Depression Screening: Patient Health Questionnaire-9

Abstract

Depression is an important public health concern, affecting millions of people globally. Depression is often underdiagnosed and undertreated in primary care settings, leading to decreased quality of life and increased comorbidities and mortality risks. At the project site currently, only an annual depression screening is used, which leads to underdiagnosis and delayed diagnosis of depression. The purpose of this quantitative, quasi-experimental project was to determine if or to what degree the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) would impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida over four weeks. Lewin’s change theory and Nola Pender’s Heath Promotion Theory will be the theoretical underpinnings of the project. Data will be collected from PHQ-9 depression questionnaires and EMR databases. Data analysis will be done with the use of a t-test. Convenience sampling will be used to involve 150 participants. The main clinical findings will detect whether there is a relationship between the use of PHQ-9 and the improved rates of newly diagnosed depression patients in the primary care setting. The project findings can be beneficial for improved practices of depression screening and early detection, beneficial for patients’ health outcomes. Future research using larger samples and randomized clinical trials are necessary for supporting the generalizability of the project findings.

Dedication

The primary goal of this project is to help the community rediscover the value of mental health. Naturally, the research dedicated to helping people cannot be created without support from the people around. I am grateful to every individual willing to contribute to the development of this research, as their genuine involvement is what made this project stand out from others and present a unique value to the community.

While this paper is dedicated to every person and clinician seeking new ways to support mental health awareness, I would like to specifically address my husband and two beautiful kids, whose love and support inspired me to make the most out of my potential and this project. Without their endless belief in my potential, I could not be nearly as passionate and persistent about the research you are about to read. Moreover, thinking about my daughters inspires me to find every possible way to help them live a happier life, so I dedicate the findings of this study to them as my contribution to their future.

I also dedicate this research to the chair and my preceptor, as I am enternally grateful for the support and guidance provided by my superiors. Their expertise and wisdom helped me find the strength and courage to follow the topic I have always wanted to study. Without the assistance of my mentors, I would not be able to transform my research into a plausible project potentially adopted by clinicians.

I want to say a special thank you to the Grand Canyon Univesrity for giving me knowledge and opportunity to pursue my career and explore the specifics of health care with the help of an empirical projec

Introduction to the Project

Depression is one of the leading causes of disability worldwide and exceedingly prevalent comorbidity in several other mental and physical disorders. Over 300 million people of all ages globally have depression (Maurer et al., 2018). The prevalence of major depression in adults age 18 or older in the United States is about 8%, which makes it one of the most common mental health issues nationwide (Brody et al., 2018). The consequences of failing to properly diagnose and treat depression can be detrimental, including those of suicidal ideations and attempts. Therefore, effective and timely screening is necessary for the timely diagnosis and management of depression. However, there is evidence that despite a high prevalence of depression, associated risks, and official recommendations for screening, it is underused in clinical settings, with only 5% of adults age 18 and older being screened for depression in a primary care setting (Costantini et al., 2020; Indu et al., 2018). At the project site, they only do an annual depression screening for all patients. In the absence of routine screening, less than 50% of patients with depression receive treatment (Kondova et al., 2018). Therefore, more comprehensive depression screening protocols can be beneficial for improved patient outcomes.

Numerous self-report screening tools exist and can be used in clinical practice and research for depression recognition. Research has shown the Patient Health Questionnaire (PHQ) to be a useful screening tool for monitoring depressive symptoms and identifying major depressive disorder (MDD). In particular, the PHQ-9 is a 9-item version of PHQ, which is based on the nine diagnostic criteria for depressive disorder (Brody et al., 2018). This brief and simple questionnaire can be used as a one or two-item screening method. PHQ-9 has been demonstrated to be reliable and valid and recommended for use in Western populations (Doi et al., 2018).

The purpose of this direct practice intervention project is to implement routine depression screening intervention using the PHQ-9 tool into a primary care site. The discussion that follows includes a background of the problem, advancements in scientific knowledge, purpose, and significance of the project, including proposed clinical questions, methodology and rationale for its choice, definitions of terms, project assumptions, and limitations.

Background of the Project

Mental health disorders impose a great financial burden on society and individuals (Wu et al., 2021). Safe and effective delivery of health services is complicated with numerous risks of failure, which may have adverse effects on patients’ safety and outcomes. This is even more prevalent in patients with significant comorbidities, such as cancer, diabetes, tuberculosis, and others, when practitioners’ main focus can be placed on other conditions, and thus, depression can be underdiagnosed or diagnosed late. There is a fundamental need for improved screening, early detection, and management protocols of depression in different levels of care, including primary care facilities.

Early recognition, intervention, and surveillance are critical for preventing the adverse effects of undiagnosed and untreated depression. However, there is little evidence as to the optimal timing and intervals between screening attempts. More evidence is necessary for defining the efficient screening intervals. Among the existing hypotheses on the most appropriate screening intervals, there are two main approaches of screening every patient who has not been screened before, screening high-risk patients, considering their comorbidities or life events, and routine screening, which implies screening everyone despite their apparent symptoms and life situations. Therefore, US Preventive Services Task Force (USPSTF) recommends depression screening as a way of early detection and intervention, but further research is necessary for developing evidence-based protocols on using specific screening tools with more or less certain time intervals (Costantini et al., 2020).

Problem Statement

Whereas depression is highly prevalent in adults age 18 or older, depression screening is often underused due to the restrictions in time and financial resources (Grapp et al., 2019; Sun et al., 2020). As a result, some cases of depression can remain undiagnosed and untreated or diagnosed late. During physical exam in a primary care setting, depressive symptoms may be not obvious, mainly because the purpose of the visit is to address other health aspects. Patients usually do not share information about depression or their mood disorders unless specifically asked about it.

It is not known if or to what degree if or to what degree the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) would impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida over four weeks.

Purpose of the Project

The purpose of this quantitative quasi-experimental project is to determine if or to what degree the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) would impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida over four weeks. The implementation of the PHQ-9 will be measured numerically, calculating the number of patients who are newly diagnosed. The dependent variable is the rates of newly diagnosed depression in patients. This PHQ-9 implementation will represent the independent variable of the project.

A review of electronic health records (EHRs) will be conducted examining patient charts 30 days prior to the intervention, collecting the information on the number of depression screenings performed and newly diagnosed patients detected. In addition, data will be collected documenting if patients reported depressive symptoms themselves or if depression risks were detected by the health care professional, determining patient or professional next to each detected case. Based on the clinical questions, patients newly diagnosed with depression will represent the project’s dependent variables.

The data will be collected in the course of the project and analyzed after the 4-week project finishes. These results will be compared to corresponding pre-intervention data. The purpose of this quality improvement project will be to detect if the use of PHQ-9 tool for routine depression screening can improve the rates of newly diagnosed patients with depression in adults age 18 or older in a primary care setting.

Clinical Question(s)

The following clinical questions guide this quantitative project:

• Q1: To what degree does the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida?

Patients with untreated depression have a decrease in their quality of life and decrease in healthcare outcomes. Untreated depression has been linked to an increased risk of suicide and poor outcomes with coexisting comorbidities. Comprehensive screening tools are needed to improve screening practices and enhance the rates of early detection and timely management of depression not only for patients who present with depressive symptoms complaints or belong to high-risk groups, but also those who have hidden symptoms of depression. The independent variable is the PHQ-9 depression screening tool for routine screening of all patients older than 18 years old. The dependent variable is the number of newly diagnosed depression patients.

Advancing Scientific Knowledge

This project will contribute to the existing evidence on the use of depression screening by addressing the gap in evidence on effective timing of screening, detecting whether routine depression screening can be effective in a primary care setting.

This project implementation shows how Lewin’s Change Theory and Nola Pender’s Health Promotion Theory can be used to implement a quality improvement project. Lewin’s theory incorporates three main stages, unfreezing, changing and refreezing, which will be applied for the implementation of an intervention, which was meant to improve practice. By incorporating Nola Pender’s Heath Promotion Theory, this project will focus on increasing a patient’s level of wellbeing. According to Pender’s model, health is a positive dynamic state, rather than absence of disease. In this context, depression screening of patients who have no complaints about their mental state is a method of preventing or early detection of depression and other mental health issues. By incorporating Lewin’s Change Theory, this project will pass through the main stages of change implementation, which are unfreezing changing and refreezing, to successfully launch the intervention of routine depression screening through the use of PHQ-9 screening tool.

Significance of the Project

Depression is recognized as one of the most common mental disorders and one of the main causes of disability worldwide and in the United States. Despite its high prevalence, with 300 million people globally living with this condition, it can remain undetected and untreated (De Joode et al., 2019). Despite the recommendations of the official guidelines, depression screening tools are underused, and the condition can be unrecognized, in particular in cases when patients have essential comorbidities, such as cancer or tuberculosis, and thus clinicians’ main focus is on addressing the physical health concern, whereas mental health status should not be underestimated either (Levis et al., 2020). Moreover, unrecognized, and untreated depression can be life-threatening, because its severe cases can be associated with suicidal ideations or even attempts. Furthermore, the adverse effects of depression on the current quality of life can be essential, as in some cases depression can have negative effects on social status, interfering with relationships and employment or reducing the effectiveness of treatment programs for some chronic diseases (Pence et al., 2019).

This project is in conformity with other research findings from primary care settings, claiming that adult depression screening is a necessity, which would benefit patients who have hidden or even more evident depression symptoms (Carroll et al., 2020; Tomitaka et al., 2018). Identifying patients at risk early in their depression state or even before the condition is developed through early diagnosis and the use of interventions and precautionary measures. Current research promotes the idea that early depression detection is essential for the efficient treatment, and both clinicians and patients should be encouraged to initiate screening, risk assessment and early intervention (Levis et al., 2019).

Rationale for Methodology

This project will use a quantitative methodology aimed at evaluating a relationship between the independent and dependent variables for the purpose of supporting or questioning existing theories (Creswell & Creswell, 2018). A quantitative method was chosen to use statistical analysis and establish if a mathematical relationship exists between variables. The analysis of numerical findings will be used for making the analysis objective. The objective of this quality improvement project will be to determine if or to what degree the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) would impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida.

Qualitative methods would not suffice for the purpose of this project, because they would only allow describing the perceptions of phenomena from the point of view of individual experiencing them. Whereas a qualitative method can be used to collect subjective opinions and interpret them, quantitative method can be used to establish statistically significant relationships. Therefore, findings of a quantitative research are more reliable, credible, consistent and less time consuming, because statistical analysis can be automatized, whereas transcribing and interpreting the findings of qualitative research requires a lot of time.

Nature of the Project Design

The design used in this DPI project will be quantitative method with quasi-experimental design. The choice of this design is explained by the needs of a quality improvement project evaluating the role of depression screening in newly diagnosed depression patients. A quasi-experimental design will allow determining a cause-and -effect relationship between the variables, however, without randomization of participants, which is done for the convenience purposes.

The project will be occurring within four weeks. It will start by explaining the clinicians the importance of depression screening for at-risk patient groups, followed by implementation of PHQ-9 screening offered to adults age 18 and older. The target population of the project will include the adult age 18 and older patients that has not been diagnosed or treated for depression. Six primary care practitioners will be working at a primary care setting in Florida.

Nominal and scaled data measurement will be used for the data analysis in this quantitative project. The data will be described and evaluated with the help of descriptive and inferential statistics. A paired-sample t-test will be used in this project both to determine if association between the dependent and independent variables exists and is statistically significant.

Definition of Terms

Depression is defined as a mental state associated with a lack of positive affect and a range of negative emotional, cognitive, and behavioral symptoms. Depression is a mood disorder, which may present with more obvious or hidden symptoms. Taking into account the variety of underlying causes and severity of possible complications, early detection of depression is essential for timely and effective management and prevention of adverse effects (Tomitaka et al., 2018).

• Patient health questionnaire -9 (PHQ-9). PHQ-9 is a 9-item self-administered screening tool, addressing the main risk factors, allowing to assess individual depression risks (Appendix B) (Doi et al., 2018).
• Healthcare professionals. Healthcare professionals are qualified individuals working in healthcare, including physicians, registered nurses, and other trained staff with healthcare qualifications.
• Electronic Health Records (EHRs). EHRs are digitalized charts, containing personal health histories, treatment plans and health practitioners’ comments.

Assumptions, Limitations, Delimitations

Assumptions can be defined as beliefs which are accepted as true by investigators and readers. Limitations are the potential weaknesses detected in the project design, which are out of the investigators’ control and may potentially lead to bias and inconsistencies in the project findings. Delimitations are defined measures used by the investigators for the purpose of improving control over variables.

• Assumptions. It was assumed that the PHQ-9 screening tool will be used properly and according to the instructions. Health professionals’ efforts are significant for the optimal implementation of the project, but it depends on a variety of factors, such as the workload and professionals’ motivation, which are difficult to predict and control. However, it can be assumed that healthcare workers are expected to do their work properly, and thus, including PHQ-9 into the list of responsibilities will allow assuming that the workers approach PHQ-9 implementation as throroughly as the rest of other responsibilities. It was assumed that all patients will answer the questions appropriately, providing relevant and objective information about their mental health status. Wheras mental health disorders are stigmatized, there is likelihood that some patients may choose to conceal their symptoms. However, dealing with mood disoders, like depression, asking about perceived wellbeing is the most appropriate way to collect data on perceived individual wellbeing.
• Limitations. The perceived project limitations include short time frame of 4 weeks to gather data and providers’ workload, which may impact visit time and time spent on PHQ-9 administration (Galenkamp et al., 2017). Another limitation is the lack of sample randomization, explained with the investigators’ convenience. All the patietns visiting the primary care setting will be screened and invited to take part in the project. However, this sample will not be representative of wider population groups. Additionally, the sample size will be relatively small, the data will be collected in only one setting, which limits possibilities for generalizability of findings. The findings will be valid for only one setting, which may have certain specifics and thus differ from other locations.
• Delimitations. This project will be implemented in one primary care setting in Florida. If the project findings demonstrate the connection between the use of PHQ-9 screening tool and improved rates of newly diagnosed depression patients, the findings can be used for direct practice improvement. The findings of this project will be disseminated in the primary care setting, contributing to the evidence-based approach implemented in the setting.

