Journal of Research in Health Sciences
Volume:21 Issue: 2, Spring 2021

Numerous studies have been conducted to seek a better understanding of disparities in adverse pregnancy outcomes. The present study aimed to explore racial differences in influential socio-demographic, economic, and environmental factors in women who have had a low birth weight (LBW) infant (outcome variable). Study Design: A cross-sectional study.

This study used data from the Fragile Families and Child Wellbeing Study (FFCWS). Univariate and multivariate analyses were performed.

The obtained results pointed to statistical racial differences between Non-Hispanic (NH) Black and NH White women in the socio-demographic variable of marital status (P<0.001).  Regarding the assessed economic stability variables, employment status (P=0.032), poverty level (P<0.001), earnings (P=0.038), and federal government assistance paying for rent (P=0.007) were statistically significant across the two racial groups.  The environmental factors that were statistically significant across racial groups were living in public housing projects (P=0.018), car ownership (P<0.001), and neighborhood safety (P=0.010).  The results of the multivariate models revealed that NH Black race and government assistance to pay rent were associated with an increased likelihood of LBW, while being married, having health care coverage, and living in public housing were associated with a decreased likelihood.

As evidenced by the obtained results, there were statistically significant racial differences in sociodemographic, economic, and environmental/physical characteristics associated with adverse pregnancy outcomes.

Currently, the worldwide prevalence and incidence of multidrug-resistant tuberculosis (MDR-TB) is drastically increasing. The main objective of this study was modeling the time-to-death of patients with MDR-TB at St. Peter’s Specialized Hospital, Addis Ababa, Ethiopia, by using various parametric shared frailty models. Study Design: A retrospective study design was used.

The study population was TB patients with MDR at St. Peter’s Specialized Hospital from January 2016 through December 2019. Exponential, Weibull, and log-normal were used as baseline hazard functions with the gamma and inverse Gaussian frailty distributions. All the models were compared based on Akaike’s Information Criteria.

The overall median time to death was 11 months and 123 (33.5%) patients died. Patients who lived in rural areas had shorter survival time than those who lived in urban areas with an accelerated factor of 0.135 (P=0.002). Patients with a history of anti-TB drug consumption had a short survival time than those without such a history with an accelerated factor of 0.02 (P=0.001). The variability (heterogeneity) of time to death of patients in the region for the selected model (Weibull-inverse Gaussian shared frailty model) was =0.144 (P=0.027).

The MDR-TB patients with weight gain, khat and alcohol consumption, clinical complication of pneumothorax and pneumonia, extrapulmonary TB, and history of anti-TB drug consumption as well as those who lived in rural areas had a shorter survival time, compared to others. There was a significant heterogeneity effect in the St. Peter’s Specialized Hospital. The best model for predicting the time to death of MDR-TB patients was Weibull-inverse Gaussian shared frailty model.

This study aimed to determine the secondary attack rate (SAR) and its determinants to describe the clinical features and epidemiological aspects of patients and determine the risk factors of COVID-19 among household contacts in Hamadan Province, west of Iran. Study design: A cohort study.

In this cohort study, a total of 323 index cases and 989 related close contacts ages more than 15 years old (family members, relatives, and co-workers) were enrolled using a manual contact tracing approach, and all participants were tested by reverse transcription polymerase chain reaction test. In this research, the frequency of symptoms was assessed, the SAR among contacts of index cases was calculated, and the risk factors of COVID-19 were evaluated by the logistic regression model.

The secondary attack rate for total household members of index cases was estimated at 31.7% (95% CI: 28.8-34.7). It was found that among household contacts, the highest SARs were related to spouses 47.1% (95% CI: 38.7-55.7) and grandparents/parents 39.3% (95% CI: 29.4, 49.9) of index cases, who had also higher risks to become secondary cases (adjusted odds ratio [OR]=2.98, 95% CI: 1.31-6.75 and adjusted OR=2.76, 95% CI: 1.18-6.44, respectively). Considering the occupation of contacts, unemployed and retired people and housewives were most susceptible for transmission of COVID-19. It was revealed that cough was the most prevalent symptom among index and secondary cases.

Our findings indicated that spouses and grandparents/parents of index cases were the most susceptible individuals for COVID-19 transmission. Prolonged exposure with index case before COVID-19 diagnosis raised the chance of infection among secondary cases.

This study aimed to analyze the demographic and epidemiological features of identified COVID-19 cases in Kazakhstan. Study design: A cross-sectional study.

This cross-sectional study aimed to analyze COVID-19 cases (n=5116) collected from March 13 to June 6, 2020, in Kazakhstan. The data were obtained from a state official medical electronic database. The study investigated the geographic and demographic data of patients as well as the association of COVID-19 cases with gender and age. The prevalence of symptoms, the presence of comorbidities, complications, and COVID-19 mortality were determined for all patients.

The mean±SD age of the patients in this study was 34.8±17.6 years, and the majority (55.7%) of COVID-19 cases were male and residents of cities (79.6%). In total, 80% of the cases had the asymptomatic/mild form of the disease. Cough (20.8 %) and sore throat (17.1%) were the most common symptoms among patients, and pneumonia was diagnosed in 1 out of 5 cases. Acute respiratory distress syndrome (ARDS) was recorded in 1.2% of the patients. The fatality rate was 1% in the study population and lethality was 2.6 times higher in males compared to females.  Each additional year in age increased the probability of COVID-19 infection by 1.06 times. The presence of cardiovascular, diabetes, respiratory, and kidney diseases affected the rate of mortality (P<0.05).

