Which one of the following statements is NOT true for taking a decision on screening for disease?
The sequence of events leading to disability and handicap is:
Disability-adjusted life years (DALYs) include:
Predictive accuracy of a screening test depends on the following EXCEPT:
Which one of the following is NOT a function of Epidemiology?
In a cohort of 500 women attending antenatal clinic, 70 % had ultrasonography (USG). This cohort was followed up at delivery. Of the women who had USG, 70 delivered low birth weight (LBW) babies; whereas of the women, who did not undergo USG, 50 delivered LBW babies. The incidence of LBW babies among women who had USG is:
Poor hand hygiene of a mess worker in a university college mess led to Hepatitis A cases in the hostel inmates. What type of epidemic will this exposure present with? 1. Propagated 2. Common source-continuous exposure 3. Common source-point exposure Select the correct answer using the code given below:
A depression screening tool developed in specialty clinics performs poorly when used in primary care settings. The positive predictive value drops from 85% to 25%. What is the most likely explanation?
A cohort study follows 5,000 nurses for 20 years to study night shift work and breast cancer risk. After 10 years, 30% of participants have been lost to follow-up. What is the most significant threat to study validity?
A screening test for a rare disease has 99% sensitivity and 95% specificity. In a population where the disease prevalence is 0.1%, what is the most important consideration for implementing this test?
Explanation: ***Proportion of false negatives is high*** - A **high proportion of false negatives** means that many individuals with the disease are missed by the screening test. - This is **absolutely undesirable** for screening and directly contradicts the fundamental principle that screening tests should have **high sensitivity**. - This statement is clearly NOT true as a criterion for making screening decisions. *Sensitivity and specificity are high* - **High sensitivity** means the test correctly identifies most people who have the disease, minimizing false negatives. - **High specificity** means the test correctly identifies most people who do not have the disease, minimizing false positives. - Both are desirable characteristics according to Wilson-Jungner screening criteria. *Disease prevalence should be high* - This statement is **oversimplified and not strictly accurate** according to Wilson-Jungner criteria. - The actual criterion is: **"The condition should be an important health problem"** - based on disease burden, severity, and consequences, NOT necessarily high prevalence. - Many successful screening programs target **low prevalence diseases** (e.g., phenylketonuria in newborns, congenital hypothyroidism). - What matters is the **positive predictive value (PPV)** and cost-effectiveness, which are influenced by prevalence but don't require "high" prevalence. *Disease is lethal* - The Wilson-Jungner criterion states the disease should have **"serious consequences if left untreated"** - this includes but is not limited to lethality. - Screening is justified for diseases causing **significant morbidity or mortality** where early detection improves outcomes. - The disease should be sufficiently serious to warrant the costs and potential harms of screening.
Explanation: ***Disease → Impairment→Disability→Handicap*** - This sequence follows the **WHO International Classification of Impairments, Disabilities, and Handicaps (ICIDH, 1980)** model, which correctly illustrates the progression from a health condition to its societal consequences. - A **disease** or health condition leads to **impairment** (loss or abnormality of body structure/function), which then restricts activities (**disability**), and ultimately impacts social roles (**handicap**). *Disease → Disability→Impairment→Handicap* - This order is incorrect because **impairment** (a problem in body function or structure) logically precedes **disability** (a difficulty executing tasks). - **Disability** arises from the *functional limitation* caused by impairment, not the other way around. *Disease → Disability→Handicap→Impairment* - This sequence is incorrect as **impairment** is the initial consequence of disease on a functional level, occurring before **disability** and **handicap**. - **Handicap** represents the societal and environmental disadvantage, which is the final stage in this classic WHO model. *Disease → Handicap→Impairment→Disability* - This order is incorrect because **handicap**, which refers to a social disadvantage, is the last step in the disablement process, following impairment and disability. - **Impairment** is a direct result of the disease, and **disability** follows from that impairment.
