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Improving the quality of cause of death data for public health policy: are all 'garbage' codes equally problematic?
BMC Medicine ( IF 7.0 ) Pub Date : 2020-03-09 , DOI: 10.1186/s12916-020-01525-w
Mohsen Naghavi ₁ , Nicola Richards ₂ , Hafiz Chowdhury ₂ , James Eynstone-Hinkins ₃ , Elisabeth Franca ₄ , Michael Hegnauer ₅ , Ardeshir Khosravi ₆ , Lauren Moran ₃ , Lene Mikkelsen ₂ , Alan D Lopez ₂

Affiliation

All countries need accurate and timely mortality statistics to inform health and social policy debates and to monitor progress towards national and global health development goals. In many countries, however, civil registration and vital statistics (CRVS) systems are poorly developed. Consequently, the statistics they produce are not fit for purpose. In part, this arises because the physicians certifying cause of death (COD) have either not been adequately trained in how to complete a death certificate according to the current International Statistical Classification of Diseases – Version 10 (ICD-10) [1], or they fail to appreciate the public health importance of what is often perceived as a largely administrative task [2]. This can be reinforced by cultural attitudes and perceptions among hospital administrators, who are generally unaware of the critical contribution that accurate medical certification of CODs makes to generating essential public health intelligence that can be used for planning.

Unsurprisingly, these system deficiencies usually result in a high proportion of CODs being assigned to ‘garbage’ codes [3]. These have little or no public health value because they are too vague, are an immediate or intermediate COD, or are impossible as an underlying cause of death (UCOD). For example, septicaemia is often chosen as the underlying or precipitating COD when it is, in fact, the immediate cause arising from a many possible UCODs including communicable or non-communicable diseases, or an injury [3]. Prevention strategies would differ markedly depending on the UCOD; hence the importance of correct certification.

Garbage codes bias a country’s true pattern of mortality. Studies of the quality of mortality statistics carried out in Thailand [4], Sri Lanka [5], and Iran [6], for example, have repeatedly found that the population’s likely true mortality pattern was considerably different from the pattern reported by the CRVS system. These discrepancies have been largely attributed to physicians’ extensive use of garbage codes.

The rationale for identifying garbage codes is that, by doing so, certifying physicians and coders can be encouraged and trained to avoid unspecific ICD codes that are unlikely to be useful in guiding disease and injury control strategies. Specifically, national health planners must understand which misdiagnoses have the greatest impact on policy decions. Rather than classifying garbage codes according to the type of error (see Naghavi et al. [3]), an alternative classification is therefore needed, based on the severity of the impact that particular garbage codes might have in seriously misinforming public policy.

Accordingly, we have adapted the classification of garbage codes used by the Global Burden of Disease study to guide efforts to improve CRVS data quality. We focus more on the likely policy implications of various types of misdiagnosis of the true UCOD. The four distinct levels of garbage codes are defined as:

Level 1 (very high) – codes with serious policy implications. These are causes (e.g. septicaemia) for which the true UCOD might belong to any one of three broad cause groups: communicable or non-communicable diseases, or injuries. We simply don’t know. Such errors potentially grossly misinform understanding of the extent of an epidemiological transition in a population.
Level 2 (high) – codes with substantial policy implications. These are causes for which the true UCOD is likely to belong to one, or at most two, of the three broad groups, (e.g., ‘essential (primary) hypertension’). While not greatly altering the understanding of the broad composition of mortality in a population, these codes might considerably affect the comparative importance of leading causes within broad disease categories.
Level 3 (medium) – codes with important policy implications. These are causes for which the true underlying UCOD is likely to be within the same ICD chapter. For instance, ‘unspecified cancer’ still identifies the death as being attributed to cancer, thus has some policy value, although greater type (site) specificity is required because different strategies are applied for different sites of cancer (e.g. breast versus lung).
Level 4 (low) – codes with limited policy implications. These are diagnoses for which the true UCOD is likely to be confined to a single disease or injury category (e.g. unspecified stroke would still be assigned as a stroke death). The implications of unusable causes classified at this level will therefore, generally, be much less important for public policy.

To better focus data quality improvement efforts, this new classification only identifies the garbage codes that are truly unhelpful for policy and are used frequently by physicians to certify deaths; namely, levels 1–3. This excludes, for example, ‘unspecified pneumonia’, which although considered a garbage code in the Global Burden of Disease study, given its relevance for research and technology development [4], can be ignored in this public health oriented framework since we believe it provides sufficient information to guide public health interventions. Morever, any public health-orientated garbage code classification must be realistic about countries’ diagnostic capacity at different levels of development. For example, to reliably distinguish between haemorrhagic and ischaemic stroke, a computed tomography scan or magnetic resonance image is usually necessary – technologies that are not widely available in low- and middle-income countries.