The results demonstrated a high proportion (40%) of the asymptomatic type of coronavirus infection in the Kazakhstan population. The severity of COVID-19 symptoms and lethality were directly related to the age of patients and the presence of comorbidities.

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by a newly identified coronavirus. Our knowledge about the survival rate and prognostic factors of the disease is not established well. Therefore, this study aimed to identify the risk factors associated with the survival of COVID-19 cases in Hamadan province, West of Iran. Study design: A retrospective cohort study

This retrospective cohort study was performed in Hamadan province, West of Iran. The study included patients that referred to the provincial hospitals from February 20 to September 20, 2020. The follow-up of each subject was calculated from the date of onset of respiratory symptoms to the date of death. Demographic and clinical characteristics were extracted from patients’ medical records. Kaplan-Meier method, Flemington-Harrington test, and Cox regression were used for data analysis.

The overall 1, 5, 10, 20, 30 and 49-day survival rates were estimated at 99.57%, 95.61%, 91.15%, 87.34%, 86.91%, and 86.74%, respectively. Furthermore, survival time showed a significant association with age, gender, history of traveling to contaminated areas, co-morbidity, neoplasms, chronic diseases, and hospital units.

In conclusion, elderly people, male gender, and comorbidities presented a greater risk of death. Therefore, it is important to pay more attention to this group of people to reduce the incidence and consequences after infection.

Breast cancer is one of the most common causes of death among women worldwide and the second leading cause of death among Iranian women. The incidence of this malignancy in Iran is 22 per 100,000 women. These patients have long-term survival time with advances in medical sciences. The present study aimed to identify the risk factors of breast cancer using Cox proportional hazard and Cox mixture cure models. Study design: It is a retrospective cohort study.

In this cohort study, we recorded the survival time of 140 breast cancer patients referred to Ali Ibn Abitaleb Hospital in Rafsanjan, Iran, from 2001 to 2015. The Kaplan-Meier curve was plotted; moreover, two Cox proportional hazards and the Cox mixture cure models were fitted for the patients. Data analysis was performed using SAS 9.4 M5 software.

The mean age of patients was reported as 47.12 ±12.48 years at the commencement of the study. Moreover, 83.57% of patients were censored. The stage of disease was a significant variable in Cox and the survival portion of Cox mixture cure models (P=0.001). The consumption of herbal tea, tumor size, duration of the last lactation, family history of cancer, and the type of treatment were significant variables in the cured proportion of the Cox mixture cure model (P=0.001).

The Cox mixture cure model is a flexible model which is able to distinguish between the long-term and short-term survival of breast cancer patients. For breast cancer patients, cure effective factors were the stage of the disease, consumption of herbal tea, tumor size, duration of the last lactation, family history, and the type of treatment.

The basic reproduction number (R0) is an important concept in infectious disease epidemiology and the most important parameter to determine the transmissibility of a pathogen. This study aimed to estimate the nine-month trend of time-varying R of COVID-19 epidemic using the serial interval (SI) and Markov Chain Monte Carlo in Lorestan, west of Iran. Study design: Descriptive study.

This study was conducted based on a cross-sectional method. The SI distribution was extracted from data and log-normal, Weibull, and Gamma models were fitted. The estimation of time-varying R0, a likelihood-based model was applied, which uses pairs of cases to estimate relative likelihood.

In this study, Rt was estimated for SI 7-day and 14-day time-lapses from 27 February-14 November 2020. To check the robustness of the R0 estimations, sensitivity analysis was performed using different SI distributions to estimate the reproduction number in 7-day and 14-day time-lapses. The R0 ranged from 0.56 to 4.97 and 0.76 to 2.47 for 7-day and 14-day time-lapses. The doubling time was estimated to be 75.51 days (95% CI: 70.41, 81.41).

Low R0 of COVID-19 in some periods in Lorestan, west of Iran, could be an indication of preventive interventions, namely quarantine and isolation. To control the spread of the disease, the reproduction number should be reduced by decreasing the transmission and contact rates and shortening the infectious period.

This study aimed at assessing how population density (PD), aging index (AI), use of public transport (URPT), and PM10 concentration (PI) modulated the trajectory of the main COVID-19 pandemic outcomes in Italy, also in the recrudescence phase of the epidemic. Study design: Ecological study.

For each region, we recovered data about cases, deaths, and case fatality rate (CFR) recorded since both the beginning of the epidemic and September 1, 2020. Data about total hospitalizations were included as well.

PD correlated with, and was the best predictor of, total and partial cases, total and partial deaths, and total hospitalizations. Moreover, URPT correlated with, and was the best predictor of, total CFR. Besides, PI correlated significantly with total and partial cases, total and partial deaths, and total hospitalizations.

PD explains COVID-19 morbidity, mortality, and severity while URPT is the best predictor of disease lethality. These findings should be interpreted with caution due to the ecological fallacy.