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What is disease surveillance and the activities involved in the process.
Describe the various methods of disease surveillance.
Discuss the objectives and components of a disease surveillance system.
Sources of epidemiological data and some limitations to the use of these data.
Discuss the role of healthcare professionals in disease surveillance.
Calculate survival rates with the aid of practical examples.
Discuss the types of variables and methods of data presentation.
Disease surveillance is a systematic and ongoing collection, analysis, interpretation, and dissemination of data regarding diseases and health conditions. It is a crucial component of public health, helping authorities monitor the spread of diseases, understand patterns, and implement effective control measures. By gathering data from various sources such as hospitals, laboratories, and communities, disease surveillance provides real-time information about the occurrence and distribution of diseases. This information enables public health officials to detect outbreaks early, track the progression of diseases, and assess the impact of control measures. Additionally, disease surveillance plays a vital role in the identification of emerging threats, allowing for timely response to prevent widespread outbreaks. Timely and accurate surveillance data can inform evidence-based decision-making, resource allocation, and policy development, ultimately contributing to the prevention and control of diseases. WHO (2019), underscores the significance of surveillance in guiding public health interventions, stating that surveillance systems are essential for tracking health trends, planning, and evaluating programs, as well as stimulating research.
The Objectives of Disease Surveillance Systems
The main role of disease surveillance is to predict, observe, and minimize harm caused by epidemic or pandemic as well as contribute to the growing body of knowledge on the determinants for these diseases with respect to the specific population. Disease surveillance therefore aims to:
- Promptly identify emerging diseases and health risks for the population.
- Track changes in disease frequency and prevalence.
- Collate morbidity and mortality data to inform health system planning.
- Identify risk factors associated with diseases.
- Monitor the occurrence and outcome of disease such as cancer, asthma, chemical poisoning, injuries, etc.
- Monitor for the completeness of vaccination coverage and the protection of the population from multi-drug resistant organisms.
- Assess control and preventative measures for efficacy and improvements.
Uses of Surveillance
By this, you should be able to depict some of the uses of disease surveillance data. They are used to identify signs of an epidemic, calculate morbidity, identify high-risk groups in the population, test hypotheses, plan and implement control measures, predict the future burden of diseases, monitor disease trends, and mobilize health resources.
Indication for Use and Characteristics of Disease Surveillance Systems
A disease surveillance system is essential for monitoring and controlling public health threats. There are several indications highlighting the need for disease surveillance systems:
Disease with High Incidence and Prevalence- Diseases that affect a large portion of the population require continuous surveillance. For example, diseases like malaria in certain regions of Africa or diabetes globally necessitate surveillance systems to understand the scope of the issue, assess the effectiveness of interventions, and allocate resources appropriately.
High Mortality Rate, Including Premature Mortality- Diseases with high mortality rates, such as certain types of cancer or cardiovascular diseases, require monitoring to identify trends, assess risk factors, and evaluate the impact of healthcare interventions. Surveillance helps in understanding the causes of premature deaths and implementing preventive measures.
Disease with Epidemic Potential- Diseases that can cause widespread outbreaks, such as influenza or Ebola virus disease, require constant surveillance. Early detection of outbreaks through surveillance enables rapid response, containment, and prevention of further spread, minimizing the impact on public health.
Information Gaps on New Diseases- With the emergence of new diseases like COVID-19, surveillance becomes crucial to fill information gaps. Surveillance systems help in understanding the transmission dynamics, identifying vulnerable populations, and assessing the efficacy of treatments and vaccines. Constant monitoring and analysis of data are vital to respond effectively to new and evolving health threats.
Biological or Chemical Threats- Surveillance is essential for monitoring the deliberate or accidental release of biological or chemical agents, including bioterrorism events. A robust surveillance system can detect unusual patterns of illness or identify specific biological or chemical agents, enabling rapid response and mitigating the impact of such threats.
An effective disease surveillance system should possess several key characteristics to ensure its efficiency and usefulness in public health efforts. These desired characteristics include:
Simplicity
The surveillance system must be simple and easy to use while maintaining its core purpose or function. Complex systems increase the risk of under-reporting and have negative consequences for data accuracy and timeliness.
