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WQ.182/2024
WRITTEN QUESTION TO THE MINISTER FOR HEALTH AND SOCIAL SERVICES
BY DEPUTY K.M. WILSON OF ST. CLEMENT
QUESTION SUBMITTED ON TUESDAY 7th MAY 2024
ANSWER TO BE TABLED ON WEDNESDAY 15th MAY 2024
Question
“In relation to the Mortality Report for 2022, will the Chief Minister –
- provide details of how the Island’s mortality statistics are used by the Government to inform policy development;
- advise whether the use of the statistics by Government is consistent with how other jurisdictions use their mortality statistics;
- advise whether the percentage increase in deaths is an indication of ‘excess deaths’ caused by the COVID-19 pandemic; and
- consider amending future reports to include statistics in absolute terms (numbers higher than expected), relative terms (percentage higher than expected), and age stratification data?”
Answer
- Provide details of how the Island’s mortality statistics are used by the Government to inform policy development
Mortality statistics are a cornerstone of health data and hold significant value across various policy areas. Within government, they play a critical role in:
• Guiding the commissioning of services, prioritisation of resources, and informing intervention planning and design.
• Inform programmes such as screening programmes, by providing insights into leading causes of death and trends over time.
• Contributing to population projections, aiding in broader demographic analysis. This also enables the government to look at future demand on services, condition management, and prioritising intervention and prevention programmes.
• healthcare planning and policy development for the island, where such data serves as a foundational tool for allowing policymakers to target interventions where they are most needed.
Practically, mortality statistics inform services related to deceased individuals, including cremations, morgues, and funeral arrangements.
Moreover, mortality statistics are instrumental in identifying emerging health threats and evaluating the effectiveness of public health interventions. During crises like pandemics, they facilitate real-time monitoring and response planning, enabling timely interventions to mitigate adverse outcomes.
In assessing policy impact, mortality statistics serve as a crucial benchmark, allowing policymakers to gauge effectiveness and identify areas for improvement. They also contribute to calculations for Healthy life expectancy (which are part of the future jersey indicators), reflecting overall population health.
Furthermore, mortality statistics monitor various policy areas beyond suicide prevention and substance use, including chronic disease prevention, infectious disease control, and maternal and child health. The comparability of local mortality statistics offers several benefits, including trend identification, benchmarking, impact evaluation, resource allocation, and international collaboration, facilitating informed decision-making and effective public health strategies.
- Advise whether the use of the statistics by Government is consistent with how other jurisdictions use their mortality statistics
Mortality statistics play a fundamental role in guiding healthcare policies and strategies across jurisdictions. While there may be some variation in their specific applications due to differences in healthcare systems and priorities, the overarching principles guiding their use remain consistent.
Firstly, mortality statistics serve as a cornerstone for governments in shaping healthcare policies and strategies. By analysing mortality trends, policymakers can identify areas of concern and allocate resources effectively to address pressing health issues.
Secondly, mortality statistics are vital components of public health surveillance systems. They enable governments to monitor disease trends, detect outbreaks early, and evaluate the effectiveness of disease prevention and control measures. Real-time monitoring allows for the implementation of targeted interventions to curb the spread of infectious diseases and mitigate their impact on public health.
During public health emergencies, such as pandemics or natural disasters, mortality statistics play a crucial role in guiding emergency preparedness and response efforts. By tracking mortality rates in real-time, governments can assess the severity of the crisis, allocate resources efficiently, and implement timely interventions to save lives and prevent further harm.
Moreover, mortality statistics provide a means for governments to evaluate the impact of healthcare policies, programmes, and interventions over time. By comparing mortality rates before and after the implementation of specific measures, policymakers can gauge effectiveness and make informed adjustments to improve health outcomes.
Additionally, mortality statistics facilitate international benchmarking, allowing governments to compare health outcomes with those of other jurisdictions. This comparative analysis helps identify best practices, learn from successful interventions, and drive continuous improvements in population health globally.
Addressing the limitations in mortality statistics in Jersey, such as the lack of available deprivation data and the absence of ethnicity recording on death certificates, would enable policymakers to better understand and address health inequalities. Incorporating such data would provide insights into disparities in health outcomes among different population groups and inform targeted strategies to mitigate them effectively.
