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Case study: Paper 1 May 2021 - Multi Marketing

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Business Management Paper 1 May 2021

Case Study: Multi Marketing

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• Paper 1 Pre-release case study
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Aims and overview of the Paper 1 assessment

The Paper 1 examination for May 2022 has been rescheduled for Friday 28th April 2023 (morning session for Exam zones B and C, and afternoon session for Exam zone A). For details of Exam zone, click the link here .

The Paper 1 examination for November 2023 is scheduled for Monday 24th October 2023 (afternoon session). This is the final examination of the Paper 1 in its current format.

Please click the icon below to see the revised timings and weightings that were used for the Paper 1 exams for the M21 to N22 sessions. These adaptations were made by the IB in recognition of the huge challenges that students and teachers faced during the prolonged coronavirus pandemic and school closures.

Revised timings, marks, and weights for IB Business Management exams (May 2021 - Nov 2022)

Revised timings

1 SL candidates answer only one question (not two) from Section A, plus the complusory question in Section B.

2 Section C ( CUEGIS essay) has been removed from the May 2022 Paper 2 exam.

3 HL students answer two questions from Section A and the compulsory question in Section B. Section C of the examination (including unseen data/information for the PU case study) has been removed for M22.

4 Section C (CUEGIS essay) has also been removed from the M21 - N22 Paper 2 exam.

Revised marks and weightings (SL)

Revised marks and weightings (HL)

Source : M21 adapted assessment: Modified marks, weightings and examination times, p.11

Download a PDF version of the revised timings, marks, and weights by clicking the link here .

Please note that the IB has announced to Heads of School and DP Coordinators that the above changes will also apply to the examinations in May 2022 and November 2022.

The main aims and overview of the Paper 1 assessment are as follows:

Promotes a holistic approach to IB Business Management

Assesses all 5 topics of the IB BM syllabus

Section C: Extended response (essay) question (HL Only)

Carries 30% in the examination for SL and 35% for HL

Demands extensive analytical and critical thinking skills.

The pre-issued case study

Each exam session, the IB releases a pre-issued case study for the external examinations. This is made available on the PRC (Programme Resources Centre) - speak with your IB Coordinator if you are not sure how to access this resource.

For the May examination, the pre-issued case study is made available in February. For the November examination, the pre-issued case study is available on the PRC in August.

The case study is based on a hypothetical organization, which covers all 5 units in the syllabus. It can act as a useful stimuls for students when revising for both Paper 1 and Paper 2. Note: students must not use this organization for their CUEGIS essay in Paper 2, Section C!

FAQ 1: So, what would happen if a student decided to write about the organization featured in Paper 1 for their CUEGIS essay in Section C of Paper 2?

The CUEGIS essay in Paper 2 must be based on a real-world organization. The organization featured in the Paper 1pre-issued case study is a fictitious business. Therefore, students will be penalized if they choose to use the business organization featured in Paper 1!

Note: Schools are responsible for providing the formulae sheet + a clean copy of the pre-issued case study for students in the external exam. It is therefore useful to practice this by setting students a mock/trial exam for Paper 1.

Paper 1 – Standard Level

Section A: answer 2 of 3 structured questions

10 marks per question; there are no AO3 questions in Section A

Additional unseen additional material in Section B

Section B: answer the compulsory structured question worth 20 marks

Total = 40 marks

Time = 1 hour 15 mins examination

FAQ 2: What happens if a student answers all three questions in Section A?

This is not a recommended strategy! However, in such a case, the examiner will mark all three answers and award the top two marks. However, there is an opportunity cost if answering all three questions in Section A because students are likely to run out of time for Section B of the exam.

Paper 1 – Higher Level

Additional unseen material will be provided in the actual Paper 1 exam

Answer the compulsory question worth 20 marks

HL extension topics may be assessed

Total = 60 marks

Time = 2 hour 15 mins examination

FAQ 3: How do the HL and SL Paper 1 examinations differ from each other?

Sections A and B are very similar for SL and HL, although the latter can include HL extension or HL only topics as mentioned in the Guide (page 54). The main difference is Section C for HL only students, which is a single question worth 20 marks. This requires students to answer a business strategy question based on the pre-released material and the additional stimulus materials in Section B and Section C of the examination.

 ATL Activity - What do you know about the Paper 1 assessment?

What do you know about the Paper 1 assessment? Use any resource you have access to (such as the IB Business Management Guide) to answer the following questions:

Click the banner above to see details of two workshops being hosted by InThinking to support teachers preparing for the M22 Paper 1 examination.

For lesson ideas, research tasks, and other resources for the various IB Paper 1 case studies , click on the relevant hyperlink below:

May 2023 case study (Skandvig Terra PLC)

Nov 2022 case study (ELE PLC)

May 2022 case study (Peacewick University)

November 2021 case study (MegaminMining)

May 2021 case study (Multi Marketing)

November 2020 case study (Ducal Aspirateurs)

May 2020 case study (P&S Trawlers)

BM May 2021 Paper 1 Case Study - Multi Marketing

IB Business Management May 2021 Paper 1 Case Study - Multi Marketing

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Author: Paul Hoang

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Open Access

Peer-reviewed

Research Article

Mapping the global geography of cybercrime with the World Cybercrime Index

Roles Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliations Department of Sociology, University of Oxford, Oxford, United Kingdom, Canberra School of Professional Studies, University of New South Wales, Canberra, Australia

Roles Conceptualization, Investigation, Methodology, Writing – original draft

Affiliations Department of Sociology, University of Oxford, Oxford, United Kingdom, Oxford School of Global and Area Studies, University of Oxford, Oxford, United Kingdom

Roles Formal analysis, Methodology, Writing – review & editing

Affiliations Department of Sociology, University of Oxford, Oxford, United Kingdom, Leverhulme Centre for Demographic Science, University of Oxford, Oxford, United Kingdom

Roles Funding acquisition, Methodology, Writing – review & editing

Affiliation Department of Software Systems and Cybersecurity, Faculty of IT, Monash University, Victoria, Australia

Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Affiliation Centre d’études européennes et de politique comparée, Sciences Po, Paris, France

• Miranda Bruce, 
• Jonathan Lusthaus, 
• Ridhi Kashyap, 
• Nigel Phair, 
• Federico Varese

• Published: April 10, 2024
• https://doi.org/10.1371/journal.pone.0297312
• Peer Review
• Reader Comments

Cybercrime is a major challenge facing the world, with estimated costs ranging from the hundreds of millions to the trillions. Despite the threat it poses, cybercrime is somewhat an invisible phenomenon. In carrying out their virtual attacks, offenders often mask their physical locations by hiding behind online nicknames and technical protections. This means technical data are not well suited to establishing the true location of offenders and scholarly knowledge of cybercrime geography is limited. This paper proposes a solution: an expert survey. From March to October 2021 we invited leading experts in cybercrime intelligence/investigations from across the world to participate in an anonymized online survey on the geographical location of cybercrime offenders. The survey asked participants to consider five major categories of cybercrime, nominate the countries that they consider to be the most significant sources of each of these types of cybercrimes, and then rank each nominated country according to the impact, professionalism, and technical skill of its offenders. The outcome of the survey is the World Cybercrime Index, a global metric of cybercriminality organised around five types of cybercrime. The results indicate that a relatively small number of countries house the greatest cybercriminal threats. These findings partially remove the veil of anonymity around cybercriminal offenders, may aid law enforcement and policymakers in fighting this threat, and contribute to the study of cybercrime as a local phenomenon.

Citation: Bruce M, Lusthaus J, Kashyap R, Phair N, Varese F (2024) Mapping the global geography of cybercrime with the World Cybercrime Index. PLoS ONE 19(4): e0297312. https://doi.org/10.1371/journal.pone.0297312

Editor: Naeem Jan, Korea National University of Transportation, REPUBLIC OF KOREA

Received: October 11, 2023; Accepted: January 3, 2024; Published: April 10, 2024

Copyright: © 2024 Bruce et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The dataset and relevant documents have been uploaded to the Open Science Framework. Data can be accessed via the following URL: https://osf.io/5s72x/?view_only=ea7ee238f3084054a6433fbab43dc9fb .

Funding: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 101020598 – CRIMGOV, Federico Varese PI). FV received the award and is the Primary Investigator. The ERC did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funder website: https://erc.europa.eu/faq-programme/h2020 .

Competing interests: The authors have declared that no competing interests exist.

Introduction

Although the geography of cybercrime attacks has been documented, the geography of cybercrime offenders–and the corresponding level of “cybercriminality” present within each country–is largely unknown. A number of scholars have noted that valid and reliable data on offender geography are sparse [ 1 – 4 ], and there are several significant obstacles to establishing a robust metric of cybercriminality by country. First, there are the general challenges associated with the study of any hidden population, for whom no sampling frame exists [ 5 , 6 ]. If cybercriminals themselves cannot be easily accessed or reliably surveyed, then cybercriminality must be measured through a proxy. This is the second major obstacle: deciding what kind of proxy data would produce the most valid measure of cybercriminality. While there is much technical data on cybercrime attacks, this data captures artefacts of the digital infrastructure or proxy (obfuscation) services used by cybercriminals, rather than their true physical location. Non-technical data, such as legal cases, can provide geographical attribution for a small number of cases, but the data are not representative of global cybercrime. In short, the question of how best to measure the geography of cybercriminal offenders is complex and unresolved.

There is tremendous value in developing a metric for cybercrime. Cybercrime is a major challenge facing the world, with the most sober cost estimates in the hundreds of millions [ 7 , 8 ], but with high-end estimates in the trillions [ 9 ]. By accurately identifying which countries are cybercrime hotspots, the public and private sectors could concentrate their resources on these hotspots and spend less time and funds on cybercrime countermeasures in countries where the problem is limited. Whichever strategies are deployed in the fight against cybercrime (see for example [ 10 – 12 ]), they should be targeted at countries that produce the largest cybercriminal threat [ 3 ]. A measure of cybercriminality would also enable other lines of scholarly inquiry. For instance, an index of cybercriminality by country would allow for a genuine dependent variable to be deployed in studies attempting to assess which national characteristics–such as educational attainment, Internet penetration, or GDP–are associated with cybercrime [ 4 , 13 ]. These associations could also be used to identify future cybercrime hubs so that early interventions could be made in at-risk countries before a serious cybercrime problem develops. Finally, this metric would speak directly to theoretical debates on the locality of cybercrime, and organized crime more generally [ 11 – 14 ]. The challenge we have accepted is to develop a metric that is both global and robust. The following sections respectively outline the background elements of this study, the methods, the results, and then discussion and limitations.

Profit-driven cybercrime, which is the focus of this paper/research, has been studied by both social scientists and computer scientists. It has been characterised by empirical contributions that have sought to illuminate the nature and organisation of cybercrime both online and offline [ 15 – 20 ]. But, as noted above, the geography of cybercrime has only been addressed by a handful of scholars, and they have identified a number of challenges connected to existing data. In a review of existing work in this area, Lusthaus et al. [ 2 ] identify two flaws in existing cybercrime metrics: 1) their ability to correctly attribute the location of cybercrime offenders; 2) beyond a handful of examples, their ability to compare the severity and scale of cybercrime between countries.

