traumatic experiences
Table 2 shows the conditional adjusted odds of disorder onset at each age, comparing those experiencing their first unexpected death experience at that age to those who did not experience unexpected death at that age. Incidence proportions and sample sizes that form the basis of comparison for each age group and each disorder are given in Online Table 3 .
Association between age of first unexpected death of a loved one and onset of major depressive episode, dysthymia, manic episode, and alcohol disorder compared with those who did not experience unexpected death of a loved one (N=27,534).
Age of first unexpected death experience | Major depression (N=7,881) | Dysthymia (N=2,051) | Manic episode (N=1,855) | Alcohol disorder (N=11,197) | ||||||
---|---|---|---|---|---|---|---|---|---|---|
N (total ) | OR | (95% C.I.) | OR | (95% C.I.) | OR | (95% C.I.) | OR | (95% C.I.) | ||
: | 750 | 3.56 | (1.54–8.23) | 2.84 | (0.59–13.58) | 1.22 | (0.48–3.11) | 1.81 | (0.73–4.53) | |
1397 | 1.62 | (1.05–2.52) | 0.91 | (0.35–2.34) | 0.93 | (0.60–1.45) | 0.93 | (0.77–1.13) | ||
2,164 | 1.24 | (0.94–1.63) | 0.77 | (0.41–1.46) | 0.94 | (0.56–1.56) | 1.11 | (0.86–1.44) | ||
1,685 | 1.47 | (1.12–1.93) | 1.37 | (0.72–2.59) | 0.69 | (0.36–1.32) | 1.3 | (0.90–1.88) | ||
1,426 | 1.5 | (1.10–2.03) | 2.41 | (1.17–4.94) | 1.08 | (0.50–2.35) | 0.93 | (0.60–1.44) | ||
1,297 | 1.53 | (1.13–2.06) | 1.91 | (1.04–3.51) | 2.14 | (1.19–3.83) | 1.27 | (0.86–1.87) | ||
1,145 | 2.25 | (1.61–3.16) | 1.31 | (0.59–2.91) | 2.08 | (1.05–4.13) | 1.36 | (0.90–2.05) | ||
1033 | 1.43 | (0.97–2.11) | 1.24 | (0.61–2.54) | 2.65 | (1.32–5.31) | 1.12 | (0.66–1.90) | ||
787 | 4.33 | (3.03–6.17) | 3.78 | (1.74–8.22) | 2.01 | (0.85–4.79) | 3.01 | (1.80–5.03) | ||
620 | 2.94 | (1.86–4.65) | 4.67 | (2.33–9.34) | 5.71 | (1.85–17.56) | 2.68 | (1.03–6.99) | ||
440 | 5.77 | (3.31–10.06) | 4.07 | (1.63–10.17) | 1.99 | (0.24–16.21) | 4.94 | (2.07–11.81) | ||
313 | 7.45 | (3.93–14.12) | 6.77 | (2.24–20.53) | -- | -- | 7.89 | (2.62–23.80) | ||
201 | 10.66 | (4.42–25.70) | -- | -- | 4.71 | (0.89–24.94) | 3.14 | (1.25–7.92) | ||
324 | 2.47 | (1.26–4.82) | 2.14 | (0.37–12.34) | 5.66 | (1.12–28.54) | 3.23 | (2.02–5.16) |
As shown in Table 2 , unexpected death was associated with increased odds of each mood and alcohol use disorder examined in at least one age group, with significant associations tending to cluster in older age groups. Increased odds of major depressive episodes were observed in 12 of 14 age groups, dysthymia in 6 age groups, manic episodes in 5 age groups, and alcohol disorders in the six age groups occurring after age 45.
Table 3 shows associations between age of first unexpected death experience and onset of anxiety disorders. Of the 14 age groups tested, odds of disorder onset was heightened in all 14 age groups for PTSD, 13 age groups for panic disorder, 7 age groups for generalized anxiety disorder (all after age 40), 5 age groups for specific phobia (all after age 40), and age groups for social phobia.
Association between age of first unexpected death of a loved one and onset of generalized anxiety disorder, PTSD, social phobia, specific phobia, and panic disorder compared with those who did not experience unexpected death of a loved one (N=27,534).
Age of first unexpected death experience | GAD (N=2,646) | PTSD (N=3,423) | Social Phobia (N=2,332) | Specific phobia (N=5,197) | Panic disorder | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
N (total ) | OR | (95% C.I.) | OR | (95% C.I.) | OR | (95% C.I.) | OR | (95% C.I.) | OR | (95% C.I.) | ||
750 | 2.57 | (0.89–7.42) | (3.38–6.00) | 0.96 | (0.60–1.54) | 0.76 | (0.58–1.01) | (1.56–8.50) | ||||
1397 | 0.65 | (0.20–2.04) | (3.18–5.12) | 0.95 | (0.71–1.27) | 0.83 | (0.63–1.09) | (1.08–4.02) | ||||
2,164 | 0.71 | (0.38–133) | (3.48–5.49) | 0.80 | (0.59–1.09) | 1.15 | (0.89–1.49) | (1.10–2.24) | ||||
1,685 | 1.24 | (0.73–2.13) | (6.74–10.98) | 0.92 | (0.49–1.72) | 0.88 | (0.56–1.37) | (1.15–2.28) | ||||
1,426 | 1.41 | (0.78–2.53) | (5.69–9.59) | 1.51 | (0.67–3.39) | 1.46 | (0.88–2.16) | (1.11–2.44) | ||||
1,297 | 1.27 | (0.75–2.17) | (5.81–11.21) | 0.90 | (0.35–2.33) | 1.40 | (0.87–2.23) | (1.27–2.78) | ||||
1,145 | 1.53 | (0.94–2.48) | (8.09–15.19) | 1.02 | (0.39–2.72) | 1.12 | (0.69–1.82) | (1.58–3.72) | ||||
1033 | (1.15–3.56) | (6.40–13.70) | 1.80 | (0.79–4.09) | (1.12–3.12) | (0.72–2.06) | ||||||
787 | (1.18–3.41) | (8.37–18.58) | (1.29–5.78) | (1.57–3.96) | (1.82–5.03) | |||||||
620 | (1.26–4.02) | (12.38–32.58) | (1.53–7.89) | (1.31–4.76) | (1.05–3.79) | |||||||
440 | (1.45–5.46) | (17.42–56.45) | -- | -- | 2.29 | (0.98–5.35) | (2.49–15.26) | |||||
313 | (2.47–15.49) | (16.22–85.54) | -- | -- | (3.18–22.28) | (1.12–12.10) | ||||||
201 | (3.81–24.46) | (5.52–23.35) | -- | -- | (2.48–9.20) | |||||||
324 | (1.77–7.28) | (6.45–28.70) | -- | -- | -- | (2.14–13.74) |
To assess the role of reporting bias in these associations, we conducted a sensitivity analysis by removing 8,218 individuals who reported their first unexpected death experience more than 10 years before the time of the survey. Results are shown in Online Table 4 . We could not estimate incidence for age 5–9, though for other age groups, results were robust and stronger associations were often observed compared with the results from the total sample.
In the online supplementary figures , we show the proportion of respondents with a given psychiatric disorder onset at each age interval separately for respondents with no unexpected death experiences and for those with an experience at that same age interval. That is, among those who had a lifetime diagnosis of PTSD ( eFigure 1 ), for example, we show the proportion with onset at age 5–9, 10–14, 15–19, etc., among those with the first unexpected death of a loved one at age 5–9. We then show the distribution of onset by age among those with an unexpected death at age 10–14, and then the distribution among those with an unexpected death at age 15–19, continuing through age 70+. We also show the distribution among those with no unexpected death experience. We show these age-of-onset distributions for dysthymia ( efigure 2 ), depressive episode ( efigure 3 ), manic episodes ( efigure 4 ), panic disorder ( efigure 5 ), generalized anxiety disorder ( efigure 6 ), alcohol disorders ( efigure 7 ), social phobia ( efigure 8 ), and specific phobia ( efigure 9 ). For most disorders, a marked increase was observed in onset frequency in the time period during which the unexpected death occurred, with the exception of social and specific phobia for which little association with unexpected death was observed.
Table 4 shows the associations between number of unexpected deaths and number of episodes of each psychiatric disorder as well as total number of psychiatric disorder episodes across all disorders. Increasing exposure to unexpected death was associated with a monotonic increase in number of total psychiatric disorder episodes. Compared to those with no unexpected deaths, individuals with 1, 2–3, and 4+ deaths had 1.18 (95% C.I. 1.09–1.72), 1.25 (95% C.I. 1.12–1.41), and 1.72 (95% C.I. 1.44–2.04) times the number of total psychiatric disorder episodes, based on a Poisson distribution. When examined separately for each disorder, increases in episode frequency were particularly notable for major depression and PTSD episodes (see Table 4 ).
