Assessment Item 2: Research Assignment

In 2021, it was estimated that worldwide, 38.4 million people were living with HIV (PLWH). In the same year, approximately 650 000 people died from AIDS-related illnesses worldwide (UNAIDS).

However, the rate of people dying from AIDS-related illnesses varies among countries, and little is known about the extent of this problem in the adult population of Epiland. Therefore, you are the epidemiologist who will lead a project with the following goals:

A)    Quantify the overall crude mortality and age-specific mortality among people living with HIV in Epiland.
B)    Explore potential risk factors associated with death among people living with HIV.
C)    Determine if the association between tuberculosis and death among people living with HIV is modified by treatment with antiretrovirals.

You are required to write a research paper on any of the disease scenarios and risk factors. You will be required to provide the Abstract, Introduction, Results, Discussion (including strengths and limitations, implications for policy and future research) and Conclusion sections for the paper. You will be provided with the Aims and Methods section of the research paper.

•    The length of the abstract should be 250 words.
•    The length of the main paper (Introduction, Results, Discussion, Conclusion) should be 2000 words ± 5% (1900-2100 words), excluding references.
•    You should reference (background/introduction and discussion) according to the AMA Referencing style (11th edition) following the guide available on the JCU Library Referencing website: https://libguides.jcu.edu.au/ama
•    Please format your assignment in readable font size, for example, Arial 12 or Times New Roman font size 12.
•    Pages should be numbered and formatted with 1.5 line spacing and 2.5 cm page margins, headers, and footers.

Mortality and risk factors risk in adult people living with HIV (PLWH): A study From Epiland

Aims
A)    Quantify the overall crude mortality ang age-specific mortality among people living with HIV in Epiland.
B)    Explore potential risk factors associated with death among people living with HIV.
C)    Determine if the association between tuberculosis and death among people living with HIV is modified by treatment with antiretrovirals.

Methods
Setting and Study population.
This was a retrospective cohort study conducted in the National Institute of HIV of Epiland (a country from East Asia). This Institute is a national public referral centre for HIV/AIDS care. The Institute invites all patients that receive HIV primary care in its facilities to participate in its research projects. In these patients, every 6 months, sociodemographic and behavioural data were collected via standardized questionnaires. A health professional took a medical history and performed a physical examination and laboratory testing was conducted. Participants who missed a scheduled study visit were followed up by study personnel via mobile phone contacts and home visits.

The data from this study includes PLWH at least 18 years old who were diagnosed between January 1, 2010, and December 31, 2015. This Institute covers around 40% of the estimated HIV population from Epiland. All records in the databases had undergone quality control and trained staff updated the clinical databases using medical records, laboratory results and ART usage information. All HIV- cases included in the study were confirmed in a laboratory.

Dependent and independent variables. The outcome variable was all-cause mortality (death by any cause) with the certification of death by a medical practitioner, or a verbal confirmation of death from a relative or friend.

Six potential risk factors/exposures associated with the outcome in the study population were studied. These were:
•    Having a CD4 count <350 cells/mm3 (<350 cells/mm3 vs ≥350 cells/mm3)
•    Having active tuberculosis as a co-infection (yes vs no)
•    Living in a rural area (rural area vs urban area)
•    Not using highly active antiretroviral therapy-HAART (never used vs have used)
•    Being unemployed (unemployed vs employed)
•    Being undernourished (Yes vs no) 

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THE DATA TO ANALYSE:

Section 1: Crude Mortality on people living with HIV.

The study involved 44,467 persons living with HIV. At the end of the study there were a total of 2,379 deaths in the cohort over 83,850 person-years of observation. The number of deaths were also captured by age, and these results are summarized on the below table:

*Missing data on age for 1,441 persons. So, the data to calculate age-specific mortality is 43,026 persons.

With this information, estimate the following in this study population:

•    The overall crude mortality rate in this population and age-specific mortality rate in this population. For this, you will need to report these measures in two different ways:
o    Overall crude mortality rate in this population and age-specific mortality rate in this population (expressed as deaths per 1000 persons.)
o    Overall crude mortality rate in this population and age-specific mortality rate in this population (expressed as deaths per 1000 person-years.)
•    In the Discussion Section, please compare your results according to the way to express the denominator. Comment which of these two ways will be the more appropriate to report.
 
Section 2: Potential exposures associated with death in the study population.

For each of the potential risk factors, estimate:

•    Relative measure of association between each factor and death in the study population (This measure should be appropriate according to the study design)
•    If the measure of association indicates a higher risk of death owing to the risk factor, you need to calculate the population attributable fraction

Your tabulated data is presented below:

Section 3: Determine if the use of HAART modifies the association between tuberculosis co-infection and the risk of death in PLWH.

