Zero-inflated Regression Research Articles

Inbreeding Effect on Maternal Mortality and Fertility in the Habsburg Dynasty.

We investigated inbreeding effects on longevity and fertility in the House of Habsburg, one of the principal royal dynasties of Europe. A total number of 124 Habsburg marriages, involving 107 men and 124 women, in the period of approximately 1450-1800 were considered for the analysis. Kinship and inbreeding coefficients were computed from genealogical information, which included more than 8000 individuals. We found a significant negative association between age of death and inbreeding coefficient (F) in those women who had children (regression coefficient b = -1.06, p = 0.0008). This result led us to investigate possible inbreeding effects on maternal mortality in the period of 4 weeks after the childbirth. A strong inbreeding depression on maternal survival was detected through the Kaplan-Meier curve for groups of women with different level of inbreeding (log-rank test p = 0.0001) and the Cox proportional hazards regression analysis (hazard ratio = 2.36, p = 0.0008). Effect on fertility was also found as more inbred women had longer interbirth intervals (b = 154.66, p = 0.022). Effects of male or female inbreeding on the number of children per woman were not detected through zero-inflated regression models suggesting that reproductive compensation might be occurring among the more inbred and less-fecund women. The effect of inbreeding in adulthood in the Habsburg lineage was at least as important as that previously reported on prereproductive survival. To our knowledge, our results are the first evidence of an inbreeding effect on maternal mortality in humans.

Exploring Sugary Drink Consumption and Perceptions among Primary-School-Aged Children and Parents in Australia

Background: Sugar-sweetened beverages (SSBs) account for a significant proportion of sugar in the diet of children and are directly associated with obesity in this group. While there have been many studies on adolescent SSB consumption, few studies have examined the predictors of SSB consumption in primary-school-aged children. The aim of this study was to understand the degree to which a child’s consumption across a range of beverages is influenced by their own attitudes and by their parents’ attitudes and parents’ consumption behaviours. Methods: A survey of 1611 Australian parent–child dyads asked children (aged 4–11) and their parents to rate a variety of drinks in terms of healthiness, taste, and cost and indicate the amount of these drinks consumed in a typical week. Zero-inflated regression analyses were conducted to identify the strength of association between children’s weekly beverage consumption, their perceptions of each beverage, their parents’ perceptions, and their parents’ weekly beverage consumption. Results: Parental consumption of a specific beverage was the strongest predictor of child consumption of that beverage, more so than the children’s perceptions of the beverage. Conclusions: These findings provide insights for developing parent and child education programmes to reduce SSB consumption.

Exploring the role of temperature and other environmental factors in West Nile virus incidence and prediction in California counties from 2017-2022 using a zero-inflated model.

West Nile virus (WNV) is the most common mosquito-borne disease in the United States, resulting in hundreds of reported cases yearly in California alone. The transmission cycle occurs mostly in birds and mosquitoes, making meteorological conditions, such as temperature, especially important to transmission characteristics. Given that future increases in temperature are all but inevitable due to worldwide climate change, determining associations between temperature and WNV incidence in humans, as well as making predictions on future cases, are important to public health agencies in California. Using surveillance data from the California Department of Public Health (CDPH), meteorological data from the National Oceanic and Atmospheric Administration (NOAA), and vector and host data from VectorSurv, we created GEE autoregressive and zero-inflated regression models to determine the role of temperature and other environmental factors in WNV incidence and predictions. An increase in temperature was found to be associated with an increase in incidence in 11 high-burden Californian counties between 2017-2022 (IRR = 1.06), holding location, time of year, and rainfall constant. A hypothetical increase of two degrees Fahrenheit-predicted for California by 2040-would have resulted in upwards of 20 excess cases per year during our study period. Using 2017-2021 as a training set, meteorological and host/vector data were able to closely predict 2022 incidence, though the models did overestimate the peak number of cases. The zero-inflated model closely predicted the low number of cases in winter months but performed worse than the GEE model during high-transmission periods. These findings suggests that climate change will, and may be already, altering transmission dynamics and incidence of WNV in California, and provides tools to help predict incidence into the future.

A new discrete distribution with applications to radiation, smoking, and health data

In this study, a flexible discrete distribution is introduced by compounding Epanechnikov-exponential and Poisson distributions. Many mathematical features of the distribution are obtained. Several point estimators of the unknown parameter are proposed. The Monte Carlo simulation study is carried out to compare these estimators. Three methods are proposed to construct the approximate confidence intervals for the distribution parameter. A novel zero-inflated regression model is given based on the proposed discrete distribution as an alternative to zero-inflated geometric and zero-inflated Poisson models. The usability of the distribution and the count regression model is demonstrated by some practical data analyses, such as the number of dicentric chromosomes observed after exposure to radiation at doses of 0.600, survey data on health, and smoking habits.

