Depression above the plateau: The relationship between altitude and depression risk

doi:10.3724/SP.J.1041.2024.01773

Abstract

Abstract:

Research on the connection between altitude and depression in China remains scarce, despite the country’s extensive plateau regions. While the impact of altitude on physical health is well documented, existing studies have largely focused on social adaptation or physical well-being, leaving the relationship between altitude and depression underexplored. This study addresses this gap by examining how altitude affects depression among Chinese residents.

Data were drawn from various sources, including the 2016?2020 China Family Panel Studies, the 2015 China Health and Retirement Longitudinal Study, the 2016 survey on physical and mental health in Yushu, and statistics from the China Urban Statistical Yearbook and China Statistical Yearbook. The study involved several statistical techniques, including multilevel linear models, mediation models, and epidemiological case-control methods. The results revealed a significant positive association between altitude and depression, even when personal factors such as age, sex, education, and chronic illness were controlled for, along with regional factors such as temperature, PM2.5 concentration, per capita GDP, and hospital bed availability. Notably, per capita GDP and C-reactive protein levels were found to mediate this relationship. The risk of depression in individuals living at altitudes of 500?1, 000 meters, 1, 000?2, 000 meters, and 4, 000?6, 000 meters was 1.53?1.79 times, 1.67?2.25 times, and 9 times greater, respectively, than the risk in for individuals living below 500 meters.

The study also revealed age-specific variations in the risk of depression. Middle-aged and elderly individuals living at altitudes between 500?2, 000 metres are more vulnerable to depression than younger individuals are. In contrast, young people residing at altitudes of 4, 000?6, 000 metres face a greater risk of depression than their older counterparts do. These findings highlight the diverse impacts of altitude on the risk of depression across different age groups.

In conclusion, this study highlights altitude as a significant risk factor affecting depression among Chinese residents, which suggests the need for targeted mental health interventions that consider both age and altitude. It also challenges the traditional view of age and mental health, suggesting that both individual and environmental factors should be considered in mental health prevention and intervention strategies.

Key words: depression risk, plateau, altitude, age difference

WU Zhengyu, WANG Fei, WANG Dewen, LIU Zhengkui. (2024). Depression above the plateau: The relationship between altitude and depression risk. Acta Psychologica Sinica, 56(12), 1773-1787.

Figures/Tables 11

References 91

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Variable	Random Intercept Model					Random Slope Model					Random Slope + Random Intercept Model
Variable	β	SE	t	95% CI	p	β	SE	t	95% CI	p	β	SE	t	95% CI	p
Intercept	9.06	0.92	9.81	7.25~10.87	<0.001	8.81	0.62	14.24	7.60~10.02	<0.001	8.96	0.92	9.75	7.16~10.77	<0.001
Level-1 Individual Level
Age	?0.01	0.00	?1.85	?0.01~0.01	0.064	?0.01	0.00	?1.91	?0.01~0.01	0.056	?0.01	0.00	?1.86	?0.01~0.01	0.064
Gender	?0.68	0.06	?11.31	?0.79~?0.56	<0.001	?0.67	0.06	?11.24	?0.79~?0.56	<0.001	?0.67	0.06	?11.30	?0.79~?0.56	<0.001
Years of Education	?0.12	0.01	?15.63	?0.14~?0.11	<0.001	?0.12	0.01	?15.85	?0.14~?0.11	<0.001	?0.12	0.01	?15.65	?0.14~?0.11	<0.001
Marital Status	?0.78	0.08	?9.96	?0.93~?0.62	<0.001	?0.81	0.08	10.34	?0.96~?0.65	<0.001	?0.78	0.08	?9.99	?0.93~?0.62	<0.001
Urban/Rural	?0.18	0.07	?2.73	?0.32~?0.05	0.006	?0.19	0.06	?2.96	?0.32~?0.06	0.003	0.18	0.07	?2.67	?0.31~?0.05	0.008
Employment Status	0.46	0.07	6.63	0.33~0.60	<0.001	0.46	0.07	6.61	0.33~0.60	<0.001	0.47	0.07	6.63	0.33~0.60	<0.001
Chronic Illness Status	1.59	0.08	19.18	1.43~1.75	<0.001	1.61	0.08	19.47	1.45~1.78	<0.001	1.59	0.08	19.21	1.43~1.75	<0.001
Self-Reported Income	?0.53	0.03	?18.76	?0.59~?0.48	<0.001	?0.53	0.03	?19.86	?0.59~?0.48	<0.001	?0.53	0.03	?18.78	?0.59~?0.48	<0.001
Level-2 Regional Level
Altitude	0.46	0.13	2.83	0.14~0.78	0.005	0.68	0.20	3.39	0.29~1.08	0.001	0.49	0.18	2.66	0.13~0.85	0.008
Per Capita GDP	?0.06	0.02	?3.44	?0.12~?0.03	0.001	?0.10	0.01	?9.30	?0.12~?0.08	<0.001	?0.08	0.02	?4.21	?0.12~?0.05	<0.001
Hospital Beds/10,000	?0.01	0.01	?0.55	?0.02~0.01	0.580	?0.01	0.00	?1.10	?0.01~0.01	0.269	?0.01	0.01	?0.44	?0.02~0.01	0.663
Average Annual Temp.	?0.01	0.02	?0.08	?0.05~0.05	0.938	0.01	0.02	0.84	?0.02~0.04	0.399	0.01	0.02	0.21	?0.04~0.05	0.834
Average Annual Precipitation	0.39	0.25	1.57	?0.10~0.88	0.116	0.42	0.14	3.10	0.15~0.68	0.002	0.38	0.23	1.68	?0.06~0.83	0.093
Average Annual Sunshine Duration	0.03	0.16	0.16	?0.29~0.35	0.869	0.14	0.11	1.29	?0.07~0.34	0.196	0.06	0.16	0.37	?0.25~0.37	0.712
Average Annual PM2.5	?0.01	0.01	?0.23	?0.01~0.01	0.817	?0.01	0.00	?0.75	?0.01~0.01	0.457	?0.01	0.01	?0.51	?0.02~0.01	0.608

