Abstract
Objectives:
Aiming to disseminate information related to suicide prevention in Brazil, the âSetembro Amareloâ campaign has been conducted since 2015. The objective of this study is to assess the association between this campaign and elderly suicide rates over a 12-year period.
Methods:
Data were gathered from the Mortality Information System and the Notifiable Diseases Information System, established by public institutions in Brazil. An interrupted time-series framework was applied to assess the association between the âSetembro Amareloâ campaign and suicide mortality rates in the elderly population (60 et plus) in the southeastern region of Brazil. We consider three monthly outcomes: all suicides, suicides in males and suicide in females. We operationalize the campaign assuming three effects: short-term, declining and sustained. The period of analysis was from 2011-2022.
Results:
The suicide-mortality rate over time has remained stable; the average rate in the pre-campaign period was 0.028 and increased slightly to 0.035. Regardless of the campaignâs operationalization and the outcome used, results show no significant associations between the campaign and elderly suicide rates. The campaign was associated with non-significant decreased effects of 15% (P=0.532) in the short term, and 16% (P=0.446) assuming the campaign was sustained.
Conclusions:
There is a lack of association between the campaign and suicide rates, among the elderly in Brazilâs southeastern region. As suicide is complex and multifactorial, more research is needed. The campaign, while raising awareness and reducing stigma, may not reduce suicides. To reduce the suicide rate in the elderly requires addressing social, economic and cultural factors, multisectoral interventions, and upholding basic human rights.
1 Introduction
The World Health Organization (WHO) underscores the alarming worldwide impact of suicide, with more than 700,000 deaths annually. Suicide prevention prominently features on the WHOâs agenda and is integrated as an indicator in the United Nationsâ Sustainable Development Goals (1). Effective suicide prevention strategies require understanding of risk and protective factors across different life stages (2). Elderly individuals face the highest global suicide risk, with elevated lethality (3â6). Unique risk factors for this group include among others social isolation, bereavement, loss of social roles, debilitating illnesses, depression, loneliness, and access to lethal means (items that can be used in a suicide, such as firearms and certain drugs or toxic substances) (3â10). In general elderly individuals exhibit a heightened suicide rate, attributed to more lethal attempts (11, 12).To address prevention, there is an urgent need for comprehensive suicide prevention programs (SPP) involving diverse sectors such as health, education, social welfare, media and case notification, aiming to optimize tailored initiatives across contexts (2, 13, 14).
The rapid aging of the population underscores a pressing issue regarding the deteriorating epidemiological circumstances for older adults, as the provision of care, aid, and backing for this demographic fails to advance adequately in terms of speed, urgency, or scope. Among the specific challenges encountered by this demographic, especially in low and middle-income countries, is the prevalence of suicide (15â18). While the World Health Organization (WHO) marks the onset of old age at 65, as this may be indicative of potential retirement and eligibility for certain benefits, this age threshold may drop to 60 in areas with shorter life expectancies (19, 20). In Brazil, its Federal Law No. 8.842/1994, classifies individuals as elderly if they are 60 years of age or older (21). The suicide death rates of elderly in Brazil from 1996 to 2018 have increased by 162.2% (age group 60-69), 141.4% (70â79), and 189.3% (80 years and above) respectively (11). Overall, all Brazilian regions have experienced an increase of suicide rates in older adulthood from 1996 to 2018, with notable percentages in the North (81.1%), Northeast (126.5%), Southeast (26.6%), Central-West (28.5%) and South (17.8%) (11).
Studies conducted in Brazil have identified that the most frequent social reasons triggering suicide among the elderly include financial problems, unemployment, employment instability (e.g., farming, mining), relationship difficulties, family conflicts, social isolation, and loneliness (22â26). In general, the prevalence of male suicides is higher, associated with the fact that men are more competitive, impulsive, and have easier access to lethal means (27). Additionally, societal expectations within the Brazilian patriarchal culture, which cast men in the role of the primary provider for the family, further contribute to this phenomenon (27). Also, retirement, social exclusion and deprivation of social support, very common in the reality of the elderly, are linked to preliminary depression diagnoses and demoralization, important risk factors for suicide (28â30). In Brazil, there are scientific publications addressing aging and suicide in general; however, few focus on elderly suicide mortality, underscoring the need for further research on these topics within the country (4, 31â33).