Association between number of unexpected deaths of loved ones and number of episodes of psychiatric disorders (N=27,534).
All measured psychiatric disorders | Major depressive episode | Dysthymia | Manic episodes | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
N | Median number of episodes | IDR | (95% C.I.) | IDR | (95% C.I.) | IDR | (95% C.I.) | IDR | (95% C.I.) | |||
Number of unexpected losses | 4+ | 1,950 | 6 | 1.72 | (1.44, 2.04) | 1.52 | (1.16, 1.99) | 1.08 | (0.89, 1.32) | 1.34 | (0.91, 1.98) | |
2–3 | 3,632 | 5 | 1.25 | (1.12, 1.41) | 1.22 | (0.97, 1.52) | 1.04 | (0.86, 1.27) | 1.05 | (0.70, 1.57) | ||
1 | 7,879 | 5 | 1.18 | (1.09, 1.27) | 1.01 | (0.88, 1.16) | 0.93 | (0.81, 1.07) | 1.05 | (0.79, 1.40) | ||
0 | 13,680 | 4 | 1 | 1 | 1 | 1 | ||||||
IDR | (95% C.I.) | IDR | (95% C.I.) | IDR | (95% C.I.) | IDR | (95% C.I.) | IDR | (95% C.I.) | |||
Number of unexpected losses | 4+ | 1,949 | 1.42 | (0.72, 2.78) | 1.03 | (0.69, 1.54) | 0.89 | (0.63, 1.25) | 1.39 | (1.09, 1.78) | 1.11 | (0.93, 1.33) |
2–3 | 3,936 | 0.89 | (0.67, 1.18) | 1.22 | (0.89, 1.67) | 0.83 | (0.63, 1.10) | 1.1 | (0.88, 1.37) | 0.95 | (0.84, 1.07) | |
1 | 7,872 | 1.04 | (0.80, 1.34) | 1.06 | (0.80, 1.39) | 0.92 | (0.70, 1.21) | 1.02 | (0.85, 1.22) | 0.99 | (0.88, 1.11) | |
0 | 13,478 | 1 | 1 | 1 | 1 | 1 |
Unexpected death of a loved one is most frequently cited as the most severe potentially traumatic experience in one’s life, even among individuals with a high burden of lifetime stressful experiences. Unexpected death is associated with heightened vulnerability for onset of virtually all commonly occurring psychiatric disorders that we assessed. This heightened incidence risk is observable from childhood through late adulthood for major depression, PTSD, and panic disorder, and is particularly concentrated in older age groups for manic episodes, phobias, and alcohol use disorders. However, it is also notable that the majority of individuals in the present study did not have the onset of any disorder in the wake of unexpected death of a loved one.
There are several pathways through which experiencing a loved one’s death may influence psychiatric disorders. Bereavement is a major life stressor, and stressful life experiences in general are associated with later onset of many physical and mental disorders ( 34 – 36 ). A variety of cognitive, affective, and neurobiological mechanisms linking stress exposure to the onset of mental disorders have been identified ( 37 ) and work in this area in relation to death of a loved one is ongoing ( 11 ). There may also be mechanisms associated specifically with the consequences of bereavement, given that attachment relationships play a critical role in human experience ( 9 ). Available evidence indicates that lack of social support is an important predictor of depression ( 38 ); sudden loss of social support may thus engender increased psychiatric sequelae. Further, evidence indicates that bereavement following hospitalization of a loved one in an Intensive Care Unit has significant effects on a wide range of psychological and biological measures ( 39 ). Loss of a romantic partner regularly engenders separation distress, with yearning and longing for the loved one, disruption of self concept (e.g. ( 40 )) and these could potentially trigger the onset of a mood or anxiety disorder. Among children, in particular, the death of an attachment figure can have important maladaptive consequences ( 41 ). Sudden death of a loved one might therefore have consequences specific to attachment loss as well as those explainable by stress mechanisms. Death is the most obviously permanent and extreme form of loss, and sudden death is one of the more difficult forms of bereavement. It remains to be seen whether and how other types of loss, e.g. abandonment, incarceration, separation, deportation, might be similar or different from the response to sudden death. Moreover, several important time-varying constructs such as attachment loss, disruption of sense of self, loss of social support and stress reactivity likely interact to impact mental health and well-being; future research could be enhanced by explanatory models to guide us in understanding and testing intersecting associations among these important relationship-centered experiences.
We observed heightened risk for onset of a range of mood, anxiety and alcohol use disorders after the unexpected death of a loved one all along the life course. Previous research in children ( 6 ), widows ( 7 ), survivors of disasters ( 42 ), and older adults ( 43 ) suggests that the risk of depression and PTSD is elevated following the death of a loved one. However, the association between unexpected death and a range of psychiatric disorders across the life course has not previously been examined using population-based data. We found robust and pervasive associations between unexpected death and other mood and anxiety disorders as well as alcohol use disorders, with many of these associations clustered among those in older age groups. First incidence of a psychiatric disorder is relatively rare in old age compared with younger ages; these data indicate that psychiatric disorder onset in older age is commonly concomitant with the death of a loved one. The underlying developmental mechanisms that underlie this clustering of risk at older ages are an important area for future research. However, we note that unexpected death was associated consistently with elevated odds of new onsets of PTSD, panic disorder, and depressive episodes at all stages of the life course. It is particularly notable that these pervasive associations between unexpected death and onset of specific disorders are maintained even after adjustment for psychiatric comorbidity.
The present study also provides novel data supporting an association between unexpected death and onset of manic episodes in a general population sample across the life course. An increase in risk of manic episodes after death of a loved one has been suggested by a number of case reports ( 18 – 26 ), and a study based on the Danish psychiatric register found that suicide of a mother or sibling was strongly associated with increased risk for mania/mixed episodes ( 44 ). Our results suggest that unexpected death of a loved one may be a substantial risk factor for the onset of a manic episode, especially among older adults, and even among those with no prior history of mood, anxiety, or alcohol disorders. The observation of mania in response to traumatic events has been discussed in the literature for over a century ( 45 ), yet the specific mechanisms for this association remains unclear. Our findings should alert clinicians to the possible onset of mania after an unexpected death in otherwise healthy individuals.
We further document that the number of lifetime episodes of mood, anxiety, alcohol disorders increases as the number of unexpected deaths experiences increases. This suggests that prior unexpected death experiences do not offer protection from mental health problems following a later unexpected death experience; rather, each unexpected death experience is associated with similar elevations in risk for novel onsets of mood, anxiety, and alcohol disorder episodes ( 46 ).
We note several limitations to the present study that should be addressed in future research. Grief symptoms ( 47 ) were not assessed in the National Epidemiologic Survey on Alcohol and Related Conditions. A loved one’s death typically evokes a recognizable grief reaction, characterized by yearning and longing, intense sorrow and emotional pain, preoccupation with thoughts and memories of the deceased, a sense of disconnection from ongoing life, and disturbance of self concept and sense of self ( 8 , 47 ). Grief can resemble major depression and PTSD ( 48 ), and it is now clear that some bereaved individuals develop aberrant grief reactions ( 43 ). Further, we did not have information on the nature of the relationship between the respondent and the deceased loved one or the circumstances of the death. Grief intensity, frequency and duration as well as ensuing psychiatry morbidity might vary depending on the relationship to the deceased ( 49 ). For example, among children, death of a parent is especially difficult ( 50 ), and among adults, death of a child or a spouse is especially difficult ( 3 ). Therefore, it is possible that these specific types of close relationships account for the increased in risk observed in these data. Further, the assessment did not distinguish between violent and non-violent unexpected death. According to DSM-5, only unexpected violent death of a loved one can be coded as an inciting potential trauma. It is possible that violent death accounts for the relations we found between unexpected death and PTSD or other disorders. However, the literature indicates that PTSD symptoms do occur following nonviolent death ( 4 ). Future studies with information regarding between violent and non-violent death will be helpful in understanding these relations further; for example future research might contrast psychiatric sequalae after unexpected versus anticipated death to determine the extent to which the suddenness of a loved one’s death impacts psychiatric disorders. The description of a death as ‘unexpected,’ is a subjective judgment and we do not have information about reliability or validity of this judgment. However, a subjective judgment is commonly used in studies of unexpected death. The age of both unexpected death and onset of psychiatric disorder is retrospectively reported, which introduces reporting biases. However, analyses restricted to those who reported a first unexpected death experience within 10 years of the interview showed similar or slightly stronger results than among the whole sample, mitigating against reporting bias as an explanation for our findings. Finally, diagnoses were not confirmed by clinicians and were assessed with lay interviewers using structured questionnaires.
