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JOURNAL OF WOMEN’S HEALTH Volume 23, Number 9, 2014 ª Mary Ann Liebert, Inc. DOI: 10.1089/jwh.2014.4824 Predictors of Postpartum Depression Wayne Katon, MD,1 Joan Russo, PhD,1 and Amelia Gavin, PhD 2 Abstract Objective: To examine sociodemographic factors, pregnancy-associated psychosocial stress and depression, health risk behaviors, prepregnancy medical and psychiatric illness, pregnancy-related illnesses, and birth outcomes as risk factors for post-partum depression (PPD). Methods: A prospective cohort study screened women at 4 and 8 months of pregnancy and used hierarchical logistic regression analyses to examine predictors of PPD. The study sample include 1,423 pregnant women at a university-based high risk obstetrics clinic. A score of ‡ 10 on the Patient Health Questionnaire-9 (PHQ-9) indicated clinically significant depressive symptoms. Results: Compared with women without significant postpartum depressive symptoms, women with PPD were significantly younger ( p < 0.0001), more likely to be unemployed ( p = 0.04), had more pregnancy associated depressive symptoms ( p < 0.0001) and psychosocial stress ( p < 0.0001), were more likely to be smokers (p < 0.0001), were more likely to be taking antidepressants (ADs) during pregnancy ( p = 0.002), were less likely to drink any alcohol during pregnancy ( p = 0.02), and were more likely to have prepregnancy medical illnesses, including diabetes ( p = 0.02) and neurologic conditions ( p = 0.02). Conclusion: Specific sociodemographic and clinical risk factors for PPD were identified that could help physicians target depression case finding for pregnant women. Introduction T he post-partum period is a high-risk time for development of major depressive episodes. Two systematic reviews have found that 7%–13% of women will experience a serious episode of postpartum depression (PPD).1,2 Women who experience PPD have an increased risk of future depressive episodes and resulting functional impairment.3,4 PPD has been shown to adversely affect maternal functioning and is a risk factor for poor mother–infant bonding, subsequent delayed child developmental milestones,5–7 and child and adolescent mental health disorders.8,9 Several systematic reviews have examined risk factors for development of PPD.1,2 Risk factors found to be associated with moderate to high risk of PPD include depression or anxiety during pregnancy, stressful life events, low levels of social support, previous history of depression, and the personality factor of neuroticism.1,2 Pregnancy-related complications such as preeclampsia, premature labor, and other labor-related complications were associated with significant but lower level of risk in most studies.1 Markers of lower socioeconomic status such as unemployment and lower educational attainment have also been associated with significant but lower risk of PPD.1,2 1 2 The systematic reviews found that a limitation of the literature was that few studies included the full wide range of potential risk factors for PPD, such as sociodemographic factors; prepregnancy medical illness; health risk behaviors such as smoking, drug and alcohol use; depression history prior to and during pregnancy; psychosocial stress; intimate partner violence during pregnancy; pregnancy-related complications such as gestational diabetes and pregnancy-related hypertension; and adverse birth outcomes such as preterm birth, low birth weight, and fetal death.1,2 The purpose of this study was to examine a wide range of socio-demographic factors, health risk behaviors, depression history, prepregnancy medical illness, pregnancy-related illnesses, and birth outcomes as risk factors for PPD. Materials and Methods Participants in this study were women receiving prenatal care at the University Obstetrics Clinic between January 2004 and June 2011, who delivered at the University of Washington Hospital. The university’s Obstetrics and Gynecology Clinic and Obstetrics Inpatient Service have linked electronic records. Questionnaires assessing mood and other important sociodemographic, medical, and behavioral information were Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, Washington. University of Washington School of Social Work, Seattle, Washington. 753 754 introduced as a quality improvement initiative in January 2004.10 All women receiving obstetrical care and completing at least one survey during their second or third trimester as well as at 6-week postpartum follow-up were eligible for the study. Women presented to this tertiary care clinic at different pregnancy trimesters, and therefore, some had only one questionnaire completed during pregnancy and others had two (i.e., 4- and 8-month questionnaires). Clinic staff were responsible for screening patients with the survey questionnaire and once completed, obtaining written informed consent to link medical records with survey results. Given the nature of a busy, urban obstetrics clinic, staff were unable to get a small percentage of questionnaires completed. Exclusion criteria included being < 15 years of age at the time of delivery or inability to complete the questionnaire due to language difficulty or mental incapacity. All procedures were approved by the University of Washington Human Subjects Institutional Review Board. Study variables and measures The Patient Health Questionnaire-9 (PHQ-9) was used to assess depressive symptoms during the second or third trimester and postpartum.11 A continuous PHQ-9 severity measure was used as an independent variable. When questionnaires were filled out at both 4 and 8months, the mean PHQ-9 score was used. A PHQ-9 of ‡ 10 was utilized as the main outcome variable to define significant depressive symptoms at the post-partum visit. A PHQ-9 of ‡ 10 has been found in obstetrics and gynecology (Ob-Gyn) patients to have the highest sensitivity (73%) and specificity (98%), compared to a structured psychiatric interview diagnosis of major depression. Information about antidepressant (AD) use in pregnancy