Building attribution into a cybercrime index is challenging. Often using technical data, cybersecurity firms, law enforcement agencies and international organisations regularly publish reports that identify the major sources of cyber attacks (see for example [ 21 – 24 ]). Some of these sources have been aggregated by scholars (see [ 20 , 25 – 29 ]). But the kind of technical data contained in these reports cannot accurately measure offender location. Kigerl [ 1 ] provides some illustrative remarks:

Where the cybercriminals live is not necessarily where the cyberattacks are coming from. An offender from Romania can control zombies in a botnet, mostly located in the United States, from which to send spam to countries all over the world, with links contained in them to phishing sites located in China. The cybercriminal’s reach is not limited by national borders (p. 473).

As cybercriminals often employ proxy services to hide their IP addresses, carry out attacks across national boundaries, collaborate with partners around the world, and can draw on infrastructure based in different countries, superficial measures do not capture the true geographical distribution of these offenders. Lusthaus et al. [ 2 ] conclude that attempts to produce an index of cybercrime by country using technical data suffer from a problem of validity. “If they are a measure of anything”, they argue, “they are a measure of cyber-attack geography”, not of the geography of offenders themselves (p. 452).

Non-technical data are far better suited to incorporating attribution. Court records, indictments and other investigatory materials speak more directly to the identification of offenders and provide more granular detail on their location. But while this type of data is well matched to micro-level analysis and case studies, there are fundamental questions about the representativeness of these small samples, even if collated. First, any sample would capture cases only where cybercriminals had been prosecuted, and would not include offenders that remain at large. Second, if the aim was to count the number of cybercrime prosecutions by country, this may reflect the seriousness with which various countries take cybercrime law enforcement or the resources they have to pursue it, rather than the actual level of cybercrime within each country (for a discussion see [ 30 , 31 ]). Given such concerns, legal data is also not an appropriate approach for such a research program.

Furthermore, to carry out serious study on this topic, a cybercrime metric should aim to include as many countries as possible, and the sample must allow for variation so that high and low cybercrime countries can be compared. If only a handful of widely known cybercrime hubs are studied, this will result in selection on the dependent variable. The obvious challenge in providing such a comparative scale is the lack of good quality data to devise it. As an illustration, in their literature review Hall et al. [ 10 ] identify the “dearth of robust data” on the geographical location of cybercriminals, which means they are only able to include six countries in their final analysis (p. 285. See also [ 4 , 32 , 33 ]).

Considering the weaknesses within both existing technical and legal data discussed above, Lusthaus et al. [ 2 ] argue for the use of an expert survey to establish a global metric of cybercriminality. Expert survey data “can be extrapolated and operationalised”, and “attribution can remain a key part of the survey, as long as the participants in the sample have an extensive knowledge of cybercriminals and their operations” (p. 453). Up to this point, no such study has been produced. Such a survey would need to be very carefully designed for the resulting data to be both reliable and valid. One criticism of past cybercrime research is that surveys were used whenever other data was not immediately available, and that they were not always designed with care (for a discussion see [ 34 ]).

In response to the preceding considerations, we designed an expert survey in 2020, refined it through focus groups, and deployed it throughout 2021. The survey asked participants to consider five major types of cybercrime– Technical products/services ; Attacks and extortion ; Data/identity theft ; Scams ; and Cashing out/money laundering –and nominate the countries that they consider to be the most significant sources of each of these cybercrime types. Participants then rated each nominated country according to the impact of the offenses produced there, and the professionalism and technical skill of the offenders based there. Using the expert responses, we generated scores for each type of cybercrime, which we then combined into an overall metric of cybercriminality by country: the World Cybercrime Index (WCI). The WCI achieves our initial goal to devise a valid measure of cybercrime hub location and significance, and is the first step in our broader aim to understand the local dimensions of cybercrime production across the world.

Participants

Identifying and recruiting cybercrime experts is challenging. Much like the hidden population of cybercriminals we were trying to study, cybercrime experts themselves are also something of a hidden population. Due to the nature of their work, professionals working in the field of cybercrime tend to be particularly wary of unsolicited communication. There is also the problem of determining who is a true cybercrime expert, and who is simply presenting themselves as one. We designed a multi-layered sampling method to address such challenges.

The heart of our strategy involved purposive sampling. For an index based entirely on expert opinion, ensuring the quality of these experts (and thereby the quality of our survey results) was of the utmost importance. We defined “expertise” as adult professionals who have been engaged in cybercrime intelligence, investigation, and/or attribution for a minimum of five years and had a reputation for excellence amongst their peers. Only currently- or recently-practicing intelligence officers and investigators were included in the participant pool. While participants could be from either the public or private sectors, we explicitly excluded professionals working in the field of cybercrime research who are not actively involved in tracking offenders, which includes writers and academics. In short, only experts with first-hand knowledge of cybercriminals are included in our sample. To ensure we had the leading experts from a wide range of backgrounds and geographical areas, we adopted two approaches for recruitment. We searched extensively through a range of online sources including social media (e.g. LinkedIn), corporate sites, news articles and cybercrime conference programs to identify individuals who met our inclusion criteria. We then faced a second challenge of having to find or discern contact information for these individuals.

Complementing this strategy, the authors also used their existing relationships with recognised cybercrime experts to recruit participants using the “snowball” method [ 35 ]. This both enhanced access and provided a mechanism for those we knew were bona fide experts to recommend other bona fide experts. The majority of our participants were recruited in this manner, either directly through our initial contacts or through a series of referrals that followed. But it is important to note that this snowball sampling fell under our broader purposive sampling strategy. That is, all the original “seeds” had to meet our inclusion criteria of being a top expert in the first instance. Any connections we were offered also had to meet our criteria or we would not invite them to participate. Another important aspect of this sampling strategy is that we did not rely on only one gatekeeper, but numerous, often unrelated, individuals who helped us with introductions. This approach reduced bias in the sample. It was particularly important to deploy a number of different “snowballs” to ensure that we included experts from each region of the world (Africa, Asia Pacific, Europe, North America and South America) and from a range of relevant professional backgrounds. We limited our sampling strategy to English speakers. The survey itself was likewise written in English. The use of English was partly driven by the resources available for this study, but the population of cybercrime experts is itself very global, with many attending international conferences and cooperating with colleagues from across the world. English is widely spoken within this community. While we expect the gains to be limited, future surveys will be translated into some additional languages (e.g. Spanish and Chinese) to accommodate any non-English speaking experts that we may not otherwise be able to reach.

Our survey design, detailed below, received ethics approval from the Human Research Advisory Panel (HREAP A) at the University of New South Wales in Australia, approval number HC200488, and the Research Ethics Committee of the Department of Sociology (DREC) at the University of Oxford in the United Kingdom, approval number SOC_R2_001_C1A_20_23. Participants were recruited in waves between 1 August 2020 and 30 September 2021. All participants provided consent to participate in the focus groups, pilot survey, and final survey.

Survey design

The survey comprised three stages. First, we conducted three focus groups with seven experts in cybercrime intelligence/investigations to evaluate our initial assumptions, concepts, and framework. These experts were recruited because they had reputations as some of the very top experts in the field; they represented a range of backgrounds in terms of their own geographical locations and expertise across different types of cybercrime; and they spanned both the public and private sectors. In short, they offered a cross-section of the survey sample we aimed to recruit. These focus groups informed several refinements to the survey design and specific terms to make them better comprehensible to participants. Some of the key terms, such as “professionalism” and “impact”, were a direct result of this process. Second, some participants from the focus groups then completed a pilot version of the survey, alongside others who had not taken part in these focus groups, who could offer a fresh perspective. This allowed us to test technical components, survey questions, and user experience. The pilot participants provided useful feedback and prompted a further refinement of our approach. The final survey was released online in March 2021 and closed in October 2021. We implemented several elements to ensure data quality, including a series of preceding statements about time expectations, attention checks, and visual cues throughout the survey. These elements significantly increased the likelihood that our participants were both suitable and would provide full and thoughtful responses.

The introduction to the survey outlined the survey’s two main purposes: to identify which countries are the most significant sources of profit-driven cybercrime, and to determine how impactful the cybercrime is in these locations. Participants were reminded that state-based actors and offenders driven primarily by personal interests (for instance, cyberbullying or harassment) should be excluded from their consideration. We defined the “source” of cybercrime as the country where offenders are primarily based, rather than their nationality. To maintain a level of consistency, we made the decision to only include countries formally recognised by the United Nations. We initially developed seven categories of cybercrime to be included in the survey, based on existing research. But during the focus groups and pilot survey, our experts converged on five categories as the most significant cybercrime threats on a global scale:

• Technical products/services (e.g. malware coding, botnet access, access to compromised systems, tool production).
• Attacks and extortion (e.g. DDoS attacks, ransomware).
• Data/identity theft (e.g. hacking, phishing, account compromises, credit card comprises).
• Scams (e.g. advance fee fraud, business email compromise, online auction fraud).
• Cashing out/money laundering (e.g. credit card fraud, money mules, illicit virtual currency platforms).

After being prompted with these descriptions and a series of images of world maps to ensure participants considered a wide range of regions/countries, participants were asked to nominate up to five countries that they believed were the most significant sources of each of these types of cybercrime. Countries could be listed in any order; participants were not instructed to rank them. Nominating countries was optional and participants were free to skip entire categories if they wished. Participants were then asked to rate each of the countries they nominated against three measures: how impactful the cybercrime is, how professional the cybercrime offenders are, and how technically skilled the cybercrime offenders are. Across each of these three measures, participants were asked to assign scores on a Likert-type scale between 1 (e.g. least professional) to 10 (e.g. most professional). Nominating and then rating countries was repeated for all five cybercrime categories.

This process, of nominating and then rating countries across each category, introduces a potential limitation in the survey design: the possibility of survey response fatigue. If a participant nominated the maximum number of countries across each cybercrime category– 25 countries–by the end of the survey they would have completed 75 Likert-type scales. The repetition of this task, paired with the consideration that it requires, has the potential to introduce respondent fatigue as the survey progresses, in the form of response attrition, an increase in careless responses, and/or increased likelihood of significantly higher/lower scores given. This is a common phenomenon in long-form surveys [ 36 ], and especially online surveys [ 37 , 38 ]. Jeong et al [ 39 ], for instance, found that questions asked near the end of a 2.5 hour survey were 10–64% more likely to be skipped than those at the beginning. We designed the survey carefully, refined with the aid of focus groups and a pilot, to ensure that only the most essential questions were asked. As such, the survey was not overly long (estimated to take 30 minutes). To accommodate any cognitive load, participants were allowed to complete the survey anytime within a two-week window. Their progress was saved after each session, which enabled participants to take breaks between completing each section (a suggestion made by Jeong et al [ 39 ]). Crucially, throughout survey recruitment, participants were informed that the survey is time-intensive and required significant attention. At the beginning of the survey, participants were instructed not to undertake the survey unless they could allocate 30 minutes to it. This approach pre-empted survey fatigue by discouraging those likely to lose interest from participating. This compounds the fact that only experts with a specific/strong interest in the subject matter of the survey were invited to participate. Survey fatigue is addressed further in the Discussion section, where we provide an analysis suggesting little evidence of participant fatigue.

