Improving population‐level refractive error monitoring via mixture distributions Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.1111/opo.13105
Introduction Sampling and describing the distribution of refractive error in populations is critical to understanding eye care needs, refractive differences between groups and factors affecting refractive development. We investigated the ability of mixture models to describe refractive error distributions. Methods We used key informants to identify raw refractive error datasets and a systematic search strategy to identify published binned datasets of community‐representative refractive error. Mixture models combine various component distributions via weighting to describe an observed distribution. We modelled raw refractive error data with a single‐Gaussian (normal) distribution, mixtures of two to six Gaussian distributions and an additive model of an exponential and Gaussian (ex‐Gaussian) distribution. We tested the relative fitting accuracy of each method via Bayesian Information Criterion (BIC) and then compared the ability of selected models to predict the observed prevalence of refractive error across a range of cut‐points for both the raw and binned refractive data. Results We obtained large raw refractive error datasets from the United States and Korea. The ability of our models to fit the data improved significantly from a single‐Gaussian to a two‐Gaussian‐component additive model and then remained stable with ≥3‐Gaussian‐component mixture models. Means and standard deviations for BIC relative to 1 for the single‐Gaussian model, where lower is better, were 0.89 ± 0.05, 0.88 ± 0.06, 0.89 ± 0.06, 0.89 ± 0.06 and 0.90 ± 0.06 for two‐, three‐, four‐, five‐ and six‐Gaussian‐component models, respectively, tested across US and Korean raw data grouped by age decade. Means and standard deviations for the difference between observed and model‐based estimates of refractive error prevalence across a range of cut‐points for the raw data were −3.0% ± 6.3, 0.5% ± 1.9, 0.6% ± 1.5 and −1.8% ± 4.0 for one‐, two‐ and three‐Gaussian‐component and ex‐Gaussian models, respectively. Conclusions Mixture models appear able to describe the population distribution of refractive error accurately, offering significant advantages over commonly quoted simple summary statistics such as mean, standard deviation and prevalence.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/opo.13105
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/opo.13105
- OA Status
- hybrid
- Cited By
- 1
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319460569
Raw OpenAlex JSON
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https://openalex.org/W4319460569Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/opo.13105Digital Object Identifier
- Title
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Improving population‐level refractive error monitoring via mixture distributionsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-02-07Full publication date if available
- Authors
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Tim Fricke, Lisa Keay, Serge Resnikoff, Nina Tahhan, Ingrid Ornella Koumbo mekountchou, Prakash Paudel, Lauren N. Ayton, Alexis Ceecee Britten‐Jones, Suhyun Kweon, Josephine C. H. Li, Ling Lee, Peter Wagner, Rebecca Weng, Boris Béranger, Jake OlivierList of authors in order
- Landing page
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https://doi.org/10.1111/opo.13105Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/opo.13105Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/opo.13105Direct OA link when available
- Concepts
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Gaussian, Refractive error, Mathematics, Approximation error, Population, Mixture model, Statistics, Gaussian network model, Optics, Physics, Sociology, Visual acuity, Quantum