Summary and Organization of the Remainder of the Project

Depression is a serious and widely spread mental health disorder, accounting for a substantial number of deaths worldwide and causing a serious financial burden (Inegbenosun, 2021). By contrast to other mental health disorders and physical health conditions, the signs of depression can be not obvious, making depression overlooked and underestimated. Therefore, depression may remain undiagnosed and untreated, potentially leading to complications, such as suicidal ideations and even attempts. Early screening and diagnosis of depression are essential for timely and effective treatment (Keum et al., 2018). At the project implementation site there is a lack of standardized protocols on the use of a depression screening tool and time intervals for its use in this primary care site in Florida. As a result, depression screening is underused. This project will establish if the use of PHQ-9 depression screening for all patients age 18 and older can improve the rates of newly diagnosed depression patients.

Chapter 2 will present a synthesis of literature and research available on the related topic of PHQ-9 screening and its role in evaluating the risks and diagnosing depression. Chapter 3 will describe project methodology and design as well as steps taken to analyze the research findings. Chapter 4 will present findings and analysis of the data. Chapter 5 will interpret the findings and discuss their significance, in context of current research on PHQ-9 screening and its role in evaluating the risks of depression and its early detection.

Literature Review

Depression is the leading cause of medical disability and increased medical costs in the United States and worldwide. The annual estimated costs of depression treatment in the United States are between $83 – 125 billion (Cho & Crisafio, 2018). Living with a mental health disorder is associated with significant impairments of the quality of life. By contrast to the common misconceptions, according to which depression is mood swing, which an individual may control, and which can be easily removed without medical interventions (Muñoz-Navarro et al., 2017). So, by contrast to the stereotype discussed above, depression is a serious mental health condition, affecting functioning of many systems and requiring timely recognition and effective management through pharmacological interventions and repeated psychotherapy counselling sessions. Research estimates that up to 50% of major depression goes unrecognized and undiagnosed (Costantini et al., 2020). Currently at the project site, they only do an annual screening of depression.

This chapter will discuss the review of the literature on depression and screening and theories that were used as the framework for implementation. The literature included in this chapter was found through an extensive search for relevant scholarly works in online databases and journals. A comprehensive search of the body of literature was conducted to address the use of PHQ-9 in a primary care setting in Cumulative Index of Nursing and Allied Health Literature (CINAHL), PubMedm PsycINFO. The terms used for this search included ‘depression’, ‘PHQ-9’, ‘depression screening’. The original search included over 500 search results, which were later refined through the use of additional filters, including publication since 2017 and English as the language of publication. After removing duplicated articles and studies conducted in unrelated settings, 50 articles were selected for review in this project. Research articles included retrospective cohort studies, systematic reviews, randomized control trials and meta-analysis. The articles were evaluated as to the strength of evidence, analyzed, synthesized, and divided according to themes and subthemes.

The literature was then grouped into 3 main themes of Depression, Depression Screening and Screening Tools. Under the main theme of Depression, the sub themes are Types of Depression, Patient Management, Underdiagnosed and Undertreated Depression. Under the main theme of Depression Screening, the sub themes are Early Detection, Risk Factors, and Monitoring Changes in Symptoms. Under the main theme of Screening Tools, the sub themes are PHQ-9, GAD and BDI.

Background

The global prevalence of depression as the leading cause of disability has skyrocketed from the fourth leading cause to the first most important factor in the previous two decades (Shah et al., 2020). Recognized as one of the most common mental health disorders in the primary care settings, depression can be detected in at least 10% of all patient population. The prevalence of depression is estimated at 15% in patients visiting family physicians, 30% in patients with chronic diseases and 20% in patients with diabetes mellitus type 2 (Shah et al., 2020).

Commonly appreciated for its brevity, convenience, and validity for the different groups of patients, including various age and ethnic groups, the PHQ-9 questionnaire has been translated into numerous other languages. It makes PHQ-9 an international depression screening tool, which, however, requires further research and analysis (Sun et al., 2020). At the same time, the questionnaire is criticized for the risks of receiving false positive and false negative results, further leading to inappropriate interventions and even adverse effects from drug-drug interaction, polypharmacy, and others.

Theoretical Foundations

For this project implementation, a change theory was used to facilitate the change and a nursing theory was used to guide the implementation. The nursing theory used as a framework for this project is Nola Pender’s Self-Care Theory. The change theory is Kurt Lewin’s model of change.

Kurt Lewin (1951) has pioneered the study of organizational development, incorporating change as its important component. Lewin’s theory of change describes the process in which nurse leaders detect irrelevant knowledge and reject it, replacing with better alternatives (Lewin, 1951). According to Lewin’s theory, complex systems in general and healthcare organizations in particular, undergo the influence of counter-fighting driving forces, namely the restraining forces, aiming at maintaining status quo and resisting changes and driving forces, initiating the processes leading to changes (Lewin, 1951). The tension between the restraining and driving forces accounts for the equilibrium inside of the organization. However, the implementation of the planned change initiatives, based on Lewin’s three-step model is necessary for changing the status quo and launching the practice improvement initiative. The following three steps will be needed for the implementation of PHQ-9 in the chosen primary care setting: unfreezing, changing, and refreezing. The first step of unfreezing is associated with creating problem awareness by demonstrating the existing problem. The literature review and extensive evidence base presented in this project will be used as a pattern for undoing the existing equilibrium and pushing forces towards future change. The problem of underdiagnosing depression in adults and insufficient use of screening, which may lead to serious negative consequences, represent the first step of unfreezing the system. The second step is changing through seeking alternatives, which is realized through demonstrating the advantages of routine screening with the use of PHQ-9, based on the past studies of this topic. The third step of refreezing, consisting in establishing a new equilibrium in the form of the use of PHQ-9 as a new normal practice and resisting further change. The stages in this third step include celebrating success and effectiveness of the new model and further monitoring of key performance indicators, such as the number of screenings performed, the time spent, and the number of depression cases diagnosed. Lewin’s Change Theory was previously used for enhancing rates of depression detection as a staged guideline for implementing and sustaining practice change, for example, in a study on improving postpartum depression screening by Russomagno and Waldrop (2019).

Lewin’s change theory will be combined with Nola Pender’s Heath Promotion Theory. According to the definition offered by Pender, health is a positive dynamic state, broader than the absence of disease (Pender, 2011). Aimed at increasing the client’s health-related quality of life, Pender’s model emphasizes the multidimensional nature of persons, pursuing the goals of health. Applying this theory to clinical practice, depression screening is as important as addressing the physical symptoms. Implementing depression screening for patients who have no particular complaints about their mental health at the moment is in compliance with the multi-dimensional nature of health. Depression can be an influential factor, contributing to physical conditions and restricting their treatment. Lewin’s theory and Pender’s theory are not contradictory and can be used in combination to meet the goals of this project.

Review of the Literature

A literature review was conducted to establish what is already known and unknown about unrecognized depression and lack of screening for depression. This section provides a synthesis of the current literature on the project variables and related constructs. The review was organized into three main sections corresponding to the themes that emerge in the literature. The following main themes identified were: Depression, Depression Screening and Depression Screening Tools.

Depression

Depression is an important public health concern, a burdensome chronic recurrent mental health condition. According to Sun et al. (2020), MDD accounts for 35% of disability-adjusted life-years and the leading most prevalent psychiatric disorder globally. MDD requires a comprehensive and systematic treatment, which is made possible through measurement-based care (Brody et al., 2018; Maurer et al., 2018). Numerical measurements are necessary for early detection of depression, monitoring the changes in symptoms and guiding the treatment strategies (De Joode et al., 2019). The importance of accurate disease evaluation and monitoring is emphasized in the guidelines of the American Psychological Association. The effective management of MDD requires detecting a convenient and effective tool to be implemented for measuring the severity of depression and monitoring therapeutic response to treatment.

Types of depression

Ettman et al. (2020) conducted a study using PHQ-9 to estimate the prevalence of major depression symptoms among US adults before and during COVID-19 pandemic. The method used was a nationally representative survey studying 2 population-based surveys. The sample included 1441 participants who completed the survey. The main conclusion was that the prevalence of depression symptoms in US adults was more than 3-fold higher during the pandemic than before it. COVID-19 can be considered as a traumatic event, causing physical, emotional, and psychological harm to all participants. Furthermore, the policies that were developed to stop the spread of the pandemic disrupted daily routines for most people in the US, creating additional stressors. Whereas the prevalence of depression symptoms was higher in every category during COVID-19, having more financial resources and savings and being married were associated with lower rates of depression symptoms (Ettman et al., 2020).

Oquendo et al. (2019) investigated the topic of major depressive disorder in physicians, which should be distinguished from professional burnout. Whereas burnout and depression have partially overlapping symptoms, physician depression and suicide prevention are often underestimated. One of the possible reasons for underdiagnosed and undertreated depression offered by Oquendo et al. (2019) is stigma surrounding mental health diagnoses even among healthcare workers. While burnout is considered as a condition caused by external factors, it can be regarded as a more acceptable diagnosis, which, however, will not allow timely and effective treatment for the underlying condition.

Iob et al. (2020) investigated the association between hair cortisol and plasma C-reactive protein with the longitudinal symptoms of persistent depressive disorder. The results were collected for a 14-year period for a large sample of adults.7699 participants were interviewed, and hair samples were collected from 5451 individuals. However, after extraneous variables, such as undetectable cortisol level and current infections, interfering with the results were excluded, the final sample included 5784 participants. The results have revealed an association between higher levels of cortisol and CRP levels and persistent depressive symptoms. These two biomarkers were stronger associated with somatic rather than cognitive symptoms of depression. According to Iob et al. (2020) their study has the potential to search for biomarkers of depression and develop more targeted treatments for different types of depression.

Forneris et al. (2019) investigated the role of psychological therapies for preventing seasonal affective disorder (SAD), defined as a seasonal pattern of recurrent major depressive episodes occurring in autumn and in winter and remitting in spring. The objective of this study was to assess the effectiveness of psychological therapies, compared with no treatment, other types of psychological therapies, pharmacotherapy, light therapy, melatonin, and other strategies, in preventing episodes of SAD in individuals with history of SAD. Meta-analysis and systematic review were conducted to collect the evidence on psychological therapies aimed at preventing SAD. The results of this study indicated that there is no sufficient evidence that psychological therapies can be more efficient than other preventive options. The decision for or against the use of these strategies should be based on patient preferences and other preventive interventions used.

Shorey et al. (2018) investigated the prevalence and incidence of postpartum depression in healthy mothers without prior history of depression and those who gave birth to healthy full-term infants. The study used a systematic review method, collecting data from 58 articles. The incidence of postpartum depression was estimated at 12% with significant regional variations in different geographical locations, with the prevalence being the highest in Middle East and the lowest in Europe. Laos, there was evidence that the rates of postpartum depression were the highest in 6 months postpartum.

Patient management

Main treatment options for depression include psychotherapy, pharmacotherapy, transcranial magnetic stimulation (TMS) and electroconvulsive therapy (ECT). In a study conducted by Cuijpers et al. (2020), meta-analysis was used to examine the effects of 15 different types of psychotherapy on managing adult depression. Among therapies, included into this review, there were acceptance and commitment therapy, mindfulness-based cognitive behavior therapy, life review therapy, self-examination therapy, guided self-help, brief psychodynamic therapy, and others. Taking into account that there are no official definitions for the 15 types of therapy, three very broad generic categories can be distinguished, which are psychodynamic, cognitive-behavioral, and humanistic categories. The study was based on 385 comparisons between an intervention and control group. Whereas the main conclusion was that the 15 types of psychotherapy may be effective for managing adult depression, the evidence for the most types was lacking consistency due to the risk of bias in the majority of studies. After adjusting the findings with the risks of bias, which were present in 70% of studies, there were only two types of therapy, which remained significant, namely the ‘Coping with Depression’ course and self-examination therapy (Cuijpers et al., 2020).

Cuijpers et al. (2018) conducted a meta-analysis study to verify whether problem-solving therapy (PST) is effective, compared to control groups and other treatments. The clinical questions included whether PST is effective, whether its effects are comparable to other treatments and if significant sources of heterogeneity could be identified. 30 randomized trials were included into this study on PST effectiveness, with a total of 3530 patients included in trials comparing the effects of PST to effects of other therapies and the effects of pharmacotherapy. The main conclusion made by Cuijpers et al. (2018) was that PST can be an effective treatment for depression, but the effects are comparable to other therapies and no significant differences between PST and other therapies were found.

Gaspar et al. (2019) assessed the rates and determinants of pharmacological and psychotherapy and adherence to official guidelines for treating major depressive disorder. The study used a retrospective claims design as its methodology. The records of 2007 – 2016 for a population of 24, 579 patients with major depressive disorder, were tracked to analyze the determinants and antidepressant adherence. The main finding was that most patients (54.7%) quit receiving either antidepressants or psychotherapy after 5 months after being diagnosed with depression. Gaspar et al. (2019) concluded that treatment guideline recommendations were not followed, and improvement is necessary in multiple areas to enhance effective treatment.