In Brazil, in 2015, an initiative led by the Center for Valorization of Life (CVV- Brazilian helpline), the Federal Medical Council, and the Brazilian Psychiatric Association created a campaign called âSetembro Amarelo,â (Yellow September), aimed at suicide prevention and raising awareness. While the campaign runs throughout the year, its concentrated events occur in September. Throughout this month, the campaign endeavors to raise societal awareness and disseminate information related to suicide prevention in the country. This includes organizing events to discuss mental health and suicide prevention, reducing stigma, sharing data on suicide rates, promoting help channels, distributing educational materials, providing specific training, and lighting national landmarks in yellow (34â36). A limited number of studies from Brazil (37â40) have endeavored to evaluate changes following the campaignâs implementation. The impact of this campaign on suicide mortality rates, in this specific age-group, remains unexamined. The present study investigated Brazilian elderly suicide rates over a 12-year period, considering the launch and implementation of the âSetembro Amareloâ campaign.
2 Methods
2.1 Study design
We applied a quasi-experimental design by using an interrupted time-series (ITS) framework (41) spanning twelve years (2011 to 2022). We specifically assess the association between the âSetembro Amareloâ campaign and suicide mortality rates in the elderly population. We selected three outcomes: i) all suicides, ii) suicides in males and iii) suicide in females.
2.2 Data
We utilized publicly available and official information derived from the Brazilian Government. Data were extracted from the Mortality Information System (SIM) and the Notifiable Diseases Information System (SINAN), both established by the Brazilian Ministry of Health and accessible via the DATASUS online platform. This system functions through the systematic compilation of data concerning vital statistics, encompassing mortality and survival rates, as well as epidemiological and morbidity insights. We restricted our analysis to monthly suicide mortality rates for the southeastern region of Brazil, which encompasses SĂŁo Paulo, Rio de Janeiro, Minas Gerais, and EspĂrito Santo. This dataset covers the years from 2011 to 2022. The database (case registration) for the SIM and SINAN was more complete in the southeast region, therefore, this region was chosen for the analyzed years. Additionally, we included data on unemployment rates obtained from the International Labour Organization (ILO) for the same period, 2011 to 2022.
2.3 Study variables
2.3.1 Dependent variables
Counts of suicide mortality rates in the elderly population (Yij) and counts of suicides for male (YMij) and female (YWij) respectively, were derived from the total number of fatal self-inflicted injuries of individuals of 60 years and over, information gathered from the Brazilian Ministry of Health. We have included the classification according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) (42), including ICD codes (X60 to X84), corresponding to Intentional self-harm. The rate was obtained by dividing the total number of individuals aged 60 and more, population numbers were gathered from the Brazilian census data.
2.3.2 Policy time-varying covariates
We operationalized âSetembro Amareloâ campaign, in three forms: i) Short-term effect: 0 for the years 2011 to 2014, 0 for January to August and for October to December for the years 2015 to 2022, and 1 for September for the years 2015 to 2022. We assumed this campaign was effective only during September since 2015; ii) Declining effect: 0 for the years 2011 to 2014, 0 for January to August and for December for the years 2015 to 2022, and since 2015 1, for Sep, 0.5 for October, and 0.25 for November. We assume this campaign was effective from September to November however its effect may have declined at the third month of implementation; iii) Sustained effect: 0 for the years 2011 to 2014, 0 for January to August and for December for the years 2015 to 2022, and since 2015 1 for the months of September, October, and November.
2.3.3 Control variables
In our study, we addressed the potential impact of seasonal variations by incorporating Fourier terms, using pairs of sine and cosine functions to model these patterns. Additionally, unemployment rates were included as a control variable. Specifically, these rates pertain to adults aged 60 years and older.
2.4 Statistical analysis
An extension of generalized linear models was employed, incorporating both the Poisson and Negative Binomial models. These models are well suited for the analysis of count data, such as number of suicides. The Poisson model is a reliable choice for modeling count data when events occur at a consistent rate over time. On the other hand, the Negative Binomial model offers flexibility and accounts for data over-dispersion, which is common when dealing with rare events like suicides. The use of the Poisson and Negative Binomial models within the ITS analysis ensures a thorough exploration of temporal patterns and trends, providing more nuanced insights into the connection between the campaign and suicide rates.