mechanics, DemographyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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57Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2263495255, https://openalex.org/W2030380178, https://openalex.org/W2078231330, https://openalex.org/W2017008451, https://openalex.org/W1977556576, https://openalex.org/W2147258443, https://openalex.org/W2992567980, https://openalex.org/W2103841198, https://openalex.org/W6675783201, https://openalex.org/W4205366930, https://openalex.org/W2168175751, https://openalex.org/W2796810390, https://openalex.org/W2903808120, https://openalex.org/W2169961704, https://openalex.org/W1981262947, https://openalex.org/W1860433754, https://openalex.org/W2125318387, https://openalex.org/W1547566977, https://openalex.org/W2132481976, https://openalex.org/W2037562453, https://openalex.org/W2092801958, https://openalex.org/W2768063215, https://openalex.org/W3134096163, https://openalex.org/W2945744371, https://openalex.org/W2763820101, https://openalex.org/W2141431127, https://openalex.org/W14228426, https://openalex.org/W2011700895, https://openalex.org/W3117135862, https://openalex.org/W128427397, https://openalex.org/W2016114180, https://openalex.org/W2010086463, https://openalex.org/W6653022962, https://openalex.org/W2017113456, https://openalex.org/W2012816078, https://openalex.org/W2082158170, https://openalex.org/W2034929876, https://openalex.org/W1964389331, https://openalex.org/W2153188962, https://openalex.org/W2144786093, https://openalex.org/W1987430566, https://openalex.org/W3147448391, https://openalex.org/W2890452242, https://openalex.org/W2012445740, https://openalex.org/W1495974046, https://openalex.org/W2165576114, https://openalex.org/W2897787090, https://openalex.org/W2023489546, https://openalex.org/W1993502633, https://openalex.org/W4245033619, https://openalex.org/W2221875974, https://openalex.org/W81211234, https://openalex.org/W2145860152, https://openalex.org/W4200593484, https://openalex.org/W4378948881, https://openalex.org/W2582743722, https://openalex.org/W2064058097 |
| referenced_works_count | 57 |
| abstract_inverted_index.1 | 199 |
| abstract_inverted_index.a | 52, 85, 138, 176, 179, 262 |
| abstract_inverted_index.US | 236 |
| abstract_inverted_index.We | 28, 41, 78, 107, 151 |
| abstract_inverted_index.an | 75, 97, 101 |
| abstract_inverted_index.as | 317 |
| abstract_inverted_index.by | 242 |
| abstract_inverted_index.in | 10 |
| abstract_inverted_index.is | 12, 206 |
| abstract_inverted_index.of | 7, 32, 61, 90, 100, 113, 126, 134, 140, 166, 257, 264, 303 |
| abstract_inverted_index.to | 14, 35, 45, 56, 73, 92, 129, 169, 178, 198, 298 |
| abstract_inverted_index.± | 210, 213, 216, 219, 223, 272, 275, 278, 282 |
| abstract_inverted_index.1.5 | 279 |
| abstract_inverted_index.4.0 | 283 |
| abstract_inverted_index.BIC | 196 |
| abstract_inverted_index.The | 164 |
| abstract_inverted_index.age | 243 |
| abstract_inverted_index.and | 3, 23, 51, 96, 103, 121, 146, 162, 183, 192, 221, 230, 237, 246, 254, 280, 287, 289, 321 |
| abstract_inverted_index.eye | 16 |
| abstract_inverted_index.fit | 170 |
| abstract_inverted_index.for | 142, 195, 200, 225, 249, 266, 284 |
| abstract_inverted_index.key | 43 |
| abstract_inverted_index.our | 167 |
| abstract_inverted_index.raw | 47, 80, 145, 154, 239, 268 |
| abstract_inverted_index.six | 93 |
| abstract_inverted_index.the | 5, 30, 109, 124, 131, 144, 159, 171, 201, 250, 267, 300 |
| abstract_inverted_index.two | 91 |
| abstract_inverted_index.via | 71, 116 |
| abstract_inverted_index.0.06 | 220, 224 |
| abstract_inverted_index.0.5% | 274 |
| abstract_inverted_index.0.6% | 277 |
| abstract_inverted_index.0.88 | 212 |
| abstract_inverted_index.0.89 | 209, 215, 218 |
| abstract_inverted_index.0.90 | 222 |
| abstract_inverted_index.1.9, | 276 |
| abstract_inverted_index.6.3, | 273 |
| abstract_inverted_index.able | 297 |
| abstract_inverted_index.both | 143 |
| abstract_inverted_index.care | 17 |
| abstract_inverted_index.data | 83, 172, 240, 269 |
| abstract_inverted_index.each | 114 |
| abstract_inverted_index.from | 158, 175 |
| abstract_inverted_index.over | 310 |
| abstract_inverted_index.such | 316 |
| abstract_inverted_index.then | 122, 184 |
| abstract_inverted_index.used | 42 |
| abstract_inverted_index.were | 208, 270 |
| abstract_inverted_index.with | 84, 187 |
| abstract_inverted_index.(BIC) | 120 |
| abstract_inverted_index.0.05, | 211 |
| abstract_inverted_index.0.06, | 214, 217 |
| abstract_inverted_index.Means | 191, 245 |
| abstract_inverted_index.data. | 149 |
| abstract_inverted_index.error | 9, 38, 49, 82, 136, 156, 259, 305 |
| abstract_inverted_index.large | 153 |
| abstract_inverted_index.lower | 205 |
| abstract_inverted_index.mean, | 318 |
| abstract_inverted_index.model | 99, 182 |
| abstract_inverted_index.range | 139, 263 |