In sum, we designed the survey to protect against various sources of bias and error, and there are encouraging signs that the effects of these issues in the data are limited (see Discussion ). Yet expert surveys are inherently prone to some types of bias and response issues; in the WCI, the issue of selection and self-selection within our pool of experts, as well as geo-political biases that may lead to systematic over- or under-scoring of certain countries, is something we considered closely. We discuss these issues in detail in the subsection on Limitations below.

This “type” score is then multiplied by the proportion of experts who nominated that country. Within each cybercrime type, a country could be nominated a possible total of 92 times–once per participant. We then multiply this weighted score by ten to produce a continuous scale out of 100 (see Eq (2) ). This process prevents countries that received high scores, but a low number of nominations, from receiving artificially high rankings.

The analyses for this paper were performed in R. All data and code have been made publicly available so that our analysis can be reproduced and extended.

We contacted 245 individuals to participate in the survey, of which 147 agreed and were sent invitation links to participate. Out of these 147, a total of 92 people completed the survey, giving us an overall response rate of 37.5%. Given the expert nature of the sample, this is a high response rate (for a detailed discussion see [ 40 ]), and one just below what Wu, Zhao, and Fils-Aime estimate of response rates for general online surveys in social science: 44% [ 41 ]. The survey collected information on the participants’ primary nationality and their current country of residence. Four participants chose not to identify their nationality. Overall, participants represented all five major geopolitical regions (Africa, the Asia-Pacific, Europe, North America and South America), both in nationality and residence, though the distribution was uneven and concentrated in particular regions/countries. There were 8 participants from Africa, 11 participants from the Asia Pacific, 27 from North America, and 39 from Europe. South America was the least represented region with only 3 participants. A full breakdown of participants’ nationality, residence, and areas of expertise is included in the Supporting Information document (see S1 Appendix ).

Table 1 shows the scores for the top fifteen countries of the WCI overall index. Each entry shows the country, along with the mean score (out of 10) averaged across the participants who nominated this country, for three categories: impact, professionalism, and technical skill. This is followed by each country’s WCI overall and WCI type scores. Countries are ordered by their WCI overall score. Each country’s highest WCI type scores are highlighted. Full indices that include all 197 UN-recognised countries can be found in S1 Indices .

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https://doi.org/10.1371/journal.pone.0297312.t001

Some initial patterns can be observed from this table, as well as the full indices in the supplementary document (see S1 Indices ). First, a small number of countries hold consistently high ranks for cybercrime. Six countries–China, Russia, Ukraine, the US, Romania, and Nigeria–appear in the top 10 of every WCI type index, including the WCI overall index. Aside from Romania, all appear in the top three at least once. While appearing in a different order, the first ten countries in the Technical products/services and Attacks and extortion indices are the same. Second, despite this small list of countries regularly appearing as cybercrime hubs, the survey results capture a broad geographical diversity. All five geopolitical regions are represented across each type. Overall, 97 distinct countries were nominated by at least one expert. This can be broken down into the cybercrime categories. Technical products/services includes 41 different countries; Attacks and extortion 43; Data/identity theft 51; Scams 49; and Cashing out/money laundering 63.

Some key findings emerge from these results, which are further illustrated by the following Figs 1 and 2 . First, cybercrime is not universally distributed. Certain countries are cybercrime hubs, while many others are not associated with cybercriminality in a serious way. Second, countries that are cybercrime hubs specialise in particular types of cybercrime. That is, despite a small number of countries being leading producers of cybercrime, there is meaningful variation between them both across categories, and in relation to scores for impact, professionalism and technical skill. Third, the results show a longer list of cybercrime-producing countries than are usually included in publications on the geography of cybercrime. As the survey captures leading producers of cybercrime, rather than just any country where cybercrime is present, this suggests that, even if a small number of countries are of serious concern, and close to 100 are of little concern at all, the remaining half are of at least moderate concern.

Base map and data from OpenStreetMap and OpenStreetMap Foundation.

https://doi.org/10.1371/journal.pone.0297312.g001

https://doi.org/10.1371/journal.pone.0297312.g002

To examine further the second finding concerning hub specialisation, we calculated an overall “Technicality score”–or “T-score”–for the top 15 countries of the WCI overall index. We assigned a value from 2 to -2 to each type of cybercrime to designate the level of technical complexity involved. Technical products/services is the most technically complex type (2), followed by Attacks and extortion (1), Data/identity theft (0), Scams (-1), and finally Cashing out and money laundering (-2), which has very low technical complexity. We then multiplied each country’s WCI score for each cybercrime type by its assigned value–for instance, a Scams WCI score of 5 would be multiplied by -1, with a final modified score of -5. As a final step, for each country, we added all of their modified WCI scores across all five categories together to generate the T-score. Fig 3 plots the top 15 WCI overall countries’ T-scores, ordering them by score. Countries with negative T-scores are highlighted in red, and countries with positive scores are in black.

Negative values correspond to lower technicality, positive values to higher technicality.

https://doi.org/10.1371/journal.pone.0297312.g003

The T-score is best suited to characterising a given hub’s specialisation. For instance, as the line graph makes clear, Russia and Ukraine are highly technical cybercrime hubs, whereas Nigerian cybercriminals are engaged in less technical forms of cybercrime. But for countries that lie close to the centre (0), the story is more complex. Some may specialise in cybercrime types with middling technical complexity (e.g. Data/identity theft ). Others may specialise in both high- and low-tech crimes. In this sample of countries, India (-6.02) somewhat specialises in Scams but is otherwise a balanced hub, whereas Romania (10.41) and the USA (-2.62) specialise in both technical and non-technical crimes, balancing their scores towards zero. In short, each country has a distinct profile, indicating a unique local dimension.

This paper introduces a global and robust metric of cybercriminality–the World Cybercrime Index. The WCI moves past previous technical measures of cyber attack geography to establish a more focused measure of the geography of cybercrime offenders. Elicited through an expert survey, the WCI shows that cybercrime is not universally distributed. The key theoretical contribution of this index is to illustrate that cybercrime, often seen as a fluid and global type of organized crime, actually has a strong local dimension (in keeping with broader arguments by some scholars, such as [ 14 , 42 ]).

While we took a number of steps to ensure our sample of experts was geographically representative, the sample is skewed towards some regions (such as Europe) and some countries (such as the US). This may simply reflect the high concentration of leading cybercrime experts in these locations. But it is also possible this distribution reflects other factors, including the authors’ own social networks; the concentration of cybercrime taskforces and organisations in particular countries; the visibility of different nations on networking platforms like LinkedIn; and also perhaps norms of enthusiasm or suspicion towards foreign research projects, both inside particular organisations and between nations.

To better understand what biases might have influenced the survey data, we analysed participant rating behaviours with a series of linear regressions. Numerical ratings were the response and different participant characteristics–country of nationality; country of residence; crime type expertise; and regional expertise–were the predictors. Our analysis found evidence (p < 0.05) that participants assigned higher ratings to the countr(ies) they either reside in or are citizens of, though this was not a strong or consistent result. For instance, regional experts did not consistently rate their region of expertise more highly than other regions. European and North American experts, for example, rated countries from these regions lower than countries from other regions. Our analysis of cybercrime type expertise showed even less systematic rating behaviour, with no regression yielding a statistically significant (p < 0.05) result. Small sample sizes across other known participant characteristics meant that further analyses of rating behaviour could not be performed. This applied to, for instance, whether residents and citizens of the top ten countries in the WCI nominated their own countries more or less often than other experts. On this point: 46% of participants nominated their own country at some point in the survey, but the majority (83%) of nominations were for a country different to the participant’s own country of residence or nationality. This suggested limited bias towards nominating one’s own country. Overall, these analyses point to an encouraging observation: while there is a slight home-country bias, this does not systematically result in higher rating behaviour. Longitudinal data from future surveys, as well as a larger participant pool, will better clarify what other biases may affect rating behaviour.

There is little evidence to suggest that survey fatigue affected our data. As the survey progressed, the heterogeneity of nominated countries across all experts increased, from 41 different countries nominated in the first category to 63 different countries nominated in the final category. If fatigue played a significant role in the results then we would expect this number to decrease, as participants were not required to nominate countries within a category and would have been motivated to nominate fewer countries to avoid extending their survey time. We further investigated the data for evidence of survey fatigue in two additional ways: by performing a Mann-Kendall/Sen’s slope trend test (MK/S) to determine whether scores skewed significantly upwards or downwards towards the end of the survey; and by compiling an intra-individual response variability (IRV) index to search for long strings of repeated scores at the end of the survey [ 43 ]. The MK/S test was marginally statistically significant (p<0.048), but the results indicated that scores trended downwards only minimally (-0.002 slope coefficient). Likewise, while the IRV index uncovered a small group of participants (n = 5) who repeatedly inserted the same score, this behaviour was not more likely to happen at the end of the survey (see S7 and S8 Tables in S1 Appendix ).

It is encouraging that there is at least some external validation for the WCI’s highest ranked countries. Steenbergen and Marks [ 44 ] recommend that data produced from expert judgements should “demonstrate convergent validity with other measures of [the topic]–that is, the experts should provide evaluations of the same […] phenomenon that other measurement instruments pick up.” (p. 359) Most studies of the global cybercrime geography are, as noted in the introduction, based on technical measures that cannot accurately establish the true physical location of offenders (for example [ 1 , 4 , 28 , 33 , 45 ]). Comparing our results to these studies would therefore be of little value, as the phenomena being measured differs: they are measuring attack infrastructure, whereas the WCI measures offender location. Instead, looking at in-depth qualitative cybercrime case studies would provide a better comparison, at least for the small number of higher ranked countries. Though few such studies into profit-driven cybercrime exist, and the number of countries included are limited, we can see that the top ranked countries in the WCI match the key cybercrime producing countries discussed in the qualitative literature (see for example [ 3 , 10 , 32 , 46 – 50 ]). Beyond this qualitative support, our sampling strategy–discussed in the Methods section above–is our most robust control for ensuring the validity of our data.

