Author: Azeem Majeed

I am Professor of Primary Care and Public Health, and Head of the Department of Primary Care & Public Health at Imperial College London. I am also involved in postgraduate education and training in both general practice and public health, and I am the Course Director of the Imperial College Master of Public Health (MPH) programme.

Measures of Disease Frequency: Incidence and Prevalence

In this post, I will discuss methods used to measure the frequency of disease: incidence and prevalence. These are essential tools for governments, health care planners, doctors, public health specialists, and epidemiologists in their efforts to protect the health of the public.

Incidence is the rate at which new cases of a disease occur in a population during a specified time period. It is calculated by dividing the number of new cases by the population at risk during that time period.

Incidence Rate =  Number of new cases / Person-time at risk  × N

Where N is a number such as 1,000 or 100,000.

For example, if there were 100,000 myocardial infarctions in England each year, the annual incidence would be 1.75 per 10,000 people (100,000 / 57,000,000 x 10,000).

Prevalence is the proportion of individuals in a population who have a disease or other health outcome of interest at a specified point in time (point prevalence) or during a specified period of time (period prevalence). It is calculated by dividing the number of people with the disease by the total population.

Point Prevalence: The number of cases at a specific point in time.

Point Prevalence = Number of cases at a point in time / Total population at that time x N

For example, if there are 4,000,000 people with diabetes in England, the point prevalence of diabetes is 7.0 per 100 people of 7.0% (4,000,000 / 57,000,000 x 100).

Period Prevalence: The number of cases over a specific period.

Period Prevalence = Number of cases during a time period / Average population during the period x N

What methods are used to provide the data needed to measure incidence and prevalence?

There are a range of methods used to measure incidence and prevalence, depending on the specific disease or health outcome being studied and the available resources. Some common methods include:

Surveillance systems: Surveillance systems are used to collect data on the occurrence of disease or other health events on an ongoing basis. This data can be used to calculate incidence and prevalence rates, as well as to track trends over time.

Cohort studies are observational studies that follow a group of people over time to track the occurrence of disease or other health outcomes. Cohort studies can be used to calculate incidence rates, as well as to identify risk factors for disease.

Cross-sectional studies are observational studies that collect data on a group of people at a single point in time. Cross-sectional studies can be used to calculate prevalence rates, but they cannot be used to calculate incidence rates (unless they are repeated over time: serial cross-sectional studies).

With the greater use of electronic health records by the NHS and other health systems, these are now increasingly used to calculate measure of disease frequency such as incidence and prevalence. But these data do have limitations. For example, some problems may not be well-recorded in electronic health records as they rely on patients presenting to health services; and errors an omissions in the coding of clinical data are also common.

How do we interpret incidence and prevalence?

Incidence and prevalence rates can be influenced by a variety of factors, including the following:

Age: Incidence and prevalence rates often vary by age. For example, some diseases are more common in children, while others are more common in adults.

Sex: Incidence and prevalence rates also vary by sex. For example, some diseases are more common in men, while others are more common in women.

Race and ethnicity: Incidence and prevalence rates can also vary by race and ethnicity. For example, some diseases are more common in certain racial and ethnic groups.

Geography: Incidence and prevalence rates can also vary by geographic location. For example, some diseases are more common in certain countries or regions.

All of these factors must be considered when interpreting incidence and prevalence data. For example, if comparing the incidence of a disease in two different countries, it is important to make sure that the populations being compared are similar in terms of age, sex, race and ethnicity, and other relevant factors.

How are incidence and prevalence used?

Disease surveillance: Incidence and prevalence data can be used to track the occurrence of disease or other health events over time and to identify areas where there may be increased risk.

Research: Incidence and prevalence data can be used to identify risk factors for disease, to develop new diagnostic tests and treatments, and to evaluate the effectiveness of public health interventions.

Healthcare programme planning and evaluation: Incidence and prevalence data can be used to plan and evaluate health services and public health programmes, such as vaccination programmes and screening programmes.