Comparison of different statistical models for the analysis of fracture events: findings from the Prevention of Falls Injury Trial (PreFIT)

BackgroundFractures are rare events and can occur because of a fall. Fracture counts are distinct from other count data in that these data are positively skewed, inflated by excess zero counts, and events can recur over time. Analytical methods used to assess fracture data and account for these characteristics are limited in the literature.MethodsCommonly used models for count data include Poisson regression, negative binomial regression, hurdle regression, and zero-inflated regression models. In this paper, we compare four alternative statistical models to fit fracture counts using data from a large UK based clinical trial evaluating the clinical and cost-effectiveness of alternative falls prevention interventions in older people (Prevention of Falls Injury Trial; PreFIT).ResultsThe values of Akaike information criterion and Bayesian information criterion, the goodness-of-fit statistics, were the lowest for negative binomial model. The likelihood ratio test of no dispersion in the data showed strong evidence of dispersion (chi-square = 225.68, p-value < 0.001). This indicates that the negative binomial model fits the data better compared to the Poisson regression model. We also compared the standard negative binomial regression and mixed effects negative binomial models. The LR test showed no gain in fitting the data using mixed effects negative binomial model (chi-square = 1.67, p-value = 0.098) compared to standard negative binomial model.ConclusionsThe negative binomial regression model was the most appropriate and optimal fit model for fracture count analyses.Trial registrationThe PreFIT trial was registered as ISRCTN71002650.

An Integrated Patient-Centred Medical Home (PCMH) Care Model Reduces Prospective Healthcare Utilisation for Community-Dwelling Older Adults with Complex Needs: A Matched Observational Study in Singapore.

The global ageing population is associated with increased health service use. The PCMH care model integrates primary care and home-based care management to deliver comprehensive and personalised healthcare to community-dwelling older adults with bio-psycho-social needs. We examined if an integrated PCMH reduced healthcare utilisation burden of older persons in Singapore. We compared the healthcare utilisation between the intervention group and coarsened exact matched controls for a follow-up of 15 months. Baseline matching covariates included socio-demographics, health status, and past healthcare use. We accounted for COVID-19 social distancing effects on health-seeking behaviour. The intervention group consisted of 165 older adults with complex needs. We analysed national administrative healthcare utilisation data from 2017 to 2020. We applied multivariable zero-inflated regression modelling and presented findings stratified by high (CCI ≥ 5) and low disease burden (CCI < 5). Compared to controls, there were significant reductions in emergency department (β = -0.85; 95%CI = -1.55 to -0.14) and primary care visits (β = -1.70; 95%CI = -2.17 to -1.22) and a decrease in specialist outpatient visits (β = -0.29; 95%CI = -0.64 to 0.07) in the 3-month period immediately after one-year enrolment. The number of acute hospitalisations remained stable. Compared to controls, the intervention group with high and low comorbidity burden had significant decreases in primary care use, while only those with lower comorbidity burden had significant reductions in utilisation of other service types. An integrated PCMH appears beneficial in reducing healthcare utilisation for older persons with complex needs after 1 year in the programme. Future research can explore longer-term utilisation and scalability of the care model.

Evaluation of negative binomial and zero-inflated negative binomial models for the analysis of zero-inflated count data: application to the telemedicine for children with medical complexity trial

BackgroundTwo characteristics of commonly used outcomes in medical research are zero inflation and non-negative integers; examples include the number of hospital admissions or emergency department visits, where the majority of patients will have zero counts. Zero-inflated regression models were devised to analyze this type of data. However, the performance of zero-inflated regression models or the properties of data best suited for these analyses have not been thoroughly investigated.MethodsWe conducted a simulation study to evaluate the performance of two generalized linear models, negative binomial and zero-inflated negative binomial, for analyzing zero-inflated count data. Simulation scenarios assumed a randomized controlled trial design and varied the true underlying distribution, sample size, and rate of zero inflation. We compared the models in terms of bias, mean squared error, and coverage. Additionally, we used logistic regression to determine which data properties are most important for predicting the best-fitting model.ResultsWe first found that, regardless of the rate of zero inflation, there was little difference between the conventional negative binomial and its zero-inflated counterpart in terms of bias of the marginal treatment group coefficient. Second, even when the outcome was simulated from a zero-inflated distribution, a negative binomial model was favored above its ZI counterpart in terms of the Akaike Information Criterion. Third, the mean and skewness of the non-zero part of the data were stronger predictors of model preference than the percentage of zero counts. These results were not affected by the sample size, which ranged from 60 to 800.ConclusionsWe recommend that the rate of zero inflation and overdispersion in the outcome should not be the sole and main justification for choosing zero-inflated regression models. Investigators should also consider other data characteristics when choosing a model for count data. In addition, if the performance of the NB and ZINB regression models is reasonably comparable even with ZI outcomes, we advocate the use of the NB regression model due to its clear and straightforward interpretation of the results.