Along with contributing to theoretical debates on the (local) nature of organized crime [ 1 , 14 ], this index can also contribute to policy discussions. For instance, there is an ongoing debate as to the best approaches to take in cybercrime reduction, whether this involves improving cyber-law enforcement capacity [ 3 , 51 ], increasing legitimate job opportunities and access to youth programs for potential offenders [ 52 , 53 ], strengthening international agreements and law harmonization [ 54 – 56 ], developing more sophisticated and culturally-specific social engineering countermeasures [ 57 ], or reducing corruption [ 3 , 58 ]. As demonstrated by the geographical, economic, and political diversity of the top 15 countries (see Table 1 ), the likelihood that a single strategy will work in all cases is low. If cybercrime is driven by local factors, then mitigating it may require a localised approach that considers the different features of cybercrime in these contexts. But no matter what strategies are applied in the fight against cybercrime, they should be targeted at the countries that produce the most cybercrime, or at least produce the most impactful forms of it [ 3 ]. An index is a valuable resource for determining these countries and directing resources appropriately. Future research that explains what is driving cybercrime in these locations might also suggest more appropriate means for tackling the problem. Such an analysis could examine relevant correlates, such as corruption, law enforcement capacity, internet penetration, education levels and so on to inform/test a theoretically-driven model of what drives cybercrime production in some locations, but not others. It also might be possible to make a kind of prediction: to identify those nations that have not yet emerged as cybercrime hubs but may in the future. This would allow an early warning system of sorts for policymakers seeking to prevent cybercrime around the world.

Limitations

In addition to the points discussed above, the findings of the WCI should be considered in light of some remaining limitations. Firstly, as noted in the methods, our pool of experts was not as large or as globally representative as we had hoped. Achieving a significant response rate is a common issue across all surveys, and is especially difficult in those that employ the snowball technique [ 59 ] and also attempt to recruit experts [ 60 ]. However, ensuring that our survey data captures the most accurate picture of cybercrime activity is an essential aspect of the project, and the under-representation of experts from Africa and South America is noteworthy. More generally, our sample size (n = 92) is relatively small. Future iterations of the WCI survey should focus on recruiting a larger pool of experts, especially those from under-represented regions. However, this is a small and hard-to-reach population, which likely means the sample size will not grow significantly. While this limits statistical power, it is also a strength of the survey: by ensuring that we only recruit the top cybercrime experts in the world, the weight and validity of our data increases.

Secondly, though we developed our cybercrime types and measures with expert focus groups, the definitions used in the WCI will always be contestable. For instance, a small number of comments left at the end of the survey indicated that the Cashing out/money laundering category was unclear to some participants, who were unsure whether they should nominate the country in which these schemes are organised or the countries in which the actual cash out occurs. A small number of participants also commented that they were not sure whether the ‘impact’ of a country’s cybercrime output should be measured in terms of cost, social change, or some other metric. We limited any such uncertainties by running a series of focus groups to check that our categories were accurate to the cybercrime reality and comprehensible to practitioners in this area. We also ran a pilot version of the survey. The beginning of the survey described the WCI’s purpose and terms of reference, and participants were able to download a document that described the project’s methodology in further detail. Each time a participant was prompted to nominate countries as a significant source of a type of cybercrime, the type was re-defined and examples of offences under that type were provided. However, the examples were not exhaustive and the definitions were brief. This was done partly to avoid significantly lengthening the survey with detailed definitions and clarifications. We also wanted to avoid over-defining the cybercrime types so that any new techniques or attack types that emerged while the survey ran would be included in the data. Nonetheless, there will always remain some elasticity around participant interpretations of the survey.

Finally, although we restricted the WCI to profit-driven activity, the distinction between cybercrime that is financially-motivated, and cybercrime that is motivated by other interests, is sometimes blurred. Offenders who typically commit profit-driven offences may also engage in state-sponsored activities. Some of the countries with high rankings within the WCI may shelter profit-driven cybercriminals who are protected by corrupt state actors of various kinds, or who have other kinds of relationships with the state. Actors in these countries may operate under the (implicit or explicit) sanctioning of local police or government officials to engage in cybercrime. Thus while the WCI excludes state-based attacks, it may include profit-driven cybercriminals who are protected by states. Investigating the intersection between profit-driven cybercrime and the state is a strong focus in our ongoing and future research. If we continue to see evidence that these activities can overlap (see for example [ 32 , 61 – 63 ]), then any models explaining the drivers of cybercrime will need to address this increasingly important aspect of local cybercrime hubs.

This study makes use of an expert survey to better measure the geography of profit-driven cybercrime and presents the output of this effort: the World Cybercrime Index. This index, organised around five major categories of cybercrime, sheds light on the geographical concentrations of financially-motivated cybercrime offenders. The findings reveal that a select few countries pose the most significant cybercriminal threat. By illustrating that hubs often specialise in particular forms of cybercrime, the WCI also offers valuable insights into the local dimension of cybercrime. This study provides a foundation for devising a theoretically-driven model to explain why some countries produce more cybercrime than others. By contributing to a deeper understanding of cybercrime as a localised phenomenon, the WCI may help lift the veil of anonymity that protects cybercriminals and thereby enhance global efforts to combat this evolving threat.

Supporting information

S1 indices. wci indices..

Full indices for the WCI Overall and each WCI Type.

https://doi.org/10.1371/journal.pone.0297312.s001

S1 Appendix. Supporting information.

Details of respondent characteristics and analysis of rating behaviour.

https://doi.org/10.1371/journal.pone.0297312.s002

Acknowledgments

The data collection for this project was carried out as part of a partnership between the Department of Sociology, University of Oxford and UNSW Canberra Cyber. The analysis and writing phases received support from CRIMGOV. Fig 1 was generated using information from OpenStreetMap and OpenStreetMap Foundation, which is made available under the Open Database License.

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Energy, Economy, and Climate Interactions: Challenges and Opportunities - Volume II

The Spatial Effect of Integrated Economy on Carbon Emissions in the Era of Big Data: A Case Study of China Provisionally Accepted

• 1 School of Economic and Management, Xi'an University of Technology, China
• 2 School of Business and Circulation, Shaanxi Polytechnic Institute, China

The final, formatted version of the article will be published soon.

The digital economy has the characteristics of resource conservation, which can solve China's high carbon emissions problems. The digital economy can quickly integrate with the real economy, forming an integrated economy. However, it is still unclear whether an integrated economy can effectively reduce carbon emissions and achieve China's 'dual carbon goals'. Therefore, this study takes 30 provinces in China as the research object, constructs the integration economy index system through the statistical data from 2011-2021, and explores the spatial effect of the impact of the integration economy on carbon emissions by using principal component analysis, coupled coordination model and spatial econometric model. The research results are as follows. (1) From 2011 to 2021, the comprehensive economy showed a trend of increasing yearly (from 0.667 to 0.828), and carbon emissions showed a slow decrease (from 0.026 to 0.017). (2) Due to the infiltration of China's economic development from the eastern to the western, the spatial distribution of the integrated economy shows a decreasing trend from east to west. The spatial distribution of carbon emissions may be related to China's industrial layout of heavy industry in the northern, and light industry in the southern, showing a trend of low in the south and high in the north. (3) The integrated economy can significantly reduce carbon emissions (the coefficients of influence, -0.146), and the reduction effect will be more obvious if spatial spillover effects are taken into account (-0.305). ( 4) The eastern coast, the middle reaches of the Yangtze River, and the middle reaches of the Yellow River economic zones all increase carbon emissions at a certain level of significance (0.065, 0.148, and 3.890). The Northeast, South Coastal and Southwest economic zones significantly reduce carbon emissions (-0.220, -0.092, and -0.308). The results of the Northern Coast and Northwest are not significant (-0.022 and 0.095). ( 5) China should tailor regional economic development policies, such as strengthening investment in digital infrastructure in the Northwest Economic Zone and fully leveraging the spatial spillover effects of integrated economy in the Northeast, Southern Coastal, and Southwest Economic Zones to reduce carbon emissions.

Keywords: Integrated economy, carbon emissions, digital economy, Real economy, spatial effect, China

Received: 22 Jan 2024; Accepted: 11 Apr 2024.

Copyright: © 2024 Wang, Ke and Lei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Miss. Qian Ke, Xi'an University of Technology, School of Economic and Management, Xi'an, 710048, Shaanxi, China

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Published on 10.4.2024 in Vol 10 (2024)

Chronic Disease Patterns and Their Relationship With Health-Related Quality of Life in South Korean Older Adults With the 2021 Korean National Health and Nutrition Examination Survey: Latent Class Analysis

Authors of this article:

Original Paper

• Mi-Sun Lee, PhD   ; 
• Hooyeon Lee, MD, PhD  

Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Corresponding Author:

Hooyeon Lee, MD, PhD

Department of Preventive Medicine

College of Medicine

The Catholic University of Korea

222, Banpo-daero

Seoul, 06591

Republic of Korea

Phone: 82 2 3147 8381

Fax:82 2 532 3820

Email: [email protected]

Background: Improved life expectancy has increased the prevalence of older adults living with multimorbidities, which likely deteriorates their health-related quality of life (HRQoL). Understanding which chronic conditions frequently co-occur can facilitate person-centered care tailored to the needs of individuals with specific multimorbidity profiles.

Objective: The study objectives were to (1) examine the prevalence of multimorbidity among Korean older adults (ie, those aged 65 years and older), (2) investigate chronic disease patterns using latent class analysis, and (3) assess which chronic disease patterns are more strongly associated with HRQoL.

Methods: A sample of 1806 individuals aged 65 years and older from the 2021 Korean National Health and Nutrition Examination Survey was analyzed. Latent class analysis was conducted to identify the clustering pattern of chronic diseases. HRQoL was assessed by an 8-item health-related quality of life scale (HINT-8). Multiple linear regression was used to analyze the association with the total score of the HINT-8. Logistic regression analysis was performed to evaluate the odds ratio of having problems according to the HINT-8 items.

Results: The prevalence of multimorbidity in the sample was 54.8%. Three chronic disease patterns were identified: relatively healthy, cardiometabolic condition, arthritis, allergy, or asthma. The total scores of the HINT-8 were the highest in participants characterized as arthritis, allergy, or asthma group, indicating the lowest quality of life.

Conclusions: Current health care models are disease-oriented, meaning that the management of chronic conditions applies to a single condition and may not be relevant to those with multimorbidities. Identifying chronic disease patterns and their impact on overall health and well-being is critical for guiding integrated care.

Introduction

The prevalence of chronic conditions among older populations is growing due to the progressive increase in life expectancy [ 1 ]. Multimorbidity, defined as the co-occurrence of 2 or more chronic conditions, continues to increase worldwide, presenting one of today’s major challenges to health at the individual and population levels [ 1 , 2 ]. Certain chronic diseases tend to co-occur more often than expected by chance because they share pathophysiological pathways [ 2 , 3 ].

