DOI: 10.31038/PEP.2021233

Introduction

Worldwide, cancer is the second leading cause of death, with one of every six deaths caused by cancer [1]. There were 17 million new cases and 9.6 million cancer deaths worldwide in 2018, including approximately 1.7 million new U.S. cases and 600,000 U.S. cancer deaths [3]. The total financial cost of cancer in 2010 was estimated at 1.16 trillion U.S. dollars [1].

There have been significant reductions in cancer mortality, thanks to improved screening, early detection, and better treatment [1]. However, the worldwide incidence of cancer is expected to increase to 27.5 million per year by 2040 [28], a 62% increase from 2018. The U.S. expects an increase to over 1.9 million new cases per year by 2020, largely due to an aging Caucasian population and a growing African American population [5].

The World Health Organization states that “30-50% of all cancer cases are preventable. Prevention offers the most cost-effective long-term strategy for the control of cancer” [26]. Cancer can be prevented by reducing exposure to environmental risk factors, modifying lifestyle factors that are linked to cancers, and protecting against the effects of risk-factor exposures [26].

Tobacco is one of the most widely known and most modifiable risk factors for cancer and the process in determining this illustrates the value of the systematic study of cancer causes [15]. Lung cancer is the most common cancer in the world after skin cancer and the most deadly [24]. Before smoking became widespread, lung cancer was rare; however, as cigarette production and smoking increased, lung cancer became much more common. Smoking tobacco was found to be associated with lung cancer around the mid-20^th century when a study showed that smokers were more likely to have cancer than non-smokers [15]. This relationship was confirmed by epidemiological and prospective studies, experiments, pathological observations, and chemical analyses [15]. Smoking was also found to be a risk factor for many other types of cancers and diseases, and tobacco is now understood to be associated with 33% of cancers and 22% of cancer-related deaths worldwide [28]. Cigarette smoking is associated with 80%-90% of lung cancer deaths in the U.S. [25]. Deaths caused by smoking cigarettes have an average latency of about 25 years; lung cancer deaths are expected to reach about 2 million per year during the 2020s or the 2030s [15].

As a result of the overwhelming evidence that smoking is a causal factor for cancer, there have been many anti-smoking initiatives. These efforts include preventing smoking initiation, helping smokers quit the habit, and reducing exposure to second-hand smoke [11]. Smoking cessation reduces cancer risk and can improve outcomes for cancer patients. Smoking cessation can reduce lung cancer risk by as much as 85% after 16 years of cessation compared to non-cessation [13]. Due to tobacco control measures that were implemented in the U.S. in the mid-1950s, about 32% (795,851) of the lung cancer deaths that would have occurred during 1975-2000 were prevented; the benefits of these measures will continue [11]. These huge reductions in deaths, suffering, and costs were possible because good epidemiological evidence uncovered the link between smoking and cancer.

Other cancer-prevention strategies that have grown out of accumulating epidemiological studies include reducing alcohol consumption [10], vaccinating against certain viruses [17], and improving diet and exercise [4]. The International Agency for Research on Cancer (IARC) determined that alcohol was carcinogenic after reviewing studies that showed an association between alcohol consumption and certain cancers [8]. One study involving eight European countries estimated that for 2008, 3% of cases in women and 10% of cases in men were due to alcohol consumption [20]. A U.S. study determined that 3.2% – 3.7% (18,200 to 21,300) of all cancer deaths in 2009 were attributable to alcohol consumption [12].

Many viruses have been shown to cause or be associated with certain cancers [17]. Individuals and health care providers can take preventive steps such as vaccinations, follow-up treatment to minimize the risk of developing cancer, and screening to maximize chances of early detection of cancer [17]. And because obesity, diet, and sedentariness have proven to be risk factors that are related and modifiable, individuals can make lifestyle changes to reduce their cancer risk while gaining other health benefits [4].

There have been improvements in cancer survival rates due to improvements in cancer detection and treatment, but the progress made applies to relatively few cancers [16]. Also, this does not spare patients the ordeal, financial cost, and disability of cancer treatment. Screening guidelines are available for very few cancer types, so many cancers are detected at later stages and, therefore, have a lower survival rate [9]. In addition, incidence rates of some of these poorly understood cancer types are increasing. Cancer prevention is the least costly and most desirable approach to combat the expected increase in cancer incidence [26]. However, to achieve this we need epidemiological research that focuses on identifying risk factors for poorly understood cancer types.

A traditional epidemiological approach, such as the “Cancer Prevention Studies” (CPS), requires a large enough study group, long follow-up, and is costly [7]. Therefore, this approach is limiting, especially for poorly understood cancers, which tend to be rarer cancers. In addition, the research landscape has changed significantly. Information technology was one of the most significant technological developments of the twentieth century and has affected every aspect of our lives. It has made us very interconnected to people, activities, and the environment. Determining any effect of these connections is difficult due to the complexity and numerosity. Fortunately, these technological developments have also made significant advances that can be applied to health research. We have, are generating, and are capturing more data about many different aspects of our lives than ever before. We need to use current technology and data to overcome the limitations of the traditional epidemiological approach. We must develop reliable, efficient, and cost-effective research methods to identify possibly risk factors for poorly understood cancers.

