States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model
<i>Background</i>: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of...
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doaj-11904a8d8b984461a285eae5ff6b90042021-07-23T13:43:56ZengMDPI AGInternational Journal of Environmental Research and Public Health1661-78271660-46012021-07-01187449744910.3390/ijerph18147449States Transitions Inference of Postpartum Depression Based on Multi-State Markov ModelJuan Xiong0Qiyu Fang1Jialing Chen2Yingxin Li3Huiyi Li4Wenjie Li5Xujuan Zheng6Health Science Center, Shenzhen University, Shenzhen 518060, ChinaHealth Science Center, Shenzhen University, Shenzhen 518060, ChinaHealth Science Center, Shenzhen University, Shenzhen 518060, ChinaHealth Science Center, Shenzhen University, Shenzhen 518060, ChinaHealth Science Center, Shenzhen University, Shenzhen 518060, ChinaHealth Science Center, Shenzhen University, Shenzhen 518060, ChinaHealth Science Center, Shenzhen University, Shenzhen 518060, China<i>Background</i>: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. <i>Methods:</i> In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. <i>Results</i>: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42–0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46–2.27). <i>Conclusions:</i> The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD.https://www.mdpi.com/1660-4601/18/14/7449multi-state Markov modelpostpartum depressiontransition probabilityproactive prevention |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Juan Xiong Qiyu Fang Jialing Chen Yingxin Li Huiyi Li Wenjie Li Xujuan Zheng |
spellingShingle |
Juan Xiong Qiyu Fang Jialing Chen Yingxin Li Huiyi Li Wenjie Li Xujuan Zheng States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model International Journal of Environmental Research and Public Health multi-state Markov model postpartum depression transition probability proactive prevention |
author_facet |
Juan Xiong Qiyu Fang Jialing Chen Yingxin Li Huiyi Li Wenjie Li Xujuan Zheng |
author_sort |
Juan Xiong |
title |
States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model |
title_short |
States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model |
title_full |
States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model |
title_fullStr |
States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model |
title_full_unstemmed |
States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model |
title_sort |
states transitions inference of postpartum depression based on multi-state markov model |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1661-7827 1660-4601 |
publishDate |
2021-07-01 |
description |
<i>Background</i>: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. <i>Methods:</i> In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. <i>Results</i>: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42–0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46–2.27). <i>Conclusions:</i> The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD. |
topic |
multi-state Markov model postpartum depression transition probability proactive prevention |
url |
https://www.mdpi.com/1660-4601/18/14/7449 |
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