Conclusions: Incidence and prevalence are two important measures of disease frequency. These measures can be used to track health trends over time, to identify risk factors for disease, and to plan and evaluate public health and healthcare interventions. It is important to interpret incidence and prevalence data carefully, considering all of the factors that can influence these rates.

New Awareness Campaign to Help Reduce Hospital Admissions for Urinary Tract Infections

A new campaign from NHS England and the UKHSA aims to raise awareness about the prevalence and risks of urinary tract infections (UTIs), particularly among older people and carers, and to reduce hospital admissions related to UTIs.

The campaign offers advice on preventive measures. It emphasizes the importance of staying hydrated, going to the toilet as soon as the need arises, and maintaining hygiene in the genital area. Resources, including posters, are being made available to healthcare services, charities, royal colleges, and care homes to disseminate this information as widely as possible.

The guidance comes ahead of a potentially busy winter season for the NHS, a time when the health service is usually under increased pressure. As part of a larger effort to manage healthcare resources, the campaign encourages the use of alternative services like NHS 111, community pharmacists, and urgent care walk-in centres for less critical cases. This is in line with the broader NHS plan of expanding out-of-hospital care options, including “hospital at home” services and urgent community response teams.

UTIs are particularly dangerous for older adults. Prompt action and early treatment are stressed as critical for managing UTIs and preventing severe outcomes like sepsis or death.

The campaign is part of a larger effort to prepare for increased demand during the winter months and aims to improve public awareness and self-care measures to reduce the need for hospital admissions.

What issues do NHS clinicians need to consider in using this guidance?

1. It is more difficult to diagnose UTIs in older people. Younger people (who will nearly all be women) will usually present with the “classical” symptoms of  UTI – such as frequency, dysuria, urgency and haematuria. Older people can have these symptoms but they can also present with problems such as confusion, agitation, functional decline or lethargy where there is a large overlap with other conditions; making diagnosis more challenging.

2. Another challenge in older people is that some will have asymptomatic bacteriuria (i.e. bacteria in the urine that are not causing problems). When the bacteria are detected, doctors will often treat the patient with antibiotics when the medication may not be needed.

3. Spotting infections early requires knowledge of the symptoms and signs and how these differ in younger and older people. There is also a need to be aware of the complications of UTIs such as sepsis or pyelonephritis and to treat these early.

4. Doctors and patients need to balance the benefits of early diagnosis treatment with the risks of overtreatment with antibiotics. Not all UTIs need antibiotic treatment and some may resolve without it. Overuse of antibiotics contributes to antibiotic resistance as well as putting patients at risk of side effects.

5. Finally, these kind of single issue campaigns will be of limited value unless there is adequate capacity in the NHS for patients to be assessed promptly. Otherwise, patients will end up waiting a long time for appointments with the risk their condition may worsen while waiting for treatment.

Making Sense of Sensitivity, Specificity and Predictive Value: A Guide for Patients, Clinicians and Policymakers

In this post, I will discuss sensitivity, specificity and positive predictive value in relation to diagnostic and screening tests. Many more people have become aware of these measures during the Covid-19 pandemic with the increased use of lateral flow and PCR tests.

In clinical practice and public health, sensitivity, specificity, and predictive value are important measures of the performance of diagnostic and screening tests. These measures can help clinicians, public health specialists and the public to understand the accuracy of a test and to make informed decisions about its use in patient care.

Sensitivity: The proportion of people with a disease who test positive on a diagnostic or screening test.

Sensitivity = True Positives / (True Positives + False Negatives)

Specificity: The proportion of people without a disease who test negative on a diagnostic or screening test.

Specificity = True Negatives / (True Negatives + False Positives)

Positive predictive value (PPV): The proportion of people who test positive on a diagnostic test who actually have the disease.

Positive Predictive Value = True Positives / (True Positives + False Positives)

Negative predictive value (NPV): The proportion of people who test negative on a diagnostic test who actually do not have the disease.

