, (3) not different, (4) higher in the poorest, or (5) much higher in the poorest. The survey also asked respondents how confident they were in their selection. To choose conditions to model using this Selective Ecological approach, we found the set of conditions with differences in rates between poorest billion and non-poorest from the expert perception survey and which showed agreement with the direction of the association in the ecological model in over half of age groups (S1 Appendix pp 16–19). To determine conditions that experts thought varied between the poorest billion and non-poorest, we treated the answers on the expert survey as ordinal and used a non-parametric one-sample two-sided sign test to test for a difference in the median from the “No Difference” response. We modeled each of these selected conditions except for those in which the number of countries with non-zero rates in a given age and sex group was too small (fewer than 10 countries) to create stable results. We used the national-level death and YLD rates for both the poorest and non-poorest within countries for the remaining conditions that did not have alignment between the expert survey and the observed ecological relationships. We conducted analysis at the lowest levels of the GBD hierarchies (most specific conditions and ages, and by sex) and aggregated to create internally consistent results. We report results for the poorest billion from the Selective Ecological approach, though we show results for other approaches in the S1 Appendix (pp 37–119) and discuss differences. These other approaches primarily differed in relation to assumptions about within-country burden gradients (assuming none or using ecological relationships to assume a gradient) and the definition of the poorest billion population (population in poorest countries or the household survey population definition). Uncertainty bounds are available on the online GBD results tool; however, random draws from the underlying distributions are used to propagate uncertainty. Without these draws, we were unable to propagate uncertainty when aggregating our results. In many cases, we present the resulting point estimate from the Selective Ecological approach and the range for all 5 approaches. While we conducted our analysis for the most specific age groups within the GBD up to the age group 95 and older, we group the ages above 80 when we present age-specific results because such a small proportion of the population in the poorest billion is above that age. We report rates, rate differences, and rate ratios of YLLs, YLDs, and DALYs to describe the overall levels as well as absolute and relative differences in morbidity and mortality between populations. We used the GBD 2017 age standard for age-standardized results [28]. Analyses were conducted using R version 3.3.1, 3.3.3, and 3.6.1 [33]. The study was submitted to the Harvard Faculty of Medicine IRB office and found exempt from further review (IRB17-0615).

AI Digest

Study Justification:

This study aimed to quantify the burden of disease among the world’s poorest billion people, which has not been systematically examined for almost two decades. Understanding the magnitude and causes of disease in this specific population is crucial for global development efforts and aligns with the Sustainable Development Goals.

Highlights:

– The study used a multidimensional poverty index to define the population in extreme poverty.
– Disease burden estimates from the 2017 Global Burden of Disease Study were adjusted to account for within-country variation in rates.
– The composition of disease burden among the poorest billion was found to be 65% communicable, maternal, neonatal, and nutritional diseases, 29% non-communicable diseases, and 6% injuries.
– Rates of non-communicable diseases were 44% higher in the poorest billion compared to high-income regions.
– Rates of communicable, maternal, neonatal, and nutritional diseases were 2,147% higher, and rates of injuries were 86% higher in the poorest billion compared to high-income regions.

Recommendations:

– Addressing the burden of disease among the world’s poorest billion should prioritize interventions targeting communicable, maternal, neonatal, and nutritional diseases.
– Efforts to reduce non-communicable diseases and injuries should also be part of the overall strategy to improve health outcomes in this population.

Key Role Players:

– Health practitioners and researchers with expertise in disease and poverty in low- and middle-income countries.
– Policy makers and government officials responsible for healthcare and poverty alleviation programs.
– Non-governmental organizations (NGOs) and international development agencies involved in global health initiatives.

Cost Items for Planning Recommendations:

– Funding for healthcare infrastructure and services in low- and middle-income countries.
– Investments in preventive measures, such as vaccination programs and access to clean water and sanitation.
– Support for maternal and child health programs, including prenatal care and nutrition interventions.
– Resources for education and awareness campaigns on disease prevention and healthy lifestyles.
– Capacity building and training for healthcare professionals in underserved areas.
– Research and development of affordable and effective treatments for communicable and non-communicable diseases.
– Monitoring and evaluation systems to track the impact of interventions and adjust strategies accordingly.
Please note that the cost items provided are general categories and not actual cost estimates. The specific budget requirements would depend on the context and scope of the interventions implemented.