Sonmez et al. (2019) used systematic review and meta-analysis to assess the effectiveness of TMS for treating depression. The analysis was based on 18 articles with three randomized control trials among them. The analysis of randomized trials has demonstrated the cumulative effects of TMS. The meta-analysis has revealed that TMS can potentially improve depressive symptom severity, but the method is associated with financial and time burdens, which can be problematic for patients. The study methodologies were considered as acceptable, but further efforts can be necessary for enhancing the techniques and blinding. A study by Ross et al. (2018) was aimed at assessing the cost-effectiveness of electroconvulsive therapy (ECT) compared with pharmacotherapy/psychotherapy for managing depression in US adults. The methodology used in this study was a decision analytic model used for collecting data from meta-analyses, randomized trials, and observational studies. The main conclusion was that ECT may be an effective and cost-effective treatment option for US adult with treatment-resistant depression.

From a health-economic standpoint, ECT should be prescribed after failure of 2 or more lines of pharmacotherapy/psychotherapy. According to Ross et al. (2018), ECT can be significantly more effective than pharmacotherapy, with 50% to 60% of patients experiencing rapid remissions (compared with 10% to 40% in pharmacotherapy/psychotherapy). ECT is more effective if it is introduced earlier and along with immediate clinical effects, it was associated with long-term benefits, such as reduced psychiatric hospitalization rates and lower long-term risks of suicide and all-cause mortality.

Underdiagnosed and undertreated depression

Pálinkás et al. (2019) investigated the prevalence of untreated depression in patients with hypertension and diabetes mellitus. The data contained records of 2027 patients, which was retrieved from a primary care program. Beck Depression inventory was used to diagnose depression. Multiple logistic regression analysis was used to establish the association between untreated depression and secondary healthcare utilization. The frequency of untreated depression was estimated at 27%, with untreated severe depression detected in 7% of patients. The untreated severe depression was associated with increased number of visits and related healthcare expenses. The main conclusion was that screening for depression in patients with hypertension and diabetes is reasonable and feasible.

Samples et al. (2020) investigated depression screening patterns and the role of screening in detecting and managing depression in primary care setting. The study used a cross-sectional analysis, based on nationally representative survey data of visits to outpatient physicians from 2005 to 2015. The sample included 16887 patients 12 years or older. Logistic regression as used to estimate the odds of depression diagnosis screening (3%), mainly for the patients who presented with depressive symptoms complaints.

The main conclusion was that physicians used depression screening selectively and only based on patients’ presenting symptoms. However, even modest increases in screening rates were associated with meaningful increases in rates of depression identification and treatment in primary care.

The objective of the study conducted by Briggs et al. (2018) was to quantify the burden of undetected and untreated depression and death ideation in geriatric population. The design of the study was cross-sectional analysis, used for estimating the prevalence, detecting the factors associated with untreated depression. The sample included over 7000 community-dwelling individuals aged 50 or older. The participants who were not prescribed antidepressants or antipsychotics were defined as untreated. The question used to define death ideation was whether the individual felt like he would rather be dead in the last month. The study revealed that two-thirds of depressed older people were not prescribed pharmacotherapy. Therefore, it is important to enhance awareness on the importance of depression screening and further management in both primary care physicians and patients.

Waitzfelder et al. (2018) conducted a retrospective observational study to describe patient characteristics associated with depression treatment initiation and the choice of the treatment strategy. The sample included 241251 adults, who were diagnosed with depression in primary care settings. The rates of newly diagnosed depression and initiated treatment was 36% with treatment initiation commonly associated with depression severity. The main conclusion was that for only 53% of patients with a PHQ-9 score of 10 or higher were initiated a depression treatment. The use of psychotherapy was more common than medication in minority patients.

Therefore, screening for depression in primary care is essential for improving detection, treatment, and outcomes for patients with depression. Further steps are necessary to improve treatment initiation, which is currently suboptimal and overcome disparities in treatment strategies implemented.

Therefore, as it can be seen from this theme review, depression is a burdensome and highly prevalent mental health disorder, which may have significant adverse effects on quality of life and wellbeing. Stigmatized even by health workers, depression often remains underdiagnosed and undertreated. The choice of the most appropriate therapy and early initiation of treatment requires timely and effective depression screening, which is made possible with the use of effective screening tools and guidelines.

Depression screening

Whereas depression can be reliably diagnosed and treated in primary care, the condition is frequently underdiagnosed and left untreated in primary care settings. Main factors contributing to the problem of poor rates of diagnosing and treating depression in primary care include the inadequate number of mental health workers, stigma associated with mental conditions and lack of locally validated screening tools. However, screening tools, if selected and applied appropriately, can be helpful for accurately identifying patients with depressive disorders and initiating appropriate management in primary care settings (Cheung et al., 2019).

Early detection

Kim et al. (2017) noted that mental health disorders are common among different groups of population, but the ageing population is an increased risks for developing depression symptoms. Kim et al. (2017) proposed using simple unobtrusive sensing system using passive infra-red motion sensors to track the daily activities of the elderly individuals living alone and using this data by machine learning early detection of depression risks. After sensor data was collected for 20 participants over three months, it was used for differentiating between normal activities and mild depression. The study findings were 96% accurate, compared to the same patients screened and diagnosed with screening tools. Therefore, non-intrusive sensor monitoring, and machine learning can be promising cost-effective algorithms used for early detection of depression.

A study conducted by Rosanti et al. (2020) was aimed at determining the health education intervention which could increase early detection of depression based on family. The study used a quasi-experimental design with one group pretest-posttest method. The sample size included 382 families in Indonesia. The data were analyzed with the use of Chi-square test and statistical analysis software. The key findings were that family-based health educational programs were associated with a statistically significant improvement in attitudes, behaviors, and early detection rates of depression in adults. The study confirmed the hypothesis that family-based educational intervention can contribute to early detection of depression.

Shah et al. (2020) noted that with the growth of social networks, people share their emotions, feeling and thoughts online and this information can be used for early detection of depression risks. Shah et al. (2020) suggested a hybrid model for detecting depression through the analysis of textual posts. Shah et al. (2020) concluded that the use of deep learning can eb beneficial for early detection of depression risks in active users of social media, and this approach can be used as complimentary despite the privacy concerns of textual posts.

Kondova et al. (2018) noted that depression can often remain undetected in primary care, and the development of comprehensive screening tools is necessary for early detection of depression in primary care. Kondova et al. (2018) conducted a study aimed at differentiating individuals at risk of depression who prefer psychotherapy from those choosing pharmacotherapy. After PHQ-2 and PHQ-9 were used to screen 83 individuals for their depression risks, the main conclusion was that 55% of individuals had indications of mild to moderate depression. Over 50% of individuals with detected depression risks had an underlying chronic comorbidity. The main conclusion and recommendation by Kondova et al. (2018) were to make depression screening a routine part of healthcare, with particular attention being paid to patients with chronic diseases who are at increased risks of hidden depression symptoms being unrecognized and undiagnosed.

Risk factors

Cheung et al. (2019) investigated the role of gastrointestinal microbiome in the development of psychiatric disorders, particularly major depressive disorder. The study used a meta-analysis and literature review to identify human case-control studies investigating relationships between depression and microbiota. Six eligible studies were used to investigate the mechanisms and products of bacterial metabolism relating to the etiology of depression. The main conclusion was that there was no statistically significant effect of gut microbiota impairments on depression symptoms. This conclusion can be partially explained with the differences in study designs and further research is necessary to investigate whether gut microbiota specifics can be considered as the risk factor for depression and other mental health disorders.

Jokela et al. (2017) researched the role of inflammatory markers, such as C-reactive protein in individuals diagnosed with depression. The data was collected from three cross-sectional studies. Inflammation was associated with a number of depression symptoms, such as anhedonia, lack of energy, reduced appetite, sickness behavior. The main finding that inflammation may lead to depression or be indicative of depression. Further research can be necessary for determining whether changes in inflammation are associated with changes in the specific depression symptoms.

Oh et al. (2018) noted that seasonal allergies have been associated with mental health problems. The study was based on multivariable logistic regression models, which were used to examine relationships between lifetime allergies and lifetime depressions. Oh et al. (2018) concluded that the relationships between seasonal allergies and depression were stronger for men than for women. The main clinical finding was that seasonal allergies are an important risk factor for developing mental health problems. Thus, individuals presenting with seasonal allergies should be screened for mental health disorders and referred to specialized services or treated in primary care if necessary.

Pence et al. (2018) investigated if the role of improved depression screening and evidence-based depression treatment can be effective for shortening the duration of depressive episodes in adults living with HIV. The underlying problem was that HIV is an important risk factor of depression, which commonly affects adults with HIV and complicates the management of HIV. The objective of this study was to examine the association between increased prevalence of depression and multiple HIV care indicators. The conclusion was that greater chronicity of depression leads to increased likelihood of failure at multiple points in the curse of HIV care. More advanced protocols for early detection and effective treatment of depression in individuals with HIV is necessary, which could potentially shorten the course and prevent repeated incidences of depression, improving patients’ clinical outcomes.

Jia et al. (2017) discussed the association between depression and risks of cancer. It was hypothesized that depression can be associated with increased risks of developing cancer. However, further multinational research and larger samples are needed to confirm this hypothesis. Systematic review and meta-analysis were used to investigate the incidence of cancer from 25 studies. The study by Jia et al. (2017) has shown a small and positive association between depression and occurrence of cancer, but further research is needed to confirm this association on larger samples.

Monitoring changes in symptoms

Sun et al. (2020) assessed the reliability of PHQ-9 for screening, diagnosing, and tracking changes in symptoms in psychiatric hospitals in China. 109 outpatients and inpatients meeting the criteria of major depression according to the Diagnostic and Statistical Manual of Mental Disorders were included into the study sample. The study evaluated the construct validity and criterion validity of the screening tool. The conclusion was that PHQ-9 showed good validity and reliability, coupled with high adaptability for psychiatric clinic use. The tool is simple and reliable for not only diagnosing depression, but also tracking the changes and improvements in symptoms in the course of treatment.

Das et al. (2019) used systematic review of randomized controlled trials to establish the efficacy of various depression treatment strategies and the tools to be used for tracking changes in depression symptoms. A total of 21 studies with 4563 patients were included into analysis. In comparison to usual care and placebo effects in treating patients with depression, ET and CBT have revealed more positive outcomes. Whereas the comparison between the different treatment methods were not conclusive, further investigation on the most effective tools for tracking changes in depression symptoms in response to treatment can be needed.

A study conducted by Akincigil and Matthews (2017) was aimed at examining the national rates and patterns of screening for depression in primary care. Despite the recommendation for using depression screening as a part of routine primary care, it is often underused, and depressive symptoms often remain undetected by primary care physicians. The study used secondary data analysis from 2012 to 2013 National Ambulatory Medical Care Survey. As a result, 33,653 patient visits were included in the analysis. The total rate of depression screening was estimated at 4%. Therefore, the rates of depression screening were very low and further improvements are needed to develop more comprehensive protocols for depression screening as well as tracking the changes of symptoms in depression treatment.

A study conducted by Park and Zarate (2019) used a case study to analyze the screening and treatment strategies used for depression diagnosis, using a specific case. The main clinical findings of this article were that screening for depression is essential in primary care settings. The choice of treatment strategies depends on associated side effects and patient’s individual health history, such as coexisting chronic conditions and drug reactions. The conclusion was that psychotherapy can be first-line treatment for moderate depression, whereas antidepressant drugs have been more beneficial for moderate to severe depression cases. Further research is necessary for detecting validity of screening tools for monitoring the changes in depression symptoms in response to depression treatment.

To sum it up, depression screening is important for early detection of depression, its timely management and monitoring of changes in symptoms when treatment is initiated. In some cases, the patients can be lacking awareness on their depression symptoms, but in other cases, the patients may want to conceal their symptoms due to stigma surrounding mental health conditions. Instead of subjective interviewing by physicians in primary care, which can be biased and insufficient, more objective screening with numerical evaluation of symptoms severity should be used.

Screening Tools

The PHQ-9 screening tool has been developed on the basis of the Patient Health Questionnaire for the purpose of detecting depression and other mental health conditions and monitoring changes in symptoms with the course of time after treatment has been implemented. Many extensive studies validated the accuracy of PHQ-9 being used for assessing the risks of depression in a primary care setting (Mufson et al., 2021). Additionally, some studies have revealed that individuals who have been screened had better chances to receive appropriate interventions for depression.

PHQ-9

Another important criterion of PHQ-9 validation is comparing its sensitivity and reliability with other similar tools and scales. In a meta-analysis study conducted by Sun et al. (2020), the validity and reliability of PHQ-9 was ranked higher than the same characteristics of DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, fourth edition). The study was comprised of results from 6000 subjects and concluded that PHQ-9 can be used as not only a self-administered screening tool, but also an effective tool for monitoring changes in the symptoms severity. The adaptations of PHQ-9 have been translated into differ rent languages, including Chinese, French, Arabic, Korean, Greek and others. A meta-analysis of 17 studies conducted in different countries has revealed that PHQ-9 is a valid and reliable screening tool, which can be used in different populations in different countries.

In some cases, when a more specific screening tool is available, PHQ-9 still can be used as a valid and accurate diagnostic instrument. Thus, even though Edinburgh Postnatal Depression Scale (EPDS) is a recommended legacy screening measure for perinatal depression, PHQ-9 can also be used as a screening tool of perinatal populations with similar levels of sensitivity and specificity (Wang et al., 2020). Another study by Do et al. (2021) evaluated the validity of PHQ-9, compared to two other instruments, the Well-Being Index and Perceived Stress Scale. The findings of the project by Do et al. (2021) have revealed that the use of PHQ-9 can be validated as effective for screening for antenatal depression and suicide risks.

