https://doi.org/10.4081/gh.2024.1307
The distribution of cardiovascular diseases in Tanzania: a spatio-temporal investigation
Submitted: 3 May 2024
Accepted: 21 August 2024
Published: 10 September 2024
Accepted: 21 August 2024
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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.
Authors
Cardiovascular Disease (CVD) is currently the major challenge to people’s health and the world’s top cause of death. In Tanzania, deaths due to CVD account for about 13% of the total deaths caused by the non-communicable diseases. This study examined the spatio-temporal clustering of CVDs from 2010 to 2019 in Tanzania for retrospective spatio-temporal analysis using the Bernoulli probability model on data sampled from four selected hospitals. Spatial scan statistics was performed to identify CVD clusters and the effect of covariates on the CVD incidences was examined using multiple logistic regression. It was found that there was a comparatively high risk of CVD during 2011-2015 followed by a decline during 2015-2019. The spatio-temporal analysis detected two high-risk disease clusters in the coastal and lake zones from 2012 to 2016 (p<0.001), with similar results produced by purely spatial analysis. The multiple logistic model showed that sex, age, blood pressure, body mass index (BMI), alcohol intake and smoking were significant predictors of CVD incidence.
Amini M, Zayeri F, Salehi M, 2021. Trend analysis of cardiovascular disease mortality, incidence, and mortality-to-incidence ratio: results from global burden of disease study 2017. BMC Public Health 21:1–12.
DOI: https://doi.org/10.1186/s12889-021-10429-0
Amsalu E, Liu M, Li Q, Wang X, Tao L, Liu X, Luo Y, Yang X, Zhang Y, Li W, Li X, Wang W, Guo X, 2021. Spatial-temporal analysis of cause-specific cardiovascular hospital admission in Beijing, China. Int J Environ Health Res 31:595–606.
DOI: https://doi.org/10.1080/09603123.2019.1677862
Anselin L, 1995. Local Indicators of Spatial Association—LISA. Geogr Anal 27:93–115.
DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Azimi A, Bagheri N, Mostafavi SM, Furst MA, Hashtarkhani S, Amin FH, Eslami S, Kiani F, VafaeiNezhad R, Akbari T, Golabpour A, Kiani B, 2021. Spatial-time analysis of cardiovascular emergency medical requests: enlightening policy and practice. BMC Public Health 21:7.
DOI: https://doi.org/10.1186/s12889-020-10064-1
Baptista EA, Queiroz BL, 2022. Spatial analysis of cardiovascular mortality and associated factors around the world. BMC Public Health 22:1556.
DOI: https://doi.org/10.1186/s12889-022-13955-7
Chaiyasong S, Huckle T, Mackintosh AM, Meier P, Parry CDH, Callinan S, Viet Cuong P, Kazantseva E, Gray-Phillip G, Parker K, Casswell S, 2018. Drinking patterns vary by gender, age and country-level income: cross-country analysis of the International Alcohol Control Study. Drug Alcohol Rev 37:S53–S62.
DOI: https://doi.org/10.1111/dar.12820
Colpani V, Baena CP. Jaspers L, van Dijk GM, Farajzadegan Z, Dhana K, Tielemans MJ, Voortman T, Freak-Poli R, Veloso GGV, Chowdhury R, Kavousi M, Muka T, Franco OH, 2018. Lifestyle factors, cardiovascular disease and all-cause mortality in middle-aged and elderly women: a systematic review and meta-analysis. European Journal of Epidemiology, 33:831–845.
DOI: https://doi.org/10.1007/s10654-018-0374-z
Damen JAAG, Hooft L, Schuit E, Debray TPA, Collins GS, et al., 2016. Prediction models for cardiovascular disease risk in the general population: systematic review. Br Med J 353. doi.org/10.1136/bmj.i2416
DOI: https://doi.org/10.1136/bmj.i2416
Hamid S, Groot W, Pavlova M, 2019. Trends in cardiovascular diseases and associated risks in sub-Saharan Africa: a review of the evidence for Ghana, Nigeria, South Africa, Sudan and Tanzania. Aging Male 22:169–76.
DOI: https://doi.org/10.1080/13685538.2019.1582621
Hung Y-C, 2023. A review of Monte Carlo and quasi-Monte Carlo sampling techniques. In Scott D, Lu H (eds). Wiley Interdisciplinary Reviews (WIRE) Computational Statistics. doi.org/10.1002/wics.1637
DOI: https://doi.org/10.1002/wics.1637
Jana M, Sar N, 2016. Modeling of hotspot detection using cluster outlier analysis and Getis-Ord Gi* statistic of educational development in upper-primary level, India. Modeling Earth Systems and Environment, 2:1–10.