One systematic review reported multimorbidity patterns, especially cardiometabolic conditions, mental health issues, and musculoskeletal disorders [ 4 ]. Another study showed 3 multimorbidity groups: cardiovascular and metabolic diseases, mental health problems, and allergic diseases [ 5 , 6 ]. Hypertension, dyslipidemia, stroke, and diabetes mellitus are highly likely to co-occur [ 2 ], with hypertension having stronger connections with hyperlipidemia and diabetes than other pairs of morbidities in Korean older adults [ 2 ]. Health-related quality of life (HRQoL) is an important health outcome indicator in the aging process. Previous observational studies have shown the negative effects of multimorbidity on HRQoL [ 7 , 8 ].

While multimorbidity studies have applied a variety of methodologies, such as cluster analysis, factor analysis, and latent class analysis (LCA), none of these approaches enable direct comparisons. LCA is preferred to conventional clustering because it uses probability-based classification methods to select an optimal number of classes based on various diagnostic tests [ 3 , 9 ], which allows researchers to group individuals into a number of latent classes and then analyze the differences between those classes [ 10 ].

South Korea, which became an aged society in 2017, is the world’s fastest aging country, with more than 14% of its population aged 65 years and older [ 6 ]. Further, among Korean adults aged 50 years and older, 39% have 2 or more chronic diseases [ 3 ]. However, few studies have applied LCA to extract the multimorbidity patterns among Korean older adults. Understanding which chronic conditions frequently co-occur can facilitate person-centered care tailored to the needs of individuals with specific profiles of multimorbidity [ 7 , 8 , 11 , 12 ]. One of the first steps is to identify the prevalence and distinct patterns of multimorbidity to inform clinical guidelines and facilitate integrated care [ 9 , 10 ]. Chronic conditions tend to cluster together into multimorbidity patterns. However, further replicability of results between studies that use different methodologies is an important step toward moving from exploratory to confirmatory approaches.

Therefore, in this study, we aimed to (1) examine the prevalence of multimorbidity among Korean older adults, (2) investigate chronic disease patterns using LCA, and (3) assess which chronic disease patterns are more strongly associated with HRQoL using data taken from the 2021 Korean National Health and Nutrition Examination Survey (KNHANES).

Data and Study Population

The KNHANES is conducted annually to assess the health and health-related behaviors of the Korean population based on Article 16 of the National Health Promotion Act. It collects comprehensive data on the entire Korean population’s sociodemographic characteristics, health behaviors, health status, biochemical profiles, disease history, and nutrient intake [ 4 , 5 ]. A stratified, multistage probability sampling method was used. Participants included Korean family members aged 1 year and older in the selected households, corresponding to 9682 individuals. The health interview and health examination surveys were conducted in mobile examination centers, while the nutrition survey was performed by visiting households [ 6 ]. Of the 9682 eligible participants, 7090 (response rate=73.2%) ultimately participated in the survey. Of these survey respondents, we finally selected 1806 individuals aged 65 years and older.

Measurements

Chronic diseases.

Multimorbidity is indicated by the presence of 2 or more of the diseases in a single individual. The diagnosis of chronic conditions was based on whether the respondent had ever been diagnosed with a chronic disease by a doctor and received treatment for that disease [ 3 , 7 , 8 ]. When the prevalence of multimorbidity is low, the sample size for certain combinations of multimorbid conditions may be insufficient to provide reliable results [ 9 ]. Therefore, our measurement of multimorbidity was limited to 9 chronic conditions with a prevalence of at least 3% among the older people measured in the KNHANES [ 10 , 11 ]: hypertension, dyslipidemia, myocardial infarction, stroke, diabetes mellitus, arthritis, osteoarthritis, asthma, or allergic rhinitis.

Health-Related Quality of Life

The HRQoL was assessed by the 8-item health-related quality of life scale (HINT-8), an instrument developed in 2014 as a quality-of-life scale reflective of Korean culture [ 5 ]. The items on the HINT-8 are difficulty climbing stairs, pain, lack of vitality, difficulty working, depression, difficulty in memory, sleep problems, and unhappiness [ 12 ]. Each question is rated at 4 levels (none, mild, moderate, and severe problems). The total score ranges from 4 to 32 points. Higher scores on the HINT-8 indicate a poorer quality of life. These 4 levels were divided into one group having “no problems” and the other having “problems” (mild, moderate, and severe problems) [ 13 - 15 ]. In this study, the Cronbach α coefficient of the HINT-8 indicated satisfactory internal consistency (α=0.817).

The covariate variables were age, sex, household income, education level, employment status, marital status, current smoking status, and current drinking status. Household income included wages, pensions, bank interest, social security benefits, and unemployment benefits [ 16 ]. Marital status was classified as married, cohabiting, divorced, bereaved, or separated. Current alcohol consumption was defined as adults who consumed alcohol at least once in the previous month [ 4 , 17 ]. Current smoking was defined as an adult with a lifetime smoking history of 5 packs or more who also currently smokes [ 17 ]. Household income was defined as the monthly average gross income divided by an equivalence factor to adjust for differences in household size and composition [ 18 ] and was categorized into quintiles. This study reclassified the quintiles into 3 groups (upper 2 quintiles, middle 2 quintiles, and lower 2 quintiles). Education level was categorized as middle school graduation or below, high school graduation, and college graduation or above. Employment status was categorized as employed or unemployed.

Statistical Analysis

Latent class analysis.

LCA is a statistical procedure used to identify qualitatively different subgroups within populations who often share certain outward characteristics [ 19 ]. Subgroups are referred to as latent groups (or classes). To detect latent groups, LCA uses participants’ responses to categorical variables. Individuals with similar chronic disease patterns are then classified into distinct subgroups, and a label is derived for each pattern based on the salient characteristics [ 20 ]. This special case of person-centered mixture modeling can thus identify the latent subpopulations within a sample based on patterns of responses to observed variables [ 19 ].

LCA was conducted using the R-based Jamovi (version 2.3.24; The Jamovi Project). Models with 1-5 classes were estimated. The likelihood ratio statistic (G 2 ), Akaike information criterion, Bayesian information criterion, and entropy value (0.0-1.0, ≥0.70 is acceptable) were used to identify the optimal number of classes [ 21 ]. The final selection model considered class distinction, plot interpretability, and estimated sample size. The multiple fit statistics indicated that a 3-class model was the best fit for this study’s data (G 2 =–522, Akaike information criterion 14,352, Bayesian information criterion 14,604, entropy 0.876; P =.01; Table S1 and Figure S1 in Multimedia Appendix). These 3 classes were called the relatively healthy group (class 1), the cardiometabolic condition group (class 2), and the arthritis, allergy, or asthma group (class 3).

Association Analysis

After determining the 3 latent classes, descriptive statistics, analysis of variance, and chi-square tests were used to analyze the prevalence distribution of the participants’ sociodemographic characteristics and HRQoL. Multiple linear regression analyses were conducted to assess the association between the contribution of the latent classes and the total score of the HINT-8 after adjusting for age, sex, household income, education level, marital status, employment status, current smoking status, and current drinking status.

Multiple logistic regression analyses were conducted separately for each HINT-8 subfactor (difficulty in climbing stairs, pain, lack of vitality, difficulty working, depression, memory difficulties, sleep problems, and unhappiness). The odds ratios (ORs) and 95% CIs were calculated after adjusting for the covariates. A multistage cluster sampling design was adopted using the SPSS Complex Sample, SPSS/WIN (version 25.0; IBM Corp) program.

Ethical Considerations

The 2021 KNHANES was approved by the institutional review board (IRB) of the Korea Centers for Disease Control and Prevention (2018-01-03-5C-A). This study used publicly available secondary, deidentified data. The IRB review exemption was approved by the IRB of the Catholic University of Korea.

Chronic Disease Patterns

Table 1 presents the estimated probabilities of belonging to the 3 classes. Class 1 patients had the lowest probability of having any of the 9 chronic diseases. Class 2 patients had a high probability of having hypertension, dyslipidemia, diabetes mellitus, myocardial infarction, and stroke. Class 3 patients had a high probability of having arthritis, osteoarthritis, allergic rhinitis, or asthma. Figure 1 shows the distribution of the three latent classes according to the 9 chronic diseases.

Table 2 presents the sociodemographic characteristics, chronic disease patterns, and HRQoL according to the latent classes. Of this study’s sample of 1806 Korean older adults, 42.7% (771/1806) were male participants. The mean age was 73.36 (SD 5.18) years. The prevalence of multimorbidity in the sample was 54.8%. The mean HRQoL measured by the HINT-8 was 14.07 (SD 3.89).

Among the 3 latent classes, class 1 (741/1806, 41.03%) had the lowest prevalence of current smoking and drinking. The mean number of chronic diseases was 0.57 (SD 0.59). The class 2 (636/1806, 35.22%) group with cardiometabolic conditions had a mean 2.65 (SD 1.06) chronic diseases per individual. Among them, 71.9% (447/622) experienced pain, 67.2% (418/622) lacked vitality, 44.8% (278/620) had depression, and 74% (459/620) felt unhappy. Of the class 3 group (429/1806, 23.75%) that included individuals with arthritis, allergies, or asthma, 79.8% (340/426) of the participants reported difficulty in climbing stairs, 81.2% (346/426) reported pain, 55.2% (342/426) reported depression, and 64.6% (275/426) reported sleep problems. The mean number of chronic diseases in class 3 was 2.50 (SD 1.13). Compared with class 1 participants, those participants classified into classes 2 and 3 were older, more likely to be female participants, and had lower levels of education.

a These indices had the highest probabilities.

a N/A: not applicable.

b ANOVA was performed for the continuous variables.

c HINT-8: 8-item health-related quality of life instrument.

Associations Between Multimorbidity Classes and HRQoL

Table 3 presents the associations among the 3 latent classes and the total scores of the HINT-8. Class 2 patients had higher HRQoL scores by 1.14 points than class 1. Class 3 patients had higher HRQoL scores by 2.30 points than class 1 patients, which was significant.

Table 4 presents the adjusted ORs and shows the associations between chronic disease patterns and HRQoL based on the HINT-8 items. The class 2 group had a higher OR of difficulty in climbing stairs (OR 1.49, 95% CI 1.08-2.06), pain (OR 1.58, 95% CI 1.13-2.23), and unhappiness (OR 1.28, 95% CI 1.03-1.69) than the class 1 group. The class 3 group had a higher OR of difficulty in climbing stairs (OR 1.92, 95% CI 1.35-2.71), pain (OR 1.88, 95% CI 1.32-2.67), lack of vitality (OR 1.35, 95% CI 1.02-1.79), depression (OR 2.51, 95% CI 1.09-4.98), sleep problems (OR 1.46, 95% CI 1.01-2.10), and unhappiness (OR 1.54, 95% CI 1.13-2.26) than the class 1 group. Generally, the class 3 group was more likely to have problems in all 8 items of the HINT-8, even in the absence of a statistically significant result for the lack of vitality and memory difficulties.

a P <.001.

b N/A: not applicable.

a Multiple logistic regression analysis was performed by classifying the dependent variable as binary.

b OR: odds ratio.

