Get Our e-AlertsSubmit Manuscript
Health Data Science / 2022 / Article

Perspective | Open Access

Volume 2022 |Article ID 9893703 | https://doi.org/10.34133/2022/9893703

Pengfei Li, Lin Ma, Jue Liu, Luxia Zhang, "Surveillance of Noncommunicable Diseases: Opportunities in the Era of Big Data", Health Data Science, vol. 2022, Article ID 9893703, 3 pages, 2022. https://doi.org/10.34133/2022/9893703

Surveillance of Noncommunicable Diseases: Opportunities in the Era of Big Data

Received30 Apr 2022
Accepted12 May 2022
Published07 Jun 2022

1. Introduction

Noncommunicable diseases (NCDs) are the leading cause of mortality, accounting for 70% of deaths worldwide, and have become one of the major challenges for human sustainable development in the 21st century [1]. The pandemic of coronavirus disease 2019 (COVID-19) interrupted NCD services in 75% of the countries—public health campaigns and NCD surveillance efforts gave way to pandemic control [2], which further imposes challenges to fight NCDs. Among the key components of strategy for the prevention and control of NCDs, surveillance is a crucial one to track and monitor the major risk factors and intervention effects, which is defined as “the ongoing systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice, closely integrated with timely dissemination of these data to those who need to know” [3, 4]. The capacity to undertake surveillance varied substantially and was inadequate in many low-income and middle-income countries. It is hoped that integration of multiple existing data sources into a comprehensive NCD surveillance framework could enhance the NCD surveillance efforts [5].

2. The Current NCD Surveillance System

To guide the implementation of medical and public health measures to control the diseases, Alwan et al. proposed the national surveillance framework for NCDs that encompassed key risk factors, outcomes, health-system response interventions, and health-system capacity [6]. Considering the need of monitoring incidence and the emphasis on representativeness, the periodic population-based survey is one of the most important components of the surveillance system.

The National Health and Nutrition Examination Survey (NHANES) in the United States initiated in the 1960s is one of the early endeavors to monitor the health and nutritional status of nation-wide population. Starting in 1999, NHANES became a continuous program focusing on a variety of health and nutrition measurements each year [7]. Similar population-based surveys have been launched in low-income and middle-income countries (LMICs) with a high burden of NCDs [6]. For example, in China, the nutrition and chronic disease surveys have been combined since 2002 and the resulting China National Nutrition and Health Surveys provide information of “how the social and economic transformation of Chinese society is affecting the health and nutritional status of its population” [8].

Population-based surveys could be extremely manpower, time, and material resource-consuming, limiting NCD surveillance capacity, especially in LMLCs. For example, among 22 LMLCs with a high burden of NCDs, only 50% of them have reported population-based information on risk factors for NCDs [6]. Even for countries with risk factor information, concerns exist regarding the accuracy, quality, and standardization of data [6]. Furthermore, the composition of global burden of diseases has displayed a dynamic trend [9], and the current surveillance system might not have the resilience to include emerging NCDs in a timely manner. For instance, chronic kidney disease (CKD) has only been recognized as an important public health issue worldwide in the past 20 years [10], while the surveillance programs (especially national programs initiated by the government) for CKD are extremely limited [11]. In addition, during major events such as the COVID-19 outbreak [2], risk behaviors affecting health could change rapidly. As a result, the current periodic survey-based system might not be adequate to identify those acute changes. The resource consumption, incomplete reporting, and time lags of existing surveillance systems might damage the ability of governments and health organizations to capture NCD trends and manage unmet needs.

3. Big Data and NCD Surveillance Systems

The digitalization in medicine and the advent of big data analytics have introduced novel opportunities for NCD surveillance. The widespread use of electronic health records (EHR), the accumulating administrative and claim data at national level, and other types of datasets have enabled us to pursue solutions to population health issues previously thought impossible [5, 12]. Instead of extrapolating from the data obtained from samples at high costs to make inferences about a population of national level, it is possible to use EHR data across health organizations at the national population level to provide a real-world picture [13]. The practice-driven and longitudinal nature of real-world data enable the inclusion of multiple risk factors, outcomes, and intervention effect evaluation, which are all important elements of NCD surveillance. Furthermore, integrating and analyzing enriched data resources have introduced the feasibility of quick identification and response to changing situations associated with NCD surveillance [14]. As an illustration of the value of multiple data resources, the surveillance of CKD as an emergent NCD is used as an example in the following.