Purpose

Our main objective in this study was to identify cancer types that represent a health burden, but for which environmental and lifestyle risk factors are poorly understood (i.e., without an established causal risk factor). We used a combination of inclusion and exclusion criteria to identify these cancers. The inclusion criteria were cancer types 1) without screening guidelines; 2) with low survival rates; and 3) with increasing incidence. Cancer screening aims to detect cancer before the individual becomes symptomatic, and early detection usually results in more successful treatment and greater survival [27]. Currently, only four types of cancer – breast, cervical, colorectal, and lung – have broadly accepted screening recommendations [16]. The cancers with low survival rates tend to be cancers that are more difficult to detect and to treat. An upward trending cancer indicates a growing concern that should be investigated to identify risk factors and reduce incidence. The primary exclusion criterion was cancer types with established causal risk factors. By default, cancer types with screening guidelines, without low survival rates, and/or without increasing incidence were excluded.

Secondarily, we propose a new methodological approach to study the etiology of these rare cancers that maximizes data utilization without the need for costly epidemiological studies, such as the “Cancer Prevention Studies”. This design allows exploration of the relationships of selected cancer with various potential risk factors without the financial and feasibility barriers of traditional epidemiological designs.

Methods

We used data from the National Cancer Institute’s (NCI’s) Surveillance, Epidemiology, and End Results (SEER) Program. The SEER Program collects cancer incidence and survival data for every cancer case reported from population-based cancer registries covering approximately 34 percent of the U.S. population spanning 19 geographic areas. The program started in 1975 with nine registries and now collects data from twenty-one registries. Based on the broad coverage area, the data collected by the SEER Program is representative of the U.S. population [21].

SEER data are coded to ICD-O-3 and are grouped by major cancer site/histology [22]. The data have 102 groups in a hierarchical format, ranging from all sites to miscellaneous. SEER incidence data have both the rate (per 100,000 and age-adjusted to the 2000 U.S. population) and count. Survival data provide observed, expected, and relative rates. Incidence trend data show overall percent change, annual percent change (APC), and the rate for each year. For this study, we used incidence data for 2011 through 2016 and survival data for the five-year period 2011 to 2015.

Starting with all 102 groups of cancers from the SEER database, we compiled data for incidence, survival rate, and trend. We added data on whether the cancers had recommended screening guidelines. Our selection criteria for cancer groups representing a health burden were groupings that do not have recommended screening guidelines, had a positive APC over the 6-year period (2011 to 2016), and had a 5-year relative survival rate of less than 70%. From the cancers meeting all three criteria, we removed groupings that were poorly defined, groupings containing “Other” or “Not Otherwise Specified”, and groupings that were too broadly defined (e.g., “Female Genital System”). We removed “Liver and Intrahepatic Bile Duct” since it includes the sub-category “Liver” that did not meet the criteria, but the sub-category “Intrahepatic Bile Duct” is in the final list (Table 1). We implemented the primary exclusion criterion by reviewing the websites of the American Cancer Society (ACS) (Cancer.org) and the National Cancer Institute (NCI) (Cancer.gov) to identify risk factors, causal and non-causal, for each of the cancers initially selected based on epidemiological measures. None of the cancers that met the inclusion criteria had established causal factors, so none were excluded (Table 2).

Table 1: Cancers meeting inclusion criteria

¹Rate per 100,000 population, age-adjusted to the 2000 U.S. standard population.
Shaded groupings were removed for reasons stated above

Table 2: Application of exclusion criterion using information from the ACS website

Results

The main epidemiological measures for the 12 groupings of poorly understood cancers are presented in Table 1. The grouping of “Oral Cavity and Pharynx” is a major grouping with sub-groupings “Tongue” and “Oropharynx”, and all three are in the final list. There are four groupings of the digestive system – “Small Intestine”, “Appendix”, “Intrahepatic Bile Duct”, and “Pancreas”. There are two leukemias – “Acute Lymphocytic Leukemia” and “Chronic Myeloid Leukemia”. The other groupings are “Soft Tissue including Heart”, “Penis”, and “Myeloma”.

The cancers included in the final list have varying statistics (Table 1). The age-adjusted incidence rates of poorly understood cancers in the final list range from 0.40 to 12.23 cases per year per 100,000 population. The incidence rates for all the 102 original groupings ranged from 0.00063 to 85.28 cases per year per 100,000 population. The final list of cancers has 5-year survival rates ranging from 7.67% for Pancreatic Cancer to 69.13% for Cancer of the Appendix. Cancer of the Appendix has the highest upward incidence trend (APC=16.46), followed by Intrahepatic Bile Duct (APC=8.70), Oropharynx (APC=2.62), and Tongue (APC=1.76). The other cancers have upward incidence trends of less than 1.00% APC. While there are some known risk factors for these cancers, there are no known causal risk factors.

Some of these cancers, such as soft tissue cancer including heart, have a more robust set of potential risk factors in the ACS classification of risk factors but a substantially larger number of potential risk factors classified by NCI. Others like “Acute Lymphocytic Leukemia” have a large number of potential risk factors in both ACS and NCI classifications. Tobacco, infections, radiation, and immunosuppressive medications were stated more as general risk factors for many of these poorly understood cancers. Some cancer-specific risk factors are viruses, diseases, syndromes, and poor nutrition, but the overall opinion is that very little is known about the causes of these cancers.

