Communicable disease mortality trends and characteristics of infants in rural China, 1996-2015

Geographical disparities in infant mortality in the rural areas of China: a descriptive study, 2010-2018

BACKGROUND

The infant mortality rate (IMR) is considered a basic measure of public health for countries around the world. The specific aim of our study was to provide an updated description of infant mortality rate among different regions in rural China, and assess the trends and causes of the IMR geographical disparities.

METHODS

Data were collected from China's Under-5 Child Mortality Surveillance System(U5CMSS). The annual number of deaths and causes of death were adjusted using a 3-year moving average underreporting rate based on annual national data quality control results. The average annual decline rate (AADR) and the relative risk (RR) of the IMR and cause-specific infant mortality were calculated by Poisson regression and the Cochran-Mantel-Haenszel method. Data analysis was completed by SAS software.

RESULTS

There was an apparent decrease in infant mortality in rural China from 2010 to 2018, at the AADR of 11.0% (95%CI 9.6-12.4), 11.2% (95%CI 10.3-12.1) and 6.6% (95%CI 6.0-7.3) in the eastern, central and western rural areas, respectively. The IMR was highest in the western rural area, followed by the central and eastern rural areas. Compared with the eastern rural area, the RR of infant mortality in the central rural area remained at 1.4-1.6 and increased from 2.4 (95%CI 2.3-2.6) in 2010-2012 to 3.1 (95% CI 2.9-3.4) in 2016-2018 in the western rural area. Pneumonia, preterm birth /LBW and birth asphyxia were the leading causes of infant deaths in the western rural area. Mortality rates of these three causes fell significantly in 2010-2018 but contributed to a higher proportion of deaths in the western rural area than in the central and western rural ares.

CONCLUSIONS

Our study indicated that the infant mortality rate dropped significantly from 2010 to 2018, however, geographical disparities of IMR in rural China are still persist. Therefore, there is an urgent need for public health programmes and policy interventions for infants in western rural China.

Postnatal Mental Health, Hand Washing Practices, and Infant Illness in Rural China

Background: Maternal mental health problems play an important role in infant well-being. Although western countries have extensively studied the associations between maternal mental disorders, hygiene practices and infant health, little is known in developing settings. This study investigates the correlations between postnatal mental health problems, hand washing practices and infant illness in rural western China.

Methods: A total of 720 mothers of infants aged 0–6 months from four poor counties in rural western China were included in the survey. Mental health symptoms were assessed using the Depression, Anxiety, and Stress Scale-21 (DASS-21). Questions about infant illness and hand washing practices followed evaluative surveys from prior studies. Adjusted ordinary least squares regressions were used to examine correlations between postnatal mental health (depression, anxiety, and stress) symptoms, hand washing practices, and infant illness outcomes.

Results: Maternal depression, anxiety and stress symptoms were significantly associated with reduced hand washing overall and less frequent hand washing after cleaning the infant's bottom. Mental health symptoms were also associated with a higher probability of infants showing two or more illness symptoms and visiting a doctor for illness symptoms. Individual hand washing practices were not significantly associated with infant illness; however, a composite measure of hand washing practices was significantly associated with reduced probability of infant illness.

Conclusion: Postnatal mental health problems are prevalent in rural China and significantly associated with infant illness. Policy makers and practitioners should investigate possible interventions to improve maternal and infant well-being.

Determinants of the Underlying Causes of Mortality during the First Wave of COVID-19 Pandemic in Saudi Arabia: A Descriptive National Study

Since the emergence of the COVID-19 pandemic, the mortality statistics are constantly changing globally. Mortality statistics analysis has vital implications to implement evidence-based policy recommendations. This study aims to study the demographic characteristics, patterns, determinants, and the main causes of death during the first half of 2020, in the Kingdom of Saudi Arabia (KSA). Methodology: A retrospective descriptive study targeted all death (29,291) registered in 286 private and governmental health settings, from all over KSA. The data was extracted from the ministry of health’s death records after the ethical approval. The International Classification of Diseases (ICD-10) and WHO grouping, were used to classify the underlying causes of deaths. The collected data were analyzed using the appropriate tables and graphs. Results: 7055 (24.9%) died at the middle age (40–59 year), and 19,212 (65.6%) were males, and 18,110 (61.8%) were Saudi. The leading causes of deaths were non-communicable diseases (NCDs) 15,340 (62.1%), mainly Cardiovascular diseases (CVDs) 10,103 (34.5%). There was a significant relationship between the main causes of deaths and sex (p < 0.05) and nationality (p = 0.01). Conclusion: NCDs mainly CVDs are the leading cause of death. The COVID-19 mortalities were mainly in males, and old age > 55 year. The lockdown was associated with a reduction in the NCDs and Road traffic accidents mortalities.

Customer relationship management analysis of outpatients in a Chinese infectious disease hospital using drug-proportion recency-frequency-monetary model

BACKGROUND

Identifying the patient types with different economic values can be useful for hospital development.

OBJECTIVE

This work uses the theory of customer relationship management (CRM) to analyze the outpatients in the hospital for infectious diseases in Shanghai, China.

METHODS

A total of 2,271,020 data elements of outpatients in the research unit between August 2009 and December 2019 were extracted, analyzed and cleaned to obtain 171,107 valid data elements (1 element per person). The main diseases were viral hepatitis B (VHB) and acquired immunodeficiency syndrome (AIDS), and the average percentage of drug expenditure was 80.39 %. We innovatively expanded the classic RFM (R: recency, F: frequency, M: monetary) model in CRM to the dRFM (d: percentage of drug expenditure) model. We selected the best clustering algorithm from the K-means, Kohonen and two-step clustering methods to find the optimal model to distinguish the types of patients with different economic values and the best decision-making algorithm from the C5.0, CART classification regression tree, CHAID and QUEST algorithms to verify the model.

RESULTS

After performing two rounds of K-means clustering analysis on three models: RFM, RFM + dRFM and dRFM, and 97,855 data elements were retained. The RFM + dRFM model was the optimal model, clustering the patients into 3 types: potential patients (24.2 %) to be retained, with a high drug expenditure and the last visit in more than 19.06 months, high-value patients (24.5 %) to be attracted, with the last visit in about 6.66 months; basal patients (51.3 %) to be kept, with the last visit in about 3.7 months. The model was then verified using the C5.0 decision tree algorithm with an accuracy rate of 99.97 %.

CONCLUSION

This objective CRM analysis of the patients in the hospital for infectious diseases using the dRFM model accurately identified different types of patients, providing an objective and effective basis for hospital management.