python3 -c " import pdfplumber with pdfplumber.open('/tmp/workspace/attachments/da1ac3ea-08c8-45d8-abfb-7789c6585d0e/cureus-0018-00000100576.pdf') as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): print(f'\n===== PAGE {i+1} =====') text = page.extract_text() if text: print(text[:3000]) "

find /tmp/workspace -name "*.pdf" 2>/dev/null; ls /tmp/workspace/attachments/ 2>/dev/null || echo "no attachments dir"

cureus 100576 CVD cardiovascular risk prevalence community medicine article

https://www.cureus.com/articles/100576

cardiovascular risk factors prevalence community India cross-sectional

mkdir -p /tmp/workspace/cvd-project && ls /tmp/workspace/cvd-project/

cat << 'PYEOF' > /tmp/workspace/cvd-project/create_cvd_article.py from docx import Document from docx.shared import Pt, Inches, RGBColor, Cm from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.enum.style import WD_STYLE_TYPE from docx.oxml.ns import qn from docx.oxml import OxmlElement import copy doc = Document() # ── Page margins (Cureus-style narrow margins) ── for section in doc.sections: section.top_margin = Cm(2.5) section.bottom_margin = Cm(2.5) section.left_margin = Cm(2.5) section.right_margin = Cm(2.5) # ── Base font ── normal = doc.styles["Normal"] normal.font.name = "Times New Roman" normal.font.size = Pt(11) # Helper: add heading def add_heading(text, level=1, color=RGBColor(0x2E, 0x74, 0xB5)): p = doc.add_heading(text, level=level) p.alignment = WD_ALIGN_PARAGRAPH.LEFT for run in p.runs: run.font.color.rgb = color run.font.bold = True run.font.size = Pt(13) if level == 1 else Pt(11) run.font.name = "Arial" return p # Helper: add paragraph def add_para(text, bold=False, italic=False, size=11, align=WD_ALIGN_PARAGRAPH.JUSTIFY): p = doc.add_paragraph() p.alignment = align run = p.add_run(text) run.font.name = "Times New Roman" run.font.size = Pt(size) run.bold = bold run.italic = italic return p # Helper: add table def add_table_with_header(headers, rows, title=""): if title: tp = doc.add_paragraph(title) tp.alignment = WD_ALIGN_PARAGRAPH.CENTER for run in tp.runs: run.bold = True run.font.name = "Arial" run.font.size = Pt(10) table = doc.add_table(rows=1 + len(rows), cols=len(headers)) table.style = "Table Grid" # Header row hdr_cells = table.rows[0].cells for i, h in enumerate(headers): hdr_cells[i].text = h for para in hdr_cells[i].paragraphs: for run in para.runs: run.bold = True run.font.name = "Arial" run.font.size = Pt(9) para.alignment = WD_ALIGN_PARAGRAPH.CENTER hdr_cells[i]._tc.get_or_add_tcPr() shading = OxmlElement("w:shd") shading.set(qn("w:val"), "clear") shading.set(qn("w:color"), "auto") shading.set(qn("w:fill"), "2E74B5") hdr_cells[i]._tc.tcPr.append(shading) for para in hdr_cells[i].paragraphs: for run in para.runs: run.font.color.rgb = RGBColor(0xFF, 0xFF, 0xFF) # Data rows for r_idx, row_data in enumerate(rows): row_cells = table.rows[r_idx + 1].cells for c_idx, val in enumerate(row_data): row_cells[c_idx].text = str(val) for para in row_cells[c_idx].paragraphs: for run in para.runs: run.font.name = "Times New Roman" run.font.size = Pt(9) para.alignment = WD_ALIGN_PARAGRAPH.CENTER doc.add_paragraph() # ═══════════════════════════════════════════════════════════ # TITLE BLOCK # ═══════════════════════════════════════════════════════════ title_para = doc.add_paragraph() title_para.alignment = WD_ALIGN_PARAGRAPH.CENTER title_run = title_para.add_run( "Prevalence of Cardiovascular Disease Risk Factors Among Adults in an\n" "Urban Community: A Cross-Sectional Study" ) title_run.bold = True title_run.font.name = "Arial" title_run.font.size = Pt(16) title_run.font.color.rgb = RGBColor(0x1F, 0x49, 0x7D) doc.add_paragraph() # Authors auth_para = doc.add_paragraph() auth_para.alignment = WD_ALIGN_PARAGRAPH.CENTER auth_run = auth_para.add_run("[Your Name]¹, [Co-Author Name]²") auth_run.font.name = "Arial" auth_run.font.size = Pt(11) auth_run.bold = True # Affiliations aff_para = doc.add_paragraph() aff_para.alignment = WD_ALIGN_PARAGRAPH.CENTER aff_run = aff_para.add_run( "¹ Department of Community Medicine, [Your Medical College], [City], India\n" "² Department of Community Medicine, [Your Medical College], [City], India" ) aff_run.font.name = "Times New Roman" aff_run.font.size = Pt(10) aff_run.italic = True # Corresponding author corr_para = doc.add_paragraph() corr_para.alignment = WD_ALIGN_PARAGRAPH.CENTER corr_run = corr_para.add_run( "Corresponding Author: [Your Name] | Email: [youremail@institution.edu]" ) corr_run.font.name = "Times New Roman" corr_run.font.size = Pt(10) # DOI / Date line doi_para = doc.add_paragraph() doi_para.alignment = WD_ALIGN_PARAGRAPH.CENTER doi_run = doi_para.add_run("Received: [Date] | Accepted: [Date] | Published: [Date]") doi_run.font.name = "Times New Roman" doi_run.font.size = Pt(10) doc.add_paragraph() # Horizontal rule simulation hr = doc.add_paragraph("─" * 95) hr.alignment = WD_ALIGN_PARAGRAPH.CENTER for run in hr.runs: run.font.color.rgb = RGBColor(0x2E, 0x74, 0xB5) run.font.size = Pt(8) doc.add_paragraph() # ═══════════════════════════════════════════════════════════ # ABSTRACT # ═══════════════════════════════════════════════════════════ add_heading("Abstract", level=1) abstract_sections = [ ("Background and Objective: ", "Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality globally, " "including in India. Identifying the prevalence and distribution of modifiable CVD risk factors " "in community settings is essential for targeted public health interventions. This study aimed to " "assess the prevalence of CVD risk factors among adults aged 30 years and above residing in an " "urban area."), ("Methods: ", "A community-based cross-sectional study was conducted among [N] adults aged ≥30 years selected " "by systematic random sampling from an urban field practice area. A pretested, structured " "questionnaire was used to collect sociodemographic data. Anthropometric measurements, blood " "pressure, fasting blood glucose, and lipid profiles were recorded. CVD risk was assessed using " "the World Health Organization (WHO)/International Society of Hypertension (ISH) risk prediction " "charts. Data were analysed using SPSS version [XX]."), ("Results: ", "Of the [N] participants, [XX]% were male and [XX]% were female. The mean age was [XX ± SD] years. " "The prevalence of hypertension was [XX]%, diabetes mellitus [XX]%, obesity (BMI ≥30 kg/m²) [XX]%, " "dyslipidaemia [XX]%, smoking [XX]%, and physical inactivity [XX]%. Overall, [XX]% of participants " "had at least one major CVD risk factor. High CVD risk (≥30% 10-year risk) was observed in [XX]% " "of participants. Advancing age (≥50 years), male sex, and central obesity were significantly " "associated with higher CVD risk (p < 0.05)."), ("Conclusion: ", "The prevalence of CVD risk factors is high in this urban community. There is an urgent need for " "community-level screening programmes, health education on lifestyle modification, and early " "pharmacological management of modifiable risk factors to reduce the CVD burden."), ] for label, content in abstract_sections: p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY run_label = p.add_run(label) run_label.bold = True run_label.font.name = "Times New Roman" run_label.font.size = Pt(11) run_content = p.add_run(content) run_content.font.name = "Times New Roman" run_content.font.size = Pt(11) doc.add_paragraph() # Keywords kw_para = doc.add_paragraph() kw_para.alignment = WD_ALIGN_PARAGRAPH.LEFT kw_run1 = kw_para.add_run("Keywords: ") kw_run1.bold = True kw_run1.font.name = "Times New