Negative Predictive Value = True Negatives / (True Negatives + False Negatives)

How do we Interpret sensitivity, specificity, and predictive value?

Sensitivity and specificity are linked measures. A test with high sensitivity is good at identifying people with a disease, but it may also produce false positives in people who do not have the disease. A test with high specificity is good at identifying people who do not have a disease, but it may also produce false negatives in people who do have the disease. In general, as sensitivity increases, specificity decreases; and vice versa.

Positive Predictive Value (PPV) depends on the prevalence of the disease in the population being tested. In a population with a high prevalence of disease, a positive test result is more likely to be a true positive. Conversely, in a population with a low prevalence of disease, a positive test result is more likely to be a false positive.

In clinical and public health practice this means that a test can have a high sensitivity and specificity but if it is being carried out in a population with a low prevalence, most positive tests are false positives; thereby limiting the value of a positive test. This is why a test can vary in its performance in primary care (where prevalence of a condition is often low) and in hospital care (where prevalence will generally be higher).

The Covid-19 pandemic brought global attention to the importance of diagnostic test parameters such as sensitivity, specificity and positive predictive value. Initial Covid-19 tests often prioritised sensitivity to capture as many positive cases as possible. However, as the pandemic progressed, the need for more specific tests became clear to minimise false positives that could distort public health strategies. For example, a false positive test could result in a person isolating or staying off work or school unnecessarily.

A test with a high Negative Predictive Value means that it is good at ruling out disease in people who test negative. This is important for public health interventions, such as contact tracing, where it is important to identify people who are unlikely to be infected with a disease so that they can be excluded from further monitoring and isolation.

The pandemic underscored that no single measure—sensitivity, specificity, or predictive value—could offer a complete picture of a test’s effectiveness.

Example of a diagnostic test: A Covid-19 test has a sensitivity of 90%, meaning that 90% of people with a Covid-19 infection will test positive on the test. The test has a specificity of 98%, meaning that 98% of people without Covid-19 will test negative on the test.

The PPV of the test will vary depending on the prevalence of Covid-19 in the population being tested. For example, if 5% of people in a population have Covid-19, then the PPV of the test will be 70%. This means that 70% of people who test positive on the test will actually have Covid-19.

If the prevalence of Covid-19 is 1%, then the PPV will be 31%. This means that 31% of people who test positive on the test will actually have Covid-19. Hence, at times of low prevalence, many positive Covid-19 tests will be wrong.

You can use a Positive Predictive Value Calculator to see how changing sensitivity, specificity and prevalence alters the result.

Screening tests have also become more important as health systems across the world try to detect conditions such as cancer earlier in their clinical course in an attempt to improve health outcomes survival.

Example of a screening test: A mammogram is a screening test for breast cancer. It has a sensitivity of 85%, meaning that 85% of women with breast cancer will have a positive mammogram. The mammogram has a specificity of 90%, meaning that 90% of women without breast cancer will have a negative mammogram. The PPV of the mammogram will vary depending on the prevalence of breast cancer in the population being screened. For example, if the prevalence of breast cancer in a population is 1%, then the PPV of the mammogram will be 8%. This means that 8% of women who have a positive mammogram will actually have breast cancer. Hence, many women who don’t have breast cancer will need investigation to confirm the result of their screening test.

Conclusion: Sensitivity, specificity, and predictive value are important concepts in the evaluation of diagnostic and screening tests. Clinicians, public health specialists and the public should understand the performance of a test before using it in patient care.

In addition to sensitivity, specificity, and predictive value, there are other factors that clinicians should consider when choosing a diagnostic or screening test, such as the cost of the test, the risks and benefits of the test, and the availability of alternative tests.

No diagnostic or screening test is perfect. All tests have the potential to produce false positives and false negatives. Clinicians, the public and policy-makers should use judgment to interpret the results of any test; and to make decisions about patient care, screening programmes and public health policy.