The diagnosis of PTSD in this data also deserves comment. Two issues are noteworthy. First, the National Epidemiologic Survey on Alcohol and Related Conditions followed DSM-IV and permitted unexpected death to qualify as a potential traumatic event. DSM-5 requires that the death be directly witnessed or both violent and unexpected, thus some cases identified here may not meet DSM-5 criteria. However, this has been a controversial issue in the field and we believe it is useful to know the prevalence of PTSD diagnoses after unexpected death more generally. The second issue is that not all respondents had a lifetime occurrence of an event that qualified as a potential trauma. A large majority of the sample (80%) did experience a potential trauma, though this was not consistent across different age groups. Therefore, our estimates of the association between unexpected death of a loved one and the development of PTSD include individuals who were not at risk for PTSD. However, the estimates we report in the present paper are overall population-average estimates of PTSD risk following unexpected death, thus of clinical interest.
In summary, we found a significant relationship between the onset of a mood, anxiety and alcohol use disorder and the unexpected death of a loved one. These results suggest bereavement may be a useful lens in examining the etiology of psychiatric illness. Clinically, our results highlight the importance of considering a possible role for loss of close personal relationships through death in assessment of psychiatric disorders, especially among older adults without a prior history of mental disorder. Clinicians should query the loss of key relationships through death over the patient’s life course, and especially around the period of onset of symptoms. It may be important to address the patient’s response to death of a loved one in order to optimize treatment outcome and reduce the likelihood of illness recurrence.
Supplemental figures and tables, acknowledgements.
This study was supported by National Institutes of Health Grants MH092526 (McLaughlin), MH093612 (Koenen), MH078928 (Koenen), MH60783 (Shear), MH70741 (Shear), and AA021511 (Keyes).
Financial disclosure/conflict of interest : The authors report no conflicts of interest and have no financial relationships with commercial interests.
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Part 2 Lucy’s Story
Nicole Graham
Lucy has experienced the symptoms of mental illness during her lifespan; however, it was not until her early twenties that she was formally diagnosed with bipolar affective disorder. In the case study below, we explore the symptomology that Lucy experienced in the lead up to and post diagnosis. Lucy needs to consider her mental illness in relation to her work as a Registered Nurse and as she continues to move through the various stages of adulthood.
By the end of this case study, you should be able to:
Lucy’s small group of friends describe her as energetic and ‘a party person’. Although she sometimes disappears from her social group for periods of time, her friends are not aware that Lucy experiences periods of intense depression. At times Lucy cannot find the energy to get out of bed or even get dressed, sometimes for extended periods. As she gets older, these feelings and moods, as she describes them, get more intense. She loves feeling high on life. This is when she has an abundance of energy, is not worried about what people think of her and often does not need to sleep. These are the times when she feels she can achieve her goals. One of these times is when she decides to become a nurse. She excels at university, loves the intensity of study, practice and the party lifestyle. Emergency Nursing is her calling. The fast pace, the quick turnaround matches her endless energy. The fact that she struggles to stay focused for extended periods of time is something she needs to consider in her nursing career, to ensure it does not impact negatively on her care.
Unfortunately, Lucy has experienced challenges in her career. For example, her manager often comments on her mental illness after she had openly disclosed her diagnosis. It is challenging for her to hear her colleagues speak badly about a person who presents with mental illness. The stigma she hears directed at others challenges her. She is also very aware that it could be her presenting to the Emergency Department when she is unwell and in need of further support. Lucy is constantly worried that her colleagues will read her medical chart and think she is unsafe to practice.
While the symptoms that cause significant distress and disruption to her life began in her late teens, they intensified after she commenced antidepressant medication after the loss of her child. She subsequently ceased taking them due to side effects. These medications particularly impact on her ability to be creative and reduce her libido and energy. By the time she turns 18, she notices more frequent, intense mood swings, often accompanied by intense feelings of anxiety. During her high periods, Lucy enjoys the energy, the feeling of euphoria, the increased desire to exercise, her engagement with people, and being impulsive and creative. Lucas appreciates her increased libido. However, during these periods of high mood, Lucy also has impaired boundaries and is often flirtatious in her behaviour towards both friends and people she doesn’t know. She also increases her spending and has limited sleep. Lucas is often frustrated by this behaviour, leading to fights. On occasion Lucas slaps her and gets into fights with the people she is flirting with. These periods can last days and sometimes weeks, always followed by depressive episodes.
When she is in the low phases of her mood, Lucy experiences an overwhelming sense of hopelessness and emptiness. She is unable to find the energy to get out of bed, shower or take interest in simple daily activities. Lucas gets frustrated and dismisses Lucy’s statements of wanting to end her life as ‘attention seeking’. Lucy often expresses the desire to leave this world when she feels this way. When Lucas seeks support from the local general practitioner, nothing really gets resolved. The GP prescribes the medication; Lucy regains her desire to participate in life; then stops the medication due to side effects which extend to gastrointestinal upsets, on top of the decrease in libido and not feeling like herself. When Lucy is referred to a psychologist, she does not engage for more than one session, saying that she doesn’t like the person and feels they judge her lifestyle. When the psychologist attempts to explore a family history of mental illness, Lucy says no- one in her family has it and dismisses the concept.
The intense ups and downs are briefly interrupted with periods of lower intensity. During these times, Lucy feels worried about various aspects of her life and finds it challenging to let go of her anxious thoughts. There are times when Lucy has symptoms like racing heart, gastrointestinal update and shortness of breath. She spends a great deal of time wanting her life to be better. Her desire to move on from Lucas and to start a new life becomes more intense. Lucy is confident this is not a symptom of depression; it is just that she is unhappy in her relationship. Lucy starts to consider career options, feeling that not working affects her lifestyle, freedom and health. As she explores different options on the internet, Lucy comes across a chat room. Using the chat name ‘Foxy Lady 20’, she develops new friendships. She finds herself talking a lot with a man named Lincoln who lives on the Gold Coast.
After a brief but intense period talking with Lincoln online, Lucy abruptly decides to leave Lucas and her life in Bundaberg to move in with Lincoln. Lincoln, aged 26, 5 years older than Lucy, owns a modest home on the Gold Coast and has stable employment at the local casino. Their relationship progresses quickly and within a month Lincoln has proposed to Lucy. They plan to marry within 12 months.
Lucy is now happy with her life and feels stable. She decides to pursue a degree in nursing at the local university. Lucy enrols and makes many new friends, enjoying the intensity of study and a new social scene. Her fiancé Lincoln also enjoys the social aspects of their relationship. During university examination periods, Lucy experiences strong emotions. At the suggestion of an academic she respects, she makes an appointment with the university counselling service. After the first 3 appointments, Lucy self-discovers, with the support of her counsellor, that she might benefit from a specialist consultation with a psychiatrist. She comes to recognise that her symptoms are not within the normal range experienced by her peers. Lincoln is incredibly supportive and attends the appointments with Lucy, extending on the information she provides. Lucy reveals information about her grandmother, who was considered eccentric, and known for her periods of elevated mood and manic behaviour. The treating psychiatrist suggests Lucy may be living with bipolar affective disorder and encourages her to trial the medication lithium.
Lucy does not enjoy the side effects of decreased energy, nausea and feeling dazed and ceases taking the lithium during the university break period. This causes Lucy to again experience an intense elevation of her mood, accompanied by risk-taking behaviours. Lucy goes out frequently, nightclubbing and being flirtatious with her friends. She becomes aggressive towards a woman who confronts Lucy about her behaviour with her boyfriend in the nightclub. This is the first time Lucy exhibits this type of response, along with very pressured speech, pacing and an inability to calm herself. The police are called. They recommend Lucy gets assessed at the hospital after hearing from Lincoln that she has ceased her medication. Lucy is admitted for a brief period in the acute mental health ward. After stabilising and recommencing lithium, Lucy returns to the care of her psychiatrist in the community. The discharge notes report that Lucy had been previously diagnosed with bipolar disorder, may also be experiencing anxiety related symptoms, and have personality vulnerabilities.
Lucy is in the final year of her university studies when she has a professional experience placement in the emergency ward. Lucy really enjoys the fast pace, as well as the variety of complex presentations. Lucy feels it matches her energy and her desire for frequent change. After she completes her studies, Lucy applies and is successful in obtaining a position at the local hospital. Throughout her initial graduate year, Lucy balances life with a diagnosis of mental illness as well as a program of her own self-care. She finds the roster patterns in particular incredibly challenging and again becomes unwell. She goes through a period of depression and is unable to work. During this period, Lucy experiences an overwhelming sense of hopelessness and considers ending her life. Again, she requires a higher level of engagement from her treating team. Lucy agrees she is not fit to work during this time and has a period of leave without pay to recover. She has disclosed to her manager that she has been diagnosed with a mental illness and later discusses how shift work impacts her sleep and her overall mental wellbeing.