was obtained from the self-report questionnaire.11 Self-report of AD use in pregnancy has been found to have high concordance with pharmacy records.12 Sociodemographic information on age, marital status, race/ethnicity, education, and employment, as well as general health history, health risk behaviors, social history, and psychosocial stressors were collected during either the second or third trimester. Chronic medical problems prior to pregnancy were screened for with a standard list that included hypertension, diabetes, asthma, thyroid disorders, migraines, arthritis, seizure disorders, heart failure, cancer, and other heart disease. Tobacco status was assessed using the SmokeFree Families prenatal screen that was developed to screen for smoking during pregnancy.13 Women with any current smoking were classified as smokers. Diagnosis of pregnancy-induced/gestational hypertension was based on outpatient and inpatient physician International Classification of Diseases, Ninth Revision (ICD-9) diagnoses of 642.3, 642.4, 642.5, 642.6, and 642.7, respectively.14 Diagnosis of gestational diabetes mellitus (GDM) was determined by a physician ICD-9 diagnosis of 648.8 in the outpatient or inpatient medical record.15 GDM is clinically defined as glucose intolerance with the first recognition or onset in pregnancy;16 therefore, this diabetes category could potentially include women with previously unrecognized type 2 diabetes. The Prenatal Psychosocial Profile Stress Scale is an 11item self-report scale that measures perceived current hassles and stressors.17 Women indicate the extent to which each KATON ET AL. item is a current hassle or stressor on a 4-point Likert scale [1 (no stress) to 4 (severe stressor)] with a range of 11 to 44. It has been shown to have high reliability and validity in pregnant populations. The three-question Abuse Assessment Screen has been validated in pregnant patients as a sensitive and specific screen for intimate partner violence.18 Each item is rated as ‘‘yes’’ or ‘‘no,’’ and the percentage of women with at least one measure of intimate partner violence was described. The revised four item alcohol screening questionnaire, the T-ACE (Take [number of drinks], Annoyed, Cut-down, Eye-opener), was employed to assess use of alcohol during pregnancy. The T-ACE modification of the alcohol screening questionnaire, the CAGE (Cut-down, Annoyed, Guilt, Eyeopener), substitutes the guilt question with an alcohol tolerance question.19 The T-ACE has been found to outperform obstetric staff assessment of alcohol use by pregnant women.19 Women with any use of alcohol during pregnancy were identified with the T-ACE. Offspring birth weight, gestational age at birth, and fetal death were obtained from study participants’ computerized medical records. Low birth weight was based on a gestational weight threshold of 2,500 g. Pre-term birth was determined as less than 37 weeks of completed gestation. Statistical analyses We selected the study sample from the entire screening sample (N = 3,039). The 1,423 women whose data were included in the study were compared with the 1,616 women who were excluded based on missing key data on baseline demographic and clinical variables using Fischer exact tests and t-tests. Due to significant inclusion group differences, non-response propensity scores were created using baseline variables (demographics, medical conditions, health risk behaviors, pregnancy variables, and depression). The inverse of the probability of not responding was used to weight our regression analyses. We compared baseline variables for the women with and without PPD (having a postpartum PHQ-9 score ‡ 10) using Fischer Exact tests and t-tests. Hierarchical logistic regression analyses were used to predict the odds of PPD. We first examined the unadjusted association between prepregnancy PHQ-9 scores and PPD. In the next model, we added demographic variables and reevaluated the odds for prepregnancy depression. The third model contained the demographics and medical conditions. The fourth model added health-related behaviors to the model, and the fifth model added pregnancyrelated variables. Lastly, we calculated a final model predicting PPD, including all odds ratios and their 95% confidence intervals for all the predictor variables. Because an increase of five points on the PHQ-9 is associated with significant clinical change,20 we recalculated the odds of PPD based on pregnancy PHQ-9 total scores formed by creating groups with five-point intervals, adjusting for all other study variables. Lastly, we conducted a sensitivity analysis by examining the final model without the use of the propensity weights. Results A total of 3,039 women were screened either at four months or eight months (or at both time periods) of pregnancy. Of these, 1,515 women were excluded due to lack of a PREDICTORS OF POSTPARTUM DEPRESSION postpartum assessment (the vast majority of these women attended post-partum visits at clinics closer to their homes rather than the university high-risk obstetrics clinic); 84 were excluded due to only filling out the 8-month questionnaire which had no questions on medical history and 17 were excluded due to lack of data on birth outcomes (preterm labor or low birth weight), leaving a study sample of 1,423. Univariate analyses comparing the pregnancy data for those women who were and were not included in this study revealed significant group differences. Those women not eligible for this study were slightly younger ( p < 0.001, although the difference in means was only 1.3 years), less likely to be college educated ( p < 0.001), and more likely to be single ( p < 0.001), non-white ( p < 0.001), and unemployed ( p < 0.001) than the women included in the study sample. In addition, ineligible women reported being slightly more depressed ( p < 0.003), 0.5 difference on PHQ-9), were more likely to have a baby die ( p < 0.01), and less likely to 755 have GDM ( p < 0.006) or preeclampsia ( p < 0.02) than those women retained for analysis. Due to these