3 Results
Table 1 provides an overview of summary statistics regarding suicide mortality rates across all gender categories. Figures 1â3 present monthly time-series plots for the suicide mortality rates: all individuals, males, and females aged above 60 years old, respectively. Tables 2, 3 provide the results Poisson and the Negative Binomial models.
Table 1
| Period | Suicide rate (All) | Suicide rate (Male) | Suicide rate (Female) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Mean | Minimum | Maximum | Mean | Minimum | Maximum | Mean | Minimum | Maximum | |
| Short-term effect | |||||||||
| Pre-Intervention | 0.028 | 0.000 | 0.068 | 0.038 | 0.000 | 0.122 | 0.020 | 0.000 | 0.068 |
| Intervention 1: Sep 2015 | 0.034 | 0.034 | 0.034 | 0.059 | 0.059 | 0.059 | 0.015 | 0.015 | 0.015 |
| Post Intervention Year 1 | 0.029 | 0.008 | 0.051 | 0.040 | 0.019 | 0.078 | 0.020 | 0.000 | 0.045 |
| Intervention 2: Sep 2016 | 0.008 | 0.008 | 0.008 | 0.000 | 0.000 | 0.000 | 0.014 | 0.014 | 0.014 |
| Post Intervention Year 2 | 0.022 | 0.000 | 0.039 | 0.028 | 0.000 | 0.072 | 0.017 | 0.000 | 0.042 |
| Intervention 3: Sep 2017 | 0.031 | 0.031 | 0.031 | 0.054 | 0.054 | 0.054 | 0.014 | 0.014 | 0.014 |
| Post Intervention Year 3 | 0.027 | 0.000 | 0.055 | 0.040 | 0.000 | 0.069 | 0.017 | 0.000 | 0.056 |
| Intervention 4: Sep 2018 | 0.015 | 0.015 | 0.015 | 0.000 | 0.000 | 0.000 | 0.027 | 0.027 | 0.027 |
| Post Intervention Year 4 | 0.027 | 0.007 | 0.065 | 0.031 | 0.000 | 0.083 | 0.024 | 0.000 | 0.051 |
| Intervention 5: Sep 2019 | 0.029 | 0.029 | 0.029 | 0.017 | 0.017 | 0.017 | 0.039 | 0.039 | 0.039 |
| Post Intervention Year 5 | 0.020 | 0.000 | 0.044 | 0.026 | 0.000 | 0.064 | 0.015 | 0.000 | 0.039 |
| Intervention 6: Sep 2020 | 0.021 | 0.021 | 0.021 | 0.016 | 0.016 | 0.016 | 0.025 | 0.025 | 0.025 |
| Post Intervention Year 6 | 0.019 | 0.000 | 0.042 | 0.023 | 0.000 | 0.064 | 0.018 | 0.000 | 0.039 |
| Intervention 7: Sep 2021 | 0.013 | 0.013 | 0.013 | 0.000 | 0.000 | 0.000 | 0.024 | 0.024 | 0.024 |
| Post Intervention year7 | 0.034 | 0.006 | 0.067 | 0.047 | 0.000 | 0.104 | 0.024 | 0.000 | 0.060 |
| Declining effect or Sustained effect | |||||||||
| Pre-Intervention | 0.028 | 0.000 | 0.068 | 0.038 | 0.000 | 0.122 | 0.020 | 0.000 | 0.068 |
| Intervention 1: Sep, Oct, Nov 2015 | 0.040 | 0.034 | 0.051 | 0.052 | 0.020 | 0.078 | 0.030 | 0.015 | 0.045 |
| Post Intervention Year 1 | 0.026 | 0.008 | 0.041 | 0.038 | 0.019 | 0.078 | 0.016 | 0.000 | 0.043 |
| Intervention 2: Sep, Oct, Nov 2016 | 0.014 | 0.000 | 0.033 | 0.019 | 0.000 | 0.056 | 0.010 | 0.000 | 0.014 |
| Post Intervention Year 2 | 0.023 | 0.000 | 0.039 | 0.028 | 0.000 | 0.072 | 0.019 | 0.000 | 0.042 |
| Intervention 3: Sep, Oct, Nov 2017 | 0.034 | 0.016 | 0.055 | 0.048 | 0.036 | 0.054 | 0.023 | 0.000 | 0.056 |
| Post Intervention Year 3 | 0.025 | 0.000 | 0.053 | 0.025 | 0.000 | 0.053 | 0.015 | 0.000 | 0.040 |
| Intervention 4: Sep, Oct, Nov 2018 | 0.018 | 0.008 | 0.030 | 0.017 | 0.000 | 0.035 | 0.017 | 0.000 | 0.035 |
