Analysis of the situation of socio economic factors affecting health and disease in vietnam before and during the covid Phân tích thực trạng các yếu tố kinh tế xã hội ảnh hưởng đến sức khỏe và bệnh tật ở Việt Nam trước và trong thời kỳ Covid
OVERVIEW
Reasons for choosing the topic
Vietnam has seen significant improvements in public health, with notable advancements in life expectancy, reduced infant and child mortality rates under five, and lower malnutrition levels compared to previous years However, the Central Highlands, Northern Midlands, and mountainous areas still face greater health challenges than other regions.
Regional and demographic differences significantly influence health indicators, particularly due to varying illness patterns and healthcare needs across age groups The aging population, especially those aged 65 and older, is likely to increase the burden of non-communicable diseases such as diabetes, cancer, and cardiovascular conditions Additionally, while socio-economic development can lead to higher pollution levels and unhealthy lifestyles, it also enhances nutrition and access to health information and services, positively affecting overall health However, disparities in healthcare access and population distribution across regions with differing economic development levels continue to impact health outcomes.
Vietnam's economic system has achieved remarkable milestones, with consistent growth rates around 8% in recent years The poverty rate has dramatically decreased from 58% in 1993 to 11.3% in 2009 and under 4% in 2019, while average household income and living conditions have significantly improved Over the past thirty years, Vietnam has navigated a challenging reconstruction period, facing external disruptions such as the Asian financial crisis of 1997 and the global financial crises of 2008 Despite these challenges, the economy has demonstrated impressive resilience and continued development However, the unique epidemiological events of 2020 have posed significant impacts on Vietnam, mirroring global trends.
[3] A core tenet of the healthcare sector is patient safety To improve health care for the entire population, slow the spread of illness in the community, and contribute to
The Prime Minister has authorized a comprehensive plan aimed at preventing both communicable and non-communicable diseases from 2015 to 2025, which is essential for the country's socioeconomic development and citizens' access to healthcare A robust health system must prioritize patient safety by enhancing medical care standards, ensuring environmental security, and effectively managing risks such as infection control and the safe use of medications and equipment To provide high-quality medical care, countries need to establish protocols that prevent medical accidents and incorporate lessons learned from past incidents, with contributions from patients, hospitals, and healthcare professionals.
Socioeconomic status (SES) is a significant indicator of health conditions, typically assessed through a combination of factors such as education, wealth, housing, and employment Researchers can estimate the importance of these variables using methods like principal component analysis or by assigning them equal weights SES is recognized as a crucial factor affecting global health, particularly by organizations like UNICEF Malnutrition is a leading cause of high child mortality rates in developing countries, and studies, including one by Dr Chao et al in 2018, indicate that wealth disparities contribute to increased child death rates, while rates among high-income groups are on the decline.
The Covid-19 pandemic has exposed significant shortcomings in the Vietnamese government's planning, policy-making, and resource allocation, particularly with the emergence of new variants like Delta and Omicron, posing a serious challenge to the health system The novelty of these pathogens necessitates adaptive responses to alleviate the pressure on healthcare Global research has highlighted the link between socioeconomic indicators and health risk factors, yet Vietnam lacks a dedicated team or organization to investigate the relationship between socioeconomic status and immunization efforts as well as overall public health My ongoing study aims to address this gap by exploring these critical connections.
This analysis explores the relationships between various indicators to forecast the potential for a new pandemic wave Although my involvement is limited, the insights gained will aid policymakers in enhancing their public health research in Vietnam.
Research Objectives
The COVID-19 pandemic has led to significant losses in Vietnam, affecting individuals, society, and the economy while revealing critical weaknesses in the country's health system Many lives have been lost due to the virus, with healthcare services already strained before the outbreak and now facing unprecedented pressure A robust and reliable health system is essential for protecting the population, highlighting that investing in health is a crucial investment rather than merely a controllable expense.
Healthcare spending remains heavily skewed towards hospitals instead of prioritizing primary care, curative treatments, and preventive health measures Strengthening the resilience and preparedness of health systems is essential for future improvements Advancements in digital health and integrated care offer promising opportunities for systemic reform It is crucial to explore the relationship between socio-economic status and health outcomes to guide government and relevant sectors in implementing timely and effective policies.
Societal issues such as income, education, employment status, and housing conditions significantly influence perceptions of health While higher income can lead to longer, more stressful working hours, it also allows individuals to afford essential health resources like nutritious food and safe housing Those with higher education levels often possess greater knowledge about health-related practices Conversely, residing in unhealthy or polluted environments greatly elevates the risk of illness and mortality Additionally, poor working conditions and unemployment can adversely affect mental health, highlighting the intricate relationship between health and its social determinants.