Some studies have revealed that PHQ-9 can be inferior in its sensitivity for diagnosing depression in some populations. Thus, a study by Guerra et al. (2018) has demonstrated that PHQ-9 is more conservative and less sensitive than HAM-D for diagnosing depression in individuals with heart failure.

PHQ-9 limitations

The limitations associated with some diagnostic tools used for depression screening include restrictions by cost, time necessary to administer the tool, and in case with self-administered tools, like PHQ-9, the self-reported answers of patients, which can be biased and affected by stigma.

Another important limitation of using PHQ-9 depression screening is its diagnostic accuracy for some groups of patients. Thus, Hartung et al. (2017) have concluded that the use of PHQ-9 is not as accurate and effective as the use of a standardized diagnostic interview, for example. Furthermore, a study conducted by Manea et al. (2017) revealed possibility of allegiance effects of the PHQ-9 authorship, which may lead to the researchers’ bias. In other words, the findings of the same questionnaire differed, depending upon whether it was conducted by its authors or not. Further research on the authors’ role in delivering and interpreting self-administered PHQ-9 can be necessary for establishing the tool validity and developing measures for addressing its restrictions.

Advantages of PHQ-9 depression screening

The PHQ-9 screening tool has been developed on the basis of the Patient Health Questionnaire for the purpose of detecting depression and other mental health conditions and monitor changes in symptoms with the course of time after treatment has been implemented. Many extensive studies validated the accuracy of PHQ-9 being used for assessing the risks of depression in a primary care setting (Mufson et al., 2021). Additionally, some studies have revealed that individuals who have been screened had better chances to receive appropriate interventions for depression.

Convenience and cost-effectiveness of PHQ-9

As one of the largely under-diagnosed yet treatable mental health conditions, depression presents a substantial healthcare burden which could be reduced through improved screening and management. Jia et al. (2017) stated that a two-stage screening comprised of administering PHQ-2 and PHQ-9 subsequently was cost-effective, estimated at only $1,726 per one patient. The two-item patient health questionnaire (PHQ-2) is followed by a 9-item questionnaire (PHQ-9) if a person screened positive on the first brief version of the questionnaire (Boothroyd et al., 2019). The extended version comprised of 9 questions significantly improves the specificity of the test, resulting in a positive predictive value in the target patient population. However, even after depression is successfully diagnosed, in many cases the condition can remain undertreated (Tomitaka et al., 2018). According to Jia et al. (2017), detection of depression symptoms is only the first important step in the management of the condition, which should eb followed by developing an intervention and carefully monitoring the changes in the patient’s condition. For this latter purpose of monitoring, the same PHQ-9 tool can be used with the higher score revealing more severe symptoms and a drop in the score associated with the improvement in the patient’s condition.

As one of the most inexpensive screening tools, PHQ-9 has a number of advantages, recognized both by the practitioners and patients. The convenient format, which enables patients to complete screening online or in a paper-based version on their own, while the practitioner can be easy with some other formalities. This approach not only saves time resources, but it is also essential for the patient’s comfort. Whereas mental health is a sensitive topic, often surrounded by stigma, discussing questions related to it can cause discomfort. However, by using the intuitive format, in which patients are expected to rank their answers, the PHQ-9 creates a favorable environment for comfortable and confidential screening (Stocker et al., 2021). However, the risks that the patients will be trying to modify their answers or to conceal something in order to produce a positive impression still exist and should not be underestimated.

To sum it up, PHQ-9 has been validated in official clinical guidelines as a reliable and cost-effective tool, which can be used for early detection of depression and monitoring the symptoms change in adults. Compared to its alternatives, even more specifically developed for diagnosing depression in narrower populations, such as patients with comorbidities, certain age groups and prenatal population, PHQ-9 has been proven effective and either as effective than its alternatives, or even more reliable. However, there is certain criticism of PHQ-9 for its conservatism and insufficient sensitivity or possibility of false positive results in detecting depression in high-risk populations.

Summary

Chapter 2 discussed the existing literature and gaps in knowledge regarding the use of PHQ-9 for screening adults for the risks of depression and monitoring the changes in the patients’ mental health status if the test is positive. Depression is a common mental health disorder with associated high risks of comorbidities and complicated treatment of those comorbidities. According to an extensive body of studies, PHQ-9 is a valid, reliable, and sensitive depression screening tool, appropriate for the use in adults in primary care settings (Carroll et al., 2020; Costantini et al., 2020). Comprehensive screening, early detection, and tracking changes in symptoms in response to treatment are the important steps to improved clinical pratices. Compared to its alternatives, including specialized measurements, like a measurement for postpartum populations or cardiovascular, diabetes patients and patients with other chronic comorbidities, PHQ-9 is as powerful and accurate as its alternatives or even exceeding other depression screening tools (Grapp et al., 2019). The only study, which prioritized the use of an alternative approach over the use of PHQ-9 was the study of depression screening in cancer patients (Grapp et al., 2019; Hartung et al., 2017.). As to the use of PHQ-9 in various ethnic and age groups, it has proven to be effective and valid, including its adaptations and translations into other languages (Patel et al., 2019). Chapter 2 presented theoretical foundations for the implementation of direct practice improvement in a primary care setting, namely Lewin’s change theory. According to Lewin’s theory, the implementation of change consists of unfreezing of the system, when the need for change is demonstrated, the stage of actual change and then the stage of refreezing, when the system restores its balance with the changed and improved practice integrated into it. Additionally, this chapter discussed the theme of barriers to the effective use of PHQ-9 in healthcare practitioners and patients, as well as the ways to overcome these barriers, with this project being one of the ways to address the problem. By using an evidence-based approach, collecting related evidence, and later disseminating it, this project contributes to the improved nursing practice. Chapter 3 will discuss methodology and design of this project chosen for investigating the effectiveness of PHQ-9 for early detection of depression.

Methodology

The purpose of this quantitative quasi-experimental project is to determine if or to what degree the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) would impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida over four weeks.

Depression is one of the leading causes of morbidity and mortality globally and in the United States. More than 300 millions of people of all ages in the world have depression (Maurer et al., 2018). The estimated prevalence of depression in adults age 18 and older in the United States is 8%, which makes depression one of the most common mental health disorders. However, despite the high prevalence and the variety of available screening tools and official recommendations for screening, only 5% of adults age 18 and older are screened for depression in a clinical setting (Costantini et al.). The failure to properly and timely screen, detect and treat depression may have serious detrimental consequences, including suicidal ideations and attempts. Additionally, depression is associated with low energy, loss of interests and apathy, which may interfere with treatment of many conditions, which require lifestyle improvements. Thus, depression may be an important influential factor, decreasing the effectiveness of interventions in individuals with chronic diseases, such as diabetes mellitus or hypertension. The importance of depression and its screening is often underestimated.

Statement of the Problem

It is not known if or to what degree if or to what degree the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) would impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida over four weeks.

Current research promotes the idea that early depression detection is essential for the efficient treatment, and both clinicians and patients should be encouraged to initiate screening, risk assessment and early intervention (Levis et al., 2019). Early recognition, intervention and surveillance are critical for preventing the adverse effects of undiagnosed and untreated depression. The use of PHQ-9 as a depression screening tool in the clinical setting under consideration requires additional training of healthcare providers, resources, and adjustments in planning, which would allow making routine screening for depression a part of the clinical practice at the project site.

Clinical Question

The following clinical question will guide this quantitative project:

• Q1: To what degree does the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida?

Compared to the use of PHQ-9 for routine screening, the current practice does not imply the use of routine screening for all patients, who are not in high-risk groups. The independent variable using the nominal level of measurement in this project is the implementation of PHQ-9 tool. The dependent variable is the number of reported newly diagnosed cases of depression, in adults age 18 or older. The number of post-intervention newly diagnosed cases will be compared to the baseline number of new depression diagnoses at the site in the 4 weeks prior to implementation.

Project Methodology

This project will use a quantitative research method, which would allow collecting measurable data and establishing if the implementation of the project improved the patients’ outcomes. The choice of the quantitative research method is explained with the need to collect numerical data to measure the variables and then determine if there is a relationship between them. Therefore, the main factors, which affected the choice of a quantitative methodology included the nature of variables and the need to determine if there is a cause-and-effect relationship through the use of statistical analysis and quantifiable variables. The wording of the clinical question is relatively narrow, and thus the answer requires the collection and further analysis of numeric data.

In contrast to qualitative research, quantitative projects use deductive reasoning and generalizations. A qualitative methodology is a subjective method that is hard to measure. The data in a qualitative methodology is not measured with numbers but rather explains or describes the characteristics of the data set which makes it hard to quantify.

Project Design

The design selected for this project is a quasi-experimental design. The project will collect data from only one isolated clinical setting, and the sample will be non-randomized. The project will be implemented at the clinical site. Quantitative methods use statistical analysis to detect whether a mathematical relationship exists between the numerical variables, establishing if the project was effective for improving practices in a clinical setting. The quasi-experimental design is appropriate for testing the effectiveness of PHQ-9 for routine depression screening in a clinical setting, but it is associated with increased risks of internal and external validity threats, which will be discussed later in this chapter.

The relationship between the independent variable of the PHQ-9 implementation, and the dependent variable, referred to as outcome variable, expressed through the number of newly diagnosed depression cases, will be based on numerical data, and further evaluated with the help of statistical analysis.

There are three other quantitative designs that were not selected for this project: descriptive, correlational, and a true experimental design.

A descriptive design is not a good fit for this project because it does not allow testing the effects of the intervention, and thus it would not be effective for answering the clinical question. A correlational design was not used because the project used no control group and because of the type of the relationship tested. A true experimental design was not used because it would be impossible to fully control the experiment in all of its stages. Instead, the project will be conducted in true clinical setting, which allows only partial control of the experiment.

Population and Sample Selection

The project will be conducted in a relatively small primary care clinic in Miami, Florida with the total population of about 4500 patients. The major population is comprised of diverse ethnic groups, living in the neighborhood of the clinic and rarely from other parts of the city. The target adult population of age 18 and older, represents about 85% of the clinic population.

The main inclusion criteria will include the patient’s age and time of visit during the project implementation. The only exclusion criterion will be the current mental health diagnosis of depression, which would make additional screening abundant and unnecessary. Using the convenience sampling, this project will use a sample of 30 participants. A convenience non-randomized sampling method will be used for the convenience of this project. A power analysis done to determine the minimum recommended sample size for this project calculate the minimum recommended number of participants as 27. Taking into account that some of the participants may fail to complete the study, the number of participants in the sample was increased to 30. All the patients visiting primary care and matching major inclusion criteria, which are the age older than 18 and not being diagnosed with depression earlier, will be included into the sample. Routine depression screening will be completed during primary care visits and the results will be used for further investigation.

Instrumentation or Sources of Data

The data collected for the purposes of this project will include the number of newly diagnosed cases of depression in adult patient age 18 or older. The demographic data, health and family history of participants will be retrieved from EHRs. The demographic data will be collected on the participants’ age, gender, ethnicity, education, employment, family composition to analyze and describe the demographic characteristic of the sample and collect data for further generalization opportunities.

The instrument being used for implementation is the PHQ-9 questionnaire. The PHQ-9 has been developed as a quickly administered depression screening tool for fast-paced clinical settings (Manea et al., 2017). With only 9 items included into the questionnaire, PHQ-9 may require only a couple of minutes for completing it, and then it is scored rapidly. This DPI project will use PHQ-9 for routine screening of all adult patients to assess its effectiveness in detecting cases of depression.

Validity

The potential internal validity threats in this project will include response bias and participants’ attitudes, when the participants can be attempting to conceal their emotions, because of stigma surrounding mental health conditions and the social stereotypes of having to show the best side of life and hide the reverse side of reality and moments of weakness.

The diagnostic validity of PHQ-9 was established in the previous studies, estimated as sensitivity of 88% and specificity of 88% (Sun et al., 2020). The relatively high levels of reliability ad validity of the tool have indicated that its psychometric properties are sound. Therefore, the collection of data through the use of PHQ-9 will be associated with valid and reliable results.

Reliability

The reliability of the project refers to its integrity in data collection and analysis. The appropriateness of the experiment conditions. The presence of uncontrolled and unpredictable variables can interfere with the reliability of this study. An important factor, threatening the reliability of the project is the participants’ readiness to cooperate and to provide viable answers to the questionnaire. Additionally, for testing distributions in data, the project can use a chi-square test of independence, which will allow evaluating the strength of association between the variables on a scale of 0 to 1 (Creswell & Creswell, 2018).

Data Collection Procedures

The implementation of PHQ-9 will begin, and all patients meeting criteria will be offered a self-administered PHQ-9 during their visits. During the visit, the provider will review and determine those at risk and determine if further intervention is needed. The provider will determine diagnosis code as appropriate.

Once implementation is completed the data of new depression cases will be collected from the EMR database and analyzed. In the past study conducted by Ingram et al. (2020), EMR data was valid to predict and investigate the prevalence of depression, as a simple algorithm was used to generalize most EHR data sets.

Data Analysis Procedures

To determine if there is a relationship between the variables, this project will use a t-test (Cho & Crisafio, 2018). This test will allow differentiating between systematic and random factors, affecting the project outcomes. First, data will be collected from the PHQ-9 questionnaires and reports on them. The raw data will be preparing through the use of Microsoft Excel, in which it will be ordered and visualized through the use of graphs and tables.

Next, data will be entered into the Statistical Package for the Social Sciences (SPSS) database version 26.0 and the t-test analysis ran. T-test is the most appropriate type of test for this project because it allows establishing statistically significant relationships between the intervention and outcome, if any, and establishing whether the relationship was statistically significant (Gray et al., 2020).