Sensitivity
The system should be sensitive enough to detect even small changes in disease occurrence. It should be able to pick up both sporadic cases and outbreaks. Positive Predictive Value (PPV), measures the probability that a reported case is a true case. A high PPV ensures that reported cases are likely to be actual cases.
Specificity
The system should be specific in identifying particular diseases or health events. Specificity ensures that reported cases are related to the targeted disease or condition, reducing false alarms and unnecessary responses. Negative Predictive Value (NPV), the probability that an individual with a negative test is a true negative can be used to determine the specificity of a test.
Timeliness
Timely data collection, analysis, and reporting are critical. Rapid response to outbreaks or health threats depends on receiving information as quickly as possible. Depending on the nature of the health event, morbidity and mortality associated with certain diseases can change rapidly influencing the public health response to the specific health event.
Flexibility
The system should be adaptable to changing circumstances, including emerging diseases or new patterns of existing diseases. Flexibility allows for the incorporation of new variables or diseases into the surveillance process. Different countries, based on their economic situation, will face varying resource challenges. A flexible surveillance system is one that can be adapted to a wide variety of healthcare settings regardless of resource challenges.
Representativeness
The data collected should be representative of the population under surveillance. Biases in data collection can skew the results and lead to incorrect conclusions impacting healthcare planning and delivery. The system should provide information that is valuable for decision-making. It should help public health officials and policymakers understand disease patterns, assess interventions, and allocate resources effectively.
Acceptability
The system should be accepted by both the public and healthcare professionals. Trust and cooperation from these groups are essential for accurate reporting and effective surveillance.
Data Quality
Accurate, complete, and reliable data are crucial. Regular training and supervision of personnel involved in data collection can help maintain high data quality.
Stability
The surveillance system should be stable over time. Consistency in data collection methods and reporting formats allows for meaningful trend analysis.
Ethical Considerations
Respect for individuals’ privacy and confidentiality of health information is paramount. Ethical guidelines must be followed in the collection and use of surveillance data.
Integration
Ideally, the surveillance system should be integrated with other public health systems and databases. Integration allows for a comprehensive understanding of health issues and supports coordinated responses.
Activities in a Disease Surveillance Model
Surveillance begins with the systematic collection of data from various sources such as hospitals, laboratories, clinics, and community health centers. These data include information about the occurrence of diseases, patient demographics, and other relevant details.
Data collected are analyzed to identify patterns, trends, and anomalies. Epidemiologists and public health experts use statistical methods to analyze the data, helping them understand the spread of diseases and the factors contributing to their occurrence.
Analyzed data are interpreted to draw meaningful conclusions. This step involves understanding the implications of the data, such as identifying potential outbreaks, assessing the effectiveness of interventions, and evaluating the impact of diseases on public health.
Surveillance data, along with the interpreted results, are reported to Ministry of Health and Wellness (MOHW) as well as other relevant health authorities regionally and globally. Timely and accurate reporting is crucial for informing decision-making processes and implementing appropriate control measures.
Disease surveillance systems are designed to detect outbreaks early. By continuously monitoring data, public health officials can identify unusual spikes in disease occurrences, enabling rapid response to contain the outbreak and prevent its further spread.
Surveillance activities also involve continuous monitoring of diseases and their trends. By evaluating the effectiveness of control measures and interventions, public health officials can make necessary adjustments to their strategies, ensuring a more targeted and efficient response to diseases.
Surveillance data and findings are disseminated to healthcare professionals, policymakers, and the public. Transparent communication of information helps raise awareness, educates the public about preventive measures, enables healthcare providers to make informed decisions about patient care, prioritize and mobilize healthcare resources as needed.
Methods of Disease Surveillance
Various methods are employed for data collection in disease surveillance, including routine reporting, alert systems, outbreak investigations, and surveys. Routine surveillance is conducted for high-impact diseases, some of which are also monitored globally. Data is typically reported on a weekly or monthly basis. Alert systems play a critical role in notifying authorities immediately when a case of a disease with epidemic potential occurs, serving as an early warning system for epidemic-prone disease outbreaks. During declared outbreaks, data collection may become ad hoc and more intensive.
In situations where existing disease surveillance systems do not provide answers to specific questions, active surveillance methods, such as surveys and other tailored data collection techniques, become essential. These approaches allow health authorities to gather targeted information and fill gaps in understanding. By employing a variety of data collection methods, disease surveillance systems can enhance their responsiveness, accuracy, and effectiveness in monitoring and controlling diseases.
