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一项关于移动医疗应用程序和区域政策对深圳阳性筛查后产后抑郁转介接受度的纵向研究。

A longitudinal study of a mHealth app and regional policies on the uptake of postpartum depression referral after positive screening in Shenzhen.

机构信息

School of Public Health, Suzhou Medical College of Soochow University, Suzhou, 215006, China.

Women's Health Care Department, ShenZhen Pingshan Maternal and Child Health Hospital, Pingshan District, Shenzhen, 518122, China.

出版信息

BMC Pregnancy Childbirth. 2024 Aug 14;24(1):536. doi: 10.1186/s12884-024-06733-3.

DOI:10.1186/s12884-024-06733-3
PMID:39143520
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11325571/
Abstract

BACKGROUND

Postpartum depression (PPD) has received widespread attention. Shenzhen has been running a large-scale program for PPD since 2013. The program requires mothers to self-assess when applying information technology to PPD screening beginning in 2021. The purpose of this study was to conduct a longitudinal analysis of the impact of mHealth apps on the health-seeking behaviors of PPD patients.

METHODS

Longitudinal data from districts in the Shenzhen Maternal and Child Health Management Information System (MCHMIS) for ten years was used in this study. Referral success rate (RSR, successful referrals to designated hospitals as a percentage of needed referrals) was used to assess health-seeking behavior. Trend χ2 tests were used to assess the overall trend of change after the implementation of mHealth in ten districts in Shenzhen. Interrupted Time Series Analysis (ITSA) was employed to assess the role of the mHealth app in changing patient health-seeking behaviors.

RESULTS

For the results of the trend χ2 tests, the ten districts of Shenzhen showed an upward trend. For the ITSA results, different results were shown between districts. Nanshan district, Longhua district, and Longgang district all demonstrated an upward trend in the first-year application of the mHealth app. Nanshan district and Longgang district both exhibited an upward trend in terms of sustained effects.

CONCLUSIONS

There is a difference in the performance of the mHealth app across the ten districts. The results show that the three districts with better health resource allocation, Nanshan, Longgang, and Longhua districts, demonstrated more significant mHealth app improvements. The mHealth app's functions, management systems, and health resource allocation may be potential factors in the results. This suggests that when leveraging mHealth applications, the first step is to focus on macro-level area resource allocation measures. Secondly, there should be effective process design and strict regulatory measures. Finally, there should also be appropriate means of publicity.

摘要

背景

产后抑郁症(PPD)受到广泛关注。深圳自 2013 年以来一直在开展大规模的 PPD 计划。该计划要求母亲在 2021 年开始使用信息技术进行 PPD 筛查时进行自我评估。本研究旨在对 mHealth 应用程序对 PPD 患者健康寻求行为的影响进行纵向分析。

方法

本研究使用了深圳市妇幼保健管理信息系统(MCHMIS)十年的地区纵向数据。转诊成功率(RSR,指定医院转诊成功的比例)用于评估健康寻求行为。趋势 χ2 检验用于评估深圳十个区实施 mHealth 后的总体变化趋势。中断时间序列分析(ITSA)用于评估 mHealth 应用程序在改变患者健康寻求行为方面的作用。

结果

趋势 χ2 检验的结果显示,深圳的十个区呈上升趋势。对于 ITSA 结果,不同的区表现出不同的结果。南山、龙华和龙岗区在 mHealth 应用程序的第一年应用中均呈上升趋势。南山和龙岗区在持续效果方面均呈上升趋势。

结论

十个区的 mHealth 应用程序表现存在差异。结果表明,在卫生资源配置较好的三个区,南山、龙岗和龙华区,mHealth 应用程序的改进更为显著。mHealth 应用程序的功能、管理系统和卫生资源配置可能是结果的潜在因素。这表明,在利用 mHealth 应用程序时,第一步是关注宏观区域资源配置措施。其次,应进行有效的流程设计和严格的监管措施。最后,还应该有适当的宣传手段。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/0b3ca6917a57/12884_2024_6733_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/281f8f14b52a/12884_2024_6733_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/0e213ee0d252/12884_2024_6733_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/e15d2663d3ed/12884_2024_6733_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/5beef423caba/12884_2024_6733_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/0b3ca6917a57/12884_2024_6733_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/281f8f14b52a/12884_2024_6733_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/0e213ee0d252/12884_2024_6733_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/e15d2663d3ed/12884_2024_6733_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/5beef423caba/12884_2024_6733_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd3b/11325571/0b3ca6917a57/12884_2024_6733_Fig5_HTML.jpg

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