differences, we created nonresponse propensity weights utilizing the variables in Table 1, and have applied these weights to our regression analyses. Table 1 displays the descriptive data for the groups with and without PPD. A total of 6.7% of women had a PHQ-9 score of ‡ 10 during pregnancy, and 5.8% had a score of ‡ 10 at the postpartum check. Women with PPD reported significantly more depressive symptoms during pregnancy than women without PPD. In addition, women with PPD were significantly younger, less likely to be married, less educated, and more likely to report being unemployed than women without PPD. In terms of medical conditions, women with PPD in comparison with women without PPD reported higher rates of diabetes, migraines, and a trend ( p = 0.06) for hypertension and neurological conditions (0.07). Women with PPD were more likely to report current smoking, taking an Table 1. Descriptive Data for Women With and Without Post-Partum Depression (PPD) Variablesa mean (SD) or percent (n) Pregnancy PHQ-9 Total Scoreb Pregnancy PHQ-9 ( ‡ 10)c Pregnancy minor depression PHQ-9 (5–9)c Ageb Racec White African American Hispanic Other Marriedc At least some collegec Unemployedc Total sample N = 1,423 PHQ-9 (0–9) no PPD n = 1,340 Depression 3.4 (3.4) 3.1 (3.1) 6.7 (95) 4.9 (66) 4.4 (62) 4.1 (55) Demographic variables 31.5 (5.9) 31.7 (5.9) 73.8 (1006) 74.1 (953) 5.6 (76) 5.4 (70) 4.6 (63) 44 (56) 16.0 (218) 16.1 (207) 89.4 (1257) 90.5 (1197) 85.5 (1200) 86.6 (1143) 40.1 (564) 38.7 (511) Prepregnancy medical conditions Asthmac 10.7 (152) 10.4 (139) Diabetesc 7.3 (104) 6.9 (92) 7.2 (103) 7.0 (93) GI disordersc 4.7 (67) 4.5 (60) Heart conditionsc Hypertensionc 6.6 (94) 6.3 (84) Migrainec 14.1 (200) 13.2 (176) 2.0 (28) 1.8 (24) Neurological conditionsc 7.0 (99) 6.8 (91) Thyroid problemsc Health-related behaviors 5.5 (78) 4.0 (53) Current cigarette smokingc Taking an antidepressantc 7.0 (100) 6.2 (83) 14.1 (201) 14.3 (190) Drinking alcohol during pregnancyc 2.5 (36) 2.3 (30) Intimate partner violence during the past yearc Stress total scoreb 14.3 (3.4) 14.0 (3.1) Pregnancy-related variables GDMc 20.7 (294) 20.7 (278) Preeclampsiac 21.2 (301) 20.6 (277) Low birth weightc 11.5 (164) 10.8 (145) Preterm birthc 15.0 (214) 14.2 (190) Fetal deaths 0.35 (5) 0.37 (5) a PHQ-9 ( ‡ 10) PPD n = 83 p Value from t-test or Fisher’s Exact test 7.8 (5.3) 34.9 (29) 8.4 (7) 0.0001 0.0001 0.09 28.5 (6.3) 0.0001 68.8 7.8 9.1 14.2 72.3 68.7 63.9 (53) (6) (7) (11) (60) (57) (53) 0.17 15.7 14.5 12.0 8.4 12.0 29.6 4.9 9.6 (13) (12) (10) (7) (10) (24) (4) (8) 0.14 0.02 0.12 0.11 0.06 0.0001 0.07 0.37 30.5 20.5 11.6 7.2 18.3 (25) (17) (11) (6) (4.9) 0.0001 0.0001 0.54 0.02 0.0001 19.3 28.9 22.9 28.9 0 (16) (24) (19) (24) (0) 0.89 0.10 0.002 0.001 1.0 0.0001 0.0001 0.0001 Only a mean of 2.7% of the model variables were missing. Mean (standard deviation [SD]). Percent (n). GI, gastrointestinal; GDM, gestational diabetes mellitus; PHQ-9, Patient Health Questionnaire-9; PPD, post-partum depression. b c 756 KATON ET AL. AD [women taking an AD had higher PHQ-9 scores than women not treated with ADs: PHQ-9 = 6.1 (4.5) versus 3.1 (3.2), p < 0.001], being a victim of intimate partner violence in the past year, and had higher stress scores than women without PPD. The percentage of women with GDM and preeclampsia during the pregnancy did not differ between the depression groups. However, having an infant with low birth weight or having a preterm birth were both significantly more prevalent in the women with PPD. There were five fetal deaths, all in women without PPD. Table 2 presents the logistic regression results. The unadjusted odds ratio (OR) for PHQ-9 scores assessed during pregnancy was 1.25 (95% confidence interval [95% CI] = 1.21– 1.29). The addition of the demographic variables and medical conditions did little to change this result. The addition of the health-related behaviors minimally reduced the odds to 1.10 (1.05–1.15). The addition of the pregnancy-related variables did not change the odds ratio. Therefore, there is a 10% increase in the odds of reporting PPD for every one-point increase in PHQ-9 score assessed during pregnancy. A clinically significant five-point increase in PHQ-9 scores assessed during pregnancy resulted in a 70% increase in the odds of PPD [OR = 1.70, 95% CI = 1.34–2.16, p = .0001], after adjusting for all the demographic, medical conditions, health-related behaviors, and pregnancy-related variables. The final model in Table 2 shows the odds ratios and their 95% confidence intervals for all included variables. In addition to depression, women with PPD were significantly more likely to be younger, to be unemployed and to have prepregnancy diabetes and neurological conditions, to be smokers; to report using Ads, and Table 2. Risk Factors Associated with Post-Partum Depression Model Unadjusted total PHQ-9 score assessed during pregnancy Total PHQ-9 Score assessed during pregnancy adjusted for demographic variablesa Total PHQ-9 Score assessed during pregnancy adjusted for demographic variablesa and medical conditionsb Total PHQ-9 Score assessed during pregnancy adjusted for demographic variables,a medical conditions,b and health-related behaviorsc Final multi-variable model Total PHQ-9 Score assessed during pregnancy Demographic variablesa Age White race Married Some college Unemployment Prepregnancy medical conditionsb Asthma Diabetes GI disorders Heart conditions Hypertension Migraines Neurological conditions Thyroid problems Health-related behaviorsc Current cigarette smoking Taking an antidepressant Drinking alcohol during pregnancy Intimate partner violence within the past year Stress total score Pregnancy-related variablesd GDM Preeclampsia Low birth weight Preterm birth Odds ratio (95% CI) for total PHQ-9 Score (1 point) p Value 1.25 (1.21–1.29) 1.22 (1.18–1.26) 0.0001 0.0001 1.21 (1.17–1.25) 0.0001 1.10 (1.05–1.16) 0.0001 1.10 (1.05–1.15) 0.0001 0.94 1.01 1.14 1.18 1.50 (0.91–0.97) (0.69–1.48) (0.73–1.77) (0.77–1.82) (1.02–2.21) 0.0001 0.95 0.58 0.44 0.04 0.61 1.98 0.74 1.92 0.70 1.40 2.37 0.70 (0.37–1.00) (1.12–3.52) (0.42–1.32) (0.98–3.79) (0.39–1.25) (0.93–2.11) (1.12–5.02) (0.34–1.46) 0.05 0.02 0.31 0.06 0.23 0.10 0.02 0.34 2.84 2.23 0.46 0.53 1.14 (1.80–4.48) (1.35–3.68) (0.24–0.90) (0.24–1.13) (1.09–1.19) 0.0001 0.002 0.02 0.10 0.0001 0.68 1.43 1.38 1.07 (0.40–1.13) (0.95–2.17) (0.79–2.43) (0.64–1.81) 0.13 0.09 0.26 0.79 The above analyses are propensity weighted. a Demographic variables: age, race, marital status, education, unemployment. b Prepregnancy medical conditions: asthma, hypertension, diabetes, neurological condition, heart condition, GI condition, thyroid problems, migraines. c Health-related behaviors: current cigarette smoking, taking an antidepressant, any domestic violence in the past year, drinking alcohol during pregnancy, and stress total score. d Pregnancy-related variables: GDM, preeclampsia, low birth weight, and preterm birth. 