The quality of age data: Comparison between two recent Indian censuses 2001–2011

Yadav, Akhilesh; Vishwakarma, Minakshi; Chauhan, Shekhar

doi:10.1016/j.cegh.2019.09.005

Cited by 6 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such age heaping is a well-recognized data issue in different contexts [e.g., ( 32 )] and can be especially problematic in developing countries [e.g., ( 33 )]. It is a particularly relevant issue for the RDD approach, which compares observations around an age cutoff—e.g., 44-year-olds versus 45-year-olds—since people who report their age as, say, a multiple of five could differ in essential ways from those who do not, and then our comparisons could be confounded by such differences.…”

Section: Methodsmentioning

confidence: 99%

Measuring vaccine effectiveness from limited public health datasets: Framework and estimates from India’s second COVID wave

et al. 2022

View full text Add to dashboard Cite

Despite an urgent need, authorities in many countries are struggling to track COVID vaccine effectiveness (VE) because standard VE measures cannot be calculated from their public health data. Here, we use regression discontinuity design (RDD) to estimate VE, motivated by such limitations in public health records from West Bengal, India. These data cover 8,755,414 COVID vaccinations (90% ChAdOx1 NCov-19, almost all first doses, until May 2021), 8,179,635 tests, and 141,800 hospitalizations. The standard RDD exploits age-based vaccine eligibility; we also introduce a new RDD-based VE measure that improves on the standard one when better data are available. Applying these measures, we find a VE of 55.2% (95% confidence interval: 44.5 to 65.0%) against symptomatic disease, 80.1% (63.3 to 88.8%) against hospitalizations, and 85.5% (24.8 to 99.2%) against intensive care/critical care/high dependency admissions or deaths. Other data-deficient countries with age-based eligibility for any vaccine—and not just COVID vaccines—can also use these easy-to-implement measures to inform their own immunization policies.

show abstract

Section: Methodsmentioning

confidence: 99%

Measuring vaccine effectiveness from limited public health datasets: Framework and estimates from India’s second COVID wave

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The study used the rate of age not stated (per 1,000 population) to assess the quality of the age-sex data, while Whipple"s and Myers"s Blended Indexes were applied to assess age heaping in the data. Age heaping is defined as the propensity of innumerate respondents to report their age round to a close convenient even number in the multiple of 5 or 10 (Yadav et al, 2020). Data in five years age groups were analysed using Age Sex Ratio and the United Nations Age Sex Accuracy Index to establish possible undercounting of the population and displacement of individuals between age groups.…”

Section: Techniquesmentioning

confidence: 99%

“…The available literature on data quality in developing countries reveals a gloomy picture. In countries such as India census data quality on age and sex is still said to have a rough or very rough quality on the Whipple"s index (WI) scale (Yadav et al, 2020). The phenomenon was likewise observed in the demographic health surveys (DHS) data collected in five South Asian countries Afghanistan, India, Nepal, Bangladesh, and Pakistan using the WI (Singh et al, 2022).…”

Section: Introductionmentioning

confidence: 95%

Evaluation of Age-Sex Data Collected in the 1991, 2001 and 2011 Population and Housing Censuses of Namibia

Shipanga

Shinyemba

2023

Asian J. Pop. Sci.

View full text Add to dashboard Cite

This paper examined the accuracy of age-sex data reported in the Namibia population censuses with the aim to establish patterns and trends in the quality of age-sex reporting. To date, Namibia has conducted three population and housing censuses. However, it is not clear if there has been any advancement in the quality of data collected on age and sex over the years. The study followed a cross-sectional study design using the 1991, 2001 and 2011 population censuses data from Namibia Statistics Agency. We computed Whipple’s and Myers’s Blended indexes to measure age heaping. The age-sex ratio and UN joint score were used to ascertain the possibility of population undercounts and displacement between age groups. The results show that age heaping was fairly accurate in all three censuses. Analysis of single-age distribution showed a minimal digit preference and avoidance across all three censuses. Likewise, Myers blended index portrayed that terminal digits 0, 1, 2, 5, 8 and 9 were preferred the most by both sexes in all three censuses. The Whipple’s Index showed a two-point decline from 106 in 1991 to 104 in 2001 and 2011 suggesting an improvement in age reporting. The UN joint score for Namibia found that the data collected in all three censuses were inaccurate in terms of age-sex displacement.

show abstract

“…Self-reported data of the Indian census suffer from a number of issues, such as age ignorance, carelessness in calculating the proper age, intentional misstatement, and misinterpretation of the query,[ 2 ] digit preference, and age not stating. [ 3 ]…”

Section: Introductionmentioning

confidence: 99%

Spatial Patterns of Heaping in Age Data among Literates, Illiterates, and Numeracy–Literacy Correlates: A Cross-Sectional Analysis of Census 2011, of India

Datta,

Sinha

2024

Indian Journal of Community Medicine

View full text Add to dashboard Cite

Background: Accurate information on age is an essential prerequisite for demographic and epidemiological studies. This study analyzed the age data quality among the literate and illiterate (combined, rural, urban) population and examined the association between data quality and literacy. Material and Method: Secondary data on age statistics and literacy were obtained from census 2011. We measured age data quality for literates and illiterates (combined, rural, urban) by transforming Whipple’s index known as ABCC, abbreviated based on surnames of the developers A’Hearn, Baten, and Crayen (2009). Correlation tests between literacy and ABCC were performed. RStudio (Version 1.3.1073) was used. Result: Computed ABCC indices in majority states (union territories) for literates (data quality rough) were higher than illiterates (data quality very rough). Urban data among literates and rural data among illiterates were comparatively superior. Correlation between ABCC and literacy rates for (i) literate combined (R = 0.84, P = 3.5e-10), (ii) literate rural (R = 0.8, P = 1.1e-08), (iii) literate urban (R = 0.8, P = 1e-08), (iv) illiterate combined (R = 0.54, P = 9e-04), (v) illiterate rural (R = 0.48, P = 0.0034), and (vi) illiterate urban (R = 0.73, P = 6.4e-07) was significant. Age data quality for both literates and illiterates was poor. There was heaping at terminal digits “0” and “5” even among literates, which contradicts the theoretical expectation of quality data among literates. Conclusion: Correlations between data quality and literacy were significant, with comparatively lower magnitude among illiterates, which indicates the role of literacy in yielding quality data. Awareness, training, ADHAAR-based enumeration, and digitization may be suggested for better age data.

show abstract

The quality of age data: Comparison between two recent Indian censuses 2001–2011

Cited by 6 publications

References 4 publications

Measuring vaccine effectiveness from limited public health datasets: Framework and estimates from India’s second COVID wave

Measuring vaccine effectiveness from limited public health datasets: Framework and estimates from India’s second COVID wave

Evaluation of Age-Sex Data Collected in the 1991, 2001 and 2011 Population and Housing Censuses of Namibia

Spatial Patterns of Heaping in Age Data among Literates, Illiterates, and Numeracy–Literacy Correlates: A Cross-Sectional Analysis of Census 2011, of India

Contact Info

Product

Resources

About