Abstract
Hospital Cancer Registries serve as a vital source of information for clinical and epidemiological research, allowing the evaluation of patient care outcomes through therapeutic protocol analysis and patient survival assessment. This study aims to assess the trend of incompleteness in the epidemiological variables within the Hospital Cancer Registry of a renowned oncology center in a Brazilian state. An ecological time-series study was conducted using secondary data from the Hospital Santa Rita de Cássia Cancer Registry in Espírito Santo between 2000 and 2016. Data completeness was categorized as follows: excellent (<5%), good (5%–10%), fair (10%–20%), poor (20%–50%), and very poor (>50%), based on the percentage of missing information. Descriptive and bivariate statistical analyses were performed using the free software RStudio (version 2022.07.2) and R (version 4.1.0). The Mann–Kendall test was used to assess temporal trends between the evaluated years, and the Friedman test was employed to evaluate quality scores across the years. Among the variables assessed, birthplace, race/color, education, occupation, origin, marital status, history of alcohol and tobacco consumption, previous diagnosis and treatment, the most important basis for tumor diagnosis, tumor-node-metastasis staging (TNM) staging, and clinical tumor staging by group (TNM) showed the highest levels of incompleteness. Conversely, other epidemiological variables demonstrated excellent completeness, reaching 100% throughout the study period. Significant trends were observed over the years for history of alcohol consumption (P < .001), history of tobacco consumption (P < .001), TNM staging (P = .016), clinical tumor staging by group (TNM) (P = .002), first treatment received at the hospital (P = .012), disease status at the end of the first treatment at the hospital (P < .001), and family history of cancer (P < .001), and tumor laterality (P = .032). While most epidemiological variables within the Hospital Santa Rita de Cássia Cancer Registry exhibited excellent completeness, some important variables, such as TNM staging and clinical staging, showed high levels of incompleteness. Ensuring high-quality data within Cancer Registries is crucial for a comprehensive understanding of the health-disease process.
Keywords: cancer epidemiology, health surveillance, hospital cancer registry, oncology, prostate neoplasms
1. Introduction
Globally, non-communicable diseases are the leading causes of illness and death in the population.[1] Among non-communicable diseases, malignant neoplasms hold a significant position and contribute to a substantial number of annual deaths worldwide (41 million deaths yearly, corresponding to 71%), accounting for 9.3 million cases, second only to cardiovascular diseases.[2,3] In Brazil, the National Cancer Institute (INCA) estimates a yearly occurrence of 704,080 new cancer cases during the 2023 to 2025 triennium. Among men, prostate cancer is projected to be the most prevalent type (30%), followed by colon and rectal cancers (9.2%).[4]
Importantly, population studies reveal an unequal geographic distribution in the incidence and aggressiveness of cancer, suggesting the influence of hereditary characteristics and lifestyle habits on the risk of developing the disease.[2,5–7]
The hospital cancer registry (HCR) has been implemented in many low- and middle-income countries, particularly in Asia and Latin America, to fulfill various objectives, including providing information on patient diagnosis and treatment as well as specific tumor characteristics and clinical outcomes within the hospital setting. However, the data collected may be subject to potential bias depending on the organization of the healthcare system, capturing a more or less representative subgroup of cancer patients.[8]
In Brazil, the INCA has developed the HCR to standardize technical offerings and provide national-level training to enhance hospital management for cancer patients’ care. HCRs compile data on cancer cases diagnosed or treated within an institution.[9] Furthermore, HCRs serve as a valuable resource for clinical-epidemiological research, allowing the evaluation of results of therapeutic protocols and the analysis of patient survival rates.[8,10–14] The objective of the present study is to assess the trend of incompleteness in the epidemiological variables within the HCR of a renowned oncology center in a Brazilian state.
2. Methods
2.1. Study design
This is an ecological time-series study utilizing secondary data from the HCR database at Hospital Santa Rita de Cássia (HSRC) in Espírito Santo (ES) between 2000 and 2016. The data were obtained from comprehensive databases maintained by the Health Secretariat of ES.