Variable	Random Intercept Model					Random Slope Model					Random Slope + Random Intercept Model
Variable	β	SE	t	95% CI	p	β	SE	t	95% CI	p	β	SE	t	95% CI	p
Intercept	9.06	0.92	9.81	7.25~10.87	<0.001	8.81	0.62	14.24	7.60~10.02	<0.001	8.96	0.92	9.75	7.16~10.77	<0.001
Level-1 Individual Level
Age	?0.01	0.00	?1.85	?0.01~0.01	0.064	?0.01	0.00	?1.91	?0.01~0.01	0.056	?0.01	0.00	?1.86	?0.01~0.01	0.064
Gender	?0.68	0.06	?11.31	?0.79~?0.56	<0.001	?0.67	0.06	?11.24	?0.79~?0.56	<0.001	?0.67	0.06	?11.30	?0.79~?0.56	<0.001
Years of Education	?0.12	0.01	?15.63	?0.14~?0.11	<0.001	?0.12	0.01	?15.85	?0.14~?0.11	<0.001	?0.12	0.01	?15.65	?0.14~?0.11	<0.001
Marital Status	?0.78	0.08	?9.96	?0.93~?0.62	<0.001	?0.81	0.08	10.34	?0.96~?0.65	<0.001	?0.78	0.08	?9.99	?0.93~?0.62	<0.001
Urban/Rural	?0.18	0.07	?2.73	?0.32~?0.05	0.006	?0.19	0.06	?2.96	?0.32~?0.06	0.003	0.18	0.07	?2.67	?0.31~?0.05	0.008
Employment Status	0.46	0.07	6.63	0.33~0.60	<0.001	0.46	0.07	6.61	0.33~0.60	<0.001	0.47	0.07	6.63	0.33~0.60	<0.001
Chronic Illness Status	1.59	0.08	19.18	1.43~1.75	<0.001	1.61	0.08	19.47	1.45~1.78	<0.001	1.59	0.08	19.21	1.43~1.75	<0.001
Self-Reported Income	?0.53	0.03	?18.76	?0.59~?0.48	<0.001	?0.53	0.03	?19.86	?0.59~?0.48	<0.001	?0.53	0.03	?18.78	?0.59~?0.48	<0.001
Level-2 Regional Level
Altitude	0.46	0.13	2.83	0.14~0.78	0.005	0.68	0.20	3.39	0.29~1.08	0.001	0.49	0.18	2.66	0.13~0.85	0.008
Per Capita GDP	?0.06	0.02	?3.44	?0.12~?0.03	0.001	?0.10	0.01	?9.30	?0.12~?0.08	<0.001	?0.08	0.02	?4.21	?0.12~?0.05	<0.001
Hospital Beds/10,000	?0.01	0.01	?0.55	?0.02~0.01	0.580	?0.01	0.00	?1.10	?0.01~0.01	0.269	?0.01	0.01	?0.44	?0.02~0.01	0.663
Average Annual Temp.	?0.01	0.02	?0.08	?0.05~0.05	0.938	0.01	0.02	0.84	?0.02~0.04	0.399	0.01	0.02	0.21	?0.04~0.05	0.834
Average Annual Precipitation	0.39	0.25	1.57	?0.10~0.88	0.116	0.42	0.14	3.10	0.15~0.68	0.002	0.38	0.23	1.68	?0.06~0.83	0.093
Average Annual Sunshine Duration	0.03	0.16	0.16	?0.29~0.35	0.869	0.14	0.11	1.29	?0.07~0.34	0.196	0.06	0.16	0.37	?0.25~0.37	0.712
Average Annual PM2.5	?0.01	0.01	?0.23	?0.01~0.01	0.817	?0.01	0.00	?0.75	?0.01~0.01	0.457	?0.01	0.01	?0.51	?0.02~0.01	0.608