The choice between active and passive surveillance methods hinges on the unique characteristics of the disease or event and the prevailing circumstances. For rapidly spreading and high-impact diseases such as COVID-19, passive surveillance proves to be generally suitable due to its real-time data collection ability. It allows for swift reporting, essential for immediate responses and containment efforts.
On the other hand, diseases like measles, known for sporadic outbreaks, often require a nuanced approach. A combination of active and passive surveillance methods might be necessary, with active surveillance playing a pivotal role in the initial stages. Active methods can effectively identify and help control the outbreak, providing crucial insights into the disease’s patterns.
For chronic or persistent diseases like HIV/AIDS, which maintain a steady presence within communities, passive surveillance emerges as the most appropriate method. Its continuous, non-intrusive data collection allows for long-term monitoring, enabling healthcare systems to track the disease’s progression, allocate resources, and assess the impact of interventions over time. By tailoring surveillance methods to the specific nature of each disease, public health responses can be more targeted, efficient, and ultimately effective.
Passive Disease Surveillance
Passive disease surveillance relies on healthcare providers and the public to report cases to health authorities voluntarily. In this system, health professionals report diseases or health events when they encounter them, and individuals also self-report if they suspect they have a specific disease. Available data on reportable diseases are used and reporting can be made mandatory by local health authorities or medical officer of health. Passive surveillance is less resource-intensive but might suffer from underreporting, as it relies on individuals and healthcare workers to initiate the reporting process.
Examples
- The National Notifiable Diseases Surveillance System (NNDSS)
- The World Health Organization’s Global Individual Case Safety Reports (ICSRs) for Pharmacovigilance.
Global Influenza Surveillance and Response System (GISRS)
Active Disease Surveillance
Active disease surveillance involves proactive efforts to seek out information about diseases. This often means health authorities initiate the data collection process, conducting regular, systematic data collection from healthcare facilities or communities. Healthcare staff makes periodic visits to identify new cases of diseases or death from diseases. This method provides more accurate and timely information but requires more resources and manpower.
Examples
The Centers for Disease Control and Prevention’s (CDC) Active Bacterial Core surveillance (ABCs), which actively collects data on bacterial infections in select geographic areas
The European Antimicrobial Resistance Surveillance Network (EARS-Net), which actively monitors antimicrobial resistance patterns in Europe.
These systems involve regular and systematic collection of data from specific sources, enabling health authorities to track diseases effectively and implement timely interventions.
Table 4.1. Strengths, Weaknesses, and Limitations to the Use of Disease Surveillance Data
Passive surveillance is a cornerstone of most disease monitoring systems, wherein data is continuously collected. Diseases are categorized into three main classes based on their severity, dictating the reporting protocols. In Jamaica are:
Class 1 communicable diseases, which must be reported immediately on suspicion via telephone or in writing, are instances like accidental poisoning, maternal death, and diseases such as cholera, TB, HIV/AIDS, Poliomyelitis, malaria, hepatitis B, and leprosy. Notably, a recent addition to this class is COVID-19, reflecting the dynamic nature of disease surveillance and response.
Class 2 health events, reported weekly, include diseases like influenza, Hepatitis A, and dengue fever.
Class 3 health events, reported monthly, encompass diseases such as chickenpox, mumps, and certain sexually transmitted infections like chlamydia and gonorrhea, which are excluded from Class 1.
This hierarchical classification ensures a structured approach to disease reporting, allowing for prompt responses to emerging health threats while facilitating comprehensive data analysis.
Sentinel Reporting
Sentinel surveillance involves the targeted monitoring of specific sites, events, or providers that are representative of a larger population. These sites or providers are carefully selected to provide early warning of disease outbreaks or trends. For example, a sentinel surveillance system for influenza may involve a network of selected healthcare providers who report the number of influenza-like illness cases they see each week. This data can provide insight into the prevalence and trends of influenza in the community.
Serial health surveys are conducted at regular intervals to collect data on the health status, behaviors, and risk factors of a population. These surveys often use random or stratified sampling to ensure a representative sample. Examples include the Behavioral Risk Factor Surveillance System (BRFSS) in the United States, which collects information on various health-related behaviors like smoking and physical activity. Serial surveys provide valuable information for tracking changes in health indicators over time.
