Sample Size Estimation in Prevalence Studies

Arya, Ravindra; Antonisamy, Belavendra; Kumar, Sushil

doi:10.1007/s12098-012-0763-3

Cited by 233 publications

(192 citation statements)

References 3 publications

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“…A sample size of 210 was chosen to measure the prevalence of E. coli contamination with a margin of error less than 4%, assuming a 95% confidence level and a population size of 450 eligible households estimated from census data. This margin of error was chosen to meet the convention of measuring prevalences > 10% or < 90% to a precision of at least ±5% points 18 ; the sample size was calculated using a finite population correction, which is appropriate when the sample size is large relative to the size of the total study population. 19 Enrollment.…”

Section: Methodsmentioning

confidence: 99%

“Improved” But Not Necessarily Safe: An Assessment of Fecal Contamination of Household Drinking Water in Rural Peru

Heitzinger¹,

Rocha²,

Quick³

et al. 2015

The American Society of Tropical Medicine and Hygiene

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Abstract. The indicator used to measure progress toward the Millennium Development Goal (MDG) for water is access to an improved water supply. However, improved supplies are frequently fecally contaminated in developing countries. We examined factors associated with Escherichia coli contamination of improved water supplies in rural Pisco province, Peru. A random sample of 207 households with at least one child less than 5 years old was surveyed, and water samples from the source and storage container were tested for E. coli contamination. Although over 90% of households used an improved water source, 47% of source and 43% of stored water samples were contaminated with E. coli. Pouring or using a spigot to obtain water from the storage container instead of dipping a hand or object was associated with decreased risk of contamination of stored water (adjusted prevalence ratio [aPR] = 0.58, 95% confidence interval [CI] = 0.42, 0.80). Container cleanliness (aPR = 0.67, 95% CI = 0.45, 1.00) and correct handwashing technique (aPR = 0.62, 95% CI = 0.42, 0.90) were also associated with decreased contamination risk. These findings highlighted the limitations of improved water supplies as an indicator of safe water access. To ensure water safety in the home, household water treatment and improved hygiene, water handling, and storage practices should be promoted.

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Section: Methodsmentioning

confidence: 99%

“Improved” But Not Necessarily Safe: An Assessment of Fecal Contamination of Household Drinking Water in Rural Peru

Heitzinger¹,

Rocha²,

Quick³

et al. 2015

The American Society of Tropical Medicine and Hygiene

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“…A sample size of at least 384 was arrived at based on a probabilistic prevalence of 50% in the absence of prevalence values from the literature 14 , an allowable error (d) of 5% and z statistic of 1.96 (confidence interval of 95%) -all inputted into the formula to calculate sample size in cross-sectional studies 15 .…”

Section: Methodsmentioning

confidence: 99%

Non-clinical factors and predictors of self-ratings of oral health among young adolescents in a rural Nigerian population

Lawal

Dauda

2018

Braz. J. Oral Sci.

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Aim: The aim of the study was to assess the non-clinical predictors of self-rating of oral health among young adolescents in a Nigerian rural population. Methods: A cross-sectional survey was conducted among adolescents of 11-13 years old in Igboora, Nigeria. Information on self-rating of oral health, self-assessed satisfaction with oral health condition and tooth appearance, pain history, consultation with the dentist and oral hygiene measures were obtained using structured questionnaires translated to the local language. Data were analysed using SPSS version 23; Chi Square and logistic regression were used to establish associations between variables and predictors with p value < 0.05 statistically significant. Results: A total of 400 respondents participated in the study. Most 346 (86.5%) rated their oral health positively. Those who expressed dissatisfaction with the appearance of their teeth, 17 (44.7%) dissatisfaction with their oral health condition, 25 (45.5%) had toothache in the preceding six months, 44 (19.7%) perceived a need for dental treatment, 43 (16.7%) or cleaned their teeth once daily or less frequently, 37 (20.9%), rated their oral health poorly (p < 0.001, p < 0.001, p = 0.001, p = 0.012, p < 0.001, respectively). The significant predictors of self-rating of oral health were self-assessed satisfaction with oral health condition, toothache in the preceding six months and frequency of tooth cleaning. Conclusion: Satisfaction with oral health condition, toothache in the preceding six months and frequency of tooth cleaning are factors that predict self-rating of oral health in young adolescents in the rural community studied.

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“…A total of 300 samples were collected based on a previous related prevalence rate of 18% for HBV obtained from the formula for sample size estimation in prevalence studies given by: [18,19].…”

Section: Methodsmentioning

confidence: 99%

Demographic Study on Hepatitis A Infections among Outpatients of Selected Hospitals within Kaduna Metropolis, Nigeria

Waje¹,

John²,

Yusha’u³

et al. 2017

Int Arch Public Health Community Med

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Sample Size Estimation in Prevalence Studies

Cited by 233 publications

References 3 publications

“Improved” But Not Necessarily Safe: An Assessment of Fecal Contamination of Household Drinking Water in Rural Peru

“Improved” But Not Necessarily Safe: An Assessment of Fecal Contamination of Household Drinking Water in Rural Peru

Non-clinical factors and predictors of self-ratings of oral health among young adolescents in a rural Nigerian population

Demographic Study on Hepatitis A Infections among Outpatients of Selected Hospitals within Kaduna Metropolis, Nigeria

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