A review of accessibility of administrative healthcare databases in the Asia-Pacific region

Miléa, D.; Azmi, Soraya; Reginald, Praveen; Verpillat, Patrice; François, Clément

doi:10.3402/jmahp.v3.28076

Cited by 29 publications

(30 citation statements)

References 42 publications

(25 reference statements)

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“…In Asia, China had more EMRs/medical record data sources available for researches. An Asia‐Pacific study corroborates our results for China, as the authors also reported EMRs as the major generic data source available for analysis . The increased number of EMR sources in China owes to the implementation of the national health reform plan wherein the government aims to have a more regularized health care distribution and hospital management system .…”

Section: Discussionsupporting

confidence: 84%

“…In the present study, following a systematic approach, a total of 2635 unique data sources were identified, of which 300 were mapped. A similar study was conducted to identify generic administrative datasets but was limited to Asia‐Pacific countries only . Furthermore, the study focused only on the level of accessibility of each dataset without evaluating their suitability for research.…”

Section: Discussionmentioning

confidence: 99%

“…A similar study was conducted to identify generic administrative datasets but was limited to Asia-Pacific countries only. 20 Furthermore, the study focused only on the level of accessibility of each dataset without evaluating their suitability for research. Another study used a web-based survey to gather information from 13 generic health care databases in the Asia-Pacific region, 21 but the authors did not ascertain the suitability for research for each of the selected databases.…”

Section: Discussionmentioning

confidence: 99%

“…An Asia-Pacific study corroborates our results for China, as the authors also reported EMRs as the major generic data source available for analysis. 20 The increased number of EMR sources in China owes to the implementation of the national health reform plan wherein the government aims to have a more regularized health care distribution and hospital management system. 22 Globally and in The key strength of this study lies in its unique methodology, wherein using a systematic literature review approach with a systematic search and a comprehensive data abstraction tool, the most important RWD sources worldwide were identified, and mapped.…”

Section: Discussionmentioning

confidence: 99%

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Identification and mapping of worldwide sources of generic real‐world data

Wang¹,

Lahoz

Jawla

et al. 2019

Pharmacoepidemiology and Drug

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Purpose The demand for real‐world data as supportive evidence to traditional clinical studies has increased in the past few years. The present study aimed to identify worldwide generic sources of real‐world data and to assess completeness and suitability of selected real‐world evidence (RWE) data sources to conduct prespecified research. Methods A systematic literature review was conducted to identify generic (non‐disease specific) sources of real‐world data in Medline and Embase from January 1, 2010 to September 8, 2015. Data sources used in observational studies were identified and summarized based on their geographical distribution and the type of data. In the next step, the selected data sources were critically evaluated for their completeness. Results A total of 10,069 identified publications were screened, leading to 2635 unique data sources across 102 countries. Europe had the maximum number of data sources (n = 1163) followed by United States (n = 578), and Asia, Middle East, and African Countries (n = 374). The most common type of identified data sources across all countries was structured data sources, ie, administrative databases and registries. Of the identified data sources, 300 were selected for further investigation. From the selected databases, ~50% had confirmed information on over 60% of the investigated variables, ~61% were suitable for epidemiological research, and 60% had possibility of linkage. Conclusions The present study applied a systematic literature review approach and identified available generic sources of real‐world data worldwide, in addition to the United States and Europe, which are suitable for conducting pre‐defined researches and support future RWE studies.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Identification and mapping of worldwide sources of generic real‐world data

Wang¹,

Lahoz

Jawla

et al. 2019

Pharmacoepidemiology and Drug

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“…This study is a retrospective observational analysis using de-identified, individual-level data obtained from the health claims database of the Japan Medical Data Center (JMDC), Tokyo, between 2005 and 2017. [11][12][13][14][15] This database contains medical claims data provided by multiple health insurance services for employees and their family members. Included are patient demographics, diagnosis codes recorded as standardized local codes of diseases defined by the Medical Information System Development Center mapped to the International Classification of Diseases (ICD)-10 codes, prescriptions, medical procedures and examinations.…”

Section: Study Design and Source Of Datamentioning

confidence: 99%

Lipid‐lowering medication is associated with decreased risk of diabetic retinopathy and the need for treatment in patients with type 2 diabetes: A real‐world observational analysis of a health claims database

Kawasaki

Konta

Nishida

2018

Diabetes Obesity Metabolism

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The Structured Process to Identify Fit‐For‐Purpose Data: A Data Feasibility Assessment Framework

Gatto

Campbell

Rubinstein

et al. 2021

Clin Pharma and Therapeutics

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To complement real-world evidence (RWE) guidelines, the 2019 Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real-world Evidence (SPACE) framework elucidated a process for designing valid and transparent real-world studies. As an extension to SPACE, here, we provide a structured framework for conducting feasibility assessments-a step-by-step guide to identify decision grade, fit-for-purpose data, which complements the United States Food and Drug Administration (FDA)'s framework for a RWE program. The process was informed by our collective experience conducting systematic feasibility assessments of existing data sources for pharmacoepidemiology studies to support regulatory decisions. Used with the SPACE framework, the Structured Process to Identify Fit-For-Purpose Data (SPIFD) provides a systematic process for conducting feasibility assessments to determine if a data source is fit for decision making, helping ensure justification and transparency throughout study development, from articulation of a specific and meaningful research question to identification of fit-for-purpose data and study design. BACKGROUNDAccess to extensive and diverse real-world data (RWD) sources has grown exponentially over the past decade. [1][2][3] Receptivity to using RWD in real-world evidence (RWE) to complement clinical trial evidence has simultaneously increased, 4-7 leading to more frequent inclusion of RWD studies in regulatory and payer submission packages, 8,9 but with mixed success. Whereas particular therapeutic areas, such as oncology and rare diseases, have historically utilized RWE, advances are being made to understand the optimal settings for producing RWE fit for decision making by regulators, payers, and health technology assessment agencies. 10 Standardssuch as guidance documents, step-by-step processes, and templates, developed to guide researchers on the design and conduct of RWD studies-support validity and transparency, and ultimately bolster confidence in RWE. These good practices cover the continuum 11 from articulating a clear research question 12 to transparency in study conduct and reporting of results, [13][14][15][16] and include consideration of the hypothetical target trial, 12,17 identifying confounders by constructing causal diagrams, 12,18,19 identifying a fit-for-purpose design, 12,20 protocol development, [21][22][23][24][25][26][27] and visualizing the study design. 20 A Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent RWE (SPACE) framework elucidated a step-by-step process for designing valid and transparent real-world studies and provides templates to capture decision making and justification at each step. 12 The structured template for planning and reporting on the implementation of RWE studies (STaRT-RWE) picks up where SPACE leaves off, providing detailed templates to capture the final design and implementation details (e.g., specific algorithms for each study variable). Taken...

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A review of accessibility of administrative healthcare databases in the Asia-Pacific region

Cited by 29 publications

References 42 publications

Identification and mapping of worldwide sources of generic real‐world data

Identification and mapping of worldwide sources of generic real‐world data

Lipid‐lowering medication is associated with decreased risk of diabetic retinopathy and the need for treatment in patients with type 2 diabetes: A real‐world observational analysis of a health claims database

The Structured Process to Identify Fit‐For‐Purpose Data: A Data Feasibility Assessment Framework

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