Evaluating the Uptake of the NHS App in England

Our new study published in the British Journal of General Practice examines uptake of the NHS App in England. The NHS App was launched in January 2019 as a “front door” to digitally enabled health services, allowing patients to access their personal health information online. With the advent of the COVID-19 pandemic, the app saw a significant increase in downloads, especially with the introduction of the COVID Pass feature. However, the uptake of the app has revealed some important trends and inequalities that need to be addressed.

The Study

A comprehensive observational study used monthly NHS App user data at general-practice level in England from January 2019 to May 2021. Different statistical models were applied to assess changes in the level and trend of use of various functionalities of the app, particularly before and after the first COVID-19 lockdown.

Key Findings

Between January 2019 and May 2021, the NHS App was downloaded 8,524,882 times and registered 4,449,869 users. Intriguingly, the app experienced a 4-fold increase in downloads after the introduction of the COVID Pass feature, which allows users to prove their COVID-19 vaccination status. However, the data also revealed disparities in app registration based on sociodemographic factors:

  • There were 25% fewer registrations in the most deprived practices.
  • Largest-sized practices had 44% more registrations.
  • Registration rates were 36% higher in practices with the highest proportion of registered White patients.
  • Practices with a larger proportion of 15–34-year-olds saw 23% more registrations.
  • Surprisingly, practices with the highest proportion of people with long-term care needs saw 2% fewer registrations.

What This Means

The findings indicate that while the NHS App has proven to be an useful tool, especially in the times of the Covid-19 pandemic, its usage is not uniform across various sociodemographic groups. This raises questions about accessibility and the digital divide, which could ultimately impact the quality of patient care and health outcomes.

Further Steps

While the app has clearly benefited a significant number of people, it’s crucial to understand the reasons behind these patterns of inequalities. Further research is essential to delve deeper into these trends and how they may affect the patient experience.

Understanding these dynamics can guide improvements to the app, making it more inclusive and effective for all users. Policymakers, developers, and healthcare providers need to work together to ensure that digital health services like the NHS App are accessible and beneficial to everyone, regardless of their socio-economic status or demographic background.

Conclusion

The NHS App has seen a considerable increase in usage since the onset of the Covid-19 pandemic, highlighting its essential role in modern healthcare. However, the unequal patterns in its uptake call for a focused approach to ensure it serves as an inclusive platform for all. Further research is crucial to uncover the underlying reasons for these disparities and to work towards a more equitable healthcare system.

Guidance for NHS staff on writing support letters for patients for applications for PIP or ESA.

Doctors and other NHS professionals in England are often asked to write in support of patients applying for benefits such as Personal Independence Payments (PIP) or Employment Support Allowance (ESA); which support people with disabilities and long-term health conditions.

These benefits are vital for people suffering from long-term health conditions and disabilities, offering them financial help that can significantly improve their wellbeing and quality of life. Given the critical nature of these benefits and the stringent criteria often applied during the assessment process, the letters we write can play an essential role in securing this much-needed support for our patients.

Here is some guidance on how to write a more effective and relevant letter of support based on my long experience as an NHS doctor in writing such letters.

1. Introduce yourself and describe your relationship to the patient, including how long you have known them and in what capacity. This will help establish your credibility as a reliable source of information in support of their application for a personal independence payment or another state benefit.

2. Provide a detailed description of the patient’s medical conditions, including any diagnoses they have received, how their medical conditions affect their daily life, and any symptoms they experience. Focus on the most relevant conditions to their application first. For example, if the patient is applying for PIP due to mobility problems, you should focus on their mobility impairments and how they affect their daily life and ability to work. Also include any medication they are taking and any past medical or surgical interventions.

3. If the patient is applying for Employment & Support Allowance (ESA), explain how their medical condition affects their ability to work. Describe any physical or cognitive limitations they have, or how their symptoms interfere with their ability to perform tasks required for their job. Aim to give precise descriptions of their conditions; for example if they have heart failure, what is the severity?