Database linkage involves the integration of data from multiple sources to create a more comprehensive dataset for analysis. This method is often used to monitor chronic diseases or specific health outcomes. For instance, linking data from hospital records, cancer registries, and mortality databases can help track cancer incidence, treatment, and survival rates. By merging and analyzing these datasets, researchers can gain a better understanding of disease patterns and outcomes.
Active surveillance may involve healthcare providers or laboratories actively reporting specific diseases or events to public health authorities. For example, healthcare providers may be required to report cases of notifiable diseases, such as tuberculosis or HIV, to the local health department. This ensures that public health agencies receive timely and accurate information about specific diseases.
Active surveillance can also include special studies or investigations conducted in response to specific public health concerns. For instance, during an outbreak investigation, public health teams may actively collect data from affected individuals, healthcare facilities, and community sources to identify the source and control the outbreak. Let us take a look at this example:
In Jamaica’s healthcare system, the Sentinel Surveillance System operates through a strategic network of selected healthcare facilities known as sentinel stations. These stations, including hospitals and clinics, are meticulously identified. Each sentinel station designates a sentinel officer, often a clerk or community health aide, responsible for daily tracking of individuals reporting specific health conditions of interest.
The crucial data collected at these sentinel sites are then transmitted to the Epidemiology unit within the Ministry of Health and Wellness (MOHW). From there, the information is further disseminated to international bodies, notably the Caribbean Epidemiology Centre (CAREC) in Trinidad and Tobago and subsequently to the Centers for Disease Control and Prevention (CDC).
Key sentinel sites in Jamaica include the National Chest Hospital, focusing on cases related to TB and HIV/AIDS, and prominent hospitals like Bustamante Hospital for Children, Spanish Town Regional Hospital, Mandeville Regional Hospital, and Cornwall Regional Hospital. Ensuring seamless coordination, the medical officers of health for respective parishes collaborate closely with local virology labs and the National Public Health Laboratory. This collaboration ensures the proper treatment of samples and accurate reporting, vital for maintaining data integrity, which is then integrated into global repositories managed by institutions like the CDC and the World Health Organization (WHO).
Additionally, the system incorporates stringent thresholds triggering immediate action. For instance, if the reported cases rise above a specific threshold, such as greater than 5 for cholera or gastroenteritis cases, or for singular instances of diseases like TB, anthrax, Hepatitis A and B, Rabies, and certain neoplasms, an alert is generated. This mechanism ensures rapid response and targeted interventions, enhancing the effectiveness of Jamaica’s disease surveillance efforts.
Case Definition in Disease Surveillance Tools
Case definitions are standard criteria used to identify and classify individuals with a particular disease or health condition. These definitions are crucial for consistency in data collection, analysis, and reporting. They usually consist of three categories: suspected cases, probable cases, and confirmed cases.
Table 4.2. Standardized Case Definitions used in Disease Surveillance Tools
While standardized case definitions are fundamental for consistency and comparability across regions, they can pose challenges in countries with varying access to health resources and diagnostic tools.
Limited Access to Healthcare- In regions with limited healthcare access, individuals might not seek medical help for mild symptoms, leading to underreporting and a skewed representation of the disease’s actual prevalence.
Differences in Diagnostic Capabilities- Discrepancies in the availability of diagnostic tools and skilled personnel can result in varying confirmation rates. Some regions might lack advanced laboratories, leading to delayed or inaccurate diagnoses.
Epidemiological Context- Socioeconomic factors, cultural practices, and local epidemiological contexts can influence disease presentation and transmission. Standardized definitions might not always capture these nuances, leading to misclassification of cases.
Resource Allocation- Relying solely on standardized definitions can misguide resource allocation. Regions with higher access to diagnostics might receive more resources, potentially leaving underserved areas underreported and under-resourced.
It is therefore crucial for countries to adapt case definitions to their specific contexts. This allows for a more accurate representation of the disease’s prevalence, ensures appropriate allocation of resources, and facilitates targeted public health interventions tailored to the local needs and capacities of the health system.