Roman" kw_run1.font.size = Pt(11) kw_run2 = kw_para.add_run( "cardiovascular disease; risk factors; prevalence; community medicine; cross-sectional study; " "hypertension; diabetes; dyslipidaemia; urban population; India" ) kw_run2.font.name = "Times New Roman" kw_run2.font.size = Pt(11) doc.add_paragraph() hr2 = doc.add_paragraph("─" * 95) hr2.alignment = WD_ALIGN_PARAGRAPH.CENTER for run in hr2.runs: run.font.color.rgb = RGBColor(0x2E, 0x74, 0xB5) run.font.size = Pt(8) doc.add_paragraph() # ═══════════════════════════════════════════════════════════ # 1. INTRODUCTION # ═══════════════════════════════════════════════════════════ add_heading("1. Introduction", level=1) add_para( "Cardiovascular disease (CVD) encompasses a group of disorders of the heart and blood vessels, " "including coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic " "heart disease, congenital heart disease, and deep vein thrombosis and pulmonary embolism. According " "to the World Health Organization (WHO), CVD is the number one cause of death globally, claiming " "approximately 17.9 million lives each year, which represents 32% of all global deaths [1]." ) add_para( "In India, the burden of CVD has been rising dramatically over the past three decades. The age-" "standardised CVD mortality rate in India was 272 per 100,000 population in 2016, higher than the " "global average of 235 per 100,000 [2]. Indians develop CVD approximately a decade earlier than " "Western populations, with a larger proportion of deaths occurring in the working-age group of " "30-69 years [3]. Urbanisation, sedentary lifestyle, dietary transitions, and psychosocial stress " "have contributed to the epidemic of risk factors including hypertension, type 2 diabetes mellitus, " "dyslipidaemia, and obesity." ) add_para( "Modifiable risk factors - hypertension, diabetes mellitus, dyslipidaemia, tobacco use, physical " "inactivity, unhealthy diet, and obesity - account for more than 80% of the preventable CVD burden. " "Community-based studies identifying the local prevalence of these risk factors are essential for " "designing targeted preventive programmes. However, data from urban field practice areas attached to " "medical institutions remain sparse. The present study was therefore undertaken to assess the " "prevalence of CVD risk factors and the 10-year CVD risk among adults aged ≥30 years in an urban " "community." ) add_para( "The findings of this study will provide a local evidence base for the health authorities and " "contribute to community medicine training by identifying actionable targets for primary prevention " "at the population level." ) doc.add_paragraph() # ═══════════════════════════════════════════════════════════ # 2. MATERIALS AND METHODS # ═══════════════════════════════════════════════════════════ add_heading("2. Materials and Methods", level=1) add_heading("2.1 Study Design and Setting", level=2) add_para( "A community-based cross-sectional study was conducted from [Month Year] to [Month Year] in the " "urban field practice area of the Department of Community Medicine, [Medical College Name], [City], " "India. The field practice area has a total registered population of approximately [XX,XXX], " "comprising [XX] wards." ) add_heading("2.2 Study Population and Sample Size", level=2) add_para( "All adults aged ≥30 years residing in the study area for at least six months prior to the survey " "were eligible for inclusion. Participants with known chronic kidney disease, established CVD " "(prior myocardial infarction or stroke), or who were pregnant were excluded. Sample size was " "calculated using the formula n = Z²pq/d², taking the expected prevalence of any CVD risk factor " "as [XX]% (based on previous studies), 95% confidence level (Z = 