Força da prova

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 utilized national-level disease burden estimates from the 2017 Global Burden of Disease Study and adjusted these to account for within-country variation in rates. They also conducted a survey of health practitioners and researchers to gather expert opinions on the relationship between poverty and disease. However, the abstract does not provide information on the sample size or representativeness of the survey participants, which could affect the reliability of the expert opinions. To improve the evidence, the authors could provide more details on the survey methodology and ensure a diverse and representative sample of participants. Additionally, the abstract does not mention any limitations or potential biases in the study, which should be addressed to enhance the overall strength of the evidence.

Resumo

We downloaded publicly available data from the GBD Study 2017. The available data contain population, incidence, prevalence, deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) by causes of morbidity and mortality for 23 age groups, by sex, and in 195 countries and territories [1, 28, 29]. We used the estimates for the year 2017. For poverty estimates, we used a set of eight household-level indicators from the Multidimensional Poverty Index (MPI) created by the Oxford Poverty and Human Development Initiative (OPHI). Typically, the OPHI MPI is estimated by aggregating ten indicators in three dimensions including education, living standards, and health [30]. We excluded the health indicators because our analysis examined health as an outcome. We defined the poorest billion as those living in households deprived in five or more of the eight categories in our poverty index, which include child school attendance, highest educational attainment, electricity, sanitation, safe water, floor material, cooking fuel, and a set of assets (details in S1 Appendix, p 3). The data to classify populations according to these indicators came from representative country surveys (S1 Appendix, pp 4–11), and more detail on this multidimensional definition of poverty can be found elsewhere [31]. The prevalence of people in the poorest billion was estimated by sex and five-year age groups, ending with and including a group for those 80 years and older. This non-monetary approach had advantages in terms of capturing multiple dimensions of poverty, being derived from microdata that allowed for age- and sex-specific estimates, and being available across a broad set of countries [20]. The poverty indicators have clear theoretical links by which to influence disease—use of biomass fuels is associated with household air pollution, lack of sanitation and safe drinking water is linked to diarrhea and malnutrition, dirt floors provide environments for particular pathogens, maternal and childhood education have well established links to mortality, and certain household assets are linked with general resources and wealth, access to information, and mobility to access health care [20]. Prevalence of people in the poorest billion could be estimated in 105 countries from surveys since 2005. Ninety-one of these surveys (87%) were from 2010 or after. For countries with surveys prior to 2010, only Madagascar, Somalia, and Bolivia had more than one million people estimated in the poorest billion, based on 2017 populations. In addition, there were two low-income, seven lower-middle-income countries, and 22 upper-middle-income countries, according to the World Bank list of economies (calendar year 2017), in which surveys to create the poverty index were not available and in which disease burden data existed (S1 Appendix, pp 4–11) [32]. From these countries without surveys, we assumed that the prevalence of people with five or more deprivations was equal to the age- and sex-specific average by country income group among countries with surveys. This constituted less than 5% of the total population in the poorest billion. We defined the populations in high-income countries to be entirely outside the poorest billion. To define populations consistent with the GBD demographic and disease burden estimates, we applied the proportions of people in the poorest billion from the survey data in each age, sex, and location to the corresponding age-, sex-, and location-specific populations from the GBD study for the year 2017. We used the proportion of under-5-year-olds in the poorest billion as the proportion for the early neonatal (0–6 days), late neonatal (7–27 days), post neonatal (28 days to under 1 year), and 1–4 year old age groups from GBD, as well as the proportion of 80 year-olds and over in the poorest billion for the 80–84, 85–89, 90–94, 95 and older age groups. We found 838 million people with 5 or more deprivations on our poverty index across these countries. Including the low- and middle-income countries without surveys, we added 34 million additional people. In total, our “poorest billion” population contained 873 million people. Though this population was not precisely one billion, we refer to this population of interest as the poorest billion to remain consistent with literature describing this population. To create disease burden estimates for the poorest billion, we utilized GBD 2017 estimates. We employed five strategies