Over time, Lucy develops strategies to maintain wellness. However, she describes her relationship with the Nursing Unit Manager as strained, due to her inability to work night shift as her medical certificate shows. Lucy says she is often reminded of the impact that her set roster has on her colleagues. Lucy also feels unheard and dismissed when she raises workplace concerns, as her manager attributes her feelings to her mental health deteriorating. Lucy has a further period when her mental health deteriorates. However, this time it is due to a change in her medication.
As Lucy and Lincoln have a desire to have a child, Lucy was advised that she cease lithium in favour of lamotrigine, to reduce the risk of harm to the baby. Lucy ceases work during the period when her mental health deteriorates during the initial phase of changing medication. Lucy recommences lithium after she ceases breastfeeding their son at 4 months, with good effect and returns to work.
Sometimes people do not agree with a diagnosis of mental illness, which can be incorrectly labelled as ‘denial’ by health professionals. It is possible that the person is unable to perceive or be aware of their illness. This inability of insight is termed anosognosia (Amador, 2023). The cause of anosognosia in simple terms can be due to a non-functioning or impaired part of the frontal lobe of the brain, which may be caused by schizophrenia, bipolar disorder or other diseases such as dementia (Kirsch et al., 2021).
As healthcare workers will likely care for someone who is experiencing anosognosia, it is important to reflect on how you may work with someone who does not have the level of insight you would have hoped. Below is a roleplay activity whereby you can experience what it might be like to communicate with someone experiencing anosognosia. Reflect on your communication skills and identify strategies you could use to improve your therapeutic engagement.
Learning objectives.
Two people assume role of either service user or clinician. If time permits, switch roles and repeat.
Reflect and discuss your experiences, both as Lucy and as the clinician. Identify and discuss what was effective and what were the challenges.
Identify professional development opportunities and develop a learning plan to achieve your goals.
Fisher (2022) suggests there are large numbers of health professionals who live with mental illness and recognise the practice value that comes with lived experience. However, the author also notes that as stigma is rife within the health care environment, disclosing mental illness can trigger an enhanced surveillance of the health professional’s practice or impede professional relationships (Fisher, 2022).
It is evident that the case studies derived from Lucy’s life story are complex and holistic care is essential. The biopsychosocial model was first conceptualised in 1977 by George Engel, who suggests it is not only a person’s medical condition, but also psychological and social factors that influence health and wellbeing (Engel,2012).
Below are examples of what you as a health professional could consider in each domain.
Developing skills through engaging in reflective practice and professional development is essential. Each person is unique, which requires you as the professional to adapt to their particular circumstances. The resources below can help you develop understanding of both regulatory requirements and the diagnosis Lucy is living with.
In this case study, Lucy’s symptoms of mental illness emerge in her teenage years. Lucy describes periods of intense mood, both elevated and depressed, as well as potential anxiety-related responses. It is not until she develops a therapeutic relationship with a university school-based counsellor that she realises it might be beneficial to engage the services of a psychiatrist. After she is diagnosed with bipolar affective disorder she engages in treatment. Lucy shares her experience of both inpatient and community treatment as well as her professional practice requirements in the context of her mental illness.
Amador, X. (2023). Denial of anosognosia in schizophrenia. Schizophrenia Research , 252 , 242–243. https://doi.org/10.1016/j.schres.2023.01.009
Engel, G. (2012). The need for a new medical model: A challenge for biomedicine. Psychodynamic Psychiatry, 40 (3), 377–396. https://doi.org/10.1521/pdps.2012.40.3.377
Fisher, J. (2023). Who am I? The identity crisis of mental health professionals living with mental illness. Journal of Psychiatric and Mental Health Nursing . Advance online publication. https://doi.org/10.1111/jpm.12930
Kirsch, L. P., Mathys, C., Papadaki, C., Talelli, P., Friston, K., Moro, V., & Fotopoulou, A. (2021). Updating beliefs beyond the here-and-now: The counter-factual self in anosognosia for hemiplegia. Brain Communications , 3 (2), Article fcab098. https://doi.org/10.1093/braincomms/fcab098
Case Studies for Health, Research and Practice in Australia and New Zealand Copyright © 2023 by Nicole Graham is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.
Through personal stories, these case studies illustrate various pathways to care for students and some of the processes that postsecondary service providers have followed to build on-and-off-campus partnerships, agreements and referrals. The aim of these case studies is to illustrate real-life examples of promising practices for supporting students with complex needs.
We also hope that they help stimulate creative ideas and allow for reflection on ways your campus can adopt some of these practices within your context. As much as possible, these case studies illustrate ways the different service models (i.e. case management, triage etc.) play out in practice. In addition, some of the case studies have also tried to exemplify specific challenges such as transitions, effective supports for Aboriginal and International students, and early psychosis intervention.
This case study highlights a pathway to the Early Psychosis Intervention (EPI) service… Read Lily’s Story
This case study highlights the Successful Transitions Online and Mentoring Program (STOMP)… Read Sally’s Story
This story showcases a Case Management approach to supporting student mental health… Read David’s Story
Arjun’s story highlights the Early Psychosis Intervention Ontario Network (EPION) service… Read Arjun’s Story
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Global trends in economics, climate and technology are weighing on young adults, a report finds. It recommends overhauling how we approach mental health care.
By Christina Caron
Chloé Johnson, 22, has been feeling hopeless lately.
She’s struggling to focus on classes at her local community college in Dallas while also working full-time, making $18 an hour as a receptionist.
Her car broke down, so the $500 that she had managed to save will now go toward a down payment for a used vehicle.
And she was recently passed over for a promotion.
“Right now it just feels, like, very suffocating to be in this position,” said Ms. Johnson, who was diagnosed last year with bipolar II disorder, depression and A.D.H.D. “I’m not getting anywhere or making any progress.”
It’s an endless loop: Ms. Johnson’s mental health has worsened because of her financial difficulties and her financial problems have grown, partly because of the cost of mental health treatment but also because her disorders have made it more difficult to earn a college degree that could lead to a more lucrative job.
“I’ve failed several classes,” she said. “I burn out really easily, so I just give up.”
The mental health of adolescents and young adults has been on the decline and it’s partly because of “harmful megatrends” like financial inequality, according to a new report published on Tuesday in the scientific journal The Lancet Psychiatry. The global trends affecting younger generations also include wage theft , unregulated social media , job insecurity and climate change , all of which are creating “a bleak present and future for young people in many countries,” according to the authors.
The report was produced over the course of five years by a commission of more than 50 people, including mental health and economic policy experts from several continents and young people who have experienced mental illness.
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My journey as a mom and a psychiatrist facing my son's bipolar illness..
Posted August 15, 2024 | Reviewed by Gary Drevitch
The purpose of this blog will be to describe aspects of the mental illness journey that my son “Bill” and I took together: the illness episodes, the emergency room visits, hospitalizations, medication issues, and interludes of stability and success. I will share my thoughts about aspects of his treatment that worked, treatment dilemmas that families face, legal decisions, and finally the tremendous strain that we all face confronting a terrible illness with inadequate resources.
In writing about my struggles, I hope to help others to understand mental illness better in themselves, or in their friends and loved ones. Every family’s situation is different, yet there are common elements, including recognizing the warning signs, getting help, understanding the different medications and their side effects, learning the ropes of the mental health system, and taking action to improve care. My ultimate goal is for this account to expand awareness and recognition that we are not alone in this struggle.
It has been almost three years since I found my son dead in his apartment in Madison, Wisconsin. I keep asking myself if I could have done more. If I could have saved him. His illness and death particularly haunt me because I am a professor of psychiatry at the University of Pennsylvania. Both my research and my clinical work focus on major depression and bipolar illness. Treating bipolar illness is what I do every day. So how could my own son have died from this terrible illness? How could I not save him?
Piecing together what happened with “Bill” involves several intertwined threads. One is the raw power of psychosis to dramatically change a person’s ability for self-direction and choice—in other words, free will . In classical philosophy , free will has two aspects: the freedom to do otherwise and the power of self-determination. In Bill’s case, and for many others with severe psychosis, it was questionable whether he had the power of self-determination once he started into a psychotic episode .
Because his psychosis was brought about by mania , it took the form of grandiose delusions and paranoia . He alternately thought he was auditioning to be Director of the CIA, that his thoughts were controlled by a computer, or that he was leading a rebel army against occupying forces. Some of these experiences naturally terrified him. But many were accompanied by an extremely elevated mood: “10,000 times higher than the highest marijuana high,” is how he once described it. He wrote about his drive to become manic again, to re-experience the high of mania.
This led to a third thread: his desire to avoid taking his medication, partly because of the side effects but even more because he simply wanted to feel better. I didn’t fully understand these different threads—the power of psychosis to undermine self-determination, the desire to re-experience the high of manic mood elevation, and the drive to avoid taking medicines—until it was too late.