Principal Findings and Comparison With Previous Work

This study investigated the prevalence of multimorbidity, chronic disease patterns, and the association between latent classes and quality of life among Korean older adults using LCA. Of the sample of 1806 Korean older adults, 54.8% had multimorbidities, with a mean number of chronic conditions of 1.82 (SD 1.35). This prevalence of multimorbidity was lower than that found by previous studies in other countries. At least two-thirds of those older than 65 years have multimorbidities in the United Kingdom and the United States [ 22 , 23 ], while approximately 80% of Australians aged 65 years and older have 3 or more chronic conditions [ 8 ]. The standardized national prevalences of multimorbidity in Japan are approximately 50% for ages between 60 and 69 years and 60% for ages between 70 and 74 years [ 24 ].

Using LCA, this study identified 3 chronic disease groups: a relatively healthy group without multimorbidities, a cardiometabolic condition group, and an arthritis, allergy, or asthma group. The cardiometabolic condition group had a high probability of having hypertension, dyslipidemia, diabetes mellitus, myocardial infarction, and stroke, while the other group had a high probability of experiencing arthritis, osteoarthritis, allergic rhinitis, or asthma. Similar disease patterns to those found in this study have been noted previously [ 3 , 25 - 28 ]. Cardiometabolic diseases are one of the most reproducible multimorbidity patterns [ 29 ]. The association between dietary patterns and multimorbidity within the cardiometabolic domain suggests the importance of dietary interventions in preventing and managing multimorbidity [ 30 ].

Our findings were also similar to the results of another study among Koreans aged 50 years and older that classified hypertension, hyperlipidemia, diabetes, and stroke as a single latent class of multimorbidity using data from the KNHANES conducted in 2013 and 2014 [ 3 ]. Another study using a network analysis approach indicated that hypertension had stronger connections with hyperlipidemia and diabetes than other pairs of morbidities in older adults, using data from the KNHANES conducted between 2010 and 2018. The substantial overlap between hypertension, diabetes, and hyperlipidemia in etiology and disease mechanisms might have resulted in this phenomenon [ 2 ]. The further replicability of results between studies that use different methodologies is an important step toward moving from exploratory to confirmatory approaches.

This study indicated that the association between chronic disease patterns and HRQoL differed by multimorbidity group. The cardiometabolic condition and arthritis, allergy, or asthma groups had lower HRQoL than the relatively healthy group [ 31 - 34 ]. Moreover, the total scores of the HINT-8 were higher in the arthritis, allergy, or asthma group than in the cardiometabolic condition group, indicating a lower quality of life [ 35 ].

Compared with older adults with a single chronic disease, those with multiple chronic conditions face mental health-related social needs and social isolation [ 36 , 37 ]. Their low quality of life and combination of chronic diseases may diminish their overall health [ 28 ]. Thus, these chronic disease patterns can be used to support better screening, targeted management, and integrated health services for older adults with chronic diseases [ 32 , 38 , 39 ]. Further research is required to investigate whether multimorbidity associations are similar to biological pathways or the result of interactions.

Current health care models are disease-oriented, meaning that the management of chronic conditions applies to a single condition and may not be relevant to those with multimorbidities [ 40 ]. Individuals with multimorbidities typically receive services from multiple health care specialists who only focus on a single health condition and do not view them in their entirety. A key challenge faced by individuals with multimorbidities, and health care professionals is thus fragmented clinical care, which creates miscommunication; this may result in inconsistent health care messages and could lead to an increase in polypharmacy [ 28 ]. Consequently, further research is needed to develop guidelines and approaches for the management of multimorbidity and identify chronic disease groups.

Limitations

This study had some limitations. First, it focused on a limited range of chronic diseases, specifically those with a prevalence of at least 3% as reported by the KNHANES; hence, the list of chronic conditions may not have been exhaustive. If more diseases are considered, a higher prevalence could be estimated [ 41 ]. However, while we performed an additional LCA including all the chronic diseases registered in the KNHANES data set, the samples for certain combinations of multimorbidities were insufficient to provide reliable results despite our relatively large sample overall [ 9 ].

Second, the findings could differ if a broader scope of chronic conditions is considered along with the different aspects of the conditions. A long illness negatively impacts the quality of life of chronic patients [ 42 ]. Moreover, information on disease severity may have helped distinguish between patterns; unfortunately, these data were unavailable.

Third, the participants self-reported their chronic diseases, increasing the risk of reporting bias. However, self-reported data are the most appropriate alternative in the absence of objective diagnoses and have been shown to provide solid estimates [ 9 , 43 ].

Finally, the selected class name in LCA may not always accurately represent class membership [ 20 ]. However, any issues arising from this so-called “naming fallacy” may be mediated by presenting the distribution of the selected indicators for each class (Table 1) to allow an individual interpretation. Despite these limitations, the chronic disease patterns among older adults were examined using nationally representative data.

Conclusions

This study identified 3 distinct chronic disease groups among Korean older adults: a relatively healthy group without multimorbidities, a cardiometabolic condition group, and an arthritis, allergy, or asthma group. Those in the latter group had a lower quality of life than those in the cardiometabolic condition group. Understanding these combinations of long-term conditions may help target disease prevention and improve care integration efforts. The further replicability of results between studies that use different methodologies is an important step toward moving from exploratory to confirmatory approaches. Future research on the effectiveness of interventions aimed at preventing and improving the management of multimorbidities is needed.

Data Availability

The data sets generated during and/or analyzed during this study are available in the Korean National Health and Nutritional Examination Survey website repository.

Conflicts of Interest

None declared.

Latent class model identification and model fit statistics and elbow plot of the latent class model fit.

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Abbreviations

Edited by A Mavragani; submitted 29.05.23; peer-reviewed by I Del Cura-González, R Meng; comments to author 06.10.23; revised version received 03.01.24; accepted 04.03.24; published 10.04.24.

©Mi-Sun Lee, Hooyeon Lee. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 10.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

• Open access
• Published: 12 April 2024

A prospective observational cohort study of covid-19 epidemiology and vaccine seroconversion in South Western Sydney, Australia, during the 2021–2022 pandemic period.

• Daniela Potter 1 , 2 , 3 ,
• Jason Diep 3 ,
• Colleen Munro 3 ,
• Noelle Lin 3 ,
• Ramon Xu 3 ,
• Jeffrey Wong 3 ,
• Robert Porritt 4 ,
• Michael Maley 4 ,
• Hong Foo 4 &
• Angela Makris 1 , 2 , 3  

BMC Nephrology volume  25 , Article number:  131 ( 2024 ) Cite this article

Metrics details

It is known that COVID-19 disproportionally adversely affects the immunocompromised, including kidney transplant recipients (KTR), as compared to the general population. Risk factors for adverse outcomes and vaccine seroconversion patterns are not fully understood. Australia was uniquely positioned to reduce initial case numbers during the 2021–2022 pandemic period due to its relative isolation and several significant public health interventions. South-Western Sydney Local Heath District was one of the predominant regions affected.

A single centre, prospective cohort study of prevalent renal transplant recipients was conducted between 25th July 2021 and 1st May 2022. Baseline characteristics, COVID-19 vaccination status, COVID-19 diagnosis and outcomes were determined from the electronic medical record, Australian vaccination register and Australian and New Zealand Dialysis and Transplant Registry. Assessment of vaccine-induced seroconversion was assessed with ELISA in a subpopulation. Analysis was performed using SPSS v.28.

We identified 444 prevalent transplant recipients (60% male, 50% diabetic, median age 58 years (Interquartile range (IQR)21.0) and eGFR 56 ml/min/1.73m 2 (IQR 21.9). COVID-19 was identified in 32% ( n  = 142) of patients, of which 38% ( n  = 54) required hospitalisation and 7% ( n  = 10) died. At least one COVID-19 vaccination was received by 95% ( n  = 423) with 17 (4%) patients remaining unvaccinated throughout the study period. Seroconversion after 2 and 3 doses of vaccine was 22% and 48% respectively. Increased COVID-19 related deaths were associated with older age (aOR 1.1, 95% CI 1.004–1.192, p  = 0.040), smoking exposure (aOR 8.2, 05% CI 1.020-65.649, p  = 0.048) and respiratory disease (aOR 14.2, 95%CI:1.825–110.930, p  = 0.011) on multi-variable regression analysis. Receipt of three doses of vaccination was protective against acquiring COVID-19 (aOR 0.48, 95% CI 0.287–0.796, p  = 0.005) and death (aOR 0.6, 95% CI: 0.007–0.523, p  = 0.011), but not against hospitalisation ( p  = 0.32). Seroconversion was protective for acquiring COVID-19 on multi-variable regression independent of vaccination dose (aOR 0.1, 95%CI: 0.0025–0.523, p  = 0.011).

Conclusions

COVID-19 was associated with a high mortality rate. Older age, respiratory disease and prior smoking exposure may be risk factors for increased mortality. Vaccination of 3 doses is protective against acquiring COVID-19 and death, however not hospitalisation. Antibody response is protective for acquiring COVID-19, however seroconversion rates are low.

Peer Review reports

Introduction

It is known that COVID-19 disproportionally adversely affects the immunocompromised, including kidney transplant recipients (KTR), as compared to the general population. The advent of specific COVID-19 therapies and novel vaccination improved outcomes, however mortality rates for organ transplant recipients from large cohort studies remained as high as 14% into 2021 [ 1 , 2 ]. Factors predicting mortality are not fully understood but age, cardiovascular disease, diabetes, and certain immunosuppression regimens have been suggested [ 1 , 3 , 4 , 5 , 6 , 7 ]. KTRs were prioritised for vaccine administration, however, were not included in original vaccination trials [ 8 , 9 ]. Subsequent data suggests conventional 2-dose regimens are insufficient for KTRs, with 3 doses potentially ineffective against later strains such as BA.1 (Omicron) [ 10 , 11 , 12 ]. The primary course of vaccination was extended, between March 2021 and July 2022, to 5 doses in Australia, however adequate ongoing vaccination strategies are unclear [ 13 ].