95% CI, 95% confidence interval. PREDICTORS OF POSTPARTUM DEPRESSION to endorse more stress and less alcohol use during pregnancy in comparison with the women without PPD. For our sensitivity analysis, we ran the same complete model without the propensity weights. The significance and odds ratios for all the variables were similar for the propensity weighted and unweighted models, except for four variables which became nonsignificant: unemployment ( p = 0.37), asthma ( p = 0.41), neurological problems ( p = 0.31), and alcohol consumption during pregnancy ( p = 0.18). These variables were less robustly associated with PPD in the weighted model, and all four of these variables also occurred in higher rates in women who were excluded from the study, which probably accounts for differences in the weighted versus unweighted samples. Discussion PPD is a very common disorder that causes significant functional impairment and increases risk of poor mother– infant bonding and delays in infant development. Therefore, enhancing the understanding of vulnerability factors could raise awareness for obstetricians and primary care physicians about high-risk populations. Our data show that depressive symptoms in pregnancy, AD use at the time of pregnancy screening, younger age, unemployment, prepregnancy diabetes and neurologic disorders, smoking, less alcohol use during pregnancy, and a high degree of psychosocial stressors were independent predictors of risk of PPD. Of these, neither smoking nor prepregnancy diabetes (or other medical disorders) were found to be associated with risk in the previous meta-analyses1,2 but were likely not included as predictors in many previous studies. Our findings emphasize that one of the highest risk factors for PPD, which is potentially modifiable, is depressive symptoms in pregnancy. We found that for every one-point change in depressive symptoms there was an associated 10% increased risk of PPD. A significant clinical increase of five-points on the PHQ-9 was associated with an approximately 70% increased risk of PPD. These data emphasize the importance of improving case-finding of patients with depression using well-validated tools such as the PHQ-9 coupled with development of effective primary care or Ob-Gyn evidence-based depression interventions. Screening for depression and referral out-of-clinic to mental health specialists is not likely to be effective due to data showing that approximately half of primary care patients with depression do not follow through with primary care referrals to mental health specialists.21 Collaborative depression care interventions integrated into medical clinics which include a physician-supervised care manager, longitudinal measurement of depressive symptoms, and increasing intensity of care based on persistent symptoms have been shown to be an effective way to improve quality of treatment of depression, and depressive and functional outcomes, in both primary care22 and Ob-Gyn settings.23 The finding that younger, unemployed, psychosocially stressed women with adverse health habits such as smoking, and development of depressive disorders and medical disorders in early adulthood, are vulnerable to PPD is supported by epidemiologic data showing that women growing up in socially disadvantaged environments tend to have greater risk for both medical and psychiatric disorders, which often develop at younger ages than women growing up in less vulnerable situations.24,25 Recent research has focused on the 757 effect of stressors over a woman’s life-course in adding to risk of pregnancy-related complications, low birth weight, preterm labor, and PPD.26 Prospective studies suggest that maternal exposure to low socioeconomic status in childhood and exposure to violence/mental health issues in childhood was associated with low birth weight in offspring.27–30 An emerging literature suggests that causal mechanisms link maternal early-life risk and offspring birth weight. Specifically, through a causal pathway that includes adolescent substance use and prenatal substance use,31,32 researchers have shown that maternal exposure to maltreatment and economic disadvantage during early childhood is associated with offspring low birth weight. Physicians strongly advise women to quit smoking during pregnancy due to its association with risk of low birth weight.33 Depression during pregnancy has been found to increase risk of not being able to quit smoking.34 Many pregnant women do quit smoking, and it is possible that those in our sample who had not quit by the 4- or 8-month screening had both stronger nicotine dependence and more psychological vulnerabilities that they cope with by smoking.34,35 We also found that prepregnancy diabetes was associated with a higher risk of PPD. Lower socioeconomic status,36 childhood adversity,37 and depression38 have been found to be associated with a higher risk of prepregnancy metabolic abnormalities and diabetes, suggesting that development of this disorder early in a woman’s life may reflect psychological and social vulnerabilities. Obesity and prepregnancy diabetes are also linked to the risk for pregnancy-induced