Female specialists were more likely to respond to a postal questionnaire about drug treatments for Alzheimer disease

OBJECTIVE: To assess demographic predictors of response for specialists who were mailed a postal questionnaire on drug treatments for Alzheimer disease. STUDY DESIGN AND SETTING: The questionnaire was sent to 317 specialists in Quebec, Canada. Demographic variables included specialty, urban/rural practice, language, sex, and 'number of years since receiving a medical license.' The specialists were stratified according to responder status (i.e., respondent/nonrespondent), and respondents were further stratified as early or late responders. Variables differing between these strata were entered into logistic regression models to see if they predicted response. RESULTS: Only 'female sex' was a predictor of response in the respondent/nonrespondent analysis (OR 2.03; 95% CI 1.17, 3.53). No demographic variables predicted early or late response. CONCLUSION: Researchers planning postal questionnaires should target male specialists with modified or additional mailings to increase response and reduce the potential for nonresponse bias. Caution should be exercised when comparing early vs. late responders as a means of assessing nonresponse bias.     

Mathematical coupling may account for the association between baseline severity and minimally important difference values

ObjectiveTo generate anchor-based values for the “minimally important difference” (MID) for a number of commonly used patient-reported outcome (PRO) measures and to examine whether these values could be applied across the continuum of preoperative patient severity.Study Design and SettingSix prospective cohort studies of patients undergoing elective surgery at hospitals in England and Wales. Patients completed questionnaires about their health and health-related quality of life before and after surgery. MID values were calculated using the mean change score for a reference group of patients who reported they were “a little better” after surgery minus the mean change score for those who said they were “about the same.” Pearson's correlation was used to examine the association between baseline severity and change scores in the reference group. Baseline severity was expressed in two ways: first in terms of preoperative scores and second in terms of the average of pre- and postoperative scores (Oldham's method).ResultsOf the 10 PRO measures examined, eight demonstrated a moderate or high positive association between preoperative scores and MID values. Only two measures demonstrated such an association when Oldham's measure of baseline severity was used.ConclusionIn general, there is little association between baseline severity and MID values. However, a moderate association persists for some measures, and it is recommended that researchers continue to test for this relationship when generating anchor-based MID values from change scores.     

Nonresponse bias in a mail survey of physicians

With the increased pressure on survey researchers to achieve high response rates, it is critical to explore issues related to nonresponse. In this study, the authors examined the effects of nonresponse bias in a mail survey of physicians (N = 3,400). Because slightly more than one half of the sample did not respond to the survey, there was potential for bias if nonresponders differed significantly from responders with respect to key demographic and practice variables. They analyzed response status and timing of response with respect to five variables: gender, region, specialty, urbanicity, and survey length. The potential consequences of nonresponse bias on the survey estimates were then analyzed. Men were more likely to respond, as were physicians receiving a shorter questionnaire. Repeated follow-up attempts reduced gender response bias because male physicians were more likely to be early responders. Overall, higher response rates were not associated with lower response bias.     

Modeling strategies to improve parameter estimates in prognostic factors analyses with patient-reported outcomes in oncology

Purpose

The inclusion of patient-reported outcome (PRO) questionnaires in prognostic factor analyses in oncology has substantially increased in recent years. We performed a simulation study to compare the performances of four different modeling strategies in estimating the prognostic impact of multiple collinear scales from PRO questionnaires.

Methods

We generated multiple scenarios describing survival data with different sample sizes, event rates and degrees of multicollinearity among five PRO scales. We used the Cox proportional hazards (PH) model to estimate the hazard ratios (HR) using automatic selection procedures, which were based on either the likelihood ratio-test (Cox-PV) or the Akaike Information Criterion (Cox-AIC). We also used Cox PH models which included all variables and were either penalized using the Ridge regression (Cox-R) or were estimated as usual (Cox-Full). For each scenario, we simulated 1000 independent datasets and compared the average outcomes of all methods.