Furthermore, mortality statistics have the potential to offer insights beyond disease-specific interventions, shedding light on broader social determinants of health. Analysing mortality patterns across demographic groups enables policymakers to identify disparities and develop targeted social policies to address underlying socio-economic factors contributing to premature mortality.
The absence of dependable data around residential status in mortality statistics can compromise the accuracy of population estimates, hinder the identification of vulnerable populations, impede efforts to assess geographic disparities, limit health equity analysis, and constrain the exploration of social determinants of health. Therefore, efforts to improve data collection on residential status are crucial for enhancing the utility and reliability of mortality statistics for informing public health policies and interventions.
- Advise whether the percentage increase in deaths is an indication of ‘excess deaths’ caused by the COVID-19 pandemicl;;;;.
The percentage increase in deaths in Jersey can indeed serve as an indication of potential excess deaths caused by the COVID-19 pandemic. During a pandemic of this nature, excess deaths may arise due to various factors, including the direct impact of the virus and indirect effects such as strain on the healthcare system, disruptions in medical care, and broader social determinants of health.
To decide whether the percentage increase in deaths in Jersey is indicative of excess deaths attributable to COVID-19, it is crucial to compare the observed number of deaths during the pandemic period with historical data from previous years. This comparison allows us to assess whether the observed deaths surpass the expected baseline level, accounting for factors such as population growth and aging, which may naturally influence mortality rates over time.
Furthermore, analysing mortality data alongside COVID-19-related mortality statistics can provide valuable insights into the virus's overall impact on mortality rates. If the percentage increase in deaths aligns with the onset and progression of the COVID-19 outbreak, it strengthens the case for attributing the excess deaths to the pandemic.
However, further analysis and interpretation of mortality data by public health experts and epidemiologists are necessary to conclusively determine whether the observed percentage increase in deaths in Jersey can be attributed to excess deaths caused by the COVID-19 pandemic. This comprehensive analysis would provide a clearer understanding of the pandemic's impact on mortality rates and inform targeted public health responses accordingly.
- Consider amending future reports to include statistics in absolute terms (numbers higher than expected), relative terms (percentage higher than expected), and age stratification data?”
Providing both the actual number of excess deaths and presenting statistics in relative terms might allow Jersey to gain a comprehensive understanding of the impact of the COVID-19 pandemic.
The actual number of excess deaths may offer clarity regarding the scale of the impact, allowing policymakers, healthcare professionals, and the public to grasp the magnitude of the issue and plan appropriate responses accordingly.
Similarly, presenting statistics in relative terms, such as percentages higher than expected, may provide valuable context, and facilitate comparisons across different time periods and population groups. This approach may help assess the proportional increase in mortality rates and offers insight into the severity of the situation relative to historical norms.
Furthermore, analysing mortality data by age groups may highlight the disproportionate impact of COVID-19 on certain demographics, particularly older adults. Age-specific data should enable the identification of vulnerable populations and inform targeted interventions and resource allocation efforts. Additionally, age stratification should allow for the detection of any disparities in mortality rates among different age groups, aiding in the development of tailored responses.
By incorporating these additional elements into future reports, Public Health can enhance the comprehensiveness and usefulness of its mortality statistics analysis. This approach may facilitate more informed decision-making, better resource allocation, and the implementation of more effective public health interventions to address the impacts of the COVID-19 pandemic on the population. However, it is important to note that there several considerations arise when Public Health analyse mortality statistics, particularly when dealing with relatively small numbers. Policies and interventions informed by these statistics must acknowledge the limitations and challenges associated with small data sets to ensure decisions are well-founded. Recognising the heightened levels of statistical uncertainty inherent in small numbers is paramount. This uncertainty can significantly impact the accuracy and reliability of interpretations derived from mortality statistics, highlighting the necessity for careful analysis.
Small data sets also present challenges in detecting meaningful trends or patterns in mortality data. Understanding this limitation is crucial for accurately interpreting mortality trends and avoiding misinterpretations that may lead to inappropriate policy responses. Additionally, small increases in deaths may not always signify a significant public health concern and could align with the expected range of variation.
Addressing these complexities is essential for making informed decisions about resource allocation, intervention strategies, and public health measures, especially when dealing with small numbers of excess deaths.