hypertension and GMD, which may increase patient’s perception of stress during pregnancy and risk of PPD.39 AD use is likely a marker of depressive episodes that occurred prior to pregnancy. However, AD use in observational studies of medical populations is usually an example of confounding by severity, since only approximately half of patients in primary care are accurately recognized by physicians as having depression, and only half of those diagnosed are effectively treated.40 Thus, AD prescriptions may be correlated with more severe and persistent episodes of depression, which are more likely to be recognized but are still often inadequately treated. We examined whether there was evidence of less than adequate treatment in patients in our sample who were treated with ADs and found significantly more depressive symptoms based on the PHQ-9 in these patients compared to those not treated with ADs. The strengths of this study include the large sample size, using propensity weights to allow use of the full screening sample, and inclusion of a full range of predictor variables. Limitations include study of a population from one large university clinic in one geographical region of the United States, lack of use of structured psychiatric interviews for diagnosis of depression and history of prior depressive episodes, and not assessing body mass index (BMI) or social support. However, the PHQ-9 has been validated in a large Ob-Gyn study of 3,000 patients and found to have high sensitivity and specificity for the diagnosis of major depression based on structured psychiatric interview,11 and we did assess prepregnancy diabetes, hypertension, and gestational diabetes—all of which are associated with prepregnancy BMI. Moreover, the PHQ-9 is being widely used in quality improvement efforts in many primary care settings. We also found a lower rate of probable major depression based on a PHQ-9 score of ‡ 10 during pregnancy 758 (6.7%) and postpartum periods (5.8%) than has been found in many previous studies. The relatively high educational level and the high percentage of married women in our sample may have served as protective factors for development of affective illness. The finding that less alcohol use during pregnancy was associated with a high risk of PPD was surprising and may reflect the limitations of the questionnaire used, which identified a minority of women with ‘‘any use’’ of alcohol but very few with abuse or dependence problems. It also may reflect underreporting of alcohol use. The single 6-week postpartum screen for depression may have missed some women who develop PPD within a three-month period. However, a recent large study found that most post-partum episodes began within the first postpartum month.41 Conclusion In summary, we found that younger age, unemployment, antenatal depressive symptoms, taking ADs, psychosocial stressors, prepregnancy chronic physical illnesses (diabetes and neurologic conditions), and smoking were independent predictors of development of PPD. Attention to these risk factors may help primary care and Ob-Gyn physicians focus depression case-finding efforts. Disclosure Statement No competing financial interests exist. References 1. Robertson E, Grace S, Wallington T, Stewart DE. Antenatal risk factors for postpartum depression: A synthesis of recent literature. Gen Hosp Psychiatry 2004;26:289–295. 2. Schmied V, Johnson M, Naidoo N, et al. Maternal mental health in Australia and New Zealand: A review of longitudinal studies. Women Birth 2013;26:167–178. 3. Nott PN. Extent, timing and persistence of emotional disorders following childbirth. 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J Perinat Med 2013:1–8. 36. Stringhini S, Batty GD, Bovet P, et al. Association of lifecourse socioeconomic status with chronic inflammation and type 2 diabetes risk: The Whitehall II prospective cohort study. PLoS Med 2013;10:e1001479. 759 37. Scott KM, Von Korff M, Angermeyer MC, et al. Association of childhood adversities and early-onset mental disorders with adult-onset chronic physical conditions. Arch Gen Psychiatry 2011;68:838–844. 38. Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan: A meta-analysis. Diabetes Care 2008;31:2383–2390. 39. Mutsaerts MA, Groen H, Buiter-Van der Meer A, et al. Effects of paternal and maternal lifestyle factors on pregnancy complications and perinatal outcome. A populationbased birth-cohort study: The GECKO Drenthe cohort. Hum Reprod 2014;29:824–834. 40. Katon WJ, Unutzer J, Simon G. Treatment of depression in primary care: Where we are, where we can go. Med Care 2004;42:1153–1157. 41. Di Florio A, Forty L, Gordon-Smith K, et al. Perinatal episodes across the mood disorder spectrum. JAMA Psychiatry 2013;70:168–175. Address correspondence to: Wayne Katon, MD Department of Psychiatry and Behavioral Sciences University of Washington School of Medicine Campus Box 356560 1959 Northeast Pacific Street Seattle, WA 98195 E-mail: wkaton@uw.edu Copyright of Journal of Women's Health (15409996) is the property of Mary Ann Liebert, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. ORIGINAL RESEARCH Maternal and neonatal health outcomes following the implementation of an innovative model of nurse practitioner-led care for diabetes in pregnancy Giuliana O. Murfet, Penny Allen & Tania J. Hingston Accepted for publication 28 August 2013 Correspondence to P. Allen: e-mail: penny.allen@utas.edu.au Giuliana O. Murfet MSc MNurs (Nurse Practitioner) RN CDE Nursing Practitioner Diabetes Centre, Tasmanian Health Organisation – North West, Burnie, Tasmania, Australia Penny Allen BA (Hons) MPH PhD Research Fellow Rural Clinical School, University of Tasmania, Burnie, Tasmania, Australia Tania J. Hingston MBBS (Hons) FRANZCOG Consultant Obstetrician and Gynaecologist Maternity Department, North West Private Hospital, Burnie, Tasmania, Australia M U R F E T G . O . , A L L E N P . & H I N G S T O N T . J . ( 2 0 1 4 ) Maternal and neonatal health outcomes following the implementation of an innovative model of nurse practitioner-led