Potential Bias and Mitigation

A potential source of bias in this project may include the convenience sampling. Despite the restricted opportunities for making generalizations, the non-randomized sampling method is used for convenience purposes. Another important source of bias can be the patients themselves and the providers or primary investigators influence on opinionated responses. To minimize the risks of these sources of bias, the project will use routine screening, which means administering the questionnaire to every adult patient, instead of administering it to those that the providers or investigator consider as high-risk group.

Ethical Considerations

The project will comply with the core principles of Belmont Report, namely the principles of respect, justice, and beneficence. The implemented project will cause no added harm to patients or healthcare practitioners, emphasizing the importance of fairness, trust and respect for all participants and their rights. All personal identifiers will be coded, and personal information will be protected, while the project data will be shared only with the primary investigators. The numerical codes will be used for the demographic characteristics and health history. The information will be kept in the primary investigator’s computer, protected with several stages of authentication, and it will be deleted from the hard drive two years after the project completion.

Summary

This chapter presented a detailed discussion of the DNP quality improvement project methodology, including the rationale for the choice of methodology, design, sampling method, with its limitations, reliability, and validity threats. The method chosen for this project is a quantitative quasi-experimental design. The numerical data in the form of the number of newly diagnosed depression cases will be collected from PHQ-9 and EHRs. A t-test will be used for analyzing data and detecting causal relationship between the independent variable of PHQ-9 tool implementation for routine depression screening and the dependent outcome variable of the number of newly diagnosed depression. Despite the restrictions imposed by the use of a convenience sampling and limited time frame, the data from this project will answer the clinical question on whether and to what degree the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) would impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida. Chapter 4 will further present more detail on data collection and analysis procedures that will be used in this quality improvement project.

Data Analysis and Results

As one of the leading causes of disability worldwide, affecting over 300 million people of all ages globally, depression requires early recognition and effective timely intervention (Maurer et al., 2018). Despite the detrimental effects of undiagnosed and untreated depression, including suicidal ideations and attempts and overall decreased quality of life, depression is often undiagnosed or diagnosed late. The primary care setting in urban Florida, chosen for this project, could benefit from the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9), used as routine screening for all patients age 18 or older visiting the setting to improve the rates of screening and timely diagnosing of depression.

It is not known if or to what degree the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) would impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida over four weeks. The current practice is only annual depression screening for adult patients age 18 or older. The project will use a quantitative methodology with a quasi-experimental design. The purpose of this project will be to identify if there is a statistically significant relationship between the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) and depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida over four weeks. A chi-square test will be used to identify if the relationship between the implementation of PHQ-9 and the rates of newly-diagnosed depression patients is statistically significant. The chapter will include a detailed review and analysis of the collected data, the interpretation of data and a summary of the project findings.

Descriptive Data

The target population selected for sampling of this project will include adult patients age 18 or older in an urban primary care clinic in Florida. This age group was chosen because the statistical data on depression rates is usually divided into adult and pediatric population, which can be explained with the different processes in children and adults, different social determinants and levels of responsibility, leading to different risks of depression. This primary care facility provides family care to various groups of population, including low-income and minority groups. The clinic population includes patients diagnosed with chronic conditions, such as diabetes, hypertension, allergies, cancer and others, increasing the risks of developing depression. Additionally, postpartum period and corresponding hormonal processes and lack of night sleep can be precursors of postpartum depression. Among 150 participants included into sampling, # completed a self-administered PHQ-9 questionnaire, while the remaining # participants did not complete the questionnaire, due to the lack of time or poor health at the moment of visit. The only exclusion criterion will be the current mental health status of depression that has already been diagnosed prior to the visit. The age, gender, ethnicity, marital status, postpartum period and education level of the population included into sampling are documented in table 1.

Table 1. Characteristics and Number of participants.

The participants older than 18 years old were included into the non-randomized convenience sample during their visits to the primary care facility, based on their age as the main inclusion criterion. No written consent was obtained from the patients, because only data obtained from electronic health records and reports were used in the project. All the participants were screened with the use of a self-administered PHQ-9 test. The results of the screening revealed that 15 participants, who did not have any special mental health status prior to screening, were newly diagnosed with depression.

Descriptive data was significant to this project, so that the primary investigator could identify the trends in the demographic characteristics of the sample, which was not randomized, but obvious emphasis on certain groups of population should be avoided or taken into consideration when analyzing and interpreting the collected data. Some of the demographic characteristics, such as belonging to ethnic minorities or being a postpartum female, may increase the risks of depression. Additionally, there is a two-fold relationship between chronic conditions and depression; some disorders may lead to depression symptoms, and depression symptoms may complicate the treatment process and interfere with the treatment outcomes. It demonstrates the significance of descriptive data and explains its value to this project.

Data Analysis Procedures

The data collection approach and analysis procedures selected as appropriate for investigating the chosen clinical questions of this project included routine depression screening of all patients older than 18 years old, using the PHQ-9 self-administered questionnaire. The next step was further diagnosing of individuals scoring higher than 5 on this scale, which indicates possibility of mild depression, with the score of 10 – 14 indicating moderate depression, and score 15 and more indicating severe depression. The next stage of data collection was comparing the number of newly diagnosed depression patients with the number of depression diagnosis rates within a similar period in the past, when routine screening with the use of PHQ-9 was not applied. Secondary outcomes of the project included improved quality of life, energy levels, healthier lifestyles, and health outcomes in patients with chronic conditions, such as diabetes, hypertension and other.

The screening procedure was performed in the form of a self-administered PHQ-9 questionnaire, completed usually within 3 to 10 minutes, depending on the patient’s individual preferences. After the depression risk level was assessed, mild-, moderate- and high-risk patients were referred to the following diagnosis procedures, prescribed by the protocols. Further, the individuals, whose diagnosis was confirmed, received an individual treatment plan. In most cases, treatment strategies included pharmacotherapy, psychotherapy sessions or both, depending on the patient’s current condition, past health history, existing comorbidities and polypharmacy. The patients also received education on the recommended lifestyle changes and potential risks of untreated depression. In some cases, the patients also required psychological counselling in order to help them overcome stigma surrounding mental health conditions and help them recognize their condition and special needs.

The doctors’ reports on the number of patients at risk of depression and those diagnosed with depression, along with the data retrieved from the EHRs will be stored in the primary investigator’s personal computer for the following three years. The computer is safe and protected with password, whereas the collected personal data is coded to avoid patient identification. After three years of protected storage, the collected data and the project reports will be removed from the hard drive and destroyed, making restoration impossible.

The responsibilities of the primary investigator included extracting data and analyzing it. An Excel spreadsheet was used to compare the number of newly diagnosed depression patients within the four weeks in which the practice improvement project was conducted and the retrospective data on the number of depression diagnoses within a similar time period of four weeks, before the PHQ-9 routine screening was implemented. To respect the participants’ anonymity and confidentiality, the patient identification data was excluded from the Excel file, and the numerical codes were used to provide complete information, but make it free of unintended confidentiality breaches.

Reminders to use routine PHQ-9 self-administered questionnaires for diagnosing depression were manually added to the practitioners’ daily schedules for each patient age 18 or older. The randomization of the sample was not applied, but rather the patients were added to the sample until the target number of 150 was reached (8 of these participants were not able to complete the screening due to their personal reasons and were excluded from the sample as a result). In some cases, patients age 18 or older were not screened with the use of PHQ-9 due to the lack of time, motivation or other human factors, typical of human research. Therefore, despite the reminders and the updated depression screening protocols, some of the patients were not screened and thus could be underdiagnosed.

The internal reliability and validity were achieved through the use of the chi-square test, applied as a statistical method to analyze findings. The main advantages of the chi-square test are the fact that equality of variances in compared groups, which plays an important role for this project. In other words, the parametric assumptions cannot be conveyed in this project.

A sufficient number of participants were involved into this practice improvement project for internal validity. The p-value, connected with the level of significance (α) was set at 5% or p<.05. If p-value was lower or equal to the set significance level, the collected data would be defined as inconsistent, leading to rejection of the null hypothesis (Melnyk & Fineout-Overholt, 2015). Higher power, effect and sample size could increase the internal validity of the project and its outcome variables.

The possible risks of error in the project, which could potentially limit the validity and reliability, include the practitioners’ failures to administer the tool or the patients’ intentional or unintentional misuse of the PHQ-9 template. It is possible that due to the stigma of mental health disorders, participants might want to reduce their risks of being diagnosed, trying to predict their score on the report and taking steps to reduce the risks. For 6 % of all patients, the questionnaire was completed with the answer “No” to all questions, which could signal the attempts to conceal the real status and elevated levels of anxiety.

Results

The mandatory parts of the quality improvement project are descriptive and inferential statistics. To measure correlation between current and retrospective rates of newly diagnosed depression patients, a non-parametric chi-square test was used. The inferential statistics was used to analyze the numerical characteristics of data.

Table 2 will illustrate the association between the implementation of PHQ-9 tool in patients age 18 or older and the rates of newly diagnosed patients with depression. Before the intervention, the number of newly diagnosed depression patients was about 4%, while after the intervention, about 11 % of patients were diagnosed with depression.

Table 2. The effect of the intervention on the outcome variable. The number of depression diagnoses before and after the intervention.

The use of p reveals a statistically significant relationship between the implementation of PHQ-9 screening tool and improvement in the number of newly diagnosed depression patients. The prevalence of depression diagnosis, which changed after the intervention, clearly demonstrates the statistically significant improvement in practice, deserving further consideration and/ or reduplication.

Summary

In sum, the results of the project have revealed a statistically significant relationship between the intervention and outcome variables, in the form of the PHQ-9 use for routine depression screening and the number of newly diagnosed depression patients. The pre-intervention occasional screening allowed diagnosing only 4% of the sample with depression, while the post-intervention number of newly-diagnosed depression patients was estimated at 11 % of the total number of participants. The post-intervention secondary results included the improved quality of life, energy levels and health outcomes, as a result of depression therapies. Chapter 5 will discuss the implications of the project’s findings, including clinical and practical implications. Additionally, the future recommendations for further research on related topics, and improved internal validity and generalizability, will be provided to the investigators to improve the quality of projects and their outcomes.

Summary, Conclusions, and Recommendations

Depression is an important healthcare concern, associated with increased morbidity, mortality, reduced quality of life and substantial financial burden. Some categories of population, such as postpartum women and people with significant chronic or autoimmune conditions, such as diabetes, seasonal allergies, cancer and others, or people experiencing significant life hardships, are at increased risk of developing depression. Left unrecognized and untreated, depression may lead to detrimental consequences, such as suicidal ideations and attempts, poorer health outcomes. Timely recognition is critical for effective management and preventing the negative consequences of depression. Unlike healthcare concerns having more distinct physical manifestations, depression and many other mental health conditions can be not obvious. Furthermore, most mental health conditions are stigmatized, leading to patients’ reluctance to recognize their symptoms and attempts to conceal the condition and/ or manage it without professional help. For this reason, timely and effective depression screening is central to managing the problem of depression in adult patients. The current practice in the chosen primary care setting in urban Florida is annual screening of patients. However, there is evidence that routine depression screening can be effective for detecting more cases of depression and preventing depression complications.

Evidence demonstrates that the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) can improve depression diagnosis rates, decrease the prevalence of undiagnosed and untreated depression and related complications in patients age 18 and older (Costantini et al., 2020). This project was designed to use a quantitative method and a quasi-experiemntal design to assess whether the implementation of the PHQ-9 routine depression screening can be effective for improving the rates of newly diagnosed depression cases in a primary care setting in urban Florida. The project is important for enhancing understanding of the importance of depression, its adverse effects, timely screening and detection.

Summary of the Project

This project used a quantitative methodology and quasi-experimental design based on an intervention to assess if or to what degree the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) would impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida over four weeks. The choice of this multi-modal interventional approach can be explained with its relevance for the scope and requirements of this quality improvement evidence-based project in the selected primary care facility in urban Florida. The clinical question explored in this project was to what degree does the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) would impact depression diagnosis rates when compared to current practice among adults age 18 or older in a primary care setting in urban Florida.

This project was based on implementing routine depression screening for all primary care visits within a four-week time. By contrast to the current practice, in which only annual screenings or screenings in certain life situations (postpartum or after an accident) were completed, in this project, PHQ-9 was offered to all patients older than 18 years old and having proper English language competence. After the phase of active screening was completed, the rates of newly diagnosed depression cases were compared to the rates in a similar period using ony annual screenings. As a result, the project will demonstrate if the number of depression cases which would remain undiagnosed without routine screening is statistically significant.

This chapter summarizes the project findings, accomplishments and sheds light on the theoretical framework, used in the project and possible clinical and practical implications of its findings. Additionally, this chapter discusses the strengths and limitations of the project through the lens of analysis of the internal and external validity and credibility of the methodology chosen for the project. Acknowledging certain limitations of the chosen approach, the chapter offers solutions, which could enhance validity of findings and their generalizability, which could be valuable for future projects.

To discuss the applicability of the project findings in current clinical practice, the chapter will analyze the needs of adult patients and primary care practitioners in context of mental health status screening. For this purpose, the chapter discusses the implementation of the project in the chosen primary care setting. Taking into account the global prevalence of depression and possible detrimental consequences of leaving it undiagnosed and untreated, the topic of routine depression screening is relevant and its significance is likely to continue growing.