Higher education is often linked to increased income, healthier living conditions, and reduced smoking rates Research suggests that significant income disparities can exacerbate population health issues and lead to greater health inequalities (Pickett and Wilkinson, 2015).
Research in Vietnam reveals that high population density contributes to numerous health problems, particularly infectious diseases, with significant disparities in population growth across various regions.
According to the UN population model, Vietnam is characterized by a "Golden Population Structure," as a significant proportion of its population is of working age compared to dependents This demographic shift will lead to a notable change in the demand for pediatric and reproductive health care, particularly with a large number of women entering childbearing age Additionally, there is a growing sex disparity at birth, and despite an increase in life expectancy, the overall quality of the population remains low, with Vietnam's Human Development Index (HDI) reflecting an average value.
Industrialization, urbanization, migration, and lifestyle modifications:
Health care systems in large cities are facing challenges due to increasing rural-urban migration, which can lead to various health issues The 2019 Population and Housing Census, conducted by the General Statistics Office in collaboration with the United Nations Population Fund, revealed that 6.4 million migrants aged five and older represent 7.3% of the total population The primary motivations for migration include family relocation and employment opportunities, accounting for 36.8% and 35.5% of cases, respectively Notably, the South East region attracts the highest percentage of work-related migrants at 50.3%, highlighting its strong job prospects and continued appeal for those seeking better opportunities.
Urbanization and rapid industrialization pose significant challenges to the healthcare system, exacerbating health concerns The increasing reliance on urban infrastructure leads to air and water pollution, further jeopardizing public health Additionally, the development of social infrastructure is crucial, particularly in enhancing access to essential health services.
14 clean water, waste treatment, sewage water, medical facilities, educational institutions, housing, etc., has also lagged behind population expansion well
The median annual temperature has increased by 0.7°C, leading to significant changes in ecosystems and a rise in tropical and infectious diseases Rainfall patterns have shifted, with lower precipitation in July and August and increased rainfall in September, impacting the establishment and development of certain disease carriers Additionally, sea levels are rising by approximately 3 mm annually, causing higher tides and waterlogging in various regions.
The spread of infectious diseases, particularly among low-income populations, poses significant threats to human health, exacerbated by climate change Notable examples include the rise of SARS, influenza A (H5N1), and various dengue-related issues in Asia, highlighted by the recent resurgence of dengue fever in Vietnam.
Natural disasters also have a significant negative influence on people's health due to the loss of safe water supplies, a shortage of food, injuries, collisions, and problems receiving medical care
The hygiene factor is based on the proportion of homes that utilize sanitary water sources, hygienic toilets, and the standard of air in the living area
The Northern Midlands and Mountainous areas exhibit the lowest sanitary water consumption rate at 61.5% Alarmingly, less than half of the homes in the Northern Mountainous Midlands (26.1%), North Central and Central Coast (47.3%), Central Highlands (46.5%), and Mekong Delta (42.4%) are equipped with sanitary latrines.
The surge in the number of automobiles and motorbikes, coupled with swift urbanization and city development, has made traffic the leading contributor to urban air pollution, accounting for 70% of the total emissions in cities.
Based on research results and surveys of socio-economic issues related to health and disease This research aims at 02 main objectives:
(01) Determine the current state of health and socio-economic indicators of Vietnam in the period from 1990 to 2021
(02) Assess the correlation between health indicators and socio-economic status before and during Covid – 19 and provides recommendations for more reference perspectives on this issue
RATIONALE
Subjects of study
For my research, I gathered socio-economic and health-related datasets from Vietnam spanning 30 years, from 1990 to 2021 This timeline was selected to analyze the changes and correlations between socio-economic variables and health before and during the Covid-19 pandemic, with 2021 marking the pandemic's impact The data aligns with published statistics from the General Statistics Office of Vietnam, the World Bank, UNICEF, and other reputable sources.
