Keywords:
Health data-gaps
Data quality
Maternal-Child Health
Measurement challenges
Good sustainable Data practices

  • Abstract

    Data metrics in public health programs and interventions play a crucial role in reflecting the health and well-being of the population, along with the structural and biosyndemic factors influencing health, public health interventions, and system resources. The management of health data has emerged as a key focus area within the sustainable global agenda, aiming to ensure effective, efficient, timely, and safe quality healthcare through evidence-based clinical practices and high-quality data systems that support quality improvement strategies and interventions to achieve optimal standards of care. Recognized internationally as an integral part of the unfinished health goals and the Sustainable Development Agenda 2030, a high-quality health information system is deemed essential for positively impacting health outcomes. However, emerging economies like India face the significant challenge of developing complex data platforms to track SDG health goals effectively, thereby providing useful insights for policymakers, academics, researchers, and program implementers. This paper aims to contribute to the growing knowledge on the implications of poor data quality and the performance of health information systems. It outlines the availability, completeness, and accuracy of data related to key performance indicators in the maternal and child health domain, evaluates the capacity and readiness of healthcare systems to generate high-quality data metrics, and explores the challenges, determinants, and gaps in achieving a sustainable and effective data management system. The study utilizes a mixed-methods approach and the WHO-DQAT data quality framework, employing objective-subjective assessments, desk analysis, cross-sectional field validation surveys, and integrated mixed-triangulation data tools. By measuring data quality dimensions and identifying potential gaps and determinants, the paper sheds light on deficiencies in the structure, function, and capabilities of the system, particularly in data management processes. It emphasizes the importance of strengthening statistical capacities and creating dedicated data management systems at state and national levels, and urges stakeholders to recognize the significance of data in health information systems for evidence-based policymaking.