care for diabetes in pregnancy. Journal of Advanced Nursing 70 (5), 1150–1163. doi: 10.1111/jan.12277 Abstract Aim. To investigate maternal and neonatal outcomes following implementation of a nurse practitioner-led model of care for diabetes in pregnancy. Background. Diabetes in pregnancy increases the risk of adverse health outcomes in mothers and infants. The management of diabetes in pregnancy is crucial to reduce poor outcomes. Design. Uncontrolled before-after intervention study. Methods. International Classification of Diseases codes were used to identify pregnancies suspected of being complicated by diabetes. Demographic, health, diabetes and maternity data were extracted from hospital records. Adverse maternal and neonatal outcomes were compared pre- (2003–2006) and postintervention (2010–2011). Adjusted relative risks (aRR) were calculated using the glm command in Stata. Results. A total of 261 pregnancies were included: 112 pre-intervention and 149 managed under the nurse practitioner-led model. There were 37 women with preexisting diabetes (26 T1DM, 11 T2DM) and 195 with gestational diabetes. Referrals to dieticians and diabetes educators increased, while referrals to physicians decreased. There was no decrease in the risk of adverse maternal outcomes for all women with DIP or women with GDM. However, there was a 24% decrease in adverse neonatal outcomes overall and a 40% decrease among infants of women with gestational diabetes. Conclusion. The study demonstrated that nurse practitioner-led models of care for diabetes in pregnancy are feasible. The findings suggest that the model reduced adverse neonatal outcomes. By improving information provision, support and care coordination, the model is particularly valuable in rural areas, where access to medical specialists is often restricted. Keywords: diabetes, model of care, nurse practitioner, nurses, nursing, pregnancy 1150 © 2013 John Wiley & Sons Ltd JAN: ORIGINAL RESEARCH Why is this research or review needed? • This research describes an innovative model of nurse practitioner-led care for the women with pregnancies complicated Nurse practitioner-led care for diabetes in pregnancy mentation of nurse practitioner-led models of care for DIP in rural regions. This study evaluated a nurse practitionerled DIP clinic in the sparsely populated North West of the island state of Tasmania, Australia. by diabetes. • Published studies of nurse-led care models for the management of diabetes in pregnancy differ from the model presented in this paper. • Few studies have evaluated the effectiveness of nurse-led models for reducing adverse maternal and neonatal health outcomes in pregnancies complicated by diabetes. What are the key findings? • The nurse practitioner-led model of care increased referrals to • The model resulted in decreased referrals to physicians for • There was a 24% reduction in the risk of adverse neonatal dieticians and diabetes educators. diabetes management. outcomes among all women with diabetes in pregnancy in the postintervention period and a 40% reduction in the risk of adverse neonatal outcomes among women with gestational diabetes. • The model may have played an important role in reducing adverse neonatal outcomes. How should the findings be used to influence policy/ practice/research/education? • The findings support the implementation of nurse practitioner-led care models for women with pregnancies complicated by diabetes. Introduction The prevalence of diabetes in pregnancy (DIP) has escalated in developed and developing countries over the past 10–15 years (Dabelea et al. 2005, Hunt & Schuller 2007, Baraban et al. 2008, Bell et al. 2008). This presents a challenge to health professionals and healthcare systems as DIP increases the risk of adverse outcomes for both the mother and child (Farrell et al. 2002, Temple et al. 2002, Dunne et al. 2003, Clausen et al. 2005, Macintosh et al. 2006, Hapo Study Cooperative Research Group et al. 2008, Peticca et al. 2009). Women with DIP living in rural Australia are likely to be at greater risk of complications due to poor levels of access to specialist care and barriers to accessing diabetes self-management information (King & Wellard 2009). A potential solution to this problem is the imple© 2013 John Wiley & Sons Ltd Background DIP encompasses pregnancies in women with pre-existing type 1 diabetes (T1DM), pre-existing type 2 diabetes (T2DM) or gestational diabetes mellitus (GDM). During the early stages of pregnancy, particularly in the first trimester when nausea and vomiting are common, the mother with either T1DM or T2DM may find it difficult to maintain blood glucose levels to target, resulting in recurrent hyperglycaemic or hypoglycaemic events, which are associated with pregnancy loss (Jovanovic et al. 2005). Pre-eclampsia is a serious maternal complication found in approximately 7–13% of women with DIP (McIntyre et al. 2004, Temple et al. 2006, Peticca et al. 2009), compared with approximately 3% in normoglycaemic pregnancies (McIntyre et al. 2004). As a consequence of pre-eclampsia, glycaemic changes and/or foetal complications, the pregnancy may not reach full term. Pre-term labour accounts for around 31–38% of pregnancies complicated by diabetes (Ferrara et al. 2012). A further concern is the approximate 25–6 fold increased risk of perinatal and early neonatal mortality compared with the general population (Dunne et al. 2003, Macintosh et al. 2006). Pregnancies that continue to near full term may be complicated by difficulties encountered during birth. The large, macrosomic baby born vaginally may result in perineal lacerations requiring repair (Berard et al. 1998). On occasion, the macrosomic infant is simply too large for a vaginal delivery. Consequently, women with DIP typically have high rates of caesarean delivery with a large Canadian study reporting caesarean rates of 52% among women with T1DM and 38% among women with T2DM or GDM (Peticca et al. 2009). Infants born to women with DIP are at increased risk of growth