The study data consisted of 29 columns (corresponding to socioeconomic and health variables) and 33 lines (corresponding to the period of study work from 1990 to 2021)
1 Covid: ( 1: before Covid – 19, 2: during Covid -19)
2 Year: number of years (from 1990 to 2021)
3 deaths_rate_under5 : Under-5 mortality rate
4 deaths_rate_adolescents_1014: Adolescent mortality rates between 10 and
5 deaths_rate_children_0509: Child mortality rate from 5 to 9 years old
6 immunization_measles_child_1223: measles vaccination rates in children aged 12 to 23 months
7 life_expectancy: The rate of a person's life expectancy is calculated in years
8 crude_fertility_rate: crude mortality rate (%)
9 crude_mortality_rate: crude birth rate (%)
10 natural_increase_population: natural population growth rate (%)
11 ifn_deaths: infant mortality rate (%)
12 BCG_vaccinated: BCG vaccination rate
13 DTP1_vaccinated: rate of vaccination with the first dose of DTP vaccine
14 DTP2_vaccinated: vaccination rate of second dose DTP vaccine
15 surviving_infants_measles1_vaccinated: infant survival after first measles vaccination
16 newborn_TT_vaccinated: proportion of infants vaccinated with TT vaccine
17 IPV_vaccinated: IPV vaccination rate
18 MCV_vaccinated: vaccination rate of MCV vaccine first dose
19 MCV2_vaccinated: vaccination rate for the second dose of MCV vaccine
20 gdp_growth: GDP growth rate
21 unemployment_rate: annual unemployment rate
22 inflation_rate: annual inflation rate
23 population_growth: annual population growth rate
25 net_trade: annual net trade
26 gni_growth: GNI growth index
28 export_gns: Export of goods and services
29 import_gns: import of goods and services
Research progress
Identifying and understanding the problem is a crucial first step in the problem-solving process, as it highlights the specific issue that the thesis aims to address.
The analysis of socio-economic factors impacting health and epidemics, particularly before and during the Covid-19 pandemic, explores the critical question of how these factors influence public health While existing global studies provide valuable insights, there is a notable lack of similar research in Vietnam, especially in the context of the recent Covid-19 outbreak Therefore, conducting this research to validate the hypothesis is essential for understanding the relationship between socio-economic conditions and public health outcomes.
Given the limited data and scarce resources for relevant research in Vietnam, this issue can be approached through an analysis of socioeconomic and health indicators.
Analyze data and generate results
20 the same time, applying simple correlation methods will be the approaches to the problem of the topic
Once the problem is identified, the next thing is to build an analytical dataset Here I collect data of indices from available sources, each taking the timeline from
1990 to 2021 These resources are collected by organizations by year and the public on the internet such as the World Bank, General Statistics Office, and UNICEF
The data from the aforementioned organizations is generally well-organized with minimal missing information To ensure consistency for analysis, I utilized Python and Excel to standardize the data types and eliminate any redundant entries.
Once I have the dataset, I conduct an analysis using analytical tools to describe and give my opinion on each indicator
Through that, draw conclusions, answer the initial question, and make appropriate suggestions.
Data analysis tools
Stata is a powerful statistical software that offers advanced statistical techniques, efficient data management tools, and an excellent system for generating publication-quality tables and graphs, making it easy and quick to use.
Microsoft Excel, a key component of the Microsoft Office suite, is a powerful digital spreadsheet tool designed for organizing data, creating graphical representations, and performing various computations Each cell within Excel is identified by a unique address, combining its column and row names, such as A1 or D2 The program accommodates an extensive grid with 1,048,576 rows and 16,384 columns, allowing users to manage large datasets efficiently This functionality, often referred to as cell referencing, enhances data manipulation and analysis capabilities in Excel.
R is an essential programming language for those in need of robust data analysis and software development solutions Widely recognized in fields such as mathematics, data mining, and bioinformatics, R offers a versatile array of extension packages that enhance its capabilities in statistics and graphics.
R is a potent statistical analysis tool, however, tests of R's support for data analysis and prediction also revealed some of the following findings:
- Appropriate for tasks involving statistical analysis and graphing;
- A command-based working environment that is sophisticated and tough to abuse;
- Enter data in.csv or.txt file structure for tabular analysis; the program does not enable connecting directly to the database;
R is a vector-based programming language that enables computations on vectors without the need for loops For example, when applying a function to a vector, each element can be incremented by one simultaneously.
If we don't utilize a vector, we must loop while iterating through the list of elements in n+1 operations using an array of n elements
- R is a statistical language that is popular and has advantages in the fields of genetics, statistical data, and biological learning [14].
Data Processing
3.4.1 Primary data processing a) Health and disease index variables
A1: Mortality rate, under-5 (per 1,000 live births)
The under-five mortality rate refers to the probability a newborn would die before reaching exactly five years of age, expressed per 1,000 live births
A2: Probability of dying among adolescents ages 10-14 years (per 1,000)
Traumas, such as drowning and road traffic accidents, are significant causes of mortality and permanent impairment in older children and early adolescents aged 5 to 14 years In contrast, preterm birth, birth trauma, and infectious illnesses are the leading causes of death in children under 5 Notably, children and teenagers are the least affected age groups concerning COVID-19 infection-related mortality, representing approximately 8% of all cases and just 0.1% of global fatalities.
A3: Probability of dying among children ages 5-9 years (per 1,000)
The survival rates of young children are assessed through the death rate of those aged 5 to 9 years Typically, this age group's mortality rate is lower than that of infants and newborns Nevertheless, this stage is vital, as children become more mobile and independent during these formative years.