Real-World Evidence of Aripiprazole Tablets with Sensor: Treatment Patterns and Impacts on Psychiatric Healthcare Resource Utilization.

Maintaining adherence to antipsychotic (AP) medication is often challenging. Aripiprazole tablets with sensor (AS) contain an ingestible event marker and communicate with wearable patches and a smartphone app to provide objective medication ingestion data. This study evaluated real-world treatment patterns of AS usage and its impact on psychiatric healthcare resource utilization (HCRU). This retrospective, observational cohort study identified individuals who initiated AS between 1/1/2019 and 6/30/2020 with 3 months baseline and 6 months of follow-up data using a commercial medical and pharmacy claims database (Clarivate). Controls were propensity score-matched (4:1) to AS initiators based on age (±2 years), sex, diagnosis (major depressive disorder [MDD], schizophrenia, bipolar I disorder [BP-I], other), insurance, and baseline oral AP use (yes/no). Days of AP supply were evaluated using a general regression model. The frequency of psychiatric HCRU during follow-up was compared between groups using a zero-inflated regression model. Most AS initiators were diagnosed with MDD (61.2%) and were women (61.2%); mean age was 37.7 years (standard deviation: 14.1). Most AS initiators (53.1%) continued treatment for >60 days (mean days of supply = 77). After adjusting for covariates, AS initiators had 41% more days of AP supply during follow-up compared with controls (P <0.0001) and significantly lower adjusted odds ratios (ORs) for psychiatric outpatient visits (adjusted OR = 0.80; P <0.05), emergency department visits (adjusted OR = 0.11; P <0.05), inpatient visits (adjusted OR = 0.42; P <0.05), and other medical services (adjusted OR = 0.25; P <0.05). Participants who implemented AS had significantly more days of AP supply and fewer psychiatric care visits. These preliminary results suggest AS usage can help build regular medication-taking habits and holds promise for reducing psychiatric HCRU. Additional studies with larger sample sizes are warranted to inform clinical practice and coverage decisions.

Geo-spatial risk factor analysis for drug overdose death in South Florida from 2014 to 2019, and the independent contribution of social determinants of health

PurposeThe physical environment and social determinants of health have been shown to influence health behaviors including drug use and fatal drug overdose. The current research examines the effects of the built environment, social determinants of health measures and aggregated risk from the built environment at neighborhood-level on drug overdose death locations in Miami-Dade County, Florida. MethodsRisk Terrain Modeling (RTM) was used to assess the place features risk factors that significantly increase the risk of drug overdose death spatially in Miami-Dade County ZIP Code Tabulation Areas, Florida from 2014 to 2019. An aggregated neighborhood risk of fatal drug overdose measure was developed by averaging the risk per grid cell from the RTM within census block groups each year. Six logistic and zero-inflated regression models were built to examine the effects of three indices of incident-specific social determinants of health (IS-SDH) measures and aggregated risk measures separately, and simultaneously on drug overdose death locations each year. ResultsSeven place features including parks, bus stops, restaurants and grocery stores were significantly related to the occurrence of fatal drug overdoses. When examined separately, one or more indices of the IS-SDH were significant covariates of drug overdose locations in some years. When examined simultaneously, the three indices of the IS-SDH and aggregated risk of fatal drug overdose measure could be all significant in certain years. ConclusionsThe patterns of high-risk areas and place features identified from the RTM related to drug overdose deaths may be used to inform the placement of treatment and prevention resources. A multi-factor approach that combines an aggregated neighborhood risk measure reflecting the risk from the built environment and the incident-specific social determinants of health measures can be used to identify the drug overdose death locations in certain years.

Association of climate factors with dengue incidence in Bangladesh, Dhaka City: A count regression approach

BackgroundIn Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease. MethodsA total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg−1), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values. ResultsThe mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall. ConclusionThe findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.