Summary of Findings and Conclusion

The clinical question that was guiding the implementation of this project was to what degree the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) impact depression diagnosis rates when compared to current practice among adults in a primary care setting in urban Florida. The data was collected on depression screening results and practices before and after the intervention with the goal of answering this question. The analysis of the collected data has revealed that there was a 375 % increase (4 and 15 diagnosed depression cases before and after the intervention respectively) in the rates of depression diagnosis. This finding has revealed a statistically significant relationship between the implementation of Spitzer et al.’s Patient Health Questionnaire-9 (PHQ-9) for routine depression screening and resulting increase in depression diagnosis rates (p<0.001).

In chapter 2, the literature review was conducted to identify the main themes and subthemes related to the main clinical question. The review of themes focused on depression prevalence, types and treatment strategies, the importance of timely screening and most effective screening tools. The findings of this project are applied to the analysis of the project significance and advancement of scientific knowledge, which were included into chapter 1.

• Depression. Depression is a burdensome and highly prevalent mental health disorder, one of the leading causes of disability worldwide. Detailed and timely measurement of depression symptoms, which can be achieved through screening, is necessary for defining depression degree and monitoring changes and improvements after the treatment is initiated. Main treatment options include psychotherapy, pharmacotherapy, transcranial magnetic stimulation and electroconvulsive therapy. According to Pálinkás et al. (2019), the rates of untreated depression are estimated at 27%, with the rates of severe untreated depression estimated at 7%. Being left untreated, depression may have significant detrimental consequences on quality of life and health status.
• Depression Screening. Depression can be reliably diagnosed and treated in primary care settings. However, depression often remains undiagnosed and untreated in primary care settings. The main reason for the lack of effective depression screening and diagnosing in primary care include understaffed facilities, stigma surrounding mental health conditions and lack of locally validated screening tools. The choice of effective screening tools and following implementation of these tools is important for accurate identification of patient with depressive disorders and following appropriate management of their conditions. Understanding of certain influential factors, such as gastrointestinal microbiome, seasonal allergies, chronic conditions, cancer can be valuable for effective screening. Das et al. (2019) concluded that PHQ-9 screening tool is one of the most effective screening methods, which allows not only detecting depression, evaluating its degree but also monitoring changes after the treatment is initiated.
• Depression Screening Tools. After comparing PHQ-9 with other depression screening tools, based on literature review, it can be concluded that PHQ-9 can be beneficial for timely detection, management of depression and monitoring its symptoms in the course of treatment. Das et al. (2019) concluded that PHQ-9 can be effectively used for diagnosing patients with various underlying conditions and in different situation, including postpartum period among others. Even in cases when a more specific depression screening tool is available, PHQ-9 still can be used with identical levels of sensitivity and specificity (Wang et al., 2020).

Implications

Theoretical Implications. The theoretical foundations which were used to guide this project included Nola Pender’s Health Promotion theory and Lewin’s Change Theory. These theories were used to enhance the effectiveness of PHQ-9 implementation in the primary care setting. Pender’s theory emphasized the importance of timely screening and diagnosing depression as a part of broader self-care and health promotion principles. Lewin’s 3-stage change theory was applied for properly understanding the deficits in current practices and providing rationale for improvement (unfreeze stage), actually implementing change (change stage) and returning to the stable state of the system, with the updated practices (refreeze stage).

Practical Implications. The insights from this project can be used for addressing a significant health care concern of mental health conditions left undiagnosed and untreated. Whereas depression screening can be effectively used in primary care settings, it is often underused due to the lack of staff and uncertainties as to the most effective screening tools and proper timing for depression screening. The project’s findings should be adapted to the needs of primary care settings. Currently, depression screening is applied only annually, without direct recommendations as to when and how to use depression screening tools. However, the findings of this project will reveal if the use of routine depression screening with Spitzer et al.’s PHQ-9 tool can improve the rates of depression diagnosing in primary care.

Future Implications

The opportunities for the future implications of the project’s findings combine the use of the evidence-based practices of depression screening in primary care settings and making the issue of regular and effective mental health screening more visible and investigated. Future practice improvement projects addressing related topics could explore the effectiveness of applying PHQ-9 in various primary care settings, potentially distinguishing between groups of patients with various comorbidities, frequently associated with depression. Another option is investigating the efficacy of PHQ-9 use, compared to other depression screening tools.

The future implications for the project findings are the changes in the clinical practices, with the use of PHQ-9 for routine depression screening. This practice improvement will foster early diagnosis of deression and appropriate timely management of this condition. This approach will be beneficial both for the health care providers, who will make their routines more efficient and for patients’ outcomes, which heavily depend on timely detection of depression. Since depression may have negative impact on treatmen of comorbid chronic conditions, management of mental health issues may be associate with improved physical health at the same time.

The detected weaknesses of the project design included the inaccuracies in the use of PHQ-9 by individual practitioners or the patients’ attempts of reputation management and attempts to conceal certain symptoms. Additional limitations could be the relatively small sample size, the setting restricted to only one primary care clinic, the use of non-randomized sampling method, restricted time in which the project was conducted (4 weeks). The strengths of the project were the convenience of administering PHQ-9, its format and ability to cover various dimensions of the mental health condition, also including the numerical measurements of symptoms. The use of the screening tool will not be associated with significant psychological discomfort in patients and itwill not lead to substantial anxiety in participating patients. The potential benefits of the project’s implementation outweigh its potential risks, without creating any additional stressors for the patients.

Recommendations

Recommendations for Future Projects

The recommendations for future projects focus mainly on expanding the sample size and including a variety of primary care settings. In this way, and by randomizing samples, future projects could create better opportunities for generalizations. Currently, the data retrieved from the intervention cannot be extrapolated to the whole US population even when considering urban areas, as collecting primary evidence from the state of Florida exclusively can result in imposing bias on th other regions and populations.

The first recommendation is to use bigger samples and include various primary care settings, to reduce the influence of the setting specifics and decrease corresponding bias. Conducting primary research in a single setting creates a series of biases both in terms of the patients exposed to the study and the professionals assigned to the patient screening. Thus, for example, in case of the present project, including one Florida-based primary care setting is likely to produce a rather homogenous result due to the level of personnel awareness of diagnostics procedure and the potential approach to the depression screening within the setting. As a result, the patterns of communication and PHQ-9 screening relevant to this population segment cannot be identical in other settings in and outside Florida. In order to promote precision, future research should use a comparative model that addresses various health care establishments with different populations of interest.

The second recommendation is to use randomized samples, which would be representative of certain population groups, instead of using convenience sample, in which all participants matching the inclusion criteria and available in the given setting are invited to participate. A convenience sampling model, which can be extremely useful in a research setting with minimal resources, creates a bias to the generalization of the study. Indeed, when employed, the population is likely to represent a group that is most likely to be accessed in the area. As a result, if the overwhelming majority of the sample is comprised of cisgender white individuals, the success of depression screening cannot be extrapolated to the general population characterized by diversity and different levels of emotional predisposition to mental illnesses. Hence, it is imperative for future projects not only to use randomized sampling but to randomize the study population based on the empirical data on different social groups’ predisposition to depressive disorders.

The third recommendation is to ensure that the screening tool is used properly, and education for health care practitioners can be necessary in order to emphasize the potential benefits and the importance of accurate data entry. Indeed, in order to secure the quality outcomes, the quality of the very intervention should be proven. While the present project had time limitations to conducting a full-scale training program among the professionals, future projects should pay more attention to groundwork of the screening, including education and screening practice among the clinicians. In such a way, data obtained from the questionnaire is more likely to correspond the reality.

The fourth recommendation is to provide technical support to the project. It is possible that the use of electronic reminders to administer the depression screening tool during a visit could improve the use of the tool and reduce the number of cases when the tool was not administered due to the lack of time or mere forgetfulness. Currently, the majority of the medical records of the patients are electronic, which makes it easier to track the patient’s progress. However, nowadays, the reminder system for the EHRs includes vaccination and obligatory medical check-ups according to an existing medical condition, paying little or no attention to mental health of the patients. Setting a reminder for the PHQ-9 procedure will help both nurses and patients address the risks of depression as frequently as twice a year. The increased frequency of screening due to reminders will yield more objective data both during the research and future implementation of the PHQ-9 approach.

Finally, it is important to make use of the screening tool as easy as possible for the patients. It is recommended that the patients be given a choice whether to use a paper-based or a digitized screening tool. Creating a QR-code with a link to the screening tool could be an effective way to administer the PHQ-9. Time efficiency plays an extremely important role in the patient’s willingness to embrace preventive health assessments. For the majority of patients, spending fifteen to twenty minutes more in order to undergo questionnaire is a major problem, so the convenience of an electronic PHQ-9 questionnaire will encourage more participants to enter future projects. The optimal outcome would be to make sure that an online screening tool can be connected directly to the patient’s EHR profile while preserving data security through name-coding. In such a way, the results of the screening can be later connected to other health characteristics of a patient, establishing regularities in one’s physical health and risk of depression.

Recommendations for Practice. Judging by the findings of this project, Spitzer et al.’s PHQ-9 screening tool can and should be implemented in the chosen primary care setting in urban Florida. First, by including routine depression screening with the use of PHQ-9 into the standard primary care visit protocol, the primary care setting would be able to improve early depression diagnosis and timely depression management rates. According to Uwadiale et al. (2021), “early screening and detection for potential MDD cases can reduce the severity and duration of maladaptive symptoms” (p. 272). In such a way, PHQ-9 implementation is likely to reduce the overall rate of depression statewide.

The second recommendation for PHQ-9 adoption addresses the fact that frequency of screening saves depression treatment costs across the medical facilities. The treatment of a major depressive disorder, when diagnosed late, requires costly therapy, regular psychological counseling, and medical check-ups. However, when detected early, depression treatment can be limited to counseling, saving money for both the patient and the facility. Hence, it is highly recommended clinical settings embrace the habit of routine PHQ-9 depression screening.

Third, disseminating evidence on the importance of regular depression screening and the hazards of leaving depression undiagnosed and untreated is important for reducing the negative consequences of undiagnosed depression and preventing its detrimental consequences. Even when diagnosed, late-stage depressive disorders bring unexpected challenges to one’s mental health, creating a barrier to treatment. Hence, it is important to understand that detection of a depressive disorder when it is already visible to the majority of people is considered a late intervention. During the explicit manifestation of depression, the patient is likely to refuse treatment because they do not have enough resources to battle the condition. For this reason, early screening should be implemented as a means of preventing mortifying outcomes.

Finally, the present project emphasizes the value of screening automation in health care practice. In order to encourage patients to take care of their mental health in advance, the prevention tools should be simplistic and accessible. Hence, the introduction of the online access the PHQ-9 questionnaire with the help of a QR-code or telehealth mobile app is likely to initiate a pivotal change in people’s interest in their mental health, as they are no longer required to have a full-scale appointment to be diagnosed with early stages of depressive disorders.

References

Akincigil, A., & Matthews, E. B. (2017). National rates and patterns of depression screening in primary care: Results from 2012 and 2013. Psychiatric Services, 68(7), 660-666.

Boothroyd, L., Dagnan, D., & Muncer, S. (2019). PHQ-9: One factor or two?. Psychiatry Research, 271, 532-534.

Briggs, R., Tobin, K., Kenny, R. A., & Kennelly, S. P. (2018). What is the prevalence of untreated depression and death ideation in older people? Data from the Irish longitudinal study on aging. International Psychogeriatrics, 30(9), 1393-1401.

Brody, D. J., Pratt, L. A., & Hughes, J. P. (2018). Prevalence of depression among adults aged 20 and over: United States, 2013-2016. Centers for Disease Control and Prevention.

Carroll, H. A., Hook, K., Perez, O. F. R., Denckla, C., Vince, C. C., Ghebrehiwet, S., Ando, K., Touma, M., Borba, C., Fricchione, G. & Henderson, D. C. (2020). Establishing reliability and validity for mental health screening instruments in resource-constrained settings: Systematic review of the PHQ-9 and key recommendations. Psychiatry Research, 291, 113 – 119.

Cheung, S. G., Goldenthal, A. R., Uhlemann, A. C., Mann, J. J., Miller, J. M., & Sublette, M. E. (2019). Systematic review of gut microbiota and major depression. Frontiers in Psychiatry, 10, 34.

Cho, S. H., & Crisafio, A. (2018). Depression and Primary Care: A Review of the Prevalence, Burden, Costs, Screening, and Treatment Strategies. Journal of Psychiatry Behavioral Health Forecast, 1 (1), 1002.

Costantini, L., Pasquarella, C., Odone, A., Colucci, M. E., Costanza, A., Serafini, G., Aguglia, A., Murri, M., B., Brakoulias, V., Amore, M., Ghaemi, S. N., & Amerio, A. (2020). Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review: Screening for depression in primary care with PHQ-9. Journal of Affective Disorders, 279, 473-483. Creswell, J.W. & Creswell, J.D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5^th ed.). Thousand Oaks: CA. Sage Publications.

Cuijpers, P., de Wit, L., Kleiboer, A., Karyotaki, E., & Ebert, D. D. (2018). Problem-solving therapy for adult depression: An updated meta-analysis. European Psychiatry, 48(1), 27-37.

Cuijpers, P., Karyotaki, E., de Wit, L., & Ebert, D. D. (2020). The effects of fifteen evidence-supported therapies for adult depression: A meta-analytic review. Psychotherapy Research, 30(3), 279-293.

Das, A., Roy, B., Schwarzer, G., Silverman, M. G., Ziegler, O., Bandyopadhyay, D., Philpotts, L. L., Sinha, S., Blumenthal, J. & Das, S. (2019). Comparison of treatment options for depression in heart failure: A network meta-analysis. Journal of Psychiatric Research, 108, 7-23.