Results

The Cox-R showed similar or better performances with respect to the other methods, particularly in scenarios with medium–high multicollinearity (ρ?=?0.4 to ρ?=?0.8) and small sample sizes (n?=?100). Overall, the Cox-PV and Cox-AIC performed worse, for example they did not select one or more prognostic collinear PRO scales in some scenarios. Compared with the Cox-Full, the Cox-R provided HR estimates with similar bias patterns but smaller root-mean-squared errors, particularly in higher multicollinearity scenarios.

Conclusions

Our findings suggest that the Cox-R is the best approach when performing prognostic factor analyses with multiple and collinear PRO scales, particularly in situations of high multicollinearity, small sample sizes and low event rates.

     

Preoperative Deprescribing for Medical Optimization of Older Adults Undergoing Surgery: A Systematic Review

ObjectiveTo summarize the evidence for preoperative deprescribing and its effect on postoperative outcomes in older adults undergoing surgery.DesignSystematic review.Setting and ParticipantsAll available studies.MethodsWe searched EMBASE, Cumulative Index of Nursing and Allied Health (CINAHL), and PubMed from inception to January 12, 2021. Settings included outpatient settings during the waiting period for surgery (ie, preoperative clinic) through to the preoperative period in the hospital. Participants who were older adults, aged ≥65 years, undergoing planned or emergency surgery with deprescribing or medication-related interventions were included for review.ResultsWe identified 3 different methods of deprescribing intervention delivery during the preoperative period: geriatrician-led (n = 2), interdisciplinary team-led (n = 8), and pharmacist-led (n = 6). Outcomes were related to health care utilization, patient outcomes, and medication changes; however, results were difficult to compare because of heterogeneous outcomes within the topics. Overall, results were either positive or neutral.Conclusions and ImplicationsThe evidence for deprescribing during the preoperative period for older adults undergoing surgery is weak because of the heterogeneity of intervention delivery and outcomes, inclusion of nonoperative cases in some studies, and low power. This review highlights the need for future research, which may consider the following: (1) interdisciplinary approach, (2) coordination of deprescribing efforts with primary care provider from the waiting period for surgery up to after hospital discharge, and (3) validated deprescribing criteria such as STOPP/START that is easy to implement. It is important to note that results yielded positive and neutral results, not negative ones, which should reassure clinicians to implement deprescribing for older adults during the surgical period. Additionally, policy initiatives such as integrated electronic medical records or increased reimbursement of deprescribing efforts for primary care providers and/or hospitals should be pursued to prevent adverse postoperative events for this population.     

Evaluation of Noncompletion Bias and Long-Term Adherence in a 10-Year Patient-Reported Outcome Monitoring Program in Clinical Routine

Background

Currently there is little knowledge on real-life sustainability of routine patient-reported outcome (PRO) measurement and the representativeness of collected data.

Late response and item nonresponse in the Finbalt Health Monitor survey

BACKGROUND: The Finbalt Health Monitor is a collaborative system for monitoring the health-related behaviour, practices and lifestyles in Estonia, Finland, Latvia and Lithuania. This system is based on nationally representative samples and self-administered mailed questionnaires. In comparing the results of national surveys, the awareness of the direction and socioeconomic patterning of the response bias is essential. METHODS: The data were gathered from the cross-sectional surveys conducted in 1998 from Estonia (n = 1362), Finland (n = 3504), Latvia (n = 2322) and Lithuania (n = 1874). An analysis was made of the prevalence of late response, completeness of information obtained from respondents and the magnitude of response bias on the prevalence estimates of health behaviour indicators. RESULTS: The response rates were comparatively high: 68% in Estonia, 70% in Finland, 77% in Latvia and 62% in Lithuania. Late response was weakly related to age, education or place of residence. The total proportion of missing information was below 10% and the sociodemographic patterning for this missing information was similar in all countries. Thus, older and less-educated respondents had more missing information on their questionnaires. Response bias of the prevalence estimates was minimal when it was calculated by using information obtained from late respondents. CONCLUSIONS: The level of nonresponse and missing information was comparable in different countries, not information on health behaviour. Therefore special efforts are needed to design a questionnaire form which appears equally relevant to all respondent groups. The follow-up mailings were an effective way to increase the total response rate, but it was unlikely that they provided an effective way to reach the 'hard core' nonrespondents.     