Dispersion insensitive truncated models for inflated count data with upper bound

Zero-inflated regression models are generally accepted for modeling data with inflated amounts of zeros. The principle used in constructing the distribution for these models has been extended to modeling certain types of data with inflation at the upper bound of the support. An excellent example of this is the zero and κ-inflated truncated Poisson regression model which utilizes the Poisson distribution. However, the equi-dispersion nature of the Poisson distribution makes the model less suitable for data that are under-dispersed and over-dispersed. Models based on exponentiated exponential geometric distribution (EEGD) are presented as alternatives for modeling any count data with an upper bound, irrespective of the nature of dispersion. The models are obtained by truncating the EEGD at the top and inflating at either or both zero and κ. A simulation study is conducted to examine the performances of the models under certain conditions. The result shows that the closer the dispersion parameter c is from the equi-dispersion line, the better the estimates for over-dispersed data, while the farther the dispersion parameter from the equi-dispersed line, the better the estimates for an under-dispersed model. The application of the models is illustrated using data from the Behavioral Risk Factor Surveillance System 2011.

A new zero–inflated discrete Lindley regression model

Recently, providing a new count regression model is very popular for many researchers. These count regression models are constructed by using a new discrete distribution or one of the existing distributions in the literature. In this paper, we consider a new zero-inflated regression model as an alternative to the zero-inflated regression models. We present two real data applications to illustrate the usefulness of the suggested regression model in modeling data, and compare the competitor models such as Poisson, discrete Lindley, and zero-inflated regression models. We provide a new count regression model which is useful in modeling overdispersed data.

Migrant and native women’s perceptions of prenatal care communication quality: the role of host-country language proficiency

BackgroundDespite the potentially significant impact of women-prenatal care provider communication quality (WPCQ) on women’s perinatal health, evidence on the determinants of those perceptions is still lacking, particularly among migrant women.MethodsWe aimed to examine the effect of women’s host-country language proficiency on their perceived WPCQ. We analyzed the data of 1210 migrant and 1400 native women who gave birth at Portuguese public hospitals between 2017 and 2019 and participated in the baMBINO cohort study. Migrants’ language proficiency was self-rated. Perceived WPCQ was measured as a composite score of 9 different aspects of self-reported communication quality and ranged from 0 (optimal) to 27.ResultsA high percentage of women (29%) rated communication quality as “optimal”. Zero-inflated regression models were fitted to estimate the association between language proficiency and perceived WPCQ. Women with full (aIRR 1.35; 95% CI 1.22,1.50), intermediate (aIRR 1.41; 95% CI 1.23,1.61), and limited (aIRR 1.72; 95% CI 1.45,2.05) language proficiencies were increasingly more likely to have lower WPCQ when compared to natives.ConclusionsFacilitating communication with migrant women experiencing language barriers in prenatal care could provide an important contribution to improving prenatal care quality and addressing potential subsequent disparities in perinatal health outcomes.

An empirical study of relationships between urban lighting indicators and night-time light radiance

Night-time light (NTL) radiance has a great potential in analyses of dynamic changes in patterns of human activities, and socio-economic and demographic factors. However, most of those analyses are focused on factors at global scales such as the population size, gross domestic product, electric power consumption, fossil fuel carbon dioxide emission etc. In this study we investigate the relationships between three urban lighting indicators and monthly averaged NTL radiance obtained from NASA’s Black Marble monthly NTL composites for 4 study areas in the Czech Republic at local scale. The Pearson correlation analysis was used to identify a strength of the correlations between the indicators and radiance at near-nadir for two different snow conditions. The results from the correlation show that radiance has a strong positive correlation with the number of streetlighting points and their total nominal power, while for the average mast height there were observed moderate correlation coefficients. However, the areas with larger scales have higher correlation coefficients. Moreover, we found that the correlation coefficients are higher for snow-covered condition radiances. Generalized linear (GL) regression analysis was used to examine an association between the radiance and selected indicators. Owing to the excess zeros and overdispersion in the data, the zero-inflated regression performs better than the GL regression. Results from the regression analysis evince a statistically significant relationship between the radiance and selected indicators.