Discussion

Our study identified 12 cancers as poorly understood because a causal risk factor has not yet been identified. These 12 cancers, though not among the cancers with highest health burden in the United States and worldwide — a ranking mostly reserved for lung, colorectal, prostate, breast and cervical cancer — represent a moderate health burden if their count is taken in aggregate. Therefore, preventing these cancers and improving population health will be possible if we identify their causal risk factors (exposures).

Identifying associations between exposures and cancer can be done through cohort or case-control studies [18, 19]. A cohort study can provide strong evidence of causal associations. ACS’ Cancer Prevention Studies (CPS-I, CPS-II, CPS-3) are large scale, prospective, cohort studies [7]. These studies required large scale recruitment of subjects, survey completion by the subjects, and long follow-up periods. Initially, the aim was to determine the relationship between smoking and mortality from diseases. CPS-3 aims to determine the causes and protectants of cancer by looking at lifestyle, exposure, biology, and environment [7]. The results of CPS-I and II have identified significant factors that affect health and diseases progression. This information identified the areas to focus resources in order to combat diseases. These studies have provided valuable information that has helped to improve health; however, they require significant manpower and follow-up time. CPS-I had 68,000 volunteers in 25 states, a cohort of almost one million participants (men and women) and ran from 1959 through 1972. The CPS-II cohort was established in 1982 through recruitment of 1.2 million men and women in 50 states by 77,000 volunteers. CPS-3, with over 30,000 volunteers, enrolled over 304,000 participants from across the United States and Puerto Rico from 2006 through 2013. CPS-II and CPS-3 are still ongoing [7, 14].

Two common features of the poorly understood cancers identified in this study are they are rare and have low survival rates (< 70%). These features limit the choices of study designs; however, the low survival rates indicate their severity and justify the need for studying these cancers. A cohort study of these cancers would be challenging. The first challenge for studying these cancers is finding a large enough number of eligible, willing subjects to form a reliable study group. Second, based on the long follow-up required, the expected loss of study subjects might make any results obtained unreliable. Third, these challenges would increase the costs of studying these cancers. In addition, any study results obtained might not be useful due to low power. This justifies a case-control approach to investigating these cancers. A case-control approach selects subjects based on the outcome (e.g., presence/absence of one of these rare cancers) and measures the prior exposure event retrospectively. Compared to a cohort study, this approach would require fewer subjects, less time, and less funding.

We intend to use a case-control design and informatic-derived analytical techniques to identify potential risk factors for these poorly understood and somewhat rare cancers. Our aim is to combine various secondary datasets that traditionally have not been analyzed together for the purpose of performing exploratory data analyses and subsequent generation of hypotheses about unknown risk factors for these cancers. We believe this approach is novel due to the use of only secondary data and informatic-derived imputation methods and analyses. Using logistic regression to impute missing attributes in the dataset will produce a more complete dataset with sociodemographic, behavioral, and environmental attributes.  The application of geographic information system (GIS) analyses, association mining, cluster analyses, and contrast mining to this dataset could reveal valid relationships.

The term “poorly understood” is often used to describe many different aspects of diseases, ranging from etiology to outcomes. However, criteria for assigning the term to any aspect of disease have not been established. There is research on individual cancer types and sub-types that are termed “poorly understood”, but the publications do not provide objective justification for assigning the term. We believe this approach is also relatively novel due to the use of set measures for selecting poorly understood cancers.

We aim to include in the study multiple types of factors (environmental, behavioral, sociodemographic, clinical) against multiple types of poorly understood cancers as in the Environmental Public Health Tracking Network (EPHTN) of Wisconsin conducted by Hanrahan et al, 2004. The Wisconsin EPHTN was established to generate and test hypotheses for environmental causes of childhood cancers [6]. However, by using more types of factors, this proposed study can also examine interactions of the factors against cancer type(s) in all age groups.

Using data from the Missouri Cancer Registry, University of Missouri (MU) Healthcare, U.S. Census, Behavioral Risk Factor Surveillance System, and the Environmental Protection Agency, we will create datasets that have data on cancer incidence, health care records, demographics, behavioral risk factors, and environmental factors.

We will start by identifying the records of new cases of the cancers of interest in the Missouri Cancer Registry (i.e., incidence cancer cases). We will then identify if these patients also exist in the MU Healthcare electronic health records (EHR). For these matches, we will link and merge the records for the individuals, including cancer diagnosis and all available sociodemographic attributes, in both datasets as well as medications and procedure information from the MU healthcare system dataset. From the EHR dataset, we will also select un-matched patients (non-cancer patients or patients without a cancer of interest) that have similar demographic characteristics to the matched subjects. The selected patients will form control pools from which we will select our controls.