Until I read his journal writings about his drive to avoid medication, I didn’t grasp how extremely ill he was, how there were repeated episodes when he was no longer himself once he’d been annihilated by illness. I didn’t realize he had a strong compulsion to invite the illness in again to feel high, something he described vividly as an addiction to mania, not dissimilar to opiate addiction.
Yvette Sheline, MD, is a professor of psychiatry at the University of Pennsylvania whose career has been devoted to understanding brain mechanisms of treatment effects, plasticity, and mood dysregulation. She is also the mother of a son with bipolar disorder who committed suicide.
Sticking up for yourself is no easy task. But there are concrete skills you can use to hone your assertiveness and advocate for yourself.
Transforming the understanding and treatment of mental illnesses.
Información en español
March 27, 2024 • Feature Story • 75th Anniversary
At a Glance:
President George H. W. Bush proclaimed the 1990s “ The Decade of the Brain ,” urging the National Institutes of Health, the National Institute of Mental Health (NIMH), and others to raise awareness about the benefits of brain research.
“Over the years, our understanding of the brain—how it works, what goes wrong when it is injured or diseased—has increased dramatically. However, we still have much more to learn,” read the president’s proclamation. “The need for continued study of the brain is compelling: millions of Americans are affected each year by disorders of the brain…Today, these individuals and their families are justifiably hopeful, for a new era of discovery is dawning in brain research.”
Still, despite the explosion of new techniques and tools for studying the brain, such as functional magnetic resonance imaging (fMRI), many mental health researchers were growing frustrated that their field was not progressing as quickly as they had hoped.
For decades, researchers have studied mental disorders using diagnoses based on the Diagnostic and Statistical Manual of Mental Disorders (DSM)—a handbook that lists the symptoms of mental disorders and the criteria for diagnosing a person with a disorder. But, among many researchers, suspicion was growing that the system used to diagnose mental disorders may not be the best way to study them.
“There are many benefits to using the DSM in medical settings—it provides reliability and ease of diagnosis. It also provides a clear-cut diagnosis for patients, which can be necessary to request insurance-based coverage of healthcare or job- or school-based accommodations,” said Bruce Cuthbert, Ph.D., who headed the workgroup that developed NIMH’s Research Domain Criteria Initiative. “However, when used in research, this approach is not always ideal.”
Researchers would often test people with a specific diagnosed DSM disorder against those with a different disorder or with no disorder and see how the groups differed. However, different mental disorders can have similar symptoms, and people can be diagnosed with several different disorders simultaneously. In addition, a diagnosis using the DSM is all or none—patients either qualify for the disorder based on their number of symptoms, or they don’t. This black-and-white approach means there may be people who experience symptoms of a mental disorder but just miss the cutoff for diagnosis.
Dr. Cuthbert, who is now the senior member of the RDoC Unit which orchestrates RDoC work, stated that “Diagnostic systems are based on clinical signs and symptoms, but signs and symptoms can’t really tell us much about what is going on in the brain or the underlying causes of a disorder. With modern neuroscience, we were seeing that information on genetic, pathophysiological, and psychological causes of mental disorders did not line up well with the current diagnostic disorder categories, suggesting that there were central processes that relate to mental disorders that were not being reflected in DMS-based research.”
Concerned about the limits of using the DSM for research, Dr. Cuthbert, a professor of clinical psychology at the University of Minnesota at the time, approached Dr. Thomas Insel (then NIMH director) during a conference in the autumn of 2008. Dr. Cuthbert recalled saying, “I think it’s really important that we start looking at dimensions of functions related to mental disorders such as fear, working memory, and reward systems because we know that these dimensions cut across various disorders. I think NIMH really needs to think about mental disorders in this new way.”
Dr. Cuthbert didn’t know it then, but he was suggesting something similar to ideas that NIMH was considering. Just months earlier, Dr. Insel had spearheaded the inclusion of a goal in NIMH’s 2008 Strategic Plan for Research to “develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures.”
Unaware of the new strategic goal, Dr. Cuthbert was surprised when Dr. Insel's senior advisor, Marlene Guzman, called a few weeks later to ask if he’d be interested in taking a sabbatical to help lead this new effort. Dr. Cuthbert soon transitioned into a full-time NIMH employee, joining the Institute at an exciting time to lead the development of what became known as the Research Domain Criteria (RDoC) Framework. The effort began in 2009 with the creation of an internal working group of interdisciplinary NIMH staff who identified core functional areas that could be used as examples of what research using this new conceptual framework looked like.
The workgroup members conceived a bold change in how investigators studied mental disorders.
“We wanted researchers to transition from looking at mental disorders as all or none diagnoses based on groups of symptoms. Instead, we wanted to encourage researchers to understand how basic core functions of the brain—like fear processing and reward processing—work at a biological and behavioral level and how these core functions contribute to mental disorders,” said Dr. Cuthbert.
This approach would incorporate biological and behavioral measures of mental disorders and examine processes that cut across and apply to all mental disorders. From Dr. Cuthbert’s standpoint, this could help remedy some of the frustrations mental health researchers were experiencing.
Around the same time the workgroup was sharing its plans and organizing the first steps, Sarah Morris, Ph.D., was a researcher focusing on schizophrenia at the University of Maryland School of Medicine in Baltimore. When she first read these papers, she wondered what this new approach would mean for her research, her grants, and her lab.
She also remembered feeling that this new approach reflected what she was seeing in her data.
“When I grouped my participants by those with and without schizophrenia, there was a lot of overlap, and there was a lot of variability across the board, and so it felt like RDoC provided the pathway forward to dissect that and sort it out,” said Dr. Morris.
Later that year, Dr. Morris joined NIMH and the RDoC workgroup, saying, “I was bumping up against a wall every day in my own work and in the data in front of me. And the idea that someone would give the field permission to try something new—that was super exciting.”
The five original RDoC domains of functioning were introduced to the broader scientific community in a series of articles published in 2010 .
To establish the new framework, the RDoC workgroup (including Drs. Cuthbert and Morris) began a series of workshops in 2011 to collect feedback from experts in various areas from the larger scientific community. Five workshops were held over the next two years, each with a different broad domain of functioning based upon prior basic behavioral neuroscience. The five domains were called:
At each workshop, experts defined several specific functions, termed constructs, that fell within the domain of interest. For instance, constructs in the cognitive processes domain included attention, memory, cognitive control, and others.
The result of these feedback sessions was a framework that described mental disorders as the interaction between different functional processes—processes that could occur on a continuum from normal to abnormal. Researchers could measure these functional processes in a variety of complementary ways—for example, by looking at genes associated with these processes, the brain circuits that implement these processes, tests or observations of behaviors that represent these functional processes, and what patients report about their concerns. Also included in the framework was an understanding that functional processes associated with mental disorders are impacted and altered by the environment and a person’s developmental stage.
Over time, the Framework continued evolving and adapting to the changing science. In 2018, a sixth functional area called sensorimotor processes was added to the Framework, and in 2019, a workshop was held to better incorporate developmental and environmental processes into the framework.;
Since its creation, the use of RDoC principles in mental health research has spread across the U.S. and the rest of the world. For example, the Psychiatric Ratings using Intermediate Stratified Markers project (PRISM) , which receives funding from the European Union’s Innovative Medicines Initiative, is seeking to link biological markers of social withdrawal with clinical diagnoses using RDoC-style principles. Similarly, the Roadmap for Mental Health Research in Europe (ROAMER) project by the European Commission sought to integrate mental health research across Europe using principles similar to those in the RDoC Framework.;
Dr. Morris, who has acceded to the Head of the RDoC Unit, commented: “The fact that investigators and science funders outside the United States are also pursuing similar approaches gives me confidence that we’ve been on the right pathway. I just think that this has got to be how nature works and that we are in better alignment with the basic fundamental processes that are of interest to understanding mental disorders.”
The RDoC framework will continue to adapt and change with emerging science to remain relevant as a resource for researchers now and in the future. For instance, NIMH continues to work toward the development and optimization of tools to assess RDoC constructs and supports data-driven efforts to measure function within and across domains.
“For the millions of people impacted by mental disorders, research means hope. The RDoC framework helps us study mental disorders in a different way and has already driven considerable change in the field over the past decade,” said Joshua A. Gordon, M.D., Ph.D., director of NIMH. “We hope this and other innovative approaches will continue to accelerate research progress, paving the way for prevention, recovery, and cure.”