To achieve this aim, you conducted a stratified analysis. You can find here two tables. The first only includes the study population who has used HAART. The second table only includes the study population who reported never used HAART. In both tables, the association of tuberculosis and death is shown.


Study population who has used HAART (N=30949)

Study population who has never used HAART (N=13518)
 

Estimate:
•    The stratum-specific measure of association
•    Compare these stratum-specific measures of association with the crude association (section 2)
 
DISCUSSION

We expect here that you discuss the results of each section. Please note that we expect that in your Discussion:
•    The study's results are interpreted and linked to the study's aims and research question.
•    The findings are synthesised in the context of the relevant literature with appropriate references. Valid conclusions are drawn based on data presented in the context of existing literature.
•    There is a reflection on the study and the issues raised.
•    Application of results and implications for current and future knowledge, practice and policy are recognised.
•    The strengths and weaknesses of the study are considered.

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Assessment Task 2: Structure and Criteria Sheet

Abstract 
The abstract is written in a scientific tone using a logical background/ methods results/conclusions structure.
There is a clear presentation of the paper’s general purpose, which orientates the reader to the Public Health issue being addressed in the paper.
The results are clearly presented, and conclusions are made based on the results.

Introduction/Background 
Clear explanation of the health/disease condition being examined and why it is significant. Literature relevant to the topic is clearly summarised to contextualise the research and provide a clear rationale. There is a clear summary of what is already known on the topic, and precise identification of the limitations of current research. Key published studies on this topic are included, with appropriate critique.

Results
Data analyses are accurate and consistent.
The presentation of the data/results is clear and reflects the stated aims.
Results are appropriately summarised and presented, and there is demonstrated clarity of presentation and technical competence (e.g., figures, tables).
Epidemiological terms and concepts are thoughtfully used to describe the study’s findings.

Discussion: 
The study’s results are interpreted and linked to the study’s aims and research question. The findings are synthesised in the context of the relevant literature with appropriate references. Valid conclusions are drawn based on data presented in the context of existing literature.
There is a reflection on the study and the issues raised.
Application of results and implications for current and future knowledge, practice and policy are recognised. The strengths and limitations of the study are addressed.
Application of results and implications for current and future research, practice and policy are recognised.

Conclusions 
There is a brief overall summary of the main findings and implications. The conclusions were presented and supported in a literate and effective manner.

ANSWERS

Abstract

This study aims to investigate the risk factors associated with HIV by evaluating crude mortality rates among people, age-specific death rates and deaths related to the risk factors.

This research quantified all results to explore potential risk factors for the study population to evaluate if the relationship between tuberculosis and death is affected by treatment with antiretrovirals.

The aim of this research ae achieved by collecting the primary data from the people living with HIV from Epiland. This study followed a cross-sectional design and collected data from people over a period of time between January 2010 to December 2015.

The research collected sociodemographic and behavioural data every 6 months using standardised questionnaires. Participants in this study represent nearly 40% of people from Epiland living with HIV. The finding of this research shows that the crude mortality rate for the study participants was 12.9 per 1000 which increases with age.

Hence, the risk of death is higher among older adults living with HIV. Furthermore, research revealed several risk factors for people living with HIV that are low CD4 count such as (<350 cells/mm³) active tuberculosis, living in rural areas, unemployment and no use of HAART.

Out of these risk factors, low CD4 has been found a major risk factor linked with high mortality among the study population. The cross-sectional design of this research and focus on a specific region are the two major limitations. However, a large sample and effective presentation of results show the reliability. 

1. Introduction

This research paper focuses on a disease scenario of HIV and its risk factors. It aims to quantify crude mortality among the population living with HIV, and potential risk factors related to deaths. In addition, it explains the link between tuberculosis and HIV deaths modified by treatment with antiretrovirals.

The objectives of this research are achieved by collecting primary from people living in Epiland. The study population include individual living with HIV. Recent statistics show that nearly 39 million people in the world are living with HIV.

Similarly, 1 million people die each year with this disease.1 Hence, this research is essential to evaluate the possible risk factors and crude mortality that can be used to develop effective strategies to prevent HIV-related deaths.

2. Results

The results of this research are presented in three different sections that are based on the objective of this research. The first section presents the crude mortality rate by analysing data collected from study participants from the Epiland.