DOI: https://doi.org/10.1007/s40808-016-0122-x
Julin B, Willers C, Leksell J. Lindgren P, Looström-Muth K, Svensson AM, Lilja M, Dahlström T, 2018. Association between sociodemographic determinants and health outcomes in individuals with type 2 diabetes in Sweden. Diabetes Metab Res Rev 34:e2984.
DOI: https://doi.org/10.1002/dmrr.2984
Kapologwe NA, Meara JG, Kengia JT, Sonda Y, Gwajima D, Alidina S, Kalolo A, 2020. Development and upgrading of public primary healthcare facilities with essential surgical services infrastructure: A strategy towards achieving universal health coverage in Tanzania. BMC Health Serv Res 20:218
DOI: https://doi.org/10.1186/s12913-020-5057-2
Keino BC, Carrel M, 2023. Spatial and temporal trends of overweight/obesity and tobacco use in East Africa: subnational insights into cardiovascular disease risk factors. International J Health Geogr 22:1–18.
DOI: https://doi.org/10.1186/s12942-023-00342-7
Kiani B, Raouf Rahmati A, Bergquist R, Hashtarkhani S, Firouraghi N, Bagheri N, Moghaddas E, Mohammadi A, 2021. Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018. BMC Public Health 21:1093.
DOI: https://doi.org/10.1186/s12889-021-11157-1
Koliaki C, Liatis S, Kokkinos A, 2019. Obesity and cardiovascular disease: revisiting an old relationship. Metabolism 92:98–107.
DOI: https://doi.org/10.1016/j.metabol.2018.10.011
Kulldorff M, 1999. Spatial scan statistics: models, calculations and applications. In Scan Statistics and Applications, pp. 303–22. d
DOI: https://doi.org/10.1007/978-1-4612-1578-3_14
Kulldorff M, 1997. A Spatial Scan Statistic. Communi Stat-Theor M 26:1481-96.
DOI: https://doi.org/10.1080/03610929708831995
Kundu J, Kundu S, 2022. Cardiovascular disease (CVD) and its associated risk factors among older adults in India: Evidence from LASI Wave 1. Clin Epidemiol Glob Health 13:100937.
DOI: https://doi.org/10.1016/j.cegh.2021.100937
Mayige M, Kagaruki G, Ramaiya K, Swai A, 2012. Non communicable diseases in Tanzania : A call for urgent action. Tanzan J Health Res 13:378-86.
DOI: https://doi.org/10.4314/thrb.v13i5.7
Mena C, Sepúlveda C, Fuentes E, Ormazábal Y, Palomo I, 2018. Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search. Geospat Health 13:11–19.
DOI: https://doi.org/10.4081/gh.2018.587
Ottaru TA, Kwesigabo GP, Butt Z, Rivera AS, Chillo P, Siril H, Hirschhorn LR, Feinstein MJ, Hawkins C, 2022. Ideal Cardiovascular Health: Distribution, Determinants and Relationship with Health Status among People Living with HIV in Urban Tanzania. Glob Heart 17:1157
DOI: https://doi.org/10.5334/gh.1157
Rajabi M, Mansourian A, Pilesjö P, Åström D O, Cederin K, Sundquist K, 2018a. Exploring spatial patterns of cardiovascular disease in Sweden between 2000 and 2010. Scand J Public Health 46:647–58.
DOI: https://doi.org/10.1177/1403494818780845
Rana R, Singhal R, 2015. Chi ‑ square Test and its Application in Hypothesis Testing. J Pract Cardiovasc Sci 1:69-71.
DOI: https://doi.org/10.4103/2395-5414.157577
Ranganathan P, Pramesh CS, Aggarwal R, 2017. Common pitfalls in statistical analysis : Logistic regression. Perspect Clin Res 8:148-51.
DOI: https://doi.org/10.4103/picr.PICR_87_17
Rao H, Shi X, Zhang X, 2017. Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009-2016. BMC Infect Dis 17:1–12.
DOI: https://doi.org/10.1186/s12879-017-2643-y
Rodgers JL, Jones J, Bolleddu SI, Vanthenapalli S, Rodgers LE, Shah K, Karia K, Panguluri SK, 2019. Cardiovascular Risks Associated with Gender and Aging. J Cardiovasc Dev Dis 6:19.
DOI: https://doi.org/10.3390/jcdd6020019
Rodrigues PCO, Santos ES, Ignotti E, Hacon SS, 2015. Space-time analysis to identify areas at risk of mortality from cardiovascular disease. Biomed Res Int 2015:841645.
DOI: https://doi.org/10.1155/2015/841645
Roerecke M, Rehm J, 2014. Alcohol consumption, drinking patterns, and ischemic heart disease: A narrative review of meta-analyses and a systematic review and meta-analysis of the impact of heavy drinking occasions on risk for moderate drinkers. BMC Med 12:1–11.