Implications for mortality data systems

To help countries identify the pattern and extent of garbage coding in their COD data, this new typology of garbage codes has been included in the data quality assessment tool, Analysis of Causes of National Deaths for Action (ANACONDA) developed by the University of Melbourne’s Bloomberg Philanthropies Data for Health Initiative in partnership with the Swiss Tropical and Public Health Institute at the University of Basel [7].

This tool allows countries to identify not only the relative importance of different categories of garbage codes, but also the ICD codes that are most commonly misused within each of these three levels. Strategies to improve COD data quality in hospitals should address the most commonly used garbage codes from all categories. However, clearly, greater emphasis should be given to reducing the frequency of those codes, which have the greatest potential to seriously distort the evidence-base for public health policy designed to reduce premature mortality.

Not applicable.

COD:: Cause of death
CRVS:: Civil registration and vital statistics
ICD:: International Statistical Classification of Diseases and Related Health Problems
UCOD:: Underlying cause of death

1.
Rampatige R, Mikkelsen L, Hernandez B, Riley I, Lopez A. Systematic review of statistics on causes of deaths in hospitals: strengthening the evidence for policy-makers. Bull World Health Org. 2014;92:807–16.
- Article
- Google Scholar
2.
Ahern RM, Lozano R, Naghavi M, Foreman K, Gakidou E, Murray CJL. Improving the public health utility of global cardiovascular mortality data: the rise of ischemic heart disease. Pop Health Metrics. 2011;9:1.
- Article
- Google Scholar
3.
Naghavi M, Makela S, Foreman K, O’Brien J, Pourmalek F, Lozano R. Algorithms for enhancing public health utility of national causes-of-death data. Pop Health Metrics. 2010;8:9–22.
- Article
- Google Scholar
4.
Pattaraarchachai J, Rao C, Polprasert W, Porapakkam Y, Pao-in W, Wansa S, et al. Cause-specific mortality patterns among hospital deaths in Thailand: validating routine death certification. Pop Health Metrics. 2010;8:12–23.
- Article
- Google Scholar
5.
Rampatige R, Gamage S, Peirkis S, Lopez AD. Assessing the reliability of causes of death reported by the vital registration system in Sri Lanka: medical records review in Colombo. Health Inf Manage J. 2013;42(3):20–8.
- Google Scholar
6.
Khosravi A, Rao C, Naghavi M, Taylor R, Jafari N, Lopez AD. Impact of misclassification on measures of cardiovascular disease mortality in the Islamic Republic of Iran: a cross-sectional study. Bull World Health Org. 2008;86:688–96.
- Article
- Google Scholar
7.
Mikkelsen L, Moesgaard K, Hegnauer M, Lopez AD. ANACONDA: A new tool to improve mortality and cause of death data. BMC Medicine. 2020. https://doi.org/10.1186/s12916-020-01521-0.

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This paper is based on a technical meeting held at the Melbourne School of Population and Global Health, University of Melbourne, on 27–28 February 2018. The authors acknowledge inputs at the meeting from Saman Gamage, Deirdre Mclaughlin, Tim Moore, Rasika Rampatige and Ian Riley, University of Melbourne; Pamela Groenewald, South Africa Medical Research Council; and Jomilynn Rebenal, Ministry of Health, the Philippines.

This study was funded by an award from Bloomberg Philanthropies to the University of Melbourne to support the Data for Health Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Affiliations

Institute of Health Metrics and Evaluation, University of Washington, 2301 5th Ave, Seattle, WA, 98121, USA
- Mohsen Naghavi
Melbourne School of Population and Global Health, University of Melbourne, 207 Bouverie Street, Carlton, VIC, 3053, Australia
- Nicola Richards
- , Hafiz Chowdhury
- , Lene Mikkelsen
- & Alan D. Lopez
Australian Bureau of Statistics, 295 Ann Street, Brisbane, QLD, 4000, Australia
- James Eynstone-Hinkins
- & Lauren Moran
University of Minas Gerais, Belo Horizonte, Minas Gerias, 31270-901, Brazil
- Elisabeth Franca
Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
- Michael Hegnauer
Ministry of Health and Medical Education, District 2, Eyvanak Blvd, Tehran, Iran
- Ardeshir Khosravi

Authors

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Contributions

MN, ADL, and LM conceptualised earlier versions of the classification and led the workshop from which this manuscript is derived. NR drafted first version of the manuscript. LM and ADL finalised the manuscript. MN, HC, JE-H, EF, MH, AK and LMo provided comments on the draft manuscript. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Lene Mikkelsen.

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The authors declare that they have no competing interests.