The data for the individual subjects and controls will lack values for many sociodemographic, behavioral, and environmental attributes of interest in this research but will have geographic identifiers that will be used to impute such values. Using demographic, behavioral, and environmental attributes from individuals in similar sociodemographic categories as the study cancer cases but from other datasets and the geographical identifiers available in both datasets, an imputation process will be used to ascribe values of these attributes to the study cancer cases. These geographic identifiers will be used to ascribe extrapolated and imputed values of demographic and environmental attributes to the cancer cases. We will use logistic regression for this imputation analysis. The resulting datasets will be significantly enriched for hypothesis generation analyses of the associations between cancer and potential risk factors that otherwise could not have been studied. This type of analytical approach is only hypothesis generating because of the many possible biases originating from the extrapolation and imputation processes.

We will also analyze the enriched dataset using geographic information system (GIS) analyses, association mining, cluster analyses, contrast mining, and statistical analyses. GIS analyses can determine the proximity to regulated environmental activities. Association mining will identify associations between cancer(s) and the attributes within the dataset. Cluster analysis will be used to group cancers based on similarities and might identify different cancers that have one or more common factors. Contrast mining will be used to identify differences among different cancers and cancer groups by comparing the factors associated with each. Statistical analyses will be used to model relationships within the dataset and determine the odds ratios and 95% confidence intervals for associations within the dataset.

The results of this design and analyses approach are expected to benefit prevention and control strategies for these rare cancers. Currently, the rarity of these cancers and the prohibitive costs of established epidemiological studies of cancer etiology make it infeasible for research-funding institutions to support studies of these cancers. The findings of this study and the accompanying big-data driven case-control study should help guide research agencies’ decisions to fund further investigation into specific cancers and risk factors relationships they postulate.

If progress is not made regarding cancer prevention and control, the medical cost of cancer in the U.S. could rise to $207 billion by 2020 [2]. The increasing burden of cancer will have an even greater impact on low- and middle-income countries. These countries already bear the burden of 70% of cancer deaths, are at a financial disadvantage due to the significant financial cost of cancer, and lack the resources to detect and adequately treat cancer [1, 23].

Conclusion

This study is a first step toward our overall research goal to identify possible causal risk factors for poorly understood cancers. This first step systematically identified the cancer types that are severe and trending up but for which the risk factors are poorly understood. A major limitation is the low incidence for these cancers. This low power makes it highly infeasible to study these specific cancers using cohort studies. We propose a novel approach to generate hypotheses for the associations of these poorly understood cancers with multiple risk factors. This approach circumvents historical limitations of cost and feasibility of implementation that culminated with these cancers being currently classified as poorly understood.

We propose to use current health information technology and data to develop methods to overcome the limitations of the traditional epidemiological approach and identify possible risk factors for these poorly understood cancers. A big-data driven approach to identifying risk factors maximizes the size of the study group, is more cost effective than the traditional approach, eliminates the problem of lost study subjects, and reduces the time to obtain results. We believe it is the least costly and most feasible approach to identify risk factors for poorly understood cancers.

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DOI: 10.31038/PEP.2021232

Abstract

Background: Tanzania is implementing Infection Prevention and Control (IPC) in health care settings using Standard Based Management and Recognition model. The country has developed standards that are used to assess compliance of IPC best practices in health facilities. In order to compare country’s standards with international standards of IPC achievement, the World Health Organization checklist has been used to assess six hospitals.

Aim: To evaluate the Infection Prevention and Control compliance mean scores by using national Standard Based Management and Recognition tool and WHO’s IPC Assessment Framework at Facility Level (IPCAF – Facility tool).

Methods: A comparative cross-sectional evaluation on IPC compliance using national SBM-R tool and IPCAF Facility (WHO tool) was done in January and May 2020 respectively. We conducted evaluation in six hospitals – four regional referral hospitals (RRHs) namely Bukoba RRH, Maweni RRH, Sekou Toure RRH, Temeke RRH, and two zonal referral hospitals which are Benjamin Mkapa Hospital and Mbeya Zonal Referral Hospital.

Results: Temeke Regional Referral Hospital showed the highest infection prevention and control compliance scores with difference in scores when using SBM-R tool and IPCAF Facility WHO tool of 28 (CI 27.74-28.26) p<0.0001; while the lowest difference mean score was from Sekou Toure RRH, which was 1 (CI 0.74 – 1.26) p<0.0001.

Conclusion: There was a significant difference between the mean scores when evaluation was done using SBM-R tool and the WHO-IPCAF tool in all health care facilities. Generally the scores were average in all cases.

Key words

Infection Prevention and Control, Standards Based Management and Recognition, IPCAF Facility WHO

Background

Compliance with Infection Prevention and Control (IPC) guidelines and standards in low-and middle- income countries (LMICs) continues to be a challenge [1]. This has been attributed to inadequate resources, poor infrastructure and other contextual factors which call for more research to identify approaches that work well in LMICs [2, 3]. Gaps in knowledge of IPC implementation strategies among Nurses in sub-Saharan Africa have been reported mainly on “understanding of which, in what combination, and in what context implementation strategies should be best utilized to ensure their safety and that of their patients” [4]. Analysis of a global situation on implementation of the IPC core components at national level reveal that “most countries have IPC programmes and guidelines, but have not invested adequate resources and neither have they translated them in to implementation and monitoring [5].

