Original Articles
3 September 2024
Vol. 19 No. 2 (2024)
Performance of a negative binomial-GLM in spatial scan statistic: a case study of low-birth weights in Pakistan
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
1068
Views
377
Downloads
88
HTML
Authors
Spatial cluster analyses of health events are useful for enabling targeted interventions. Spatial scan statistic is the stateof- the-art method for this kind of analysis and the Poisson Generalized Linear Model (GLM) approach to the spatial scan statistic can be used for count data for spatial cluster detection with covariate adjustment. However, its use for modelling is limited due to data over-dispersion. A Generalized Linear Mixed Model (GLMM) has recently been proposed for modelling this kind of over-dispersion by incorporating random effects to model area-specific intrinsic variation not explained by other covariates in the model. However, these random effects may exhibit a geographical correlation, which may lead to a potential spatial cluster being undetected. To handle the over-dispersion in the count data, this study aimed to evaluate the performance of a negative binomial- GLM in spatial scan statistic on real-world data of low birth weights in Khyber-Pakhtunkhwa Province, Pakistan, 2019. The results were compared with the Poisson-GLM and GLMM, showing that the negative binomial-GLM is an ideal choice for spatial scan statistic in the presence of over-dispersed data. With a covariate (maternal anaemia) adjustment, the negative binomial-GLMbased spatial scan statistic detected one significant cluster covering Dir lower district. Without the covariate adjustment, it detected two clusters, each covering one district. The district of Peshawar was seen as the most likely cluster and Battagram as the secondary cluster. However, none of the clusters were detected by GLMM spatial scan statistic, which might be due to the spatial correlation of the random effects in GLMM.
Altmetrics
Downloads
Download data is not yet available.
Citations
Anderson NH, Titterington DM, 1997. Some methods for investigating spatial clustering, with epidemiological applications. J R Stat Soc Ser A: Stat Soc 160:87–105. DOI: https://doi.org/10.1111/1467-985X.00047
An Q, Wu J, Fan X, Pan L, Sun W, 2016. Using a negative binomial regression model for early warning at the start of a hand foot mouth disease epidemic in Dalian, Liaoning Province, China. PLoS One 11: e0157815. DOI: https://doi.org/10.1371/journal.pone.0157815
Baig JA, Jamal MM, Jamal J, Musarrat M, 2020. To determine the association of maternal anemia with perinatal outcome in tertiary care hospital. Pak. Armed Forces Med. J 70:302–7.
Bilancia M, Demarinis G, 2014. Bayesian scanning of spatial disease rates with Integrated Nested Laplace Approximation (INLA). Stat Methods Appt 23:71–94. DOI: https://doi.org/10.1007/s10260-013-0241-8
Blencowe H, Krasevec J, De Onis M, Black RE, An X, Stevens GA, Borghi E, Hayashi C, Estevez D, Cegolon L, Shiekh S, 2019. National, Regional, and Worldwide Estimates of Low Birthweight in 2015, with Trends from 2000: A Systematic Analysis. Lancet Glob Health 7:e849–60. DOI: https://doi.org/10.1016/S2214-109X(18)30565-5
Cook AJ, Gold DR, Li Y, 2007. Spatial cluster detection for censored outcome data. Biometrics 63:540–49. DOI: https://doi.org/10.1111/j.1541-0420.2006.00714.x
Dean CB, 1992. Testing for overdispersion in poisson and binomial regression models. J Am Stat Assoc 87:451–57. DOI: https://doi.org/10.1080/01621459.1992.10475225
DHIS KP, 2019. Annual Report. Available from: https://www.dghskp.gov.pk/reports.html
Engidaw MT, Eyayu T, Tiruneh T, 2022. The effect of maternal anaemia on low birth weight among newborns in Northwest Ethiopia. Sci Rep 12:15280. DOI: https://doi.org/10.1038/s41598-022-19726-z
Frévent C, Ahmed MS, Marbac M, Genin M, 2021. Detecting spatial clusters in functional data: new scan statistic approaches. Spat Stat 46:100550. DOI: https://doi.org/10.1016/j.spasta.2021.100550
Gómez-Rubio V, Moraga P, Molitor J, Rowlingson B. 2019. DClusterm: model-based detection of disease clusters. J Stat Softw 90:1–26. DOI: https://doi.org/10.18637/jss.v090.i14
Grimson RC, 1993. Disease clusters, maxima, and p‐values. Stat Med 12:1773–94. DOI: https://doi.org/10.1002/sim.4780121906
Huang L, Kulldorff M, Gregorio D, 2007. A spatial scan statistic for survival data. Biometrics 63:109–18. DOI: https://doi.org/10.1111/j.1541-0420.2006.00661.x
Iqbal A, Kanwal M, Rani N, Abbas S, Lakhan S, Pawan N. A, 2023. A prospective study to assess the correlation of neonatal birth weight with the hemoglobin level of the mother during pregnancy. Pakistan J Med Health Sci 17:799. DOI: https://doi.org/10.53350/pjmhs2023171799
Iqbal S, Tanveer A, Khan Z, Junaid KM, Mushtaq N, Ali N, 2022. Risk factors of low birth weight in Pakistan. Pakistan J Med Health Sci 16:1163. DOI: https://doi.org/10.53350/pjmhs221631163