The definition of quality dimensions proposed by Lima et al (2009)[15] was used, where completeness is determined by the proportion of fields containing non-null values. Additionally, for the completeness analysis, the classification proposed by Romero and Cunha (2006)[16] was adopted. The percentage of missing data was categorized as follows: excellent (<5%), good (5%–10%), fair (10%–20%), poor (20%–50%), or very poor (≥50%). Therefore, completeness refers to the extent to which the analyzed fields are filled, measured by the proportion of notifications with a category other than those indicating missing data. In this study, a field filled with the category “ignored,” the numeral zero, an unknown date, or a term indicating missing data were considered incomplete.[16,17]
2.2. Study population and data collection procedures
The study utilized secondary data from the state of ES, situated in southeastern Brazil. The ES Oncology Care Network encompasses 3 health regions: North/Center, Metropolitan, and South.[14] Within this network, the oncology hospital unit at HSRC maintains a well-structured and operational HCR. In addition, the HSRC oncology hospital unit, which is the only Highly Complex Oncology Center in ES, is a reference in Oncology for the entire state of ES, Brazil. The hospital maintains a philanthropic characteristic and allocates 60% of its services to patients of the Brazilian Unified Health System-SUS. As a result, it also receives patients from other Brazilian states, such as: from the south of the state of Bahia, east of the state of Minas Gerais and north of the state of Rio de Janeiro. It has a structured HCR that has been operating since 2000, with its databases annually forwarded to the Integrating System of the HCR (SIS-RHC). Furthermore, HSRC operates the Oncological Care Line, which establishes a systematic framework for cancer care across the state of ES. The objectives of the care line include reducing neoplasm-related mortality, enhancing accessibility to diagnostic and treatment procedures for cancer, and improving overall healthcare accessibility throughout the state.[18]
For this study, a total of 6545 observations for prostate cancer spanning the period from 2000 to 2016 were extracted from the HSRC HCR database. All cases recorded as either analytic or non-analytic were included in the analysis. Data collection for this study occurred between August 2021 and December 2021 at Health Secretariat of ES. The period from 2000 to 2016 was chosen for analysis due to the consolidation of data from all hospitals within the Oncology Care Network in the state of ES. Until December 2016, these hospitals submitted their respective HCR data, which were processed by the Epidemiological Surveillance of the state. It is important to note that the COVID-19 pandemic presented challenges and delays for hospitals in processing the submission of HCR to the Epidemiological Surveillance network, mainly due to operational reasons. To ensure the consistency and reliability of the data for this study, it was decided to maintain the standardization of the historical series, allowing for the utilization of consolidated data from this particular hospital.
2.3. Variables
The tumor registration form of the Brazilian hospital registry integrator[9] encompasses several epidemiological variables, including: gender; age; place of birth; race/skin color; schooling; occupation; provenance; marital status; history of alcohol consumption; history of tobacco consumption; date of first hospital visit; date of first tumor diagnosis; previous diagnosis and treatment; most important basis for tumor diagnosis; primary tumor location; detailed primary tumor location; histological type of primary tumor; tumor-node-metastasis staging (TNM) staging; clinical tumor staging by group (TNM); date of initiation of treatment; main reason for not performing antineoplastic treatment in the hospital; first treatment received at the hospital; disease status at the end of the first hospital treatment; date of death; family history of cancer; forwarding source; tumor laterality; occurrence of more than 1 primary tumor; first clinic attended; first treatment clinic; and relevant exams for tumor diagnosis and treatment planning.
The HCR tumor registration form serves multiple purposes, including gathering information from medical records, providing a concise summary of the case, and serving as a data entry document for inputting information into the computerized databases of the Brazilian hospital registry integrator.[9] The content of this form is designed to meet the information requirements of hospitals with a cancer registry, adhering to standardization guidelines established by the World Health Organization and the International Agency for Research on Cancer. These guidelines have been validated through expert consensus at meetings coordinated by the INCA.[9]
2.4. Statistical analysis
Statistical analyses were conducted using the free software RStudio (version 2022.07.2) and R (version 4.1.0). The completeness of the data was described based on the observed relative frequency and their corresponding completeness scores. The Friedman test[19] was employed to compare score classifications across different years. Additionally, the Mann–Kendall test[20,21] was utilized to assess the presence of statistically significant temporal trends over the evaluated years. A significance level of 5% was adopted for all analyses.