De Joode, J. W., Van Dijk, S. E., Walburg, F. S., Bosmans, J. E., Van Marwijk, H. W., de Boer, M. R., Tulder, M. & Adriaanse, M. C. (2019). Diagnostic accuracy of depression questionnaires in adult patients with diabetes: A systematic review and meta-analysis. PloS One, 14(6), e0218512.

Do, P. H., Vo, V. T., Luong-Thanh, B. Y., Nguyen, L. H., Valdebenito, S., Eisner, M.,… & Dunne, M. (2021). 728 Comparative performance of WHO-5, PHQ-9 and PSS Scale for screening antenatal depression and suicide risk. International Journal of Epidemiology, 50(1), 168-176.

Doi, S., Ito, M., Takebayashi, Y., Muramatsu, K., & Horikoshi, M. (2018). Factorial validity and invariance of the Patient Health Questionnaire (PHQ)-9 among clinical and non-clinical populations. PloS One, 13(7), e0199235.

Ettman, C. K., Abdalla, S. M., Cohen, G. H., Sampson, L., Vivier, P. M., & Galea, S. (2020). Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA Network Open, 3(9), e2019686-e2019686.

Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175-191.

Forneris, C. A., Nussbaumer‐Streit, B., Morgan, L. C., Greenblatt, A., Van Noord, M. G., Gaynes, B. N.,… & Gartlehner, G. (2019). Psychological therapies for preventing seasonal affective disorder. Cochrane Database of Systematic Reviews, 5 (21), 1465 – 1858.

Galenkamp, H., Stronks, K., Snijder, M. B., & Derks, E. M. (2017). Measurement invariance testing of the PHQ-9 in a multi-ethnic population in Europe: The HELIUS study. BMC Psychiatry, 17(1), 1-14.

Gaspar, F. W., Zaidel, C. S., & Dewa, C. S. (2019). Rates and determinants of use of pharmacotherapy and psychotherapy by patients with major depressive disorder. Psychiatric Services, 70(4), 262-270.

Grapp, M., Terhoeven, V., Nikendei, C., Friederich, H. C., & Maatouk, I. (2019). Screening for depression in cancer patients using the PHQ-9: The accuracy of somatic compared to non-somatic items. Journal of Affective Disorders, 254, 74-81.

Gray, J., Grove, S. & Sutherland, S. (2020). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence. Elsevier – Health Sciences Division. ISBN 9781433832154

Guerra, T. D. R. B., Venancio, I. C. D., Pinheiro, D. M. D. M., Mendlowicz, M. V., Cavalcanti, A. C. D., & Mesquita, E. T. (2018). Methods of screening for depression in outpatients with heart failure. International Journal of Cardiovascular Sciences, 31, 414-421

Hartung, T. J., Friedrich, M., Johansen, C., Wittchen, H. U., Faller, H., Koch, U.,… & Mehnert, A. (2017). The Hospital Anxiety and Depression Scale (HADS) and the 9‐item Patient Health Questionnaire (PHQ‐9) as screening instruments for depression in patients with cancer. Cancer, 123(21), 4236-4243.

Hatton, C. M., Paton, L. W., McMillan, D., Cussens, J., Gilbody, S., & Tiffin, P. A. (2019). Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare. Journal of Affective Disorders, 246, 857-860.

Indu, P. S., Anilkumar, T. V., Vijayakumar, K., Kumar, K. A., Sarma, P. S., Remadevi, S., & Andrade, C. (2018). Reliability and validity of PHQ-9 when administered by health workers for depression screening among women in primary care. Asian Journal of Psychiatry, 37, 10-14.

Inegbenosun, H. (2021). Implementation of depression screening with the Patient Health Questionnaire-9 (PHQ-9) at a Radiation Oncology Department. The American Journal of Geriatric Psychiatry, 29 (4), 92 – 93.

Ingram, W. M., Baker, A. M., Bauer, C. R., Brown, J. P., Goes, F. S., Larson, S., & Zandi, P. P. (2020). Defining major depressive disorder cohorts using the EHR: multiple phenotypes based on ICD-9 codes and medication orders. Neurology, Psychiatry and Brain Research, 36, 18-26.

Iob, E., Kirschbaum, C., & Steptoe, A. (2020). Persistent depressive symptoms, HPA-axis hyperactivity, and inflammation: the role of cognitive-affective and somatic symptoms. Molecular Psychiatry, 25(5), 1130-1140.

Jia, Y., Li, F., Liu, Y. F., Zhao, J. P., Leng, M. M., & Chen, L. (2017). Depression and cancer risk: A systematic review and meta-analysis. Public Health, 149, 138-148.

Jokela, M., Virtanen, M., Batty, G. D., & Kivimäki, M. (2017). Inflammation and specific symptoms of depression. JAMA Psychiatry, 73(1), 87-88.

Keum, B. T., Miller, M. J., & Inkelas, K. K. (2018). Testing the factor structure and measurement invariance of the PHQ-9 across racially diverse US college students. Psychological Assessment, 30(8), 1096.

Kim, J. Y., Liu, N., Tan, H. X., & Chu, C. H. (2017). Unobtrusive monitoring to detect depression for elderly with chronic illnesses. IEEE Sensors Journal, 17(17), 5694-5704.

Kondova, A., Todorova, A., Tsvetkova, A., Arnaoudova, M., Andreevska, K., & Grekova, D. (2018). Screening and risk assessment for depression in community pharmacy-pilot study. Journal of IMAB–Annual Proceeding Scientific Papers, 24(1), 1928-1931.

Levis, B., Benedetti, A., & Thombs, B. D. (2019). Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: Individual participant data meta-analysis. BMJ, 365 (11), 476 – 483.

Levis, B., Benedetti, A., Ioannidis, J. P., Sun, Y., Negeri, Z., He, C., Wu, Y., Krishnan, A., Bhandari, P. M., Neupane, D., Imran, D., Rice, D., Riehm, K., Saadat, Azar, M., Boruff, Cuijpers, J., Gilbody, S., Kloda, L. & Thombs, B. D. (2020). Patient Health Questionnaire-9 scores do not accurately estimate depression prevalence: Individual participant data meta-analysis. Journal of Clinical Epidemiology, 122, 115-128.

Lewin, K. (1951). Field theory in social science. Harper and Row, New York.

Manea, L., Boehnke, J. R., Gilbody, S., Moriarty, A. S., & McMillan, D. (2017). Are there researcher allegiance effects in diagnostic validation studies of the PHQ-9? A systematic review and meta-analysis. BMJ Open, 7(9), e015247.

Maurer, D. M., Raymond, T. J., & Davis, B. N. (2018). Depression: Screening and diagnosis. American Family Physician, 98(8), 508-515.

Muñoz-Navarro, R., Cano-Vindel, A., Medrano, L. A., Schmitz, F., Ruiz-Rodríguez, P., Abellán-Maeso, C. & Hermosilla-Pasamar, A. M. (2017). Utility of the PHQ-9 to identify major depressive disorder in adult patients in Spanish primary care centres. BMC Psychiatry, 17(1), 1-9.

Oh, H., Koyanagi, A., DeVylder, J. E., & Stickley, A. (2018). Seasonal allergies and psychiatric disorders in the United States. International Journal of Environmental Research and Public Health, 15(9), 1965.

Oquendo, M. A., Bernstein, C. A., & Mayer, L. E. (2019). A key differential diagnosis for physicians—major depression or burnout?. JAMA Psychiatry, 76(11), 1111-1112.

Pálinkás, A., Sándor, J., Papp, M., Kőrösi, L., Falusi, Z., Pál, L. & Döme, P. (2019). Associations between untreated depression and secondary health care utilization in patients with hypertension and/or diabetes. Social Psychiatry and Psychiatric Epidemiology, 54(2), 255-276.

Park, L. T., & Zarate, C. A. Jr. (2019). Depression in the primary care setting. New England Journal of Medicine, 380(6), 559-568.

Patel, J. S., Oh, Y., Rand, K. L., Wu, W., Cyders, M. A., Kroenke, K., & Stewart, J. C. (2019). Measurement invariance of the patient health questionnaire‐9 (PHQ‐9) depression screener in US adults across sex, race/ethnicity, and education level: Nhanes 2005–2016. Depression and Anxiety, 36(9), 813-823.

Pence, B. W., Mills, J. C., Bengtson, A. M., Gaynes, B. N., Breger, T. L., Cook, R. L. & Mugavero, M. J. (2018). Association of increased chronicity of depression with HIV appointment attendance, treatment failure, and mortality among HIV-infected adults in the United States. JAMA Psychiatry, 75(4), 379-385.

Pence, B. W., Stockton, M. A., Mphonda, S. M., Udedi, M., Kulisewa, K., Gaynes, B. N., & Hosseinipour, M. C. (2019). How faithfully do HIV clinicians administer the PHQ-9 depression screening tool in high-volume, low-resource clinics? Results from a depression treatment integration project in Malawi. Global Mental Health, 6(21), 1 – 8.

Pender, N. (2011). Health promotion model manual. 

Rahman, N., Wang, D. D., Ng, S. H. X., Ramachandran, S., Sridharan, S., Khoo, A.,… & Tan, X. Q. (2018). Processing of electronic medical records for health services research in an academic medical center: Methods and validation. JMIR Medical Informatics, 6(4), e10933.

Rosanti, E., Machmud, R., Nurdin, A. E., & Afrizal, A. (2020). Health education intervention on increasing early detection of depression based on family. Open Access Macedonian Journal of Medical Sciences, 8(E), 331-333.

Ross, E. L., Zivin, K., & Maixner, D. F. (2018). Cost-effectiveness of electroconvulsive therapy vs pharmacotherapy/psychotherapy for treatment-resistant depression in the United States. JAMA Psychiatry, 75(7), 713-722.

Samples, H., Stuart, E. A., Saloner, B., Barry, C. L., & Mojtabai, R. (2020). The role of screening in depression diagnosis and treatment in a representative sample of US primary care visits. Journal of General Internal Medicine, 35(1), 12-20.

Shah, F. M., Ahmed, F., Joy, S. K. S., Ahmed, S., Sadek, S., Shil, R., & Kabir, M. H. (2020). Early depression detection from social network using deep learning techniques. In 2020 IEEE Region 10 Symposium (TENSYMP) (pp. 823-826). IEEE.

Shorey, S., Chee, C. Y. I., Ng, E. D., Chan, Y. H., San Tam, W. W., & Chong, Y. S. (2018). Prevalence and incidence of postpartum depression among healthy mothers: A systematic review and meta-analysis. Journal of Psychiatric Research, 104, 235-248.

Sonmez, A. I., Camsari, D. D., Nandakumar, A. L., Voort, J. L. V., Kung, S., Lewis, C. P., & Croarkin, P. E. (2019). Accelerated TMS for depression: a systematic review and meta-analysis. Psychiatry Research, 273, 770-781

Stocker, R., Tran, T., Hammarberg, K., Nguyen, H., Rowe, H., & Fisher, J. (2021). Patient Health Questionnaire 9 (PHQ-9) and General Anxiety Disorder 7 (GAD-7) data contributed by 13,829 respondents to a national survey about COVID-19 restrictions in Australia. Psychiatry Research, 298 (11), 113 – 122.

Sun, Y., Fu, Z., Bo, Q., Mao, Z., Ma, X., & Wang, C. (2020). The reliability and validity of PHQ-9 in patients with major depressive disorder in psychiatric hospital. BMC Psychiatry, 20(1), 1-7.

Tomitaka, S., Kawasaki, Y., Ide, K., Akutagawa, M., Yamada, H., Ono, Y., & Furukawa, T. A. (2018). Distributional patterns of item responses and total scores on the PHQ-9 in the general population: Data from the National Health and Nutrition Examination Survey. BMC Psychiatry, 18(1), 1-9.

Uwadiale, A., Cordaro, M., Brunett, K., Stern, M., & Howard, K. (2021). Lessons learned about the need for early screening for depression during the first months of the COVID-19 pandemic in the United States. Issues in Mental Health Nursing, 272-281.

Waitzfelder, B., Stewart, C., Coleman, K. J., Rossom, R., Ahmedani, B. K., Beck, A. & Simon, G. E. (2018). Treatment initiation for new episodes of depression in primary care settings. Journal of General Internal Medicine, 33(8), 1283-1291

Wang, L., Kroenke, K., Stump, T. E., & Monahan, P. O. (2020). Screening for perinatal depression with the patient health questionnaire depression scale (PHQ-9): A systematic review and meta-analysis. General Hospital Psychiatry, 68, 74-82

Wu, Y., Zhu, B., Chen, Z., Duan, J., Luo, A., Yang, L., & Yang, C. (2021). New insights into the comorbidity of coronary heart disease and depression. Current Problems in Cardiology, 46(3), 400 – 413.

Appendix A

Appendix A

Developed by Drs. Robert L. Spitzer, Janet B.W. Williams, Kurt Kroenke and colleagues, with an educational grant from Pfizer Inc. No permission required to reproduce, translate, display, or distribute.

Appendix B

Appendix C

Appendix D

The SPSS database is set up with all variables coded to compare between or within the comparison groups. A comparison may be made within the same individual and it coded 1 for before and 2 after the intervention. Or if measuring between individuals, the data would be coded the same 1 for before and 2 after as noted in the Group Column. Software supplied by Grand Canyon University.

Appendix E

How to Make APA Format Tables and Figures Using Microsoft Word

Tables vs. Figures:

• See APA Publication Manual, Chapter 7 for additional details (APA, 2019).
• Tables consist of words and numbers where spatial relationships usually do not indicate any numerical information.
• Tables should be used to present information that would be too wordy, repetitive, or difficult to read as text.
• Figures typically communicate numerical information using spatial relations. For example, as you move up the Y axis of bar graph the scores usually go up.

