Evidence map›Paper›PMID 30373577›Full record

ArticleBMC health services research2018

Impact of an intelligent chronic disease management system on patients with type 2 diabetes mellitus in a Beijing community.

Wei Xue-Juan, Wu Hao, Ge Cai-Ying, Liu Xin-Ying, Jia Hong-Yan, Wang Li, Guo Xiao-Ling, Liu Wan-Ying, Gao Wen-Juan, Liang Wan-Nian

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Wei Xue-JuanFangzhuang Community Health Service Center of Capital Medical University, Building No.1, 3rd quarter of FangQunYuan, Fangzhuang residence community, Fengtai District, Beijing, China.
Wu HaoFangzhuang Community Health Service Center of Capital Medical University, Building No.1, 3rd quarter of FangQunYuan, Fangzhuang residence community, Fengtai District, Beijing, China.
Ge Cai-YingHealth and Family Planning Commission, Badadi, Wenti Rd. No. 2, Fengtai District, Beijing, China.
Liu Xin-YingFangzhuang Community Health Service Center of Capital Medical University, Building No.1, 3rd quarter of FangQunYuan, Fangzhuang residence community, Fengtai District, Beijing, China.
Jia Hong-YanFangzhuang Community Health Service Center of Capital Medical University, Building No.1, 3rd quarter of FangQunYuan, Fangzhuang residence community, Fengtai District, Beijing, China.
Wang LiFangzhuang Community Health Service Center of Capital Medical University, Building No.1, 3rd quarter of FangQunYuan, Fangzhuang residence community, Fengtai District, Beijing, China.
Guo Xiao-LingFangzhuang Community Health Service Center of Capital Medical University, Building No.1, 3rd quarter of FangQunYuan, Fangzhuang residence community, Fengtai District, Beijing, China.
Liu Wan-YingHealth and Family Planning Commission, Badadi, Wenti Rd. No. 2, Fengtai District, Beijing, China.
Gao Wen-JuanCollege of General Practice and Continuing Education of Capital Medical University, Xitoutiao No. 10, Youanmenwai, Fengtai District, Beijing, China.
Liang Wan-NianCollege of General Practice and Continuing Education of Capital Medical University, Xitoutiao No. 10, Youanmenwai, Fengtai District, Beijing, China. medicalscience@yeah.net.ORCID http://orcid.org/0000-0003-1764-5502
Capital Medical University · CNNational Health and Family Planning Commission · CN

Funding

Capital Health Development Research and Special Projects 2014-2-7051High-level Technical Personnel Training Program for Beijing Healthcare System 2014-3-105High-level Technical Personnel Training Program for Beijing Healthcare System 2014-3-106
6 · The paper itself

Abstract

backgroundRapid demographic and economic changes have made chronic disease the number one health issue in China, contributing to more than 80% of the country's 10.3 million annual deaths and nearly 70% of its total disease burden (Wang et al., Toward a Healthy and Harmonious Life in China: Stemming the Rising Tide of Non-Communicable Diseases, 2011; Yip and Hsiao, Lancet 384: 805-18, 2014). Diabetes is a major contributor to the chronic disease burden and is experienced by nearly 11% of the adult population of China (Yang et al., N Engl J Med 362:1090-101, 2010). In response to the challenges of chronic disease, the Chinese government initiated comprehensive health care reforms nationwide in 2009. A key measure was a hierarchical diagnosis and treatment system for monitoring and reducing chronic diseases and improving the community health service system (Barber et al., Health Policy Plan 29:367-78, 2014). Primary hospitals, such as community health service centers, are the main gatekeepers for management of diabetes and other chronic diseasesin China. In recognition of the need for a more patient-centered approach, the Chinese government has piloted a program incorporating methods of diabetes self-management for chronic care: the Happy Life Club (Browning et al., Front in Public Health 2:181, 2015). This program is modeled on a similar program developed in Australia (Kelly et al., Aust J Prim Health 9:186-9, 2003). The ICDMS is an important tool in the implementation of patient-centered programs targeting chronic health issues, and its success is determined by factors, such as frequent contact between patients and doctors and effective website training for patients. This retrospective study used de-identified data from the Fangzhuang (Beijing) intelligent chronic disease management system (ICDMS) database to evaluate the effect of an intelligent chronic disease management system on selected Beijing community patients who have type 2 diabetes mellitus (T2DM).

methodsA comparative study before and after ICDMS implementation was performed to evaluate the effect of ICDMS on the rates of follow-up and laboratory examinations, measurement rates of blood glucose and lipids, glycosylated hemoglobin (HbA1c) and blood lipid levels, as well as the corresponding health parameters. Continuous variables and categorical variables were analyzed using paired t-test and McNemar's tests, respectively.

resultsA total of 2451 T2DM patients met inclusion/exclusion criteria. Compared with the pre-index period, the laboratory examination, rates of blood glucose and blood lipids increased significantly in the post-index period (p < 0.001). Triglyceride (TC) levels decreased significantly from 5.22 mmol/L to 5.11 mmol/L (p < 0.05), and high density lipoprotein-cholesterol (HDL-C) levels increased significantly from 1.35 mmol/L to 1.48 mmol/L (p < 0.05). The control rate of TC increased from 24.86 to 29.76% (p = 0.079). The control rate of low density lipoprotein-cholesterol (LDL-C) increased from 12.16 to 13.97% (p = 0.421), while the control rate of HDL-C increased significantly from 68.60 to 78.77%. Importantly, Compared with the patients with HbA1C above 7% in the pre-index period, the mean HbA1c decreased significantly from 7.84 to 6.94%((p < 0.001) in the post-index period, and the control rate of HbA1c was 57.43%.

conclusionsThe intelligent chronic disease management system is an effective tool in the management of T2DM and should be promoted by the Community Health Service Center in China as well as in other developing countries.

Indexed as

AdolescentAdultAgedBeijingBlood GlucoseChinaCholesterol, LDLChronic DiseaseCommunity Health ServicesDiabetes Mellitus, Type 2FemaleGlycated HemoglobinHumansLipidsMaleMiddle AgedBlood GlucoseCholesterol, LDLGlycated HemoglobinLipidsCommunityFangzhuang (Beijing)Intelligent chronic disease management systemType 2 diabetes

Identifiers

PMID30373577
PMCPMC6206887
OpenAlexW2898618675

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.