The risk of injury-related fatalities, particularly from drowning and traffic accidents, rises significantly For instance, a five-year-old child in a specific year faces an increased probability of not reaching the age of ten, based on the prevailing age-specific mortality rates.
A4: Immunization, measles (% of children ages 12-23 months)
Life expectancy at birth reflects the mortality trends within a population and is influenced by numerous factors affecting individuals across all age groups It indicates the average lifespan a newborn can anticipate, providing insight into the overall health and well-being of a community.
The ratio, often multiplied by 1,000, is between the total number of live births in a population during a given year and the population at the midpoint of that year
The crude death rate, expressed as the number of deaths per 1,000 individuals, represents the ratio of annual fatalities to the median population.
A8: The rate of natural increase of population
To calculate the natural growth rate, divide the number of births and deaths in a year by the midyear population, then multiply the result by 1,000 This calculation reveals the difference between the crude birth rate and the crude death rate.
The infant mortality rate reflects the grim likelihood that a child born within a specific year may not live to see their first birthday, a statistic determined by the death rates of infants during that timeframe.
The Bacillus Calmette-Guérin (BCG) vaccine, named after its creators Albert Calmette and Camille Guérin, is an effective vaccine against tuberculosis (TB) It is recommended that healthy newborns in countries where TB is prevalent receive one dosage of this vaccine.
Tuberculosis (TB) and leprosy are addressed promptly, with routine vaccinations administered primarily to high-risk children in areas where TB is less common In such regions, suspected cases are diagnosed and treated on an individual basis.
A11: Rate of vaccination with the first dose of DTP vaccine
The DTP vaccine, also known as the DPT vaccine, is a vital combination immunization that effectively protects against diphtheria, pertussis (whooping cough), and tetanus It includes diphtheria and tetanus toxoids, along with pertussis antigens or inactivated cells of the bacteria that cause pertussis Toxoid vaccines stimulate an immune response by using inactivated toxins produced by the infections they aim to prevent, rather than targeting the pathogens directly This mechanism helps the body build immunity against the harmful effects of the toxins associated with these diseases.
A12: Vaccination rate of second dose DTP vaccine
A13: Infant survival after first measles vaccination
The measles vaccination guards against contracting the disease Those who fail to acquire immunity after receiving one dosage do so after receiving a second
Measles outbreaks typically cease when vaccination rates hit 92%, but a decline in vaccination can trigger new outbreaks While the vaccine has proven to be effective for an extended period, it remains unclear if its effectiveness diminishes over time.
A14: Proportion of infants vaccinated with TT vaccine
The tetanus toxoid (TT) vaccine is essential for preventing tetanus, with a recommended schedule of five doses during childhood and a sixth dose in adolescence To maintain immunity, booster shots should be administered every 10 years.
Almost everyone is immediately immune after three treatments, but further doses every 10 years are advised to maintain immunity [19]
Polio vaccines are available in two types: oral and intravenous The World Health Organization (WHO) advises that all children be vaccinated to combat polio As a result of these vaccinations, there has been a significant reduction in polio cases globally.
A16: Vaccination rate of MCV vaccine first dose
Young adults and teenagers are at a higher risk for meningococcal disease, which can be life-threatening and lead to death within hours To help protect your child's health, it is crucial to have discussions about meningococcal illness and the importance of vaccinations.
24 meningococcal immunization with a doctor For some additional groups, including preteens and teenagers, the CDC advises meningococcal vaccination
The CDC advises MenACWY vaccination for all preteens between the ages of 11 and 12
Around the age of 16, all teenagers
Teenagers continue to receive protection throughout the years when they are most at risk thanks to the booster shot at age 16
A17: Vaccination rate for the second dose of MCV vaccine b) Socio-economic indicator variables
GDP measures the total value of goods and services produced within a country during a specific period It considers all production within a country's borders
B2: Unemployment, total (% of total labor force)
Individuals seeking employment while currently employed are classified as unemployed, regardless of age This classification serves as a global indicator for comparing unemployment rates accurately It accounts for seasonal variations and measures the percentage of individuals without work within the labor force, which includes both employed and unemployed individuals.