Ishioka F, Kawahara J, Mizuta M, Minato SI, Kurihara K, 2019. Evaluation of hotspot cluster detection using spatial scan statistic based on exact counting. Jpn J Stat Data Sci 2:241–62. DOI: https://doi.org/10.1007/s42081-018-0030-6
Jung I. 2009. A generalized linear models approach to spatial scan statistics for covariate adjustment. Stat Med 28:1131–43. DOI: https://doi.org/10.1002/sim.3535
Jung I, Kulldorff M, Klassen AC, 2007. A spatial scan statistic for ordinal data. Stat Med 26:1594–1607. DOI: https://doi.org/10.1002/sim.2607
Klassen AC, Kulldorff M, Curriero F, 2005. Geographical clustering of prostate cancer grade and stage at diagnosis, before and after adjustment for risk factors. Int J Health Geogr 4:1–16. DOI: https://doi.org/10.1186/1476-072X-4-1
Kulldorff M, 1997. A spatial scan statistic. Commun Stat Theory Methods 26:1481–96. DOI: https://doi.org/10.1080/03610929708831995
Leyso NLC, Palatino MC, 2020. Detecting local clusters of under-5 malnutrition in the province of Marinduque, Philippines using spatial scan statistic. Nutr Metab Insights 13:1178638820940670. DOI: https://doi.org/10.1177/1178638820940670
Li M, Shi X, Li X, Ma W, He J, Liu T, 2019. Sensitivity of disease cluster detection to spatial scales: an analysis with the spatial scan statistic method. Int J Geogr Inf Sci 33:2125–52. DOI: https://doi.org/10.1080/13658816.2019.1616741
Nava M. 2014. Generalized Linear Regression Models for Count Data. California State University, Long Beach; 2014. Available from: https://books.google.com/books?id=DaB90AEACAAJ
NIPS D, 2013. Pakistan Demographic and Health Survey 2012–13. Available from: https://dhsprogram.com/pubs/pdf/FR290/FR290.pdf
Planning Commission, MP, 2013. Pakistan Millennium Development Goals Report 2013. Available from: https://www.undp.org/pakistan/publications/pakistan-mdgs-report-2013
Rana SS, Sharma S, Chand A, Malla R, 2013. Relationship between maternal haemoglobin and fetal weight. Nepal Journal of Obstetrics &Gynaecology 8:37-40. DOI: https://doi.org/10.3126/njog.v8i1.8860
Rashid HU, Khan MN, Imtiaz A, Ullah N, Dherani M, Rahman A, 2020. Post-traumatic stress disorder and association with low birth weight in displaced population following conflict in malakand division, pakistan: a case-control study. BMC Pregnancy and Childbirth 20:1–8. DOI: https://doi.org/10.1186/s12884-020-2841-2
Sheehan TJ, DeChello LM, 2005. A space-time analysis of the proportion of late stage breast cancer in Massachusetts, 1988 to 1997. Int J Health Geogr 4:1–9. DOI: https://doi.org/10.1186/1476-072X-4-15
Stoklosa J, Blakey R V, Hui FKC, 2022. An overview of modern applications of negative binomial modelling in ecology and biodiversity. Diversity 14:320. DOI: https://doi.org/10.3390/d14050320
Ullah S, Burney SMA, Rasheed T, Burney S, Barakzia MAK, 2023. Space-time cluster analysis of anemia in pregnant women in the province of Khyber Pakhtunkhwa, Pakistan (2014-2020). Geospat Health 18:1192. DOI: https://doi.org/10.4081/gh.2023.1192
Ullah S, Daud H, Dass SC, Fanaee-T H, Kausarian H, Alamgir, 2020. space-time clustering characteristics of tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015–2019. Int J Environ Res Public Health 17:1413. DOI: https://doi.org/10.3390/ijerph17041413
Ullah S, Nor NHM, Daud H, Zainuddin N, Gandapur MSJ, Ali I, Alamgir. 2021. Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020). Geospat Health 16:961. DOI: https://doi.org/10.4081/gh.2021.961
WHO, 2014. Global Nutrition Targets 2025: Low Birth Weight Policy Brief. Available from: https://media.tghn.org/articles/WHO_NMH_NHD_14.5_eng.pdf
Zahra T, Mumtaz U, Riffat N, Mushtaq F, Cheema MH, Mahmud T, 2022. Factors associated with low birthweight among newborns delivered at term in a tertiary care hospital in Lahore. J. Fatima Jinnah Med. Univ16:20–26. DOI: https://doi.org/10.37018/MXDD8087
Zhang T, Lin G, 2009. Spatial scan statistics in loglinear models. Comput Stat Data Anal 53:2851–58. DOI: https://doi.org/10.1016/j.csda.2008.09.016
How to Cite
Performance of a negative binomial-GLM in spatial scan statistic: a case study of low-birth weights in Pakistan. (2024). Geospatial Health, 19(2). https://doi.org/10.4081/gh.2024.1313
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Most read articles by the same author(s)
- Sami Ullah, Nurul Hidayah Mohd Nor, Hanita Daud, Nooraini Zainuddin, Muhammad Salim Javed Gandapur, Iftikhar Ali, Alamgir Khalil, Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020) , Geospatial Health: Vol. 16 No. 1 (2021)
- Sami Ullah, Hanita Daud, Sarat C. Dass, Habib Nawaz Khan, Alamgir Khalil, Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach , Geospatial Health: Vol. 12 No. 2 (2017)
- Sami Ullah, SM Aqil Burney, Tariq Rasheed, Shamaila Burney, Mushtaq Ahmad Khan Barakzia, Space-time cluster analysis of anemia in pregnant women in the province of Khyber Pakhtunkhwa, Pakistan (2014-2020) , Geospatial Health: Vol. 18 No. 2 (2023)