to aggregate the burden for the world’s poorest populations. We report results from one method and the range across methods in the main text, and we show comparisons in the S1 Appendix (pp 37–119). We called the main approach presented in the results the Selective Ecological approach. The population in the poorest billion was defined as described using the household survey poverty data. Rather than using country-level burden estimates for both the poorest and non-poorest within each country (an approach we also took, S1 Appendix pp 12–13), we sought to inform estimates using ecological relationships between disease burden and poverty across countries. By age, sex, and cause, we conducted mixed-effects linear regressions predicting rates of death and YLDs across countries, using the prevalence of poorest billion population as a covariate and including a random effect for region to better isolate associations with poverty prevalence from other regional differences. We then made predictions from these models for hypothetical groups in which either 100% of the population was in the poorest billion or 0% was in the poorest billion. We scaled these estimates to the national-level GBD estimates in each country, such that the population-weighted average rate for the poorest and non-poorest in each country was consistent with the national-level estimate. We called this the Full Ecological approach (presented as one of the five approaches, S1 Appendix pp 12–15). Cross-country associations between disease rates and poverty are not necessarily consistent with the within-country associations. To understand how rates of disease may vary by socioeconomic status within LMICs to the best of our knowledge, we conducted a survey of perceived relationships between poverty and disease in LMICs among 97 health practitioners and researchers with a broad range of disease expertise and experience working in or researching health in LMICs. The S1 Appendix (pp 16–17) characterizes the participants in greater detail. Participants answered questions about their perception of relationships between rates of diseases and poverty within LMICs, indicating whether they thought occurrence (defined in survey instructions as incidence) rates and fatality (defined as case fatality) rates were (1) much higher in the non-poorest, (2) higher in the non-poorest, (3) not different, (4) higher in the poorest, or (5) much higher in the poorest. The survey also asked respondents how confident they were in their selection. To choose conditions to model using this Selective Ecological approach, we found the set of conditions with differences in rates between poorest billion and non-poorest from the expert perception survey and which showed agreement with the direction of the association in the ecological model in over half of age groups (S1 Appendix pp 16–19). To determine conditions that experts thought varied between the poorest billion and non-poorest, we treated the answers on the expert survey as ordinal and used a non-parametric one-sample two-sided sign test to test for a difference in the median from the “No Difference” response. We modeled each of these selected conditions except for those in which the number of countries with non-zero rates in a given age and sex group was too small (fewer than 10 countries) to create stable results. We used the national-level death and YLD rates for both the poorest and non-poorest within countries for the remaining conditions that did not have alignment between the expert survey and the observed ecological relationships. We conducted analysis at the lowest levels of the GBD hierarchies (most specific conditions and ages, and by sex) and aggregated to create internally consistent results. We report results for the poorest billion from the Selective Ecological approach, though we show results for other approaches in the S1 Appendix (pp 37–119) and discuss differences. These other approaches primarily differed in relation to assumptions about within-country burden gradients (assuming none or using ecological relationships to assume a gradient) and the definition of the poorest billion population (population in poorest countries or the household survey population definition). Uncertainty bounds are available on the online GBD results tool; however, random draws from the underlying distributions are used to propagate uncertainty. Without these draws, we were unable to propagate uncertainty when aggregating our results. In many cases, we present the resulting point estimate from the Selective Ecological approach and the range for all 5 approaches. While we conducted our analysis for the most specific age groups within the GBD up to the age group 95 and older, we group the ages above 80 when we present age-specific results because such a small proportion of the population in the poorest billion is above that age. We report rates, rate differences, and rate ratios of YLLs, YLDs, and DALYs to describe the overall levels as well as absolute and relative differences in morbidity and mortality between populations. We used the GBD 2017 age standard for age-standardized results [28]. Analyses were conducted using R version 3.3.1, 3.3.3, and 3.6.1 [33]. The study was submitted to the Harvard Faculty of Medicine IRB office and found exempt from further review (IRB17-0615).

Materiais

Inovações

The study mentioned in the provided text focuses on the burden of disease among the world’s poorest billion people and highlights the disparities in disease rates between this population and high-income regions. While the text does not explicitly mention innovations for improving access to maternal health, we can make potential recommendations based on the information provided:

1. Mobile Health (mHealth) Solutions: Utilize mobile technology to provide maternal health information, reminders, and access to healthcare services. This can include SMS/text message reminders for prenatal care appointments, educational videos or audio messages on maternal health topics, and telemedicine consultations.

2. Community Health Workers: Train and deploy community health workers to provide maternal health services and education in remote or underserved areas. These workers can conduct prenatal visits, provide antenatal care, assist with childbirth, and offer postnatal support.

3. Telemedicine: Implement telemedicine platforms to connect pregnant women in remote areas with healthcare providers. This allows for remote consultations, monitoring of high-risk pregnancies, and timely access to medical advice.

4. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to pregnant women for accessing maternal health services. These vouchers can cover the cost of prenatal care, delivery, and postnatal care, ensuring that women can afford essential healthcare services.

5. Maternal Health Education Programs: Develop and implement comprehensive maternal health education programs that target women, families, and communities. These programs can focus on promoting healthy behaviors during pregnancy, childbirth, and postpartum, as well as raising awareness about the importance of skilled birth attendance and emergency obstetric care.

It’s important to note that these recommendations are based on general innovations in maternal health and may not directly align with the specific findings of the mentioned study. Further research and analysis would be required to identify the most effective innovations for improving access to maternal health in the context of the study.

AI Innovations Description

The recommendation to improve access to maternal health based on the provided information is to focus on addressing the burden of communicable, maternal, neonatal, and nutritional (CMNN) diseases among the world’s poorest billion people. This can be achieved through the following strategies:

1. Strengthening healthcare systems: Invest in improving healthcare infrastructure, including facilities, equipment, and trained healthcare professionals, in regions with high poverty rates. This will ensure that pregnant women have access to quality maternal healthcare services.

2. Increasing awareness and education: Implement comprehensive maternal health education programs that target communities living in extreme poverty. These programs should focus on raising awareness about the importance of antenatal care, skilled birth attendance, and postnatal care, as well as promoting healthy behaviors during pregnancy.

3. Enhancing access to essential maternal health services: Ensure that essential maternal health services, such as prenatal care, skilled birth attendance, emergency obstetric care, and postnatal care, are available and accessible to women living in extreme poverty. This may involve establishing mobile clinics or outreach programs to reach remote and underserved areas.

4. Addressing social determinants of health: Recognize and address the social determinants of poor maternal health outcomes among the poorest billion, such as inadequate nutrition, lack of access to clean water and sanitation, and limited education. Implement interventions that target these underlying factors to improve maternal health outcomes.

5. Collaboration and partnerships: Foster collaboration between governments, non-governmental organizations, and international agencies to mobilize resources and coordinate efforts to improve access to maternal health services. This can include sharing best practices, leveraging funding opportunities, and advocating for policy changes that prioritize maternal health.

By implementing these recommendations, it is possible to develop innovative solutions that can significantly improve access to maternal health for the world’s poorest billion people.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals in areas with high poverty rates can improve access to maternal health services.

2. Mobile health clinics: Implementing mobile health clinics that travel to remote and underserved areas can provide essential maternal health services to women who have limited access to healthcare facilities.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services, education, and support in their communities can help bridge the gap in access to maternal healthcare.

4. Telemedicine: Utilizing telemedicine technologies to provide remote consultations, prenatal care, and monitoring can improve access to maternal health services, especially in areas with limited healthcare resources.

5. Maternal health education: Implementing comprehensive maternal health education programs that target women, families, and communities can increase awareness about the importance of maternal health and encourage early and regular prenatal care.

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 to measure the impact of the recommendations, such as the number of women accessing prenatal care, the number of skilled birth attendants present during deliveries, and the reduction in maternal mortality rates.

2. Data collection: Collect baseline data on the selected indicators in the target population. This can be done through surveys, interviews, and existing health records.

3. Intervention implementation: Implement the recommended interventions in specific areas or communities. Ensure proper monitoring and evaluation mechanisms are in place to track the progress and effectiveness of the interventions.

4. Data analysis: Analyze the data collected post-intervention to assess the impact of the recommendations on the selected indicators. Compare the post-intervention data with the baseline data to determine the extent of improvement.

5. Statistical modeling: Use statistical modeling techniques to simulate the impact of scaling up the interventions to larger populations or different geographical areas. This can help estimate the potential impact on access to maternal health services.

6. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the results and identify potential limitations or uncertainties in the methodology.

7. Policy recommendations: Based on the findings, provide evidence-based policy recommendations to scale up successful interventions and address any gaps or challenges identified during the simulation.

It is important to note that the methodology described above is a general framework and can be tailored to specific contexts and resources available for data collection and analysis.

Autor

Dima Health

Statistics:

Citations: 4

Identifiers:

DOI: 10.1371/journal.pone.0253073

Research Areas:

Disability, Disparities, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Social Determinants, Violence and Injury

Study Countries:

Madagascar, Multi-Countries, Somalia

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

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