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Naghavi, M., Richards, N., Chowdhury, H. et al. Improving the quality of cause of death data for public health policy: are all ‘garbage’ codes equally problematic?. BMC Med 18, 55 (2020). https://doi.org/10.1186/s12916-020-01525-w

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Received: 03 February 2020
Accepted: 11 February 2020
Published: 09 March 2020
DOI: https://doi.org/10.1186/s12916-020-01525-w

Keywords

Mortality
Cause of death
Garbage codes
Unusable and insufficiently specified codes
ICD-10
Ill-defined codes
Policy
Planning

中文翻译：

为公共卫生政策提高死因数据的质量：所有“垃圾”法规是否同样存在问题？

所有国家都需要准确，及时的死亡率统计数据，以为卫生和社会政策辩论提供信息，并监测实现国家和全球卫生发展目标的进展。但是，在许多国家，民事登记和人口动态统计（CRVS）系统开发不完善。因此，他们产生的统计数据不符合目的。造成这种情况的部分原因是，根据当前的《国际疾病分类统计》（第10版）[1]，未对证明死亡原因（COD）的医生进行过有关如何完成死亡证明的充分培训，或者他们没有意识到通常被认为是一项行政任务的公共卫生重要性[2]。医院管理人员之间的文化态度和看法可以加强这一点，

毫不奇怪，这些系统缺陷通常导致将大量COD分配给“垃圾”代码[3]。由于它们太模糊，是直接或中间的化学需氧量，或者不可能作为潜在的死因（UCOD），因此它们几乎没有公共卫生价值。例如，当败血症实际上是由许多可能的UCOD（包括传染病或非传染病）或伤害引起的直接原因时，常常将败血症选为潜在的或沉淀的COD [3]。预防策略根据UCOD会明显不同；因此，正确认证的重要性。

垃圾法规对一个国家的真实死亡率产生了偏见。例如，在泰国[4]，斯里兰卡[5]和伊朗[6]进行的死亡率统计质量研究再次发现，该人群可能的真实死亡率模式与CRVS报告的模式大不相同。系统。这些差异主要归因于医生对垃圾代码的广泛使用。

识别垃圾代码的理由是，通过这样做，可以鼓励和培训证明医生和编码员，避免使用不太明确的ICD代码，这些代码不太可能对指导疾病和伤害控制策略有用。具体来说，国家卫生计划制定者必须了解哪些误诊对政策决策的影响最大。因此，不是根据错误的类型对垃圾代码进行分类（参见Naghavi等人[3]），而是需要根据特定垃圾代码在严重误导公共政策方面可能产生的影响的严重性，进行替代分类。

因此，我们调整了全球疾病负担研究中使用的垃圾代码分类，以指导改善CRVS数据质量的工作。我们将重点更多地放在对真正的UCOD进行各种类型的误诊的可能的政策含义上。垃圾代码的四个不同级别定义为：

1级（非常高）–具有严重政策影响的代码。这些是真正的UCOD可能属于以下三个广泛原因类别之一的原因（例如败血病）：传染病或非传染病或伤害。我们根本不知道。这些错误可能严重误导人们对人群中流行病学转变程度的理解。
2级（高）–具有实质性政策含义的代码。这些都是真正的UCOD可能属于三大类中的一类或最多二类的原因（例如，“原发性（原发性）高血压”）。这些法规虽然不会极大地改变人们对广泛死亡率构成的理解，但可能会大大影响主要疾病在广泛疾病类别中的相对重要性。
3级（中级）–具有重要政策含义的代码。这些都是真正的基础UCOD可能位于同一ICD章中的原因。例如，“未指明的癌症”仍将死亡确定为归因于癌症，因此具有一定的政策价值，尽管需要更大的类型（部位）特异性，因为对不同部位的癌症（例如乳腺癌和肺癌）采用了不同的治疗策略。
级别4（低）–具有政策影响的代码。对于这些诊断，真正的UCOD可能仅限于单一疾病或损伤类别（例如，未指定的中风仍将归为中风死亡）。因此，对于公共政策而言，归类于此级别的无法使用的原因的含义通常将不那么重要。

为了更好地关注数据质量改进工作，此新分类仅标识了垃圾代码，这些垃圾代码确实对策略无济于事，并且医生经常使用这些垃圾代码来证明死亡。即1-3级。例如，这不包括“非特定性肺炎”，尽管它在全球疾病负担研究中被视为垃圾法规，但鉴于其与研究和技术开发的相关性[4]，在我们面向公共卫生的框架中可以忽略不计，因为我们相信提供足够的信息来指导公共卫生干预措施。此外，任何面向公共卫生的垃圾分类标准都必须对各国在不同发展水平上的诊断能力具有现实意义。例如，为了可靠地区分出血性和缺血性中风，

对死亡率数据系统的影响

为了帮助各国识别其COD数据中的垃圾编码模式和范围，这种新的垃圾代码类型已包含在数据质量评估工具中，这是由墨尔本大学彭博分校开发的“全国因行动死亡原因分析”（ANACONDA）与巴塞尔大学瑞士热带与公共卫生研究所合作开展的“慈善事业数据促进健康计划” [7]。