Mediation Pathway	Effect Type	Effect Size	Standard Error	95% CI Lower	95% CI Upper	Proportion of Total Effect (%)
Altitude→Per Capita GDP→Depression Score	Total Effect	0.41	0.06	0.31	0.52	?
	Direct Effect	0.16	0.01	0.46	0.87	38.01%
	Indirect Effect	0.26	0.02	0.21	0.30	61.99%
Using Matched Data: Altitude→Per Capita GDP→Depression Score	Total Effect	1.39	0.11	1.17	1.58	?
	Direct Effect	1.00	0.12	0.75	1.22	71.94%
	Indirect Effect	0.39	0.06	0.28	0.50	28.06%
Altitude→C-Reactive Protein→Depression Score	Total Effect	1.093	0.160	0.785	1.403	?
	Direct Effect	1.087	0.013	0.063	0.113	99.46%
	Indirect Effect	0.006	0.003	0.001	0.013	0.54%

Mediation Pathway	Effect Type	Effect Size	Standard Error	95% CI Lower	95% CI Upper	Proportion of Total Effect (%)
Altitude→Per Capita GDP→Depression Score	Total Effect	0.41	0.06	0.31	0.52	?
	Direct Effect	0.16	0.01	0.46	0.87	38.01%
	Indirect Effect	0.26	0.02	0.21	0.30	61.99%
Using Matched Data: Altitude→Per Capita GDP→Depression Score	Total Effect	1.39	0.11	1.17	1.58	?
	Direct Effect	1.00	0.12	0.75	1.22	71.94%
	Indirect Effect	0.39	0.06	0.28	0.50	28.06%
Altitude→C-Reactive Protein→Depression Score	Total Effect	1.093	0.160	0.785	1.403	?
	Direct Effect	1.087	0.013	0.063	0.113	99.46%
	Indirect Effect	0.006	0.003	0.001	0.013	0.54%

Age (years)	Altitude (meters)	2016 CES-D 8 scores			2018 CES-D 8 scores			2020 CES-D 8 scores
Age (years)	Altitude (meters)	> = 9	<3	OR	> = 9	<3	OR	> = 9	<3	OR
18~30	500~1 000	84	135	1.57	78	87	1.60	60	91	1.02
18~30	<500	608	1 534	1.57	590	1 055	1.60	541	834	1.02
31~40	500~1 000	77	94	1.63	91	67	1.84	68	76	1.12
31~40	<500	545	1 083	1.63	599	813	1.84	590	737	1.12
41~50	500~1 000	148	118	2.44	129	90	1.88	88	68	1.87
41~50	<500	764	1 486	2.44	776	1 020	1.88	548	790	1.87
51~60	500~1 000	127	125	1.69	129	101	1.86	108	85	1.68
51~60	<500	828	1 374	1.69	807	1 175	1.86	656	869	1.68
61~70	500~1 000	99	93	1.66	122	92	1.81	87	67	1.80
61~70	<500	797	1 245	1.66	812	1 106	1.81	526	732	1.80
71~	500~1 000	58	43	1.81	76	55	1.77	40	37	1.77
71~	<500	449	603	1.81	429	551	1.77	241	392	1.77
Overall	500~1 000	593	608	1.79	625	492	1.81	451	424	1.49
Overall	<500	3 991	7 325	1.79	4 013	5 720	1.81	3 102	4 354	1.49
18~30	1 000~2 000	208	364	1.44	233	268	1.55	218	268	1.25
18~30	<500	608	1 534	1.44	590	1 055	1.55	541	834	1.25
31~40	1 000~2 000	208	228	1.81	238	154	2.10	231	193	1.50
31~40	<500	545	1 083	1.81	599	813	2.10	590	737	1.50
41~50	1 000~2 000	350	317	2.15	370	229	2.09	256	200	1.75
41~50	<500	764	1 486	2.15	776	1 002	2.09	548	750	1.75
51~60	1 000~2 000	294	233	2.09	366	194	2.75	310	188	2.18
51~60	<500	828	1 374	2.09	807	1 175	2.75	656	869	2.18
61~70	1 000~2 000	277	161	2.69	288	131	2.99	181	121	2.42
61~70	<500	797	1 245	2.69	812	1 106	2.99	526	852	2.42
71~	1 000~2 000	132	58	3.06	132	51	3.32	90	54	2.71
71~	<500	449	603	3.06	429	551	3.32	241	392	2.71
Overall	1 000~2 000	1 469	1 361	1.98	1 627	1 027	2.25	1 286	1 024	1.80
Overall	<500	3 991	7 325	1.98	4 013	5 702	2.25	3 102	4 434	1.80