Inflation, as measured by the consumer price index (CPI), refers to the fluctuation in the prices of goods and services that certain households typically purchase
The phenomenon of population growth denotes the steady increase in the overall count of persons within a population or scattered community.B5: GDP growth
Data analysis methods
3.5.1 Application research models and research hypotheses
Forecasting and evaluation have long been essential in guiding future development across various industries, including healthcare, where experts focus on infectious disease prediction Analyzing and projecting infectious diseases is crucial for proactive prevention, allowing us to mitigate the harm caused by outbreaks Understanding the evolution of these diseases in different socioeconomic and geographic contexts is vital for identifying their cycles, formulating strategies, and effectively zoning outbreaks to contain their spread Numerous predictive models have been developed in the medical field, with Ross's 1911 model serving as a foundation for Macdonald's enhancements in 1957, particularly for malaria research Recent models, such as the SIR (Susceptible-Infected-Recovered) and SIS (Susceptible-Infected-Susceptible), aim to forecast disease transmission based on the number of infected individuals and mosquitoes These models, rooted in the SIR framework, categorize the affected population into three classes, thereby facilitating effective disease management and response.
- Disease-prone class S: People in this class have never had the disease and are at risk of being infected
- Infected Class I: People in this class already have the disease and are likely to transmit the disease to others
- The class that is cured of disease R: The people in this class have been cured of the disease or have died from sickness
In the SIR model, the incubation period is minimal, and a sufficiently large population sample ensures accurate approximations The forecast results remain reliable when the epidemic unfolds rapidly, maintaining a stable population in the affected area The extent of disease spread is determined by analyzing these factors.
In a model where individuals in class S become infected by those in class I, the transmission occurs at a rate of R, leading to an increase in the infected population This results in the equation S + I → 2I, indicating that one person from class S transitions to class I, effectively doubling the number of infected individuals.
𝐼→ 𝑅 : People in class I will recover from illness after a while (or die 𝛼 from illness) and switch to class R at a rate 𝑎 > 0
Each class in the SIR model is a function of time t consisting of 𝑆(𝑡),𝐼(𝑡) and
The infected class's growth rate is proportional to the number of infected and susceptible individuals, represented by the equation rSI, where r is a positive constant indicating the rate of loss from the infected class The cure rate for infected individuals is also proportional to their number, denoted by ai, where a is a positive constant, and 1/a indicates the duration of infection Due to a short incubation period, susceptible individuals become infected immediately upon exposure to pathogens By analyzing the input values, one can determine the parameters of this model, which is designed to predict the numbers of susceptible S(t), infected I(t), and recovered R(t) over time t Mathematical epidemiological models can effectively yield accurate predictions for systems that are not overly complex.
Time series analysis methods and models (timeseries) are now often utilized in forecasting, research, and other domains forecast the course of an illness By using
28 time series analysis models throughout time, regression models may be used to overcome the drawbacks of logically regression models (logistics)
To enhance the accuracy of epidemic warning models, it is essential to adopt a multivariable approach that accounts for temporal autocorrelation in series data Researchers have utilized time series analysis and predictive modeling techniques to examine the interplay between geographic factors, time, and various influences on the incidence of specific diseases.
To effectively implement machine learning analysis using modern and precise models, a substantial data set is essential In this research, I propose fundamental and traditional techniques to align with the data I have collected Consequently, I will introduce two analytical models, with further details provided in the following sections.
02 - Traditional time series analysis techniques
This study expands on previous research linking national income to health outcomes by investigating the interaction effects of income, sector-specific socioeconomic indicators, and multidimensional socioeconomic status (SES) characteristics It focuses on the impact of various SES indices on well-being, mortality, vaccination rates, and life expectancy Additionally, it examines the relationships between these factors and how they influence all-cause mortality, aiming to uncover the causal mechanisms behind the connections among SES indices, happiness levels, and mortality rates.
In statistical analysis, modeling is used to define the relationship between independent and dependent variables, allowing for an accurate representation of their connection This process results in an estimation equation that enables us to predict the value of the dependent variable, which is unknown, based on the known value of the independent variable.
- I am seeking inspiration for developing a fresh product utilizing presently available products.;
- Predict the time series of the stock market and indices
The regression equation y = F(x, θ) + e illustrates the process of estimating the target value y through a function F that incorporates one or more predictor variables (x1, x2, , xn), a set of parameters (θ1, θ2, , θn), and an error term (e).
A linear regression technique may be used if the relationship between the predicted factors and the outcome can be approximated by a straight line
Figure 3.3 Example of linear regression (Source: Javapoint – Linear Regression)
Linear regression with a single forecast can be represented by the equation:
Y i = β 0 + β 1 X i (3.3) where Y i = Dependent variable, β 0 = constant/Intercept, β 1 = Slope/Intercept,
This algorithm explains the linear relationship between the dependent(output) variable y and the independent(predictor) variable X using a straight line Y= B0 +
Figure 3.4 Linear regression model representation (Source: Analytics Vidhya)
Multivariate linear regression involves the use of two or more predictor variables, allowing for more complex relationships to be modeled Unlike single-variable regression, the resulting regression line cannot be visualized in two-dimensional space Instead, the model extends the equation of simple linear regression to accommodate multiple predictors, represented mathematically as y = θ1 + θ2x1 + θ3x2 + + θnxn−1 + e This approach enhances the analysis by incorporating various factors influencing the dependent variable.