Impact of Dementia on Long-Term Hip Fracture Surgery Outcomes: An Electronic Health Record Analysis

ObjectiveOlder adults with dementia are at higher risk for sustaining hip fracture and their long-term health outcomes after surgery are usually worse than those without dementia. Widespread adoption of electronic health records (EHRs) may allow hospitals to better monitor long-term health outcomes in patients with dementia after hospitalization. This study aimed to (1) estimate how dementia influences discharge location, mortality, and readmission 180 days and 1 year after hip fracture surgery in older adults, and (2) demonstrate the feasibility of using selection-bias reduced EHR data for research and long-term health outcomes monitoring.DesignRetrospective observational cohort study using EHRs.Setting and ParticipantsA cohort of 1171 patients over age 65 years who had an initial hip fracture surgery between October 2015 and December 2018 was extracted from EHRs of one health system; 376 of these patients had dementia.MethodsLogistic regression was applied to estimate influences of dementia on discharge disposition and Cox proportional hazards model for mortality. The Fine and Gray regression model was used to analyze readmission, accounting for the competing risk of death. To reduce selection bias in EHRs, inverse probability of treatment weighting using propensity scores was implemented before modeling.ResultsDementia had significant impacts on all outcomes: being discharged to facilities [odds ratio (OR) = 2.11, 95% confidence interval (CI) 1.19–3.74], 180-day mortality [hazard ratio (HR) = 1.69, 95% CI 1.20–2.38], 1-year mortality (HR = 1.78, 95% CI 1.33–2.38), 180-day readmission (HR = 1.62, 95% CI 1.39–1.89), and 1 year readmission (HR = 1.39, 95% CI 1.21–1.58).Conclusions and ImplicationsDementia was a significant risk factor for worse long-term outcomes. The inverse probability of treatment weighting approach can be used to reduce selection bias in EHR data for research and monitoring long-term health outcomes in the target population. Such monitoring could foster collaborations with post-acute and long-term health care services to improve recovery outcomes in patients with dementia after hip fracture surgery.     

A prenotification letter increased initial response,whereas sender did not affect response rates

ObjectiveTo find ways to improve response rates of medical and health surveys. We investigated whether a prenotification letter instead of a second reminder and varying senders of the questionnaires would affect response rates.Study Design and SettingWe present the results of two studies. In the first study, four groups were compared that either received a prenotification letter (group 1 and 2) or a second reminder letter (group 3 and 4); received the questionnaire from either a research institute (group 1 and 3) or a health insurance company (HIC; group 2 and 4). In the second study, we compared two groups that received the questionnaire sent by either a HIC or a hospital. Response rates, response speed, respondent characteristics, item nonresponse, and mean scores on quality aspects and global ratings were compared.ResultsResponse rates did not differ significantly between groups. Prenotification groups returned their questionnaires faster. No other significant differences were found for response speed, respondent characteristics, item nonresponse, or mean scores.ConclusionA prenotification letter does only increase initial response speed and does not increase total response rates. A prenotification letter should be considered when quick response is desirable. Varying senders had no effect on response rates.     

Preoperative Sleep Questionnaires Identify Medical Emergency Team Activation in Older Adults