Minimum Data Elements to be Collected
In disease surveillance, collecting minimum data elements is essential for understanding the patterns, impact, and risk factors associated with diseases in a population. Demographic and socio-economic data, including information on age, gender, ethnicity, education, employment, nutrition, and access to basic needs, provide critical insights into the social determinants of health. Understanding the socio-economic status of a population helps in identifying vulnerable groups with limited access to healthcare, enabling targeted interventions and resource allocation. For example, monitoring nutrition levels can indicate malnutrition rates, which are essential in assessing the overall health and resilience of a community. By collecting these data elements, disease surveillance programs can identify disparities and design policies addressing social inequalities, ultimately enhancing the overall health outcomes of the population.
Mortality rates are key indicators in disease surveillance, providing valuable information about the number of deaths in a specific population over a defined period. Crude mortality rate calculates the total number of deaths per 1,000 people in a given population, offering a general overview of mortality patterns. Proportionate mortality, on the other hand, focuses on the proportion of deaths attributed to specific diseases or conditions, offering insights into the major causes of mortality in the community. Case fatality rate, expressed as a percentage, assesses the severity of a disease by measuring the proportion of individuals diagnosed with a particular disease who die from it. These rates are vital for prioritizing public health efforts, directing resources towards prevalent and high-risk diseases, and evaluating the effectiveness of interventions, enabling a more targeted and impactful disease surveillance strategy.
Morbidity rates, including incidence, attack rate, and prevalence, provide detailed information about the occurrence and spread of diseases in a population. Incidence rate calculates the number of new cases of a specific disease within a defined population over a specific period, aiding in understanding disease trends and identifying potential outbreaks. Attack rate, expressed as a percentage, measures the proportion of people at risk who develop the disease during an outbreak, providing crucial information about the disease’s contagiousness and risk factors. Prevalence rate, on the other hand, estimates the total number of existing cases (both old and new) within a population at a specific point in time. These morbidity rates offer real-time insights into disease transmission patterns, help in implementing preventive measures, and enable timely responses to contain outbreaks. By collecting and analyzing these morbidity rates, disease surveillance programs can detect emerging diseases, track the progression of known diseases, and design targeted interventions, ultimately minimizing the impact of diseases on public health
Sources of Surveillance Data and Limitations to Use
Sources
Clinical Records
Hospitals, clinics, and healthcare facilities maintain clinical records that provide valuable information about diagnosed diseases, treatments, and patient demographics.
Laboratory Reports
Diagnostic laboratories provide data on confirmed cases of various diseases, allowing for accurate identification and tracking of infectious agents.
Pharmacy Records
Data from pharmacies can indicate trends in the purchase of specific medications, suggesting the prevalence of certain illnesses in a community.
Health Surveys
Surveys conducted in communities or populations provide insights into health behaviors, risk factors, and prevalence of specific diseases.
Notifiable Disease Reporting
Healthcare providers and laboratories are required to report certain diseases to public health authorities, forming a primary source of surveillance data.
Mortality Data
Death certificates or death registries provide information about causes of death, helping identify diseases with high mortality rates.
Environmental Data
Information on environmental factors such as air quality, water contamination, and climate patterns can be crucial for surveillance, especially for diseases influenced by the environment.
Social Media and Internet Data
A more contemporary trend which is gaining momentum is monitoring social media and online forums as they can provide early indications of disease outbreaks and public sentiment regarding health issues
Limitations
Underreporting
Not all cases are reported, leading to underestimation of disease prevalence and hindering accurate analysis.
Data Quality
Incomplete, inaccurate, or inconsistent data can distort the understanding of disease patterns and trends.
Selection Bias
Data might be biased toward certain demographics or regions, affecting the representativeness of the surveillance findings.
Diagnostic Advances
Changes in diagnostic techniques and criteria over time can affect the comparability of data collected from different periods.
Resource Disparities
Variations in healthcare access and resources can result in uneven surveillance coverage, leading to disparities in reported data.
Privacy Concerns
Striking a balance between data collection for public health and protecting individuals’ privacy rights is a challenge, especially in the era of electronic health records.
Limited Scope
Surveillance systems might not capture non-traditional healthcare-seeking behaviors, alternative medicine practices, or diseases with mild symptoms, leading to gaps in the data.
Data Interpretation
Interpreting surveillance data requires expertise, and misinterpretation can lead to inappropriate interventions or resource allocation.