4. When describing how the patient’s condition impacts their daily life, focus on the activities of daily living that they have difficulty with. For example, you could mention if they have difficulty dressing, bathing, cooking, shopping, cleaning, or managing their finances. Also describe any problems the patients has in managing their health and their medical conditions.

5. Use specific examples to illustrate how the patient’s condition affects their daily life and ability to work. For example, you might describe a time when the patient experienced a symptom flare-up that prevented them from completing a task at home or attending work.

6. Emphasize the patient’s need for financial support through benefits such as PIP or ESA. Explain how this support would help them manage their condition and improve their quality of life. With the cost of living crisis, these benefits are now essential for many people.

7. Remember to keep the letter factual, polite, concise and to the point, and to focus on the patient’s medical conditions (both physical and mental health problems) and how they impact their ability to work and carry out essential daily activities.

Some doctors argue they should not be writing such letters as they detract from the time available from providing core medical services. But obtaining support from a PIP or ESA can improve a patient’s well-being, which ultimately is also beneficial for the NHS and society.

In England, the NHS is funding social prescribers to work with general practices and writing such letters of support is often delegated to these social prescribers who can take over this task from health professionals such as general practitioners and therapists.

Financial problems will have a big impact on people’ health and well-being and it is important that NHS staff do their best to support patients who have difficult financial circumstances because of their health problems.

Understanding and Managing Sport-Related Concussion in Primary Care

The importance of the global emphasis on physical activity for health cannot be understated. However, it is crucial to address one of the adverse effects of contact sports—specifically, sport-related concussions. Sport-Related Concussion is a traumatic brain injury caused by a direct blow to the head, neck, or body resulting in an impulsive force being transmitted to the brain.

Sport-Related Concussion can present with a wide range of signs and symptoms, and can affect a person’s thinking, concentration, memory, mood, and behaviour. These incidents are common and account for a significant number of emergency department visits. They also have some long-term risks, including cognitive and neurological problems.

Recent publications, like the consensus statement from the Concussion In Sport Group and the UK Government’s landmark concussion guidance, offer valuable insights in the management of Sport-Related Concussion. This blog – based on our recent article in the British Journal of General Practice – aims to provide guidance on recognising, diagnosing, and managing Sport-Related Concussion within the context of primary care.

 The Changing Landscape of Sport-Related Concussion

In 2016, over 1% of emergency department visits in England and Wales were attributed to concussions. Up to 60% of these involved children and adolescents. A 2021 UK House of Commons report criticised the current awareness level about Sport-Related Concussion in the UK’s NHS, indicating a need for better recording and treatment procedures.

 Recognising Sport-Related Concussion

Symptoms of Sport-Related Concussion can range from cognitive issues to mood changes. Anyone with a suspected concussion should be immediately removed from the field of play and assessed by an appropriate healthcare professional within 24 hours of the injury. Those working in sport will be aware of specialist assessment tools pertaining to individual sports that aid clinicians when diagnosing concussion. The UK Government guidance provides a list of ‘red flags’ that require immediate assessment.

Once Sport-Related Concussion has been recognised or diagnosed, a short period (24–48 hours) of relative rest is advised, where only light-intensity physical activity that does not, or only minimally, exacerbates symptoms is undertaken. Subsequently, a logical graduated return to school/work and then sport can be started, where progression through stages is dependent on minimal and transient (the CSIG advise <1 hour) exacerbation of symptoms.

Sport-specific assessment tools exist for diagnosing concussion, such as the Sport Concussion Assessment Tool (SCAT6). These tools are most effective within 72 hours of the injury and evaluate symptoms, cognitive function, and coordination.

 Managing Sport-Related Concussion in Primary Care

Primary care doctors play an essential role in managing Sport-Related Concussion. Initial management includes:

– Advising a short period of relative rest (24-48 hours)

– Reducing screen time and cognitive load

– No alcohol, solitary time, or driving within the first 24 hours

Patients with persistent symptoms beyond 28 days should be referred for a more comprehensive assessment. Gradual return to normal activities is advised, strictly adhering to symptom-dependent progress.

