Direct answer
The site uses different source standards for different kinds of claims. A regulatory claim should come from a regulator or legal source. A reimbursement claim should come from a payer, official payment rule, or documented policy. A disease-burden claim should come from a credible epidemiologic source. A market-access claim needs evidence about approval, payment, procurement, adoption, and implementation, not just a market-size estimate.
Why methodology matters
Healthcare systems are complicated because facts sit at different levels. A law may establish authority without showing implementation. A government statistic may show national totals while hiding provincial variation. A peer-reviewed article may measure one city, hospital type, or time period. A company presentation may describe a market opportunity but omit payer, procurement, or service constraints. The purpose of a source methodology is to prevent those source types from being treated as interchangeable.
For U.S.-China comparison, source discipline is especially important. In the United States, CMS, FDA, commercial payers, state agencies, hospital systems, and professional societies each answer different questions. In China, NHC, NHSA, NMPA, local governments, public hospitals, and procurement bodies also control different gates. A page is stronger when it names which gate the source supports.
Source hierarchy
| Claim type | Preferred sources | How to read them |
|---|---|---|
| Regulatory approval | FDA, NMPA, laws, implementing rules, guidance, and official registers. | Approval is permission to market under defined conditions, not proof of reimbursement or adoption. |
| Payment and reimbursement | CMS, NHSA, local payer rules, coding guidance, coverage decisions, procurement documents. | Coding, coverage, payment, and procurement are separate gates. |
| Population health | WHO, CDC, China CDC, national statistical yearbooks, peer-reviewed epidemiology. | Check year, geography, indicator definition, and data completeness. |
| Health-system structure | Official statistics, Commonwealth Fund profiles, WHO health-system materials, academic reviews. | Use structural sources for institutions, but verify current policy details separately. |
| Clinical evidence | Peer-reviewed trials, systematic reviews, guidelines, registries, and real-world evidence. | Assess population, comparator, endpoint, setting, and transferability. |
Data-quality rules
WHO's Data Quality Assurance materials emphasize that routine health information should be assessed for completeness, timeliness, internal consistency, and agreement with source documents. CDC surveillance guidance similarly treats data flow, timeliness, sensitivity, representativeness, and usefulness as part of evaluating public-health systems. Those principles are used here in plain language: if a number is national, ask what it hides; if a source is local, ask whether it travels; if a rule is current, ask when it may change; if a dataset is administrative, ask what behavior it was designed to record.
For China pages, that usually means distinguishing national policy text from provincial execution. A central ministry document may set the reform direction, while reimbursement percentages, hospital payment pilots, procurement results, and patient cost-sharing are often implemented locally. For U.S. pages, the parallel issue is separating federal Medicare rules from Medicaid state variation, commercial payer policies, hospital adoption decisions, and professional coding practice. A source is strongest when it is close to the decision being described.
Practical rule
Every substantive page should identify the source family behind its main claims. Stable institutional explanations can rely on official profiles and system reviews, while regulation, payment, procurement, and data-governance pages require more frequent review. When a page is used for business, legal, regulatory, or investment decisions, the linked primary source should be checked directly.
Research anchors
- WHO Data Quality Assurance for routine health-data quality principles.
- CDC surveillance evaluation guidance for usefulness, timeliness, sensitivity, and representativeness.
- EQUATOR Network for transparent health-research reporting guidelines.