Regression Analysis of the Factors Responsible for Live Birth and Maternal Mortality in Oyo State, Nigeria

Background: The changes in the size of the population take a systemic pattern of variation at different periods of demographic studies. Objective: This study examines the demographic analysis using different regression estimators to determine the effect of live births and factors responsible for maternal mortality in Oyo State. Methods: Analysis of child state of birth such as cephalic presentation, twin, stillbirth, triplet, and prematurity was carried out using discrete regression estimators (negative binomial (NB) regression, zero-inflated regression, poisson regression, and quasi-poisson regression) to determine the factors responsible for live birth and maternal mortality in the state. Level of significance of 5% was used to authenticate the results. Results: The result revealed that cephalic presentation, triplet, and prematurity significantly contributed to live birth at 5%. In contrast, estimated results showed that twin, stillbirth, and triplet states of a child contributed positively to the maternal mortality at 5% level of significance. Conclusion: The log-likelihood function of all the four models were performed and compared, hence the NB regression fitted the maternal mortality better, while quasi poisson regression model fitted the live birth data better. Therefore, the childbirth state of delivery statistically determines factors responsible for both live births and maternal mortality in Oyo State.

Artificial Intelligence-based CT Assessment of Bronchiectasis: The COPDGene Study.

Background CT is the standard method used to assess bronchiectasis. A higher airway-to-artery diameter ratio (AAR) is typically used to identify enlarged bronchi and bronchiectasis; however, current imaging methods are limited in assessing the extent of this metric in CT scans. Purpose To determine the extent of AARs using an artificial intelligence-based chest CT and assess the association of AARs with exacerbations over time. Materials and Methods In a secondary analysis of ever-smokers from the prospective, observational, multicenter COPDGene study, AARs were quantified using an artificial intelligence tool. The percentage of airways with AAR greater than 1 (a measure of airway dilatation) in each participant on chest CT scans was determined. Pulmonary exacerbations were prospectively determined through biannual follow-up (from July 2009 to September 2021). Multivariable zero-inflated regression models were used to assess the association between the percentage of airways with AAR greater than 1 and the total number of pulmonary exacerbations over follow-up. Covariates included demographics, lung function, and conventional CT parameters. Results Among 4192 participants (median age, 59 years; IQR, 52-67 years; 1878 men [45%]), 1834 had chronic obstructive pulmonary disease (COPD). During a 10-year follow-up and in adjusted models, the percentage of airways with AARs greater than 1 (quartile 4 vs 1) was associated with a higher total number of exacerbations (risk ratio [RR], 1.08; 95% CI: 1.02, 1.15; P = .01). In participants meeting clinical and imaging criteria of bronchiectasis (ie, clinical manifestations with ≥3% of AARs >1) versus those who did not, the RR was 1.37 (95% CI: 1.31, 1.43; P < .001). Among participants with COPD, the corresponding RRs were 1.10 (95% CI: 1.02, 1.18; P = .02) and 1.32 (95% CI: 1.26, 1.39; P < .001), respectively. Conclusion In ever-smokers with chronic obstructive pulmonary disease, artificial intelligence-based CT measures of bronchiectasis were associated with more exacerbations over time. Clinical trial registration no. NCT00608764 © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Schiebler and Seo in this issue.

New zero-inflated regression models with a variant of censoring

There is ever growing demand of modeling overdispersed count data generated by various disiplines. Excessive number of zeros and heterogeneity in the population are two main sources of the overdispersion problem. Development of new count models that are more flexible than conventional Poisson model is thus necessary in order to address such sources. This study fullfils this need by proposing a new heterogeneous Poisson model with a capture of excess zeros, namely zero-inflated Poisson–Ailamujia (ZIPA) model. In line with the aim of curing overdispersion, a censored variant of this newly suggested model is also here developed. An extensive simulation study is conducted to assess the performances of both forms of new models in terms of bias, precision and accuracy measures. Additionally, two real world applications are presented to illustrate practical implications of zero-inflated (censored) Poisson–Ailamujia models in comparison to some alternatives.

Distribution models calibrated with independent field data predict two million ancient and veteran trees in England.