In multivariate linear regression, each forecast variable is assigned a coefficient, which quantifies its influence on the target variable The algorithm calculates these coefficients during model development, allowing for an assessment of how each predictor affects the outcome.
Figure 3.5 Example of multivariate linear regression (Source: Scribbr)
Data mining encompasses various types, including table data, cross-sectional data, and time series data This thesis focuses on a time series dataset from the WHO, World Bank, UNICEF, and the General Statistics Office (GSO), covering health, epidemic, and socio-economic indicators from 1990 to 2021 The dataset illustrates the evolution of epidemic and socio-economic health indicators over time, highlighting the significance of time series data in this analysis.
Time series data consists of a sequence of values measured over time, representing a specific quantity This data can be understood as a series of vectors or values organized according to their respective timelines.
The analysis of time series data is to find the relationship between P and corresponding to the value x(t), we have the following formula:
Time series data, such as the evolving number of measles vaccinations and the changing per capita income in Vietnam, can be categorized into two forms: discrete and continuous.
In data analysis, incoherent timing can lead to non-seamless, discrete data series, while continuous data is represented by a timeline (t) and corresponding values (x(t)) The correlation within the dataset reflects the transformation of data over time To effectively analyze this relationship, the thesis employs regression analysis within the context of time series regression This method highlights the correlation between independent and dependent variables, illustrating how time influences the metrics present in the dataset.
RESEARCH FINDINGS
Current situation of socio-economic situation and health and disease in Vietnam
in Vietnam in the period from 1990 to 2021
Figure 4.1 Describe the current rate of under-5 deaths per 1000 children before and during Covid 19
Figure 4.2 Describe the current rate of adolescents from 10 to 14 deaths per 1000 children before and during Covid 19
Figure 4.3 Describe the current rate of children from 5 to 9 deaths per 1000 live birth
During the 1990s, newborn mortality rates saw a significant decline, dropping from 44.5 deaths per 1,000 live births in 1989 to 36.7 in 1999 Urban areas experienced the most substantial improvements, with nearly a 50% reduction in infant mortality rates In contrast, rural regions, where most births occurred, only achieved a modest 7% decrease in the same timeframe, highlighting the ongoing challenges in reducing infant mortality in these areas.
Over the past 30 years, child mortality rates from infancy to adolescence have significantly declined, with Vietnam recognized internationally for its success in achieving maternal and child health targets Between 1990 and 2015, the under-five mortality rate (U5MR) in Vietnam decreased by over 60%, dropping from 58 to 22.1 deaths per 1,000 live births during the implementation of the Millennium Development Goals.
Despite not meeting the target of reducing the under-five mortality rate (U5MR) by two-thirds, Vietnam has made significant progress, achieving a U5MR of 19.3, which is lower than the global average By 2015, Vietnam's rapid reduction in U5MR positioned it ahead of many other countries, reflecting its commitment to achieving Sustainable Development Goals.
Goals (SDGs) of the 2030 Agenda for Sustainable Development (SDGs) is verified to be lower than the worldwide target
There was no appreciable change in the influence of Covid-19 on the death rate for children under the age of 5, for children aged 5 to 9, and for teenagers aged
10 to 14 years during the Covid-19 pandemic The most severe age group was from
In Vietnam, children aged 6 to 12 years old represent 8% of COVID-19 cases, while those aged 13 to 17 account for 4.8% Younger age groups show lower percentages, with 0 to 2 years old at 3.6% and 3 to 5 years old at 2.8% Notably, a report from the Ministry of Health indicates that approximately 19.2% of COVID-19 cases in the country involve individuals under 18 years of age.
Figure 4.4: Chart depicting Viet Nam's crude birth and death rates from
Between 1990 and 2000, the crude fertility rate saw a significant decline from 28‰ to 18.1‰, followed by a steady fluctuation between 16 and 17‰ over the next decade From 2015 to 2019, fertility rates continued to fluctuate at low levels prior to the Covid-19 pandemic Meanwhile, the crude mortality rate experienced a slight decrease, remaining between 1 and 1.5‰ during the survey period Fertility plays a crucial role in shaping population and development strategies due to its impact on population growth, influenced by various factors including environmental, medical, social, cultural, economic, and biological elements Overall, from 1990 to 2021, changes in fertility rates were not drastic, although the Covid-19 pandemic introduced new variables that may affect these trends.