Cuthbert, B. N., & Insel, T. R. (2013). Toward the future of psychiatric diagnosis: The seven pillars of RDoC. BMC Medicine , 11 , 126. https://doi.org/10.1186/1741-7015-11-126
Cuthbert B. N. (2014). Translating intermediate phenotypes to psychopathology: The NIMH Research Domain Criteria. Psychophysiology , 51 (12), 1205–1206. https://doi.org/10.1111/psyp.12342
Cuthbert, B., & Insel, T. (2010). The data of diagnosis: New approaches to psychiatric classification. Psychiatry , 73 (4), 311–314. https://doi.org/10.1521/psyc.2010.73.4.311
Cuthbert, B. N., & Kozak, M. J. (2013). Constructing constructs for psychopathology: The NIMH research domain criteria. Journal of Abnormal Psychology , 122 (3), 928–937. https://doi.org/10.1037/a0034028
Garvey, M. A., & Cuthbert, B. N. (2017). Developing a motor systems domain for the NIMH RDoC program. Schizophrenia Bulletin , 43 (5), 935–936. https://doi.org/10.1093/schbul/sbx095
Kozak, M. J., & Cuthbert, B. N. (2016). The NIMH Research Domain Criteria initiative: Background, issues, and pragmatics. Psychophysiology , 53 (3), 286–297. https://doi.org/10.1111/psyp.12518
Morris, S. E., & Cuthbert, B. N. (2012). Research Domain Criteria: Cognitive systems, neural circuits, and dimensions of behavior. Dialogues in Clinical Neuroscience , 14 (1), 29–37. https://doi.org/10.31887/DCNS.2012.14.1/smorris
Sanislow, C. A., Pine, D. S., Quinn, K. J., Kozak, M. J., Garvey, M. A., Heinssen, R. K., Wang, P. S., & Cuthbert, B. N. (2010). Developing constructs for psychopathology research: Research domain criteria. Journal of Abnormal Psychology , 119 (4), 631–639. https://doi.org/10.1037/a0020909
BMC Primary Care volume 25 , Article number: 304 ( 2024 ) Cite this article
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Lack of access to health care is a worldwide public health crisis. In primary care it has led to increases in the implementation of nurse practitioners and heightened interest in their patient panel capacity. The aim of this study was to examine factors influencing nurse practitioner patient panel size in team-based primary care in Ontario, Canada.
We used a multiple case study design. Eight team-based primary care practices including rural and urban settings were purposively selected as cases. Each case had two or more nurse practitioners with a minimum of two years experience in the primary care setting. Interviews were conducted in-person, audio recorded, transcribed and analysed using content analysis.
Forty participants, including 19 nurse practitioners, 16 administrators (inclusive of executives, managers, and receptionists), and 5 physicians were interviewed. Patient, provider, organizational, and system factors influenced nurse practitioner patient panel size. There were eight sub-factors: complexity of patients’ health and social needs; holistic nursing model of care; nurse practitioner experience and confidence; composition and functioning of the multidisciplinary team; clerical and administrative supports, and nurse practitioner activities and expectations. All participants found it difficult to identify the panel size of nurse practitioners, calling it— “a grey area.” Establishing and maintaining a longitudinal relationship that responded holistically to patients’ needs was fundamental to how nurse practitioners provided care. Social factors such as gender, poverty, mental health concerns, historical trauma, marginalisation and literacy contributed to the complexity of patients’ needs. Participants indicated NPs tried to address all of a patient’s concerns at each visit.
Nurse practitioners have a holistic approach that incorporates attention to the social determinants of health as well as acute and chronic comorbidities. This approach compels them to try to address all of the needs a patient is experiencing at each visit and reduces their panel size. Multidisciplinary teams have an opportunity to be deliberate when structuring their services across providers to meet more of the health and social needs of empanelled patients. This could enable increases in nurse practitioner panel size.
Peer Review reports
Access to health care is a worldwide public health crisis. The supply and productivity of the primary care workforce, including nurse practitioners, (NPs) is critical to its resolution [ 1 , 2 , 3 ]. Panel size is a metric which refers to the number of patients receiving care from a single provider in one year [ 4 ]. Though commonly assessed in physician practices [ 5 , 6 , 7 , 8 ], its use in NP practice is less frequent but increasing [ 9 ]. Panel size and composition is also of interest in the context of primary care teams and is regarded as a key component of high-performing teams [ 10 , 11 , 12 ].
NPs are advanced practice nurses with graduate education that includes clinical training to assess, diagnose and manage patients with acute and or chronic illness [ 3 ]. NPs function as the most responsible provider for their own patient panels or provide care for patients in a collaborating physician’s panel [ 9 ]. Estimates of NP panel size range between 500 and 1000 patients [ 9 ]. These panel size numbers are less than what is typical for physician patient panels leading some to question the cost effectiveness of NPs [ 13 ].
Research has shown that the presence of an NP in a physician’s practice increases the physician’s patient panel size [ 4 , 5 , 6 ]. However few studies have investigated the nature of the patient panels of NPs or the factors that influence panel size and composition. Use of electronic databases, as well as validated disease burden scores, or first-voice perspectives in this research is sparse [ 9 ]. Since NPs are autonomous primary care providers in most of North America and many countries around the world [ 14 ], understanding the factors that influence their panel size is important for health workforce planning.
In Canada NPs are employed in all provinces and territories and their numbers are growing [ 15 ]. The province of Ontario was the first to formally introduce and regulate NPs 25 years ago and continues to have the largest number of NPs in the country [ 16 ]. It is also the province with the longest and most varied experience with various models of interprofessional team-based primary care [ 17 , 18 , 19 , 20 ].
Our study aim was to explore the factors influencing NP patient panel size in team-based primary care in Ontario, Canada.
We used a multiple case study design to enable understanding of the phenomena of panel size in the real-life context of rural and urban primary care practices [ 21 ]. Cases were defined as team-based primary care practices that included two or more NPs with at least two years experience in the setting.
Funding The study was funded by the Ontario Ministry of Health and Long-term Care. The grant number was 06695.
In consultation with an advisory panel of health care administrators and providers, we discussed the various team-based primary care models in Ontario and decided to include Aboriginal Health Access Centres, Community Health Centres, Family Health Teams and Nurse-Practitioner Led Clinics all of which had been in existence for at least 10 years [ 17 , 18 , 19 , 20 ]. The research team purposively selected eligible practices in rural and urban locations. To enable onsite data collection, practices were located within five hours driving from a large city.
An information letter explaining the study was emailed to the senior administrator in each potential case site. A follow-up telephone call confirmed the practice met eligibility criteria and obtained organizational permission to conduct the study, including the processes for participant recruitment. Each primary care setting and participant received a small honorarium.
Individual interviews with providers and staff explored participants’ perceptions of the barriers and facilitators to increasing NP patient panel size. Interviews were conducted in-person by a research team member between March and November 2015. A small number of interviews were conducted by telephone when requested by participants. Interviews were 30 to 50 min in length and included member checking by summarising key points [ 22 ]. To promote data saturation, data collection occurred one case at a time [ 21 ]. Interviews were conducted in person at each site over two-to-three-days, recorded and transcribed. The interview guide is available as a supplemental file.
Transcripts were coded deductively and inductively using content analysis [ 23 ]. NVivo 12 was used to manage the data [ 24 ]. First, two research team members independently coded four transcripts, compared coding, and discussed emerging themes. Remaining transcripts were independently coded by four team members. Emerging and evolving themes were discussed and refined at research team meetings, including one with the advisory panel.
When reporting the findings, the descriptor “most participants” was used for approximately 75% or more participants, “many participants” for approximately 50–74%, “some participants” for approximately 25–49% and “few participants” for less than 25%. This approach protects participant confidentiality and provides an indication of how many participants expressed a viewpoint without making interpretations beyond the sample [ 25 ].
Forty participants including 19 NPs, 16 administrators (inclusive of executives, managers, and receptionists), and 5 physicians were interviewed. Their characteristics are reported in Table 1 . Many NPs (60%) had graduate education, most (72%) were employed fulltime, and, with one exception, had three or more years experience as an NP. Many self-identified as being female (56%) and their ages ranged from 39 to 46 years. NPs reported spending between 4 and 4.8 days per week providing direct patient care.
Factors influencing NP patient panel size were identified at patient, provider, organizational, and systems levels along with eight sub-factors (Table 2 ). Participants from Aboriginal Health Access Centres and NP-Led Clinics indicated NPs had their own patient panel whereas participants from Family Health Teams and Community Health Centres indicated NPs and physicians shared a patient panel.
All participants found it difficult to identify the number of patients in NPs’ panels commenting it was “a grey area.” Panel size estimates ranged from 300 to 1800 across the eight settings. Some participants assumed that expectations for NP panel sizes were externally determined, for example, by government or health service organizations. Others perceived panel size was determined at the primary care practice level and or by each provider individually.
All participants indicated that numbers alone could not capture the complexities of many of their patients’ lives, even when social factors were included as a diagnosis. As illustrated in the following quote, this, in combination with NPs’ deep commitment to patient-centred preventative care, inevitably meant longer appointment times and likely smaller panel sizes. These concepts are further considered in the following subfactor sections organized as Patient Factors, NP Factors, Organizational Factors and System Factors.