The COVID-19 pandemic caused by Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) underscores the need to strengthen IPC practices [2]. Infection Prevention and Control interventions are key in preventing COVID-19 and Healthcare Associated Infections (HAIs). Such interventions include hand hygiene and other hospital- based IPC practices and approaches which are highly required in limited resource settings especially in LMICs [6]. However, a study in the Cochrane Database of Systematic Reviews [7] found variability in certainty of evidence on which approach is better for improving hand hygiene compliance between multimodal and simpler interventions, hence calling for more robust studies. In the area of hand hygiene, Tanzania has been cited as an example of countries in Africa with hand hygiene policies embedded in the IPC policies, i.e., national IPC guidelines, standards and tools [8].

Information available regarding HAIs do not portray the real situation in African countries [9]. Some studies have outlined effective measures which can be implemented in settings where resources are inadequate in order to improve IPC compliance and prevent HAIs. These include developing and implementing policies and procedures on HAIs accompanied with rigorous monitoring and feedback mechanism; and regular education and training of health care workers, patients and visitors on the policies and procedures [10]. A position statement of the International Society for Infectious Diseases in 2020 regarding surveillance of surgical site infections (SSIs) in LMICs noted that the burden of SSIs ranges from 8% to 30% of procedures making it the most common HAI. The statement gives key areas to address in preventing HAIs based on surveillance (collecting valid, high-quality data; linking HAIs to economic incapacity; implementing SSI surveillance within IPC programs; prioritizing IPC training for healthcare workers in LMICs to conduct broad-based surveillance; and developing a highly accurate and objective international system for defining SSIs, which can be translated globally in a straightforward manner) [11].

In October 2015, the world adopted “the 2030 Agenda for Sustainable Development”, in which goal 3 is on “ensuring healthy lives and promoting well-being for all at all ages” with a target (target 3.8) to “achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all” [12]. The target on universal health coverage (UHC) contains, as one of its pillars, the quality aspect which includes IPC. Through the lens of UHC and learning from the COVID-19 pandemic, Karamagi and colleagues have proposed a framework for health systems functionality towards UHC in the WHO African Region in which compliance of standard precautions for IPC is one of proxy indicators for vital sign “patient safety” in the domain “effective demand for essential services” [13]. This puts further emphasis to member states to strengthen compliance of IPC guidelines, standards and tools. Likewise, Storr, et al, (2016) worked to propose key points for building a policy case for IPC in the context of quality UHC as well as support and guidance to national governments. They also described the eight (8) WHO IPC core components [14]. The compliance to the WHO IPC core components in Tanzania in the past two decades is shown in Table 1.

Table 1: Compliance with the WHO- IPC core components in Tanzania: 2004 to 2021

The MoHCDGEC, in collaboration with partners reviewed the National IPC Guidelines of 2004 [15] to a new version of 2018. Also, in 2019 the MoHCDGEC reviewed the IPC standards for hospitals, 2012 [16] and updated to a new version [17] that is in line with the revised national guideline, 2018. The MoHCDGEC has also embarked into countrywide dissemination of the revised national IPC guideline (2018) to health facilities [18] in order to improve IPC practices as one of key pillars to fight threats of emerging and reemerging infectious diseases including Ebola Virus Disease (EVD) that had been affecting the neighboring country of the  Democratic Republic of Congo (DRC) [19]; and the recent pandemic of coronavirus disease of 2019 (COVID-19) which is caused by severe acute respiratory Syndrome coronavirus 2 (SARS-CoV-2) that was first reported in Wuhan city of China in December 2019 [20, 21].

In Tanzania, the standard based management recognition (SBM-R) tool has been mainly used to ensure compliance of IPC standards in hospitals [16]. The SBM‐R “consists of systematic utilization of detailed performance standards for rapid and repeated assessments of health facilities, including both clinical and support systems; identification of gaps in compliance with these standards; implementation of corrective interventions; and rewarding of achievements through recognition mechanisms” [22]. Quality Improvement Teams (QITs) are responsible for overall governance of quality improvement issues in hospitals. They provide advice to the Hospital Management Team (HMT) on matters related to quality, and supervise Work Improvement Teams (WITs) in departments [23, 24].

Strengthening compliance of IPC practices in health facilities is of utmost importance in this era of increasing emerging and re-emerging infectious diseases [25, 26]. Emerging and re-emerging diseases are global threats towards human existence. In Tanzania, the trend for emerging and reemerging diseases is increasing; and this is attributed to several factors including changes in ecology, climate and human demographics. Infectious diseases which are on the rise in Tanzania include Cholera, Rift Valley Fever, Plague, Anthrax, Swine Flu, and Dengue [27]. Among the notable emergency events in the country is the Cholera epidemic of August 2015 – July 2017, in which 30,269 cases were reported with 475 deaths (CFR of 1.6%) [28].

Tanzania has been affected by COVID-19 pandemic [29], which is an ongoing crisis all over the world. Moreover, Tanzania remains to be at high risk of EVD as neighboring DRC continues to suffer from repeated outbreaks since 2018 [30]. Hence, the need to implement effective and timely IPC measures is of paramount importance [31].