COVID-19 in Australia and South Western Sydney

Australia was protected from high case numbers during the early phases of the pandemic due to its geographical isolation, strict initial international border controls and aggressive case tracking. Although Australia comprises 6 states and two territories, each have a significant degree of independence and power in health policy making. Those states with low case numbers throughout 2020–2022, such as South and Western Australia, maintained strict international and interstate border controls, but relaxed internal restrictions with almost near normal, pre-COVID, living conditions. They enacted limited, “snap lockdowns” in response to small numbers of detected cases to keep COVID-19 suppressed, until the majority of the population could be vaccinated [ 14 ]. Within New South Wales, however, several significant outbreaks occurred in 2021–2022, prompting repeated modification of public orders and prolonged periods of community lockdown and restrictions [ 15 ]. South Western Sydney Local Health District (SWSLHD) was one of the first areas in New South Wales (NSW) to be affected by COVID-19, and experienced one of the higher reported case numbers and the highest reported deaths of any Local Health District in NSW [ 16 ]. In response to the high rates of infection, SWSLHD experienced the most restrictive lockdown regulations in NSW during the pandemic period. During the second NSW wave in 2021, several local government areas within SWSLHD were classed as “areas of concern” and had additional public orders imposed, including: a stay at home order, restrictions on entering or leaving a district except for specific work exemptions (which required a permit), not allowed to travel more than 5 km for exercise, mandatory mask wearing outside and, at one point, a 9pm to 5am curfew [ 17 ]. August and September 2021 was associated with peak B.1.617.2 (Delta) wave incidence, followed by peak BA.1 (Omicron) in January 2022 [ 16 ]. COVID-19 vaccination was available for immunosuppressed individuals in Australia from 22nd March 2021 [ 18 ]. There is also a large burden of chronic kidney disease (CKD), with SWSLHD accounting for approximately 3.3% of prevalent KTRs in Australia. SWSLHD is also diverse, multiethnic population with 54% of people speaking a language other than English, predominantly Arabic or Vietnamese, and 43% of the population were born overseas, in comparison to 29% to the rest of NSW [ 19 , 20 ].

This study was conducted in the 2nd to 3rd year of the pandemic, during two dominant strain outbreaks, B.1.617.2 (Delta) and BA.1.(Omicron), after vaccination was available for all recipients [ 15 ]. Our objective was to ascertain the impact of COVID-19 on KTRS, with a focus on acquisition, hospitalisation, and mortality from COVID-19, and to perform a serology assessment of vaccine seroconversion.

Study design

A single centre (SWSLHD) prospective cohort study of prevalent kidney transplant recipients was undertaken between 25th July 2021 and 1st May 2022.

The study was commenced prospectively, coinciding with the onset of rising COVID-19 transmission and initiation of community stay at home orders. After restrictions had ended, vaccination numbers had increased, and it was clear no further public health orders were likely to be initiated, the study was terminated. All KTRs were strongly encouraged to receive vaccination throughout the study period, via national public health messaging, family practitioner support, and nephrologist advice. The Renal department at SWSLHD undertook a program at this time to encourage immunisation by developing a multi-lingual (Arabic, Vietnamese) information letter in view of the multi-ethnic population (distributed, mailed or emailed) to all KTRs and dialysis patients. A dedicated contact nephrologist was available to answer vaccination specific queries to facilitate timely immunisation.

Participants

All prevalent KTRs, aged  ≥  18, were included in the initial observational component of the study (see ethics below). Patients were identified from an existing clinical database and cross-referenced by searching the entire health district electronic coding system for renal transplantation to reduce the risk of selection bias. After the final data collection point on 1st May 2022 the cohorts of COVID-19 positive and COVID-19 negative patients were identified.

Variables and data sources

Baseline clinical and transplant characteristics, including: age, sex, body mass index, place of birth, smoking status, primary renal disease, co-morbidities, baseline eGFR, use of any blood thinner, prior dialysis modality and modality change during study, requirement for an interpreter, number of transplants, donor type, number of mismatches, transplant vintage, baseline immunosuppression regiment, dosage and levels and administration of Anti-thymocyte globulin, were determined from the electronic medical record, the Australian and New Zealand Dialysis and Transplant Registry (ANZDATA) records, and locally available Nephrologist letters. The date and brand of each COVID-19 vaccination is recorded into the Australian Immunisation Register and electronic health record prospectively. We obtained information on every dose of COVID-19 vaccination provided to patients. We assessed the impact of increasing vaccination dose, from 1 onwards. COVID-19 diagnosis, and outcomes were determined from the electronic medical record, including date of diagnosis, administration of sotrovimab or molpurinovir, hospitalisation and level of care for COVID-19, oxygen requirement, use of dexamethasone and other adjunctive agents including baricitinib, tocilizumab, remdesivir and sarilumab, length of stay and mortality from COVID-19.

The combination of both ANZDATA records, local electronic health record and Nephrologist letters was utilised to reduce missing data and increase accuracy of data imputation. The study period encompassed a period of mandatory reporting of all positive COVID-19 polymerase chain results and rapid antigen tests to the NSW Health Service. In SWSLHD each positive result was reviewed by a dedicated COVID-19 Community Health service and documented in the electronic health record, which we anticipated would reduce the impact of sampling bias and missing data.

Serology assessment

All patients were invited to participate in the post COVID-19 vaccination serology conversion assessment component of the study. At study commencement all patients received a multi-lingual text message (English, Arabic, or Vietnamese) offering participation in COVID-19 serologic conversion testing. Additional written informed consent for this component of the study was obtained from those willing to participate. Blood tests we requested to be performed at least 14 days after their 2nd and 3rd vaccine dose. These patients were planned to be analysed as a subgroup from the main cohort.

All patient serum underwent testing at NSW Health Pathology– Liverpool, using both the Roche Elecsys Anti-SARS-CoV-2 assay (“Elecsys”) and the EUROIMMUN Anti-SARS-CoV-2 QuantiVAC ELISA (“QuantiVAC”), which have different targets. The Elecsys assay is an electrochemiluminescence assay for the qualitative detection of antibodies to SARS-CoV-2 nucleocapsid protein in human serum, and is considered reflective of wild-type infection [ 21 ]. A result (cutoff index; signal to cutoff ratio) of ≥  1.0 is considered reactive. The QuantiVAC ELISA is an enzyme immunoassay, providing quantitative in vitro determination of antibodies to the immunoglobulin class IgG against the S1 antigen and receptor binding domain of SARS-CoV-2 [ 22 ]. Detection of the anti-S1 (spike) antibody is considered to indicate either a wild-type infection or a response to vaccination. A result of < 8RU/ml was considered negative, ≥ 8-<11 RU/ml borderline and ≥ 11RU/ml positive. Utilising the results from these two assays, in conjunction with the patient’s vaccination status and any noted clinical COVID-19, it was possible to determine whether the patient’s antibody response was secondary to clinical infection or to vaccination (Supp Table  1 ).

To determine vaccine-induced seroconversion in patients who undertook serial testing, we reviewed the relationship of serial serum collections, vaccination and known COVID-19. The serum sample collected closest in time to the date of vaccination, with a minimum of 14 days post-vaccination was included in the analysis. If there was evidence of seroconversion from a reactive QuantiVac ELISA, subsequent reactive samples were not included. If there was evidence of seroconversion after a subsequent incrementing vaccine dose, with no known interval COVID-19, patients were considered to have vaccine-induced seroconversion at the incrementation. If there was no evidence of seroconversion, despite additional vaccine administration, the final sample was included to reflect this. Borderline results were considered as seroconverted in the context of immunocompromise.

This study was approved by the SWSLHD Human Research and Ethics Committee (Approval Reference: 2019/STE00860) with a waiver of consent for the initial cohort analysis of all KTRs in the district and individual informed consent for the serology component if a patient elected to participate.

Statistical methods

Data was analysed using parametric and non-parametric tests for normally distributed and non-normally distributed variables respectively. Univariate analysis was performed with chi-squared, or Fishers test as appropriate, on categorical variables, and either independent t-test or Mann-Whitney U for continuous variables. Missing data was left as null with no imputation. Any variable with > 10% missing data was not included in any model Multi-variable binary logistic regression (backward stepwise conditional) was undertaken. Probability of entry for any variable was 0.05, removal 0.1. A goodness-of-fit test was undertaken ((Hosmer-Lemeshow test) was utilised to assess the goodness of fit and stability of the model. Statistical analysis was performed using SPSS v.28. P  < 0.05 (2-sided) was considered significant. Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines were followed for the reporting of the results [ 26 ].

A total of 537 patients were initially identified. After excluding patients that were deceased ( n  = 37), already on dialysis ( n  = 26), moved out of area ( n  = 23), or lost to follow up ( n  = 7), a total of 444 patients remained for analysis (Fig.  1 .). A total of 84 patients elected to participate in testing for the seroconversion analysis, who were analysed as a subgroup. Baseline characteristics of the final 444 prevalent KTRs are shown in Table  1 . They were predominantly male (60%), with a median age 58 years (Interquartile range [IQR]21.0) and baseline mean estimated glomerular filtration rate (eGFR) of 57 ml/min/1.73m 2 (Standard Deviation [SD] 21.9). Patients were primarily deceased donor recipients (69%) due to glomerulonephritis (50%) or diabetes (15%), with a median transplant vintage of 69.0 months (IQR 111.0). The primary immunosuppression regimen consisted of prednisolone (93%), mycophenolate (80%), and tacrolimus (72%).

Flow chart of patient inclusion and COVID-19 diagnosis

Vaccination status

Vaccination status was acquired for 440 (99%) patients. By study end, 95% ( n  = 423) of patients had received at least 1 vaccination. The number of patients that received 1,2,3, or 4 vaccine doses was 4 (1%), 75 (17%), 239 (54%) and 105 (24%) respectively. 17 (4%) patients remained unvaccinated throughout the study period. The vaccines administered included Pfizer BioNTech BNT162b2 (70%), AstraZeneca ChAdOx1 nCoV-19 (26%) and Moderna mRNA-1273 (3%) (Supp Table  2 ).

COVID-19 outcomes

COVID-19 was reported in 142 (32%) patients, and of these 54 (38%) required admission for COVID-19 with 10 (7%) deaths due to COVID-19. 17 (4%) patients died from any cause during the study period, with COVID-19 accounting for 59% of all deaths.

COVID-19 diagnosis

Univariate factors associated with acquiring COVID-19 are shown in Table  2 . On multivariable analysis, an increased risk of acquiring COVID-19 was associated with male sex (aOR 1.7, 1.093–2.701, p  = 0.019), younger age (aOR, 0.98, 0.964–0.994, p  = 0.006) and lower eGFR (aOR 0.99, 0.978–0.998, p  = 0.020), after adjusting for significant univariate associations, body mass index (BMI) and diabetes. (Table  3 ). Receipt of 3 or more doses of vaccine was protective (aOR 0.48, 95% CI 0.287–0.796, p  = 0.005).

COVID-19 mortality

Deaths from COVID-19 occurred throughout the study period, with 3 deaths in September 2021, 1 death in January 2022, 4 deaths in February 2022 and 1 death in both April and May 2022. Univariate analyses are shown in Table  2 . On multivariate analysis, increased mortality due to COVID-19 was associated with older age (aOR1.1, 95%CI 1.004–1.192, p  = 0.04), respiratory disease (aOR 14.2, 95%CI 1.825–110.930, p  = 0.011) and current or past smoking exposure (aOR 8.2, 95% CI 1.020-65.649, p  = 0.048) after adjusting for significant univariate associations, sex, BMI, diabetes, and vaccination (3 + doses). Vaccination of 3 or more doses was protective (aOR 0.6, 95% CI 0.007–0.523, p  = 0.011) (Table  3 ).