We really try hard here to put the diagnosis in and even poverty comes up or social isolation , so all of those factors. But even those things don’t capture the complexities. So , I think determining a panel size based on that and a philosophy of patient-centred preventative care— rather than acute episodic all the time. That means longer appointment times , less— maybe less patients [NP, Nurse Practitioner-Led Clinic].
The composition of the patient population and the complexity of their needs was the only patient related factor influencing NP panel size.
Across all cases, most interview participants emphasized that the number one factor limiting NP panel sizes was the complexity of the health and social needs of patient populations. As one Family Health Team administrator explained; “ The more complex your patients are , the more time you’re spending with them , the higher their needs and then consequently it becomes more difficult to manage a larger panel .”
Many participants stressed that the length of appointment times was dependant on the needs of patients. Although the primary care receptionists knew most patients well and tried to predict the length of time needed for appointments, this was often not possible. The full extent of patients’ needs was often not disclosed or discovered until the time of the appointment with the NP. When this occurred, it required NPs to choose between staying on schedule or extending the length of the appointment in order to address the presenting patient’s needs. Participants indicated that NPs chose to capitalize on the opportunity to address the needs of the patient especially if the patient was unlikely or unable to return for regular follow up.
“You’re really only ready to give that injection but now you have 10 other issues , and you want to make sure you get through them because this patient may or may not come back for their follow up and it’s so important. I think that can really impact how your whole day looks”. [NP, Community Health Centre]
Participants recognized that health care funders wanted primary care practices to increase the size of NPs’ patient panels and the panels of the practice as a whole. However, participants indicated this was difficult to do without compromising their capacity to address the health and social needs of the patients already in NPs’ panels. As the following quote illustrates, they described this as the tension between providing quality care versus quantity. For participants, establishing and maintaining a supportive relationship with patients already in their practice was fundamental to providing quality care.
“I don’t know how you could have a relationship or how you could support the patient if they have 7 to 10 minutes or whatever the number might be. I recognize that they have expectations of us , being the funding body , that they need data , they need numbers. It’s a struggle with providing quality care versus quantity , especially when our patients come in with early onset aging and a higher representation of chronic diseases at an early age and issues with housing , financial , poverty —all those things. We manage health care , but in addition to health care , support for all the other areas— and it takes time.” [Administrator, Aboriginal Health Access Centre].
Participants identified that NPs provided primary care for patients with complex health and social needs. This included “patients with four or more chronic diseases with multiple complaints on any one day.” Most participants stressed that while multiple chronic illnesses were a component of the challenges patients were experiencing, mental health and substance use concerns and socioeconomic factors compounded the complexity of their lives. The following two quotes illustrate this.
“We draw from a range of socio-economic status areas in (city) and that affects many things. We have a high incidence or prevalence of mental illness , and a lot of issues are complicated through the presence of their mental illnesses {…} or make management more difficult. I’d say that’s a challenge for all our practitioners. {…} You know , you get it all just sort of wrapped together; complexities due to older patients or patients with multiple meds , and then you wrap that up with socioeconomics and mental illness— it’s huge .” [Administrator, Family Health Team] That just doesn’t mean the medical complexity , that also means the social complexity. If that client is substance-using , then I may give them a medication for an infection and they will go get that medication and then come back and say , you need to keep this medication , and then run away again , and I don’t know if they’ll come back or not. It’s totally the medical and behavioural complexity of that individual that certainly limits how many people I see in a day . [NP, Community Health Centre].
Participants further elaborated that gender, literacy, prior unattachment to a primary care provider, historical trauma and marginalisation also contributed to complexity and therefore the need for more support and more time.
“With the NPs , the responsibilities that they see and manage every day with complex patients. We have complex patients probably at a higher number and with early onset aging and historical trauma. Our patients come in with a lot more — they need a lot more support .” [Physician, Aboriginal Health Access Centre]. “We did have new families , and even with those patients , it was chronic issues. They were unattached and marginalized , and they’d never seen any primary care provider for years. And they were the frequent flyers between hospital emergency and the walk-in clinics .” [NP, NP-Led Clinic].
In summary, participants were united in their view that the complex health and social needs of patients in their primary care settings required longer appointment times with the NP, and this in turn reduced NPs’ panel size capacity.
Two provider factors influenced NP panel size: NPs’ holistic nursing model of care and their experience and confidence.
Half of NP and administrator participants in all four models of team-based primary care identified that the holistic nature of the care NPs provide was an influence on their patient panel size. A physician explained that NPs’ ability to look at patients holistically originated from nursing culture and is a value-add that they bring to primary care.
“I think one of the great things NPs add to primary care , and it’s something that’s important to all of us , is something of value in nursing culture , which is to see people holistically as people who have needs and who come in with fears and anxieties. I think that’s a value that our whole health system needs more of.” [Physician, Community Health Centre].
Providing holistic care was linked to scheduling appointment times that enabled NPs to take time to nurture a relationship built on trust and to address the multiple health and social concerns of patients. An NP explained it as follows:
“If we can be patient-centred and take the time with people to address their issues , listen to them , you’re going to get more done. You spend a lot more money when a person ends up in crisis than doing prevention and really listening carefully to people and having that relationship with them. A lot of patients come here and the biggest thing they say they notice is that we spend the time—we listen. Their doctor may have had 10 minutes with them. So , they really appreciate that and they’re more apt to follow through on your suggestions because they feel you’ve listened to their problem and have some trust.” [NP, NP-Led clinic].
Most participants indicated that experience in the role and in primary care practice influenced NP panel size. This is illustrated in the following quotes from NP and administrator participants.
“When they first started , they had hour-long appointments with the clients because , first of all , they didn’t know the computer system , and their primary care skills weren’t as fresh.” [NP, Community Health Centre]. If an NP is more novice and really having to deal with a big pile of complexity with inter-related problems and symptoms—that really does affect panel size. It affects the amount of time that the NP takes and really having to lean on his or her colleague to get advice and support.” [Administrator, Community Health Centre].
Development of confidence occurred over time, and through application of theoretical knowledge and exposure to clinical issues in the practice setting. Having opportunities to discuss questions and decisions with a colleague enabled confidence. When patients presented with complexities that required NPs to consult other providers, these consultations impacted the number of patient appointments.
The connections between experience, confidence, and an ability to practice at a faster speed are summarised by an administrator in the following quote.
“What I have noticed is because of the comfort and confidence that a more experienced NP has , he or she will be able handle a situation around a prescription or diagnosis much more quickly .” [Administrator, Community Health Centre].
There were three organisational factors influencing the panel size of NPs: multidisciplinary team roles; clerical and administrative supports, and expectations regarding NP activities.
Most NP and physician participants and many administrator participants identified that multidisciplinary teams enabled increases in NP panel size because patients could receive care from other team members. For example, dieticians provided nutrition counselling for patients with diabetes, pharmacists assisted patients with smoking cessation and social workers enabled patients to make connections with community resources and supports. Participants identified that it was important for the multidisciplinary team to think deliberately about each team member’s contribution to patient care. They indicated that when patient needs were matched with team members with the expertise to meet those needs, the panel size of NPs and the panel size of the entire practice could be increased.
If we involve more multidisciplinary team members, for instance, having a diabetic program in place or if you have a pharmacist, more registered practical nurses involved, and social work more involved, I can see NPs having more patients and expanding the panel size more. [Administrator, NP-Led Clinic].
Participants offered several examples of health care providers, who, through their education and team roles, provided specific skills and services to meet needs of patient groups. They discussed the importance of enabling all team members to practice to their full scope of practice and making deliberate decisions about involving providers with specific competencies in the care of specific patient populations. The following quote illustrates this concept.
“Truly using every provider to their full scope of practice and not booking tasks and activities that could go to another provider. So , when I think about our NPs , they’re a pretty hot commodity. We need them to see specific things with specific clients with specific issues and if they’re booking up their schedules with a blood pressure recheck or —name your episodic thing—it does take away time from being able to see other patients. So that full scope is really incredible.” [Administrator, Community Health Centre].
Some participants identified the importance of providers reflecting on their own professional and personal scope of practice as well as the scope of practice of other team members when planning how to address the needs of patients in the practice. As one NP explained
“I always think— what am I best able to provide the patient? I’m not a social worker , I’m not a great CBT (cognitive behavior therapy) counsellor— so why try to spend time counselling when I can send them to somebody else , find them resources.” [NP, Family Health Team].
Further to this, some participants indicated that multidisciplinary teams wanted opportunities to design, implement and evaluate new approaches to team collaboration. Many team members indicated that collaboration improved team building and that it enabled problem-solving that led to operational changes that contributed to panel size growth.