After the concept of CKD was defined in 2002 and existing data revealed its public health importance, the Centers for Disease Control and Prevention in the United States launched the CKD initiative to establish the first comprehensive CKD Surveillance System in 2006 [15]. The approach is to leverage existing data from a wide range of data sources, including data from both healthcare systems and data from existing surveillance programs [11]. Through the integration of various data sources available, the CKD Surveillance System can provide detailed information in support of CKD control in many important aspects.

The similar approach could also be utilized in developing countries with limited resources and capacity. In China, the prevalence of CKD is reported to be 10.8% [16] and imposes a substantial burden on the healthcare system, while CKD was not included in the existing government-initiated surveillance system. Under the national strategy to promote the application of big data, an initiative entitled China Kidney Disease Network (CK-NET) was launched in 2014. CK-NET integrates and analyzes different data source-administrative data, regional EHR, research data, and real-world data, being recognized as the most comprehensive CKD surveillance system and “an important benchmark for kidney disease surveillance in China” [17].

4. The Future of NCD Surveillance

Besides big data and data analytics, many cutting-edge technologies could be used to collect data complementing existing NCD surveillance data. As home-based wireless devices, apps, and wearable technology mature in recent years, the real-time health data generated by patients can be collected, which could capture health behaviors including patient engagement [18]. During the pandemic of COVID-19, the cutting-edge technologies are pivotal in infection surveillance. Besides wearable health monitoring sensors, virtual care technologies and Internet-of-Things could be integrated to develop smart disease surveillance systems to prevent, diagnose, and treat COVID-19 [19]. Such kinds of systems could also be instrumental for NCD surveillance [19].

Furthermore, it is increasingly recognized that health is ineluctably linked to social, environmental, and economic factors; hence, including behavioral, environmental, network, and community data into the surveillance of NCDs could lead to opportunities for interventions aimed at improving population health [20]. For example, accumulating evidence suggests that worsening economic outcomes may be a primary contributor to negative health trends among working-age US adults with low income and less education [21]. In addition, virtual digital trails such as mining social media data provide the opportunity to evaluate self-reported NCD-related attitudes and behaviors [5]. Research has shown conveying anger and fatigue on Twitter was associated with an increased risk of heart disease [22]. With the increased complexity of collected data in the surveillance system, tools from other sectors could also be utilized to develop innovative approaches for NCD surveillance. For example, using spatial analyses and geographical information systems provided a geographic perspective to explore health disparities and access to care in patients with head and neck cancer [23].

In summary, the outburst of data in both the medical area and beyond, the development of data analytics and other cutting-edge technologies, and the emergence of data-driven paradigms are transforming NCD surveillance. Although extremely promising, there are still enormous legal, ethical, political, and technical challenges to overcome. In addition, transdisciplinary efforts are unprecedentedly important in the era of big data.

Disclosure

All authors have completed the ICMJE uniform disclosure form.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Authors’ Contributions

PL and LZ conceived and designed the study. PL and LM drafted the manuscript. JL provided epidemic domain expertise. All authors critically revised the manuscript for important intellectual content. All authors approved the final version for submission. Pengfei Li and Lin Ma contributed equally to this work.

Acknowledgments

This work was supported by grants from the National Natural Science Foundation of China (grant number 72125009).