Tanzania has limited capacity of healthcare staffs in responding to potential infectious disease outbreaks. Also, HAIs prevention systems have not been effectively implemented at health facility levels; and there is limited coordination and collaboration between QITs and Working Improvement Teams (WITs) in addressing HAIs and eventually emerging and re-emerging diseases [23]. Also, in Tanzania the functionality of QITs in RRHs have been demonstrated to be inadequate, which may affect implementation of quality improvement activities [24]. Improving mentorship on prevention of HAIs offers an opportunity for optimal use of the limited resources in reducing of negative impacts on animal and human health. This paper aims at documenting the experience of IPC standards compliance in six hospitals using SBM-R approach and assesses the factors that are responsible for improved functionality of QITs and WITs as well as IPC sub-committee in the hospitals.

Objectives

The overall objective of the study was to gauge compliance with infection prevention and control in six referral hospitals by using National and World Health Organization checklists.

Specifically, the objectives of the study were to:

• Determine compliance score of IPC Standards using national IPC standards checklist in six referral hospitals;
• Determine compliance score of IPC Standards using WHO’s IPCAF- tool) in six referral hospitals; and
• Compare compliance scores between national IPC standards tool and WHO’s IPCAF- tool in six referral hospitals.

Hypothesis

It was hypothesized that mentorship would improve compliance with IPC standards and measures through: (1) strengthening the functionality of quality governance structures (QITs and WITs) by providing members with opportunity for more hands-on practice; (2) building capacity of frontline workers in various departments and wards; and (3) clarification of challenging issues observed during assessments and observations in the service areas. Conceptual framework in the implementation of IPC activities in Tanzania is shown in figure 1.

Figure 1: Conceptual framework in implementation of IPC activities in Tanzania (Credit: Bahegwa, R. P. (2021))

Methodology

Study design: A comparative cross-sectional study on IPC compliance using SBM-R tool and IPCAF – Facility WHO tool.

The national SBM-R tool has been used in Tanzania for assessment of IPC [38]. It is structured based on the functional areas within the health care facility like operating theatre, labour ward, etc. The checklist captures all standard and transmission-based precautions of IPC in all functional areas. Each functional area can score a maximum of hundred percent (100%). At the end the facility is assigned average score of all functional areas. The scores assigned are interpreted as follows: 0%-49%-poor:  the facility needs a lot of work to improve; 50%-79%-moderate: needs to improve at some areas; and 80%-100%- acceptable and more investment is needed to sustain the compliance.

The IPCAF – Facility WHO tool was tested using a robust global study (in 46 countries, 181 hospitals and 324 individuals) and revised as necessary and then approved as an effective tool for IPC improvement in healthcare facilities [39]. The assessment of the facility through the use of IPCAF – Facility WHO tool focuses on eight (8) main core components of the IPC namely the IPC programme; IPC guidelines; IPC education and training; HAI surveillance; Multimodal strategies; Monitoring/audits of IPC practices and feedback; Workload, staffing and bed occupancy; and Built environment, materials and equipment for IPC at the facility level) which are then addressed by a total of 81 indicators. These indicators are based on evidence and expert consensus and have been framed as questions with defined answers to provide an orientation for assessment. Based on the overall score achieved in the eight sections, the facility is assigned to one of four levels of IPC promotion and practice: Inadequate (0-200): IPC core components implementation is deficient. Basic, (201-400): Significant improvement is required, some aspects of the IPC core components are in place, but not sufficiently implemented. Further improvement is required. Intermediate (401-600): Most aspects of the IPC core components are appropriately implemented. The facility should continue to improve the scope and quality of implementation and focus on the development of long-term plans to sustain and further promote the existing IPC programme activities. Advanced (601-800): The IPC core components are fully implemented according to the WHO recommendations and appropriate to the needs of the facility.

The assessment using SBM-R tool was conducted in January 2020 and the assessment using WHO’s IPCAF-Facility tool was done in May 2020. Prospective documentation of IPC compliance in the six hospitals – four regional referral hospitals (RRHs) namely Bukoba RRH, Maweni RRH, Sekou Toure RRH, Temeke RRH, and two zonal referral hospitals which are Benjamin Mkapa Hospital and Mbeya Zonal Referral Hospital, was done from January to May 2020.

Target population: All operating referral health care facilities in the country (regional, zonal and national hospitals) regardless of their ownership, i.e. public or private.

Study population: Prospective documentation of IPC implementation in the six hospitals –Bukoba RRH, Maweni RRH, Sekou Toure RRH, Temeke RRH, Benjamin Mkapa Hospital and Mbeya Zonal Referral Hospital, was done. The Assessment using SBM-R tool in those hospitals was followed by implementation of developed action plans by Hospital’s QIT through an IPC Focal Person who chairs an IPC sub-committee that reports to QIT.

Assessment by using the IPCAF – Facility WHO Tool was done in May 2020, as part of efforts to institutionalize IPC skills among the hospital’s IPC sub-committee to oversee IPC implementation. Also, the QITs conducted IPC assessment in the hospital on quarterly basis and the WITs in each department (functional area) conducted assessment in their functional area on monthly basis as part of SBM-R implementation in the respective hospital. In between the assessments, there have been mentorship visit in which IPC standards implementation was assessed using the IPC Hospital Standards Assessment Tool. The mentors worked as facilitators to the QITs, WITs and IPC sub-Committees in helping them to get hands-on knowledge and skills on IPC standards, standard operating procedures, as well as scoring using the tools; which ultimately helped the members of the teams to become champions. Also, qualitative information on the functionality of the QIT, WITs, IPC sub-Committee, as well as Hospital Management Team support to and commitment to IPC practices strengthening was documented, to help understand about possibility of sustaining the teams’ performance [24].