2.5. Ethical considerations
This study received ethical approval from the Research Ethics Committee of the Federal University of ES Health Science Center, with opinion number 5433,541. Furthermore, approval and authorization were obtained from the ES State Health Department, based in Vitória, the capital city, to collect secondary data and access to restricted data associated with this research.
3. Results
3.1. Sociodemographic variables in the HSRC HCR: description of incompleteness and classification of completeness
During the study period, a total of 6545 cases of prostate cancer were registered in the HSRC HCR. The number of cases per year is as follows: 63 (2000), 168 (2001), 212 (2002), 225 (2003), 260 (2004), 288 (2005), 281 (2006), 407 (2007), 357 (2008), 377 (2009), 556 (2010), 417 (2011), 646 (2012), 520 (2013), 632 (2014), 506 (2015), and 630 (2016).
The data completeness of the sociodemographic variable “place of birth” in 2000 was classified as fair, with 14.25% missing data. However, for the following years, it was classified as excellent for 13 years and good for 3 years. The variable “race/skin color” presented 17.69% (regular) incompleteness in 2013, which was a discrepant year compared to other years, where it had excellent indexes for 14 years and good for 2 years. The variable “schooling” was classified as poor in 2009 and 2010, with 24.14% and 38.31% missing data, respectively. In other years, it was classified as excellent (8 years) or good (7 years). The variable “occupation” was classified as poor in 2006, 2007, and 2010, with 10.68%, 11.30%, and 11.69% missing data, respectively. In other years, it fluctuated between excellent (10 years) and good (4 years).
Except for the year 2003, which recorded 0.44% missing observations, the variable “origin” obtained 100% completeness and was classified as excellent in all years. The variable “marital status” was also classified as excellent in all years, with its highest incompleteness rate in 2001, with only 2.38% missing data. The variables “history of alcohol consumption” and “history of tobacco consumption” were classified as very poor in most years, with 100% and 98.41% missing data, respectively, in the year 2000. However, both variables showed excellent levels of completeness from 2010 to 2013 before returning to being classified as poor and very poor in the following years. The variables “gender” and “age” showed 100% completeness for all years evaluated. For a detailed breakdown of the completeness ratings year by year, please refer to Table 1.
Table 1.
Percentage of incompleteness and classification of completeness of the sociodemographic variables of the Hospital Santa Rita de Cássia (HSRC) hospital cancer registry (HCR) between 2000 and 2016.
| Variables | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sex | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Age | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Place of birth | Incompleteness (%) | 14,29 | 0,00 | 2,36 | 6,22 | 9,23 | 4,51 | 2,49 | 0,98 | 0,84 | 0,27 | 0,36 | 0,24 | 2,79 | 0,77 | 3,8 | 4,15 | 6,35 |
| Scoring | 3 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | |
| Race/skin color | Incompleteness (%) | 1,59 | 2,38 | 0,47 | 0,00 | 0,38 | 0,35 | 0,36 | 0,00 | 0,56 | 0,00 | 0,18 | 2,88 | 5,11 | 17,69 | 6,49 | 4,15 | 1,43 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 3 | 2 | 1 | 1 | |
| Schooling | Incompleteness (%) | 3,17 | 2,38 | 2,83 | 2,22 | 3,08 | 13,89 | 14,59 | 5,90 | 2,24 | 24,14 | 38,31 | 6,95 | 9,13 | 9,81 | 9,34 | 3,36 | 3,65 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 4 | 4 | 2 | 2 | 2 | 2 | 1 | 1 | |
| Occupation | Incompleteness (%) | 0,00 | 1,19 | 2,83 | 3,56 | 3,85 | 2,78 | 10,68 | 11,3 | 5,04 | 5,84 | 11,69 | 1,44 | 5,26 | 7,12 | 4,11 | 2,17 | 3,65 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 2 | 2 | 3 | 1 | 2 | 2 | 1 | 1 | 1 | |
| Provenance | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,44 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,15 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Marital status | Incompleteness (%) | 0,00 | 2,38 | 0,00 | 0,89 | 0,00 | 0,35 | 0,00 | 0,74 | 0,00 | 0,00 | 1,26 | 0,96 | 0,15 | 0,19 | 0,32 | 0,79 | 0,16 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| History of alcohol consumption | Incompleteness (%) | 100,00 | 90,48 | 96,70 | 95,11 | 93,85 | 91,32 | 88,97 | 83,05 | 90,2 | 84,08 | 0,54 | 0,72 | 0,15 | 0,96 | 21,99 | 69,37 | 66,98 |
| Scoring | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 1 | 1 | 1 | 1 | 4 | 5 | 5 | |
| History of tobacco consumption | Incompleteness (%) | 98,41 | 88,1 | 94,34 | 94,22 | 86,15 | 81,25 | 76,87 | 72,73 | 73,39 | 65,52 | 0,18 | 0,24 | 0,31 | 0,96 | 20,41 | 64,43 | 60 |
| Scoring | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 1 | 1 | 1 | 1 | 4 | 5 | 5 |
1 = Excellent (<5%), 2 = Good (5%–10%), 3 = Fair (10%–20%), 4 = Poor (20%–50%), 5 = Very poor (≥50%).
