The paper reviewed here is ‘Climatic Relationships with Specific Clinical Subtypes of Depression’ by Radua and colleagues. In their abstract the authors conclude that
‘The main findings were a negative 1-month delayed relationship between onset rates of episodes with melancholic features and a climatic factor mainly composed of ambient termperature/sunlight, and a negative 1-month delyaed relationship between onset rates of episodes with psychotic features and a climatic factor mainly composed of barometric pressure‘
In their introduction, the authors note the long history of a proposed association between climate and depression with holidays to specific locations suggested as a treatment as far back as the 19th century.
In the first part of the methodology section they describe their approach to gathering and analysing the climate data. They used climate data recorded between 1997 and 2004 and the data included
- Daily Mean Ambient Temperature
- Accumulated Solar Radiation
- Relative Humidity
- Barometric Pressure
The clinical data was gathered from inpatients located within a specified distance of the metereological station. Patients included were over the age of 18 years, DSM criteria were used for single and recurrent episodes of major depressive disorder, as well as those with readmissions less than 2 months after a previous admission or where the date of onset of the episode was unclear. Interestingly there were no exclusion criteria for medical illnesses which I thought meant that the sample would be more representative of a clinical inpatient sample. The exclusion criteria above seem to be a sensible means of identifying a strong temporal association between the depressive episode and the climate data. Various types of demographica data were obtained.
The authors then discuss the analysis of the data. Part of the data involved the analysis of the climate data which I am not familiar with and so to a certain extent I wasn’t able to comment sensibly onthe methodological description or the results of this part of the data analysis. Thye used a Principal Components Analysis of climate variables with a particular transformation to produced what they refer to as an ‘Anderson-Rubin climatic factor’. They add in the methodology section that
‘Therefore climatic data were transformed from several highly correlated climatic variables to three uncorrelated climatic factors‘
So they transformed their data into a few factors that could then be correlated with the clinical data. They later state that
‘Relationships between the climatic factors yielded by the PCA and the onset rates of the different clinical subtypes of depressive episodes were tested by means of an Autoregressive Integrated Moving Average (ARIMA) procedure‘
There’s a brief explanation of the procedure here but I didn’t particularly understand the justifications for it from the description so again couldn’t comment sensibly on this aspect of the analysis. SPSS 15 is used for the modelling and Bonferroni corrections are made as multiple comparisons are made between the climatic factors and the subtypes of depression.
The researchers identified 547 individuals with 770 hospitalisations. After exclusion criteria were applied there were 421 episodes. They found a few significant results. One of these was the relationship between ‘ambient temperature and sunlight’ and onset of unipolar depression with melancholic features with a 13 month delay. They argued that this 13 month delay would probably be due to an ‘artefact’ of a 1-month delay which just happened to fit better with the previous year’s data. When they repeated with a 1-month delay they found a significant relationship supporting their hypothesis. A significant correlation was also found between unipolar depression onset and barometric pressure with a 1 month delay in the former (p=0.003 after Bonferroni correction). They also found a significant correlation between onset of depression with psychotic features and barometric pressure with 2-month delay of the former.
The researchers discuss their results and some of the limitations of the study. I didn’t particularly understand all of the analysis particularly that of the climate data and the rationale behind the ARIMA procedure. Having said that the final results with p-values are easy to understand. This for me is one of the disadvantages of a cross-disciplinary study – that there is more ‘esoteric’ knowledge needed in order to fully understand the results. On the other hand, the potential rewards of such an approach are tremendous and in order to answer such a potentially clinically important question it seems hard to avoid collaboration with scientists from other disciplines. I would go back to what I said in a previous post namely that there should be an associated video file for the methodology which would take the reader/audience through the process of analysis in greater detail than is allowed in methodology section. I would argue that with such material available, hosted on a free service such as YouTube, the audience has a greater chance of fully understanding the analysis.
From this retrospective study, the researchers identify a relationship between barometric pressure, ambient temperature and sunlight and onset of various subtypes of depression as above. It will be interesting to see if this work is further replicated.
Radua J, Pertusa A and Cardoner N. Climate relationships with specific clinical subtyeps of depression. Psychiatry Research. 175. 217-220. 2010.
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