Explanation: ***Both YLL and YLD*** - **DALYs (Disability-Adjusted Life Years)** are a measure of overall disease burden, expressed as the number of years lost due to ill-health, disability, or early death. - They are calculated as the sum of **Years of Life Lost (YLL)** due to premature mortality and **Years Lost due to Disability (YLD)** for people living with a health condition or its consequences. *Neither YLL nor YLD* - This statement is incorrect because DALYs are explicitly designed to integrate both premature mortality (YLL) and the impact of non-fatal health outcomes (YLD). - Excluding both components would render DALYs meaningless as a comprehensive health metric. *Years lost to disability (YLD)* - While **YLD** is a crucial component of DALYs, focusing solely on YLD would ignore the impact of premature mortality. - A healthy life lost due to early death is as significant as years living with a disability in assessing disease burden. *Years of lost life (YLL)* - While **YLL** is an essential component, considering only YLL would overlook the burden of morbidity and disability. - Many chronic diseases cause significant disability and reduced quality of life without necessarily leading to premature death, which YLD accounts for.
Explanation: ***Disease incidence*** - **Disease incidence** refers to the rate at which new cases of a disease occur in a population over a specified period. While related to disease prevalence, it is not a direct factor in calculating the predictive accuracy of a screening test. - Predictive accuracy, specifically **positive predictive value (PPV)** and **negative predictive value (NPV)**, relies on the test's inherent properties (sensitivity and specificity) and the **prevalence** of the disease, not its incidence. *Specificity of screening test* - **Specificity** is crucial for predictive accuracy as it determines the probability that a test correctly identifies those *without* the disease. - A test with high specificity will have fewer **false positives**, which directly impacts the positive predictive value. *Disease prevalence* - **Disease prevalence** profoundly influences the predictive accuracy of a screening test. The **positive predictive value** increases with higher disease prevalence. - In populations with low disease prevalence, even highly sensitive and specific tests can yield a large number of **false positives**. *Sensitivity of screening test* - **Sensitivity** is a key determinant of predictive accuracy, as it measures the proportion of *true positives* correctly identified by the test. - A test with high sensitivity helps ensure that most individuals *with* the disease are detected, which affects both **positive and negative predictive values**.
Explanation: ***Making a clinical diagnosis*** - **Epidemiology** focuses on **population-level health patterns** and determinants of disease, not individual patient diagnosis. - Making a **clinical diagnosis** is the role of a healthcare provider based on a patient's symptoms, physical examination, and diagnostic tests. *Searching for the causes and risk factors* - A primary function of epidemiology is to **identify potential causes** and **risk factors** for diseases within populations. - This involves investigating associations between **exposures** and **health outcomes**. *Identifying syndromes* - Epidemiologists help identify new or unrecognized **patterns of disease** presentation that may constitute a **syndrome**. - This involves observing **clusters of symptoms** or conditions in a population. *To study historically the rise and fall of disease in the population* - **Historical epidemiology** tracks changes in disease incidence, prevalence, and mortality over time. - This function helps understand disease trends and the **impact of public health interventions**.
Explanation: ***20 %*** - Total women are 500, and 70% had USG, which equals 350 women. - Of these 350 women, 70 delivered **low birth weight (LBW)** babies. Therefore, the incidence is (70/350) * 100% = **20%**. *25 %* - This option would imply a higher number of LBW babies among those who had USG than the actual data. - It does not align with the calculation based on the given figures (70 LBW babies out of 350 women who had USG). *10 %* - This option represents a lower incidence and does not correspond to the calculation of (70/350) * 100%. - This value might be obtained if the total number of women with USG was incorrectly assumed to be 700. *15 %* - This option is incorrect as it does not match the calculated incidence of LBW babies among women who underwent USG. - This would mean only 52.5 LBW babies were born, which contradicts the information given that 70 delivered LBW babies.
Explanation: ***2 only*** - A mess worker with **ongoing poor hand hygiene** represents a **continuous common source exposure**. The worker continues to handle food over days or weeks with persistent poor hygiene, leading to **repeated contamination** and cases occurring over an extended period rather than clustered around a single incubation period. This produces a plateau-like epidemic curve characteristic of continuous exposure. *1 only* - **Propagated epidemic** occurs through **person-to-person transmission** (e.g., measles, chickenpox), where each case can generate new cases, creating successive waves with progressively larger peaks. Hepatitis A from a food handler is a **common source outbreak**, not propagated, as cases trace back to contaminated food rather than spreading between inmates. *1 and 3* - Option 1 (propagated) is incorrect as explained above. Option 3 (**common source-point exposure**) would apply if there was a **single, brief contamination event** (e.g., one contaminated meal), resulting in cases appearing within one incubation period with a sharp peak. The scenario describes **persistent poor hygiene** suggesting ongoing contamination, not a single point event. *1 and 2* - Option 1 (propagated) is incorrect as this is a common source outbreak from contaminated food, not person-to-person transmission.