3.2. Description of incompleteness and classification of completeness of the clinical variables of the HSRC HCR
Regarding the clinical variables, the TNM staging variable was consistently classified as very poor for all the years analyzed. It had the lowest completeness in 2009, with 97.35% missing information. The variable representing clinical tumor staging by group (TNM) also exhibited high levels of incompleteness. It was classified as very poor from 2000 to 2013, except for 2006, with a poor index with 25.62% missing information. In the most recent years analyzed, this variable showed a progressive decrease in incompleteness, with 43.67%, 36.96%, and 31.90% of missing data for 2014, 2015, and 2016, respectively. The treatment provided in the hospital was classified as very poor from 2000 to 2009, with incompleteness ranging from 72.48% to 93.45%. However, starting in 2010, the rating changed to poor, with missing data ranging between 21.35% and 38.39%.
The variable “date of start of treatment” exhibits a range of incompleteness varying from 0.27% to 16.61%, with classifications fluctuating between excellent, good, and fair. Starting from 2010, the completeness classifications shifted from excellent to regular and remained so until 2016, reaching the highest level of incompleteness in 2014.
The variable “main reason for not performing the antineoplastic treatment in the hospital” was rated as excellent or good for 13 out of the 17 years evaluated, with incompleteness ranging from 0.44% to 9.27%. However, there was a drastic change in its rating, sharply transitioning to very poor for 2006, 2008, 2009, and 2010, with 100% of observations missing each year. The variable “family history of cancer” was consistently classified as very poor throughout the study, with missing information ranging from 64.62% to 100% in all years.
The variable “source of referral” was classified as poor and regular throughout the study period, with percentages of missing data ranging from 10.87% to 30.16%. On the other hand, the variables “first treatment received at the hospital,” “tumor laterality,” “exams relevant to the diagnosis and planning of tumor therapy,” “diagnosis and previous treatment,” and “most important basis for tumor diagnosis” were consistently classified as excellent, with mean percentages of incompleteness of 0%, 15%, 0.14%, 0.67%, 0.54%, and 0.29%, respectively, for the years 2000 to 2016.
Several other clinical variables, including “type of case,” “date of the first consultation,” “date of diagnosis,” “primary location,” “detailed primary location,” “histological type of primary tumor,” “date of death,” “occurrence of more than one primary tumor,” “clinic of first visit,” and “clinic from the start of treatment,” achieved 100% completeness and were consistently classified as excellent in all years studied. Table 2 presents an overview of the incomplete details of clinical variables from 2000 to 2016.
Table 2.