Patients with obstructive sleep apnea (OSA) have increased postoperative complications that are important for patient safety and healthcare utilization. Questionnaires help identify patients at risk for OSA; however, among older adults who preoperatively self-administered OSA questionnaires, the frequency of postoperative Medical Emergency Team Activation (META), rapid response, code blue, code stroke, is unknown.ObjectivesIdentify whether having OSA questionnaires completed by patients is feasible in the preoperative clinic. Determine the frequency of META among older patients at risk for OSA.Design and interventionCohort of prospective patients independently completed 2 OSA questionnaires in a preoperative clinic, STOP-Bang (SB) and ISNORED (IS). Observers blinded to questionnaire responses recorded incidence of META.setting and participantsOf the 898 consecutive patients approached in the preoperative assessment clinic and surgical navigation center, 575 (64%) consented and completed the questionnaires in <5 minutes and were included in the analysis.MeasuresSleep questionnaire responses and frequency of inpatient postoperative META.ResultsWith an affirmative response to ≥3 questions on either questionnaire, 65% of patients enrolled were at risk for OSA. Of these, 3.1% sustained an META. In patients at risk for OSA, META occurred in 7.6% (SB+) and 7.2% (IS+) vs 2.5% (SB+) and 1.7% (IS+) for low risk. METAs were disproportionately higher among patients aged ≥65 years (6.3% vs 1.7%; P < .018), American Society of Anesthesiologists (ASA) physical status class ≥3, and IS+. All patients with META positively answered ≥3 of 15 components of the 2 questionnaires.Conclusions/ImplicationsPreoperative, self-administration of SB and IS questionnaires is feasible. Overall, 65% of those with affirmative responses to ≥3 questions were at risk for OSA and associated with a disproportionate number of postoperative META in older patients. Additionally, risk of OSA identified by preoperative sleep questionnaires was associated with postoperative META among older adults. Use of clinical tools and OSA questionnaires may improve preoperative identification of META in this population.     

Web-based data collection yielded an additional response bias--but had no direct effect on outcome scales

ObjectiveTo assess and to evaluate possible effects arising from Web-based data collection on the results of a study.Study Design and SettingWe analyzed participants of the German Weight Control Registry (GWCR) of whom 328 chose to use Web-based questionnaires and 139 preferred to participate via a traditional postal survey. Furthermore, we included data of 212 individuals sampled independently from the general population who fulfilled the study's inclusion criteria—giving us the chance to differentiate between response bias (concerning Web-based data collection) and general selection bias (concerning participation in the GWCR).ResultsIn addition to selection bias (GWCR participants are overall better educated, more likely to live in a partnership, more often female, and older than the general population), we also found a substantial response bias: Participants using the Internet were younger, better educated, and more often male compared with participants preferring the paper-and-pencil version. However, after adjusting for these differences, we found no additional direct effect of Web-based data collection on any of the outcome variables.ConclusionWeb-based epidemiologic studies still do not attract the same participants as postal surveys, even in highly industrialized countries. However, after adjusting for this bias, the same results can be expected.     