Mitigating these limitations requires ongoing efforts to improve data quality, enhance reporting mechanisms, investment in healthcare infrastructure, and ensuring the ethical use of data while maximizing its utility for public health decision-making.
Roles of the Healthcare Professional in Disease Surveillance
Pharmacists, with their expertise in medications and close patient interactions, play a vital role in disease surveillance. Their involvement strengthens the healthcare system’s ability to detect, prevent, and manage diseases effectively, ultimately improving public health outcomes.
Reporting Class 1 Health Events
Pharmacists play a crucial role in disease surveillance by promptly reporting any Class 1 health events or diseases, ensuring timely communication of potential outbreaks to health authorities. Their vigilance can aid in early detection and response efforts.
Monitoring Excess Drug Use
Pharmacists collaborate with other health professionals to monitor drug usage patterns. They can identify unusual trends, such as excessive use or requests for specific medications, which might indicate a potential disease outbreak or drug-resistant strain, contributing to early warning systems.
Staying Informed
Pharmacists keep themselves updated with disease surveillance reports sent by the local MOHW in their respective countries. They maintain awareness of disease trends and emerging health threats, enabling informed decision-making in patient care and pharmaceutical practices.
Managing Clinical Trials
Pharmacists often manage clinical trials for new drugs or treatments related to infectious diseases. Their role involves overseeing drug administration, monitoring participants, and reporting adverse events. Active involvement in clinical trials contributes valuable data to disease research and surveillance efforts.
Providing Consultations
Pharmacists serve as trusted healthcare advisors, providing consultations to patients regarding disease prevention, medication adherence, and immunizations. Their guidance ensures patients are well-informed about preventive measures and can recognize early signs of diseases, enhancing overall community health.
Collaboration with Health Professionals
Pharmacists collaborate with various health professionals, including physicians, nurses, and epidemiologists. By sharing their observations and insights, pharmacists contribute to a comprehensive understanding of disease patterns, ensuring a multidisciplinary approach to disease surveillance and response.
Table 4.3. Summary of Additional Roles and Responsibilities in Disease Surveillance
Calculating Survival Rates and Presentation of Data
We calculate survival rates using surveillance data to assess the health of the population, set priorities, evaluate intervention strategies, and plan health services. The following calculations are therefore fundamental in epidemiology and disease surveillance as they provide valuable information about disease progression, treatment effectiveness, and the impact of diseases on populations. They help healthcare professionals and policymakers make informed decisions to improve public health outcomes.
Calculating Survival Rates:
- Case Fatality Rate
- Five-Year Survival Rate
- Observed Survival Rate
- Median Survival Time
- Relative Survival Time
- Actuarial (Life Table) Method
- Kaplan-Meier Method
- Net Survival Rate
- Cohort or Period Survival Rate
Case Fatality Rate
Provides a measure of the severity of a disease by indicating the proportion of individuals diagnosed with the disease who subsequently die from it. Case fatality rate is crucial in understanding the severity and lethality of a disease outbreak. See Measures of Occurrence in Epidemiology for additional information on Case Fatality Rate.
Five-Year Survival Rate
The percentage of patients who are alive 5 years after treatment begins or 5 years after diagnosis. The rational for using 5 years is that most deaths from cancer occur during this period after diagnosis. The Five-Year survival rate is calculated as follows:
Five-year survival rates provide insights into the long-term prognosis for patients with a specific disease. It’s commonly used in cancer research to evaluate the effectiveness of treatments over a five-year period. Example- If 500 patients were diagnosed with bladder cancer, and after five years, 400 of them are still alive, the five-year survival rate would be (400/500) * 100 = 80%.
Observed Survival Rate
Observed survival rate refers to the proportion of individuals diagnosed with a specific disease who are still alive after a certain period of time following the diagnosis. It is a measure of the actual survival experience of patients in a given population.
Example- Let’s consider a group of 200 patients diagnosed with heart failure (HFref). After 5 years, 70 of these patients are still alive.
Observed survival = 70/200 * 100% = 35%
This means that 35% of the patients diagnosed with heart failure with ejection fraction less than 40% were still alive 5 years after their diagnosis.
This information is valuable for healthcare professionals and policymakers to understand the impact of HF with reduced ejection fraction on the population and to plan appropriate interventions and support systems.