Percentage of incompleteness and classification of completeness of the variables related to diagnosis and treatment of the Hospital Santa Rita de Cássia (HSRC) hospital cancer registry (HCR) between 2000 and 2016.
| Variables | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Type of case | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Date of first hospital visit | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Date of first tumor diagnosis | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Previous diagnosis and treatment | Incompleteness (%) | 4,76 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,49 | 0,00 | 0,00 | 0,72 | 0,48 | 0,77 | 0,58 | 0,32 | 0,4 | 0,63 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Most important basis for tumor diagnosis | Incompleteness (%) | 1,59 | 0,00 | 0,00 | 0,44 | 0,77 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,24 | 0,00 | 0,96 | 0,32 | 0,2 | 0,48 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Primary tumor location | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Detailed primary tumor location | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Histological type of primary tumor | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| TNM staging | Incompleteness (%) | 95,24 | 92,26 | 94,34 | 89,33 | 84,62 | 89,24 | 42,55 | 96,31 | 95,24 | 97,35 | 87,95 | 88,73 | 85,29 | 76,73 | 71,15 | 71,54 | 74,76 |
| Scoring | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | |
| Clinical tumor staging by group (TNM) | Incompleteness (%) | 79,37 | 85,12 | 89,62 | 80,44 | 74,69 | 79,17 | 25,62 | 82,06 | 80,95 | 85,41 | 69,42 | 63,55 | 65,48 | 53,27 | 43,67 | 36,96 | 31,9 |
| Scoring | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 4 | 4 | |
| Date of initiation of treatment | Incompleteness (%) | 6,35 | 6,55 | 7,08 | 6,22 | 0,77 | 0,69 | 0,36 | 0,49 | 0,28 | 0,27 | 12,41 | 11,27 | 14,24 | 15,77 | 16,61 | 10,67 | 16,19 |
| Scoring | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | |
| Main reason for not performing antineoplastic treatment in the hospital | Incompleteness (%) | 3,17 | 4,17 | 2,83 | 0,44 | 1,92 | 4,51 | 100,00 | 3,19 | 100,00 | 100,00 | 100,00 | 3,6 | 5,11 | 9,27 | 9,02 | 5,93 | 7,94 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 5 | 5 | 5 | 1 | 2 | 2 | 2 | 2 | 2 | |
| First treatment received at the hospital | Incompleteness (%) | 1,59 | 0,00 | 0,47 | 0,44 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Disease status at the end of the first hospital treatment | Incompleteness (%) | 76,19 | 93,45 | 86,79 | 76,89 | 74,62 | 73,26 | 72,95 | 72,48 | 78,99 | 81,7 | 34,53 | 22,54 | 38,39 | 21,35 | 36,71 | 37,35 | 25,4 |
| Scoring | 5 | 1 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |
| Date of death | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Family history of cancer | Incompleteness (%) | 100,00 | 97,02 | 99,53 | 96,44 | 97,69 | 97,57 | 95,37 | 96,56 | 93,28 | 87 | 87,77 | 79,62 | 77,24 | 64,62 | 78,96 | 77,67 | 85,87 |
| Scoring | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | |
| Forwarding source | Incompleteness (%) | 30,16 | 37,5 | 24,06 | 13,33 | 11,92 | 15,97 | 20,69 | 14,25 | 17,93 | 13,53 | 18,35 | 19,42 | 17,49 | 17,69 | 13,13 | 10,87 | 19,68 |
| Scoring | 4 | 4 | 4 | 3 | 3 | 3 | 4 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | |
| Tumor laterality | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,38 | 0,00 | 0,00 | 0,25 | 0,28 | 0,00 | 0,18 | 0,00 | 0,15 | 0,19 | 0,32 | 0,4 | 0,16 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Occurrence of more than 1 primary tumor | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| First clinic attended | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,24 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| First treatment clinic | Incompleteness (%) | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Relevant exams for tumor diagnosis and treatment planning | Incompleteness (%) | 0,00 | 0,60 | 0,00 | 0,00 | 4,23 | 1,39 | 0,00 | 0,49 | 4,76 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 | 0,00 |
| Scoring | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
1 = Excellent (<5%), 2 = Good (5%–10%), 3 = Fair (10%–20%),; 4 = Poor (20%–50%), 5 = Very poor (≥50%).
When comparing the scores of the epidemiological variables of the HSRC HCR, no significant difference was observed (P = .9076) in the classification of the scores. In other words, the classification remained similar from 2000 to 2016.
3.3. Trend of incompleteness of sociodemographic and clinical variables of the HSRC HCR
The Mann–Kendall test was employed to examine whether there were significant trends for sociodemographic and clinical variables over the years. The results of the test are provided in Table 3. Notably, significant decreasing trends were observed for the variables history of alcohol consumption, history of tobacco consumption, TNM staging, clinical tumor staging by group (TNM), first treatment received in the hospital, disease status at the end of the first treatment in the hospital, family history of cancer, and significant upward trends for the variable tumor laterality.
Table 3.
Evaluation of the incompleteness trend of the epidemiological variables of the Hospital Santa Rita de Cássia (HSRC) hospital cancer registry (HCR) between 2000 and 2016.
| Variable | S | P value | Trend |
|---|---|---|---|
| Place of birth | −10 | .710 | Non significant |
| Race/skin color | 35 | .160 | Non significant |
| Schooling | 36 | .149 | Non significant |
| Occupation | 34 | .174 | Non significant |
| Provenance | −3 | .881 | Non significant |
| Marital status | 15 | .554 | Non significant |
| History of alcohol consumption | −86 | <.001 * | Decrease |
| History of tobacco consumption | −90 | <.001 * | Decrease |
| Previous diagnosis and treatment | 40 | .088 | Non significant |
| Most important basis for tumor diagnosis | 14 | .559 | Non significant |
| TNM staging | −59 | .016 * | Decrease |
| Clinical tumor staging by group (TNM) | −74 | .002 * | Decrease |
| Date of initiation of treatment | 40 | .108 | Non significant |
| Main reason for not performing antineoplastic treatment in the hospital | 46 | .061 | Non significant |
| First treatment received at the hospital | −41 | .012 * | Decrease |
| Disease status at the end of the first hospital treatment | −82 | <.001 * | Decrease |
| Family history of cancer | −102 | <.001 * | Decrease |
| Forwarding source | −34 | .174 | Non significant |
| Tumor laterality | 50 | .032 * | Increase |
| Occurrence of more than 1 primary tumor | 6 | .609 | Non significant |
| Relevant exams for tumor diagnosis and treatment planning | −28 | .164 | Non significant |
In bold are the variables that were statistically significant P < 0.05.
P value < .05.
The variables that achieved 100% completeness in all years studied were not included in the Mann–Kendall test and, as a result, are not presented in Table 3. Figure 1 displays the graphs of the historical series depicting the percentage of incompleteness for the variables that exhibited significant trends according to the Mann–Kendall test from 2000 to 2016. The time series with incomplete data are represented by black lines, while the blue line represents the time trend. Among the variables analyzed, only handedness displayed a positive trend. However, as indicated in Table 3 and Figure 1 (tumor laterality), the percentage values of incompleteness for handedness are very small, ranging from 0% to 0.44%.
Figure 1.
Trend of incompleteness of variables: history of alcohol consumption; history of tobacco consumption; TNM staging; clinical tumor staging by group (TNM); first treatment received at the hospital; disease status at the end of the first hospital treatment; family history of cancer and tumor laterality for HSRC HCR data between 2000 and 2016. HCR = hospital cancer registry, HSRC = Hospital Santa Rita de Cássia.
4. Discussion
Our findings revealed that several crucial variables for understanding the health-disease process were classified as excellent. These variables include sex, age, date of diagnosis, primary location, and histological type of the primary tumor. However, despite many variables being classified as good or excellent, many clinical-epidemiological variables of relevance contained missing information. Some variables had an average of <10% missing data, such as race/skin color, education, marital status, occupation, previous diagnosis and treatment, and the most important basis for tumor diagnosis. Other variables had an incompleteness rate exceeding 50%, such as history of alcohol and tobacco consumption, TNM staging, clinical tumor staging by group (TNM), and family history. In line with our findings, a study conducted in Mato Grosso, Brazil, which examined the quality of information and assessed the completeness and consistency of the HCR, identified TNM and education as the most incomplete variables.[22]
The gender variable consistently demonstrated excellent performance throughout the study period, and its significance was acknowledged.[6] This finding can be attributed to the minimal subjectivity involved in recording this information, contributing to the results’ robustness. Other studies have reported similar findings, highlighting the excellent completeness of the gender variable.[22,23]
The completeness of the race/skin color variable fluctuated between 2011 and 2013, with an increasing trend of non-completeness during that period. However, it returned to values below 2% of missing information in 2016. Except for 2013, this variable demonstrated excellent and good completeness ratings. In the context of prostate cancer studies, the race/skin color variable holds immense significance due to the higher incidence and lower survival rates observed in individuals of African and Asian origin.[5,24] It should be noted that this variable encompasses more than just biological differences; it represents a complex variable that encapsulates socioeconomic and cultural factors, highlighting inequities in accessing healthcare, particularly in cancer diagnosis and treatment. Our findings align with other research conducted in Brazil.[14,22,23,25,26] The incompleteness and inaccuracies in recording this variable pose challenges in accurately assessing the need for programs focused on health promotion and disease prevention in vulnerable populations.[26] Furthermore, the race/skin color variable contributes to broader discussions on social inclusion, individual and political vulnerabilities, and programmatic approaches.[14,27]
Except for 2009 and 2010, where the average rate of missing observations exceeded 30%, the education variable demonstrated excellent and good scores. This variable exhibited a different pattern than other studies on HCRs in Brazil.[14,22,25,26] The education variable holds significant clinical and epidemiological relevance as it greatly influences a patient prognosis.[14] Its low incompleteness is crucial as it enables various comparisons such as early diagnosis, treatment adherence, survival evaluation, and disease recurrence.[28] Studying the level of education also allows for insights into socioeconomic situations where precise income information may be lacking.[14,28] Moreover, this variable can be associated with late diagnosis, highlighting how lower levels of education present obstacles to accessing early diagnosis, treatment, and prognosis.[22,26]
The occupation variable in the HSRC HCR exhibited varying levels of incompleteness, ranging from 0% to 11.69%, and remained classified as excellent or good for most of the years studied. Compared to other studies involving HCRs in Brazil, this variable demonstrated good completeness quality. A study assessing the completeness of occupation information in an HCR in Brazil reported a lack of information in 46% of cases (40% for men), with only a slight improvement over the years of the study.[29] Numerous studies have established associations between specific occupations and an increased likelihood of developing cancer or experiencing cancer-related mortality, underscoring the importance of capturing detailed information regarding work activities.[22,30,31] Furthermore, occupation serves as a significant diagnostic marker, particularly in cases such as lung cancer, where the work environment can be a potential source of exposure to carcinogenic agents.[32]
When it comes to recording the variables related to the history of alcohol consumption and tobacco consumption, their measurement is subjective, leading to high rates of incompleteness. This can introduce bias when quantifying the use of these substances or even result in the omission of this information.[33] However, an interesting finding emerged for the years 2010 to 2013, where these variables exhibited atypical completion rates, reaching excellent levels of completeness. This finding is particularly relevant considering the well-established carcinogenic potential of alcohol and tobacco.[34]
The completeness of clinical variables, specifically TNM staging and clinical tumor staging by group (TNM), was very poor. Similar findings have been reported in studies conducted in Brazil[14,28,30] that analyzed HCR data and classified completeness levels as “poor.” However, excellent completeness for the TNM variable has been observed in another state within the country.[26] TNM staging is a globally used classification system that holds tremendous importance in understanding the extent of the disease. This information is crucial in defining the appropriate therapeutic plan, evaluating the treatment outcomes for individuals with cancer, and facilitating standardized procedures and exchange of experiences among cancer treatment institutions.[14,22,35,36] Moreover, knowledge of staging contributes to assessing the quality of care provided to cancer patients and aids in implementing public policies focused on early diagnosis.[9,36] The staging information is also highly relevant as a widely utilized prognostic factor in survival studies.
Family history of cancer serves as a valuable marker for comprehending the health-disease process[26,37–41] and plays a significant role in early cancer detection.[42–44] Unfortunately, for the HSRC HCR, this variable exhibited a classification of very poor, with an average of 88.95% missing information during the studied period. This level of incompleteness is concerning, considering that a family history of cancer is a known risk factor for prostate cancer.[2,5,6]
Undoubtedly, there is a pressing need for high-quality data due to political commitments towards cancer information and achieving health goals related to cancer. However, it is concerning to note that only one in 3 countries worldwide possess high-quality incidence data, and only 1 in 4 has high-quality mortality statistics.[45]
5. Limitations
The present study has some limitations. Firstly, it should be noted that the study exclusively utilized data obtained from a single HCR located within a specific Brazilian state. As a result, caution must be exercised when interpreting the findings regarding their external validity and generalizability to broader populations. Secondly, although the HSRC HCR provides valuable insights into the quality of services offered, it does not comprehensively represent the underlying local, regional, or national cancer epidemiology. The data collected within the HCR is derived from patient care within a specific hospital or the number of biopsied cancers within the system, which means that the inclusion of cases is contingent upon the resources and expertise available within the respective institutions. Consequently, the aggregated cases recorded within the HCR represent only a subset of the total cases in the broader context.