References

  1. N. C. Countdown, “NCD Countdown 2020: Pathways to achieving sustainable development goal target 3.4,” Lancet, vol. 396, no. 10255, pp. 918–934, 2020. View at: Publisher Site | Google Scholar
  2. T. Lancet, “COVID-19: a new lens for non-communicable diseases,” Lancet, vol. 396, no. 10252, p. 649, 2020. View at: Publisher Site | Google Scholar
  3. H. I. Hall, A. Correa, P. W. Yoon, C. R. Braden, and Centers for Disease Control and Prevention, “Lexicon, definitions, and conceptual framework for public health surveillance,” MMWR Suppl, vol. 61, no. 3, pp. 10–14, 2012. View at: Google Scholar
  4. M. Kroll, R. K. Phalkey, and F. Kraas, “Challenges to the surveillance of non-communicable diseases--a review of selected approaches,” BMC Public Health, vol. 15, no. 1, 2015. View at: Publisher Site | Google Scholar
  5. R. D. Balicer, M. Luengo-Oroz, C. Cohen-Stavi et al., “Using big data for non-communicable disease surveillance,” The Lancet Diabetes & Endocrinology, vol. 6, no. 8, pp. 595–598, 2018. View at: Publisher Site | Google Scholar
  6. A. Alwan, D. R. Maclean, L. M. Riley et al., “Monitoring and surveillance of chronic non-communicable diseases: progress and capacity in high-burden countries,” The Lancet, vol. 376, no. 9755, pp. 1861–1868, 2010. View at: Publisher Site | Google Scholar
  7. S. Ncfh, About the National Health and Nutrition Examination Survey, 2017, https://www.cdc.gov/nchs/nhanes/about_nhanes.htm.
  8. D. Yu, L. Zhao, J. Zhang et al., “China Nutrition and Health Surveys (1982−2017),” China CDC Weekly, vol. 3, no. 9, pp. 193–195, 2021. View at: Publisher Site | Google Scholar
  9. T. Vos, S. S. Lim, C. Abbafati et al., “Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019,” The Lancet, vol. 396, no. 10258, pp. 1204–1222, 2020. View at: Publisher Site | Google Scholar
  10. Mortality G B D and Causes of Death C, “Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2015: a systematic analysis for the global burden of disease study 2013,” Lancet, vol. 385, no. 9963, pp. 117–171, 2015. View at: Google Scholar
  11. J. Radhakrishnan, G. Remuzzi, R. Saran et al., “Taming the chronic kidney disease epidemic: a global view of surveillance efforts,” Kidney International, vol. 86, no. 2, pp. 246–250, 2014. View at: Publisher Site | Google Scholar
  12. L. Zhang, H. Wang, Q. Li, M. H. Zhao, and Q. M. Zhan, “Big data and medical research in China,” BMJ, vol. 360, article j5910, 2018. View at: Publisher Site | Google Scholar
  13. K. Y. Ngiam and I. W. Khor, “Big data and machine learning algorithms for health-care delivery,” The Lancet Oncology, vol. 20, no. 5, pp. e262–e273, 2019. View at: Publisher Site | Google Scholar
  14. W. H. Organization, Integrated Surveillance of Noncommunicable Diseases (iNCD) a European Union-WHO Project, WHO Regional Office for Europe, Copenhagen, Denmark, 2015.
  15. S. L. White, S. J. Chadban, S. Jan, J. R. Chapman, and A. Cass, “How can we achieve global equity in provision of renal replacement therapy?” Bulletin of the World Health Organization, vol. 86, no. 3, pp. 229–237, 2008. View at: Publisher Site | Google Scholar
  16. L. Zhang, F. Wang, L. Wang et al., “Prevalence of chronic kidney disease in China: a cross-sectional survey,” Lancet, vol. 379, no. 9818, pp. 815–822, 2012. View at: Publisher Site | Google Scholar
  17. R. Saran, D. Steffick, and J. Bragg-Gresham, “The China Kidney Disease Network (CK-NET): "Big Data--Big Dreams",” American Journal of Kidney Diseases, vol. 69, no. 6, pp. 713–716, 2017. View at: Publisher Site | Google Scholar
  18. R. V. Milani, R. M. Bober, and C. J. Lavie, “The role of technology in chronic disease care,” Progress in Cardiovascular Diseases, vol. 58, no. 6, pp. 579–583, 2016. View at: Publisher Site | Google Scholar
  19. S. Maxwell and M. Grupac, “Virtual care technologies, wearable health monitoring sensors, and internet of medical things-based smart disease surveillance systems in the diagnosis and treatment of COVID-19 patients,” American Journal of Medical Research, vol. 8, no. 2, pp. 118–131, 2021. View at: Publisher Site | Google Scholar
  20. S. Galea, S. M. Abdalla, and J. L. Sturchio, “Social determinants of health, data science, and decision-making: forging a transdisciplinary synthesis,” PLoS Medicine, vol. 17, no. 6, article e1003174, 2020. View at: Publisher Site | Google Scholar
  21. A. S. Venkataramani, R. O’Brien, G. L. Whitehorn, and A. C. Tsai, “Economic influences on population health in the United States: toward policymaking driven by data and evidence,” PLoS Medicine, vol. 17, no. 9, article e1003319, 2020. View at: Publisher Site | Google Scholar
  22. J. C. Eichstaedt, H. A. Schwartz, M. L. Kern et al., “Psychological language on twitter predicts county-level heart disease mortality,” Psychological Science, vol. 26, no. 2, pp. 159–169, 2015. View at: Publisher Site | Google Scholar
  23. S. K. Rereddy, D. R. Jordan, and C. E. Moore, “Dying to be screened: exploring the unequal burden of head and neck cancer in health provider shortage areas,” Journal of Cancer Education, vol. 30, no. 3, pp. 490–496, 2015. View at: Publisher Site | Google Scholar

Copyright © 2022 Pengfei Li et al. Exclusive Licensee Peking University Health Science Center. Distributed under a Creative Commons Attribution License (CC BY 4.0).

 PDF Download Citation Citation
Views1225
Downloads299
Altmetric Score
Citations