Challenges and Future Directions

The NHS’s limited specialist services for treating complex or prolonged sport-related concussion symptoms create a care gap. This could be bridged by experts in sports medicine or primary care doctors with extended roles in sports medicine. Emerging technologies like Inertial Measurement Units (IMUs) in mouthguards and salivary micro-RNA samples show promise for better recognition and understanding of sport-related concussion.

Conclusions

Sport-Related Concussion is important. Effective recognition and management by general practitioners can significantly contribute to an individual’s immediate and long-term health. It is also vital for local commissioners to implement appropriate care pathways for managing this condition. By acknowledging the complexities in management and investing in ongoing research, we can create a healthcare system that supports both the benefits of physical activity and the challenges it can sometimes bring.

Identifying Potential Biases in Diagnostic Codes in Primary Care Electronic Health Records: What We Need to Know

Electronic healthcare records (EHRs) are increasingly being used to collect and store data on patient care. This data can be used for a variety of purposes, such as improving clinical care, conducting research, and monitoring population health. However, it is important to be aware of potential biases in EHR data, as these can lead to inaccurate or misleading results..

The reliability of diagnostic codes in primary care EHRs is a subject of ongoing debate and a topic we investigated in paper published in BMJ Open.

These codes not only guide clinical decisions but also shape healthcare policies, research, and even financial incentives in the healthcare system. A recent retrospective cohort study explored whether the frequency of these codes for long-term conditions (LTCs) is influenced by various factors such as financial incentives, general practices, patient sociodemographic data, and the calendar year of diagnosis. The study comes at a crucial time, shedding light on significant biases that need to be addressed.

Key Findings

The study, which involved data from 3,113,724 patients diagnosed with 7,723,365 incident LTCs from 2015 to 2022, revealed some significant findings:

Influence of Financial Incentives: Conditions included in the Quality and Outcomes Framework (QOF), a financial incentive program, had higher rates of annual coding than those not included (1.03 vs 0.32 per year, p<0.0001).

Variability Across GPs: There was a significant variation in the frequency of coding across different General Practices, which was not explained solely by patient sociodemographic factors.

Impact of Sociodemographic factors: Higher coding rates were observed in people living in areas of greater deprivation, irrespective of whether the conditions were part of QOF or not.

Covid-19: The study noted a decrease in code frequency for conditions that had follow-up time in the year 2020, likely due to the COVID-19 pandemic affecting healthcare services.

Implications for Healthcare Providers and Researchers

The findings of the study raise some pertinent questions:

Addressing Financial Incentives: If the QOF influences coding rates, how can we ensure a level playing field for conditions not included in such programs? This could impact resource allocation and healthcare planning.

Standardizing Practices: The variability in coding across GPs implies that there might be inconsistencies in how conditions are diagnosed and recorded. These inconsistencies need to be addressed to improve the quality of healthcare.

Considering Sociodemographic factors: The influence of patient sociodemographic factors suggests a need for tailored interventions, especially in areas with higher deprivation levels.

Navigating Pandemic-related Challenges: The reduction in coding during the COVID-19 pandemic indicates that external factors can significantly affect healthcare data. This demands adaptive strategies to ensure the ongoing reliability of EHRs.

Conclusions and Future Steps

As we move towards a more data-driven healthcare system, understanding the biases in primary care EHRs becomes crucial. The study suggests that natural language processing or other analytical methods using temporally ordered code sequences should account for these biases to provide a more accurate and comprehensive picture. By doing so, healthcare providers and policymakers can better tailor their strategies, ensuring more effective and equitable healthcare delivery.

Navigating the academic publishing process

I am sometimes asked by junior researchers or by the public how the academic publication process works. The academic peer review timeline varies depending on the journal, but it typically takes several months (sometimes even longer) from submission to publication.

