RMNCH+A approach India reproductive maternal neonatal child health adolescent community health nursing

https://journalofcomprehensivehealth.co.in/rmncha-a-strategi…

https://www.iapsm.org/pdf/Guidelines/RMNCH+A/RMNCH+A%20Strat…

India MMR IMR U5MR NMR TFR latest 2024 2025 NFHS SRS data

India maternal mortality ratio 2024 SRS infant mortality rate under five child mortality latest statistics

https://www.pib.gov.in/PressReleasePage.aspx?PRID=2128024

mkdir -p /home/daytona/workspace/rmnch-graphs && ls /home/daytona/workspace/rmnc…

~/rmnch-graphs/generate_graphs.py

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np

# ── colour palette ──────────────────────────────────────────────
C_BLUE   = '#1565C0'
C_RED    = '#C62828'
C_GREEN  = '#2E7D32'
C_ORANGE = '#E65100'
C_PURPLE = '#6A1B9A'
C_TEAL   = '#00695C'
C_GOLD   = '#F9A825'
BG       = '#F8F9FA'

plt.rcParams.update({
    'font.family': 'DejaVu Sans',
    'axes.facecolor': BG,
    'figure.facecolor': 'white',
    'axes.grid': True,
    'grid.alpha': 0.35,
    'grid.linestyle': '--',
})

# ════════════════════════════════════════════════════════════════
# 1. MMR TREND  (SRS + UN-MMEIG data)
# ════════════════════════════════════════════════════════════════
fig1, ax1 = plt.subplots(figsize=(10, 5.5))

mmr_years  = [1990, 2000, 2005, 2007, 2010, 2012, 2014, 2016, 2018, 2020, 2023]
mmr_india  = [556,  374,  254,  212,  178,  167,  167,  130,  113,  103,   97]   # per 100k LB; 2023 from UN-MMEIG Apr 2025
mmr_global = [430,  342,  293,  260,  216,  210,  215,  211,  221,  223,  197]   # UN-MMEIG global

ax1.plot(mmr_years, mmr_india,  marker='o', linewidth=2.8, color=C_BLUE,
         label='India MMR', zorder=5)
ax1.plot(mmr_years, mmr_global, marker='s', linewidth=2.2, color=C_RED,
         linestyle='--', label='Global MMR', zorder=4)

# RMNCH+A launch marker
ax1.axvline(2013, color=C_ORANGE, linestyle=':', linewidth=1.8, alpha=0.8)
ax1.text(2013.2, 480, 'RMNCH+A\nLaunch 2013', fontsize=8.5, color=C_ORANGE, va='top')

# SDG target line
ax1.axhline(70, color=C_GREEN, linestyle='-.', linewidth=1.6, alpha=0.8)
ax1.text(1991, 55, 'SDG Target: 70 (2030)', fontsize=8, color=C_GREEN)

# annotations on last point
ax1.annotate('97', xy=(2023, 97), xytext=(2021.5, 140),
             arrowprops=dict(arrowstyle='->', color=C_BLUE), fontsize=9, color=C_BLUE, fontweight='bold')

# labels
for yr, val in zip(mmr_years, mmr_india):
    ax1.text(yr, val + 14, str(val), ha='center', fontsize=7.5, color=C_BLUE)

ax1.set_title('India Maternal Mortality Ratio (MMR) Trend 1990–2023\n(per 100,000 live births)',
              fontsize=13, fontweight='bold', pad=12)
ax1.set_xlabel('Year', fontsize=11)
ax1.set_ylabel('MMR (per 100,000 live births)', fontsize=11)
ax1.legend(fontsize=10)
ax1.set_xlim(1988, 2025)
ax1.set_ylim(0, 620)
ax1.tick_params(axis='both', labelsize=9)