Overall, observed survival rates are essential as they provide valuable insights into the real-world impact of diseases and treatments on patients’ lives. These rates help epidemiologists and healthcare professionals assess the effectiveness of interventions, track disease progression, and compare the survival outcomes of different patient groups. Observing the survival rates over time is crucial for evaluating the success of healthcare policies and treatments, guiding public health strategies, and improving patient care.
Median Survival Time
This is the length of time that half the study population survives. Median survival time is used because the mean is affected by extreme values. Since median survival time represents the time point at which half of the patients have died. It provides a more comprehensive understanding of the disease’s progression and helps in comparing treatment effectiveness.
Relative Survival Time
Calculating relative survival time can be complex and may require statistical software and access to appropriate life tables for the general population. The relative survival estimates are typically used in cancer research, where background mortality rates are well-documented. Relative survival time compares the survival of patients with a specific disease to the expected survival of a similar group in the general population.
Notice in the equation the numerator and denominator appear to have different characteristics, that is because it is a comparison of two different groups (survival with the disease of interest and survival without the disease). Therefore, relative survival time represents the percentage of individuals in the study cohort who survived up to a specific time point compared to what would be expected in the general population, accounting for age-specific and other background mortality rates.
Actuarial (Life Table) Method
This method estimates the probability of surviving to a specific time point. It involves using a life table to calculate the probability of surviving each time interval. These probabilities are then multiplied together to obtain the overall survival rate. It is often used in clinical trials and long-term cohort studies.
Kaplan-Meier Method
This method is used for estimating survival rates when the data includes censored observations (patients who are lost to follow-up or still alive at the end of the study). Kaplan-Meier estimates survival probabilities at different time points. It takes into account both censored and uncensored data points, making it suitable for incomplete data. Kaplan-Meier method is widely used in medical research, especially in studies with varying follow-up times.
Net Survival Rate
Net survival represents the survival of patients with the disease after accounting for mortality from other causes. It involves adjusting the observed survival rate by subtracting the expected mortality from other causes in the general population.
This is particularly useful when estimating survival rates for diseases with high competing risks of death.
Cohort or Period Survival Rate
These methods calculate the proportion of individuals from a specific cohort who survive for a certain period. Cohort survival rates consider a specific group of individuals born in the same year, while period survival rates consider individuals diagnosed in the same time period. It is useful for understanding the survival patterns of specific birth cohorts or for assessing changes in survival rates over time.
It is important to choose the appropriate method based on the nature of the data and research objectives to accurately calculate and interpret survival rates in healthcare studies.
Presenting Surveillance Data
When presenting surveillance data, careful consideration of the variables involved is crucial. In epidemiology, variables are categorized into independent and dependent variables, each serving distinct roles in data analysis. Independent variables are factors that are manipulated or controlled in a study, while dependent variables are outcomes that are observed and measured.
“Does a diet with a high content of animal fat cause prostate cancer?”
The researcher is exploring the relationship between two variables: diet and prostate cancer. In this context, the independent variable is the factor that the researcher manipulates or controls, which is the diet with a high content of animal fat. This variable is plotted on the x-axis because it is the cause or predictor that is being investigated. The dependent variable, on the other hand, is the outcome that the researcher is interested in understanding, explaining, or predicting, which is the occurrence of prostate cancer. This variable is plotted on the y-axis because it is the effect or the outcome that is being observed or measured in response to changes in the independent variable.
In this scenario, the researcher is studying whether there is a relationship between the consumption of a diet high in animal fat (independent variable) and the development of prostate cancer (dependent variable). The researcher is interested in understanding if changes in the diet have any impact on the occurrence of prostate cancer. Therefore, the occurrence of prostate cancer depends on the type of diet individuals consume, making prostate cancer the dependent variable. The independent variable, diet with a high content of animal fat, is manipulated to observe its effect on the dependent variable, prostate cancer incidence.
Additionally, understanding the characteristics of the data is vital. Continuous variables, such as age or temperature, are measured on a continuous scale and can take any value within a given range. They are typically ratio or interval variables, allowing for precise mathematical operations. Discrete variables, on the other hand, are distinct and separate, often represented by whole numbers. These can be nominal (categories without inherent order) or ordinal (categories with a specific order).