1.96), and absolute precision of " "5%. After adding a 10% non-response rate, the final sample size was [N]. Participants were selected " "by systematic random sampling from the household registry." ) add_heading("2.3 Data Collection", level=2) add_para( "Data were collected by trained medical interns using a pretested, structured interview schedule. " "The questionnaire captured: (i) sociodemographic variables (age, sex, education, occupation, " "socioeconomic status by Modified Kuppuswamy scale); (ii) personal history (tobacco use, alcohol " "consumption, physical activity by Global Physical Activity Questionnaire [GPAQ]); (iii) dietary " "habits; and (iv) family history of CVD." ) add_para( "Anthropometric measurements included height (Stadiometer, ±0.1 cm), weight (calibrated digital " "scale, ±0.1 kg), and waist circumference (WC, non-stretchable tape at the umbilicus level). " "Body mass index (BMI) was calculated as weight (kg)/height² (m²). Central obesity was defined " "as WC ≥90 cm in men and ≥80 cm in women (IDF Asia-Pacific criteria)." ) add_para( "Blood pressure was measured with a calibrated aneroid sphygmomanometer after five minutes of rest, " "using the right arm in sitting position; the average of two readings taken five minutes apart was " "recorded. Hypertension was defined as SBP ≥140 mmHg or DBP ≥90 mmHg, or current antihypertensive " "use (JNC 8 criteria). Fasting venous blood samples were collected for fasting blood glucose (FBG) " "and lipid profile. Diabetes was defined as FBG ≥126 mg/dL or current antidiabetic use (ADA 2021). " "Dyslipidaemia was defined per NCEP ATP III guidelines." ) add_heading("2.4 CVD Risk Assessment", level=2) add_para( "The 10-year CVD risk was estimated using the WHO/ISH risk prediction charts for the South-East " "Asia Region (SEARO D). Participants were classified as low risk (<10%), moderate risk (10-<20%), " "high risk (20-<30%), and very high risk (≥30%). Framingham Risk Score was used as a secondary " "comparator." ) add_heading("2.5 Statistical Analysis", level=2) add_para( "Data were entered in Microsoft Excel 2019 and analysed using SPSS Version 26.0 (IBM Corp., " "Armonk, NY). Categorical variables were expressed as frequencies and percentages; continuous " "variables as mean ± standard deviation (SD). Chi-square test was used to assess associations " "between categorical variables. Binary logistic regression was performed to identify independent " "predictors of high CVD risk. A p-value of <0.05 was considered statistically significant." ) add_heading("2.6 Ethical Considerations", level=2) add_para( "The study protocol was approved by the Institutional Ethics Committee (IEC No. [XXX/Year]). " "Written informed consent was obtained from all participants. Confidentiality was maintained " "throughout." ) doc.add_paragraph() # ═══════════════════════════════════════════════════════════ # 3. RESULTS # ═══════════════════════════════════════════════════════════ add_heading("3. Results", level=1) add_heading("3.1 Sociodemographic Profile", level=2) add_para( "A total of [N] participants were enrolled with a response rate of [XX]%. Table 1 summarises the " "sociodemographic characteristics. The mean age of participants was [XX ± SD] years (range 30-[XX] " "years). [XX]% ([n]) were male and [XX]% ([n]) were female. The majority ([XX]%) belonged to the " "30-44 year age group. More than half ([XX]%) had completed secondary-level education, and [XX]% " "were employed. The predominant socioeconomic class was [Upper Middle / Lower Middle] (Modified " "Kuppuswamy scale)." ) # Table 1 add_table_with_header( headers=["Variable", "Category", "Frequency (n)", "Percentage (%)"], rows=[ ["Age (years)", "30-44", "[n]", "[XX]"], ["", "45-59", "[n]", "[XX]"], ["", "≥60", "[n]", "[XX]"], ["Sex", "Male", "[n]", "[XX]"], ["", "Female","[n]", "[XX]"], ["Education", "Illiterate","[n]","[XX]"], ["", "Primary","[n]","[XX]"], ["", "Secondary","[n]","[XX]"], ["", "Graduate & above","[n]","[XX]"], ["Socioeconomic Status","Upper (I)","[n]","[XX]"], ["", "Upper Middle (II)","[n]","[XX]"], ["", "Lower Middle (III)","[n]","[XX]"], ["", "Upper Lower (IV)","[n]","[XX]"], ["", "Lower (V)","[n]","[XX]"], ], title="Table 1: Sociodemographic characteristics of study participants (n = [N])" ) add_heading("3.2 Prevalence of Individual CVD Risk Factors", level=2) add_para( "Table 2 presents the prevalence of individual CVD risk factors. Hypertension was the most " "prevalent risk factor ([XX]%), followed by dyslipidaemia ([XX]%), physical inactivity ([XX]%), " "overweight/obesity ([XX]%), diabetes mellitus ([XX]%), tobacco use ([XX]%), and alcohol use " "([XX]%). Central obesity was present in [XX]% of participants. A significantly higher prevalence " "of hypertension (p = [XX]), diabetes (p = [XX]), and dyslipidaemia (p = [XX]) was observed in " "the ≥50-year age group compared to younger participants." ) # Table 2 add_table_with_header( headers=["CVD Risk Factor", "Overall n (%)", "Male n (%)", "Female n (%)", "p-value"], rows=[ ["Hypertension", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)", "[0.XXX]"], ["Diabetes Mellitus", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)", "[0.XXX]"], ["Dyslipidaemia", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)", "[0.XXX]"], ["Overweight (BMI 25-29.9)","[n] ([XX]%)","[n] ([XX]%)","[n] ([XX]%)","[0.XXX]"], ["Obesity (BMI ≥30)", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)", "[0.XXX]"], ["Central Obesity", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)", "[0.XXX]"], ["Tobacco Use", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)", "[0.XXX]"], ["Alcohol Use", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)", "[0.XXX]"], ["Physical Inactivity","[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)", "[0.XXX]"], ["Unhealthy Diet", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)", "[0.XXX]"], ["Family History of CVD","[n] ([XX]%)","[n] ([XX]%)","[n] ([XX]%)","[0.XXX]"], ["≥2 Risk Factors", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)", "[0.XXX]"], ], title="Table 2: Prevalence of CVD risk factors by sex (n = [N])" ) add_heading("3.3 Distribution of 10-Year CVD Risk", level=2) add_para( "Using WHO/ISH risk charts, the majority of participants ([XX]%) fell in the low-risk category " "(<10% 10-year risk). Moderate risk (10-<20%) was observed in [XX]%, high risk (20-<30%) in [XX]%, " "and very high risk (≥30%) in [XX]% of participants (Table 3). Very high CVD risk was significantly " "more prevalent in males (p < 0.001) and in those aged ≥50 years (p < 0.001)." ) # Table 3 add_table_with_header( headers=["CVD Risk Category", "10-Year Risk", "Overall n (%)", "Male n (%)", "Female n (%)"], rows=[ ["Low", "<10%", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)"], ["Moderate", "10-<20%","[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)"], ["High", "20-<30%","[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)"], ["Very High", "≥30%", "[n] ([XX]%)", "[n] ([XX]%)", "[n] ([XX]%)"], ], title="Table 3: Distribution of 10-year CVD risk by WHO/ISH risk categories (n = [N])" ) add_heading("3.4 Predictors of High CVD Risk", level=2) add_para( "Binary logistic regression analysis (Table 4) identified age ≥50 years (OR = [XX]; 95% CI " "[XX-XX]; p < 0.001), male sex (OR = [XX]; 95% CI [XX-XX]; p = [XX]), hypertension " "(OR = [XX]; 95% CI [XX-XX]; p < 0.001), diabetes mellitus (OR = [XX]; 95% CI [XX-XX]; " "p < 0.001), and central obesity (OR = [XX]; 95% CI [XX-XX]; p = [XX]) as independent " "predictors of