Examples of APA Tables

Descriptive table

Table 1. Characteristics of Variables

Note. Add notes here = (Provide any reference, 2019).

Table 1. Number of Handoff Per Groups

Note. SBAR handoff was defined as …. (IHI, 2020)

Table 1. Number of Hours Per Week Spent in Various Activities

Note. Schoolwork was defined as time spent doing class work outside of regular class time.

*statistically significant at p <.05

Chi-Square example (Group IV x Group DV)

Table 1.Crosstabulation of Gender and Chronic Pain

Note. Adjusted standardized residuals appear in parentheses below group frequencies

^**= p <.01.

t-Test Example (Dichotomous Group IV x Score DV)

Notice two separate t-test results have been reported.

Table 1. Chronic Paint Score and Exercise time for Males and Females

Note. Standard Deviations appear in parentheses below means.

^* = p <.05, ^*** = p <.001.

One Way ANOVA with 3 Groups Example (Group IV x Score DV)

Remember with an ANOVA, you have to report paired comparisons associated with post hoc or planned comparisons) for significant analyses. The results of paired comparisons are indicated by the subscripts on the means within rows. Also, notice in this table that we report the results of four separate analyses. This is the real power of tables: we can convey a large amount of information very concisely.

Table 1. Analysis of Variance for Sleep Times and Experimental Groups

Standard deviations appear in parentheses bellow means. Means with differing subscripts within rows are significantly different at the p <.05 based on Fisher’s LSD post hoc paired comparisons.

^* = p <.05, ^*** = p <.001.

Factorial ANOVA Example 2 x 3 between subject’s design

Notice that two tables are used here. The first table reports the overall results for the 2×3 factorial ANOVA, which includes the Main Effects for the two IV’s and the Interaction Effect for the two IV’s. The second table reports the means and simple effects tests for the significant interaction effect.

Table 1. Experimental Group x Sex Factorial Analysis of Variance for Sleep Scores

Table 1. Analysis of Sleep Scores for Experimental Groups by Gender

Note. Standard deviations appear in parentheses bellow means. Means with differing subscripts within rows are significantly different at the p <.05 based on Fisher’s LSD post hoc paired comparisons.

^** = p <.01

Notice that the simple effect comparing the 3 experiment groups only for females, requires follow up tests in order to determine which groups are significantly different. In this case, Fisher’s LSD test was used, and the results are represented with the different subscripts for each mean. In this case, female participants in the Aerobic exercise group did not differ from the no exercise group so they are given the same subscript (a). However, women in the control group and women in the Weight lifting group significantly differed from the Aerobic watching group and so the Weight Lifting group was labeled with a different subscript (b). The male subjects did not differ from one another, so they all share the same subscript (a).

Correlations (Scores IV x Scores IV)

Table 1. Pearson’s Product Moment Correlations for Chronic Pain Score, Exercise Attitude Scores and Physical Activity

Note. N = 96 for all analyses.

^† = p < .10, ^*= p < .05, ^**= p < .01, ^***= p < .001.

Examples of APA Figures

Generally, the same features apply to figures as have been previously provided for tables: They should be easy to read and interpret, consistent throughout the document when presenting the same type of figure, kept on one page if possible, and supplement the accompanying text or table.

If the figure is not your own work, note the source or reference where you found the figure. Write, “Reprinted from” or “Adapted from,” followed by the title of the book, article, or website where you found the figure. Include the page number where you found the figure as well if you are citing a figure from a book. If you are citing a figure from a website, you may write, “Reprinted from The Huffington Post.” Or include the author’s first and second initial as well as their surname. Use the author’s first and second initial, if available, rather than the author’s full first name. Note their last name as well.

References

American Psychological Association [APA]. (2019). Publication manual of the American Psychological Association. (7th ed.). Washington, DC; Author Microsoft Word ®. (2019).

Appendix F

Writing up your statistical results.

Identify the analysis technique

In the results section (Chapter 4), your goal is to report the results of the data analyses used to answer your project question. To do this, you need to identify your data analysis technique, report your test statistic, and provide some interpretation of the results. Each analysis you run should be related to your clinical question or PICOT. If you analyze data that is exploratory or outside your clinical question, you need to indicate this in the results.

Format test statistics

Test statistics and p values should be rounded to two decimal places (If you are providing precise p-values for future use in meta-analyses, 3 decimal places is acceptable). All statistical symbols (sample statistics) that are not Greek letters should be italicized (M, SD, t, p, etc.).

Indicate the direction of the significant difference

When reporting a significant difference between two conditions, indicate the direction of this difference, i.e. which condition was more/less/higher/lower than the other condition(s). Assume that your audience has a professional knowledge of statistics. Do not explain how or why you used a certain test unless it is unusual (i.e., such as a non-parametric test).

How to report p values

Report the exact p value (this is the preferred option if you want to make your data convenient for individuals conducting a meta-analysis on the topic).

Example: t(33) = 2.10, p =.03.

If your exact p value is less than.001, it is conventional to state merely p <.001. If you report exact p values, state early in the results section the alpha level used as a significance criterion for your tests. For example: “We used an alpha level of.05 for all statistical tests.”

If your results are in the predicted direction but are not significant, you can say your results were marginally significant. Example: Results indicated a marginally significant preference for pie (M = 3.45, SD = 1.11) over cake (M = 3.00, SD =.80), t(5) = 1.25, p =.08.

If your p-value is over.10, you can say your results revealed a non-significant trend in the predicted direction. Example: Results indicated a non-significant trending in the predicted direction indicating a preference for pie (M = 4.25, SD = 2.21) over cake (M = 3.25, SD = 2.60), t(5) = 1.75, p =.26.

Descriptive Statistics

Mean and Standard Deviation are most clearly presented in parentheses:

• The sample as a whole was relatively young (M = 19.22, SD = 3.45).
• The average age of students was 19.22 years (SD = 3.45).

Percentages are also most clearly displayed in parentheses with no decimal places:

• Nearly half (49%) of the sample was married.
• Frequencies or rates are reported including the range, mode, or median.

t-tests

There are several different designs that utilize a t-test for the statistical inference testing. The differences between one-sample t-tests, related measures t-tests, and independent samples t tests are clear to the knowledgeable reader so eliminate any elaboration of which type of t-test has been used. Additionally, the descriptive statistics provided will identify which variation was employed. It is important to note that we assume that all p values represent two-tailed tests unless otherwise noted and that independent samples t-tests use the pooled variance approach (based on an equal variances assumption) unless otherwise noted:

• There was a significant effect for gender, t(54) = 5.43, p <.001, with men receiving higher scores than women.
• Results indicate a significant preference for pie (M = 3.45, SD = 1.11) over cake (M = 3.00, SD =.80), t(15) = 4.00, p =.001.
• The 36 study participants had a mean age of 27.4 (SD = 12.6) were significantly older than the university norm of 21.2 years, t(35) = 2.95, p =.01.
• Students taking statistics courses in psychology at the University of Washington reported studying more hours for tests (M = 121, SD = 14.2) than did UW college students in general, t(33) = 2.10, p =.034.
• The 25 participants had an average difference from pre-test to post-test anxiety scores of -4.8 (SD = 5.5), indicating the anxiety treatment resulted in a significant decrease in anxiety levels, t(24) = -4.36, p =.005 (one-tailed).
• The 36 participants in the treatment group (M = 14.8, SD = 2.0) and the 25 participants in the control group (M = 16.6, SD = 2.5), demonstrated a significance difference in performance (t[59] = -3.12, p =.01); as expected, the visual priming treatment inhibited performance on the phoneme recognition task.
• UW students taking statistics courses in Psychology had higher IQ scores (M = 121, SD = 14.2) than did those taking statistics courses in Statistics (M = 117, SD = 10.3), t(44) = 1.23, p =.09.
• Over a two-day period, participants drank significantly fewer drinks in the experimental group (M= 0.667, SD = 1.15) than did those in the wait-list control group (M= 8.00, SD= 2.00), t(4) = -5.51, p=.005.

ANOVA and post hoc tests

ANOVAs are reported like the t test, but there are two degrees-of-freedom numbers to report. First report the between-groups degrees of freedom, then report the within-groups degrees of freedom (separated by a comma). After that report the F statistic (rounded off to two decimal places) and the significance level.

One-way ANOVA:

• The 12 participants in the high dosage group had an average reaction time of 12.3 seconds (SD = 4.1); the 9 participants in the moderate dosage group had an average reaction time of 7.4 seconds (SD = 2.3), and the 8 participants in the control group had a mean of 6.6 (SD = 3.1). The effect of dosage, therefore, was significant, F(2,26) = 8.76, p=.012.
• An one way analysis of variance showed that the effect of noise was significant, F(3,27) = 5.94, p =.007. Post hoc analyses using the Scheffé post hoc criterion for significance indicated that the average number of errors was significantly lower in the white noise condition (M = 12.4, SD = 2.26) than in the other two noise conditions (traffic and industrial) combined (M = 13.62, SD = 5.56), F(3, 27) = 7.77, p =.042.
• Tests of the four a priori hypotheses were conducted using Bonferroni adjusted alpha levels of.0125 per test (.05/4). Results indicated that the average number of errors was significantly lower in the silence condition (M = 8.11, SD = 4.32) than were those in both the white noise condition (M = 12.4, SD = 2.26), F(1, 27) = 8.90, p =.011 and in the industrial noise condition (M = 15.28, SD = 3.30), F (1, 27) = 10.22, p =.007. The pairwise comparison of the traffic noise condition with the silence condition was nonsignificant.
• The average number of errors in all noise conditions combined (M = 15.2, SD = 6.32) was significantly higher than those in the silence condition (M = 8.11, SD = 3.30), F(1, 27) = 8.66, p =.009.

Multiple Factor (Independent Variable) ANOVA

• There was a significant main effect for treatment, F(1, 145) = 5.43, p <.01, and a significant interaction, F(2, 145) = 3.13, p <.05.
• The cell sizes, means, and standard deviations for the 3×4 factorial design are presented in Table 1. The main effect of Dosage was marginally significant (F[2,17] = 3.23, p = .067), as was the main effect of diagnosis category, F(3,17) = 2.87, p =.097. The interaction of dosage and diagnosis, however, has significant, F(6,17) = 14.2, p =.0005.
• Attitude change scores were subjected to a two-way analysis of variance having two levels of message discrepancy (small, large) and two levels of source expertise (high, low). All effects were statistically significant at the.05 significance level. The main effect of message discrepancy yielded an F ratio of F(1, 24) = 44.4, p <.001, indicating that the mean change score was significantly greater for large-discrepancy messages (M = 4.78, SD = 1.99) than for small-discrepancy messages (M = 2.17, SD = 1.25). The main effect of source expertise yielded an F ratio of F(1, 24) = 25.4, p <.01, indicating that the mean change score was significantly higher in the high-expertise message source (M = 5.49, SD = 2.25) than in the low-expertise message source (M = 0.88, SD = 1.21). The interaction effect was non-significant, F(1, 24) = 1.22, p >.05.
• A two-way analysis of variance yielded a main effect for the diner’s gender, F(1,108) = 3.93, p <.05, such that the average tip was significantly higher for men (M = 15.3%, SD = 4.44) than for women (M = 12.6%, SD = 6.18). The main effect of touch was nonsignificant, F(1, 108) = 2.24, p >.05. However, the interaction effect was significant, F(1, 108) = 5.55, p <.05, indicating that the gender effect was greater in the touch condition than in the non-touch condition.

Chi Square

Chi-Square statistics are reported with degrees of freedom and sample size in parentheses, the Pearson chi-square value (rounded to two decimal places), and the significance level:

• The percentage of participants that were married did not differ by gender, X2(1, N = 90) = 0.89, p >.05.
• The sample included 30 respondents who had never married, 54 who were married, 26 who reported being separated or divorced, and 16 who were widowed. These frequencies were significantly different, X2 (3, N = 126) = 10.1, p =.017.
• As can be seen by the frequencies cross tabulated in Table xx, there is a significant relationship between marital status and depression, X2 (3, N = 126) = 24.7, p <.001.
• The relation between these variables was significant, X2 (2, N = 170) = 14.14, p <.01. Catholic teens were less likely to show an interest in attending college than were Protestant teens.
• Preference for the three sodas was not equally distributed in the population, X2 (2, N = 55) = 4.53, p <.05.

Correlations

Correlations are reported with the degrees of freedom (which is N-2) in parentheses and the significance level:

• The two variables were strongly correlated, r(55) =.49, p <.01.

Regression analyses

Regression results are often best presented in a table. APA doesn’t say much about how to report regression results in the text, but if you would like to report the regression in the text of your Results section, you should at least present the standardized slope (beta) along with the t-test and the corresponding significance level. (Degrees of freedom for the t-test is N-k-1 where k equals the number of predictor variables.) It is also customary to report the percentage of variance explained along with the corresponding F test.

Social support significantly predicted depression scores, b = -.34, t(225) = 6.53, p <.01. Social support also explained a significant proportion of variance in depression scores, R2 =.12, F(1, 225) = 42.64, p <.01.

Tables

Add a table or figure.

Adding a table of figure can be helpful to the reader. See the current APA Publication manual for examples. In reporting the results of statistical tests, report the descriptive statistics, such as means and standard deviations, as well as the test statistic, degrees of freedom, obtained value of the test, and the probability of the result occurring by chance (p value).

• APA style tables do not contain any vertical lines
• There are no periods used after the table number or title.
• When using columns with decimal numbers, make the decimal points line up.
• Use MS Word tables to create tables

American Psychological Association [APA]. (2019). Publication manual of the American Psychological Association (7th ed.). Washington, DC: Author.