  • References

    1. AbouZahr C, Boerma T (2005) Health information systems the foundations of public health. Bulletin of the World health organization. The International Journal of Public Health 83:578-583.
    2. Ako-Arrey DE, Brouwers MC, Lavis JN, Giacomini MK (2016) Team obotA-H. Health systems guidance appraisal—a critical interpretive synthesis. Implementation Science 11:9.
    3. Ballou DP, Pazer HL (1985) Modeling data and process quality in multi-input, multi-output information systems. Management Science 31:150-162.
    4. Bhojani U, Prashanth N, Devadasan N (2010) Under reported deaths and inflated deliveries: making sense of health management information systems in India. Paper presented at: International Conference on Health Systems Strengthening.
    5. Bhutta ZA, Das JK, Bahl R, et al (2014) Can available interventions end preventable deaths in mothers, newborn babies, and stillbirths, and at what cost? The Lancet 384:347-370.
    6. Brown S (2002) Data Quality: Relatively Critical & Critically Relative. DM REVIEW 12:62-64.
    7. Campbell DT, Fiske DW. (1959) Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological bulletin.;56(2):81.
    8. Canada S (2009) "Health indicators": Canadian Institute for Health Information.
    9. Chen S, Fonteneau F, Jütting J, Klasen S (2013) Towards a post-2015 framework that counts: developing national statistical capacity. Vancover Canada.
    10. Chen S, Francois F, Joannes J, Klasen S (2013) Towards a post 2015 framework that counts: Development national statistical capacity. Paris21. Paris France.
    11. Creswell JW, Plano Clark V, Gutmann ML, Hanson WEATyCT (2003) An expanded typology for classifying mixed methods research into designs.
    12. Creswell JW (2011) Controversies in mixed methods research. Vol 4.
    13. Creswell P, Clark V. Gutmann, Hanson (2003) Advanced mixed methods research designs. Handbook of mixed methods in social behavioral research 209-240.
    14. Dimitri S (2011) The Challenges of Monitoring and Reporting on the Millennium Development Goals in Africa by and Beyond. Journal statistique africain.
    15. Eugene J, Campbell DT, Schwartz RD, Sechrest L (1966) Unobtrusive measures: Nonreactive research in the social sciences: Rand McNally.
    16. Fukuda-Parr S, Greenstein J, Stewart D (2013) How Should MDG Success and Failure be Judged: Faster Progress or Achieving the Targets? World Development 19-30.
    17. Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath RJTLDH (2019) Practical guidance on artificial intelligence for health-care data 1:e157-e159.
    18. Goodchild MF (1993) Data models and data quality: problems and prospects. Environmental modeling with GIS 94-103.
    19. Gray JH, Densten IL (2005) Towards an integrative model of organizational culture and knowledge management. International Journal of Organisational Behaviour 9:594-603.
    20. Hák T, Janoušková S, Moldan B (2016) Sustainable Development Goals: A need for relevant indicators. Ecological Indicators 60:565-573.
    21. Hardee K (2008) MEASURE Evaluation: Data Quality Audit Tool: Guidelines for Implementation.
    22. Harikumar S (2012) Evaluation of Health Management Information Systems-A Study of HMIS in Kerala, SCTIMST.
    23. Husain Z, Saikia N, Bora RS (2012) Opportunities and challenges of health management information system in India: a case study of Uttarakhand.
    24. Jesiļevska S (2017) Data Quality Dimensions to Ensure Optimal Data Quality. Romanian Economic Journal 20:63.
    25. Kadir A, Shenoda S, Goldhagen J (2019) Effects of armed conflict on child health and development: A systematic review. PLoS one 14:e0210071.
    26. Karr AF, Sanil AP, Banks DL (2006) Data quality: A statistical perspective. Statistical Methodology 3:137-173.
    27. Kerber KJ, de Graft-Johnson JE, Bhutta ZA, Okong P, Starrs A, Lawn JE (2007) Continuum of care for maternal, newborn, and child health: from slogan to service delivery. The Lance 370:1358-1369.
    28. Kerr K, Norris T (2004) The Development of a Healthcare Data Quality Framework and Strategy. Paper presented at: ICIQ.
    29. Khandade TN, Kumar RK, Chattopadhyay SP (2013) Status of reporting at health sub-center level in Bihar. Indian journal of public health 57:283.
    30. Klein B, Rossin D (1999) Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy. Omega 27:569-582.
    31. Kumar R, Mehendale SM, Panda S, et al (2011) Impact of targeted interventions on heterosexual transmission of HIV in India. BMC public health 11:1-8.
    32. Kumar S (2005) Challenges of maternal mortality reduction and opportunities under National rural health mission-a critical appraisal. Indian journal of public health 49:163.
    33. Mari Bhat PN, Preston S, Dyson T (1984) Vital rates in India, 1961-1981. Washington, DC.: National Academy Press.
    34. Mikkelsen L, Phillips DE, AbouZahr C, et al (2015) A global assessment of civil registration and vital statistics systems: monitoring data quality and progress. Lancet 3:1395-1406.
    35. Muchhala B, Sengupta M (2015) A critique of the emerging post-2015 agenda. New Delhi: Centre for Development and Human Rights.
    36. Murray CF, JA (2000) framework for assessing the performance of health systems. New York: World Health Organization.
    37. Pandey A, Roy N, Bhawsar R, Mishra R (2010) Health Information System in India: Issues of Data Availability and Quality Demography India. 39:111-128.
    38. Poku NK, Whitman J (2011) The Millennium Development Goals and Development after 2015. Third World Quarterly Third World Quarterly 32:181-198.
    39. Prinja S, Manchanda N, Aggarwal AK, Kaur M, Jeet G, Kumar R (2013) Cost & efficiency evaluation of a publicly financed & publicly delivered referral transport service model in three districts of Haryana State, India. The Indian journal of medical research. 138:1003.
    40. Raban MZ, Dandona R, Dandona L (2009) Essential health information available for India in the public domain on the internet. BMC Public Health 9:208.
    41. Rashid J (2012) An analysis of self-accounts of children-in-conflict-with-law in Kashmir concerning the impact of torture and detention on their lives. International Social Work 55:629-644.
    42. Reddy S, Heuty A (2008) Global development goals: The folly of technocratic pretensions. Development Policy Review 26:5-28.
    43. Sachs JD (2012)From millennium development goals to sustainable development goals. The Lancet 379:2206-2211.
    44. Sahay S, Mukherjee AJE, Weekly P (2020) here is all our health data going 55:46-53.
    45. Sharma A, Rana SK, Prinja S, Kumar R. (2016) Quality of Health Management Information System for Maternal & Child Health Care in Haryana State, India. PLoS One 11:e0148449.
    46. Strong DM, Lee YW, Wang RY. (1997) Data quality in context. Communications of the ACM.40(5):103-110.
    47. Tripathi S, Sharma R, Nagarajan S (2018) Health Information Systems in India:Challenges and way forward. Germany: University of Munich.
    48. Verma R, Prinja S (2007) Over reporting of RCH services coverage and operational problems in health management information system at the sub-center level. Indian Journal of Community Medicine 32:185.
    49. Wahl B, Cossy-Gantner A, Germann S, Schwalbe NRJB (2018) Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? 3:e000798.
    50. Wang RY, Strong DM (1996) Beyond accuracy: What data quality means to data consumers. Journal of management information systems 12:5-33.
    51. WHO (2003) The immunization data quality audit (DQA) procedure: World Health Organization.
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How to cite

Uddin, N., Majid Zaman, & Fayaz, S. A. (2023). Data gaps, tenuity and measurement challenges in achieving sustainable maternal child healthcare ecosystem: an evidence based statistical evaluation. Multidisciplinary Reviews, 6(3), 2023024. https://doi.org/10.31893/multirev.2023024
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