In order to ensure that decisions for strengthening IPC practices both at the facility as well as at national coordination levels are guided by the data collected, both mentors and the QITs and WITs emphasized on the importance of ensuring data quality [40].

Data management and analysis

Data was cleaned and checked for completeness and outliers before analysis. The established scores for compliance of IPC Standards were tested for normality by using the Shapiro Wilk test. We used Wilcoxon Signed-Ranks test for related samples to determine whether there was mean score differences between assessment done using national SBM-R tool and assessment done using IPCAF– Facility WHO Tool [41].

Descriptive statistics, tables and charts were used to summarize the data. Comparison between compliance mean scores between assessments done using the SBM-R tool and IPCAF- Facility WHO Tool were tested by T test; results were considered significant at p < 0.05.

Results

Compliance score of IPC Standards using national IPC standards checklist and IPCAF-Facility WHO tools.

The IPC compliance using the national Tool has revealed 2(33.34%) out of six hospitals had poor compliance of IPC best practices. The remaining 4(66.66%) had moderate compliance. No facility achieved the excellent level; the maximum score of health facilities by using national tool was moderate. Upon using the IPCAF – Facility WHO tool, three hospitals (50.00%) were found to meet the basic compliance level; while two hospitals (33.34%) were on intermediate level and one hospital (16.67%) met the advanced compliance level. The details are shown in figure 2 and table 2 below.

Figure 2: Scores of IPC compliance using national IPC SBM-R tool and IPCAF – Facility WHO tool

Table 2: Scores of IPC compliance using national IPC SBM-R tool and IPCAF – Facility WHO tool

ZRH = Zonal Referral Hospital

Comparison of compliance scores between national IPC standards tool and IPCAF-Facility WHO tool

There was a significant difference between the mean scores done by using national SBM-R tool and IPCAF – Facility WHO tool in all facilities. The details are shown in table 3.

Table 3: Comparison of scores of IPC compliance using national IPC SBM-R tool and IPCAF – Facility WHO tool

Discussion

General outcome based on both national SBM-R and the IPCAF for facility-WHO tools

Assessment of IPC practices is key to monitor compliance, provide recommendations and hence improve quality of health care services delivery. Our assessments have delivered valuable insights into the state of art on implementation of key IPC structures and processes in Tanzania. Overall, the data gathered demonstrated that IPC is generally at a moderate level as demonstrated from the national SBM-R and IPCAF-Facility WHO tools. However, the use of IPCAF-Facility WHO tool in some areas revealed presence of all score levels: basic, poor and advanced.

Generally moderate level of IPC implementation was expected by both tools, as Tanzania is classified among the lower middle-income countries according to the World Bank classification. Even though, a considerable low number of participating hospitals which were only six (6), four 66.67% were allocated to merely an “intermediate/moderate” IPC level. This rather surprising finding could either be explained by a very strict interpretation of both National and IPCAF tools.

Besides the differences observed among the six participating referral hospitals in Tanzania with regard to the overall national and IPCAF tool scores, we noticed pronounced differences between facility scores of the respective national and IPCAF sections. Scores for Temeke RRH and Benjamin Mkapa were generally high as gauged by both tools. However, specific questions focusing on low scores of IPC compliance revealed mixed results. Maweni RRH scored low by using national SBM-R tool while Bukoba RRH scored low when using IPCAF -Facility tool.

Outcome based on the national SBM-R tool

The national SBM-R tool, which was structured based on standard and transmission-based precautions, was used to assess the following areas: hand hygiene; decontamination; safe waste management; safe handling of sharps; use of PPE; consider every person is potentially infectious and has risk to succumb infection;

These six hospitals did not consider every person (patient/clients or staff) as potentially infectious and susceptible to infection, hence the health care workers considered only those with clinical features as infectious. That was a risk not only to the healthcare workers but also to other patients/clients, community and the environment.

In healthcare settings, healthcare workers are required to use appropriate hand hygiene techniques. In these six facilities the critical moments to practice hand hygiene was not complied as per the requirement of WHO and the MoHCDGEC [42].  This finding compares with the study in Ethiopia which found that only 14.9% of health care providers in Central Gondar zone public primary hospitals, Northwest Ethiopia, had good hand hygiene compliance [43].

In addition to hand hygiene, healthcare workers are required to wear appropriate Personal Protective Equipment (PPE) whenever they provide healthcare services. However, in all the six hospitals visited, adherence was very low. This assessment is in-line with the study by Okello, et al (2017) which was conducted at St. Mary’s Hospital Lacor in Northern Uganda which found that 2% of healthcare workers do not know the purpose of PPE, 23.7% do not know how to don and doff PPEs, 13.6% do not use PPE even when indicated and 10% are not using an appropriate PPE [44].

In terms of management of sharps. healthcare workers in all the six hospitals visited were compliant in handling sharps appropriately. This includes use of sharps only once, avoiding recapping, and safe disposal in the sharp boxes. A study by Tariku, et al (2016) at Gondar University Comprehensive Specialized Hospital, Northwest Ethiopia found that 76.4% never bent needles with hands, 54.3% avoided removing used needles from disposable syringes, 87.2% placed used sharps in puncture-resistant container at point of use and 58.7% never recapped needles [45]. Handling of waste was low in all six facilities which were similar with the findings found at Gondar University Comprehensive Specialized hospital where level of adherence was 30.2% in segregation of noninfectious wastes in black color-coded dust bin, 34.4% in segregation of infectious medical wastes in yellow color-coded dust bin [45]. Appropriate patient management, and maintaining environmental cleanness, (eg. prompt and careful cleaning up spills of blood and other body fluids after the spill event) is also an area which was assessed and found to have fairly lower score in these six facilities which also happen to be similar to findings from Gondar University Comprehensive Specialized hospital which scored 38.3% [45].

In the six hospitals assessed, processing of instrument was good from cleaning to either sterilization or high-level disinfection; and this was consistent with the findings by Tariku and colleagues who reported compliance with sterilization of all reusable equipment before being used on another patient to be 73.7%. However, processing of linen was not following the standards and hence the linen used in these facilities is changing color from white to brown [45].

Cough etiquette to patients, caregivers and visitors with signs and symptoms of respiratory illness improved a lot. This was due to COVID-19 pandemic where all health facilities were implementing IPC and it was mandatory for everyone going to health facilities to wear a mask and observe cough etiquette.

Implementation of pre- and post-exposure prophylaxis (PEP): In Tanzania, the pre-exposure prophylaxis in the context of IPC in health provision setting is not recommended; however, (PEP) is recommended. The score in this area is low in terms of reporting and use of PEP which is comparable to finding by Maria, et al, (2016) in Tanzania that found that out of 357 health care workers who had a blood exposure in the previous 6 months, only 34% reported it and only 58% were offered PEP [46]. Provision of hepatitis B vaccination is the only vaccine which is given as far as the IPC is concerned. The challenges identified during assessment were lack of vaccine in the facilities and some of the health workers did not complete all the three doses, others had one and others had two doses and only a few had completed all the three as per schedule.

Outcome based on the IPCAF for facility-WHO tool

All the six hospitals were found to have IPC programmes/committees in place that are responsible for overseeing the IPC. In Tanzania these teams are QITs and IPC committees. The IPC subcommittee is a subunit of the Quality Teams. These teams were found to be actively supporting the IPC activities. However, doctors were not active and majority were nurses in these six hospitals. The other challenge of these teams is lack of advanced knowledge of IPC as recommended by WHO [47].

According to data from the assessment using IPCAF tool, the six hospitals had on-site trainers for conducting basic IPC training, as almost all hospitals reported having staff capable of performing basic IPC training. However, the trainers of basic IPC training revealed gaps especially with regard to the regularity of training and the understanding of complicated issues of IPC. The importance of consistent IPC training has been demonstrated in various publications, and given the presence of capable staff on-site, appears feasible in Tanzania hospitals [48].

The IPC guidelines were available in all six facilities. Tanzania has revised its IPC national guidelines to align with the WHO guidelines and other international updates. The guidelines have emphasized the emerging and reemerging infections as well as AMR [17]. The challenges concerning the guidelines were that not all functional areas within the health facilities were provided with the guidelines; and those that had the guidelines had limitations in terms of translating them into practice.

All the six hospitals reported documenting HAIs specifically SSIs. The documentation of the SSIs was an early inception of SSIs surveillance given the increase in awareness on the subject, following recommendations from WHO on the burden of HAIs.  We found the area of surveillance to be a big gap in all the six facilities. The data from documentation of SSIs were not analyzed and used in these six facilities as recommended by the WHO [1].

Efforts to strengthen IPC in middle lower-income countries should place emphasis on multimodal strategies [49]. The concept of multimodal strategies is new in Tanzania. Effective implementation of IPC is needed to improve healthcare delivery. However, given the rather low scores obtained by the six hospitals in terms of implementing multimodal strategies in IPC interventions, it appeared that awareness for and implementation of multimodal strategies were not yet fully achieved.

Monitoring/audits of IPC practices and feedback is the area which scored lowest in all facilities. In Tanzania, the QITs /IPC teams are required to do internal assessment, supervision and mentorship. In these six facilities neither monitoring/audits of IPC practices and feedback nor assessments, supervisions and mentorships were done.

No health facility among the six hospitals had achieved standard workload, staffing and bed occupancy. As in many other sub-Saharan African countries, the ratio of healthcare staff to patients, staff work load, and bed occupancy are significantly below standard.

In the hospitals assessed, beds are arranged less than one meter apart and, in some wards, one bed was occupied by more than one patient.

These six hospitals have built environment, materials and equipment for IPC at the facility level though not at the level that national and international standards would require. However, the facilities had ongoing improvement strategy so as to attain the required standards.

Conflict of Interests

There were no conflicts of interest amongst authors.

Disclaimer

The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.

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