DOI: https://doi.org/10.1186/s12916-014-0182-6
Roman WP, Martin HD, Sauli E, 2019. Cardiovascular diseases in Tanzania: The burden of modifiable and intermediate risk factors. J Xiangya Med 4: doi.org/10.21037/jxym.2019.07.03
DOI: https://doi.org/10.21037/jxym.2019.07.03
Sawe HR, Mfinanga JA, Lidenge SJ, Mpondo BCT, Msangi S, Lugazia E, Mwafongo V, Runyon MS, Reynolds TA, 2014. Disease patterns and clinical outcomes of patients admitted in intensive care units of tertiary referral hospitals of Tanzania. BMC Int Health Hum Rights 14:26.
DOI: https://doi.org/10.1186/1472-698X-14-26
Schissler AG, Nguyen H, Nguyen T, Petereit J, Gardeux V, 2019. Statistical Software. Wiley. Availabel from: doi.org/10.1002/9781118445112.stat00527.pub2
DOI: https://doi.org/10.1002/9781118445112.stat00527.pub2
Şener R, Türk T, 2021. Spatiotemporal analysis of cardiovascular disease mortality with geographical information systems. Appl Spat Anal Polic 14:929–45.
DOI: https://doi.org/10.1007/s12061-021-09382-7
Songchitruksa P, Zeng X, 2010. Getis-Ord spatial statistics to identify hot spots by using incident management data. Transp Res Rec 2165:42-51.
DOI: https://doi.org/10.3141/2165-05
Tang X, Zhan ZY, Rao Z, Fang H, Jiang J, Hu X, Hu Z, 2023. A spatiotemporal analysis of the association between carbon productivity, socioeconomics, medical resources and cardiovascular diseases in southeast rural China. Front Public Health 11:1–12.
DOI: https://doi.org/10.3389/fpubh.2023.1079702
United Republic of Tanzania, 2020. National Accounts of Tanzania Mainland. National Bureau of Statistics. Available from: https://www.nbs.go.tz/uploads/statistics/documents/sw-1705429384-National_Accounts_of_Tanzania_Manland_2015-2021.pdf
United Republic of Tanzania, 2024. The 2022 Population and Housing Census: Basic Demographic and Socio-Economic Profile; Tanzania Mainland. Available from: https://sensa.nbs.go.tz/publication/02.%20Mainland_Demographic_and_Socioeconomic_Profile.pdf
Vaccarezza M, Papa V, Milani D, Gonelli A, Secchiero P, Zauli G, Gemmati D, Tisato V, 2020. Sex/gender‐specific imbalance in CVD: Could physical activity help to improve clinical outcome targeting CVD molecular mechanisms in women? International Journal of Molecular Sciences, 21:1–16.
DOI: https://doi.org/10.3390/ijms21041477
Wang B, Gu K, Dong D, Fang Y, Tang L, 2022. Analysis of Spatial Distribution of CVD and Multiple Environmental Factors in Urban Residents. Comput Intell Neurosci 16:2022:9799054.
DOI: https://doi.org/10.1155/2022/9799054
WHO, 2016. Report on the status of major health risk factors for noncommunicable diseases: WHO African Region, 2015. Available from: https://www.afro.who.int/sites/default/files/2017-07/15264_who_afr-situation-ncds-15-12-2016-for-web.pdf
Xiao-dong Z, Shao-zhao Z, Xun H, Xin-xue L, Li-zhen L, 2022. Association of Residential Proximity to the Coast With Incident Myocardial Infarction: A Prospective Cohort Study. Front Cardiovasc Med 9:752964.
DOI: https://doi.org/10.3389/fcvm.2022.752964
Yan S, Liu G, Chen X, 2023. Spatiotemporal distribution characteristics and influencing factors of the rate of cardiovascular hospitalization in Ganzhou city of China. Front Cardiovasc Med 10:1–13.
DOI: https://doi.org/10.3389/fcvm.2023.1225878
Zangeneh A, Najafi F, Khosravi A, Ziapour A, Molavi H, Moradi Z, Bakhshi S, Shadmani FK, Karamimatin B, Soofi M, 2024. Epidemiological patterns and spatiotemporal analysis of cardiovascular disease mortality in Iran: Development of public health strategies and policies. Curr Probl Cardiol 49:102675.
DOI: https://doi.org/10.1016/j.cpcardiol.2024.102675
Zhang G, Zhu A, Huang Q, 2017. A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data approach for e ffi cient point pattern analysis on spatial big data. Int J Geogr Inf Sci 31:2068–97.
DOI: https://doi.org/10.1080/13658816.2017.1324975
How to Cite
Sianga, B. E., Mbago, M. C., & Msengwa, A. S. (2024). The distribution of cardiovascular diseases in Tanzania: a spatio-temporal investigation. Geospatial Health, 19(2). https://doi.org/10.4081/gh.2024.1307
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