| abstract_inverted_index.where | 204 |
| abstract_inverted_index.Korea. | 163 |
| abstract_inverted_index.Korean | 238 |
| abstract_inverted_index.States | 161 |
| abstract_inverted_index.United | 160 |
| abstract_inverted_index.across | 137, 235, 261 |
| abstract_inverted_index.appear | 296 |
| abstract_inverted_index.binned | 59, 147 |
| abstract_inverted_index.error. | 64 |
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| abstract_inverted_index.model, | 203 |
| abstract_inverted_index.models | 34, 66, 128, 168, 295 |
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| abstract_inverted_index.between | 21, 252 |
| abstract_inverted_index.combine | 67 |
| abstract_inverted_index.decade. | 244 |
| abstract_inverted_index.factors | 24 |
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| abstract_inverted_index.five‐ | 229 |
| abstract_inverted_index.grouped | 241 |
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| abstract_inverted_index.models, | 232, 291 |
| abstract_inverted_index.models. | 190 |
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| abstract_inverted_index.predict | 130 |
| abstract_inverted_index.summary | 314 |
| abstract_inverted_index.two‐, | 226 |
| abstract_inverted_index.various | 68 |
| abstract_inverted_index.−1.8% | 281 |
| abstract_inverted_index.−3.0% | 271 |
| abstract_inverted_index.(normal) | 87 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Bayesian | 117 |
| abstract_inverted_index.Gaussian | 94, 104 |
| abstract_inverted_index.Sampling | 2 |
| abstract_inverted_index.accuracy | 112 |
| abstract_inverted_index.additive | 98, 181 |
| abstract_inverted_index.commonly | 311 |
| abstract_inverted_index.compared | 123 |
| abstract_inverted_index.critical | 13 |
| abstract_inverted_index.datasets | 50, 60, 157 |
| abstract_inverted_index.describe | 36, 74, 299 |
| abstract_inverted_index.four‐, | 228 |
| abstract_inverted_index.identify | 46, 57 |
| abstract_inverted_index.improved | 173 |
| abstract_inverted_index.mixtures | 89 |
| abstract_inverted_index.modelled | 79 |
| abstract_inverted_index.observed | 76, 132, 253 |
| abstract_inverted_index.obtained | 152 |
| abstract_inverted_index.offering | 307 |
| abstract_inverted_index.relative | 110, 197 |
| abstract_inverted_index.remained | 185 |
| abstract_inverted_index.selected | 127 |
| abstract_inverted_index.standard | 193, 247, 319 |
| abstract_inverted_index.strategy | 55 |
| abstract_inverted_index.Criterion | 119 |
| abstract_inverted_index.affecting | 25 |
| abstract_inverted_index.component | 69 |
| abstract_inverted_index.deviation | 320 |
| abstract_inverted_index.estimates | 256 |
| abstract_inverted_index.published | 58 |
| abstract_inverted_index.three‐, | 227 |
| abstract_inverted_index.weighting | 72 |
| abstract_inverted_index.advantages | 309 |
| abstract_inverted_index.describing | 4 |
| abstract_inverted_index.deviations | 194, 248 |
| abstract_inverted_index.difference | 251 |
| abstract_inverted_index.informants | 44 |
| abstract_inverted_index.population | 301 |
| abstract_inverted_index.prevalence | 133, 260 |
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| abstract_inverted_index.statistics | 315 |
| abstract_inverted_index.systematic | 53 |
| abstract_inverted_index.Conclusions | 293 |
| abstract_inverted_index.Information | 118 |
| abstract_inverted_index.accurately, | 306 |
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| abstract_inverted_index.exponential | 102 |
| abstract_inverted_index.populations | 11 |
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| abstract_inverted_index.Introduction | 1 |
| abstract_inverted_index.cut‐points | 141, 265 |
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| abstract_inverted_index.model‐based | 255 |
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| abstract_inverted_index.two‐Gaussian‐component | 180 |
| abstract_inverted_index.≥3‐Gaussian‐component | 188 |
| abstract_inverted_index.three‐Gaussian‐component | 288 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5085389850 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 15 |
| corresponding_institution_ids | https://openalex.org/I165779595, https://openalex.org/I2801349370, https://openalex.org/I3131981448, https://openalex.org/I31746571 |
| citation_normalized_percentile.value | 0.54782594 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |