# Situation: A public health nurse is assigned to a coastal barangay of 3,200 residents that has never had a community assessment. The Rural Health Unit (RHU) needs baseline data for its multi-year plan. The nurse compiles these findings: Households with sanitary toilets: 58% (municipal average 86%) Diarrhea in children under 5 last year: 84 episodes among 420 children (municipal figure 8 episodes per 100 children) Fully immunized children: 93% (target 95%) Which conclusion is BEST supported by the data?

> source: MyMerci (mymerci.kr)  
> url: https://mymerci.kr/pages/nclex_q.php?qn_id=627274  
> language: ko  
> subject: Nursing Practice I — Community Health Nursing

## 문제

Situation: A public health nurse is assigned to a coastal barangay of 3,200 residents that has never had a community assessment. The Rural Health Unit (RHU) needs baseline data for its multi-year plan.

The nurse compiles these findings:
Households with sanitary toilets: 58% (municipal average 86%)
Diarrhea in children under 5 last year: 84 episodes among 420 children (municipal figure 8 episodes per 100 children)
Fully immunized children: 93% (target 95%)
Which conclusion is BEST supported by the data?

## 보기

1. Diarrhea in children under 5 is the main problem, linked to the gap in sanitary toilets **✔ 정답**
2. Diarrhea is about as common as in the rest of the municipality once the barangay's size is considered
3. Immunization is the main problem, because coverage has not reached the target
4. Toilet coverage is adequate, because more than half of the households have sanitary toilets

**정답: 1**

## 해설

Diarrhea incidence = 84 ÷ 420 × 100 = 20 episodes per 100 children, 2.5 times the municipal figure of 8. Sanitary toilet coverage is 28 points below the municipal average, a likely contributing factor. Immunization is only 2 points below target, so diarrhea linked to poor excreta disposal is the priority problem.

## 심화 해설

Interpreting the community data

The first step is to convert each finding into a rate that can be compared with the municipal reference. For diarrhea, the barangay recorded 84 episodes among 420 children under 5 years. Dividing 84 by 420 and multiplying by 100 gives 20 episodes per 100 children. The municipal figure is 8 episodes per 100 children, so the barangay rate is 2.5 times higher. This is not a small fluctuation; it represents a meaningful excess of diarrheal morbidity in this specific population.

Sanitary toilet coverage is 58%, while the municipal average is 86%. The gap is 28 percentage points. More than half of households having toilets does not make coverage adequate, because the relevant benchmark is the municipal average and the public health standard it reflects. A coverage deficit of this size means a substantial proportion of households still practice open defecation or use unsanitary facilities.

The combination of elevated diarrhea incidence and a large sanitation gap points to poor excreta disposal as a likely contributing factor. Fecal contamination of the environment creates ongoing opportunities for fecal-oral transmission of diarrheal pathogens, especially among children under 5 who have frequent hand-to-mouth contact and immature immunity.

Immunization coverage is 93% against a target of 95%. The shortfall is only 2 percentage points. While every unvaccinated child matters, this small gap does not carry the same magnitude of population-level risk as a diarrhea rate that is 2.5 times the municipal baseline. Key point! Prioritization in community health planning is based on the size of the gap between current status and the expected standard, not simply on whether a target has been fully met.

The relationship between sanitation and childhood diarrhea is well documented. In a comparative cross-sectional study in Ethiopia, households practicing open defecation showed a higher burden of diarrhea among children under 5 compared with open defecation free households, reinforcing that the absence of sanitary facilities is an independent risk factor for diarrheal disease [1]. A randomized controlled trial protocol from the same region was designed specifically to measure whether improved sanitation reduces diarrhea prevalence, incidence, and duration in children under 5, reflecting the strength of the biological and environmental link between excreta disposal and enteric infection [2]. Additional cross-sectional evidence from health center data in Ethiopia identified inadequate sanitation and hygiene conditions as factors associated with diarrhea among under-five children [3]. A study in Kenya similarly described open defecation as a route for environmental contamination that drives fecal-oral diseases [4].

Watch out! Option 2 is incorrect because the comparison has already been standardized. The barangay rate of 20 per 100 is directly comparable to the municipal rate of 8 per 100; adjusting for population size does not close the gap. Option 3 overstates the immunization issue, and option 4 misreads coverage adequacy by using a simple majority as the benchmark.

The best-supported conclusion is that diarrhea in children under 5 is the priority problem, and the deficit in sanitary toilet coverage is a plausible and modifiable contributing factor. The excess diarrhea rate is large, the sanitation gap is wide, and the two findings are consistent with established mechanisms of fecal-oral disease transmission. Immunization, by contrast, is close to target and does not explain the observed disease burden.
References (research sources)

- [1]Diarrhea and associated factors among under-five children in open defecation free and open defecation rural households of Degem district, Oromia, Ethiopia.Research articleDereje D, Hailu D, Debela SA, Yazew T, Tolesa F, Abebe B. (2024) · DOI: 10.3389/fpubh.2024.1480949

- [2]The effects of improved sanitation on diarrheal prevalence, incidence, and duration in children under five in the SNNPR State, Ethiopia: study protocol for a randomized controlled trial.RCT/clinical trialJung S, Doh YA, Bizuneh DB, Beyene H, Seong J, Kwon H (2016) · DOI: 10.1186/s13063-016-1319-z

- [3]Prevalence and Associated Factors of Diarrhea Among Under-Five Children in Maraki Health Center, Gondar Zone, Amhara Region, Ethiopia: A Cross-Sectional Study.Research articleDagnaw M, Indracanti M. (2026) · DOI: 10.1002/hsr2.72996

- [4]The Influence of Socioeconomic Factors on Open Defecation and Fecal Oral diseases in Soin ward, Kericho County, Kenya.Research articleKorir C, Ogendi GM, Donde OO. (2025) · DOI: 10.1177/11786302251400072

## 임상 시나리오

Community Assessment Priority SettingComparing local rates with municipal benchmarks
Convert raw counts into rates before drawing conclusions. Diarrhea episodes were 84 among 420 children under 5, giving 20 per 100 children, which is 2.5 times the municipal rate of 8 per 100.

Sanitary toilet coverage of 58% is 28 percentage points below the municipal average of 86%. This sanitation gap supports fecal-oral transmission as a likely contributor to the elevated diarrhea rate.

Immunization coverage of 93% is only 2 points below the 95% target, making it a lower priority than the combined diarrhea and sanitation problem.

CautionDo not judge adequacy by a simple majority. More than half of households having sanitary toilets is still a significant deficit when the municipal average is much higher and diarrheal morbidity is elevated.

## 핵심 개념

- **Diarrhea incidence rate** — Number of diarrhea episodes divided by the population at risk, multiplied by 100; here 84/420 x 100 = 20 per 100 children under 5.
- **Sanitary toilet coverage** — Percentage of households with sanitary toilets; compared against municipal average to identify environmental sanitation gaps.
- **Fully immunized child** — A child who has completed the recommended immunization schedule for age; coverage is measured against a target percentage.
- **Fecal-oral transmission** — Route by which diarrheal pathogens spread through contaminated feces, often linked to poor excreta disposal and inadequate sanitation.
- **Priority problem** — The health issue with the greatest magnitude, severity, and preventability, identified by comparing local data with benchmarks.

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