According to the 2020 statistics from the General Office of Population and Family Planning under the Ministry of Health, the birth rate has seen an increase, with fertility rates surpassing replacement levels in several regions.
The chart depicts the life expectancy of Vietnamese people and the average proportion of years expected to live before and during Covid 19 in the period 1990 and 2021
Vietnam boasts impressive life expectancy rates, with males averaging around 70 years and females reaching approximately 79 years as of 2017 This marks a significant improvement from 1990, when life expectancy was only 65 for men and 72 for women Despite already ranking among the countries with the longest lifespans, there is potential for further increases through enhancements to the healthcare system, which currently struggles with the leading causes of death: cardiovascular disease, stroke, and lung cancer The successful management of the Covid-19 pandemic and high vaccination rates have contributed to a rise in life expectancy within the population.
Figure 4.7 Immunization rate of essential infant vaccines between 2019 and 2021
From 1990 to 2021, Vietnam achieved a basic group vaccine coverage rate exceeding 83%, demonstrating the effectiveness of the government's universal vaccination strategy The country's vaccine insurance coverage consistently remained above 70% However, a recent UNICEF evaluation highlighted a significant decline in immunization rates for children, exacerbated by the disruption of vaccination services during the Covid-19 pandemic This decline is further attributed to factors such as violence, instability, and diminishing public trust in vaccines, which have strained the health system and limited available resources.
It is clear that children who are not immunized typically originate from the most underprivileged, isolated, and impoverished areas and are occasionally impacted by armed conflict
Recent statistics from the Foundation for International Health Equity reveal that 1 in 5 children from the lowest income households are not immunized, in stark contrast to only 1 in 20 children from the wealthiest households The survey indicates that unvaccinated children often live in remote areas or urban slums.
Their moms frequently don't attend school and don't participate much in household decisions The majority of these issues face low- and middle-income nations, where
1 in 10 children are not immunized in urban areas and 1 in 6 in rural regions
Recent research in Vietnam indicates that the percentage of unvaccinated children in urban areas is approximately 1.5 times higher than in rural regions, with rates of 6.3% and 4.2%, respectively Furthermore, this disparity is even more pronounced among the poorest families, where the unvaccinated rate reaches 13.5%, nearly double the 6.6% seen in the wealthiest households.
Figure 4.8 The chart interprets the growth of the gross national income (GNI) index between 1990 and 2021
Figure 4.9: The chart depicts Vietnam's GDP growth in the period 1990 – 2021 and the average value of the two periods before and during Covid 19
GDP per capita rose 3.6 times between 2002 and 2021, reaching around 3,700 USD The poverty rate dropped from 14.5% in 2010 to 3.8% in 2020 ($3.65/day, PPP 2017)[1]
Vietnam's economy has shown impressive resilience amid various challenges, supported by a robust foundation While GDP growth is projected to decline to 6.3% in 2023 from 8% in 2022 due to weak exports and domestic demand, a rebound to 6.5% is expected in 2024, aided by decreasing domestic inflation and the swift recovery of key export markets like the US, Eurozone, and China Additionally, despite the difficulties posed by the pandemic, Vietnam has seen an increase in life expectancy, attributed to effective Covid-19 control measures and high vaccination rates.
Vietnam's agricultural industry has been a crucial driver of economic growth and food security over the past 25 years, achieving an annual growth rate of 2.5-3.5% In 2020, it accounted for 14% of the country's GDP and generated 38% of jobs, showcasing its vital contribution to the nation's development despite facing unprecedented challenges.
Despite the challenges posed by the COVID-19 pandemic, Vietnam's economy has shown remarkable resilience, generating over US$48 billion in export revenues in 2021 This growth highlights the country's ability to thrive amid crises, showcasing its economic strength and perseverance.
Figure 4.10: Chart depicting fluctuations of the unemployment rate and average value before and during Covid 19 of Vietnam in the period 1990 – 2021
Between 1990 and 2000, Vietnam's unemployment rate exhibited notable fluctuations, remaining steady at 2% until it peaked at nearly 3% in 1997 In the early 2000s, the unemployment rate stabilized around 2.5-3%, but saw a significant decline in the following decade, reaching a low of just 1% in 2011 However, the onset of the global crisis brought about various challenges, causing the unemployment rate to rise again to nearly 2% over the next five years.
Figure 4.11: Unemployment rate by sex (2010-19)
Figure 4.12: Change in employment distribution by economic sector (2010-19)
Over the last decade leading up to the COVID-19 pandemic, agricultural employment in Vietnam decreased by 14 percentage points, while employment in the industrial and services sectors experienced a similar increase Notably, the distribution of employment between men and women has evolved, with a slight disparity observed in the agricultural sector Current statistics indicate a 15.3 percentage point decline in agricultural jobs, contrasting with the growth in industry and services.