retardation, macrosomia, birth trauma, polycythaemia, cardiomyopathy, thrombosis, hypoglycaemia, hypomagnesaemia, jaundice, feeding difficulties and long-term metabolic abnormalities (Inkster et al. 2006, Ponzo et al. 2006, Hawdon 2011). Respiratory distress syndrome (RDS), which affects approximately 5% of infants born to women with T1DM, (Persson et al. 2009) poses a serious risk to the survival of the neonate. A major concern is the risk of congenital abnormalities, which occur in 5–10% of infants born to women with diabetes (Farrell et al. 2002, Evers et al. 2004, McElduff et al. 2005, Macintosh et al. 2006). Congenital anomalies are seen more frequently in 1151 G.O. Murfet et al. babies born to mothers with pre-existing T1DM (Peticca et al. 2009) or T2DM, compared with women with GDM (Macintosh et al. 2006). Macrosomia is a common complication seen in babies of women who have DIP. Although there are differing definitions for macrosomia, a generally accepted definition is birthweight >4 kg and/or >90th percentile for gestational age (Negrato et al. 2012). Macrosomia can lead to pre-term delivery, stillbirth and early neonatal death (Zhang et al. 2008). Furthermore, the large baby can sustain damage as it passes through the birth canal, resulting in shoulder dystocia, fractures to the clavicle and injury to the brachial plexus and facial nerve (Berard et al. 1998). The long-term effects of GDM in women include an increased risk of recurrence in subsequent pregnancies, impaired glucose tolerance and diabetes in the future (Homko et al. 2001, Kim et al. 2007, Anderberg et al. 2011, Malinowska-Polubiec et al. 2012). Children born to women with DIP are at increased risk of insulin resistance, impaired glucose tolerance, metabolic disorders, increased BMI, changes in fat distribution, childhood obesity and developing diabetes in childhood or teenage years (Weintrob et al. 1996, Crume et al. 2011, Dabelea & Crume 2011, Yessoufou & Moutairou 2011, Sparano et al. 2013). Australian guidelines for the management of diabetes in pregnancy The management of diabetes in pregnancy is crucial to reduce adverse maternal and peri-natal outcomes. Models consisting of dietary and blood glucose advice and insulin therapy have been shown to improve glycaemic control and reduce rates of macrosomia, premature delivery, shoulder dystocia, caesarean delivery, stillbirth and neonatal mortality (Crowther et al. 2005, Temple et al. 2006, Landon et al. 2009). Given the strength of this evidence, the Australasian Diabetes in Pregnancy Society (ADIPS) guidelines recommend fasting blood glucose levels to be targeted at 40–55 mmol/ L and 2-hour postprandial levels at 40 units of insulin at review. The DNP liaised with the pharmacy departments of the two local public hospitals to enable appropriate insulins to be dispensed on the day of the clinic, as required. By August 2009, the DIPC, incorporating a multidisciplinary team [obstetrician, diabetes educator, dietician and antenatal nurse] and the use of a Management of Gestational Diabetes Protocol, was established at the public and private hospital sites. Figure 1 outlines the DNP model of care for pregnancies complicated by diabetes. In women with T1DM or T2DM, a Continuous Glucose Monitoring System (CGMS) was used to identify target areas of hyperglycaemia, with women sent to the Diabetes Centre for this monitoring. An after-hours DNE contact number was provided to women for further information and support regarding management of blood glucose levels (BGLs). Dependant on glycaemic control, use of insulin and obstetric reasons, women were reviewed on a 1–4 weekly basis. Women who screened positive for GDM were informed of diagnosis, provided information and booked into the next weekly DIPC. As per ADIPS guidelines at the time of the study, screening could occur earlier, either at 12–16 weeks if the woman had a previous history of GDM, or at presentation if the woman was symptomatic. At the DIPC, women received an initial assessment with the dietician and education with the CDE, including education about home blood glucose monitoring. At future appointments, clients were seen by the dietician and CDE simultaneously, with review by the midwife and obstetrician as necessary. The study Aim The study aimed to investigate maternal and neonatal health outcomes pre- and postimplementation of a nurse practitioner-led model of care for pregnancies complicated by diabetes in a rural locality. Design Uncontrolled before-after study of maternal and neonatal outcomes following implementation of a nurse practitionerled model of care for DIP. 1154 Sample/participants The pre-intervention audit included all pregnancies in North West Tasmania that were complicated by diabetes between July 2003–June 2006. Between late 2006–2009, one local hospital underwent major organizational and funding changes. Additionally, in 2008, a trial DIP screening programme was implemented for 12 months by one of the antenatal services with support of the Diabetes Centre as preliminary data highlighted lack of screening and management. It was, therefore, decided to exclude these periods from the study to reduce bias and potential contamination of the results from the effects of the DIPC pilot. While the nurse-led DIPC was implemented in August 2009, it took several months for the model to become embedded in local maternity services. It was therefore decided to delay the postintervention audit to cover the period from January 2010–December 2011. The inclusion criteria for the pre- and postintervention periods were: (1) Women with pre-existing diabetes who attended maternity services at either of the two local public hospitals or the one local private hospital during the study period; (2) Women diagnosed with GDM who attended maternity services at the same hospitals during the study period. The DNP used International Classification of Diseases (ICD) codes (Table 1) to identify women with pregnancies suspected of being complicated by diabetes. This was necessary as the pre-intervention audit found