1. Submission: You submit your paper to the journal. Make sure your paper is well-written, checked for spelling and grammatical errors, follows the journal’s style and formatting requirements, and that you submit your paper to a journal that is a good fit for your work.

2. Initial screening: An editor at the journal reviews your paper to make sure it is within the scope of the journal & meets the journal’s style and formatting requirements. Some articles are rejected at this stage, without external peer review (particularly, by larger journals). For example, articles may be rejected if they are outside the scope of the journal, if they are poorly written or have major methodological flaws, or do not include the relevant research checklist (such as STROBE or PRISMA). Other reasons for rejection include a lack of ethical approval or because the work duplicates something published elsewhere.

3. Peer review: The editor sends your paper to one or more external experts in your field for review. Reviewers are asked to assess the originality, significance, rigour of your research methods, & the validity of your work. They may suggest revisions to your paper or rejection.

4. Initial decision: The editor reviews the reviewers’ comments and decides whether to accept, reject, or revise your paper. Acceptance without any revisions is unusual as nearly all paper have scope to be improved. Generally, the authors have to respond to the comments from the referees and editor, and revise the paper before final acceptance.

5. Revisions: If your paper is accepted with revisions, you will be usually given a deadline to make the necessary changes. When sending back your revised paper, it is also normal practice to send a letter explaining how you have changed the paper in response to the comments.

6. Your response. Respond promptly to reviewer comments. Make sure your revisions are comprehensive and address all of the reviewer’s concerns and any comments from the editor. Be respectful and cooperative with the editor and reviewers. Finally, respond within the timescale given by the journal.

7. Final decision: Once your paper has been revised, it may be accepted without further changes; you may be asked to revise it again; or it may be rejected. Papers may be rejected if the authors do not adequately address the reviewer’s or editor’s concerns or if the revised paper still does not meet the journal’s standards. If accepted and no further changes are needed, the editor will send you a copy of the proofs for your final approval. This is your last chance to make changes.

8. Publication: Once you have approved the proofs, your paper will be published in the journal. Some journals (such as the BMJ) offer readers the opportunity to comment on a paper. It’s important to respond to these comments, which may sometimes highlight problems with your paper or suggest avenues for new research.

9. Responding to comments. When responding to comments, aim to be polit and respectful in your reply. Some comments can be constructive and others can be very critical of your paper. This post-publication review of a paper is an important part of the academic publication process. You can also engage with the broader public and research community through social media (for example, via Twitter or X). This increases the reach of your work including the likelihood it will be picked by the media or policy-makers.

10. The total time it takes to go through this process can vary from a few months to a year or more. It is important to be patient and to follow the instructions of the editor and reviewers. By doing so, you can increase the chances of your paper being published in a suitable journal.

The academic publication process is an important way to ensure the quality and accuracy of the scientific literature. By following the steps outlined in this article, researchers can increase their chances of getting their work published in a reputable journal

The Impact of Shielding and Loneliness on Physical Activity During the COVID-19 Pandemic

The COVID-19 pandemic had profound effects on many aspects of life, from healthcare to lifestyle habits. One of the most impacts has been the mental and physical well-being of individuals, particularly those who are older. Our study published in PLoS One aimed to quantify the relationship between shielding status and loneliness at the start of the pandemic and how these factors affected physical activity (PA) levels throughout the period. Conducted in London, the study surveyed 7748 cognitively healthy adults aged 50 and above from April 2020 to March 2021.

Methods

The study used the International Physical Activity Questionnaire (IPAQ) short-form to assess the physical activity levels of participants before the pandemic and six more times over the next 11 months. Linear mixed models were used to explore the relationship between shielding status and loneliness at the onset of the pandemic with physical activity over time.

Key Findings

Loneliness and Physical Activity

The study revealed that participants who felt ‘often lonely’ at the beginning of the pandemic completed significantly fewer Metabolic Equivalent of Task (MET) minutes per week during the pandemic. Specifically, they completed an average of 522 to 547 fewer MET minutes per week compared to those who felt ‘never lonely.’