6. Conclusion
Most epidemiological variables examined from the HSRC HCR in ES, Brazil, demonstrated excellent completeness. However, notable variables like TNM staging and clinical tumor staging by group (TNM) exhibited high rates of incompleteness between 2000 and 2016. Ensuring high-quality data within HCRs is crucial for enhancing our understanding of the health-disease process and evaluating the quality of hospital care delivered. Additionally, reliable and comprehensive data collection within HCRs is indispensable, as these records serve as the foundation for planning public policies and conducting research focused on cancer surveillance.
Acknowledgments
The authors gratefully acknowledge the strong support of the Secretaria de Estado da Saúde do Espírito Santo, Vitória, ES, Brazil.
Author contributions
Conceptualization: Wesley Rocha Grippa, Luís Carlos Lopes-Júnior.
Data curation: Wesley Rocha Grippa, Larissa Soares Dell’Antonio, Luciane Bresciani Salaroli, Luís Carlos Lopes-Júnior.
Formal analysis: Wesley Rocha Grippa, Luís Carlos Lopes-Júnior.
Funding acquisition: Luís Carlos Lopes-Júnior.
Investigation: Wesley Rocha Grippa and Luís Carlos Lopes-Júnior.
Methodology: Wesley Rocha Grippa and Luís Carlos Lopes-Júnior.
Project administration: Luís Carlos Lopes-Júnior.
Resources: Luís Carlos Lopes-Júnior.
Software: Wesley Rocha Grippa, Luís Carlos Lopes-Júnior.
Supervision: Luís Carlos Lopes-Júnior.
Validation: Wesley Rocha Grippa, Larissa Soares Dell’Antonio, Luciane Bresciani Salaroli, Luís Carlos Lopes-Júnior.
Visualization: Wesley Rocha Grippa, Larissa Soares Dell’Antonio, Luciane Bresciani Salaroli, Luís Carlos Lopes-Júnior.Writing – original draft: Wesley Rocha Grippa, Larissa Soares Dell’Antonio, Luciane Bresciani Salaroli, Luís Carlos Lopes-Júnior.
Writing – review & editing: Luís Carlos Lopes-Júnior.
Abbreviations:
- ES
- Espírito Santo
- HCR
- hospital cancer registry
- HSRC
- Hospital Santa Rita de Cássia
- INCA
- National Cancer Institute
This research received funding by Fundação de Amparo à Pesquisa e Inovação do Espírito Santo—FAPES, Edital FAPES/CNPq/Decit-SCTIE-MS/SESA No 09/2020—PPSUS. Termo de Outorga: 155/2021. Process Number: 2021-F0436.
This study involves human participants and was approved by an Ethics Committee or Institutional Board - Centro de Ciências da Saúde da Universidade Federal do Espírito Santo - CEP/CCS/UFES and approved under opinion no. 5.433.541 of July 18, 2022, in accordance with the relevant guidelines from the Declaration of Helsinki and the ethical principles in the National Health Council of Brazil.
The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.
The authors have no conflicts of interest to disclose.
How to cite this article: Grippa WR, Dell’Antonio LS, Salaroli LB, Lopes-Júnior LC. Incompleteness trends of epidemiological variables in a Brazilian high complexity cancer registry: An ecological time series study. Medicine 2023;102:31(e34369).
Contributor Information
Wesley Rocha Grippa, Email: wgripa@gmail.com.
Larissa Soares Dell’Antonio, Email: lissadellantonio@gmail.com.
Luciane Bresciani Salaroli, Email: lucianebresciani@gmail.com.
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