Large, citizen-science species databases are powerful resources for predictive species distribution modeling (SDM), yet they are often subject to sampling bias. Many methods have been proposed to correct for this, but there exists little consensus as to which is most effective, not least because the true value of model predictions is hard to evaluate without extensive independent field sampling. We present here a nationwide, independent field validation of distribution models of ancient and veteran trees, a group of organisms of high conservation importance, built using a large and internationally unique citizen-science database: the Ancient Tree Inventory (ATI). This validation exercise presents an opportunity to test the performance of different methods of correcting for sampling bias, in the search for the best possible prediction of ancient and veteran tree distributions in England. We fitted a variety of distribution models of ancient and veteran tree records in England in relation to environmental predictors and applied different bias correction methods, including spatial filtering, background manipulation, the use of bias files, and, finally, zero-inflated (ZI) regression models, a new method with great potential to investigate and remove sampling bias in species data. We then collected new independent field data through systematic surveys of 52 randomly selected 1-km2 grid squares across England to obtain abundance estimates of ancient and veteran trees. Calibration of the distribution models against the field data suggests that there are around eight to 10 times as many ancient and veteran trees present in England than the records currently suggest, with estimates ranging from 1.7 to 2.1 million trees compared to the 200,000 currently recorded in the ATI. The most successful bias correction method was systematic sampling of occurrence records, although the ZI models also performed well, significantly predicting field observations and highlighting both likely causes of undersampling and areas of the country in which many unrecorded trees are likely to be found. Our findings provide the first robust nationwide estimate of ancient and veteran tree abundance and demonstrate the enormous potential for distribution modeling based on citizen-science data combined with independent field validation to inform conservation planning.

A New Flexible Discrete Distribution with Application to Zero-Inflated Regression Analysis

A New Flexible Discrete Distribution with Application to Zero-Inflated Regression Analysis

140-LB: Management of Inpatient Hyperglycemia Guided by Continuous Glucose Monitoring (CGM) in Insulin-Treated Patients with Diabetes—A Randomized Clinical Trial

Inpatient use of CGM results in higher detection of hypoglycemic and hyperglycemic events compared to point of care testing (POC) but its efficacy and safety in adjusting insulin therapy has not been evaluated. This randomized controlled trial included 181 general medicine and surgery patients with type 1 (n= 18) and type 2 (n= 155) diabetes treated with a basal bolus insulin regimen. All patients underwent POC testing AC &amp; HS. Patients in the POC group wore a blinded Dexcom G6 CGM with insulin dose adjusted based on POC results; while in the CGM group, insulin adjustment was based on daily Dexcom G6 CGM profile review. Hypoglycemia alarms were set at 80 mg/dl in the CGM group. Primary endpoints were differences in time in range (70-180 mg/dl) and hypoglycemia (&amp;lt;70 mg/dl and &amp;lt;54 mg/dl) . Results: There were no differences on admission clinical characteristics, HbA1c or diabetes type between POC and CGM groups. There were no differences in mean daily glucose (186.8±39 mg/dl vs. 183.2±40 mg/dl, p=0.36) , total daily insulin dose (36.1±28 U/day vs. 40.7±29 U/day, p=0.33) , % patients with CGM values &amp;lt;70 mg/dl (39% vs. 36%, p=0.68) or &amp;lt;54mg/dl (24% vs. 14%, p=0.12) between the two groups. Among patients with ≥ 1 hypoglycemic event, compared to POC, CGM use resulted in significant reduction in hypoglycemia recurrence with an incidence-ratio for glucose &amp;lt;70mg/dl (0.53, 95% CI:0.31-0.92) and incidence-ratio for glucose &amp;lt;54mg/dl (0.37 (95% CI:0.17-0.83) . The percent time &amp;lt;70 mg/dl among those with hypoglycemia was smaller in the CGM (1.9±3.3% vs. 5.5±8.5, p=0.024) compared to the POC group, with group difference in hypoglycemia confirmed by zero-inflated Beta Regression analysis (p&amp;lt;0.001) . Conclusion: Our results indicates that the inpatient use of Dexcom G6 CGM is safe and effective in guiding insulin adjustment resulting in similar improvement in glucose control and in significant reduction of recurrent hypoglycemic events compared to POC testing. Disclosure I. Spanakis: Other Relationship; Dexcom, Inc. L. G. Singh: None. C. Gothong: None. I. Marcano: None. S. Lizama: None. K. M. Munir: None. C. Chesney: None. R. D. Maguire: None. W. H. Scott: None. L. Peng: None. G. E. Umpierrez: Research Support; AstraZeneca, Dexcom, Inc., Novo Nordisk. M. A. Urrutia: None. M. F. Scioscia: None. R. J. Galindo: Advisory Panel; Sanofi, WW International, Inc., Research Support; Dexcom, Inc., Eli Lilly and Company, Novo Nordisk. P. Vellanki: n/a. A. L. Migdal: None. G. Davis: Consultant; Medscape, Research Support; Insulet Corporation. T. Idrees: None. F. J. Pasquel: Consultant; AI Health LLC, Boehringer Ingelheim International GmbH, Dexcom, Inc., Research Support; Dexcom, Inc., Insulet Corporation, Merck &amp; Co., Inc.