Shielding and Physical Activity

Those who were advised to shield or self-isolate at the beginning of the pandemic also showed reduced levels of physical activity. They completed an average of 352 fewer MET minutes per week compared to those who were not shielding. After adjusting for demographic factors, the decrease was 228 fewer MET minutes per week.

Additional Factors

No significant associations were found between shielding, loneliness, and physical activity after further adjustments for health and lifestyle factors. This suggests that co-morbidities and health status also play an influential role.

Conclusions and Implications

The study indicates that those who were shielding or felt lonely at the start of the pandemic were likely to have lower levels of physical activity during the pandemic. Co-morbidities and health status also significantly influence these associations. Given the profound impact of physical activity on overall health, targeted interventions may be necessary to support these vulnerable populations in maintaining an active lifestyle, especially during challenging times like a pandemic.

For healthcare providers, public health professionals, and policy-makers, these findings underscore the need for comprehensive approaches that address not just the physical but also the psychological and social aspects of well-being, particularly for older adults. By understanding the interplay between these factors, we can aim for more effective public health strategies that promote a holistic approach to health and well-being, especially in times of crisis.

The Number Needed to Treat: Why is it Important in Clinical Medicine and Public Health?

You will often see the NNT mentioned in clinical guidelines; and when different health interventions are being prioritised or assessed for their clinical effectiveness and cost effectiveness. For example, the NNT was used to inform decisions to recommend statins for people with an elevated risk of cardiovascular disease.

The NNT is a measure used to quantify the effectiveness of an intervention or treatment. It is the average number of patients who need to be treated with a particular therapy for one additional patient to benefit.

How is NNT calculated?

In mathematical terms, the NNT = 1/[Absolute Risk Reduction]

Where Absolute Risk Reduction (ARR) = Control Event Rate (CER) – Experimental Event Rate (EER)

Control Event Rate (CER): The rate of an outcome in a control group.

Experimental Event Rate (EER): The rate of an outcome in an experimental group treated with the intervention.

For example, consider a drug that reduces the risk of heart attack from 4% to 2%. The ARR is 2% or 0.02 and the NNT is 50 (1/0.02). Hence, on average, 50 people will need to be treated to prevent one heart attack.

Importance in Clinical Medicine

The NNT is important in clinical medicine because it helps in the evaluation of the efficacy of treatments by offering a direct, patient-centred measure. It is also helpful in clinical decision making as it allows doctors and patients to make makes evidence-based decisions on treatment options. For example, when presented with data on the NNT, patients can consider how useful a medical intervention is for them.

The NNT also helps in the assessment of the balance between potential benefits and harms of treatment; and provides a uniform metric for comparing the effectiveness of different treatments.

Role of NNT in Public Health

The NNT is also important in public health because it provides a metric that can be used at a population level, offering insights into public health strategies; for example, it can help policy makers determine the most efficient use of healthcare resources. When combined with other metrics, the NNT can be a tool in assessing the cost-effectiveness of public health interventions such as preventive measures, screening and vaccination.

For example, the NNT was used by the UK JCVI to decide which population groups should be prioritised for booster Covid-19 vaccinations by considering how many people in different age groups would need to be vaccinated to prevent one hospital admission.

Limitations of NNT

The NNT does have some limitations. For example, it does not account for side effects or adverse reactions to medical interventions. It is also specific to the particular patient populations and settings from which the data to calculate the NNT was derived. For example, many adverse health outcomes are more common in older people. Hence, the NNT is not uniform over the population and will be lower in groups at higher risk such as the elderly.

Conclusions

Understanding NNT is crucial for both individual clinical decisions and broader public health strategies aimed at population health improvement. It provides an intuitive way to understand the practical impacts of treatment and public health interventions; and is a measure that is useful to many groups including policy makers, clinicians, public health specialists and patients.