For instance, in disease surveillance, the number of reported cases can be considered a dependent variable, as it depends on various independent factors such as population density, healthcare access, and public awareness campaigns. Age, often analyzed in epidemiological studies, is a continuous variable (ratio or interval) that can be crucial for understanding disease prevalence across different age groups.
In presenting surveillance data, it’s important to choose appropriate graphical representations and statistical analyses based on the nature of the variables. For continuous variables, histograms, box plots, and line graphs can provide insightful visualizations. For discrete variables, bar charts and pie charts are useful, especially for nominal data, while ordinal data can be represented using stacked bar charts or dot plots to show the order.
Figure 4.1 Scatter Plot Demonstrating the Association between Two Variables, X- Independent and Y-Dependent.
A scatter diagram is a powerful visual tool used to display the relationship between two variables. In this graph, each data point represents a unique combination of values for the two variables. By plotting these points on a Cartesian plane, patterns and trends within the data become apparent. The scatter diagram is particularly useful in identifying correlations between variables. When points cluster around a line running from the bottom left to the top right of the graph, it indicates a positive correlation: as one variable increases, so does the other. Conversely, if the points cluster around a line running from the top left to the bottom right, it signifies a negative correlation: as one variable increases, the other decreases. The scatter diagram provides a clear visual representation of the relationship between variables, aiding researchers and analysts in making data-driven decisions.
Understanding these variable distinctions ensures that the data is accurately represented and analyzed, allowing epidemiologists and policymakers to draw meaningful conclusions. Moreover, it enables the application of appropriate statistical tests, leading to more robust and reliable interpretations, ultimately enhancing the effectiveness of disease surveillance strategies.
Summary
Disease surveillance in epidemiology is a systematic process involving the collection, analysis, interpretation, and dissemination of health-related data to monitor and control diseases within populations. Effective surveillance systems possess certain desired characteristics, such as simplicity, ensuring easy use while maintaining core functionality. They must also be flexible, adaptable to different circumstances, and specific to the situation at hand, ensuring efficient responses to diverse health emergencies.
Disease surveillance serves as a critical public health response strategy during health emergencies. Its priorities encompass monitoring the severity of the emergency, detecting outbreaks promptly, tracking disease trends, evaluating the impact of interventions, and providing crucial information to the Ministry of Health. This information aids in decision-making, resource allocation, and the formulation of effective public health policies.
Data collection methods in disease surveillance can vary. Passive surveillance involves continuous data collection, particularly useful for monitoring diseases with steady occurrence. In contrast, active surveillance, triggered by alert systems or outbreak investigations, focuses on specific incidents. The data collected includes demographic information, mortality rates, and morbidity rates. These data elements are fundamental for various survival rate calculations, providing essential insights into disease progression and treatment outcomes.
To communicate surveillance findings effectively, diverse data presentation methods are employed. These methods ensure that complex information is presented in understandable formats, aiding healthcare professionals, policymakers, and the public in making informed decisions. Disease surveillance, with its comprehensive data collection and analysis techniques, plays a pivotal role in safeguarding public health by enabling timely responses and evidence-based interventions.
References
Centers for Disease Control and Prevention. (2017). Bioterrorism Agents/Diseases. https://emergency.cdc.gov/agent/agentlist-category.asp
Connolly, M. A. (2005). Communicable disease control in emergencies : A field manual. World Health Organization.
World Health Organization. (2019). Public Health Surveillance: A Tool for Targeting and Monitoring Interventions. https://www.who.int/whr/1996/en/whr96_ch4_en.pdf
World Health Organization. (2018). World Malaria Report 2018. https://www.who.int/malaria/publications/world-malaria-report-2018/en/
World Health Organization. (2021). Cardiovascular Diseases (CVDs). https://www.who.int/news-room/questions-and-answers/item/cardiovascular-diseases-(cvds)
World Health Organization. (2018). Ebola Virus Disease. https://www.who.int/en/news-room/questions-and-answers/item/ebola-virus-disease
World Health Organization (2020). Coronavirus disease (COVID-19) outbreak: Rights, roles and responsibilities of health workers, including key considerations for occupational safety and health. https://www.who.int/news-room/commentaries/detail/coronavirus-disease-covid-19-outbreak-rights-roles-and-responsibilities-of-health-workers-including-key-considerations-for-occupational-safety-and-health.
Last updated: 2023- 9- 29