high CVD risk (≥20% 10-year risk)." ) # Table 4 add_table_with_header( headers=["Variable", "Crude OR (95% CI)", "Adjusted OR (95% CI)", "p-value"], rows=[ ["Age ≥50 years", "[XX] ([XX]-[XX])", "[XX] ([XX]-[XX])", "<0.001"], ["Male sex", "[XX] ([XX]-[XX])", "[XX] ([XX]-[XX])", "[0.XXX]"], ["Hypertension", "[XX] ([XX]-[XX])", "[XX] ([XX]-[XX])", "<0.001"], ["Diabetes Mellitus", "[XX] ([XX]-[XX])", "[XX] ([XX]-[XX])", "<0.001"], ["Dyslipidaemia", "[XX] ([XX]-[XX])", "[XX] ([XX]-[XX])", "[0.XXX]"], ["Central Obesity", "[XX] ([XX]-[XX])", "[XX] ([XX]-[XX])", "[0.XXX]"], ["Tobacco Use", "[XX] ([XX]-[XX])", "[XX] ([XX]-[XX])", "[0.XXX]"], ["Physical Inactivity", "[XX] ([XX]-[XX])", "[XX] ([XX]-[XX])", "[0.XXX]"], ], title="Table 4: Logistic regression - predictors of high CVD risk (≥20% 10-year risk)" ) doc.add_paragraph() # ═══════════════════════════════════════════════════════════ # 4. DISCUSSION # ═══════════════════════════════════════════════════════════ add_heading("4. Discussion", level=1) add_para( "This community-based cross-sectional study assessed the prevalence of CVD risk factors and the " "10-year CVD risk among adults aged ≥30 years in an urban area. The high prevalence of multiple " "co-existing risk factors underscores the growing non-communicable disease (NCD) burden in urban " "India and the need for proactive community-level interventions." ) add_para( "The prevalence of hypertension ([XX]%) in our study is consistent with the ICMR-INDIAB study, " "which reported a hypertension prevalence of approximately 28.5% in urban India [4]. Similarly, " "the prevalence of diabetes ([XX]%) is comparable to national estimates of 11.4% reported by " "Anjana et al. (2023) [5], though urban populations tend to have higher rates than the national " "average. Dyslipidaemia prevalence ([XX]%) reflects the findings of the CURES study, which " "reported dyslipidaemia in over 50% of urban south Indian adults [6]." ) add_para( "The co-occurrence of ≥2 risk factors in [XX]% of participants is particularly alarming, as " "clustering of risk factors exponentially increases cardiovascular risk. This finding is consistent " "with data from the [comparable study], which reported clustering in approximately [XX]% of urban " "adults [7]. The metabolic syndrome-like clustering of hypertension, diabetes, central obesity, " "and dyslipidaemia observed in our cohort aligns with the concept of the 'Asian Indian phenotype', " "characterised by greater insulin resistance at lower BMI thresholds [8]." ) add_para( "The WHO/ISH risk stratification showed that [XX]% of participants had high or very high 10-year " "CVD risk. This is higher than reported by [comparable study] ([XX]%), possibly due to differences " "in age distribution and the urban setting of our study. Advancing age ≥50 years, male sex, " "hypertension, and diabetes were identified as independent predictors of high CVD risk - findings " "consistent with established literature [9,10]." ) add_para( "A major strength of this study is the inclusion of objectively measured biological parameters " "(blood pressure, glucose, lipid profile, anthropometry) rather than self-reported data alone. " "Limitations include the cross-sectional design precluding causal inference, restriction to one " "urban field practice area limiting generalisability, and possible social desirability bias in " "self-reported lifestyle behaviours." ) doc.add_paragraph() # ═══════════════════════════════════════════════════════════ # 5. CONCLUSIONS # ═══════════════════════════════════════════════════════════ add_heading("5. Conclusions", level=1) add_para( "The prevalence of CVD risk factors is high in this urban community, with a substantial proportion " "of adults at moderate to very high 10-year CVD