“What I’m seeing on our team , is really excellent team collaboration. If the team is really healthy , and functional , and can problem-solve and can do mutually supportive stuff—I think that’s a team that can handle a bigger panel size.” [Community Health Centre , Physician] .
Participants noted that with resources, such as dieticians, social workers, registered practical nurses and pharmacists, some of the health teaching previously done by NPs was now being carried out by other team members. This provided time for NPs to have more patient appointments.
“It evolves relative to practice size , the number of patients to be seen and to NP experience and confidence and availability of supports. An NP two years ago might have been doing smoking cessation on their own; now we have the support of the pharmacist to do that.” [Family Health Team , Administrator].
Most participants indicated that clerical, and administrative staff were essential for enabling NPs to increase their panel size. Many identified that their practices had standard times for different types of appointments. For example, patients needing a complete physical examination or who had mental health concerns would be booked for a one-hour appointment. Most other appointments were booked for 30 min. Participants noted that most patients were well known by clerical staff, and they knew how to adjust appointment duration times according to what patients were anticipated to need. The following quote exemplifies this perspective.
“I think each individual NP and the girls at the front have a background of all the patients— we know the patients well , and we know how to book them as far as time.” [NP -Led Clinic , Administrator] .
Many participants indicated that administrative responsibilities were a component of NPs’ workload and ranged from 0.5 to 7.5 h weekly.
“Of course , I have administrative time when I’m not seeing patients so during that time , I’m doing paperwork and reviewing labs and doing administrative things. If I had less administrative time , then maybe I would be able to see more clients but that’s part of the job” [NP, Family Health Team].
For others, administrative work included activities such as program planning, leading meetings, quality improvement and community education/outreach activities. Some participants indicated that the time NPs spent on administration lessened the available clinical time.
Most participants indicated appointment-based, direct care within the primary care practice setting was how NPs spent most of their time. In some practices NPs were expected to offer specific types of appointments that impacted their panel size, for example, same and next day patient appointments. In some practices NPs were responsible to do activities requiring more time, for example, completing the telephone follow-up calls or routine pelvic examinations for the patients of all providers in the practice.
Many participants indicated NPs did house calls or street calls noting these required more time because travel was entailed. For example, an Aboriginal Health Access Centre administrator said, “ I think we book 1 ½ hours .” The frequency of house-calls was on an as-needed basis, with one NP-Led Clinic NP reporting 28 in a year. Most NPs reported they did not have on-call responsibilities except for some who were on-call but only for critical lab results.
In summary, the three subfactors at the organizational level were composition and functioning of multidisciplinary healthcare provider teams, clerical and administrative supports and expectations regarding NP activities.
The systems level factors that influenced the panel size of NPs were restrictions on NP scope of practice and NP remuneration.
At the time of data collection, systems level factors that restricted scope of practice included barriers to referring patients to specialists, restrictions on ordering some diagnostic tests, and the inability to prescribe some medications. As one Family Health Team NP explained having these restrictions removed would save time because “having to ask can I order this or that— it just slows down the time .” The removal of the requirement for a physician’s co-signature on specialist referral requests had an impact on increasing panel size as illustrated in the following quote. “ Being able to refer now without a physician’s co-signing— that opened up a position for me in a satellite office in (location) to help with patients who didn’t have a family physician .
Funding was also an important enabling factor identified by many NPs and physicians. One NP from a Community Health Centre summarized it as follows:
If we think of wages as a system thing and we think about how it impacts on recruitment and retention, you can’t grow your clinic if you don’t keep your staff. {…} So, if you support recruitment and retention with good wages, you can grow your panel size. But your NPs get a better offer, and they go off to a better paying organization, […], then you can’t grow primary care, you can’t grow your clinic.
Through this analysis we found that the most significant factor influencing NP panel size was the time needed to address the multiple complex health and social needs experienced by patients in their practice. To accommodate for this, NPs had appointment times that varied between 15 and 30 min and some that were booked for an hour or more. Participants identified that providing care for patients impacted by the social determinants of health was important. All participants supported the value of the principle of time allocation for NPs to address patients’ broader social needs—especially patients experiencing trauma, marginalization and high-risk environments. They emphasized that NPs were ideally suited to provide care for this patient population because of NPs’ holistic, comprehensive nursing approach that included taking the time to listen and fulsomely address patients’ concerns at each appointment. Participants emphasized that concerns about panel size should not compromise the comprehensiveness of the care NPs provide to patients with complex needs because it was important from a social justice standpoint, and it reduced the risk of adverse patient outcomes.
The association between the length of appointment time and complexity has been documented from patient [ 26 ] and provider [ 27 , 28 , 29 ] viewpoints. Patients impacted by social factors, such as poverty, are also disproportionately affected by medical comorbidities [ 30 ]. When discussing complexity, participants spoke briefly about patients’ multimorbidity and gave detailed accounts of the social determinants impacting patients for whom NPs provided care. It is possible that their focus on these factors was a confirmation bias of their knowledge of the nursing profession’s focus on social determinants of health. Similar findings from studies using patient profile [ 28 , 30 ] and task allocation studies [ 31 ] make this explanation unlikely. NPs in this study prioritized identifying and taking action to address the social needs of their patients regardless of the time it took or impact it had on panel size. Although not a concern of participants in our study, the cost effectiveness of NPs’ longer appointment times has been criticized [ 12 ]. However, recent studies indicate NP-provided primary care for patients with complex health needs results in savings in downstream costs associated with hospital use [ 32 , 33 ]. Primary care is the point in health care systems where these interventions should be provided - not in the emergency room or in hospital.
Participants in our study were mindful of the need to attend to NP panel size. However, they tended to regard panel size determination as an accountability required of funders, not a tool to improve how care delivery could be better organized in the practice [ 34 ]. They were cognizant of the challenges patients faced when accessing primary care, for example, transportation and childcare, and recognized the visit was a time-limited window of opportunity to address concerns. Therefore, participants prioritized giving patients the time required to have most or all of their needs met in a single appointment. This approach aligns with Levesque et al.’s [ 35 ] broad perspective on accessibility as an opportunity to identify healthcare needs and have those needs for services fulfilled. It is also consistent with the call to action of Browne and Tarlier, who said: “With even a small investment of time, NPs operating from a critical social justice perspective could for example, attend to the biomedical needs of patients and at the same time work towards countering or challenging the social or economic policies that affect biomedical issues” [ 36 , p. 90]. Participants in our study prioritized their time for these patients but did not discuss policy work they were doing related to the social determinants of health.
While participants valued and made efforts to optimize the roles of multidisciplinary team members including registered and practical nurses, for the most part they did not discuss how the team could take a more deliberate approach to maximizing optimal functioning of all team members’ roles and the impact this could have on NP panel size. Such planning requires data, and we did not specifically ask participants to what extent they could mine electronic medical record data to inform this planning. Research has shown that NPs use EMR data to plan their own clinics [ 37 ], however, few practices are using this data to inform financial and organizational planning for the services a primary care clinic could offer and the roles of team members [ 38 ]. This could enable improvements in accessibility and equity.
Our study offers an in-depth description of the factors affecting NP patient panels in primary care in one Canadian province. Interview data were collected in 2015 before the COVID-19 pandemic and the increased use of virtual care in primary care. Future research should evaluate strategies for aligning team composition with patient population health and social needs as well as the potential impact of virtual care provision on NP panel size. Our study did not include patient interviews and not having their perspectives is a limitation. Detailed information about the structures and processes of these practice models other than what is presented in Table 1 was not obtained in our study and is a limitation to understanding the impact of context on panel size.
Multiple factors influence NP patient panel size, particularly the complexity of the health and social needs of patients. NPs have a patient-centred, holistic approach that aims to address the social determinants of health for each patient when they access primary care. Panel size concerns should not compromise the comprehensiveness and equity of the care NPs provide. Better utilization of practice-based data may enable multidisciplinary teams to optimize panel size without compromising equity and quality of care.
The qualitative data that support the findings of this study are not openly available. Participants did not consent to have their data shared.
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R.M.M. collected and analyzed data and wrote the initial manuscript text and led subsequent revisions. FD, JR and KK collected and analyzed data and reviewed and contributed to revisions of the manuscript. DB, IB, NC and EZ analyzed data and reviewed and contributed to revisions of the manuscript.
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Ethics approval was obtained from Toronto Metropolitan University Research Ethics Board (formerly Ryerson University Research Ethics Board). The file number was REB 2015-050. We obtained informed consent from all participants in each practice setting. Participants were informed they could withdraw from the study and have their data removed. All methods were carried out in accordance with relevant guidelines and regulations.
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Martin-Misener, R., Donald, F., Rayner, J. et al. Factors influencing nurse practitioner panel size in team-based primary care: a qualitative case study. BMC Prim. Care 25 , 304 (2024). https://doi.org/10.1186/s12875-024-02547-6
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