# 86% decline badge
ax1.text(0.73, 0.88, '↓ 86% decline\n(India, 1990–2023)', transform=ax1.transAxes,
         fontsize=10, color='white', fontweight='bold',
         bbox=dict(boxstyle='round,pad=0.4', facecolor=C_BLUE, alpha=0.85))
ax1.text(0.62, 0.88, 'vs ↓ 48% globally', transform=ax1.transAxes,
         fontsize=9, color=C_RED,
         bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.85, edgecolor=C_RED))

plt.tight_layout()
fig1.savefig('/home/daytona/workspace/rmnch-graphs/01_MMR_trend.png', dpi=160)
plt.close(fig1)
print("Saved 01_MMR_trend.png")

# ════════════════════════════════════════════════════════════════
# 2. IMR TREND  (SRS 2024 report — India national)
# ════════════════════════════════════════════════════════════════
fig2, ax2 = plt.subplots(figsize=(10, 5.5))

imr_years = [1990, 2000, 2005, 2010, 2012, 2014, 2016, 2018, 2019, 2020, 2021, 2022, 2023, 2024]
imr_india = [ 80,   68,   58,   47,   42,   39,   34,   32,   30,   28,   27,   26,   25,   24]  # SRS 2024

ax2.fill_between(imr_years, imr_india, alpha=0.15, color=C_TEAL)
ax2.plot(imr_years, imr_india, marker='o', linewidth=2.8, color=C_TEAL, zorder=5)

# RMNCH+A
ax2.axvline(2013, color=C_ORANGE, linestyle=':', linewidth=1.8, alpha=0.8)
ax2.text(2013.2, 72, 'RMNCH+A\n2013', fontsize=8.5, color=C_ORANGE)

# SDG target
ax2.axhline(12, color=C_GREEN, linestyle='-.', linewidth=1.6, alpha=0.8)
ax2.text(1991, 9.5, 'SDG Target: ≤12 (2030)', fontsize=8, color=C_GREEN)

# NHM target
ax2.axhline(25, color=C_PURPLE, linestyle=':', linewidth=1.4, alpha=0.7)
ax2.text(2015, 22, '12th Plan target: 25 ✓', fontsize=8, color=C_PURPLE)

for yr, val in zip(imr_years, imr_india):
    ax2.text(yr, val + 1.8, str(val), ha='center', fontsize=7.5, color=C_TEAL)

ax2.set_title('India Infant Mortality Rate (IMR) Trend 1990–2024\n(per 1,000 live births)  |  SRS 2024',
              fontsize=13, fontweight='bold', pad=12)
ax2.set_xlabel('Year', fontsize=11)
ax2.set_ylabel('IMR (per 1,000 live births)', fontsize=11)
ax2.set_xlim(1988, 2026)
ax2.set_ylim(0, 95)
ax2.tick_params(axis='both', labelsize=9)

ax2.text(0.72, 0.88, '↓ 71% decline\n(India, 1990–2023)', transform=ax2.transAxes,
         fontsize=10, color='white', fontweight='bold',
         bbox=dict(boxstyle='round,pad=0.4', facecolor=C_TEAL, alpha=0.88))

plt.tight_layout()
fig2.savefig('/home/daytona/workspace/rmnch-graphs/02_IMR_trend.png', dpi=160)
plt.close(fig2)
print("Saved 02_IMR_trend.png")

# ════════════════════════════════════════════════════════════════
# 3. U5MR & NMR TREND
# ════════════════════════════════════════════════════════════════
fig3, ax3 = plt.subplots(figsize=(10, 5.5))

u5_years = [1990, 2000, 2005, 2010, 2014, 2016, 2018, 2019, 2020, 2021, 2022, 2023]
u5mr     = [ 115,   88,   74,   59,   45,   39,   37,   35,   32,   32,   32,   25]  # SRS 2023 + UN IGME 2024
nmr      = [  53,   44,   38,   32,   26,   24,   23,   22,   20,   19,   18,   16]  # SRS approx

ax3.plot(u5_years, u5mr, marker='o', linewidth=2.8, color=C_RED,   label='U5MR', zorder=5)
ax3.plot(u5_years, nmr,  marker='^', linewidth=2.5, color=C_ORANGE, label='NMR',  zorder=4)

ax3.fill_between(u5_years, u5mr, nmr, alpha=0.12, color=C_RED)

ax3.axvline(2013, color='grey', linestyle=':', linewidth=1.6, alpha=0.7)
ax3.text(2013.2, 106, 'RMNCH+A 2013', fontsize=8.5, color='grey')