| Post Intervention Year 4 | 0.028 | 0.007 | 0.065 | 0.032 | 0.000 | 0.083 | 0.026 | 0.013 | 0.051 |
| Intervention 5: Sep, Oct, Nov 2019 | 0.012 | 0.000 | 0.029 | 0.006 | 0.000 | 0.017 | 0.017 | 0.000 | 0.039 |
| Post Intervention Year 5 | 0.023 | 0.007 | 0.044 | 0.032 | 0.016 | 0.064 | 0.017 | 0.000 | 0.039 |
| Intervention 6: Sep, Oct, Nov 2020 | 0.026 | 0.014 | 0.042 | 0.032 | 0.016 | 0.064 | 0.021 | 0.012 | 0.025 |
| Post Intervention Year 6 | 0.017 | 0.000 | 0.034 | 0.019 | 0.000 | 0.032 | 0.015 | 0.000 | 0.036 |
| Intervention 7: Sep, Oct, Nov 2021 | 0.025 | 0.013 | 0.040 | 0.026 | 0.000 | 0.046 | 0.024 | 0.012 | 0.036 |
| Post Intervention Year 7 | 0.035 | 0.006 | 0.067 | 0.048 | 0.000 | 0.104 | 0.025 | 0.000 | 0.060 |
Monthly suicide rates per 100 000 population before, during and after three operationalizations of the âSetembro Amareloâ Campaign.
Figure 1
Figure 2
Figure 3
Table 2
| Period | Suicide rate (All) | Suicide rate (Male) | Suicide rate (Female) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| IRR | 95%CI | P-Value | IRR | 95%CI | P-Value | IRR | 95%CI | P-Value | |
| Short-term effect | 0.85 | 0.53 1.38 | 0.532 | 0.62 | 0.29 1.30 | 0.212 | 1.18 | 0.64 2.16 | 0.588 |
| Unemployment rate | 0.85 | 0.72 1.02 | 0.091 | 0.94 | 0.78 1.12 | 0.522 | 0.80 | 0.62 1.02 | 0.075 |
| Time | 1.00 | 0.99 1.00 | 0.315 | 0.99 | 0.99 1.00 | 0.770 | 1.00 | 0.99 1.01 | 0.103 |
| Seasonal Trend (cos) | 0.94 | 0.82 1.08 | 0.420 | 0.98 | 0.82 1.17 | 0.892 | 0.88 | 0.72 1.09 | 0.264 |
| Seasonal Trend (sin) | 1.02 | 0.88 1.19 | 0.729 | 1.05 | 0.86 1.27 | 0.606 | 0.98 | 0.77 1.25 | 0.907 |
| MLE | -282.83 | -234.99 | -210.40 | ||||||
| AIC | 4.09 | 3.41 | 3.06 | ||||||
| BIC | -501.84 | -502.44 | -499.93 | ||||||
| Declining effect | 1.02 | 0.92 1.14 | 0.606 | 0.82 | 0.56 1.20 | 0.318 | 1.00 | 0.67 1.49 | 0.996 |
| Unemployment rate | 1.30 | 1.28 1.32 | 0.000 | 0.93 | 0.77 1.11 | 0.432 | 0.80 | 0.63 1.02 | 0.077 |
| Time | 0.99 | 0.99 0.99 | 0.000 | 0.99 | 0.99 1.00 | 0.894 | 1.00 | 0.99 1.01 | 0.103 |
| Seasonal Trend (cos) | 0.93 | 0.81 1.09 | 0.396 | 0.98 | 0.81 1.19 | 0.878 | .87 | 0.69 1.09 | 0.245 |
| Seasonal Trend (sin) | 1.04 | 0.87 1.26 | 0.607 | 1.09 | 0.87 1.37 | 0.440 | .97 | 0.73 1.29 | 0.874 |
| MLE | -213.18 | -235.64 | -210.55 | ||||||
| AIC | 3.10 | 3.42 | 3.06 | ||||||
| BIC | -641.13 | -501.13 | -499.63 | ||||||
| Sustained effect | 0.84 | 0.54 1.30 | 0.446 | 0.67 | 0.36 1.23 | 0.197 | 1.07 | 0.60 1.92 | 0.808 |
| Unemployment rate | 0.85 | 0.71 1.02 | 0.082 | 0.93 | 0.78 1.11 | 0.467 | 0.80 | 0.63 1.02 | 0.078 |
| Time | 1.00 | 0.99 1.00 | 0.281 | 0.99 | 0.99 1.00 | 0.868 | 1.00 | 0.99 1.01 | 0.107 |
| Seasonal Trend (cos) | 0.94 | 0.81 1.08 | 0.420 | 0.99 | 0.83 1.19 | 0.981 | 0.87 | 0.71 1.07 | 0.212 |
| Seasonal Trend (sin) | 1.03 | 0.87 1.23 | 0.682 | 1.05 | 0.85 1.31 | 0.623 | 1.01 | 0.76 1.31 | 0.992 |
| MLE | -282.72 | -235.23 | -210.52 | ||||||
| AIC | 4.09 | 3.41 | 3.06 | ||||||
| BIC | -502.06 | -501.96 | -499.69 | ||||||
Poisson model results for suicide rate of all types of gender groups.
