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Ans these According to the psm 3rd yr mbbs

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PSM Answers (3rd Year MBBS)

Q2: Screening Test Evaluation (FPGT for T2DM)

T2DM PresentT2DM AbsentTotal
Positive180 (TP)380 (FP)560
Negative20 (FN)3,420 (TN)3,440
Total2003,8004,000
1. Sensitivity and Specificity
  • Sensitivity = TP/(TP+FN) = 180/200 = 90%
  • Specificity = TN/(TN+FP) = 3,420/3,800 = 90%
2. Positive Predictive Value
  • PPV = TP/(TP+FP) = 180/560 = 32.1%
3. Negative Predictive Value
  • NPV = TN/(TN+FN) = 3,420/3,440 = 99.4%
4. High-risk selective screening — sensitivity or specificity? Prioritize high sensitivity. In a selective (high-risk) program the pre-test probability of disease is already elevated, so a highly sensitive test minimizes false negatives — i.e., it avoids missing true diabetics who need early treatment to prevent complications. False positives from a high-sensitivity/lower-specificity test are an acceptable trade-off because they can be resolved cheaply with the definitive confirmatory test (e.g., fasting/OGTT), whereas a missed true case (false negative) has a much higher clinical cost. This also improves PPV in a high-prevalence subgroup even though specificity may not be perfect.

Q3: Malaria Surveillance (NVBDCP indicators)

Population = 50,000; smears examined = 10,000; positive slides = 80 (Pv = 60, Pf = 20)
1. API and SPR
  • API = (Total positive slides / Population) × 1,000 = (80/50,000) × 1,000 = 1.6 per 1,000 population
  • SPR = (Positive slides / Slides examined) × 100 = (80/10,000) × 100 = 0.8%
2. Species distribution
  • Pf% = 20/80 × 100 = 25%
  • Pv% = 60/80 × 100 = 75%
3. Classification under NVBDCP / National Framework for Malaria Elimination Districts/PHC areas are stratified by API into three categories:
  • Category 1 (API < 1): Elimination phase
  • Category 2 (API 1 to <2): Pre-elimination phase ← this area falls here (API = 1.6)
  • Category 3 (API ≥ 2): Intensified control phase (high endemic)
Implication: With an API of 1.6, this catchment area is in the pre-elimination phase, not yet ready for the elimination phase. It requires intensified case detection (both active and passive), radical treatment of all confirmed cases (including primaquine for Pv relapse prevention), universal vector control (IRS/LLINs), and strengthened entomological surveillance to bring the API below 1 before it can be reclassified into the elimination category. The relatively high Pv proportion (75%) also means relapse prevention (primaquine radical cure) is a priority.

Q4: Vital Statistics

Population = 100,000; live births = 2,000; neonatal deaths = 50; post-neonatal deaths = 30; maternal deaths = 4
1. Neonatal Mortality Rate (NMR) NMR = (Neonatal deaths / Live births) × 1,000 = (50/2,000) × 1,000 = 25 per 1,000 live births
2. Infant Mortality Rate (IMR) Infant deaths = Neonatal + Post-neonatal = 50 + 30 = 80 IMR = (Infant deaths / Live births) × 1,000 = (80/2,000) × 1,000 = 40 per 1,000 live births
3. Why IMR is a key health-system indicator IMR is considered one of the most sensitive indicators of community health and socioeconomic development because:
  • It reflects the combined effect of antenatal, natal, and postnatal care coverage and quality, nutrition, immunization, safe water/sanitation, and general socioeconomic status.
  • Infants are highly vulnerable to lapses in healthcare access, so IMR responds quickly to changes in health service availability and utilization (unlike crude death rate, which changes slowly).
  • It is calculated from data that are relatively easy to collect (live births and infant deaths) and is comparable across regions/countries and over time, making it useful for monitoring program effectiveness (e.g., RCH/NHM interventions) and for international comparisons.

Epidemic Investigation — Wedding Feast Outbreak (150 guests, 45 ill within 6 hours: nausea, vomiting, abdominal cramps)

The short incubation period (within 6 hours) with vomiting and cramps but no mention of fever/bloody diarrhea is classic for a preformed toxin food poisoning — most likely Staphylococcus aureus enterotoxin or Bacillus cereus emetic toxin (incubation 1-6 hours for both).

1. Steps in investigating the outbreak (per standard epidemic investigation protocol, Park's PSM)

  1. Verify the diagnosis — clinically examine a sample of the 45 cases to confirm they genuinely have a food-poisoning-type illness (not a spurious report).
  2. Confirm the existence of an outbreak — 45/150 (30% attack rate) in a few hours from a single event is clearly in excess of the expected background rate; outbreak confirmed.
  3. Define the population at risk — all 150 guests who attended the feast (line list with names, age, sex, food eaten, time of onset).
  4. Rapid case finding — active search for all cases (including mild ones who did not seek care) using a standard case definition (e.g., "any guest at the feast who developed vomiting/diarrhea/cramps within 24 hours of the meal").
  5. Descriptive epidemiology — plot an epidemic curve (time), map cases (place if relevant), and tabulate by person characteristics (age, sex, specific food items consumed).
  6. Formulate a hypothesis about the vehicle (specific food item) and mode of transmission based on incubation period and symptom pattern.
  7. Test the hypothesis analytically — conduct a retrospective cohort study among all 150 guests, calculating food-item-specific attack rates and relative risk to identify the implicated food.
  8. Collect samples — food remnants, vomitus/stool from cases, and food-handler swabs for laboratory confirmation.
  9. Compare with laboratory/environmental findings to confirm or refine the hypothesis.
  10. Implement control measures concurrently (do not wait for the full report).
  11. Write and disseminate the report, and follow up to ensure no further spread and no similar future incidents.

2. Identifying the source and causative agent

  • Detailed food history from all 150 guests (a structured questionnaire covering every dish served, drinking water source, and exact time of onset of symptoms).
  • Retrospective cohort/food-specific attack rate analysis: for each dish, calculate attack rate among those who ate it vs those who did not, and relative risk. The food item with the highest attack rate and RR (with a dose-response and biologically plausible incubation period) is the likely vehicle.
  • Incubation period and clinical pattern correlation: 1-6 hours with vomiting predominant and no fever strongly suggests S. aureus or B. cereus toxin; a 6-24 hour incubation with watery diarrhea would suggest B. cereus diarrheal toxin or Clostridium perfringens; longer incubation (12-48h) with fever would suggest Salmonella.
  • Laboratory confirmation: culture and toxin assay on leftover food samples, stool/vomitus of cases, and swabs from food handlers (nasal/skin swabs to detect S. aureus carriage, especially from anyone with boils/skin sepsis who handled food).
  • Environmental/behavioral investigation: check food preparation, cooking temperatures, storage conditions (was cooked food held warm for a prolonged period before serving — a classic setup for toxin-producing bacterial growth), and time gap between preparation and consumption.
  • Food handler examination: screen cooks/servers for skin infections, recent illness, and hygiene practices.

3. Control and preventive measures

Immediate/case management:
  • Provide symptomatic treatment for all cases (oral rehydration solution, IV fluids for those dehydrated, antiemetics as needed); hospitalize severe cases.
  • Immediately stop serving/discard any remaining suspect food.
  • Notify the outbreak to the District Health Officer/PHC and local health authority (mandatory reporting of food poisoning outbreaks).
Source control:
  • Identify and temporarily exclude any food handler who is a carrier or has skin sepsis/boils (common S. aureus source) from food handling duties until medically cleared.
  • Correct faulty food handling practices — adequate cooking temperature, rapid cooling and refrigeration of cooked food, avoiding prolonged holding at warm/room temperature, and separating raw and cooked foods.
  • Ensure use of safe, potable water in food preparation and clean utensils.
Preventive/long-term:
  • Health education and training of caterers/cooks on food safety and personal hygiene (hand washing, use of clean attire/gloves, covering cuts and sores).
  • Periodic medical examination and health certification of food handlers, particularly those catering large gatherings.
  • Licensing, registration, and regular inspection of catering establishments under the Food Safety and Standards Act (FSSAI norms).
  • Community health education about safe food practices for social/religious gatherings.
  • Strengthen surveillance in the area to detect and respond promptly to any future clusters, and follow up the affected village for a defined period to ensure no further cases.
  • Park's Textbook of Preventive and Social Medicine (Investigation of an Epidemic; Outbreak control measures)

Solve these questions

analyse_media
Running Command

ls -la /tmp/workspace/attachments/47a8c5f1-50ac-4b5e-97a1-d7409a2b3d1d/

Running Command

cd /tmp/workspace/attachments/47a8c5f1-50ac-4b5e-97a1-d7409a2b3d1d/ && python3 -c " from PIL import Image import pillow_heif pillow_heif.register_heif_opener() img = Image.open('20260818_110401.heic') img.convert('RGB').save('converted.jpg', quality=95) print('done', img.size) "

Running Command

pip install --user pillow-heif -q && cd /tmp/workspace/attachments/47a8c5f1-50ac-4b5e-97a1-d7409a2b3d1d/ && python3 -c " from PIL import Image import pillow_heif pillow_heif.register_heif_opener() img = Image.open('20260818_110401.heic') img.convert('RGB').save('converted.jpg', quality=95) print('done', img.size) "

Running Command

pip install --user --force-reinstall --no-deps pillow-heif -q 2>&1 | tail -5; python3 -c "import pillow_heif" 2>&1

Running Command

cd /tmp/workspace/attachments/47a8c5f1-50ac-4b5e-97a1-d7409a2b3d1d/ && python3 -c " from PIL import Image import pillow_heif pillow_heif.register_heif_opener() img = Image.open('20260818_110401.heic') img.convert('RGB').save('converted.jpg', quality=95) print('done', img.size) " ls -la

Reading File
Running Command

cd /tmp/workspace/attachments/47a8c5f1-50ac-4b5e-97a1-d7409a2b3d1d/ && python3 -c " from PIL import Image img = Image.open('converted.jpg') w,h = img.size scale = 1400/w img2 = img.resize((int(w*scale), int(h*scale))) img2.save('converted_small.jpg', quality=80) import os print(os.path.getsize('converted_small.jpg')) "

Reading File
I converted and read your HEIC file. Here are complete solutions to the CPTEE Biostatistics & Epidemiology exercise.

BIOSTATISTICAL EXERCISES

Q1(A): Significance of difference between two group means

Group 1: n₁=100, Mean₁=42, SD₁=3.7 Group 2: n₂=150, Mean₂=44, SD₂=2.5
Since both samples are large (n > 30), use the Standard Error of Difference between two means (Z-test):
SE(diff) = √[(SD₁²/n₁) + (SD₂²/n₂)] = √[(3.7²/100) + (2.5²/150)] = √[0.1369 + 0.0417] = √0.1786 = 0.4226
Z = (Mean₂ − Mean₁) / SE(diff) = (44 − 42) / 0.4226 = 4.73
Since calculated Z (4.73) > tabulated Z at 5% level (1.96), the difference is statistically significant — we reject the null hypothesis. The two groups differ significantly in mean weight.

Q1(B): Crude Birth Rate (CBR) and General Fertility Rate (GFR)

Population 2001 = 1,12,000; Population 2011 = 1,30,000 Annual increase = (1,30,000 − 1,12,000)/10 = 1,800/year Estimated mid-year population for 2013 = 1,30,000 + (2 × 1,800) = 1,33,600
  • CBR = (Total live births / Mid-year population) × 1,000 = 3,900 / 1,33,600 × 1,000 = 29.2 per 1,000 population
  • Reproductive age women = 25% of 1,33,600 = 33,400
  • GFR = (Total live births / No. of women 15-44 yrs) × 1,000 = 3,900 / 33,400 × 1,000 = 116.8 per 1,000 women

Q2

(1) Mean and Mode of 11, 13, 13, 15, 18
  • Mean = (11+13+13+15+18)/5 = 70/5 = 14
  • Mode = 13 (most frequent value)
(2) Minimum sample size (Incidence rate p = 17%, q = 83%, allowable error l = 2%)
Using n = 4pq/l² : n = (4 × 17 × 83) / 2² = 5,644 / 4 = 1,411 (minimum sample size ≈ 1,411 individuals)
(3) Quartile Deviation QD = (Q₃ − Q₁)/2 = (75 − 35)/2 = 40/2 = 20
(4) Coefficient of Variation (Mean=50, SD=15) CV = (SD/Mean) × 100 = (15/50) × 100 = 30%
(5) 95% Confidence Limits of population mean BP (n=400, Mean=128, SD=12) SE = SD/√n = 12/√400 = 12/20 = 0.6 95% CI = Mean ± 1.96 × SE = 128 ± 1.96(0.6) = 128 ± 1.176 95% CI = 126.82 mm Hg to 129.18 mm Hg

EPIDEMIOLOGICAL EXERCISES

Cohort study data:
ExposureDeveloped CancerNo CancerTotal
Exposed1004,9005,000
Non-exposed15014,85015,000
1A. Incidence Rates
  • IE (Incidence among Exposed) = 100/5,000 = 0.02 = 2% (20 per 1,000)
  • INE (Incidence among Non-exposed) = 150/15,000 = 0.01 = 1% (10 per 1,000)
1B. Relative Risk and Attributable Risk %
  • RR = IE/INE = 0.02/0.01 = 2.0
  • Attributable Risk (AR) = IE − INE = 0.02 − 0.01 = 0.01 (1%)
  • AR% = [(IE − INE)/IE] × 100 = (0.01/0.02) × 100 = 50%
1C. Interpretation of RR An RR of 2.0 means individuals exposed to the carcinogenic chemical in the contaminated drinking water have twice the risk of developing oral cancer compared to unexposed individuals. Since RR > 1, this indicates a positive association between chemical exposure and oral cancer, supporting the chemical as a probable risk factor/causal agent for oral cancer in this population. The AR% of 50% further tells us that half of the oral cancer cases among the exposed group can be attributed to this chemical exposure and could theoretically be prevented by eliminating it.
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