COVID-19 hospitalisation

Of those with reported COVID-19, 62 (44%) received sotrovimab and 11 (8%) received molnupiravir (Suppl Table 6.). 54 (38%) patients required hospitalisation for COVID-19, and 16 (11%) required intensive care unit (ICU) care. 33 (23%) patients required oxygen therapy. The maximum level of oxygen required was: low flow nasal prong oxygen in 13 (9%), high flow nasal prong oxygen in 4 (3%), non-invasive ventilation in 8 (6%) and invasive ventilation in 8 (6%) patients. Sotrovimab and molnupiravir were given in the community. When provided, neither were found to be protective for hospital admission ( p  = 0.11, p  = 021 respectively). Among hospitalised patients, those who received sotrovimab had evidence of protection for ICU admission (OR 0.2, 95%CI 0.035–0.886, p  = 0.030). Median length of hospital stay was 8 days (IQR ± 13). There was an association between prior sotrovimab use and shorter length of stay (5 vs. 10 days, p  = 0.027). Vaccination with 3 doses did not impact hospital admission ( p  = 0.32), ICU admission ( p  = 0.14) or length of stay (0.54).

Immunosuppression alteration occurred frequently in hospitalised patients (85%), as compared to those who were not hospitalised (10%). Hospitalisation with COVID-19 increased the odds of a reduction of immunosuppression (OR 50.5, 95% CI 18.211-139.883, p  < 0.001), however it was not significant for those who required an ICU admission among hospitalised patients ( p  = 0.41) or mortality ( p  = 0.64). Univariate factors associated with hospitalisation for COVID-19 are shown in Table  2 .

On multivariable analysis, increased hospitalisation was associated with older age (aOR 1.0, 95% CI 1.007–1.0092, p  = 0.021), lower eGFR (aOR 0.96, 95% CI 0.994 − 0.982, p  < 0.001) and receipt of a deceased donor graft (aOR 4.1, 95% CI 1.128–14.747, p  = 0.032), after adjusting for significant univariable associations, sex, BMI and vaccination (3 doses) (Table  3 ). Vaccination was not protective.

Seroconversion

84 patients underwent serological testing, including: 71 patients who had a single test, 12 who had 2 serial tests and 1 patient who had 3 serial tests. All but one patient, had a non-reactive Eleycs assay, indicating no prior exposure to COVID-19. The single patient with a reactive Eleycs assay was not known to have had prior COVID-19, however, was transplanted overseas with limited details prior to returning to Australia before the study period. This patient had no further serological evaluation and was excluded from the seroconversion analysis, resulting in 83 patients providing 97 serological tests assessed for vaccine-induced seroconversion.

All but 2/97 tests were collected prior to documented COVID-19. These two patients participated in serial testing. Prior to known COVID-19 they were Elecsys assay and QuantiVac ELISA negative. Post COVID-19 they remained Elecsys assay negative, however seroconverted on the QuantiVac ELISA. During this interval they received additional vaccinations, incrementing from 2 to 3 doses. As it is not possible to determine if these patients seroconverted due to wild type COVID-19 infection or vaccination, the serial samples prior to known COVID-19 were analysed. Of the remaining 95 tests, 5 were excluded based on QuantiVac ELISA results: 1 patient who did not have a QuanitVac ELISA processed on initial collection 1, but undertook repeat testing which was utilised, 3 patients with serial reactive tests performed after 2 and 3 doses of vaccine with no status change, therefore sampling after the 2nd dose was included, and 1 patient with 2 serial reactive tests, both after the 4th dose of vaccine and the earlier sample was included.

This resulted in 90 analysed samples: 1, 64, 21 and 4 samples after 1,2,3 and 4 doses of vaccine respectively (Suppl Table 3). Seroconversion rates after 1, 2, 3 and 4 doses were: 0, 22%, 48%, and 75% respectively (Suppl Table 4). Overall seroconversion rate at study end was 33% (27/83).

Univariate factors associated with COVID-19 diagnosis in this subgroup are shown in Supplementary Table 5. On multivariable analysis, after adjusting for univariate associations, in addition to age and diabetes, factors associated with an increased rate of acquiring COVID-19 included Asian place of birth (aOR 9.0, 95% CI 1.803–44.888, p  = 0.007) and higher dose of prednisolone (aOR 1.5, 95% CI 1.125–1.949, p  = 0.005). Seroconversion was protective (aOR 0.1, 95% CI 0.025–0.627, p  = 0.011), independent of vaccination of 3 + doses ( p  = 0.108) (Table  3 ).

The number of hospitalised patients in this subgroup was small ( n  = 6). No hospitalised patients demonstrated evidence of seroconversion, however this did not reach statistical significance ( p  = 0.539). No patient who died underwent serology assessment.

In this large, observational study of KTRs in Australia, during a period following community stay-at-home orders and two strain outbreaks, COVID-19 resulted in significant morbidity and mortality throughout the 2021–2022 pandemic period. Over 30% of the cohort developed breakthrough COVID-19, despite 78% receiving 3 or more doses of vaccine. Early monoclonal or antiviral treatment was provided to 51% of positive patients, however 38% of patients still required hospitalisation, with death occurring in 7% [ 15 , 16 ]. Overall seroconversion rates were low, with 3 doses of vaccine achieving a seroconversion rate of 48%.

Several risk factors for mortality amongst KTRs have been suggested, including older age, sex, cardiometabolic or respiratory co-morbidities and obesity [ 1 , 3 , 4 , 5 , 6 , 7 , 11 , 23 ].. This data supports older age, respiratory disease and smoking exposure may be independent factors for mortality for COVID-19 in KTRs. On systematic review and registry data analysis, no single co-morbidity had consistently been identified as a risk factor, other than age [ 3 , 11 ].

It has been suggested certain immunosuppression regimens are associated with increased COVID-19 mortality [ 1 , 6 , 7 ]. We did not find any effect of individual immunosuppressive agent on mortality, however there were high rates of baseline steroid (93%) and anti-metabolite (89%) use. A higher dose of prednisolone was associated with increased risk of acquiring COVID-19 in the serology subset.

Prior recommendations suggested temporarily altering immunosuppressive regimens during COVID-19 infections, and we noted high rates of alteration on hospitalisation in line with this trend [ 29 ]. There was no association with ICU admission or mortality among this group, and of patients who were not hospitalised, the majority did not have drug alteration (90%). Drug alteration, therefore, is likely reflective of a response to the severity of COVID-19. Current advice, with new strain evolution, suggests immunosuppression alteration is not required, particularly in the asymptomatic or those with a mild illness, with our data supportive of this [ 30 ]. Systematic review has not supported an association between immunosuppression and mortality, and there is limited comparative data to guide reduction of immunosuppression therefore decisions should be based on individualized assessment and the risk of rejection [ 11 , 23 ].

This data covered a period until May 2022, during a predominant Omicron outbreak from January 2022, whereby most patients had received 3 or more vaccine doses [ 15 , 16 ].. The Omicron era heralded decreased virulence, however the neutralising capability after 3 doses of vaccine was suggested to be diminished [ 12 ]. In this data, vaccination of 3 or more doses was protective for death and acquiring COVID-19, with no effect on hospitalisation, ICU admission or length of stay. In the serology subset, seroconversion, independent of dose of vaccination, was protective for acquiring COVID-19. Mortality rates during periods of Omicron predominance among solid organ transplant recipients have been reported to be 3 − 4%, however hospitalisation rates have remained 24–32%, with ICU admission rates of 28–36% [ 24 , 31 ]. Ongoing hospitalisation rates remain a concern for KTRs and further data regarding vaccine schedule optimisation and seroconversion assessment, independent of vaccination dose number, is needed.

This data demonstrated protection against death, reduced rates of ICU admission and length of stay with the use of sotrovimab, with no protective effects of molnupiravir. Our study reflects a period where sotrovimab was the primary agent of choice in early COVID-19 disease (approved August 2021) as opposed to molnupirovir (approved January 2022), likely influencing our results [ 27 , 28 ].

Current Australian recommendations do not recommend either sotrovimab or molnupirovir. Tixagevimab plus cilgavimab (Evusheld) has also lost its recommendation. Nirmatrelvir plus ritonavir (Paxlovid) retains its conditional recommendation, however, its use in KTRs is challenging due to effects on calcineurin inhibitor levels [ 25 ]. Remdesivir remains recommended only for patients requiring oxygen due to symptomatic COVID-19. There remains a paucity of agents effective at treating early COVID-19 in renal transplant recipients.

This data supports concern surrounding ongoing mortality and hospitalisation risk for KTRs, in the context of low seroconversion rates despite increasing vaccination dose schedule. This reiterates vaccination of at least 3 doses, and potentially evidence of seroconversion, is protective, however, in the absence of effective early treatments, encouragement of protective behaviours, such as social distancing, mask compliance and hand hygiene should continue.

This study is limited as a single centre and results are not generalisable. As with all observational data our analyses are limited to associations. Those undertaking the serology assessment were a self-selected population, which is likely to result in unmeasured patient bias, especially with regards to protective behaviours. They were highly vaccinated, with more than 90% receiving 3 or more doses. In addition, our data spanned two predominant strain periods, Delta and Omicron, and we were not able to specify strains in individual patients. While it is likely we captured most noted infections due to mandatory government reporting, cases could have been omitted if patients did not note an infection, obtain testing, or report a positive test, resulting in potential underdiagnosis of mild and asymptomatic cases. In addition, seroconversion does not always reflect in-vivo activity of antibodies and we did not assess the effect of waning immunity over time.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

SWSLHD Renal Unit and Research Staff. NSW Health Pathology Laboratories– Liverpool. All the patients who participated in the study.

Locally (non-peer reviewed) funded (renal department trust fund).

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Daniela Potter & Angela Makris

University of New South Wales, Sydney, NSW, Australia

Department of Renal Medicine, Liverpool Hospital, Liverpool, Sydney, NSW, Australia

Daniela Potter, Jason Diep, Colleen Munro, Noelle Lin, Ramon Xu, Jeffrey Wong & Angela Makris

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All authors contributed to the work, as well as reading and approving the final manuscript. DP is the primary corresponding author and was responsible for the design of study and the primary data interpretation and drafting of work. AM was responsible for study conception, study design, data analysis and contributed to draft revisions of the work. JP, CM, NL and RX were responsible for data acquisition and analysis, as well as draft revision. JW was responsible for data interpretation and draft revisions. RP was the primary technical laboratory advisor and analyst. MM and HF were both advisors on laboratory, infectious and epidemiological components of the study and contributed to draft revisions.

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Correspondence to Daniela Potter .

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Potter, D., Diep, J., Munro, C. et al. A prospective observational cohort study of covid-19 epidemiology and vaccine seroconversion in South Western Sydney, Australia, during the 2021–2022 pandemic period.. BMC Nephrol 25 , 131 (2024). https://doi.org/10.1186/s12882-024-03560-8

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Received : 16 September 2023

Accepted : 26 March 2024

Published : 12 April 2024

DOI : https://doi.org/10.1186/s12882-024-03560-8

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