# SDG targets
ax3.axhline(25, color=C_RED, linestyle='-.', linewidth=1.3, alpha=0.6)
ax3.text(1991, 22, 'SDG U5MR Target: ≤25 (2030) ✓', fontsize=8, color=C_RED)
ax3.axhline(12, color=C_ORANGE, linestyle='-.', linewidth=1.3, alpha=0.6)
ax3.text(1991, 9.5, 'SDG NMR Target: ≤12 (2030)', fontsize=8, color=C_ORANGE)

for yr, v in zip(u5_years, u5mr):
    ax3.text(yr, v + 2, str(v), ha='center', fontsize=7.5, color=C_RED)
for yr, v in zip(u5_years, nmr):
    ax3.text(yr, v - 3.5, str(v), ha='center', fontsize=7.5, color=C_ORANGE)

ax3.set_title('India Under-5 Mortality Rate (U5MR) & Neonatal Mortality Rate (NMR)\n1990–2023  |  SRS / UN-IGME 2024',
              fontsize=13, fontweight='bold', pad=12)
ax3.set_xlabel('Year', fontsize=11)
ax3.set_ylabel('Rate per 1,000 live births', fontsize=11)
ax3.legend(fontsize=10)
ax3.set_xlim(1988, 2025)
ax3.set_ylim(0, 130)
ax3.tick_params(axis='both', labelsize=9)

ax3.text(0.68, 0.88, 'U5MR ↓ 78%  |  NMR ↓ 70%\n(India, 1990–2023)\nvs global 61% & 54%',
         transform=ax3.transAxes, fontsize=9, color='white', fontweight='bold',
         bbox=dict(boxstyle='round,pad=0.4', facecolor=C_RED, alpha=0.85))

plt.tight_layout()
fig3.savefig('/home/daytona/workspace/rmnch-graphs/03_U5MR_NMR_trend.png', dpi=160)
plt.close(fig3)
print("Saved 03_U5MR_NMR_trend.png")

# ════════════════════════════════════════════════════════════════
# 4. TFR TREND  (NFHS + SRS)
# ════════════════════════════════════════════════════════════════
fig4, ax4 = plt.subplots(figsize=(10, 5.5))

tfr_years = [1990, 1995, 2000, 2005, 2010, 2015, 2019, 2020, 2021, 2024]
tfr_india = [  3.9,  3.5,  3.1,  2.7,  2.4,  2.3,  2.2,  2.0,  2.0,  1.9]  # NFHS-5 + SRS 2024

ax4.fill_between(tfr_years, tfr_india, alpha=0.15, color=C_PURPLE)
ax4.plot(tfr_years, tfr_india, marker='D', linewidth=2.8, color=C_PURPLE, zorder=5)

ax4.axhline(2.1, color=C_GREEN, linestyle='-.', linewidth=1.8, alpha=0.85)
ax4.text(1991, 2.0, 'Replacement Level (TFR = 2.1) — Achieved ✓', fontsize=9, color=C_GREEN, va='top')

ax4.axvline(2013, color=C_ORANGE, linestyle=':', linewidth=1.8, alpha=0.8)
ax4.text(2013.2, 3.7, 'RMNCH+A 2013', fontsize=8.5, color=C_ORANGE)

for yr, val in zip(tfr_years, tfr_india):
    ax4.text(yr, val + 0.07, str(val), ha='center', fontsize=8.5, color=C_PURPLE, fontweight='bold')

ax4.set_title('India Total Fertility Rate (TFR) Trend 1990–2024\n(NFHS-5 & SRS data)',
              fontsize=13, fontweight='bold', pad=12)
ax4.set_xlabel('Year', fontsize=11)
ax4.set_ylabel('Total Fertility Rate', fontsize=11)
ax4.set_xlim(1988, 2026)
ax4.set_ylim(1.5, 4.3)
ax4.tick_params(axis='both', labelsize=9)

ax4.text(0.68, 0.88, 'TFR reached 1.9 (2024)\nBelow replacement level!', transform=ax4.transAxes,
         fontsize=10, color='white', fontweight='bold',
         bbox=dict(boxstyle='round,pad=0.4', facecolor=C_PURPLE, alpha=0.85))

plt.tight_layout()
fig4.savefig('/home/daytona/workspace/rmnch-graphs/04_TFR_trend.png', dpi=160)
plt.close(fig4)
print("Saved 04_TFR_trend.png")

# ════════════════════════════════════════════════════════════════
# 5. India vs Global comparison bar chart (2023 data)
# ════════════════════════════════════════════════════════════════
fig5, ax5 = plt.subplots(figsize=(10, 6))

indicators = ['MMR\n(per 100k LB)', 'IMR\n(per 1k LB)', 'U5MR\n(per 1k LB)', 'NMR\n(per 1k LB)', 'TFR']
india_2023  = [97,  25, 25, 16, 1.9]
global_2023 = [197, 27, 37, 18, 2.3]

x = np.arange(len(indicators))
width = 0.35

bars1 = ax5.bar(x - width/2, india_2023,  width, label='India (2023/24)', color=C_BLUE,   alpha=0.88, edgecolor='white', linewidth=0.8)
bars2 = ax5.bar(x + width/2, global_2023, width, label='Global (2023)',   color=C_RED, alpha=0.75, edgecolor='white', linewidth=0.8)

# value labels
for bar in bars1:
    h = bar.get_height()
    ax5.text(bar.get_x() + bar.get_width()/2., h + 1.5, str(h), ha='center', va='bottom',
             fontsize=10, fontweight='bold', color=C_BLUE)
for bar in bars2:
    h = bar.get_height()
    ax5.text(bar.get_x() + bar.get_width()/2., h + 1.5, str(h), ha='center', va='bottom',
             fontsize=10, fontweight='bold', color=C_RED)

ax5.set_title('India vs Global: Key RMNCH+A Indicators (2023–2024)\n(Sources: SRS 2024, UN-MMEIG 2025, UN-IGME 2024)',
              fontsize=13, fontweight='bold', pad=12)
ax5.set_ylabel('Rate / Value', fontsize=11)
ax5.set_xticks(x)
ax5.set_xticklabels(indicators, fontsize=10.5)
ax5.legend(fontsize=11)
ax5.set_ylim(0, 235)
ax5.tick_params(axis='y', labelsize=9)

plt.tight_layout()
fig5.savefig('/home/daytona/workspace/rmnch-graphs/05_India_vs_Global.png', dpi=160)
plt.close(fig5)
print("Saved 05_India_vs_Global.png")

# ════════════════════════════════════════════════════════════════
# 6. State-wise IMR bar chart (SRS 2024 — selected states)
# ════════════════════════════════════════════════════════════════
fig6, ax6 = plt.subplots(figsize=(12, 6.5))

states = ['Kerala', 'Tamil Nadu', 'Maharashtra', 'Himachal Pradesh',
          'Gujarat', 'Karnataka', 'Telangana', 'Punjab',
          'India (Avg)', 'Assam', 'Odisha', 'Uttar Pradesh',
          'Madhya Pradesh', 'Chhattisgarh']
imr_s  = [8, 11, 13, 13, 16, 15, 17, 19, 24, 29, 28, 35, 35, 36]

colors_s = ['#1B5E20' if v <= 15 else ('#F57F17' if v <= 25 else '#B71C1C') for v in imr_s]
colors_s[8] = C_BLUE  # national average highlight

bars6 = ax6.barh(states, imr_s, color=colors_s, edgecolor='white', linewidth=0.8)
for bar, val in zip(bars6, imr_s):
    ax6.text(val + 0.3, bar.get_y() + bar.get_height()/2, str(val),
             va='center', fontsize=9.5, fontweight='bold')

ax6.axvline(24, color=C_BLUE, linestyle='--', linewidth=1.8, alpha=0.8)
ax6.text(24.3, 0.3, 'National Avg = 24', fontsize=8.5, color=C_BLUE)
ax6.axvline(12, color=C_GREEN, linestyle='-.', linewidth=1.6, alpha=0.8)
ax6.text(12.3, 12.3, 'SDG Target ≤12', fontsize=8.5, color=C_GREEN)

legend_patches = [
    mpatches.Patch(color='#1B5E20', label='Low (≤15) - Near SDG target'),
    mpatches.Patch(color='#F57F17', label='Moderate (16–25) - National avg range'),
    mpatches.Patch(color='#B71C1C', label='High (>25) - Needs focus'),
    mpatches.Patch(color=C_BLUE, label='National Average'),
]
ax6.legend(handles=legend_patches, fontsize=8.5, loc='lower right')

ax6.set_title('State-wise Infant Mortality Rate (IMR) — SRS 2024\n(per 1,000 live births)',
              fontsize=13, fontweight='bold', pad=12)
ax6.set_xlabel('IMR (per 1,000 live births)', fontsize=11)
ax6.set_xlim(0, 44)
ax6.tick_params(axis='both', labelsize=9.5)
ax6.invert_yaxis()

plt.tight_layout()
fig6.savefig('/home/daytona/workspace/rmnch-graphs/06_State_IMR.png', dpi=160)
plt.close(fig6)
print("Saved 06_State_IMR.png")

# ════════════════════════════════════════════════════════════════
# 7. % Decline comparison India vs Global (1990–2023)
# ════════════════════════════════════════════════════════════════
fig7, ax7 = plt.subplots(figsize=(10, 6))

indicators2 = ['MMR', 'IMR', 'U5MR', 'NMR']
india_decline  = [86, 71, 78, 70]
global_decline = [48, 58, 61, 54]

x2 = np.arange(len(indicators2))
b1 = ax7.bar(x2 - 0.2, india_decline,  0.38, label='India % decline', color=C_BLUE,  alpha=0.88, edgecolor='white')
b2 = ax7.bar(x2 + 0.2, global_decline, 0.38, label='Global % decline', color=C_RED, alpha=0.75, edgecolor='white')

for bar in b1:
    h = bar.get_height()
    ax7.text(bar.get_x() + bar.get_width()/2., h + 0.6, f'{h}%', ha='center', fontsize=11, fontweight='bold', color=C_BLUE)
for bar in b2:
    h = bar.get_height()
    ax7.text(bar.get_x() + bar.get_width()/2., h + 0.6, f'{h}%', ha='center', fontsize=11, fontweight='bold', color=C_RED)

ax7.set_title('India vs Global: % Decline in RMNCH Indicators (1990–2023)\n(Source: UN-MMEIG 2025, UN-IGME Report 2024)',
              fontsize=13, fontweight='bold', pad=12)
ax7.set_ylabel('% Decline since 1990', fontsize=11)
ax7.set_xticks(x2)
ax7.set_xticklabels(indicators2, fontsize=13, fontweight='bold')
ax7.set_ylim(0, 105)
ax7.legend(fontsize=11)
ax7.tick_params(axis='y', labelsize=9)

# highlight India beats global
for i in range(len(indicators2)):
    ax7.annotate('India\nexceeds\nglobal!',
                 xy=(x2[i]-0.2, india_decline[i]),
                 xytext=(x2[i]+0.5, india_decline[i]+10),
                 fontsize=7, color=C_BLUE, alpha=0.7,
                 arrowprops=dict(arrowstyle='->', color=C_BLUE, alpha=0.4))
    break  # only annotate first for clarity

plt.tight_layout()
fig7.savefig('/home/daytona/workspace/rmnch-graphs/07_India_vs_Global_decline.png', dpi=160)
plt.close(fig7)
print("Saved 07_India_vs_Global_decline.png")

print("\n✅ All 7 graphs generated successfully!")

cd /home/daytona/workspace/rmnch-graphs && python generate_graphs.py