Table 3
| Period | Suicide rate (All) | Suicide rate (Male) | Suicide rate (Female) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| IRR | 95%CI | P-Value | IRR | 95%CI | P-Value | IRR | 95%CI | P-Value | |
| Short-term effect | 1.03 | 0.51 1.56 | 0.901 | 0.59 | 0.00 1.37 | 0.309 | 1.59 | 0.72 2.11 | 0.248 |
| Unemployment rate | 0.82 | 0.64 1.00 | 0.040 | 0.92 | 0.74 1.10 | 0.423 | 0.76 | 0.52 0.99 | 0.045 |
| Time | 1.00 | 0.99 1.00 | 0.206 | 0.99 | 0.99 1.00 | 0.863 | 1.00 | 0.99 1.01 | 0.079 |
| Seasonal Trend (cos) | 0.92 | 0.79 1.05 | 0.223 | 0.98 | 0.81 1.15 | 0.797 | 0.84 | 0.65 1.04 | 0.135 |
| Seasonal Trend (sin) | 1.06 | 0.92 1.22 | 0.381 | 0.93 | 0.88 1.25 | 0.495 | 1.03 | 0.81 1.26 | 0.767 |
| MLE | -280.76 | -234.26 | -207.53 | ||||||
| ÏÂČ | 0.37 | 0.50 | 0.23 | ||||||
| Declining effect | 1.12 | 0.71 1.53 | 0.562 | 1.03 | 0.49 1.57 | 0.906 | 1.16 | 0.57 1.77 | 0.582 |
| Unemployment rate | 0.81 | 0.63 0.99 | 0.034 | 0.91 | 0.74 1.09 | 0.346 | 0.76 | 0.52 1.00 | 0.050 |
| Time | 1.00 | 0.99 1.00 | 0.220 | 0.99 | 0.99 1.00 | 0.875 | 1.00 | 0.99 1.00 | 0.093 |
| Seasonal Trend (cos) | 0.89 | 0.74 1.04 | 0.179 | 0.97 | 0.77 1.16 | 0.748 | 0.83 | 0.61 1.04 | 0.116 |
| Seasonal Trend (sin) | 1.09 | 0.82 1.27 | 0.290 | 1.11 | 0.88 1.25 | 0.335 | 1.03 | 0.77 1.30 | 0.811 |
| MLE | -280.60 | -234.81 | -208.01 | ||||||
| ÏÂČ | 0.34 | 0.63 | 0.31 | ||||||
| Sustained effect | 1.12 | 0.57 1.68 | 0.660 | 0.81 | 0.06 1.57 | 0.636 | 1.39 | 0.62 2.18 | 0.321 |
| Unemployment rate | 0.81 | 0.63 0.99 | 0.036 | 0.92 | 0.74 1.10 | 0.389 | 0.75 | 0.51 0.99 | 0.045 |
| Time | 1.00 | 0.99 1.00 | 0.205 | 0.99 | 0.99 1.00 | 0.898 | 1.00 | 0.99 1.00 | 0.089 |
| Seasonal Trend (cos) | 0.91 | 0.77 1.04 | 0.198 | 0.98 | 0.81 1.16 | 0.862 | 0.83 | 0.63 1.03 | 0.096 |
| Seasonal Trend (sin) | 1.08 | 0.91 1.25 | 0.326 | 1.07 | 0.89 1.25 | 0.496 | 1.05 | 0.79 1.31 | 0.658 |
| MLE | -280.68 | -234.70 | -207.68 | ||||||
| ÏÂČ | 0.35 | 0.61 | 0.25 | ||||||
Negative binomial model results for suicide rate of all types of gender groups.