Explanation: ***Disease prevalence is lower in primary care than specialty clinics*** - **Positive predictive value (PPV)** is highly dependent on **disease prevalence**. A lower prevalence in primary care will naturally lead to a lower PPV even if the test's sensitivity and specificity remain constant. - In specialty clinics, the population has a higher pre-test probability of having the condition due to referral patterns, which inflates the PPV. *The tool is fundamentally flawed* - While a flawed tool could certainly perform poorly, the fact that it performed well (85% PPV) in specialty clinics suggests the tool itself is not fundamentally flawed in its ability to identify depression in an appropriate population. - The drop in performance is more likely due to a change in the characteristics of the population being screened, rather than an inherent flaw in the tool's design. *Patients in primary care are less honest about symptoms* - There is no evidence to suggest that patients in primary care settings are inherently less honest about their symptoms compared to those in specialty clinics. - Such an assumption is a generalization and does not account for the statistical relationship between prevalence and predictive values. *Primary care physicians are using the tool incorrectly* - While possible, there's no information provided to support this claim; the question implies the tool is being used for its intended purpose. - Improper use would affect test results, but a consistent drop in PPV across a broad setting points to a more systemic issue related to population characteristics.
Explanation: ***Loss to follow-up may introduce bias if related to exposure or outcome*** - A 30% **loss to follow-up** in a cohort study is substantial and can lead to **selection bias** if the reasons for loss are related to either night shift work (exposure) or breast cancer risk (outcome). - For example, if nurses experiencing early symptoms of breast cancer or those struggling with night shift demands selectively drop out, the study's **risk estimates** will be skewed. *Twenty years is too long for meaningful results* - A 20-year follow-up period is often necessary to observe chronic diseases like cancer, as they have long **latency periods**. - While longer study durations can increase the likelihood of loss to follow-up, the duration itself is not inherently a threat to validity when studying diseases with long developmental phases. *Nurses are not representative of the general population* - Selecting nurses as a cohort is a common and often appropriate strategy in **occupational epidemiology**, as it allows for focused study of specific exposures (like shift work) in a relatively homogenous group. - While findings might not be directly generalizable to the entire population, they can still provide valuable insights into the **exposure-outcome relationship** within this group. *The study should be converted to a case-control design* - The study began as a **cohort design**, which is strong for assessing incidence and temporal relationships between exposure and outcome. - Converting to a **case-control design** retrospectively would mean losing the prospective data collection and the ability to calculate **absolute risk**, which are strengths of the original design.
Explanation: ***Low prevalence will result in many false positives*** - In populations with **low disease prevalence**, even a test with high specificity can yield a large number of **false positives** because the vast majority of tested individuals do not have the condition. - For example, with a 95% specificity and 0.1% prevalence, 5% of healthy individuals will test positive, which is significantly more than the true positives from a 0.1% prevalence. - The **positive predictive value (PPV)** will be very low (~1.9%), meaning most positive results will be false positives. *High specificity ensures accurate results* - While high specificity (95%) means a low rate of false positives among healthy individuals, in a **rare disease** scenario, the absolute number of healthy people is very large, leading to many healthy individuals being incorrectly flagged. - This high number of false positives can lead to unnecessary follow-up procedures, anxiety, and resource strain, undermining the screening program's effectiveness. *High sensitivity makes it excellent for screening* - **High sensitivity** (99%) ensures that most true cases are identified, which is crucial for a good screening test. - However, in a low prevalence setting, high sensitivity alone does not address the issue of **false positives** among the vast majority of healthy individuals, which can overwhelm the system and cause patient harm. *The test performance is ideal for any population* - The performance of a screening test is highly dependent on the **prevalence of the disease** in the target population, especially the predictive values (PPV and NPV). - A test that performs well in a high-prevalence population may be unsuitable for screening a low-prevalence population due to a high rate of **false positives** and low PPV, making it impractical for population-wide screening.
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