The second section covers potential exposure and risk factors linked with the death of the study population. In addition, the third section determines if the use of HAART modifies the link between HIV deaths and tuberculosis.

Section 1

Overall crude mortality rate

Deaths per 1000 persons: 574/44467 * 1000 = 12.9

Deaths per 1000 person-years: 2379/83850 * 1000 = 28.4
 

Table 1: Age-specific mortality rate

Overall crude mortality rate in this population (expressed as deaths per 1000 persons): 12.9
Age-specific mortality rate in this population (expressed as deaths per 1000 persons):

Combined Results

Table 2: Overall crude mortality rate in this population and age-specific mortality rate in this population (expressed as deaths per 1000 persons)
 

Overall crude mortality rate: 12.9 deaths per 1000 persons

Table 3: Overall crude mortality rate in this population and age-specific mortality rate in this population (expressed as deaths per 1000 person-years.)

Overall crude mortality rate: 28.4 deaths per 1000 person-years

Table 4: Comparison of two methods of presenting crude mortality rate

Section 2

Relative measure of association between each factor and death in the study population (expressed as odds ratio)
| Risk factor | Odds ratio (95% CI) | |---|---|---| | CD4 count (<350 cells/mm³) | 3.52 (2.66-4.68) | | Tuberculosis as a co-infection | 2.23 (1.78-2.81) | | Living in a rural area | 1.21 (1.08-1.35) | | Never used HAART | 1.83 (1.51-2.24) | | Unemployed | 1.53 (1.33-1.76) | | Undernourished | 2.12 (1.82-2.48) |

Table 5: Population attributable fraction (PAF)

Section 3

Table 6: Stratum-specific measure of association

Comparison with crude association (section 2)

The crude odds ratio for the association between tuberculosis as a co-infection and death in the study population is 2.23 (95% CI 1.78-2.81).

The stratum-specific odds ratios for the association between tuberculosis as a co-infection and death are 1.62 (95% CI 1.24-2.10) for people who used HAART and 2.82 (95% CI 2.26-3.53) for people who never used HAART.

3. Discussion

According to Focacci, Lam and Bai (2022), the crude mortality rate shows the number of deaths in the total population during a specific time period which is presented as the deaths per 1000 people.2

The results presented in the section about crude mortality show that the overall crude mortality rate of the study population is 12.9 per 1,000 people. This result shows that out of 1000 people, 12.9 people die each year in the target population due to HIV.

Apart from this age-specific mortality rate is presented in Table 1 shows the number of people out of every 1000 in the given group who die every year due to HIV.

The numbers presented in the table show that the age-specific mortality for the people of the group between 18 to 24 is 31.7 per 1000 people which means that out of every 1000 people between age 18 to 24 31.7 die each year.

The results also show that the ages-specific crude mortality rate increases with the age of people; for example, it reaches 35.9 for people between 25 to 34 years and 55.2 for people who fall between the ages of 35 to 44.

Similarly, 67.9 of the age group between 45-55 and 99.8 with age 55 and above die each year. Based on these results it can be analysed that older adults have a higher risk of death and are more likely to die compared to young and adult people.

In comparison, results related to deaths per 1000 person-years show that the overall crude mortality rate of the population is 28.4 per 1000 person-years. It shows that out of every 1000 people from the study population will die each year per 1000 person-years. Age-specific mortality rate per 1000 person-years is different; however, results show that it also varies with the age of the population.

For example, Table 1 shows that the age-specific crude mortality rate among the study population per 1000 person-years goes from 25.2 to 93.6 for the people between the ages 18 to 55 and above. Based on the results presented above, it can be analysed that the age-specific mortality per 1000 person-years shows the risk of death in the given population and is more accurate.

Table 4 presented in the results section shows the comparison of two different methods of presenting crude mortality rate. It also shows how the crude mortality rate is presented in both methods and which is the more appropriate method.

It has been found that the person-years denominator considers the age distribution of the population; hence, it is a more accurate method of measuring crude mortality.

However, there are situations when it might not be effective to report the data related to crude mortality. For example, the situations when comparing populations with different age groups, the method that can be used as more appropriate is the deaths per 1000 people.3

The reason for choosing this method is that the presentation of crude mortality through this method is not impacted by the age distribution of a study population. Thus, this research has achieved its first objective by evaluating crude mortality and age-specific mortality among the study population.

The results presented in Section 2 show the link between all risk factors and deaths for the target population. The odds ratio is used to measure the link between a risk factor and its outcome.4 To measure it, the odds of the result in the exposed group are divided by the odds of the outcome in the unexposed group.