Exploring Nonresponse Bias in a Health Survey Using Neighborhood Characteristics

Objectives. We examined potential nonresponse bias in a large-scale, population-based, random-digit-dialed telephone survey in California and its association with the response rate.Methods. We used California Health Interview Survey (CHIS) data and US Census data and linked the two data sets at the census tract level. We compared a broad range of neighborhood characteristics of respondents and nonrespondents to CHIS. We projected individual-level nonresponse bias using the neighborhood characteristics.Results. We found little to no substantial difference in neighborhood characteristics between respondents and nonrespondents. The response propensity of the CHIS sample was similarly distributed across these characteristics. The projected nonresponse bias appeared very small.Conclusions. The response rate in CHIS did not result in significant nonresponse bias and did not substantially affect the level of data representativeness, and it is not valid to focus on response rates alone in determining the quality of survey data.Declining survey response rates over the last decade have raised concerns regarding public health research that uses population-based survey data. Response rates are commonly considered the most important indicator of the representativeness of a survey sample and overall data quality, and low response rates are viewed as evidence that a sample suffers from nonresponse bias.^1,2 Recent survey research literature, however, suggests that response rates are a poor measure of not only nonresponse bias but also data quality.^3–7The decline in survey response rates over the past several decades has led to a number of rigorous studies and innovative methods to explore the relationship between survey response rates and bias. A meta-analysis that examined response rates and nonresponse bias in 59 surveys found no clear association between nonresponse rates and nonresponse bias.⁸ Some surveys with response rates under 20% had a level of nonresponse bias similar to that of surveys with response rates over 70%. This is because nonresponse bias is either a function of both the response rate and the difference between respondents and nonrespondents in a variable of interest,⁹ or it is a function of covariance between response propensity and a variable of interest.¹⁰ Therefore, response rates alone are not the determinant of nonresponse bias of the survey estimates. Although it may be convenient to use the response rate as a single indicator of a survey''s representativeness and data quality, nonresponse bias is a property of a particular variable, not of a survey.Nonetheless, declining survey response rates increase the potential for nonresponse bias and have raised questions about the representativeness of inferences made from probability sample surveys. Inferences from surveys are based on randomization theory and assume a 100% response from the sample. Although the gap between theory-based assumptions and the reality of survey administration has always been a concern, the increasing deviation from the full response assumption increases this concern.Nonresponse is multidimensional, not a unitary outcome, and is roughly divided into 3 components: noncontact, refusal, and other nonresponse.⁹ Most examples of nonresponse compose the first 2 components. A study by Curtin et al. found that refusal rates in a telephone survey remained constant between 1979 and 2003, although the contact rates decreased dramatically.¹¹ Another study by Tuckel and O''Neill found the same pattern.¹²Arguably, different dynamics lead to noncontact and refusal.^13,14 Noncontact (e.g., unanswered phone calls in random-digit-dialed surveys) is related to accessibility. Call screening devices, phone usage, and at-home patterns affect accessibility, and calling strategy (e.g., number of call attempts and timing of calls) directly influences contact rates.^7,12 Refusal occurs only after contact is made. The decision to participate or not is an indicator of the respondent''s amenability to the survey and is also influenced by other factors.Noncontact and refusal may affect different types of potential biases, and these biases may offset one another.^7,15 For example, measures on volunteerism may be biased through noncontact because those who spend much time volunteering may be hard to reach in random-digit-dialed surveys. On the other hand, those who refuse to participate in the same survey may have opinions and behaviors related to volunteerism that differ dramatically from those of persons who are never contacted. Because aggregating noncontact and refusal may obscure our understanding of nonresponse bias, understanding detailed response behaviors along with overall nonresponse bias is important.The decline in response rates is more rapid for random-digit-dialed telephone surveys than for other survey types. The difficulties inherent in examining nonresponse bias arise from the absence of data on nonrespondents. Unlike face-to-face surveys, in which interviewers make direct observation of the sampled individual and have an opportunity to gather contextual information regardless of response status, such information is scarce in telephone surveys because interviewers do not visit the individual and the interviewer–respondent interaction, if any, remains oral and over the telephone. Follow-up with nonrespondents in a telephone survey can be conducted to study its nonresponse bias, but such efforts are resource intensive. Additionally, unless 100% participation is achieved, there still remains some level of nonresponse.Alternatively, nonresponse can be studied through the use of the geographic identifiers associated with sampled telephone numbers. Phone numbers from random-digit-dialed sampling frames can be readily associated with a limited number of geographic identifiers, such as zip codes. In addition, most phone numbers can be matched to a postal address and consequently to a census tract and county, which provides a unique opportunity to evaluate patterns of nonresponse as a function of neighborhood characteristics. A few recent nonresponse bias studies have used such contextual data.^16–19We examined potential nonresponse bias in the 2005 CHIS, a large random-digit-dialed telephone survey, by comparing a wide range of census tract–level neighborhood characteristics by response behavior as well as examining response rates across neighborhood characteristics. Although these characteristics are not specific to individual cases (households), neighborhood characteristics at the census tract level serve as useful proxy indicators of differences in the population. This is because census tracts are relatively permanent small geographic divisions with 1500 to 8000 people that are designed to be homogeneous with respect to sociodemographic characteristics.²⁰ Unlike previous studies that focused on statistical significance, we discuss substantive significance. We explored nonresponse bias in a large, population-based telephone health survey in California. We linked data from the California Health Interview Survey (CHIS) to US Census data at the tract level to compare respondents and nonrespondents across a broad range of neighborhood characteristics.     