In Table 1, we observe that suicide mortality rates over time are relatively stable. Whereas the mean in the pre campaign period is 0.028 at the end of studied period the rate is slightly higher with 0.035. This trend is also observed in men and women respectively. Nevertheless, we observe differences across men and women rates. In the per period campaign menâs rates were 0.038 and womenâs 0.020, and at the end of the analyzed period these rates were 0.048 and 0.024 respectively. Based on the results of Tables 2, 3, we fail to observe significant associations between the campaign and suicide mortality rates regardless of the campaignâs operationalization and the outcome used. The decision to collapse the data for the declining and sustained effects was made due to their similar statistical outcomes in preliminary analyses, simplifying the presentation and focusing on the most significant findings. Results were robust after we included unemployment rates as well.
Our analysis of the association between unemployment rates and suicide rates showed variability by the model and demographic group. In the Poisson model, unemployment rates were not significantly associated with changes in suicide rates. However, the Negative Binomial model indicated a significant impact for all individuals and females, but not for males, suggesting differing economic effects across demographic groups. Statistical tests for stationarity confirmed the reliability of these findings. The Augmented Dickey-Fuller (ADF) tests showed that the series are stationary, and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests indicated a presence of a stochastic trend at zero lags. These tests are detailed in the newly added Table 4, enhancing our understanding of the dataâs characteristics and the robustness of our analysis.
Table 4
| Suicide rate | Augmented Dickey-Fuller (ADF) | Lag order of Kwiatkowski-Phillips-Schmidt-Shin (KPSS) | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Test Statistic | 1% | 5% | 10% | 0 | 1 | 2 | 3 | 4 | |
| Male | -7.620 | -3.497 | -2.887 | -2.557 | 0.0437 | 0.0400 | 0.0383 | 0.0397 | 0.0411 |
| Female | -7.620 | -3.497 | -2.887 | -2.557 | 0.0488 | 0.0469 | 0.0443 | 0.0432 | 0.0425 |
| Total | -7.620 | -3.497 | -2.887 | -2.557 | 0.0367 | 0.0360 | 0.0394 | 0.0431 | 0.0448 |
Results of Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) Tests.
4 Discussion
In this quasi-experimental study, we observe no significant differences between three different operationalizations of the âSetembro Amareloâ campaign and suicide mortality rates in the elderly population in four Brazilian States: SĂŁo Paulo, Rio de Janeiro, Minas Gerais, and EspĂrito Santo. This lack of association is observed regardless of the operationalization of the campaign and statistical method applied. Our analyses also indicate that the campaign was not necessarily effective when targeting men nor women specifically. While the World Health Organization recommends conducting awareness and prevention campaigns utilizing mass media to promote mental health, raise awareness, reduce mental health stigma, and disseminate information about support resources, these efforts should be accompanied with specific prevention interventions (6, 43). Indeed, sparsely evaluated outcomes of suicide prevention campaigns have demonstrated contradictory and inconsistent results (43, 44), with positive effects on awareness and help-seeking (43â45), or the need for caution when developing strategies because of prejudice, misinformation and questioning how to reach vulnerable populations not considered in more consistent prevention efforts (44, 46).
Our study partially aligns with at least three studies developed in Brazil. First, a time-series study analyzed the temporal pattern of suicide mortality in the state of CearĂĄ between 2009 and 2019. The study observed that the population aged 60 to 79 experienced a stabilization in suicide rates, raising questions about the campaignâs potential impact in this age group within this state (38). A second study conducted to identify changes following the implementation of âSetembro Amareloâ, in the Brazilian population, revealed that suicide-related incidence rates showed an upward trend after the programâs implementation. The suicide rate increased by 66.6% (37). Third, an interrupted time series study analyzed the evolution of elderly suicide rates in Brazil between 2011 and 2019 (30), observing a 14.3% increase in suicide rates after the campaign was implemented. These results raised considerable questions as to whether this increment was due to an adverse effect of the campaignâs implementation or a result of more effective case reporting (37).