Assessment of the correlation of socio-economic indicators to health and
The Mann-Whitney test is primarily used to compare two independent random samples, making it highly sensitive to changes in the central trend position This non-parametric test assesses the difference in medians between two groups, assuming that both distributions have the same dispersion and shape.
The Mann-Whitney test serves as a valuable alternative to the T-test, particularly when the assumptions of underlying distribution are not satisfied or when data is structured hierarchically This non-parametric test is applicable at both the ordinal and proportional levels of measurement, as it relies on ranking data points.
To evaluate the statistical significance of a variable before and during the Covid-19 pandemic, I will employ the Mann-Whitney test, which requires a p-value of less than 0.05 to confirm significance.
Results when running Stata to produce the results of 02 foreign investment variables and the GINI index
Figure 4.18: Two-sample Wilcoxon rank-sum (Mann-Whitney) test
The first table provides a summary of the data
- Covid: variables described before and during the epidemic (01 – before the Covid19 epidemic, 02 – during the Covid19 epidemic)
- “Obs” represents the number of observations in each group
- “Rank sum” shows the sum of ranks for each group
- “Expected” indicates the expected rank sum for each group under the null hypothesis of no difference between the groups
- The “Combined” row provides the total number of observations and the overall rank sum
- The “Unadjusted variance” represents the variance of the rank sums
- The “Adjustment for ties” indicates whether any ties were present in the data
In this case, it is shown as 0, meaning there were no ties
- The “Adjusted variance” represents the variance adjusted for ties, if any were present
- The null hypothesis (H0) states that the distributions of the two groups are the same “z” is the test statistic calculated based on the ranks and sample sizes
- “Prob > |z|” represents the p-value for the test statistic In this case, it is 0.7222, which is greater than the conventional significance level of 0.05
- “Exact prob” is the exact p-value calculated using an exact method In this case, it is 0.7605, which is also greater than 0.05
The findings indicate that we cannot reject the null hypothesis, as there is inadequate evidence to suggest that the two groups defined by "covid" exhibit different distributions of the variable "foreign." The p-values suggest that the observed differences between these groups may be attributed to random variation.
. p-value* 0 proves that the pair is positively correlated with each other, and conversely, the pair with R-squared < 0 is negatively correlated
At the same time, as this indicator gradually approaches 1, their correlation becomes closer to each other Here are a few charts depicting the correlation of the health index and SES
Figure 4.21: Correlation and data representation of gross national income and proportion of infants vaccinated with TT (Tetanus Vaccine)
As a nation's gross national income increases, so does the percentage of children receiving vaccinations, suggesting that wealthier countries are more likely to provide immunizations for their youth This correlation can be attributed to better access to vaccines and improved healthcare infrastructure, as seen in Vietnam, where higher national income allows for enhanced financial resources dedicated to health Vietnam has developed a strong vaccination program aimed at safely and effectively immunizing both children and adults Furthermore, the country's vaccination system has successfully adapted to the challenges posed by the COVID-19 pandemic, enabling rapid large-scale vaccination efforts to protect public health.
54 that everyone has access to vaccinations Vietnam has swiftly executed the vaccination program and attained a high immunization rate as a consequence
In conclusion, the relationship between gross national income and children's vaccination rates is not significant Instead, factors such as public health systems, cultural norms, and health policies play a more crucial role in influencing vaccination prevalence among children.
Figure 4.22: Chart of correlation between crude mortality rate and import turnover of goods and services
The relationship between imports of goods and services and mortality is complex and cannot be defined by a single model, as it varies based on multiple factors Nevertheless, the accompanying graph highlights several key elements that are essential for understanding this relationship.
The economic development of a nation is often indicated by its import levels of goods and services Strong economies typically depend on imports to satisfy domestic production and consumption needs, while weaker economies may use imports to supplement local supplies However, the reliance on imported products and services can adversely affect a nation's health, potentially leading to health issues and increased mortality rates.
Importing high-quality medical services can enhance access to healthcare and reduce mortality rates, especially in nations that may pose safety risks or fail to comply with hygiene regulations.
Environmental considerations are crucial as the importation of products and services can negatively impact ecosystems and human health The demand for high-tech goods, raw materials, and energy imports may lead to significant environmental challenges, including air and water pollution and climate change, which can increase mortality rates During the Covid-19 pandemic, Vietnam faced the urgent need to import vaccines and essential goods to transition to a new normal Additionally, prior to the pandemic, Vietnam's open trade policies with neighboring countries and the global market introduced new economic, environmental, and healthcare threats that have proportionately affected the country's crude mortality rate.
Figure 4.23 Graph of the correlation between living expectations and annual GDP growth rates