Assessing research outcomes by postal questionnaire with telephone follow-up. TOTAL Study Group. Trial of Occupational Therapy and Leisure

BACKGROUND: Face-to-face assessment of research outcomes is expensive and may introduce bias. Postal questionnaires offer a cheaper alternative which avoids observer bias, but non-response and incomplete response reduce the effective sample size and may be equally serious sources of bias. This study examines the extent and potential effects of missing data in the postal collection of outcomes for a large rehabilitation trial. METHODS: Questionnaires containing a number of established scales were posted to participants in a trial of occupational therapy after stroke. Response was maximized by telephone and postal reminders, and incomplete questionnaires were followed up by telephone. Scale scores obtained by imputing values to questionnaire items missing on return were compared with those achieved by telephone follow-up. FINDINGS: Response to the initial posting was 60%, rising to 85% after reminders. Participants receiving the experimental treatment were more likely to respond without a reminder. There were no significant differences on any known factors between eventual responders and non-responders. Of the questionnaires, 43% were incomplete on return: partial responders were significantly different to complete responders on baseline disability and home circumstances. Of the incomplete questionnaires, 71% were resolved by telephone follow-up. In these, the scale scores achieved by telephone were generally higher than those derived by conventional imputation. CONCLUSION: Postal outcome assessment achieved a good response rate, but considerable effort was needed to minimize non-response and incomplete response, both of which could have been serious sources of bias.     

Development of the Cardiac Surgery Patient Expectations Questionnaire (C-SPEQ)

Purpose

Some variability in recovery and outcomes after cardiac surgery may be influenced by psychosocial aspects not routinely captured. Preliminary evidence suggests patient expectations impact health status, but there is no specific measure of expectations for cardiac surgery. The purpose of this study was to adapt an expectations scale to cardiac surgery and assess the psychometric properties of the scale.

Methods

Before surgery, 93 patients awaiting non-emergent cardiac surgery completed questionnaires, including the adapted Cardiac Surgery Patient Expectations Questionnaire (C-SPEQ). At 1 year after surgery, 68 patients completed questionnaires.

Results

Mean C-SPEQ score was 39.4 ± 9.02, and scores were normally distributed (Cronbach’s alpha = 0.86). Higher score indicated negative expectations. Higher presurgery C-SPEQ score was correlated with greater depression (r = 0.32, p = 0.01) and perceived stress (r = 0.36, p = 0.003), but not state anxiety (r = 0.18, p = 0.14), at one-year post-surgery. Higher C-SPEQ was associated with longer recovery time (B = 0.14, p = 0.006) and lower physical HRQL after surgery (B = ?0.31, p = 0.005). Higher C-SPEQ was not related to greater odds for perioperative complications (OR 1.01, p = 0.68) or readmissions <30 days (OR 1.05, p = 0.31). C-SPEQ score was not related to survival.

Conclusions

Adaptation of an expectations questionnaire to cardiac surgery patients was successful with acceptable reliability and validity. Negative expectations had a detrimental impact on recovery and HRQL following cardiac surgery but were not related to clinical outcomes. Although focus is mainly on improving clinical outcomes, there are opportunities to improve non-clinical aspects of the patient experience. Presurgical education might better prepare patients, reduce negative expectations, and improve psychosocial outcomes after cardiac surgery.