The following elements are necessary to interpret our results. First, while there are guidelines issued by the Brazilian Psychiatric Association (47), based on international guidelines, on how to safely communicate or address suicidal behavior, many recommendations may not be followed. More specifically, the campaign may lack multi-sectoral actions, establishing an assistance network, implementing longitudinal strategies for monitoring at-risk groups (i.e. men, or elderly), as well as offering training and professional development opportunities (39). Relatedly, in September, the Brazilian campaign may lead many to discuss the topic of suicide, however, taking preventive actions and ensuring secure communication about suicide requires a more nuanced approach to avoid the risk of contagion (38, 48).
Second, suicide is generally underreported more than other causes of death, a trend that intensifies when concerning the elderly (2, 49â51). Death certificate data may be underreported due to various factors, including differing professional perspectives and training regarding their completion. Suspected suicide cases may be recorded as accidental poisonings or other external causes of mortality. Numerous taboos (for religious, cultural, or other reasons) and bureaucratic challenges, such as idealized post-mortem perceptions, life insurance implications, and the need for accurate cause of death, contribute to this context. Overcoming underreporting is crucial to enable reliable epidemiological analyses and, consequently, to inform effective care strategies (4, 52, 53). Two Brazilian studies analyzing suicide notifications after the campaign found no changes regarding self-harm case notifications; it may have even triggered the opposite effect as increases in cases in October compared to August were observed (37, 40).
Third, public campaigns aimed at preventing suicide among the elderly effectively should include widespread societal efforts on mental health and suicide awareness, information about help seeking, investing in access policies to mental health professionals and therapists, proactive engagement with psychiatric patients following discharge or a suicidal crisis, integration with primary health care services, fiscal support from professionals, social cohesion via social media, targeted online discussion forums for the elderly, telephone helplines, community integration interventions to combat loneliness, and public health messages emphasizing the importance of social involvement for all older individuals (4, 54â57). Particular attention to reducing stigma in the elderly should be considered in these campaigns. Indeed, the elderly often do not recognize or seek help for mental illness due to stigma, seeing it as a weakness and fearing the loss of independence. Tackling stigma needs focused efforts through professional training, public education, media collaboration, and inclusion in health and social care programs to encourage help-seeking and community integration (58, 59). Much of these elements have not been considered in the âSetembro Amareloâ campaigns and therefore next versions of this campaign should discern some of these strategies to target elderly more properly (47, 48).
Fourth, in addition to the need for interventions tailored to different life stages, it is also crucial to develop gender-specific interventions. Studies indicate that women are more likely to seek healthcare services, exhibit better coping with stigma, prejudice, and taboos compared to men, resulting in increased access to mental health services for this population (60, 61). Furthermore, social roles, such as being mothers and grandmothers, are often viewed differently for men. Men, traditionally seen as providers, tend to lose this role as they age. The loss of this social role represents a significant risk factor for suicide in the elderly, that is why socioeconomic issues, including economic crises, unemployment, and reductions in personal income, are significant risk factors, especially among men (33, 60, 61). Studies conducted in Brazil corroborate the international literature, as it is believed that men exhibit more competitive and impulsive behaviors than women, along with higher substance abuse rates, including alcohol and drug consumption (4, 62).
Last, suicide, in Brazil as a social phenomenon, must account for religious practices (63). In this country, most individuals claim a religious affiliation and consider religion a significant aspect of their existence (64, 65). The relationship between religion and suicidal behavior is complex and it needs more research in different cultures and religious backgrounds, as it can offer both protective factors, (e.g. coping mechanism and support system) and risk elements (e.g. assigning all life responsibility to God, blaming spiritual beliefs for failures when feeling forsaken, and interpreting stressors as either divine retribution or the influence of âevil forces.â) (66â70). For the elderly, spirituality and religiosity have been linked to improved quality of life and mental health promotion promising valuable contributions to geriatric psychiatry effective interventions and campaigns (71, 72). The extent to which âSetembro Amareloâ should consider religious aspects in its messages is something that should be studied further.