该工具使各国不仅可以识别不同类别的垃圾代码的相对重要性，而且可以识别在这三个级别中的每个级别中最常被滥用的ICD代码。医院中提高COD数据质量的策略应解决所有类别中最常用的垃圾代码。但是，显然，应更加重视减少这些法规的频率，因为这些法规最有可能严重扭曲旨在减少过早死亡的公共卫生政策的证据基础。

不适用。

化学需氧量：: 死亡原因
CRVS：: 民事登记和人口动态统计
ICD：: 疾病和相关健康问题的国际统计分类
UCOD：: 潜在的死亡原因

1。
Rampatige R，Mikkelsen L，Hernandez B，Riley I，LopezA。对医院死亡原因统计数据的系统回顾：加强决策者的证据。公牛世界卫生组织。2014; 92：807–16。
- 文章
- 谷歌学术
2。
Ahern RM，Lozano R，Naghavi M，Foreman K，Gakidou E，Murray CJL。改善全球心血管疾病死亡率数据的公共卫生效用：缺血性心脏病的兴起。流行健康指标。2011; 9：1。
- 文章
- 谷歌学术
3。
Naghavi M，Makela S，Foreman K，O'Brien J，Pourmalek F，Lozano R.用于增强国家死因数据的公共卫生效用的算法。流行健康指标。2010; 8：9-22。
- 文章
- 谷歌学术
4。
Pattaraarchachai J，Rao C，Polprasert W，Porapakkam Y，Pao-in W，Wansa S等。在泰国的医院死亡中，特定原因的死亡模式：验证常规死亡证明。流行健康指标。2010; 8：12–23。
- 文章
- 谷歌学术
5，
Rampatige R，Gageage S，Peirkis S，Lopez AD。评估斯里兰卡生命登记系统报告的死因可靠性：科伦坡的病历审查。Health Inf Manage J.，2013年； 42（3）：20-8。
- 谷歌学术
6。
Khosravi A，Rao C，Naghavi M，Taylor R，Jafari N，Lopez AD。错误分类对伊朗伊斯兰共和国心血管疾病死亡率衡量的影响：一项横断面研究。公牛世界卫生组织。2008; 86：688–96。
- 文章
- 谷歌学术
7
Mikkelsen L，Moesgaard K，Hegnauer M，Lopez AD。ANACONDA：一种提高死亡率和死因数据的新工具。BMC医学。2020。https：//doi.org/10.1186/s12916-020-01521-0。

下载参考

本文基于2018年2月27日至28日在墨尔本大学墨尔本人口与全球卫生学院举行的技术会议。作者感谢Saman Gamage，Deirdre Mclaughlin，Tim Moore，Rasika Rampatige和墨尔本大学Ian Riley；南非医学研究理事会Pamela Groenewald；菲律宾卫生部Jomilynn Rebenal。

这项研究由彭博慈善基金会授予墨尔本大学一项奖项，以支持“健康数据计划”。资助者在研究设计，数据收集和分析，决定发表或准备手稿方面没有任何作用。

隶属关系

华盛顿大学健康指标与评估研究所，美国华盛顿州西雅图市第五大街2301号，邮政编码98121
- 莫森·纳加维（Mohsen Naghavi）
3053，澳大利亚，维多利亚大学，卡尔顿，Bouverie Street 207，墨尔本大学墨尔本人口与全球健康学院
- 尼古拉·理查兹（Nicola Richards）
- ，哈菲兹·乔杜里
- 莱恩·米克森（Lene Mikkelsen）
- ＆艾伦·洛佩兹（Alan D.Lopez）
澳大利亚统计局，昆士兰州布里斯班，安街295号，4000，澳大利亚
- 詹姆斯·爱因斯通·欣金斯
- ＆劳伦·莫兰（Lauren Moran）
米纳斯吉拉斯大学，贝洛奥里藏特，米纳斯吉里亚斯，31270-901，巴西
- 伊丽莎白·弗朗卡（Elisabeth Franca）
瑞士巴塞尔大学瑞士热带与公共卫生研究所，瑞士
- 迈克尔·黑格瑙尔
伊朗德黑兰，Eyvanak Blvd，第2区，卫生与医学教育部
- Ardeshir Khosravi

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会费

MN，ADL和LM概念化了该分类的早期版本，并领导了该手稿的来源研讨会。NR起草了手稿的第一版。LM和ADL最终确定了手稿。MN，HC，JE-H，EF，MH，AK和LMo提供了有关手稿草案的意见。所有作者均阅读并批准了稿件的最终版本。

通讯作者

对应于Lene Mikkelsen。

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不适用。

同意发表

不适用。

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作者宣称他们没有竞争利益。

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