     

Prenotification had no additional effect on the response rate and survey quality: a randomized trial

ObjectivesTo determine the effect of prenotification package on survey quality, including response rates, response time, percentage of nonresponse items, and cost.Study Design and SettingParticipants were randomized into two groups. In the first round mailing, participants in prenotification group received a prenotification package, whereas direct questionnaire mailing group participants received a questionnaire with prepaid return envelope only. In the second round mailing, both groups received the questionnaires. The trial was integrated into a study among 35–65-year-old female nurses in Hong Kong.ResultsA total of 367 nurses were included in the trial. A total of 362 mails were successfully delivered. The initial response rate in the first round mailing were 8.79% and 8.89% for prenotification and direct questionnaire mailing groups, respectively. After the second round mailing, the final response rate in prenotification and direct questionnaire mailing groups were 17.58% and 17.22%, respectively; no significant difference was found between the groups. There were no differences with respect to percentage of nonresponse items or response time, but the cost of prenotification group was HK$ 15.11 per response higher than direct mailing group.ConclusionPrenotification had no additional effect on the response rate and other survey quality compared with direct questionnaire mailing in a Hong Kong population.     

A comparison of responses to mailed questionnaires and telephone interviews in a mixed mode health survey

This paper compares respondents to mailed questionnaires with those nonrespondents subsequently interviewed by telephone in a survey of Massachusetts women aged 45-55 years conducted in 1981-1982. This mixed mode approach produced 8,050 responses, giving a response rate of 77%. This rate is similar to rates obtained in many surveys that employed in-person interviews, which are still widely used in health surveys but are increasingly expensive. Telephone respondents differed socioeconomically from mail respondents, suggesting that telephone follow-up of nonrespondents may have reduced nonresponse bias in this survey. Thus, a mixed mode approach may be superior to a mail-only approach with respect to this aspect of data quality. Women responding by mail were more likely to hold professional jobs, to have relatively high household incomes, and to have more years of education. Controlling for these socioeconomic differences did not, however, remove differences in reported health outcomes between mail and telephone respondents. These differences may be explained by less complete recall in the telephone interviews or they may arise from actual differences in health profiles between early (i.e., mail) and late (i.e., telephone) respondents. Although a mixed mode approach may reduce nonresponse bias, more research is required concerning the reasons for response differences between modes and to eliminate any differences caused by problems in data quality.     

Disparities in Perinatal Quality Outcomes for Very Low Birth Weight Infants in Neonatal Intensive Care

ObjectiveTo determine if hospital-level disparities in very low birth weight (VLBW) infant outcomes are explained by poorer hospital nursing characteristics.ConclusionsPoorer nursing characteristics contribute to disparities in VLBW infant outcomes in two nurse-sensitive perinatal quality standards. Improvements in nursing have potential to improve the quality of care for seven out of ten black VLBW infants who are born in high-black hospitals in this country.     

Perinatal Outcomes and Associated Factors among women with hypertensive Disorders of Pregnancy Delivered in Jimma Zone Hospitals,Southwest Ethiopia

BackgroundHypertensive disorders of pregnancy are multisystem diseases that increase the risk of adverse perinatal outcomes worldwide. It Led to early and late serious health consequence on the baby, with a significant proportion occurring in low-income countries. Hence the objective of this study was to determine perinatal outcomes and associated factors among women with hypertensive disorders of pregnancy delivered in Jimma zone hospitals.MethodA Facility based cross-sectional study design was employed from March to May 2020 on 211 hypertensive women delivered in the four randomly selected hospitals. The data were collected by reviewing medical record and face to face interview using consecutive sampling technique. Binary and multivariable logistic regression was performed to identify association.ResultNinety-one (43.1%) of fetuses developed unfavorable perinatal outcome. Inability to read and write (AOR=2.5; 95% CI:1.03–6.17), being primipara (AOR=4.6; 95% CI:1.6–13.2) and multi-para (AOR=3.1; 95% CI:1.09–9.17), Lack of antenatal care visit (AOR=4.2; 95% CI:1.2–15.01), having preeclampsia (AOR=4.2; 95% CI:1.1–16.6) and eclampsia (AOR=5.8; 95% CI:1.2–26.2) and late provision of drug (AOR=3.9;95% CI:1.9–7.9) were independent factors.ConclusionPregnancy complicated with hypertensive disorders was associated with increased unfavorable perinatal outcomes. Preeclampsia and eclampsia, inability to read and write, primipara and multipara, lack of antenatal care and late provision of drug were factors associated with unfavorable perinatal outcomes.