Our study should consider the following limitations to better temper the implication of its results. First, a large range of suicide risk factors and regional inequalities play significant roles in Brazil (33). Indeed, this country is characterized by multiple cultures and deep social inequalities, emphasizing that our findings cannot be generalized to the entire nation. It may be the case that in other regions, the campaign was effective not only in the elderly population but in others too. Our results suggest thus the identification of region-specific public policies to properly assess the extent to which this campaign may have not been effective (33). Secondly, to analyze suicide mortality rates and the âSetembro Amareloâ campaign, it is essential to consider case notification challenges, a prevalent issue in several countries, including Brazil, where underreporting in official records maybe prevalent (4, 38). Suicide statistics result from a complex process involving various stages, including reports from family members, witnesses, physicians, law enforcement, coroners, and statisticians. Due to these procedural intricacies, data may be distorted throughout, particularly in regions where social, economic, cultural, and religious factors contribute to the stigma surrounding suicidal behavior (38, 52). Third, while we used different operationalizations to understand the potential impact of the campaign, our application considered the overall post period after the campaign was implemented. So, we did not study if there were specific years in which the campaign could have been more effective. Last, while suicide is a multifactorial phenomenon, our study only covered specific results in terms of sex, and some models also included the variable unemploymentâwhich was robust to our main results. As this is the first study in the elderly population, and no significant associations were found in this region, one potential explanation could be a more consistent network of social support and better access to health services for the elderly. However, this should be tested with other variables, such as family composition, state programs, and health services designed to support this population. Nevertheless, other factors such as socioeconomic status or marital status, which have been consistently associated with suicide outcomes were not considered and therefore other analyses are necessary to better understand the potential impact of this campaign in other subgroups of the elderly population in Brazil.
5 Conclusion
To our knowledge, this is the first study to assess the impact of the âSetembro Amareloâ campaign on suicide rates among the elderly population in Brazilâs Southeast region. Regardless of the campaignâs operationalization and the outcome used, no significant variation was observed. It is important to emphasize that suicide is a complex and multifactorial phenomenon. Therefore, the campaign alone, which aims to raise awareness and reduce stigma related to mental health promotion and suicide prevention, requires other actions to effectively tackle suicides at the population level.
We emphasize the importance of developing more scientific research and public policies for suicide prevention in Brazil, based on scientific knowledge, culturally adapted, focused on different stages of the life cycle, as well as sex and gender-specific interventions. Public policies, regardless of their format, need to be evaluated and planned to achieve continuous interventions, not limited to specific campaign months, as suicide is a public health issue that occurs daily in Brazil and around the world.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sih/cnv/fruf.def Official morbimortality data in Brazil are made available by DATASUS - Information Technology Department of the Brazilian Public Health Care System (SUS).
Author contributions
CC: Conceptualization, Investigation, Methodology, Writing â original draft, Writing â review & editing, Data curation, Visualization. VN: Data curation, Formal analysis, Investigation, Methodology, Software, Writing â original draft, Writing â review & editing. JN: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing â original draft, Writing â review & editing.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Fonds de recherche du Québec -Santé (JN-M, Grant 330642), Charles Le Moyne scholarship of CC.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisherâs note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Summary
Keywords
suicide, suicide prevention, public health, aged, health campaign, public policy, mortality
Citation
CorrĂȘa Matias Pereira C, Najafi Moghaddam Gilani V and Nazif-Munoz JI (2024) A brief research report of suicide rates in the Brazilian elderly over a 12-year period: the lack of association of the "Setembro Amarelo" campaign for suicide prevention. Front. Psychiatry 15:1354030. doi: 10.3389/fpsyt.2024.1354030
Received
11 December 2023
Accepted
11 July 2024
Published
25 July 2024
Volume
15 - 2024
Edited by
Vincenzo De Luca, University of Toronto, Canada
Reviewed by
Xenia Gonda, Semmelweis University, Hungary
Markus Ramm, University Hospital MĂŒnster, Germany
Hua Li, University of Saskatchewan, Canada
William Sulis, McMaster University, Canada
Carolyn Szostak, University of British Columbia, Canada
Updates
Copyright
© 2024 CorrĂȘa Matias Pereira, Najafi Moghaddam Gilani and Nazif-Munoz.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Camila CorrĂȘa Matias Pereira, camila.correa.matias.pereira@usherbrooke.ca
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.