screening at one hospital was extremely limited. Data collection Three CDEs recorded data from maternity services records onto a data collection audit form for all pregnancies suspected of being complicated by diabetes. The lead DNP reviewed each form for completeness and cross-checked a random sample of 20% of forms with medical records for data accuracy. The audit form included information on demographics, GDM-specific screening, pre-existing diabetes-specific complication screening, referral to multidisciplinary team, monitoring of diabetes, treatments for diabetes during pregnancy and both maternal and neonatal health outcomes. Local experts in the field, including a diabetes paediatrician, diabetes nurse educator with a pregnancy portfolio and diabetes physician, reviewed the audit tool for content validity prior to use. Furthermore, an expert ‘diabetes in pregnancy’ endocrinologist (based interstate) validated the panel’s findings, benchmarking the form against current best practice evidence. BMI was not available for the pre-intervention group as height and weight © 2013 John Wiley & Sons Ltd JAN: ORIGINAL RESEARCH Nurse practitioner-led care for diabetes in pregnancy Table 1 ICD codes used to identify study participants. ICD codes O240 0241 0242 0243 0244 0245 0249 0335 0366 0350 P700 P701 P704 Pre-Existing T1DM in pregnancy Pre-Existing T2DM in pregnancy Pre-Existing DM other specified in pregnancy Pre-Existing DM unspecified in pregnancy Gestational Diabetes Mellitus, diabetes arising at >24 weeks in pregnancy Pre-existing impaired glucose regulation Diabetes mellitus in pregnancy, unspecified onset Maternal care for disproportion due to unusually large foetus (included if screening for GDM had not occurred & hypoglycaemia evident) Maternal care for excessive foetal growth (known or expected LGA) Maternal care for (suspected) Central Nervous System malformation in foetus – anencephaly, spina bifida (included if the baby was macrosomic and screening for GDM had not occurred Syndrome of infant of a mother with gestational diabetes Syndrome of infant of diabetic mother – maternal diabetes mellitus (pre-existing) affecting foetus or new born (with hypoglycaemic) Other neonatal hypoglycaemia (included if the baby was macrosomic and screening for GDM had not occurred) were not routinely measured as part of antenatal care during this period. HbA1c using Diabetes Control and Complications Trial (DCCT)% units were converted to International Federation of Clinical Chemistry (IFCC) mmol/mol units. Socio-economic status (SES) was derived from the Australian Bureau of Statistics (ABS) Socio-Economic Indexes for Areas (SEIFA) 2006, (Australian Bureau of Statistics 2006) by matching postcodes to the Index of Relative Socio-economic Advantage and Disadvantage (IRSAD), an area-level measure of socio-economic advantage to disadvantage that includes data on income, education level, unemployment, housing expenditure and assets. The higher deciles of this measure indicate relative advantage in an area. Ethical considerations Research Ethics Committee approval for the audit was granted by the Patient Care Committee of the Quality and Safety Executive Unit at the North West Regional Hospital, Tasmania and from the Executive Board of the North West Private Hospital. Patient anonymity was maintained by assigning a unique study identifier to each pregnancy and not recording patient identifiable data such as names and addresses. Data analysis Macrosomia was defined as birthweight >90th percentile for gestational age, while pre-term births were defined as births up to 37 weeks gestation. Adverse maternal © 2013 John Wiley & Sons Ltd outcomes included loss of consciousness, threatened abortion requiring cervical sutures, hypoglycaemia, diabetic ketoacidosis, metabolic complication, polyhydramnious, placenta previa, pyelonephritis, emergency caesarean section, failure to progress in labour, 2–4° tear and postpartum haemorrhage. Adverse neonatal health outcomes were defined as hypoglycaemia [BGL
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Running head: RESEARCH ARTICLE ANALYSIS

Research Article Analysis

Institution Affiliation

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RESEARCH ARTICLE ANALYSIS

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Introduction

For the research paper analysis assignment, I choose the article that was published by
Theodosios Stavrianopoulos in 2016. The aim of the article was to present the findings of a study
that Stavrianopoulos conducted. The study’s aim was to determine whether a telephone
intervention by nurses helped improve the quality of life in patients with Heart Failure (HF). The
study was conducted in a district hospital in Greece. HF patients spend a lot of time and money
with frequent hospital visits. If telephone-based interventions can produce positive results, it can
save a lot of money and time for these patients and improve their quality of life. The purpose of
this paper is to critique the article by Stavrianopoulos and judge whether it makes significant
contributions to the nursing field.
Research Question
The research question in the study was “Can a Nurse-led telephone intervention program
impact on the quality of life in patients with Heart Failure in a District Hospital of Greece?”
(Stavrianopoulos, 2016). The aim of the research was to acquire evidence that would help answer
this question. The question is clear, simple, and straightforward. The adoption of mobile phones
around the world has been rising significantly. Different services such as banking and customer
service can now be offered over the phone. The increasing adoption of mobile phones and their
use for the provision of vital services may have motivated the researcher to study this question.
Another trend that could have affected this question is the frequency in which HF patients visit
the hospital. Offering services that they accessed by visiting the hospital via telephone could
reduce the ...


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