risk. Hypertension, diabetes, dyslipidaemia, and " "central obesity are the dominant modifiable risk factors. Advancing age (≥50 years), male sex, " "hypertension, and diabetes are independent predictors of high CVD risk. Community-based screening " "programmes, targeted health education focusing on lifestyle modifications (diet, physical activity, " "tobacco cessation), and early pharmacological management of identified risk factors are urgently " "needed to mitigate the growing CVD burden in this community." ) doc.add_paragraph() # ═══════════════════════════════════════════════════════════ # ACKNOWLEDGEMENTS # ═══════════════════════════════════════════════════════════ add_heading("Acknowledgements", level=1) add_para( "The authors thank the participants for their time and cooperation, the field health workers of " "[UFPAA Name], and the laboratory staff of [Medical College Name] for their support during data " "collection." ) doc.add_paragraph() # ═══════════════════════════════════════════════════════════ # CONFLICTS OF INTEREST / FUNDING # ═══════════════════════════════════════════════════════════ add_heading("Conflicts of Interest", level=1) add_para("The authors declare no conflicts of interest.") add_heading("Funding", level=1) add_para("This research received no specific grant from any funding agency.") doc.add_paragraph() # ═══════════════════════════════════════════════════════════ # REFERENCES # ═══════════════════════════════════════════════════════════ add_heading("References", level=1) references = [ "1. World Health Organization. Cardiovascular diseases (CVDs) - Key facts. Geneva: WHO; 2021. " "Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)", "2. GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific " "mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden " "of Disease Study 2016. Lancet. 2017;390(10100):1151-1210.", "3. Prabhakaran D, Jeemon P, Roy A. Cardiovascular diseases in India: Current epidemiology " "and future directions. Circulation. 2016;133(16):1605-20.", "4. Anjana RM, Pradeepa R, Das AK, et al. Physical activity and inactivity patterns in India - " "results from the ICMR-INDIAB study (Phase-1). Int J Behav Nutr Phys Act. 2014;11:26.", "5. Anjana RM, Unnikrishnan R, Deepa M, et al. Metabolic non-communicable disease health " "report of India: the ICMR-INDIAB national cross-sectional study (ICMR-INDIAB-17). Lancet " "Diabetes Endocrinol. 2023;11(7):474-489.", "6. Mohan V, Deepa M, Farooq S, Datta M, Deepa R. Prevalence, awareness and control of " "hypertension in Chennai - The Chennai Urban Rural Epidemiology Study (CURES-52). J Assoc " "Physicians India. 2007;55:326-32.", "7. Gupta R, Gupta VP, Sarna M, et al. Prevalence of coronary heart disease and risk factors " "in an urban Indian population: Jaipur Heart Watch-2. Indian Heart J. 2002;54(1):59-66.", "8. Misra A, Khurana L. Obesity and the metabolic syndrome in developing countries. J Clin " "Endocrinol Metab. 2008;93(11 Suppl 1):S9-30.", "9. Mendis S, Puska P, Norrving B, eds. Global Atlas on Cardiovascular Disease Prevention and " "Control. Geneva: World Health Organization; 2011.", "10. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms " "for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. " "2007;297(6):611-9.", ] for ref in references: p = doc.add_paragraph(ref) p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY for run in p.runs: run.font.name = "Times New Roman" run.font.size = Pt(10) # ═══════════════════════════════════════════════════════════ # SAVE # ═══════════════════════════════════════════════════════════ out_path = "/tmp/workspace/cvd-project/CVD_Risk_Prevalence_Article.docx" doc.save(out_path) print(f"Saved to: {out_path}") PYEOF python3 /tmp/workspace/cvd-project/create_cvd_article.py