It has been found that an odds ratio less than 1 denotes a negative association and an odds ratio larger than 1 suggests a positive association between the risk factor and the outcome.

On the other hand, PAF refers to the population-attributable fraction, which represents the portion of disease cases that might be avoided with the removal of a risk factor.

The process of measuring PAF involves deducting the risk of disease in the population without the risk factor from the disease risk in the population having the risk factor, then dividing the result by the disease risk in the population with the risk factor.5

Based on the results presented in Section 2 it can be analysed that the risk factors are linked with the increased risk of mortality among the study population. Variable CD4 shows the strong link followed tuberculosis and the use of HAART. Similarly, PAF for CD4 was 58.3% which indicated that 58.3% of deaths are related to CD4 and can be prevented by eliminating CD4 count.

In addition, the results of this cross-sectional study indicate that these risk factors are essential and need to be considered when making a strategy for the given population to reduce mortality. Based on this analysis it can be evaluated that the research has achieved its second objective by explaining potential risk factors related to the death of people that are CD4 count, tuberculosis and use of HAART.

The results presented in Section 3 show that Stratum-specific odds ratios indicate the link between tuberculosis and death which is stronger among individuals who never used HAART. The reason behind this is that HAART can enhance immune function while reducing the risk of mortality associated with tuberculosis.

By comparing these stratum-specific measures of association with the crude association it can be evaluated that the use of HAART alters the relationship between death and tuberculosis as a co-infection. This indicates that a person's level of connection varies based on whether or not they use HAART. These findings indicate the achievement of the third objective of this research and the importance of preventing and treating tuberculosis for those individuals living with HIV.

This research has several limitations such as its cross-sectional design and focus on a single country. However, it has a large sample and presented adequate results to show the relationship between variables. Therefore, findings of this are reliable and can be implemented into the practice.

For example, these findings can be implemented to evaluate HIV testing and improve treatment by improving CD4 count among people with HIV. Similarly, these findings can be considered to reduce and prevent tuberculosis by reducing the risk of death.

On the other hand, changes such as the development of rural areas to improve healthcare access can be considered as living in rural areas has been identified as a risk factor in this research.

Similarly, these findings can be considered by the government and policymakers to enhance existing practices and measures for preventing the risk of deaths for people living with HIV such as tuberculosis prevention programs, new HIV policies, rural development policies and improving HAART access.

In addition, changes such as social support and employment opportunities can be considered to reduce mortality rates among people living with HIV.

4. Conclusion

Based on the above analysis it can be concluded that this research has presented significant results related to HIV deaths, risk factors and crude mortality rates for the study population along with the impact of each factor on the death rate.

Findings revealed that the crude mortality per 1000 is 12.9 for the study population and this age-specific death rate increases with the age of the target population in the study.

Similarly, it has been found that the link between tuberculosis and death is strong for the individuals who never used HAART compared to those who used HAART.

This relationship indicates that HAART is essential for people living with HIV to improve the immune system which can be considered as an important strategy to reduce mortality rates among people living with HIV.

Furthermore, major risk factors that have been identified through this research include lower CD4 count, tuberculosis, living in rural places and individuals who never used HAART.

The PAF result for CD4 indicates that around 58.3% of deaths could be prevented by eliminating the CD4 count.  Overall, findings of this research can be used to make effective strategies for people living with HIV to reduce mortality.

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References

1.Roser M, Ritchie H. HIV / AIDS. Our World in Data. Published online April 3, 2018. https://ourworldindata.org/hiv-aids#:~:text=Almost%201%20million%20people%20die
2.Focacci CN, Lam PH, Bai Y. Choosing the right COVID-19 indicator: crude mortality, case fatality, and infection fatality rates influence policy preferences, behaviour, and understanding. Humanities and Social Sciences Communications. 2022;9(1). doi:https://doi.org/10.1057/s41599-021-01032-0
3.Heuveline P, Tzen M. Beyond deaths per capita: comparative COVID-19 mortality indicators. BMJ Open. 2021;11(3): e042934. doi:https://doi.org/10.1136/bmjopen-2020-042934
4.Jackson D, Law M, Stijnen T, Viechtbauer W, White IR. A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio. Statistics in Medicine. 2018;37(7):1059-1085. doi:https://doi.org/10.1002/sim.7588
5.Khosravi A, Nielsen RO, Mansournia MA. Methods matter: population attributable fraction (PAF) in sport and exercise medicine. British Journal of Sports Medicine. 2020;54(17): bjsports-2020-101977. doi:https://doi.org/10.1136/bjsports-2020-101977